diff --git "a/1145.jsonl" "b/1145.jsonl" new file mode 100644--- /dev/null +++ "b/1145.jsonl" @@ -0,0 +1,490 @@ +{"seq_id": "280015052", "text": "import json\nfrom slots_tracker_server.charts import NUM_OF_CHARTS\n\n\n# Charts\ndef test_get_charts(client):\n rv = client.get('/charts/')\n r_data = json.loads(rv.get_data(as_text=True))\n assert isinstance(r_data, list)\n assert len(r_data) == NUM_OF_CHARTS\n", "sub_path": "slots_tracker_server/tests/test_api_charts.py", "file_name": "test_api_charts.py", "file_ext": "py", "file_size_in_byte": 265, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "json.loads", "line_number": 8, "usage_type": "call"}, {"api_name": "slots_tracker_server.charts.NUM_OF_CHARTS", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "193129252", "text": "from django.db import models\n\n# Create your models here.\nclass Tour(models.Model):\n name = models.CharField(\"Nome\", max_length=50)\n date = models.DateField(\"Data\")\n full = models.BooleanField(\"Lotado\", default=False)\n city = models.CharField(\"Cidade\", max_length=50)\n capacity = models.PositiveIntegerField(\"Capacidade\")\n available = models.PositiveIntegerField(\"Disponíveis\")\n TOUR_TYPES = (\n (\"Natureza\", \"Natureza\"),\n (\"Cultural\", \"Cultural\"),\n (\"Aventura\", \"Aventura\"))\n tour_type = models.CharField(\"Tipo\", max_length=50, choices=TOUR_TYPES)\n meeting_point = models.CharField(\"Ponto de encontro\", max_length=50)\n", "sub_path": "tours/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 645, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.db.models.Model", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 4, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 5, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "588022759", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n店面就一轮，顺便问问有没有更好的思路，算是设计题目，第一问就是一堆人出去玩，吃饭或者什么的，有人先付钱，其他人欠着，最后求每个人的balance.\n比如 person A,B,C\nTransaction 1 is 12$, A pays for A,B,C.鏈枃鍘熷垱鑷�1point3acres璁哄潧\nTransaction 2 is 10$, B pays for A,B\n\n那么最后balance就是\nA: -3\nB: -1\nC: 4 (C需要给A,B各1元和3元)\n\n设计数据结构和打印出每个人的balance.\n\n第二问是打印出谁应该给谁多少钱，比如上例中，应该是\n\nC gives 1 to B\nC gives 3 to A\n\n第一问没什么好说的，第二问的时间复杂度是可以做到o(n)的，我想的是维持两个queue,一个是欠钱的，一个是收钱的，队首print，然后balance清零出队。不知道有没有更好的思路。\n\n#在这里快速回复#\npost_newreply\n\"\"\"\n\nfrom collections import defaultdict\n\n\nclass Solution:\n def get_balance(self, transations):\n balance_dct = defaultdict(int)\n for amount, payer, payees in transations:\n balance_dct[payer] -= amount\n amount_payee = float(amount) / len(payees)\n for payee in payees:\n balance_dct[payee] += amount_payee\n return balance_dct\n\n def get_due(self, balance_dct):\n if sum(balance_dct.values()) != 0:\n raise Exception('Not balance')\n debets = sorted([it for it in balance_dct.items() if it[1] > 0], key=lambda x: x[1])\n credit = sorted([it for it in balance_dct.items() if it[1] < 0], key=lambda x: -x[1])\n give_dct = defaultdict(list)\n while debets and credit:\n debet_user, debet_val = debets.pop()\n credit_user, credit_val = credit.pop()\n bal = debet_val + credit_val\n if bal > 0:\n debets.append((debet_user, bal))\n elif bal < 0:\n credit.append((credit_user, bal))\n\n give_dct[debet_user].append((credit_user, min(debet_val, -credit_val)))\n return give_dct\n", "sub_path": "Practice/Interview/square/transaction_balance/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 2055, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "collections.defaultdict", "line_number": 31, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "53114630", "text": "##############################################################################\n#\n# Copyright (c) 2007-2009 Zope Corporation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\nimport zc.buildout.testing\nimport zc.buildout.tests\nfrom zope.testing import doctest\n\ndef setUp(test):\n zc.buildout.tests.easy_install_SetUp(test)\n zc.buildout.testing.install_develop('z3c.recipe.filetemplate', test)\n\ndef test_suite():\n return doctest.DocFileSuite(\n 'README.txt', 'tests.txt',\n setUp=setUp,\n tearDown=zc.buildout.testing.buildoutTearDown,\n optionflags=doctest.NORMALIZE_WHITESPACE,\n )\n", "sub_path": "z3c.recipe.filetemplate/z3c/recipe/filetemplate/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 1089, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "zc.buildout.testing.buildout.tests.easy_install_SetUp", "line_number": 20, "usage_type": "call"}, {"api_name": "zc.buildout.testing.buildout", "line_number": 20, "usage_type": "attribute"}, {"api_name": "zc.buildout.testing", "line_number": 20, "usage_type": "name"}, {"api_name": "zc.buildout.testing.buildout.testing.install_develop", "line_number": 21, "usage_type": "call"}, {"api_name": "zc.buildout.testing.buildout", "line_number": 21, "usage_type": "attribute"}, {"api_name": "zc.buildout.testing", "line_number": 21, "usage_type": "name"}, {"api_name": "zope.testing.doctest.DocFileSuite", "line_number": 24, "usage_type": "call"}, {"api_name": "zope.testing.doctest", "line_number": 24, "usage_type": "name"}, {"api_name": "zc.buildout.testing.buildout", "line_number": 27, "usage_type": "attribute"}, {"api_name": "zc.buildout.testing", "line_number": 27, "usage_type": "name"}, {"api_name": "zope.testing.doctest.NORMALIZE_WHITESPACE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "zope.testing.doctest", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "330047880", "text": "import pytest\nfrom selenium import webdriver\nimport allure\nimport logging\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\n@pytest.fixture()\ndef setUp():\n global driver\n driver = webdriver.Chrome(executable_path=\"/Users/aravindanathdm/Documents/Aravinda/chromedriver\")\n# After all the test case execution\n yield\n driver.close()\n\n\n@allure.step\ndef test_case_a(setUp):\n with allure.step(\"Amazon login\"):\n allure.title(\"Test case amzxon.com\")\n driver.get(\"https://www.amazon.in\")\n logger.info(\"User is on amazon.com\")\n logger.info(driver.current_url)\n logger.info(driver.get_cookies())\n allure.attach(driver.get_screenshot_as_png(),\"Success msg \",allure.attachment_type.PNG)\n\n\n@allure.step\ndef test_case_b(setUp):\n with allure.step(\"Facebook login\"):\n\n logger.info(\"User is entering URL\")\n driver.get(\"https://www.facebook.com\")\n allure.attach(driver.get_screenshot_as_png(), \"Sceenshot of this screen\", allure.attachment_type.PNG)\n logger.info(\"User is on \"+driver.current_url)\n\n# /Users/aravindanathdm/PycharmProjects/PythonSeleniumProject/allureReport/=/Users/aravindanathdm/Documents/class/Python Selenium/Reports\n\n# allureReport/StepsForAllureReport\n# /Users/aravindanathdm/Documents/class/Python Selenium/Reports\n\n# --vv --capture=fd --alluredir = /Users/aravindanathdm/Documents/class/Python Selenium/Reports\n\n\n# pytest test_allure.py --verbose --capture=fd --alluredir \"/demo/Users/aravindanathdm/Documents/class/Python Selenium/allureReport/demo/Reports\"\n\n\n", "sub_path": "AllureReport/TCPkg/test_allure.py", "file_name": "test_allure.py", "file_ext": "py", "file_size_in_byte": 1568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 7, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 10, "usage_type": "call"}, {"api_name": "allure.step", "line_number": 21, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 22, "usage_type": "call"}, {"api_name": "allure.attach", "line_number": 27, "usage_type": "call"}, {"api_name": "allure.attachment_type", "line_number": 27, "usage_type": "attribute"}, {"api_name": "allure.step", "line_number": 19, "usage_type": "attribute"}, {"api_name": "allure.step", "line_number": 32, "usage_type": "call"}, {"api_name": "allure.attach", "line_number": 36, "usage_type": "call"}, {"api_name": "allure.attachment_type", "line_number": 36, "usage_type": "attribute"}, {"api_name": "allure.step", "line_number": 30, "usage_type": "attribute"}]} +{"seq_id": "473750756", "text": "from django import http\nfrom django.shortcuts import render\n\n# Create your views here.\nfrom django.views import View\n\nfrom apps.contents.utils import get_categories\nfrom apps.goods.models import SKU, GoodsCategory, GoodsVisitCount\nfrom apps.goods.utils import get_breadcrumb\nfrom utils.response_code import RETCODE\n\n'''\n分析商品列表页\n一个页面的需求分析,先从大的方向把流程搞清楚\n再把页面中动态展示的数据分析出来(需求)\n\n再把一些需求模块化\n\n把需求简单化\n\n\n以列表数据展示为例:\n\n一 把需求写下来 (前端需要收集什么 后端需要做什么)\n 前端需要必须收集分类id,排序字段和页码是可选的\n 后端就是根据需要查询数据\n \n\n二 把大体思路写下来(后端的大体思路)\n # 1.根据分类id,把所有数据都查询出来\n # 2.如果有排序字段,再排序\n # 3.如果有分页字段再分页\n \n \n\n三 把详细思路完善一下(纯后端)\n 1.根据分类id,把所有数据都查询出来\n 2.如果有排序字段,再排序\n 3.如果有分页字段再分页\n \n\n四 确定我们请求方式和路由\n\n GET list/(?P\\d+)/(?P\\d+)/?sort=排序方法\n\n\n'''\nimport logging\nlogger = logging.getLogger('django')\n\nclass ListView(View):\n\n def get(self,request,category_id,page_num):\n\n # 一.面包屑的实现\n # 我们需要根据当前的分类,来获取他的上级/下级信息\n # ① 获取当前的分类\n try:\n category = GoodsCategory.objects.get(id=category_id)\n except Exception as e:\n logger.error(e)\n return render(request,'list.html',context={'errmsg':'没有此分类'})\n\n # ② 获取他的上/下级信息\n # 如果是三级 有3个信息\n # 如果是二级 有2个信息\n # 如果是一级 有1个信息\n # 对面包屑进行封装抽取\n breadcrumb = get_breadcrumb(category)\n\n\n\n\n\n\n # 二.列表数据\n # 1.如果有排序字段,先排序\n sort = request.GET.get('sort')\n # sort = hot 人气,根据销量排序\n # sort = price 价格,根据价格排序\n # sort = default 默认,根据create_time(上架时间)排序\n if sort == 'hot':\n order_filed = 'sales'\n elif sort == 'price':\n order_filed = 'price'\n else:\n order_filed = 'create_time'\n sort = 'default'\n # 2.根据分类id, 把所有数据都查询出来\n skus = SKU.objects.filter(category_id=category_id, is_launched=True).order_by(order_filed)\n\n\n # 3.如果有分页字段再分页\n try:\n page_num = int(page_num)\n except Exception as e:\n page_num = 0\n # 3.1导入分页类\n from django.core.paginator import Paginator\n try:\n # 3.2创建分页实例\n paginator = Paginator(skus,per_page=5)\n # 3.3获取分页数据\n page_skus = paginator.page(page_num)\n # 总页数\n total_page = paginator.num_pages\n except Exception as e:\n pass\n # 渲染页面\n context = {\n 'category': category, # 频道分类\n 'breadcrumb': breadcrumb, # 面包屑导航\n 'sort': sort, # 排序字段\n 'page_skus': page_skus, # 分页后数据\n 'total_page': total_page, # 总页数\n 'page_num': page_num, # 当前页码\n }\n\n return render(request,'list.html',context=context)\n\n\n'''\n# 热销排行页面的展示思路\n一 把需求写下来 (前端需要收集什么 后端需要做什么)\n 前端:需要把分类id传递给后端\n 后端:根据分类id查询数据\n \n\n二 把大体思路写下来(后端的大体思路)\n 1.获取分类id\n 2.查询是否有当前分类\n 3.根据分类去查询指定的数据,并进行排序,排序之后获取n条数据\n 4.ajax把列表对象转换为字典列表\n \n\n \n三 把详细思路完善一下(纯后端)\n # 1.获取分类id\n # 2.查询是否有当前分类\n # 3.根据分类去查询指定的数据,并进行排序,排序之后获取n条数据\n # 4.ajax把列表对象转换为字典列表\n \n\n四 确定我们请求方式和路由\n GET hot/cat_id/\n hot/?cat=xxxx\n \n'''\nclass HotView(View):\n\n def get(self,request,category_id):\n # 1.获取分类id\n # 2.查询是否有当前分类\n try:\n category = GoodsCategory.objects.get(id=category_id)\n except Exception as e:\n return http.JsonResponse({'code':RETCODE.NODATAERR,'errmag':'暂无此分类'})\n\n\n # 3.根据分类去查询指定的数据,并进行排序,排序之后获取n条数据\n skus = SKU.objects.filter(category=category,is_launched=True).order_by('-sales')[0:2]\n # 4.ajax把列表对象转换为字典列表\n skus_list = []\n for sku in skus:\n skus_list.append({\n 'id': sku.id,\n 'default_image_url': sku.default_image.url,\n 'name': sku.name,\n 'price': sku.price\n })\n return http.JsonResponse({'code':RETCODE.OK,'errmsg':'ok','hot_skus':skus_list})\n\n\n\n'''\n搜索引擎的原理: 类似于新华字典的索引\n\n 我是中国人 --> 搜索引擎 --> 进行分词处理 --> (我,是,中国,中国人,国人)\n\n 我 --> 我是中国人 这条记录\n 中国 -->\n\n 我是中\n 国人\n\n全文检索(搜索) --> 借助于 搜索引擎 (进行分词处理) --> 建立搜索词 和 搜索结果的对应关系\n\n 我 (我,是,中国,中国人,国人) 我是中国人\n\n\n\n1. 我们的搜索不使用like,因为like 查询效率低, 多个字段查询不方便\n\n2. 我们搜索使用全文检索\n\n3. 全文检索 需要使用 搜索引擎\n\n4. 我们的搜索引擎使用 elasticsearch\n\n\n使用 elasticsearch 实现全文检索\n\n\n数据 haystack elasticsearch\n\n'''\n\n\n\n\n\n\n\n\n\n\n\n\n\nclass DetailView(View):\n def get(self,request,sku_id):\n\n # 获取当前sku信息\n try:\n sku = SKU.objects.get(id=sku_id)\n except Exception as e:\n return render(request,'404.html')\n # 查询商品频道分类\n categories = get_categories()\n\n # 查询面包屑导航\n breadcrumb = get_breadcrumb(sku.category)\n\n\n # 构建当前商品的规格键\n sku_specs = sku.specs.order_by('spec_id')\n sku_key = []\n for spec in sku_specs:\n sku_key.append(spec.option.id)\n # 获取当前商品的所有SKU\n skus = sku.spu.sku_set.all()\n # 构建不同规格参数（选项）的sku字典\n spec_sku_map = {}\n for s in skus:\n # 获取sku的规格参数\n s_specs = s.specs.order_by('spec_id')\n # 用于形成规格参数-sku字典的键\n key = []\n for spec in s_specs:\n key.append(spec.option.id)\n # 向规格参数-sku字典添加记录\n spec_sku_map[tuple(key)] = s.id\n # 获取当前商品的规格信息\n goods_specs = sku.spu.specs.order_by('id')\n # 若当前sku的规格信息不完整，则不再继续\n if len(sku_key) < len(goods_specs):\n return\n for index, spec in enumerate(goods_specs):\n # 复制当前sku的规格键\n key = sku_key[:]\n # 该规格的选项\n spec_options = spec.options.all()\n for option in spec_options:\n # 在规格参数sku字典中查找符合当前规格的sku\n key[index] = option.id\n option.sku_id = spec_sku_map.get(tuple(key))\n spec.spec_options = spec_options\n\n\n # 渲染页面\n context = {\n 'categories': categories,\n 'breadcrumb': breadcrumb,\n 'sku': sku,\n 'specs': goods_specs,\n }\n\n return render(request,'detail.html',context)\n\n\n\n\"\"\"\n1.\n 当用户在列表页面/详情页面的时候，我们需要给后台发送一个统计访问量的请求\n 这个请求包含分类id就行\n \n 后台： \n 接收这个分类id，对他的统计个数+1\n \n2.后台要做什么\n 根据id，查询分类\n 再将当天的统计个数+1\n \n3.细化\n ① 获取分类id\n ② 根据分类id查询分类，判断分类是否存在\n ③ 我们以天为单位，如果当天没有统计数据，则应该新增统计数据\n 我们以天为单位，如果当天有统计数据，则应该更新统计数据\n ④ 返回响应\n4.请求方式和路由\n GET visit/?cat_id=xx\n detail/visit/(?P\\d+)/ \n\n\"\"\"\n\nclass VisitCategoryView(View):\n\n def get(self,request,category_id):\n # ① 获取分类id\n # ② 根据分类id查询分类，判断分类是否存在\n try:\n category = GoodsCategory.objects.get(id=category_id)\n except Exception as e:\n logger.error(e)\n return render(request,'404.html')\n\n # 我们需要查询当天的分类id记录\n # from datetime import datetime\n # now = datetime.now()\n # today_date = datetime.strptime(now,'%Y_%m-%d')\n\n from django.utils import timezone\n\n today = timezone.localdate()\n try:\n gvc = GoodsVisitCount.objects.get(date=today,category_id=category_id)\n\n except GoodsVisitCount.DoesNotExist:\n # 我们以天为单位，如果当天有统计数据，则应该更新统计数据\n GoodsVisitCount.objects.create(\n date=today,\n count=1,\n category_id=category_id\n )\n else:\n gvc.count += 1\n gvc.save()\n # ③ 我们以天为单位，如果当天没有统计数据，则应该新增统计数据\n\n # ④ 返回响应\n return http.JsonResponse({'code':RETCODE.OK,'errmsg':'ok'})\n\n", "sub_path": "meiduo_mall/apps/goods/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 10103, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.getLogger", "line_number": 49, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 51, "usage_type": "name"}, {"api_name": "apps.goods.models.GoodsCategory.objects.get", "line_number": 59, "usage_type": "call"}, {"api_name": "apps.goods.models.GoodsCategory.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "apps.goods.models.GoodsCategory", "line_number": 59, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 62, "usage_type": "call"}, {"api_name": "apps.goods.utils.get_breadcrumb", "line_number": 69, "usage_type": "call"}, {"api_name": "apps.goods.models.SKU.objects.filter", "line_number": 90, "usage_type": "call"}, {"api_name": "apps.goods.models.SKU.objects", "line_number": 90, "usage_type": "attribute"}, {"api_name": "apps.goods.models.SKU", "line_number": 90, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 102, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 119, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 149, "usage_type": "name"}, {"api_name": "apps.goods.models.GoodsCategory.objects.get", "line_number": 155, "usage_type": "call"}, {"api_name": "apps.goods.models.GoodsCategory.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "apps.goods.models.GoodsCategory", "line_number": 155, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 157, "usage_type": "call"}, {"api_name": "django.http", "line_number": 157, "usage_type": "name"}, {"api_name": "utils.response_code.RETCODE.NODATAERR", "line_number": 157, "usage_type": "attribute"}, {"api_name": "utils.response_code.RETCODE", "line_number": 157, "usage_type": "name"}, {"api_name": "apps.goods.models.SKU.objects.filter", "line_number": 161, "usage_type": "call"}, {"api_name": "apps.goods.models.SKU.objects", "line_number": 161, "usage_type": "attribute"}, {"api_name": "apps.goods.models.SKU", "line_number": 161, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 171, "usage_type": "call"}, {"api_name": "django.http", "line_number": 171, "usage_type": "name"}, {"api_name": "utils.response_code.RETCODE.OK", "line_number": 171, "usage_type": "attribute"}, {"api_name": "utils.response_code.RETCODE", "line_number": 171, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 220, "usage_type": "name"}, {"api_name": "apps.goods.models.SKU.objects.get", "line_number": 225, "usage_type": "call"}, {"api_name": "apps.goods.models.SKU.objects", "line_number": 225, "usage_type": "attribute"}, {"api_name": "apps.goods.models.SKU", "line_number": 225, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 227, "usage_type": "call"}, {"api_name": "apps.contents.utils.get_categories", "line_number": 229, "usage_type": "call"}, {"api_name": "apps.goods.utils.get_breadcrumb", "line_number": 232, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 278, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 306, "usage_type": "name"}, {"api_name": "apps.goods.models.GoodsCategory.objects.get", "line_number": 312, "usage_type": "call"}, {"api_name": "apps.goods.models.GoodsCategory.objects", "line_number": 312, "usage_type": "attribute"}, {"api_name": "apps.goods.models.GoodsCategory", "line_number": 312, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 315, "usage_type": "call"}, {"api_name": "django.utils.timezone.localdate", "line_number": 324, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 324, "usage_type": "name"}, {"api_name": "apps.goods.models.GoodsVisitCount.objects.get", "line_number": 326, "usage_type": "call"}, {"api_name": "apps.goods.models.GoodsVisitCount.objects", "line_number": 326, "usage_type": "attribute"}, {"api_name": "apps.goods.models.GoodsVisitCount", "line_number": 326, "usage_type": "name"}, {"api_name": "apps.goods.models.GoodsVisitCount.DoesNotExist", "line_number": 328, "usage_type": "attribute"}, {"api_name": "apps.goods.models.GoodsVisitCount", "line_number": 328, "usage_type": "name"}, {"api_name": "apps.goods.models.GoodsVisitCount.objects.create", "line_number": 330, "usage_type": "call"}, {"api_name": "apps.goods.models.GoodsVisitCount.objects", "line_number": 330, "usage_type": "attribute"}, {"api_name": "apps.goods.models.GoodsVisitCount", "line_number": 330, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 341, "usage_type": "call"}, {"api_name": "django.http", "line_number": 341, "usage_type": "name"}, {"api_name": "utils.response_code.RETCODE.OK", "line_number": 341, "usage_type": "attribute"}, {"api_name": "utils.response_code.RETCODE", "line_number": 341, "usage_type": "name"}]} +{"seq_id": "94935851", "text": "# -*- coding: utf-8 -*-\n\nfrom setuptools import setup, find_packages\n\nname = 'imagefavs'\nversion = '0.0.1'\nauthor = 'Tetsuya Shioda'\nauthor_email = 'shioda.tetsuya@gmail.com'\ndescription = 'Favorite Image Downloader'\n\ninstall_requires = [\n 'requests_oauthlib',\n]\n\nsetup(\n name=name,\n version=version,\n description=description,\n author=author,\n author_email=author_email,\n packages=['imagefavs'],\n install_requires=install_requires,\n entry_points={\n 'console_scripts': [\n 'imfv=imagefavs.client:main'\n ]\n }\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 563, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "setuptools.setup", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "54246539", "text": "\n# [link](https://linuxacademy.com/cp/exercises/view/id/712/module/168)\n\n\n# 5. Exercise: Interacting with External Commands\n# It’s not uncommon for a process to run on a server and listen to a port. Unfortunately, you sometimes don’t want that process to keep running, but all you know is the port that you want to free up.\n# write a script to make it easy to get rid of those pesky processes.\n# Write a script that does the following:\n# - Takes a port_number as its only argument.\n# - Calls out to lsof to determine if there is a process listening on that port.\n# - If there is a process, kill the process and inform the user.\n# - If there is no process, print that there was no process running on that port.\n\n# Python’s standard library comes with an HTTP server to start a server listening on a port 5500:\n# python -m http.server 5500\n\n# install lsof\n# sudo yum install -y lsof\n# lsof -n -i4TCP:PORT_NUMBER\n\n\nimport subprocess\nimport os\nfrom argparse import ArgumentParser\n\nparser = ArgumentParser(description='kill the running process listening on a given port')\nparser.add_argument(\"-port\", \"-p\", help=\"prot number\")\n# args = parser.parse_args\nport = parser.parse_args().port\n\ntry:\n result = subprocess.run(\n ['lsof', '-n', \"-i4TCP:%s\" % port],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\nexcept subprocess.CalledProcessError:\n print(f\"No process listening on port {port}\")\n exit(1)\nelse:\n listening = None\n for line in result.stdout.splitlines():\n if \"LISTEN\" in str(line):\n listening = line\n break\n\n if listening:\n # PID is the second column in the output\n pid = int(listening.split()[1])\n os.kill(pid, 9)\n print(f\"Killed process {pid}\")\n else:\n print(f\"No process listening on port {port}\")\n exit(1)\n\n\n", "sub_path": "0.project/autoscript/5.kill_process_by_portnum.py", "file_name": "5.kill_process_by_portnum.py", "file_ext": "py", "file_size_in_byte": 1851, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 26, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 32, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 34, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "subprocess.CalledProcessError", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.kill", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "492869778", "text": "# Copyright 2014 DreamHost, LLC\n#\n# Author: DreamHost, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\n\"\"\"State machine for managing a router.\n\n\"\"\"\n\n# See state machine diagram and description:\n# https://docs.google.com/a/dreamhost.com/document/d/1Ed5wDqCHW-CUt67ufjOUq4uYj0ECS5PweHxoueUoYUI/edit # noqa\n\nimport collections\nimport itertools\nimport logging\n\nfrom akanda.rug.event import POLL, CREATE, READ, UPDATE, DELETE, REBUILD\nfrom akanda.rug import vm_manager\n\n\nclass State(object):\n\n def __init__(self, log):\n self.log = log\n\n @property\n def name(self):\n return self.__class__.__name__\n\n def __str__(self):\n return self.name\n\n def execute(self, action, vm, worker_context, queue):\n return action\n\n def transition(self, action, vm, worker_context):\n return self\n\n\nclass CalcAction(State):\n def execute(self, action, vm, worker_context, queue):\n if DELETE in queue:\n self.log.debug('shortcutting to delete')\n return DELETE\n\n while queue:\n self.log.debug(\n 'action = %s, len(queue) = %s, queue = %s',\n action,\n len(queue),\n list(itertools.islice(queue, 0, 60))\n )\n\n if action == UPDATE and queue[0] == CREATE:\n # upgrade to CREATE from UPDATE by taking the next\n # item from the queue\n self.log.debug('upgrading from update to create')\n action = queue.popleft()\n continue\n\n elif action == UPDATE and queue[0] == REBUILD:\n # upgrade to REBUILD from UPDATE by taking the next\n # item from the queue\n self.log.debug('upgrading from update to rebuild')\n action = queue.popleft()\n continue\n\n elif action == CREATE and queue[0] == UPDATE:\n # CREATE implies an UPDATE so eat the update event\n # without changing the action\n self.log.debug('merging create and update')\n queue.popleft()\n continue\n\n elif queue[0] == POLL:\n # Throw away a poll following any other action,\n # because a create or update will automatically handle\n # the poll and repeated polls are not needed.\n self.log.debug('discarding poll event following action %s',\n action)\n queue.popleft()\n continue\n\n elif action != POLL and action != queue[0]:\n # We are not polling and the next action is something\n # different from what we are doing, so just do the\n # current action.\n self.log.debug('done collapsing events')\n break\n\n self.log.debug('popping action from queue')\n action = queue.popleft()\n\n return action\n\n def transition(self, action, vm, worker_context):\n if vm.state == vm_manager.GONE:\n return StopVM(self.log)\n elif action == DELETE:\n return StopVM(self.log)\n elif action == REBUILD:\n return RebuildVM(self.log)\n elif vm.state == vm_manager.BOOTING:\n return CheckBoot(self.log)\n elif vm.state == vm_manager.DOWN:\n return CreateVM(self.log)\n else:\n return Alive(self.log)\n\n\nclass PushUpdate(State):\n \"\"\"Put an update instruction on the queue for the state machine.\n \"\"\"\n def execute(self, action, vm, worker_context, queue):\n # Put the action back on the front of the queue.\n queue.appendleft(UPDATE)\n\n def transition(self, action, vm, worker_context):\n return CalcAction(self.log)\n\n\nclass Alive(State):\n def execute(self, action, vm, worker_context, queue):\n vm.update_state(worker_context)\n return action\n\n def transition(self, action, vm, worker_context):\n if vm.state == vm_manager.GONE:\n return StopVM(self.log)\n elif vm.state == vm_manager.DOWN:\n return CreateVM(self.log)\n elif action == POLL and vm.state == vm_manager.CONFIGURED:\n return CalcAction(self.log)\n elif action == READ and vm.state == vm_manager.CONFIGURED:\n return ReadStats(self.log)\n else:\n return ConfigureVM(self.log)\n\n\nclass CreateVM(State):\n def execute(self, action, vm, worker_context, queue):\n vm.boot(worker_context)\n return action\n\n def transition(self, action, vm, worker_context):\n if vm.state == vm_manager.GONE:\n return StopVM(self.log)\n return CheckBoot(self.log)\n\n\nclass CheckBoot(State):\n def execute(self, action, vm, worker_context, queue):\n vm.check_boot(worker_context)\n # Put the action back on the front of the queue so that we can yield\n # and handle it in another state machine traversal (which will proceed\n # from CalcAction directly to CheckBoot).\n if vm.state != vm_manager.GONE:\n queue.appendleft(action)\n return action\n\n def transition(self, action, vm, worker_context):\n if vm.state == vm_manager.GONE:\n return StopVM(self.log)\n if vm.state == vm_manager.UP:\n return ConfigureVM(self.log)\n return CalcAction(self.log)\n\n\nclass StopVM(State):\n def execute(self, action, vm, worker_context, queue):\n vm.stop(worker_context)\n if vm.state == vm_manager.GONE:\n # Force the action to delete since the router isn't there\n # any more.\n return DELETE\n return action\n\n def transition(self, action, vm, worker_context):\n if vm.state not in (vm_manager.DOWN, vm_manager.GONE):\n return self\n if vm.state == vm_manager.GONE:\n return Exit(self.log)\n if action == DELETE:\n return Exit(self.log)\n return CreateVM(self.log)\n\n\nclass RebuildVM(State):\n def execute(self, action, vm, worker_context, queue):\n vm.stop(worker_context)\n if vm.state == vm_manager.GONE:\n # Force the action to delete since the router isn't there\n # any more.\n return DELETE\n # Re-create the VM\n return CREATE\n\n def transition(self, action, vm, worker_context):\n if vm.state not in (vm_manager.DOWN, vm_manager.GONE):\n return self\n if vm.state == vm_manager.GONE:\n return Exit(self.log)\n return CreateVM(self.log)\n\n\nclass Exit(State):\n pass\n\n\nclass ConfigureVM(State):\n def execute(self, action, vm, worker_context, queue):\n vm.configure(worker_context)\n if vm.state == vm_manager.CONFIGURED:\n if action == READ:\n return READ\n else:\n return POLL\n else:\n return action\n\n def transition(self, action, vm, worker_context):\n if vm.state in (vm_manager.RESTART, vm_manager.DOWN, vm_manager.GONE):\n return StopVM(self.log)\n if vm.state == vm_manager.UP:\n return PushUpdate(self.log)\n # Below here, assume vm.state == vm_manager.CONFIGURED\n if action == READ:\n return ReadStats(self.log)\n return CalcAction(self.log)\n\n\nclass ReadStats(State):\n def execute(self, action, vm, worker_context, queue, bandwidth_callback):\n stats = vm.read_stats()\n bandwidth_callback(stats)\n return POLL\n\n def transition(self, action, vm, worker_context):\n return CalcAction(self.log)\n\n\nclass Automaton(object):\n def __init__(self, router_id, tenant_id,\n delete_callback, bandwidth_callback,\n worker_context, queue_warning_threshold):\n \"\"\"\n :param router_id: UUID of the router being managed\n :type router_id: str\n :param tenant_id: UUID of the tenant being managed\n :type tenant_id: str\n :param delete_callback: Invoked when the Automaton decides\n the router should be deleted.\n :type delete_callback: callable\n :param bandwidth_callback: To be invoked when the Automaton\n needs to report how much bandwidth\n a router has used.\n :type bandwidth_callback: callable taking router_id and bandwidth\n info dict\n :param worker_context: a WorkerContext\n :type worker_context: WorkerContext\n :param queue_warning_threshold: Limit after which adding items\n to the queue triggers a warning.\n :type queue_warning_threshold: int\n \"\"\"\n self.router_id = router_id\n self.tenant_id = tenant_id\n self._delete_callback = delete_callback\n self._queue_warning_threshold = queue_warning_threshold\n self.deleted = False\n self.bandwidth_callback = bandwidth_callback\n self._queue = collections.deque()\n self.log = logging.getLogger(__name__ + '.' + router_id)\n\n self.state = CalcAction(self.log)\n self.action = POLL\n self.vm = vm_manager.VmManager(router_id, tenant_id, self.log,\n worker_context)\n\n def service_shutdown(self):\n \"Called when the parent process is being stopped\"\n\n def _do_delete(self):\n if self._delete_callback is not None:\n self.log.debug('calling delete callback')\n self._delete_callback()\n # Avoid calling the delete callback more than once.\n self._delete_callback = None\n # Remember that this router has been deleted\n self.deleted = True\n\n def update(self, worker_context):\n \"Called when the router config should be changed\"\n while self._queue:\n while True:\n if self.deleted:\n self.log.debug(\n 'skipping update because the router is being deleted'\n )\n return\n\n try:\n additional_args = ()\n\n if isinstance(self.state, ReadStats):\n additional_args = (self.bandwidth_callback,)\n\n self.log.debug('%s.execute(%s) vm.state=%s',\n self.state, self.action, self.vm.state)\n self.action = self.state.execute(\n self.action,\n self.vm,\n worker_context,\n self._queue,\n *additional_args\n )\n self.log.debug('%s.execute -> %s vm.state=%s',\n self.state, self.action, self.vm.state)\n except:\n self.log.exception(\n '%s.execute() failed for action: %s',\n self.state,\n self.action\n )\n\n old_state = self.state\n self.state = self.state.transition(\n self.action,\n self.vm,\n worker_context,\n )\n self.log.debug('%s.transition(%s) -> %s vm.state=%s',\n old_state, self.action, self.state,\n self.vm.state)\n\n # Yield control each time we stop to figure out what\n # to do next.\n if isinstance(self.state, CalcAction):\n return # yield\n\n # We have reached the exit state, so the router has\n # been deleted somehow.\n if isinstance(self.state, Exit):\n self._do_delete()\n return\n\n def send_message(self, message):\n \"Called when the worker put a message in the state machine queue\"\n if self.deleted:\n # Ignore any more incoming messages\n self.log.debug(\n 'deleted state machine, ignoring incoming message %s',\n message)\n return False\n self._queue.append(message.crud)\n queue_len = len(self._queue)\n if queue_len > self._queue_warning_threshold:\n logger = self.log.warning\n else:\n logger = self.log.debug\n logger('incoming message brings queue length to %s', queue_len)\n return True\n\n def has_more_work(self):\n \"Called to check if there are more messages in the state machine queue\"\n return (not self.deleted) and bool(self._queue)\n", "sub_path": "akanda/rug/state.py", "file_name": "state.py", "file_ext": "py", "file_size_in_byte": 13167, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "akanda.rug.event.DELETE", "line_number": 54, "usage_type": "name"}, {"api_name": "akanda.rug.event.DELETE", "line_number": 56, "usage_type": "name"}, {"api_name": "itertools.islice", "line_number": 63, "usage_type": "call"}, {"api_name": "akanda.rug.event.UPDATE", "line_number": 66, "usage_type": "name"}, {"api_name": "akanda.rug.event.CREATE", "line_number": 66, "usage_type": "name"}, {"api_name": "akanda.rug.event.UPDATE", "line_number": 73, "usage_type": "name"}, {"api_name": "akanda.rug.event.REBUILD", "line_number": 73, "usage_type": "name"}, {"api_name": "akanda.rug.event.CREATE", "line_number": 80, "usage_type": "name"}, {"api_name": "akanda.rug.event.UPDATE", "line_number": 80, "usage_type": "name"}, {"api_name": "akanda.rug.event.POLL", "line_number": 87, "usage_type": "name"}, {"api_name": "akanda.rug.event.POLL", "line_number": 96, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 109, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 109, "usage_type": "name"}, {"api_name": "akanda.rug.event.DELETE", "line_number": 111, "usage_type": "name"}, {"api_name": "akanda.rug.event.REBUILD", "line_number": 113, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.BOOTING", "line_number": 115, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 115, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.DOWN", "line_number": 117, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 117, "usage_type": "name"}, {"api_name": "akanda.rug.event.UPDATE", "line_number": 128, "usage_type": "argument"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 140, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 140, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.DOWN", "line_number": 142, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 142, "usage_type": "name"}, {"api_name": "akanda.rug.event.POLL", "line_number": 144, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.CONFIGURED", "line_number": 144, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 144, "usage_type": "name"}, {"api_name": "akanda.rug.event.READ", "line_number": 146, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.CONFIGURED", "line_number": 146, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 146, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 158, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 158, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 169, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 169, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 174, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 174, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.UP", "line_number": 176, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 176, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 184, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 184, "usage_type": "name"}, {"api_name": "akanda.rug.event.DELETE", "line_number": 187, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.DOWN", "line_number": 191, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 191, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 191, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 193, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 193, "usage_type": "name"}, {"api_name": "akanda.rug.event.DELETE", "line_number": 195, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 203, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 203, "usage_type": "name"}, {"api_name": "akanda.rug.event.DELETE", "line_number": 206, "usage_type": "name"}, {"api_name": "akanda.rug.event.CREATE", "line_number": 208, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.DOWN", "line_number": 211, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 211, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 211, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 213, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 213, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.CONFIGURED", "line_number": 225, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 225, "usage_type": "name"}, {"api_name": "akanda.rug.event.READ", "line_number": 226, "usage_type": "name"}, {"api_name": "akanda.rug.event.READ", "line_number": 227, "usage_type": "name"}, {"api_name": "akanda.rug.event.POLL", "line_number": 229, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.RESTART", "line_number": 234, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 234, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.DOWN", "line_number": 234, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager.GONE", "line_number": 234, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager.UP", "line_number": 236, "usage_type": "attribute"}, {"api_name": "akanda.rug.vm_manager", "line_number": 236, "usage_type": "name"}, {"api_name": "akanda.rug.event.READ", "line_number": 239, "usage_type": "name"}, {"api_name": "akanda.rug.event.POLL", "line_number": 248, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 283, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 284, "usage_type": "call"}, {"api_name": "akanda.rug.event.POLL", "line_number": 287, "usage_type": "name"}, {"api_name": "akanda.rug.vm_manager.VmManager", "line_number": 288, "usage_type": "call"}, {"api_name": "akanda.rug.vm_manager", "line_number": 288, "usage_type": "name"}]} +{"seq_id": "151289296", "text": "\"\"\"\n\n The non-machine-learning game serving as background created by:\n\n Sample Breakout Game\n\n Sample Python/Pygame Programs\n Simpson College Computer Science\n http://programarcadegames.com/\n http://simpson.edu/computer-science/\n\n\n\n\"\"\"\n\n\nimport math\nimport pygame\nimport torch\ndevice = torch.device(\"cuda:0\")\ndtype = torch.float\n# Define some colors\nblack = (0, 0, 0)\nwhite = (255, 255, 255)\nblue = (0, 0, 255)\n\n# Size of break-out blocks\nblock_width = 23\nblock_height = 15\n\nclass Block(pygame.sprite.Sprite):\n \"\"\"This class represents each block that will get knocked out by the ball\n It derives from the \"Sprite\" class in Pygame \"\"\"\n\n def __init__(self, color, x, y):\n \"\"\" Constructor. Pass in the color of the block,\n and its x and y position. \"\"\"\n\n # Call the parent class (Sprite) constructor\n super(Block, self).__init__()\n\n # Create the image of the block of appropriate size\n # The width and height are sent as a list for the first parameter.\n self.image = pygame.Surface([block_width, block_height])\n\n # Fill the image with the appropriate color\n self.image.fill(color)\n\n # Fetch the rectangle object that has the dimensions of the image\n self.rect = self.image.get_rect()\n\n # Move the top left of the rectangle to x,y.\n # This is where our block will appear..\n self.rect.x = x\n self.rect.y = y\n\n\nclass Ball(pygame.sprite.Sprite):\n \"\"\" This class represents the ball\n It derives from the \"Sprite\" class in Pygame \"\"\"\n\n # Speed in pixels per cycle\n speed = 2.0\n\n # Floating point representation of where the ball is\n x = 0.0\n y = 180.0\n\n # Direction of ball (in degrees)\n direction = 200\n\n width = 10\n height = 10\n\n # Constructor. Pass in the color of the block, and its x and y position\n def __init__(self):\n # Call the parent class (Sprite) constructor\n super(Ball, self).__init__()\n\n # Create the image of the ball\n self.image = pygame.Surface([self.width, self.height])\n\n # Color the ball\n self.image.fill(white)\n\n # Get a rectangle object that shows where our image is\n self.rect = self.image.get_rect()\n\n # Get attributes for the height/width of the screen\n self.screenheight = pygame.display.get_surface().get_height()\n self.screenwidth = pygame.display.get_surface().get_width()\n # self.x = torch.rand(0, self.screenwidth - self.width)\n\n def bounce(self, diff):\n \"\"\" This function will bounce the ball\n off a horizontal surface (not a vertical one) \"\"\"\n\n self.direction = (180 - self.direction) % 360\n self.direction -= diff\n\n def read_pos(self):\n return self.rect.x\n\n def update(self):\n \"\"\" Update the position of the ball. \"\"\"\n # Sine and Cosine work in degrees, so we have to convert them\n direction_radians = math.radians(self.direction)\n\n # Change the position (x and y) according to the speed and direction\n self.x += self.speed * math.sin(direction_radians)\n self.y -= self.speed * math.cos(direction_radians)\n\n # Move the image to where our x and y are\n self.rect.x = self.x\n self.rect.y = self.y\n\n # Do we bounce off the top of the screen?\n if self.y <= 0:\n self.bounce(0)\n self.y = 1\n\n # Do we bounce off the left of the screen?\n if self.x <= 0:\n self.direction = (360 - self.direction) % 360\n self.x = 1\n\n # Do we bounce of the right side of the screen?\n if self.x > self.screenwidth - self.width:\n self.direction = (360 - self.direction) % 360\n self.x = self.screenwidth - self.width - 1\n\n # Did we fall off the bottom edge of the screen?\n if self.y > 600:\n return True\n else:\n return False\n\n\nclass Player(pygame.sprite.Sprite):\n \"\"\" This class represents the bar at the bottom that the\n player controls. \"\"\"\n\n def __init__(self):\n \"\"\" Constructor for Player. \"\"\"\n # Call the parent's constructor\n super(Player, self).__init__()\n\n self.width = 75\n self.height = 15\n self.image = pygame.Surface([self.width, self.height])\n self.image.fill((white))\n\n # Make our top-left corner the passed-in location.\n self.rect = self.image.get_rect()\n self.screenheight = pygame.display.get_surface().get_height()\n self.screenwidth = pygame.display.get_surface().get_width()\n\n self.rect.x = 0\n self.rect.y = self.screenheight - self.height\n\n def update(self, decision):\n \"\"\" Update the player position. \"\"\"\n # Get where the mouse is\n # pos = pygame.mouse.get_pos()\n # Set the left side of the player bar to the mouse position\n\n # self.rect.x = pos[0]\n if decision[0][0] == 1:\n self.rect.x -= 3\n if decision[0][1] == 1:\n pass\n if decision[0][2] == 1:\n self.rect.x += 3\n\n # Make sure we don't push the player paddle\n\n # off the right side of the screen\n if self.rect.x > self.screenwidth - self.width:\n self.rect.x = self.screenwidth - self.width\n if self.rect.x < 0:\n self.rect.x = 0\n def read_pos(self):\n return self.rect.x\n\n\nclass Network:\n\n def __init__(self):\n\n self.D_in, self.H1, self.H2, self.D_out = 4, 100, 100, 3\n self.learning_rate = 1e-6\n\n # Declaring weighs tensors\n self.w1 = torch.randn(self.D_in, self.H1, device=device, dtype=dtype)\n self.w2 = torch.randn(self.H1, self.H2, device=device, dtype=dtype)\n self.w3 = torch.randn(self.H2, self.D_out, device=device, dtype=dtype)\n\n def forward(self, input_arr):\n\n self.h1 = input_arr.mm(self.w1)\n self.h1_relu = self.h1.clamp(min=0)\n self.h2 = self.h1_relu.mm(self.w2)\n self.h2_relu = self.h2.clamp(min=0)\n y_pred = self.h2_relu.mm(self.w3)\n predicted_move = y_pred\n temp = torch.argmax(y_pred[0])\n if temp == 0:\n predicted_move[0][0], predicted_move[0][1], predicted_move[0][2] = 1, 0, 0\n if temp == 1:\n predicted_move[0][0], predicted_move[0][1], predicted_move[0][2] = 0, 1, 0\n if temp == 2:\n predicted_move[0][0], predicted_move[0][1], predicted_move[0][2] = 0, 0, 1\n\n return predicted_move\n\n\n def train(self, err, input_arr):\n\n grad_y_pred = -err\n grad_w3 = self.h2_relu.t().mm(grad_y_pred)\n grad_h2_relu = grad_y_pred.mm(self.w3.t())\n grad_h2 = grad_h2_relu.clone()\n grad_h2[self.h2 < 0] = 0\n\n grad_w2 = self.h1_relu.t().mm(grad_h2)\n grad_h1_relu = grad_h2.mm(self.w2.t())\n grad_h1 = grad_h1_relu.clone()\n grad_h1[self.h1 < 0] = 0\n\n grad_w1 = input_arr.t().mm(grad_h1)\n\n # Update weights using gradient descent\n self.w1 -= self.learning_rate * grad_w1\n self.w2 -= self.learning_rate * grad_w2\n self.w3 -= self.learning_rate * grad_w3\n\n# Call this function so the Pygame library can initialize itself\npygame.init()\nexit_program = False\nepochs = 200\nepoch=0\nerr_log = torch.randn(1, 3, device=device, dtype=dtype)\n\nnetwork = Network()\nwhile not exit_program:\n epoch += 1\n if epoch == epochs:\n break\n # Create an 800x600 sized screen\n screen = pygame.display.set_mode([800, 600])\n\n # Set the title of the window\n pygame.display.set_caption('Breakout')\n\n # Enable this to make the mouse disappear when over our window\n pygame.mouse.set_visible(0)\n\n # This is a font we use to draw text on the screen (size 36)\n font = pygame.font.Font(None, 36)\n\n # Create a surface we can draw on\n background = pygame.Surface(screen.get_size())\n\n # Create sprite lists\n blocks = pygame.sprite.Group()\n balls = pygame.sprite.Group()\n allsprites = pygame.sprite.Group()\n\n # Create the player paddle object\n player = Player()\n allsprites.add(player)\n\n # Create the ball\n ball = Ball()\n allsprites.add(ball)\n balls.add(ball)\n\n # The top of the block (y position)\n top = 80\n\n # Number of blocks to create\n blockcount = 32\n\n # --- Create blocks\n\n # Five rows of blocks\n for row in range(5):\n # 32 columns of blocks\n for column in range(0, blockcount):\n # Create a block (color,x,y)\n block = Block(blue, column * (block_width + 2) + 1, top)\n blocks.add(block)\n allsprites.add(block)\n # Move the top of the next row down\n top += block_height + 2\n initial_blocks = len(blocks)\n # Clock to limit speed\n clock = pygame.time.Clock()\n\n # Is the game over?\n game_over = False\n\n # Initialization of network input\n input_array = torch.randn(1, 4, device=device, dtype=dtype)\n\n # Main program loop\n\n while not game_over:\n\n # Network input update\n input_array[0][0] = ball.x\n input_array[0][1] = ball.y\n input_array[0][2] = ball.direction\n input_array[0][3] = player.read_pos()\n\n # On regular computer game will be slower anyway\n clock.tick(1000)\n\n # Clear the screen\n screen.fill(black)\n\n # Process the events in the game\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n exit_program = True\n\n # Update the ball and player position as long\n # as the game is not over. In case game is over, check on which side of the player\n # has the ball touched the ground.\n if ball.update():\n if ball.read_pos() > player.read_pos():\n err_log[0][0], err_log[0][1], err_log[0][2] = 0, 0, 1\n else:\n err_log[0][0], err_log[0][1], err_log[0][2] = 1, 0, 0\n game_over = True\n player.update(network.forward(input_array))\n\n # If we are done, print game over\n\n if exit_program == True:\n break\n\n # See if the ball hits the player paddle\n if pygame.sprite.spritecollide(player, balls, False):\n # The 'diff' lets you try to bounce the ball left or right\n # depending where on the paddle you hit it\n diff = (player.rect.x + player.width / 2) - (ball.rect.x + ball.width / 2)\n\n # Set the ball's y position in case\n # we hit the ball on the edge of the paddle\n ball.rect.y = screen.get_height() - player.rect.height - ball.rect.height - 1\n ball.bounce(diff)\n\n # Check for collisions between the ball and the blocks\n deadblocks = pygame.sprite.spritecollide(ball, blocks, True)\n\n # If we actually hit a block, bounce the ball\n if len(deadblocks) > 0:\n ball.bounce(0)\n\n # Game ends if all the blocks are gone\n if len(blocks) == 0:\n game_over = True\n\n # Draw Everything\n allsprites.draw(screen)\n\n # Flip the screen and show what we've drawn\n pygame.display.flip()\n\n\n network.train(err_log, input_array)\n\n\npygame.quit()", "sub_path": "game_script.py", "file_name": "game_script.py", "file_ext": "py", "file_size_in_byte": 11181, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torch.device", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.display.get_surface", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pygame.display.get_surface", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 91, "usage_type": "attribute"}, {"api_name": "math.radians", "line_number": 107, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 110, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 111, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 150, "usage_type": "call"}, {"api_name": "pygame.display.get_surface", "line_number": 155, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 155, "usage_type": "attribute"}, {"api_name": "pygame.display.get_surface", "line_number": 156, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 206, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 242, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 250, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 250, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 253, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 253, "usage_type": "attribute"}, {"api_name": "pygame.mouse.set_visible", "line_number": 256, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 256, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 259, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 259, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 262, "usage_type": "call"}, {"api_name": "pygame.sprite.Group", "line_number": 265, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 266, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 266, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 267, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 267, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 298, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 298, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 304, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 323, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 323, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 324, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 344, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 344, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 355, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 355, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 369, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 369, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 375, "usage_type": "call"}]} +{"seq_id": "180778058", "text": "import os\nimport tensorflow as tf\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ncwd='C:\\\\Users\\\\Rivaille\\\\Desktop\\\\ROkinect\\\\dataset3\\\\feng\\\\feng_learn\\\\'\nclasses={'back','front', 'side'} #\nwriter= tf.python_io.TFRecordWriter(\"train.tfrecords\") #要生成的文件\n\nfor index,name in enumerate(classes):\n class_path=cwd+name+'\\\\'\n for img_name in os.listdir(class_path):\n img_path=class_path+img_name #每一个图片的地址\n\n img=Image.open(img_path)\n img= img.resize((64,128))\n\n img_raw=img.all_vectores[index]#将图片转化为二进制格式\n example = tf.train.Example(features=tf.train.Features(feature={\n \"label\": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),\n 'img_raw': tf.train.Feature(\n int64_list=tf.train.Int64List(value=[img].astype(\"int64\")))\n })) #example对象对label和image数据进行封装\n writer.write(example.SerializeToString()) #序列化为字符串\n\nwriter.close()", "sub_path": "tf1/NN_tensorflow/makeTFrecord2.py", "file_name": "makeTFrecord2.py", "file_ext": "py", "file_size_in_byte": 1036, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "tensorflow.python_io.TFRecordWriter", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.python_io", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 16, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "name"}, {"api_name": "tensorflow.train.Example", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Features", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Int64List", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Int64List", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "319127620", "text": "#! /usr/bin/env python\n# -*- coding: utf-8 -*-\nimport io\n\n\ndef can_be_float(value):\n try:\n float(value)\n return True\n except ValueError:\n return False\n\n\ndef can_be_int(value):\n try:\n int(value)\n return True\n except ValueError:\n return False\n\n\ndef index_exists(list_to_check, index_to_check):\n try:\n list_to_check[index_to_check]\n return True\n except IndexError:\n return False\n\n\ndef are_floats(list_to_check):\n for item in list_to_check:\n if can_be_float(item) is True:\n pass\n else:\n return False\n return True\n\n\ndef plus_one(*args):\n variables = list()\n for i in args:\n variables.append(i + 1)\n return variables\n\n\ndef logwrite(logfilename, *args):\n form_string = unicode(args[0])\n for i in range(1, len(args), 1):\n form_string += '\\t'\n form_string += unicode(args[i])\n form_string += '\\n'\n with io.open(logfilename, 'a', encoding='utf-8') as logfile:\n logfile.write(form_string)\n\n\ndef is_ascii(string_to_check):\n try:\n string_to_check.decode('ascii')\n except UnicodeDecodeError:\n return False\n else:\n return True\n\n\ndef read_csv(csv_input_filename, columns_to_return):\n import csv\n with open(csv_input_filename, 'rt') as input_file:\n data = list(csv.reader(input_file, delimiter='\\t'))\n\n input_file.close()\n return tuple(map(list, zip(*data)))[:columns_to_return]\n\n\ndef write_csv(data, filename):\n import csv\n with open(filename, 'wb') as output_file:\n write = csv.writer(output_file)\n for row in data:\n write.writerow(row)\n\n output_file.close()\n\n\ndef generate_date_range(date_start, periods_count, period_size):\n import pandas\n if period_size == 'month':\n dates = pandas.date_range(date_start, periods=periods_count, freq='MS')\n elif period_size == 'hour':\n dates = pandas.date_range(date_start, periods=periods_count, freq='60 min')\n elif period_size == '15_min':\n dates = pandas.date_range(date_start, periods=periods_count, freq='15 min')\n elif period_size == 'year':\n dates = pandas.date_range(date_start, periods=periods_count, freq='AS')\n return dates\n\n\ndef draw(dates, x_size, y_size, title, y_axis_name, filename, *args):\n import os\n import matplotlib\n matplotlib.use('agg')\n import matplotlib.pyplot as plt\n plt.figure(figsize=(x_size, y_size))\n for data in args:\n plt.plot(dates, data)\n\n plt.title(title)\n plt.ylabel(y_axis_name)\n plt.xticks(rotation=25)\n plt.grid()\n plt.savefig(filename)\n os.system('gwenview %s' % filename)\n\n\ndef save_vars(filename, *args):\n import pickle\n vars_to_dump = list()\n\n with open(filename, 'w') as dumpfile:\n if len(args) == 1:\n pickle.dump(args[0], dumpfile)\n\n elif len(args) > 1:\n for i in args:\n vars_to_dump.append(i)\n pickle.dump(vars_to_dump, dumpfile)\n\n\ndef load_vars(filename):\n import pickle\n with open(filename) as dumpfile:\n variables_list = pickle.load(dumpfile)\n return variables_list\n", "sub_path": "functions_all.py", "file_name": "functions_all.py", "file_ext": "py", "file_size_in_byte": 3169, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "io.open", "line_number": 52, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 68, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.use", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "os.system", "line_number": 111, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 120, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 125, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "423419525", "text": "import bz2\nimport json\n\nfrom cmv.preprocessing.postPreprocessor import PostPreprocessor\n\nclass MetadataGenerator:\n def __init__(self, train_filename, val_filename, num_responses=2**32, extend=True,\n discourse=True, frames=True, sentiment=False):\n self.train_filename = train_filename\n self.val_filename = val_filename\n self.num_responses = num_responses\n self.extend = extend\n self.border = 'INTERMEDIATE_DISCUSSION'\n\n self.discourse = discourse\n self.frames = frames\n self.sentiment = sentiment\n \n self._data = None\n \n def _load_file(self, filename):\n pairs = []\n with bz2.BZ2File(filename) as f:\n for line in f:\n pairs.append(json.loads(line))\n return pairs\n\n @property\n def data(self):\n if self._data is not None:\n return self._data\n\n train = self._load_file(self.train_filename)\n val = self._load_file(self.val_filename)\n \n train_op, train_titles, train_pos, train_pos_indices, train_neg, train_neg_indices = self.processData(train)\n val_op, val_titles, val_pos, val_pos_indices, val_neg, val_neg_indices = self.processData(val)\n\n self._data = dict(train_op=train_op,\n train_titles=train_titles,\n train_pos=train_pos,\n train_pos_indices=train_pos_indices,\n train_neg=train_neg,\n train_neg_indices=train_neg_indices,\n val_op=val_op,\n val_titles=val_titles,\n val_pos=val_pos,\n val_pos_indices=val_pos_indices,\n val_neg=val_neg,\n val_neg_indices=val_neg_indices)\n \n return self._data\n \n def processData(self, pairs):\n op = []\n titles = []\n pos = []\n pos_indices = []\n neg = []\n neg_indices = []\n \n for pair_index,pair in enumerate(pairs):\n op.append(PostPreprocessor(pair['op_text'], op=True,\n discourse=False, frames=False).processedData)\n\n post = ''\n for comment_index,comment in enumerate(pair['negative']['comments'][:self.num_responses]):\n if self.extend:\n if comment_index > 0:\n post += '\\n' + self.border + '\\n'\n post += comment['body']\n else:\n neg.append(PostPreprocessor(comment['body'],\n discourse=self.discourse, frames=self.frames,\n sentiment=self.sentiment).processedData)\n neg_indices.append(pair_index)\n \n if self.extend:\n neg.append(PostPreprocessor(comment['body'],\n discourse=self.discourse, frames=self.frames,\n sentiment=self.sentiment).processedData)\n neg_indices.append(pair_index)\n \n post = ''\n for comment_index,comment in enumerate(pair['positive']['comments'][:self.num_responses]):\n if self.extend:\n if comment_index > 0:\n post += '\\n' + self.border + '\\n'\n post += comment['body']\n else:\n pos.append(PostPreprocessor(comment['body'],\n discourse=self.discourse, frames=self.frames,\n sentiment=self.sentiment).processedData)\n pos_indices.append(pair_index)\n\n if self.extend:\n pos.append(PostPreprocessor(comment['body'],\n discourse=self.discourse, frames=self.frames,\n sentiment=self.sentiment).processedData)\n pos_indices.append(pair_index)\n \n titles.append(PostPreprocessor(pair['op_title'], discourse=False, frames=False).processedData)\n \n return op, titles, pos, pos_indices, neg, neg_indices\n\n \n", "sub_path": "cmv/preprocessing/metadataGenerator.py", "file_name": "metadataGenerator.py", "file_ext": "py", "file_size_in_byte": 4372, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "bz2.BZ2File", "line_number": 23, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "cmv.preprocessing.postPreprocessor.PostPreprocessor", "line_number": 63, "usage_type": "call"}, {"api_name": "cmv.preprocessing.postPreprocessor.PostPreprocessor", "line_number": 73, "usage_type": "call"}, {"api_name": "cmv.preprocessing.postPreprocessor.PostPreprocessor", "line_number": 79, "usage_type": "call"}, {"api_name": "cmv.preprocessing.postPreprocessor.PostPreprocessor", "line_number": 91, "usage_type": "call"}, {"api_name": "cmv.preprocessing.postPreprocessor.PostPreprocessor", "line_number": 97, "usage_type": "call"}, {"api_name": "cmv.preprocessing.postPreprocessor.PostPreprocessor", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "221116979", "text": "from . import Thumbnail\nimport os, re\nimport StringIO\nimport logging #DEBUG\nfrom google.appengine.api import app_identity\nfrom google.appengine.ext import blobstore\nfrom google.appengine.api import urlfetch\nimport cloudstorage as gcs\nimport google.appengine.api.images as gimages\n\ndef post_data(url, payload):\n import json\n import requests\n data = json.dumps(payload)\n headers={ \"content-type\":\"application/json\"\n , \"datatype\":\"json\"\n }\n r = requests.post(url,data=data, headers=headers)\n if requests.codes.OK == r.status_code:\n pass\n else:\n pass #TODO: check all images with r.raise_for_status()\n\n\ndef _create_thumbnail_async_handle_results(rpc):\n \"\"\" does nothing \"\"\"\n pass\n #result = rpc.get_result()\n\ndef _create_thumbnail_async_callback(rpc):\n \"\"\" Use a helper function to define the scope of the callback. \"\"\"\n \"\"\" see: https://cloud.google.com/appengine/docs/python/urlfetch/asynchronousrequests#make_fetch_call\"\"\"\n return lambda: _create_thumbnail_async_handle_results(rpc)\n\nclass ThumbnailGAEasync(Thumbnail):\n \"\"\"makes Thumbnail work with GAE, extends flask.thumbnail\"\"\"\n _gs_bucket = None\n def __init__(self, app=None):\n super(self.__class__, self).__init__(app)\n self._rpcs = []\n\n # check if thumbnail already exists\n def _thumb_exists(self, thumb_url):\n #return False\n # TODO: use fast! and google intern file check\n if(len(thumb_url)>5):\n scheme = thumb_url[:5]\n if scheme in [\"http:\",\"https\"]:\n return self._url_exists(thumb_url)\n else :\n return self._gcs_file_exists(thumb_url)\n return False\n\n\n def _url_exists(self, url):\n ## google api compatible\n #from google.appengine.api import urlfetch\n #result = urlfetch.fetch(url)\n #if result.status_code == 200:\n # return True\n\n ## requests (faster than urlfetch?)\n try:\n r = requests.head(url)\n if requests.codes.OK == r.status_code: # this url repsonse is OK\n return True\n except Exception as e:\n logging.error(\"EXEPTION: flask.thumbnailsGAE._url_exists: error {}\".format(str(e)))\n return False\n\n # it's rather slow, but quite safe\n def _gcs_file_exists(self, url):\n try:\n from furl import furl\n from os import path\n thumb_gs_path = self._gs_path(url)\n stat = gcs.stat(thumb_gs_path)\n if stat.filename == thumb_gs_path:\n if stat.st_size > 0:\n f = furl(url)\n if stat.content_type.startswith(\"image/\"):\n import mimetypes\n if mimetypes.guess_type(url) == stat.content_type:\n return True\n return False\n except gcs.NotFoundError as e:\n logging.debug(\"flask.thumbnailsGAE._gcs_file_exists: url: '{}' not found\\nException: {}\".format(url, str(e)))\n return False\n logging.debug(\"flask.thumbnailsGAE._gcs_file_exists: url: '{}' return false\".format(url))\n return False\n\n def _store_thumb(self, thumb_url, thumb_pic, quality=100):\n try:\n thumb_filepath = self._gs_path(thumb_url)\n import mimetypes\n (content_type, content_encoding) = mimetypes.guess_type(thumb_url, strict=True)\n with gcs.open(thumb_filepath,\n 'w',\n content_type=content_type,\n options={'x-goog-acl': 'public-read'}) as thumb_gcs:\n fileext=content_type.split(\"/\")[-1]\n thumb_pic.save(thumb_gcs, fileext) # TODO save(thumb_gcs, fileext, QUALITY)\n except Exception as e:\n # TODO: better:\n # raise\n raise Exception(\"flask.thumbnailGAE._store_thumb: couldn't create thumbnail @ '{}'\\nError: {}\".format(thumb_url, str(e)))\n return thumb_url\n\n def _check_and_create(self, thumb_url, img_path, size, crop, bg, quality):\n \"\"\" makes async calls to create this thumbnails \"\"\"\n # TODO: do without const\n if self.app.config[\"LOCAL\"] is True:\n hostname = \"http://localhost:8080\"\n else:\n hostname = \"http://wwwjoeschroecker-pydev.appspot.com\"\n\n crop = crop or None\n bg = bg or None\n quality = quality if quality else 100\n thumb_name = thumb_url.split(\"/\")[-1:][0]\n\n url = \"/\".join((\"/storethumb/img\", thumb_name))\n\n data = { \"thumb_url\": thumb_url, \"img_path\":img_path, \"size\":size, \"crop\":crop, \"bg\":bg, \"quality\":quality }\n ## headers contains the necessary Content-Type and Content-Length\n ## datagen is a generator object that yields the encoded parameters\n ## possibly read image and send directly?\n #from poster.encode import multipart_encode\n #datagen, headers = multipart_encode({\"image1\": open(\"DSC0001.jpg\", \"rb\")})\n\n #TODO: use app engine moduls:\n #see: https://cloud.google.com/appengine/docs/python/modules/#Python_Background_threads\n #see: https://cloud.google.com/appengine/docs/python/modules/converting\n ### GAE Deferred (not possible, because can't pickle, maybe simpler storage method)\n import json\n from google.appengine.ext import deferred\n ## e.g. self.create_thumbnail(data['thumb_url'], data['img_path'], data['size'], data['crop'], data['bg'], data['quality'])\n deferred.defer(post_data, \"\".join((hostname,url)) , data)\n # ENABLE DEBUG post_data(\"\".join((hostname,url)), data)\n ## GAE Taskque ##\n #import json\n #data = json.dumps(data)\n\n ## use class Task()\n #from google.appengine.api.taskqueue import Task\n ## better using class Task.add_async\n #task = Task( url=url\n # , params={'thumbname': thumb_name} # adds ?paramname=value\n # , payload=data\n # , headers={\n # \"content-type\":\"application/json\"\n # , \"datatype\":\"json\"\n # }\n # , method=\"PUT\"\n # )\n #task.add_async()\n\n ## use taskqueue.add\n #from google.appengine.api import taskqueue\n #taskqueue.add( url=url\n # #, params={'thumbname': thumb_name}\n # , payload=data\n # , headers={\n # \"content-type\":\"application/json\"\n # , \"datatype\":\"json\"\n # }\n # , method=\"PUT\"\n # )\n\n ## RPC Style (still seems slow because of unhandled waits)##\n #url = \"/\".join((hostname, \"storethumb\", thumb_name))\n #rpc = urlfetch.create_rpc(deadline=1)\n #rpc.callback = _create_thumbnail_async_callback(rpc) # callback does nothing\n\n #import json\n #data = json.dumps(data)\n #urlfetch.make_fetch_call(rpc, url\n # , payload=data\n # , headers={\n # \"content-type\":\"application/json\"\n # , \"datatype\":\"json\"\n # }\n # , method=urlfetch.PUT\n # )\n\n #if self.app.config[\"LOCAL\"] is True:\n # rpc.wait() # the development server doesn't execute async calls in the background\n # return\n #else:\n # self._rpcs.append(rpc)\n\n return\n\n #def _clear_rpc_wait(self):\n # \"\"\" clears all waiting rpc, should be call after request received, eg. in create_thumbnail \"\"\"\n # for rpc in self._rpcs:\n # rpc.wait()\n\n\n def create_thumbnail(self, thumb_url, img_path, size, crop, bg, quality):\n if self._thumb_exists(thumb_url) is False:\n # get original image and transform to thumbail\n self._create_thumb(thumb_url, img_path, size, crop, bg, quality) # store thumbnail and return url\n return\n\n def get_serve_url(self, url):\n #TODO: serve \"pure\" gcs urls, see: http://stackoverflow.com/questions/22174903/how-to-serve-cloudstorage-files-using-app-engine-sdk\n gs_filepath = self._gs_path(url)\n # get_serving_url method => very slow\n # blob_key = blobstore.create_gs_key(\"/gs\" + gs_filepath)\n # return gimages.get_serving_url(blob_key)\n\n # TODO: serve better urls instade, served directly from gcs\n # example url: http://storage.googleapis.com/wwwjoeschroecker-pydev.appspot.com/img/frontpage-script_200x200_85.jpg\n\n #local\n if self.app.config[\"LOCAL\"] is True:\n servingurl = \"http://localhost:8080/_ah/gcs\"+gs_filepath\n else:\n servingurl = \"http://storage.googleapis.com/wwwjoeschroecker-pydev.appspot.com\"+url\n return servingurl\n\n def _gs_path(self, url):\n if self._gs_bucket is None:\n self._gs_bucket = app_identity.get_default_gcs_bucket_name()\n # gs_bucket = os.environ.get('BUCKET_NAME', gs_bucket)\n gs_bucket = self._gs_bucket\n gs_path = \"/\" + gs_bucket + url\n return gs_path\n\n def _build_thumbnail_url(self, img_url,*args):\n #TODO: maybe build url for blobstore image transformations\n # see: https://cloud.google.com/appengine/docs/python/images/#Python_Transforming_images_from_the_Blobstore\n return super(ThumbnailGAEasync, self)._build_thumbnail_url(img_url, *args)\n\n", "sub_path": "flask_thumbnails/gae_async.py", "file_name": "gae_async.py", "file_ext": "py", "file_size_in_byte": 9672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "json.dumps", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 19, "usage_type": "attribute"}, {"api_name": "requests.head", "line_number": 64, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 65, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 68, "usage_type": "call"}, {"api_name": "cloudstorage.stat", "line_number": 77, "usage_type": "call"}, {"api_name": "furl.furl", "line_number": 80, "usage_type": "call"}, {"api_name": "mimetypes.guess_type", "line_number": 83, "usage_type": "call"}, {"api_name": "cloudstorage.NotFoundError", "line_number": 86, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 87, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 89, "usage_type": "call"}, {"api_name": "mimetypes.guess_type", "line_number": 96, "usage_type": "call"}, {"api_name": "cloudstorage.open", "line_number": 97, "usage_type": "call"}, {"api_name": "google.appengine.ext.deferred.defer", "line_number": 138, "usage_type": "call"}, {"api_name": "google.appengine.ext.deferred", "line_number": 138, "usage_type": "name"}, {"api_name": "google.appengine.api.app_identity.get_default_gcs_bucket_name", "line_number": 225, "usage_type": "call"}, {"api_name": "google.appengine.api.app_identity", "line_number": 225, "usage_type": "name"}]} +{"seq_id": "328892195", "text": "# This file is part of the bapsflib package, a Python toolkit for the\n# BaPSF group at UCLA.\n#\n# http://plasma.physics.ucla.edu/\n#\n# Copyright 2017-2018 Erik T. Everson and contributors\n#\n# License: Standard 3-clause BSD; see \"LICENSES/LICENSE.txt\" for full\n# license terms and contributor agreement.\n#\nimport h5py\n\nfrom .digi_template import hdfMap_digi_template\n\n\nclass hdfMap_digi_siscrate(hdfMap_digi_template):\n \"\"\"\n Mapping class for the 'SIS crate' digitizer.\n \"\"\"\n def __init__(self, digi_group):\n \"\"\"\n :param digi_group: the HDF5 digitizer group\n :type digi_group: :class:`h5py.Group`\n \"\"\"\n # initialize\n hdfMap_digi_template.__init__(self, digi_group)\n\n # populate self.configs\n self._build_configs()\n\n @property\n def shotnum_field(self):\n \"\"\"Field name for shot number column in header dataset\"\"\"\n return 'Shot number'\n\n @property\n def _predefined_adc(self):\n \"\"\"\n Predefined (known) adc's for digitizer 'SIS crate'\n\n (See\n :attr:`~.digi_template.hdfMap_digi_template._predefined_adc`\n of the base class for details)\n \"\"\"\n return ['SIS 3302', 'SIS 3305']\n\n def _build_configs(self):\n \"\"\"\n Populates :attr:`configs` dictionary\n\n (See :meth:`~.digi_template.hdfMap_digi_template._build_configs`\n and :attr:`~.digi_template.hdfMap_digi_template.configs`\n of the base class for details)\n \"\"\"\n # self.configs is initialized in the template\n\n # collect digi_group's dataset names and sub-group names\n subgroup_names = []\n dataset_names = []\n for key in self.group.keys():\n if isinstance(self.group[key], h5py.Dataset):\n dataset_names.append(key)\n if isinstance(self.group[key], h5py.Group):\n subgroup_names.append(key)\n\n # populate self.configs\n for name in subgroup_names:\n is_config, config_name = self._parse_config_name(name)\n if is_config:\n # initialize configuration name in the config dict\n self.configs[config_name] = {}\n\n # determine if config is active\n self.configs[config_name]['active'] = \\\n self._is_config_active(config_name, dataset_names)\n\n # assign active adc's to the configuration\n self.configs[config_name]['adc'] = \\\n self._find_config_adc(self.group[name])\n\n # add 'group name'\n self.configs[config_name]['group name'] = name\n\n # add 'group path'\n self.configs[config_name]['group path'] = \\\n self.group[name].name\n\n # add adc info\n for adc in self.configs[config_name]['adc']:\n self.configs[config_name][adc] = \\\n self._adc_info(adc, self.group[name])\n\n @staticmethod\n def _parse_config_name(name):\n \"\"\"\n Parses :code:`name` to determine the digitizer configuration\n name. A configuration group name follows the format:\n\n | `config_name`\n\n (See\n :meth:`~.digi_template.hdfMap_digi_template.parse_config_name`\n of the base class for details)\n \"\"\"\n return True, name\n\n @staticmethod\n def _is_config_active(config_name, dataset_names):\n \"\"\"\n Determines if :code:`config_name` is an active digitizer\n configuration.\n\n (See\n :meth:`~.digi_template.hdfMap_digi_template._is_config_active`\n of the base class for details)\n \"\"\"\n active = False\n\n # if config_name is in any dataset name then config_name is\n # active\n for name in dataset_names:\n if config_name in name:\n active = True\n break\n\n return active\n\n def _adc_info(self, adc_name, config_group):\n \"\"\"\n Gathers information on the adc configuration.\n\n (See :meth:`~.digi_template.hdfMap_digi_template._adc_info`\n of the base class for details)\n \"\"\"\n # digitizer 'Raw data + config/SIS crate' has two adc's,\n # SIS 3302 and SIS 3305\n # adc_info = ( int, # board\n # [int, ], # channels\n # {'bit': int, # bit resolution\n # 'sample rate': (float, 'unit'),\n # 'shot average (software)': int,\n # 'sample average (hardware)': int})\n adc_info = []\n\n # build adc_info\n if adc_name == 'SIS 3302':\n # for SIS 3302\n conns = self._find_adc_connections('SIS 3302',\n config_group)\n for conn in conns:\n # define 'bit' and 'sample rate'\n conn[2]['bit'] = 16\n conn[2]['sample rate'] = (100.0, 'MHz')\n\n # keys 'shot average (software)' and\n # 'sample average (hardware)' are added in\n # self.__find_crate_connections\n\n # append info\n adc_info.append(conn)\n elif adc_name == 'SIS 3305':\n # note: sample rate for 'SIS 3305' depends on how\n # diagnostics are connected to the DAQ. Thus, assignment is\n # left to method self.__find_crate_connections.\n conns = self._find_adc_connections('SIS 3305',\n config_group)\n for conn in conns:\n # define 'bit' and 'sample rate'\n # - sample rate is defined in __find_adc_connections\n conn[2]['bit'] = 10\n\n # keys 'shot average (software)' and\n # 'sample average (hardware)' are added in\n # self.__find_crate_connections\n\n # append info\n adc_info.append(conn)\n else:\n adc_info.append((None, [None],\n {'bit': None,\n 'sample rate': (None, 'MHz'),\n 'shot average (software)': None,\n 'sample average (hardware)': None}))\n\n return adc_info\n\n @staticmethod\n def _find_config_adc(config_group):\n \"\"\"\n Determines active adc's used in the digitizer configuration.\n\n (See\n :meth:`~.digi_template.hdfMap_digi_template._find_config_adc`\n of the base class for details)\n \"\"\"\n active_adc = []\n adc_types = list(config_group.attrs['SIS crate board types'])\n if 2 in adc_types:\n active_adc.append('SIS 3302')\n if 3 in adc_types:\n active_adc.append('SIS 3305')\n\n return active_adc\n\n def _find_adc_connections(self, adc_name, config_group):\n \"\"\"\n Determines active connections on the adc.\n\n (See\n :meth:`~.digi_template.hdfMap_digi_template._find_adc_connections`\n of the base class for details)\n \"\"\"\n # initialize conn, brd, and chs\n # conn = list of connections\n # brd = board number\n # chs = list of connect channels of board brd\n #\n conn = []\n brd = None\n chs = []\n cmode = (None, 'GHz')\n\n # Build a tuple relating the adc name (adc), adc slot number\n # (slot), associated data configuration unique identifier index\n # (index), and board number (brd)\n active_slots = config_group.attrs['SIS crate slot numbers']\n config_indices = config_group.attrs['SIS crate config indices']\n info_list = []\n for slot, index in zip(active_slots, config_indices):\n if slot != 3:\n brd, adc = self.slot_to_brd(slot)\n info_list.append((slot, index, brd, adc))\n\n # filter out calibration groups and only gather configuration\n # groups\n sis3302_gnames = []\n sis3305_gnames = []\n for key in config_group.keys():\n if 'configurations' in key:\n if '3302' in key:\n sis3302_gnames.append(key)\n elif '3305' in key:\n sis3305_gnames.append(key)\n\n # Determine connected (brd, ch) combinations\n # TODO: make this section more efficient\n if adc_name == 'SIS 3302':\n for name in sis3302_gnames:\n\n # Find board number\n config_index = int(name[-2])\n brd = None\n for slot, index, board, adc in info_list:\n if '3302' in adc and config_index == index:\n brd = board\n break\n\n # Find active channels\n chs = []\n for key in config_group[name].attrs:\n if 'Enable' in key:\n tf_str = config_group[name].attrs[key]\n if 'TRUE' in tf_str.decode('utf-8'):\n chs.append(int(key[-1]))\n\n # determine 'shot average (software)'\n if 'Shot averaging (software)' \\\n in config_group[name].attrs:\n shtave = config_group[name].attrs[\n 'Shot averaging (software)']\n if shtave == 0 or shtave == 1:\n shtave = None\n else:\n shtave = None\n\n # determine 'sample average (hardware)'\n # - the HDF5 attribute is the power to 2\n # - So, a hardware sample of 5 actually means the number\n # of points sampled is 2^5\n if 'Sample averaging (hardware)'\\\n in config_group[name].attrs:\n splave = config_group[name].attrs[\n 'Sample averaging (hardware)']\n if splave == 0:\n splave = None\n else:\n splave = 2 ** splave\n else:\n splave = None\n\n # build subconn tuple with connected board, channels\n # and acquisition parameters\n subconn = (brd, chs,\n {'bit': None,\n 'sample rate': (None, 'MHz'),\n 'shot average (software)': shtave,\n 'sample average (hardware)': splave})\n if brd is not None:\n # This counters a bazaar occurrence in the\n # 'SIS crate' configuration where there's more\n # configuration subgroups in config_group than there\n # are listed in\n # config_group.attrs['SIS crate config indices']\n conn.append(subconn)\n\n # reset values\n # brd = None\n # chs = []\n\n elif adc_name == 'SIS 3305':\n for name in sis3305_gnames:\n\n # Find board number\n config_index = int(name[-2])\n brd = None\n for slot, index, board, adc in info_list:\n if '3305' in adc and config_index == index:\n brd = board\n break\n\n # Find active channels and clock mode\n chs = []\n for key in config_group[name].attrs.keys():\n # channels\n if 'Enable' in key:\n if 'FPGA 1' in key:\n tf_str = config_group[name].attrs[key]\n if 'TRUE' in tf_str.decode('utf-8'):\n chs.append(int(key[-1]))\n elif 'FPGA 2' in key:\n tf_str = config_group[name].attrs[key]\n if 'TRUE' in tf_str.decode('utf-8'):\n chs.append(int(key[-1]) + 4)\n\n # clock mode\n # the clock state of 3305 is stored in the 'channel\n # mode' attribute. The values follow\n # 0 = 1.25 GHz\n # 1 = 2.5 GHz\n # 2 = 5.0 GHz\n cmodes = [(1.25, 'GHz'),\n (2.5, 'GHz'),\n (5.0, 'GHz')]\n if 'Channel mode' in key:\n cmode = cmodes[config_group[name].attrs[key]]\n\n # determine 'shot average (software)'\n if 'Shot averaging (software)' \\\n in config_group[name].attrs:\n shtave = config_group[name].attrs[\n 'Shot averaging (software)']\n if shtave == 0 or shtave == 1:\n shtave = None\n else:\n shtave = None\n\n # determine 'sample average (hardware)'\n # - SIS 3305 has no hardware sampling feature\n splave = None\n\n # build subconn tuple with connected board, channels\n # and acquisition parameters\n subconn = (brd, chs,\n {'bit': None,\n 'sample rate': cmode,\n 'shot average (software)': shtave,\n 'sample average (hardware)': splave})\n if brd is not None:\n conn.append(subconn)\n\n # reset values\n cmode = (None, 'GHz')\n\n return conn\n\n def construct_dataset_name(self, board, channel,\n config_name=None, adc=None,\n return_info=False, silent=False):\n \"\"\"\n Constructs the HDF5 dataset name based on inputs. The dataset\n name follows the format:\n\n | `config_name` [Slot `#`: SIS `####` FPGA `#` ch `#`]\n\n (See\n :meth:`~.digi_template.hdfMap_digi_template.construct_dataset_name`\n of the base class for details)\n \"\"\"\n # TODO: Replace Warnings with proper error handling\n\n # initiate warning string\n warn_str = ''\n\n # Condition config_name\n # - if config_name is not specified then the 'active' config\n # is sought out\n if config_name is None:\n found = 0\n for name in self.configs:\n if self.configs[name]['active'] is True:\n config_name = name\n found += 1\n\n if found == 1:\n warn_str = ('** Warning: config_name not specified, '\n 'assuming ' + config_name + '.')\n elif found >= 1:\n # raise Exception(\"Too many active digitizer \"\n # \"configurations detected. Currently \"\n # \"do not know how to handle.\")\n raise ValueError(\"There are multiple active digitizer\"\n \"configurations. User must specify\"\n \"config_name keyword.\")\n else:\n raise ValueError(\"No active digitizer configuration \"\n \"detected.\")\n elif config_name not in self.configs:\n # config_name must be a known configuration\n raise ValueError('Invalid configuration name given.')\n elif self.configs[config_name]['active'] is False:\n raise ValueError('Specified configuration name is not '\n 'active.')\n\n # Condition adc\n # - if adc is not specified then the slow adc '3302' is assumed\n # or, if 3305 is the only active adc, then it is assumed\n # - self.__config_crates() always adds 'SIS 3302' first. If\n # '3302' is not active then the list will only contain '3305'.\n if adc is None:\n adc = self.configs[config_name]['adc'][0]\n warn_str += ('\\n** Warning: No adc specified, so assuming '\n + adc + '.')\n elif adc not in self.configs[config_name]['adc']:\n raise ValueError(\n 'Specified adc ({}) is not in specified '.format(adc)\n + 'configuration ({}).'.format(config_name))\n\n # search if (board, channel) combo is connected\n bc_valid = False\n d_info = None\n for brd, chs, extras in self.configs[config_name][adc]:\n if board == brd:\n if channel in chs:\n bc_valid = True\n\n # save adc settings for return if requested\n d_info = extras\n d_info['adc'] = adc\n d_info['configuration name'] = config_name\n d_info['digitizer'] = self.info['group name']\n\n # (board, channel) combo must be active\n if bc_valid is False:\n raise ValueError('Specified (board, channel) is not valid')\n\n # checks passed, build dataset_name\n if '3302' in adc:\n slot = self.brd_to_slot(board, 'SIS 3302')\n dataset_name = '{0} [Slot {1}: SIS 3302 ch {2}]'.format(\n config_name, slot, channel)\n elif '3305' in adc:\n slot = self.brd_to_slot(board, 'SIS 3305')\n if channel in range(1, 5):\n fpga = 1\n ch = channel\n else:\n fpga = 2\n ch = channel - 4\n\n dataset_name = '{0} [Slot {1}: '.format(config_name, slot) \\\n + 'SIS 3305 FPGA {0} ch {1}]'.format(fpga,\n ch)\n else:\n raise ValueError('We have a problem! Somehow adc '\n + '({}) is not known.'.format(adc))\n\n # print warnings\n if not silent:\n print(warn_str)\n\n if return_info is True:\n return dataset_name, d_info\n else:\n return dataset_name\n\n def construct_header_dataset_name(self, board, channel, **kwargs):\n \"\"\"\"Name of header dataset\"\"\"\n # ensure return_info kwarg is always False\n kwargs['return_info'] = False\n\n # get dataset naem\n dset_name = self.construct_dataset_name(board, channel,\n **kwargs)\n # build and return header name\n dheader_name = dset_name + ' headers'\n return dheader_name\n\n @staticmethod\n def slot_to_brd(slot):\n \"\"\"\n Translates the 'SIS crate` slot number to the board number and\n adc.\n\n :param int slot: digitizer slot number\n :return: (board number, adc name)\n :rtype: (int, str)\n \"\"\"\n sb_map = {5: (1, 'SIS 3302'),\n 7: (2, 'SIS 3302'),\n 9: (3, 'SIS 3302'),\n 11: (4, 'SIS 3302'),\n 13: (1, 'SIS 3305'),\n 15: (2, 'SIS 3305')}\n return sb_map[slot]\n\n @staticmethod\n def brd_to_slot(brd, adc):\n \"\"\"\n Translates board number and adc name to the digitizer slot\n number.\n\n :param int brd: board number\n :param str adc: adc name\n :return: digitizer slot number\n :rtype: int\n \"\"\"\n bs_map = {(1, 'SIS 3302'): 5,\n (2, 'SIS 3302'): 7,\n (3, 'SIS 3302'): 9,\n (4, 'SIS 3302'): 11,\n (1, 'SIS 3305'): 13,\n (2, 'SIS 3305'): 15}\n return bs_map[(brd, adc)]\n", "sub_path": "bapsflib/lapdhdf/map_digitizers/siscrate.py", "file_name": "siscrate.py", "file_ext": "py", "file_size_in_byte": 19665, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "digi_template.hdfMap_digi_template", "line_number": 16, "usage_type": "name"}, {"api_name": "digi_template.hdfMap_digi_template.__init__", "line_number": 26, "usage_type": "call"}, {"api_name": "digi_template.hdfMap_digi_template", "line_number": 26, "usage_type": "name"}, {"api_name": "h5py.Dataset", "line_number": 61, "usage_type": "attribute"}, {"api_name": "h5py.Group", "line_number": 63, "usage_type": "attribute"}]} +{"seq_id": "194688057", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed May 31 15:24:31 2017\r\n\r\n@author: Manjit\r\n\"\"\"\r\n\r\nimport process_mjmag_data as mj\r\nimport ssx_py_utils_basic as ssxutil\r\n#import pickle\r\nimport numpy as np\r\nimport matplotlib.pylab as plt\r\n#import matplotlib.gridspec as gds\r\nfrom matplotlib import animation\r\n\r\n\r\n\r\nshot = '013017r2'\r\n\r\n#time = time index [8192]\r\n#bdot = calibrated dB/dt data [3,25,8192]\r\n#timeb = time index for integrated data [8191]\r\n#b = integrated magnetic field data [3,25,8192]\r\n#bmod = modulus of b for doublets and triplets\r\n\r\ntime,bdot,timeb,b,bmod = mj.process_mjmag_data(shot)\r\n#create a new figure\r\nplt.close('all')\r\nfig = plt.figure(dpi = 300, facecolor = 'w', edgecolor = 'k')\r\n#fig.suptitle(date + 'r' + str(shot)+'\\n Right & left dotted lines indicate beginning & end of SFC, respectively', size = 10)\r\nax = plt.axes(xlim=(-4,30),ylim=(-500,500))\r\nax.axvline(x = 0, linestyle = 'dotted', color = 'black', linewidth = 2)\r\nax.axvline(x = 29.69, linestyle = 'dotted', color = 'black', linewidth = 2) \r\nfig.text(0.25,0.7, 'End of SFC', color = 'black',size = 10)\r\n#fig.text(1.5,0.4, 'Beginning of SFC', color = 'black', size = 10)\r\nax.set_ylabel('By (G)')\r\nax.set_xlabel('Position (cm)')\r\nfig.subplots_adjust(top=0.92, bottom=0.16, left = 0.15, right=0.96)\r\n\r\nt0 = 1700\r\ntf = 4500\r\nb = b[:,:,t0:tf]\r\nx = np.arange(20) * 1.5 - 2.86\r\nd, = ax.plot(x,b[0,0:20,0],'bo')\r\ns, = ax.plot(x,b[0,0:20,0],ls = 'dotted')\r\nax.set_title('Time = %.2f $\\mu s$' %(timeb[t0])) \r\n\r\ndef animate(i):\r\n d.set_data(x,b[0,0:20,i])\r\n \r\n s.set_data(x,b[0,0:20,i])\r\n ax.set_title('Time = %.2f $\\mu s$' %(timeb[t0 + i])) \r\n return d,s,\r\n \r\nanim = animation.FuncAnimation(fig,animate,frames = len(b[0,0,:]), interval = 20, blit = False)\r\nplt.show()\r\nwriter=animation.writers['ffmpeg'](fps=30)\r\nanim.save(shot+'_By.avi', writer=writer, dpi=600)\r\n\r\n", "sub_path": "Animation_test.py", "file_name": "Animation_test.py", "file_ext": "py", "file_size_in_byte": 1858, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "process_mjmag_data.process_mjmag_data", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pylab.close", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pylab.figure", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pylab.axes", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.animation.writers", "line_number": 57, "usage_type": "attribute"}, {"api_name": "matplotlib.animation", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "266987707", "text": "#!/usr/bin/python3\n\nimport sys, getopt\nimport settings\nfrom pymongo import MongoClient\n\ndef main(argv):\n username = ''\n name = ''\n try:\n opts, args = getopt.getopt(argv,\"hu:n:\",[\"username=\",\"name=\"])\n except getopt.GetoptError:\n print ('add_admin.py -u -n ')\n sys.exit(2)\n for opt, arg in opts:\n if opt == '-h':\n print ('add_admin.py -u -n ')\n sys.exit()\n elif opt in (\"-u\", \"--username\"):\n username = arg\n elif opt in (\"-n\", \"--name\"):\n name = arg\n\n if len(username) == 0 or len(name) == 0:\n print ('add_admin.py -u -n ')\n sys.exit(2) \n\n try:\n client = MongoClient('mongodb://%s:%s@%s:%s' % (settings.MONGO_USERNAME, settings.MONGO_PASSWORD, settings.MONGO_HOST, settings.MONGO_PORT))\n except AttributeError:\n client = MongoClient('mongodb://%s:%s' % (settings.MONGO_HOST, settings.MONGO_PORT))\n db = client[settings.MONGO_DBNAME]\n\n whitelist = db.whitelist\n user = {\n \"username\": username,\n \"name\": name,\n \"active\": True\n }\n\n whitelist_id = whitelist.insert_one(user).inserted_id\n\n if whitelist_id:\n print('SUCCESS')\n else:\n print('ERROR')\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n", "sub_path": "add_admin.py", "file_name": "add_admin.py", "file_ext": "py", "file_size_in_byte": 1344, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "getopt.getopt", "line_number": 11, "usage_type": "call"}, {"api_name": "getopt.GetoptError", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 26, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 29, "usage_type": "call"}, {"api_name": "settings.MONGO_USERNAME", "line_number": 29, "usage_type": "attribute"}, {"api_name": "settings.MONGO_PASSWORD", "line_number": 29, "usage_type": "attribute"}, {"api_name": "settings.MONGO_HOST", "line_number": 29, "usage_type": "attribute"}, {"api_name": "settings.MONGO_PORT", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 31, "usage_type": "call"}, {"api_name": "settings.MONGO_HOST", "line_number": 31, "usage_type": "attribute"}, {"api_name": "settings.MONGO_PORT", "line_number": 31, "usage_type": "attribute"}, {"api_name": "settings.MONGO_DBNAME", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 49, "usage_type": "attribute"}]} +{"seq_id": "285282209", "text": "from time import sleep\nimport MySQLdb as mysql\n\n\ncredentials = {\n 'host': 'localhost',\n 'port': 3306,\n 'user': 'web',\n 'password': 'r3p3atp1z',\n 'connect_timeout': 1\n}\n\n\ndef main():\n print('*' * 80)\n\n try:\n # создаем соединение с сервером MySQL\n connection = mysql.connect(**credentials)\n except mysql.Error as e:\n # объект исключения хранит информацию в свойстве args, представленное\n # кортежем\n print(str.format('[#] error: {}', e))\n print(str.format('[#] error code: {}', e.args[0]))\n print(str.format('[#] error string: {}', e.args[1]))\n exit(1)\n\n ping_result = connection.ping()\n print(str.format('[*] ping result # 1: {}', ping_result))\n\n print(str.format(\n '[*] connected with MySQL server <{}:{}>',\n credentials['host'], credentials['port']\n ))\n\n print('-' * 80)\n # получаем курсор, который позволяет выполнять SQL инструкции\n cursor = connection.cursor()\n\n try:\n # выполняет SQL запрос на сервер\n cursor.execute('select version()')\n except mysql.Error as e:\n print(str.format('[#] {}: {}', e.args[0], e.args[1]))\n exit(2)\n\n print('-' * 80)\n # извлекаем результат выполнения SQL инструкции; результатом может\n # быть либо кортеж, либо None\n row = cursor.fetchone()\n\n if row is not None:\n print(str.format('[*] MySQL version: {}', row[0]))\n\n for i in range(10):\n print('-' * 80)\n sleep(0.3)\n\n try:\n connection.ping()\n print(str.format('[*] ping result # {}', i + 1))\n except mysql.Error as e:\n print(str.format(\n '[#] error # {}: <{}:{}>', i + 1, e.args[0], e.args[1]\n ))\n\n print('-' * 80)\n print('[!] close cursor')\n\n # закрываем курсор\n cursor.close()\n\n # закрываем соединение с MySQL сервером\n connection.close()\n\n print('-' * 80)\n print('[!] close connection')\n\n print('*' * 80)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "packages/database_packages/sql/mysql_packages/mysqlclient_package/app1.py", "file_name": "app1.py", "file_ext": "py", "file_size_in_byte": 2295, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "MySQLdb.connect", "line_number": 19, "usage_type": "call"}, {"api_name": "MySQLdb.Error", "line_number": 20, "usage_type": "attribute"}, {"api_name": "MySQLdb.Error", "line_number": 43, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 57, "usage_type": "call"}, {"api_name": "MySQLdb.Error", "line_number": 62, "usage_type": "attribute"}]} +{"seq_id": "122770734", "text": "import requests\nimport bs4\nimport datetime\nimport csv\n\n# TO DO:\n# Create the option to update all files at once\n\n\n# Final da URL de dados a serem buscados\n# # URL_END = '/HTML/10_ProducaoEolicaUsina.html' # (Endereço usado a partir de 16 de Maio de 2017)\n# # URL_END = '/HTML/09_ProducaoTermicaUsina.html' # (Endereço usado a partir de 16 de Maio de 2017)\n# # URL_END = '/HTML/08_ProducaoHidraulicaUsina.html' # (Endereço usado a partir de 16 de Maio de 2017)\n\n# # URL_END = '/HTML/09_ProducaoEolicaUsina.html' # (Endereço usado até 15 de Maio de 2017)\n# # URL_END = '/HTML/08_ProducaoTermicaUsina.html' # (Endereço usado até 15 de Maio de 2017)\n# # URL_END = '/HTML/07_ProducaoHidraulicaUsina.html' # (Endereço usado até 15 de Maio de 2017)\n\nmenu = {\n 'UHE': [r'.\\hist_hidraulicas.csv', r'/HTML/08_ProducaoHidraulicaUsina.html'],\n 'UTE': [r'.\\hist_termicas.csv', r'/HTML/09_ProducaoTermicaUsina.html'],\n 'EOL': [r'.\\hist_eolicas.csv', r'/HTML/10_ProducaoEolicaUsina.html']\n}\n\nwhile True:\n FILE_MENU = input('Escolha os dados a serem atualizados (UHE ou UTE ou EOL): ').upper()\n if FILE_MENU in menu.keys():\n # print('Você escolheu {}'.format(FILE_MENU))\n # print('Isso causa a arbertura do arquivo {} e do complemento {}'.format(menu[FILE_MENU][0], menu[FILE_MENU][1]))\n break\n else:\n continue\n\narquivo = menu[FILE_MENU][0]\nURL_END = menu[FILE_MENU][1]\nURL_BEGIN = 'http://www.ons.org.br/resultados_operacao/SDRO/Diario/'\n\n\ndef find_last_date_csv(path_to_file):\n # Abre o histórico de geração e procura a última data com dados de medição\n with open(path_to_file, newline='') as csvFile:\n csvReader = csv.reader(csvFile, delimiter=';')\n lastDate = list(csvReader)[-1][-0]\n lastDate = datetime.datetime.strptime(lastDate, \"%d/%m/%Y\")\n csvFile.close()\n return lastDate.date()\n\n\ndef find_last_date_ons_site():\n # Procura no site do ONS as medições mais recentes\n reqDate = requests.get('http://www.ons.org.br/resultados_operacao/SDRO/Diario/topo.htm')\n reqDate.encoding = 'utf8'\n soupDate = bs4.BeautifulSoup(reqDate.text, \"html.parser\")\n textDate = soupDate.find_all('option')\n textDate = textDate[1].get_text()\n textDate = datetime.datetime.strptime(textDate[-10:], '%d/%m/%Y')\n return textDate.date()\n\n\ndef generate_dates_url(d1, numDays):\n # Gerando datas para a url\n dates = []\n for i in range(1, numDays + 1):\n dates.append((d1 + datetime.timedelta(days=i)).strftime(\"%Y_%m_%d\"))\n return dates\n\n\ndef get_measurements(urlFull):\n res = requests.get(urlFull)\n n = datetime.datetime.strptime(date, \"%Y_%m_%d\").date().strftime(\"%d/%m/%Y\")\n if res.ok:\n res.encoding = 'utf8'\n soup = bs4.BeautifulSoup(res.text, 'html.parser')\n text = soup.find_all('tbody')\n # A função anterior retorna um arry com todas as tabelas do frame\n # queremos a que contem os dados das usinas, supõe-se que seja a\n # maior.\n text = max(text, key=len)\n text = text.find_all('tr')\n medicoes_dia = []\n for value in range(1, len(text) - 1):\n linha = text[value].get_text().strip().replace(' \\n\\n', ';').replace('\\n', ';')\n linha = linha.split(';')\n linha.insert(0, n)\n medicoes_dia.append(linha)\n else:\n print('/t ERRO: {1}, {2}: Dados de {0} com problemas.'.format(n, res.status_code, res.reason))\n\n return medicoes_dia\n\n\n# Entrada de datas específicas para testes\n# d1 = datetime.date(2017, 5, 16)\n# d2 = datetime.date(2017, 5, 31)\nd1 = find_last_date_csv(arquivo)\nd2 = find_last_date_ons_site()\n\nprint('\\n\\nA última data de medições no arquivo local é: {}'.format(d1))\nprint('A última data de medições no site do ONS é: {}\\n\\n'.format(d2))\n\nnumDays = (d2 - d1).days\ndates = generate_dates_url(d1, numDays)\n\nif (input('Importar útimas medições? (S/N) :').upper() == ('S')) and (numDays > 0):\n print('\\n\\n')\n with open(arquivo, 'a', newline='') as csvFile:\n csvWriter = csv.writer(csvFile, delimiter=';')\n\n for date in dates:\n urlFull = URL_BEGIN + date + URL_END\n csvWriter.writerows(get_measurements(urlFull))\n print('Dados de {} importados com sucesso'.format(date))\n\n csvFile.close()\n\nelse:\n print('Nenhum dado importado')\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4385, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "csv.reader", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 51, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 64, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 73, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "198185415", "text": "from datetime import datetime\n\n\nclass LineItem():\n\n def __init__(self,\n item_number=\"\",\n description=\"\",\n unit_cost=0.0,\n sizes=None,\n created_date=datetime.now(),\n color=\"\",\n imprint_name=\"\",\n imprint_width=0.0,\n image_url=\"\"\n ):\n self.item_number = item_number\n self.description = description\n self.unit_cost = unit_cost\n self.sizes = {} if sizes is None else sizes\n self.created_date = created_date\n self.color = color\n self.imprint_name = imprint_name\n self.imprint_width = imprint_width\n self.image_url = image_url\n\n def __eq__(self, other):\n return self.item_number == other.item_number and self.description == other.description and self.unit_cost == other.unit_cost and self.color == other.color and self.imprint_name == other.imprint_name and self.image_url == other.image_url\n\n def to_dict(self):\n return {\n 'item_number': self.item_number,\n 'description': self.description,\n 'unit_cost': self.unit_cost,\n 'sizes': self.sizes,\n 'created_date': self.created_date,\n 'image_url': self.image_url,\n 'color': self.color,\n 'imprint_name': self.imprint_name,\n 'imprint_width': self.imprint_width\n }\n\n def total_cost(self):\n return self.unit_cost * sum([size for size in self.sizes.values()])\n", "sub_path": "src/models/line_items/line_item.py", "file_name": "line_item.py", "file_ext": "py", "file_size_in_byte": 1461, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "datetime.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "388974211", "text": "from ._ffi import *\nfrom ctypes import *\nfrom wasmtime import Store, FuncType, Val, Trap, Extern\nimport sys\nimport traceback\n\ndll.wasm_func_new_with_env.restype = P_wasm_func_t\ndll.wasmtime_func_new_with_env.restype = P_wasm_func_t\ndll.wasm_func_type.restype = P_wasm_functype_t\ndll.wasm_func_param_arity.restype = c_size_t\ndll.wasm_func_result_arity.restype = c_size_t\ndll.wasm_func_call.restype = P_wasm_trap_t\ndll.wasm_func_as_extern.restype = P_wasm_extern_t\ndll.wasmtime_caller_export_get.restype = P_wasm_extern_t\n\n\nclass Func(object):\n # Creates a new func in `store` with the given `ty` which calls the closure\n # given\n #\n # The `func` is called with the parameters natively and they'll have native\n # Python values rather than being wrapped in `Val`. If `access_caller` is\n # set to `True` then the first argument given to `func` is an instance of\n # type `Caller` below.\n def __init__(self, store, ty, func, access_caller=False):\n if not isinstance(store, Store):\n raise TypeError(\"expected a Store\")\n if not isinstance(ty, FuncType):\n raise TypeError(\"expected a FuncType\")\n idx = FUNCTIONS.allocate((func, ty.params(), ty.results(), store))\n if access_caller:\n ptr = dll.wasmtime_func_new_with_env(\n store.__ptr__,\n ty.__ptr__,\n trampoline_with_caller,\n idx,\n finalize)\n else:\n ptr = dll.wasm_func_new_with_env(\n store.__ptr__, ty.__ptr__, trampoline, idx, finalize)\n if not ptr:\n FUNCTIONS.deallocate(idx)\n raise RuntimeError(\"failed to create func\")\n self.__ptr__ = ptr\n self.__owner__ = None\n\n @classmethod\n def __from_ptr__(cls, ptr, owner):\n ty = cls.__new__(cls)\n if not isinstance(ptr, P_wasm_func_t):\n raise TypeError(\"wrong pointer type\")\n ty.__ptr__ = ptr\n ty.__owner__ = owner\n return ty\n\n # Gets the type of this func as a `FuncType`\n def type(self):\n ptr = dll.wasm_func_type(self.__ptr__)\n return FuncType.__from_ptr__(ptr, None)\n\n # Returns the number of parameters this function expects\n def param_arity(self):\n return dll.wasm_func_param_arity(self.__ptr__)\n\n # Returns the number of results this function produces\n def result_arity(self):\n return dll.wasm_func_result_arity(self.__ptr__)\n\n # Calls this function with the given parameters\n #\n # Parameters can either be a `Val` or a native python value which can be\n # converted to a `Val` of the corresponding correct type\n #\n # Returns `None` if this func has 0 return types\n # Returns a single value if the func has 1 return type\n # Returns a list if the func has more than 1 return type\n def call(self, *params):\n return self(*params)\n\n def __call__(self, *params):\n ty = self.type()\n param_tys = ty.params()\n if len(param_tys) != len(params):\n raise TypeError(\"wrong number of parameters\")\n param_ffi = (wasm_val_t * len(params))()\n for i, param in enumerate(params):\n val = Val.__convert__(param_tys[i], param)\n param_ffi[i] = val.__raw__\n\n result_tys = ty.results()\n result_ffi = (wasm_val_t * len(result_tys))()\n\n trap = dll.wasm_func_call(self.__ptr__, param_ffi, result_ffi)\n if trap:\n raise Trap.__from_ptr__(trap)\n\n results = []\n for i in range(0, len(result_tys)):\n results.append(extract_val(Val(result_ffi[i])))\n if len(results) == 0:\n return None\n elif len(results) == 1:\n return results[0]\n else:\n return results\n\n # Returns this as an instance of `Extern`\n def as_extern(self):\n ptr = dll.wasm_func_as_extern(self.__ptr__)\n return Extern.__from_ptr__(ptr, self.__owner__ or self)\n\n def __del__(self):\n if hasattr(self, '__owner__') and self.__owner__ is None:\n dll.wasm_func_delete(self.__ptr__)\n\n\nclass Caller(object):\n def __init__(self, ptr):\n self.__ptr__ = ptr\n\n # Looks up an export with `name` on the calling module.\n #\n # May return `None` if the export isn't found, if it's not a memory (for\n # now), or if the caller has gone away and this `Caller` object has\n # persisted too long.\n def get_export(self, name):\n # First convert to a raw name so we can typecheck our argument\n name_raw = str_to_name(name)\n\n # Next see if we've been invalidated\n if not hasattr(self, '__ptr__'):\n return None\n\n # And if we're not invalidated we can perform the actual lookup\n ptr = dll.wasmtime_caller_export_get(self.__ptr__, byref(name_raw))\n if ptr:\n return Extern.__from_ptr__(ptr, None)\n else:\n return None\n\n\ndef extract_val(val):\n a = val.get()\n if a is not None:\n return a\n return val\n\n\n@CFUNCTYPE(c_size_t, c_size_t, POINTER(wasm_val_t), POINTER(wasm_val_t))\ndef trampoline(idx, params_ptr, results_ptr):\n return invoke(idx, params_ptr, results_ptr, [])\n\n\n@CFUNCTYPE(\n c_size_t,\n P_wasmtime_caller_t,\n c_size_t,\n POINTER(wasm_val_t),\n POINTER(wasm_val_t),\n)\ndef trampoline_with_caller(caller, idx, params_ptr, results_ptr):\n caller = Caller(caller)\n try:\n return invoke(idx, params_ptr, results_ptr, [caller])\n finally:\n delattr(caller, '__ptr__')\n\n\ndef invoke(idx, params_ptr, results_ptr, params):\n func, param_tys, result_tys, store = FUNCTIONS.get(idx)\n\n try:\n for i in range(0, len(param_tys)):\n params.append(extract_val(Val(params_ptr[i])))\n results = func(*params)\n if len(result_tys) == 0:\n if results is not None:\n raise RuntimeError(\n \"callback produced results when it shouldn't\")\n elif len(result_tys) == 1:\n val = Val.__convert__(result_tys[0], results)\n results_ptr[0] = val.__raw__\n else:\n if len(results) != len(result_tys):\n raise RuntimeError(\"callback produced wrong number of results\")\n for i, result in enumerate(results):\n val = Val.__convert__(result_tys[i], result)\n results_ptr[i] = val.__raw__\n except Exception:\n exc_type, exc_value, exc_traceback = sys.exc_info()\n fmt = traceback.format_exception(exc_type, exc_value, exc_traceback)\n trap = Trap(store, \"\\n\".join(fmt))\n ptr = trap.__ptr__\n delattr(trap, '__ptr__')\n return cast(ptr, c_void_p).value\n\n return 0\n\n\n@CFUNCTYPE(None, c_size_t)\ndef finalize(idx):\n FUNCTIONS.deallocate(idx)\n pass\n\n\nclass Slab(object):\n def __init__(self):\n self.list = []\n self.next = 0\n\n def allocate(self, val):\n idx = self.next\n\n if len(self.list) == idx:\n self.list.append(None)\n self.next += 1\n else:\n self.next = self.list[idx]\n\n self.list[idx] = val\n return idx\n\n def get(self, idx):\n return self.list[idx]\n\n def deallocate(self, idx):\n self.list[idx] = self.next\n self.next = idx\n\n\nFUNCTIONS = Slab()\n", "sub_path": "wasmtime/_func.py", "file_name": "_func.py", "file_ext": "py", "file_size_in_byte": 7293, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "wasmtime.Store", "line_number": 26, "usage_type": "argument"}, {"api_name": "wasmtime.FuncType", "line_number": 28, "usage_type": "argument"}, {"api_name": "wasmtime.FuncType.__from_ptr__", "line_number": 59, "usage_type": "call"}, {"api_name": "wasmtime.FuncType", "line_number": 59, "usage_type": "name"}, {"api_name": "wasmtime.Val.__convert__", "line_number": 87, "usage_type": "call"}, {"api_name": "wasmtime.Val", "line_number": 87, "usage_type": "name"}, {"api_name": "wasmtime.Trap.__from_ptr__", "line_number": 95, "usage_type": "call"}, {"api_name": "wasmtime.Trap", "line_number": 95, "usage_type": "name"}, {"api_name": "wasmtime.Val", "line_number": 99, "usage_type": "call"}, {"api_name": "wasmtime.Extern.__from_ptr__", "line_number": 110, "usage_type": "call"}, {"api_name": "wasmtime.Extern", "line_number": 110, "usage_type": "name"}, {"api_name": "wasmtime.Extern.__from_ptr__", "line_number": 137, "usage_type": "call"}, {"api_name": "wasmtime.Extern", "line_number": 137, "usage_type": "name"}, {"api_name": "wasmtime.Val", "line_number": 174, "usage_type": "call"}, {"api_name": "wasmtime.Val.__convert__", "line_number": 181, "usage_type": "call"}, {"api_name": "wasmtime.Val", "line_number": 181, "usage_type": "name"}, {"api_name": "wasmtime.Val.__convert__", "line_number": 187, "usage_type": "call"}, {"api_name": "wasmtime.Val", "line_number": 187, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 190, "usage_type": "call"}, {"api_name": "traceback.format_exception", "line_number": 191, "usage_type": "call"}, {"api_name": "wasmtime.Trap", "line_number": 192, "usage_type": "call"}]} +{"seq_id": "495936495", "text": "\n# in order to get ret working need to change make modification in imtuils VideoStream package\n\nfrom imutils.video import VideoStream\nimport cv2\n\nvs = VideoStream(src=0).start()\nwhile True:\n ret, frame = vs.read()\n if ret == True:\n cv2.imshow(\"Frame\", frame)\n else:\n print(\"Failed to get Frame\")\n key = cv2.waitKey(1) & 0xFF\n # if the `q` key was pressed, break from the loop\n if key == ord(\"q\"):\n break\n\ncv2.destroyAllWindows()\nvs.stop()", "sub_path": "opencv_image_video/video_playback_using_videostream.py", "file_name": "video_playback_using_videostream.py", "file_ext": "py", "file_size_in_byte": 477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "imutils.video.VideoStream", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "80677376", "text": "\"\"\"\ntracking.py\n\nPerson detection and tracking.\n\"\"\"\n\nimport logging\nfrom multiprocessing import Process\n\nfrom .cv import get_centroids\n\nlogger = logging.getLogger(__name__)\n\ntry:\n import cv2\nexcept ImportError:\n logger.warning(\"Could not import cv2, continuing with no tracking\")\n cv2 = None\n\n\ndef start_tracking(shared_dict):\n logger.info(\"Tracking starting up\")\n while True:\n shared_dict['centroids'] = get_centroids()\n\n\nclass Tracking:\n def __init__(self, shared_dict):\n self.process = Process(\n target=start_tracking, args=(shared_dict,))\n self.process.daemon = True\n self.process.start()\n self.data = shared_dict\n\n def stop(self):\n self.process.terminate()\n self.process.join()\n\n def __del__(self):\n self.stop()\n", "sub_path": "control/thegrid/tracking.py", "file_name": "tracking.py", "file_ext": "py", "file_size_in_byte": 808, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "cv.get_centroids", "line_number": 24, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "425017296", "text": "#!/usr/bin/env python\r\n\r\n__author__ = \"Juan Biondi\"\r\n__credits__ = [\"Juan Biondi\"]\r\n__version__ = \"0.1\"\r\n__maintainer__ = \"Juan Biondi\"\r\n\r\n__status__ = \"Development\"\r\n\r\n\"\"\"\r\nImport Modules needed for this script.\r\n\"\"\"\r\nimport qrcode\r\nimport os\r\n\r\n\"\"\"\r\nDeclare functions for the code.\r\n\"\"\"\r\nclass GenerateQR(object):\r\n \"\"\"\r\n Global class to have it more like OOP\r\n \"\"\"\r\n \r\n def __init__(self, FileName):\r\n \"\"\"\r\n Init function to have the variables in the class\r\n \"\"\"\r\n self.FileName=FileName\r\n\r\n def create_images(self):\r\n \"\"\"\r\n This function will generate 50 .png images with a string data replacing last character on the\r\n string in order to have a uniques QR codes and names.\r\n \r\n Just have to declare a String variable with the name. It has to be longer that 3 characters.\r\n \"\"\"\r\n \r\n\r\n for i in range(1,51):\r\n FinalFileName = self.FileName[:-len(str(i))] + str(i)\r\n qr = qrcode.QRCode(version=1, error_correction=qrcode.constants.ERROR_CORRECT_L, box_size=10, border=4, )\r\n qr.add_data(FinalFileName)\r\n qr.make(fit=True)\r\n img = qr.make_image()\r\n with open('%s.png'%FinalFileName, 'wb') as f:\r\n img.save(f)\r\n\r\n \r\n \r\n\"\"\"\r\nCall the function(s) to be used on the final code.\r\n\"\"\"\r\n\r\n\r\n\r\nName = \"P01 V05 S0000000\"\r\nQR=GenerateQR(Name)\r\nQR.create_images()\r\n\r\n", "sub_path": "Test/Multiple_QRcodes.py", "file_name": "Multiple_QRcodes.py", "file_ext": "py", "file_size_in_byte": 1678, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "qrcode.QRCode", "line_number": 41, "usage_type": "call"}, {"api_name": "qrcode.constants", "line_number": 41, "usage_type": "attribute"}]} +{"seq_id": "393938232", "text": "# Copyright (C) 2013 the Institute for Institutional Innovation by Data\n# Driven Design Inc.\n#\n# Permission is hereby granted, free of charge, to any person obtaining\n# a copy of this software and associated documentation files (the\n# \"Software\"), to deal in the Software without restriction, including\n# without limitation the rights to use, copy, modify, merge, publish,\n# distribute, sublicense, and/or sell copies of the Software, and to\n# permit persons to whom the Software is furnished to do so, subject to\n# the following conditions:\n#\n# The above copyright notice and this permission notice shall be\n# included in all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\n# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n# NONINFRINGEMENT. IN NO EVENT SHALL THE MASSACHUSETTS INSTITUTE OF\n# TECHNOLOGY AND THE INSTITUTE FOR INSTITUTIONAL INNOVATION BY DATA\n# DRIVEN DESIGN INC. BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,\n# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE\n# USE OR OTHER DEALINGS IN THE SOFTWARE.\n#\n# Except as contained in this notice, the names of the Institute for\n# Institutional Innovation by Data Driven Design Inc. shall not be used in\n# advertising or otherwise to promote the sale, use or other dealings\n# in this Software without prior written authorization from the\n# Institute for Institutional Innovation by Data Driven Design Inc.\n\nimport datetime\nimport json\n\nimport pytz\nimport requests\nfrom dateutil.parser import parse\nfrom constance import config\n\ndef is_access_token_valid(request):\n \"\"\"\n Validate the request's access token with the OpenID Connect server.\n\n This function looks for the access token first in the query string, then in\n the headers. Next, it passes the access token to the \"tokenscope\"\n endpoint. When it receives the response, it first checks the status code,\n followed by the response format, and followed finally by the token\n expiration date.\n\n :param request: HTTP request\n :type request: HttpRequest\n\n :returns: determination as to whether the access token is valid\n :rtype: bool\n \"\"\"\n access_token = request.GET.get('access_token', None)\n if not access_token:\n auth_header = request.META.get('HTTP_AUTHORIZATION', None)\n if not auth_header:\n return False\n try:\n access_token = auth_header.split()[1]\n except IndexError:\n return False\n r = requests.get(config.TOKENSCOPE_ENDPOINT,\n headers={'Authorization': 'Bearer ' + access_token})\n if r.status_code / 100 != 2: # 2xx success\n return False\n try:\n reply = json.loads(r.text)\n except ValueError:\n return False\n if 'expiration' in reply and 'user_id' in reply:\n expires = parse(reply['expiration'])\n now = datetime.datetime.now(pytz.utc) # \"aware\" datetime\n if expires > now:\n return True\n return False\n", "sub_path": "oic_validation/validation.py", "file_name": "validation.py", "file_ext": "py", "file_size_in_byte": 3136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "requests.get", "line_number": 64, "usage_type": "call"}, {"api_name": "constance.config.TOKENSCOPE_ENDPOINT", "line_number": 64, "usage_type": "attribute"}, {"api_name": "constance.config", "line_number": 64, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 69, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pytz.utc", "line_number": 74, "usage_type": "attribute"}]} +{"seq_id": "128437583", "text": "from sklearn.decomposition import PCA\n\n#\n# Housekeeping\n#\nimport os\nimport shutil\nimport sys\nimport time\n\n#\n# Math\n#\nimport numpy as np\nimport math\n\n#\n# Plotting\n#\nimport matplotlib as mpl\nimport matplotlib.pylab as plt\n\n#\n# Optimizer\n#\nimport DeepEM\n\nif len( sys.argv ) < 6:\n\tprint( \"Usage: python \" + sys.argv[ 0 ] + \" { save folder } { max index } { loss percentage } { random generator seed } { number of hours to run }\" )\n\tsys.exit( 1 )\n\nsave_folder = sys.argv[ 1 ]\nmax_index = float( sys.argv[ 2 ] )\nloss_percentage = float( sys.argv[ 3 ] )\nrandom_seed = int( sys.argv[ 4 ] )\nnumber_of_hours_to_run = float( sys.argv[ 5 ] )\nnumber_of_seconds_to_run = 60. * 60. * number_of_hours_to_run\n\nif ( max_index > 3.5 ):\n\tprint( \"This index is a bit too high for the simulation mesh\" )\n\n\nmesh_size_nm = 9\ndensity_coarsen_factor = 10\nmesh_size_m = mesh_size_nm * 1e-9\nlambda_um = 0.532\n# num_lambda_values = 1\nnum_layers = 2\n\ndevice_width_voxels = 120\ndevice_height_voxels = 120\ndevice_voxels_total = device_width_voxels * device_height_voxels\nfocal_length_voxels = 120\n\nmin_relative_permittivity = 1.0**2\n\nsingle_pass_transmittance = 1 - ( loss_percentage / 100. )\ndevice_height_m = device_height_voxels * mesh_size_nm * 1e-9\nlambda_m = lambda_um * 1e-6\nloss_index = -lambda_m * np.log( single_pass_transmittance ) / ( device_height_m * 2 * np.pi )\n\nreal_permittivity = max_index**2 - loss_index**2\nimag_permittivity = 2 * np.sqrt( real_permittivity ) * loss_index\nmax_relative_permittivity = real_permittivity + 1j * imag_permittivity\n\n\ndensities = []\nfocal_abs = []\nnum_before_saving = 500\n\n# num_to_load = 10 * num_before_saving\nnum_load_epochs = 10\n\nfor epoch_num in range( 0, num_load_epochs ):\n\tdensities += list( np.load( save_folder + \"/generated_densities_\" + str( epoch_num ) + \".npy\" ) )\n\tfocal_abs += list( np.abs( np.load( save_folder + \"/generated_focal_fields_\" + str( epoch_num ) + \".npy\" ) ) )\n\n\npca = PCA()\npca.fit_transform( focal_abs )\n\nn_components_variance_max = 120\nratio_variance = np.zeros( ratio_variance )\n\nfor n_idx in range( 0, n_components_variance_max ):\n\tratio_variance.append( pca.explained_variance_ratio_( n_idx ) )\n\nnp.save( save_folder + \"/ratio_variance.npy\" )\n\n\n# principalComponents = pca.fit_transform(x)\n# principalDf = pd.DataFrame(data = principalComponents\n# , columns = ['principal component 1', 'principal component 2'])\n", "sub_path": "inverse_design/Landscape/pca_deep_em.py", "file_name": "pca_deep_em.py", "file_ext": "py", "file_size_in_byte": 2377, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "sys.argv", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "585575275", "text": "from googlesearch import search, get_page, filter_result, get_random_user_agent\n\n'''\n\tSearching a word via a query to Google Search Engine\n\t\n\t:param str word - the given word\n\t:param int stp - after how many result (index for last result) \n'''\n\n\ndef search_google(word, stp=5):\n\t# Search query\n\tquery = str(word)\n\n\tquery_result = search(query=query, tld='com', lang='en', num=5, start=0, stop=stp)\n\n\tresults = []\n\tfor res in query_result:\n\t\tres = filter_result(res)\n\t\thtml = get_page(res, get_random_user_agent())\n\n\t\tresults.append({'link': res, 'page': html})\n\n\treturn results\n\n\n\n\n\n", "sub_path": "NLP/src/googleSearch.py", "file_name": "googleSearch.py", "file_ext": "py", "file_size_in_byte": 583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "googlesearch.search", "line_number": 15, "usage_type": "call"}, {"api_name": "googlesearch.filter_result", "line_number": 19, "usage_type": "call"}, {"api_name": "googlesearch.get_page", "line_number": 20, "usage_type": "call"}, {"api_name": "googlesearch.get_random_user_agent", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "458469321", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\n# Boxplot configuration\nmedianprops = dict(linestyle='-', linewidth=2.5, color='firebrick')\nmeanpointprops = dict(marker='D', markeredgecolor='black',\n markerfacecolor='firebrick')\n\n# Data column indexes\nMINLAT = 0\nMAXLAT = 1\nMAXERROR = 2\nRMSE = 3\nRRMSE = 4\nEPS2 = 5\nEPS5 = 6\n\n# Reference values\n#ref_min_lat = 16.23 # LV\n#ref_max_lat = 50.37 # LV\nref_min_lat = 18.58 # RV\nref_max_lat = 67.25 # RV\n\n# Reading data\ndata_exp1 = np.genfromtxt(\"data/electric_data_RV_exp1.dat\")\ndata_exp2 = np.genfromtxt(\"data/electric_data_RV_exp2.dat\")\ndata_exp3 = np.genfromtxt(\"data/electric_data_RV_exp3.dat\")\n\n# Min. LAT\nmin_lat_box_plot_data = [ data_exp1[:,0], data_exp2[:,0], data_exp3[:,0] ]\nmin_lat_ref_value = [ref_min_lat, ref_min_lat, ref_min_lat]\n# Max. LAT\nmax_lat_box_plot_data = [ data_exp1[:,1], data_exp2[:,1], data_exp3[:,1] ]\nmax_lat_ref_value = [ref_max_lat, ref_max_lat, ref_max_lat]\n# Max. Error\nmax_error_box_plot_data = [ data_exp1[:,2], data_exp2[:,2], data_exp3[:,2] ]\n# RMSE\nrmse_box_plot_data = [ data_exp1[:,3], data_exp2[:,3], data_exp3[:,3] ]\n# RRMSE\nrrmse_box_plot_data = [ data_exp1[:,4], data_exp2[:,4], data_exp3[:,4] ]\n# eps < 2ms\neps2_box_plot_data = [ data_exp1[:,5], data_exp2[:,5], data_exp3[:,5] ]\n# eps < 5ms\neps5_box_plot_data = [ data_exp1[:,6], data_exp2[:,6], data_exp3[:,6] ]\n\nfig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))\n# Min. LAT\naxes[0, 0].boxplot(min_lat_box_plot_data, medianprops=medianprops, meanprops=meanpointprops, showmeans=False)\naxes[0, 0].scatter([1,2,3],min_lat_ref_value,s=200,c=\"red\",marker='D')\naxes[0, 0].set_title(\"Min.LAT\")\naxes[0, 0].set_ylabel(\"LAT (ms)\")\n#axes[0, 0].set_ylim([14,17])\n\n# Max. LAT\naxes[0, 1].boxplot(max_lat_box_plot_data, medianprops=medianprops, meanprops=meanpointprops, showmeans=False)\naxes[0, 1].scatter([1,2,3],max_lat_ref_value,s=200,c=\"red\",marker='D')\naxes[0, 1].set_title(\"Max.LAT\")\naxes[0, 1].set_ylabel(\"LAT (ms)\")\n#axes[0, 1].set_ylim([40,60])\n\n# Max. Error\naxes[0, 2].boxplot(max_error_box_plot_data, medianprops=medianprops, meanprops=meanpointprops, showmeans=False)\naxes[0, 2].set_title(\"Max.Error\")\naxes[0, 2].set_ylabel(\"LAT (ms)\")\n\n# RMSE\naxes[1, 0].boxplot(rmse_box_plot_data, medianprops=medianprops, meanprops=meanpointprops, showmeans=False)\naxes[1, 0].set_title(\"RMSE\")\naxes[1, 0].set_ylabel(\"LAT (ms)\")\n\n# RRMSE\naxes[1, 1].boxplot(rrmse_box_plot_data, medianprops=medianprops, meanprops=meanpointprops, showmeans=False)\naxes[1, 1].set_title(\"RRMSE\")\naxes[1, 1].set_ylabel(\"%\")\n\n# eps < 2ms\naxes[1, 2].boxplot(eps2_box_plot_data, medianprops=medianprops, meanprops=meanpointprops, showmeans=False)\naxes[1, 2].set_title(r\"$\\epsilon$ < 2ms\")\naxes[1, 2].set_ylabel(\"%\")\n\n# eps < 5ms\naxes[2, 0].boxplot(eps5_box_plot_data, medianprops=medianprops, meanprops=meanpointprops, showmeans=False)\naxes[2, 0].set_title(r\"$\\epsilon$ < 5ms\")\naxes[2, 0].set_ylabel(\"%\")\n\naxes[2, 1].set_visible(False)\naxes[2, 2].set_visible(False)\n\nplt.setp(axes, xticks=[y + 1 for y in range(3)],\n xticklabels=['Exp1', 'Exp2', 'Exp3'])\nfig.suptitle(\"Right Ventricle - Electric Results\",fontsize=20)\nfig.subplots_adjust(hspace=0.3)\nplt.savefig(\"electric_results_RV.pdf\")\n#plt.show()", "sub_path": "cco-project-3d-purkinje/scripts/Data-Reader/boxplot.py", "file_name": "boxplot.py", "file_ext": "py", "file_size_in_byte": 3244, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "numpy.genfromtxt", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}]} +{"seq_id": "341807432", "text": "import numpy as np\nfrom keras.models import load_model\nfrom keras.preprocessing.image import img_to_array, load_img\n\ntest_model = load_model('D:/.../CNN3/Databest_model_CNN4.h5')\nimg = load_img('D:/.../CNN4/test/RF1.jpg',False,target_size=(224,224))\nx = img_to_array(img)\nx = np.expand_dims(x, axis=0)\npreds = test_model.predict_classes(x)\nprob = test_model.predict_proba(x)\nprint(preds, prob)\n", "sub_path": "Predict.py", "file_name": "Predict.py", "file_ext": "py", "file_size_in_byte": 394, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "keras.models.load_model", "line_number": 5, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 6, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "265217896", "text": "import requests\nimport os\nfrom auth.client import JWTAuth\nfrom report.client import ReportClient\nfrom datetime import datetime, timedelta\n\n# path to the adobe analytics service account private key\njwt = JWTAuth(\"C:/users/jschlons/keys/adobe_io_private.key\")\njwt.setIss(\"9D88879D5579828F7F000101@AdobeOrg\")\njwt.setSub(\"473A0AFF5C498D430A495E7C@techacct.adobe.com\")\njwt.setClientId(\"45ec3245e84d4d40978fd8da5eeefc3d\")\n# client secret is sensitive, better store it as an evnironment variable \njwt.setClientSecret(os.environ.get(\"ADOBE_CLIENT_SECRET\"))\njwt.setMetascopes(\"https://ims-na1.adobelogin.com/s/ent_analytics_bulk_ingest_sdk\")\njwt.setCompanyId(\"dhlcom1\")\n\n# setup connection and authenticate\ncon = requests.Session()\njwt.authenticate(con)\n\n# report client configuration\nclient = ReportClient(con, \"dhlcom1\")\n\n# select report from adobe analytics internal json request format\n# the respective json can be retrieved as described here:\n# https://helpx.adobe.com/analytics/kt/using/build-api2-requests-analysis-workspace-feature-video-use.html\nclient.fromJSON(\"C:/pythonprojects/powerbi/testjson.json\")\n\n# set date range here\n# use python datetime syntax for setting the start and end date\n# refer to: https://docs.python.org/3.7/library/datetime.html\nclient.setDateRange(datetime.now() - timedelta(days=5), datetime.now())\n\n# execute and retrieve pandas dataframe\ndf = client.execute()\nprint(df)", "sub_path": "app/getData.py", "file_name": "getData.py", "file_ext": "py", "file_size_in_byte": 1398, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "auth.client.JWTAuth", "line_number": 8, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "requests.Session", "line_number": 18, "usage_type": "call"}, {"api_name": "report.client.ReportClient", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "385678565", "text": "from pandevice.panorama import Panorama\n\nfrom lib.actions import BaseAction\n\n\nclass Commit(BaseAction):\n \"\"\"\n Commit a firewall\n \"\"\"\n def run(self, firewall, device_group, sync, sync_all, exception):\n\n device = self.get_pandevice(firewall)\n if device_group and not isinstance(device, Panorama):\n raise ValueError(\n '{} is not a Panorama and does not understand device_group!'.format(firewall)\n )\n\n if isinstance(device, Panorama):\n try:\n device.commit(sync=sync, exception=exception)\n device.commit_all(sync=sync,\n sync_all=sync_all,\n devicegroup=device_group,\n exception=exception)\n except Exception as e:\n return False, \"Commit on {} rasied exception: {}\".format(firewall, e)\n else:\n try:\n device.commit_all(sync=sync, exception=exception)\n except Exception as e:\n return False, \"Commit on {} rasied exception: {}\".format(firewall, e)\n\n if not sync:\n return True, \"Commit on {} successfully requested!\".format(firewall)\n\n return True, \"Commit on {} successfully completed!\".format(firewall)\n", "sub_path": "actions/commit.py", "file_name": "commit.py", "file_ext": "py", "file_size_in_byte": 1313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "lib.actions.BaseAction", "line_number": 6, "usage_type": "name"}, {"api_name": "pandevice.panorama.Panorama", "line_number": 13, "usage_type": "argument"}, {"api_name": "pandevice.panorama.Panorama", "line_number": 18, "usage_type": "argument"}]} +{"seq_id": "29520786", "text": "from django.test import TestCase\nfrom datetime import datetime\nfrom django.utils import timezone\nfrom main_site.models import Person, LabSession, Request, Topic, Question, RequestHandler, tempStoreUserLocationLink\n\nclass TestModels(TestCase):\n @classmethod\n def setUpTestData(self):\n person1 = Person.objects.create(username = 'hello')\n person2 = Person.objects.create(username = '')\n labsesh1 = LabSession.objects.create(lab_session = 'tootil1')\n request1 = Request.objects.create(lab_session = labsesh1, request_location = 'lf31')\n request2 = Request.objects.create(lab_session = labsesh1, request_location = 'lf31', request_start = timezone.now(), request_end = timezone.now())\n topic1 = Topic.objects.create(topic_text = 'randomtopic')\n Question.objects.create(request = request1, topic = topic1, question_text = 'what is life')\n RequestHandler.objects.create(person = person1, request = request1)\n tempStoreUserLocationLink.objects.create(person = person1, location = 'tootil0', lab_session = labsesh1)\n tempStoreUserLocationLink.objects.create(lab_session = labsesh1)\n tempStoreUserLocationLink.objects.create(person = person2, location = '', lab_session = labsesh1)\n##################### Person class #######################\n def test_person_username_max_length(self):\n user = Person.objects.get(id=1)\n max_length = user._meta.get_field('username').max_length\n self.assertEquals(max_length, 8)\n\n def test_person_username_label(self):\n user = Person.objects.get(id=1)\n field_label = user._meta.get_field('username').verbose_name\n self.assertEquals(field_label, 'username')\n\n def test_person_is_username(self):\n user = Person.objects.get(id=1)\n expected_object_name = user.username\n self.assertEquals(expected_object_name, str(user))\n\n##################### LabSession class #######################\n def test_LabSession_variables_max_length(self):\n user = LabSession.objects.get(id=1)\n max_length = user._meta.get_field('lab_session').max_length\n self.assertEquals(max_length, 30)\n\n\n def test_LabSession_labels(self):\n user = LabSession.objects.get(id=1)\n field_label1 = user._meta.get_field('lab_session').verbose_name\n self.assertEquals(field_label1, 'lab session')\n\n def test_LabSession_is_lab_session(self):\n user = LabSession.objects.get(id=1)\n expected_object_name = user.lab_session\n self.assertEquals(expected_object_name, str(user))\n \n##################### Request class #######################\n# check the on_delete thing\n def test_request_max_length(self):\n user = Request.objects.get(id=1)\n max_length = user._meta.get_field('request_location').max_length\n max_length1 = user._meta.get_field('status').max_length\n self.assertEquals(max_length, 30)\n self.assertEquals(max_length1, 20)\n\n def test_request_label(self):\n user = Request.objects.get(id=1)\n field_label = user._meta.get_field('request_location').verbose_name\n field_label1 = user._meta.get_field('status').verbose_name\n field_label2 = user._meta.get_field('lab_session').verbose_name\n field_label3 = user._meta.get_field('request_made').verbose_name\n field_label4 = user._meta.get_field('request_start').verbose_name\n field_label5 = user._meta.get_field('request_end').verbose_name\n self.assertEquals(field_label, 'request location')\n self.assertEquals(field_label1, 'status')\n self.assertEquals(field_label2, 'lab session')\n self.assertEquals(field_label3, 'request made')\n self.assertEquals(field_label4, 'request start')\n self.assertEquals(field_label5, 'request end')\n\n\n def test_request_is_lab_and_request(self):\n user = Request.objects.get(id=1)\n user1 = Request.objects.get(id=2)\n expected_object_name = str(user.lab_session) + ': ' + user.request_location + ' - made at ' + str(user.request_made)\n expected_object_name1 = str(user1.lab_session) + ': ' + user1.request_location + ' - made at ' + str(user1.request_made)\n expected_status = str(user.status)\n expected_request_start = user.request_start\n expected_request_end = user.request_end\n expected_request_made = user.request_made\n self.assertEquals(expected_object_name, str(user))\n self.assertEquals(expected_object_name1, str(user1))\n self.assertEquals(expected_status, 'Not started')\n self.assertEquals(expected_request_start, None)\n self.assertEquals(expected_request_end, None)\n # self.assertEquals(expected_request_made, timezone.now())\n\n\n##################### Topic class #######################\n def test_topic_text_max_length(self):\n user = Topic.objects.get(id=1)\n max_length = user._meta.get_field('topic_text').max_length\n self.assertEquals(max_length, 30)\n\n def test_topic_text_label(self):\n user = Topic.objects.get(id=1)\n field_label = user._meta.get_field('topic_text').verbose_name\n self.assertEquals(field_label, 'topic text')\n\n def test_topic_is_topic_text(self):\n user = Topic.objects.get(id=1)\n expected_object_name = user.topic_text\n self.assertEquals(expected_object_name, str(user))\n \n##################### Question class #######################\n# on_delete testing\n def test_question_max_length(self):\n user = Question.objects.get(id=1)\n max_length = user._meta.get_field('question_text').max_length\n self.assertEquals(max_length, 30)\n\n def test_question_label(self):\n user = Question.objects.get(id=1)\n field_label = user._meta.get_field('request').verbose_name\n field_label1 = user._meta.get_field('topic').verbose_name\n field_label2 = user._meta.get_field('question_text').verbose_name\n self.assertEquals(field_label, 'request')\n self.assertEquals(field_label1, 'topic')\n self.assertEquals(field_label2, 'question text')\n\n def test_question_is_topic_and_question(self):\n user = Question.objects.get(id=1)\n expected_object_name = str(user.topic) + ': ' + str(user.question_text)\n self.assertEquals(expected_object_name, str(user))\n \n##################### RequestHandler class #######################\n# on_delete testing\n def test_person_username_label(self):\n user = RequestHandler.objects.get(id=1)\n field_label = user._meta.get_field('person').verbose_name\n field_label1 = user._meta.get_field('request').verbose_name\n self.assertEquals(field_label, 'person')\n self.assertEquals(field_label1, 'request')\n\n def test_person_is_username(self):\n user = RequestHandler.objects.get(id=1)\n expected_object_name = str(user.person) + ' ' + str(user.request)\n self.assertEquals(expected_object_name, str(user))\n \n##################### tempStoreUserLocationLink class #######################\n# on_delete testing\n def test_tempStoreUserLocationLink_max_length(self):\n user = tempStoreUserLocationLink.objects.get(id=1)\n max_length = user._meta.get_field('location').max_length\n self.assertEquals(max_length, 30)\n\n def test_temp_label(self):\n user = tempStoreUserLocationLink.objects.get(id=1)\n field_label = user._meta.get_field('person').verbose_name\n field_label1 = user._meta.get_field('location').verbose_name\n field_label2 = user._meta.get_field('lab_session').verbose_name\n self.assertEquals(field_label, 'person')\n self.assertEquals(field_label1, 'location')\n self.assertEquals(field_label2, 'lab session')\n\n def test_temp_with_person_with_location(self):\n user = tempStoreUserLocationLink.objects.get(id=1)\n expected_object_name = str(user.person) + ' ' + str(user.lab_session)\n self.assertEquals(expected_object_name, str(user))\n\n def test_temp_null_person_and_null_location(self):\n user = tempStoreUserLocationLink.objects.get(id=2)\n expected_object_name = str(user.location) + ' ' + str(user.lab_session)\n self.assertEquals(expected_object_name, str(user))\n\n def test_temp_blank_person_and_blank_location(self):\n user = tempStoreUserLocationLink.objects.get(id=3)\n expected_object_name = str(user.person) + ' ' + str(user.lab_session)\n self.assertEquals(expected_object_name, str(user))", "sub_path": "main_site/tests/test_models.py", "file_name": "test_models.py", "file_ext": "py", "file_size_in_byte": 8514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.test.TestCase", "line_number": 6, "usage_type": "name"}, {"api_name": "main_site.models.Person.objects.create", "line_number": 9, "usage_type": "call"}, {"api_name": "main_site.models.Person.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "main_site.models.Person", "line_number": 9, "usage_type": "name"}, {"api_name": "main_site.models.Person.objects.create", "line_number": 10, "usage_type": "call"}, {"api_name": "main_site.models.Person.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "main_site.models.Person", "line_number": 10, "usage_type": "name"}, {"api_name": "main_site.models.LabSession.objects.create", "line_number": 11, "usage_type": "call"}, {"api_name": "main_site.models.LabSession.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "main_site.models.LabSession", "line_number": 11, "usage_type": "name"}, {"api_name": "main_site.models.Request.objects.create", "line_number": 12, "usage_type": "call"}, {"api_name": "main_site.models.Request.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "main_site.models.Request", "line_number": 12, "usage_type": "name"}, {"api_name": "main_site.models.Request.objects.create", "line_number": 13, "usage_type": "call"}, {"api_name": "main_site.models.Request.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "main_site.models.Request", "line_number": 13, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 13, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 13, "usage_type": "name"}, {"api_name": "main_site.models.Topic.objects.create", "line_number": 14, "usage_type": "call"}, {"api_name": "main_site.models.Topic.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "main_site.models.Topic", "line_number": 14, "usage_type": "name"}, {"api_name": "main_site.models.Question.objects.create", "line_number": 15, "usage_type": "call"}, {"api_name": "main_site.models.Question.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "main_site.models.Question", "line_number": 15, "usage_type": "name"}, {"api_name": "main_site.models.RequestHandler.objects.create", "line_number": 16, "usage_type": "call"}, {"api_name": "main_site.models.RequestHandler.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "main_site.models.RequestHandler", "line_number": 16, "usage_type": "name"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects.create", "line_number": 17, "usage_type": "call"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "main_site.models.tempStoreUserLocationLink", "line_number": 17, "usage_type": "name"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects.create", "line_number": 18, "usage_type": "call"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "main_site.models.tempStoreUserLocationLink", "line_number": 18, "usage_type": "name"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects.create", "line_number": 19, "usage_type": "call"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "main_site.models.tempStoreUserLocationLink", "line_number": 19, "usage_type": "name"}, {"api_name": "main_site.models.Person.objects.get", "line_number": 22, "usage_type": "call"}, {"api_name": "main_site.models.Person.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "main_site.models.Person", "line_number": 22, "usage_type": "name"}, {"api_name": "main_site.models.Person.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "main_site.models.Person.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "main_site.models.Person", "line_number": 27, "usage_type": "name"}, {"api_name": "main_site.models.Person.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "main_site.models.Person.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "main_site.models.Person", "line_number": 32, "usage_type": "name"}, {"api_name": "main_site.models.LabSession.objects.get", "line_number": 38, "usage_type": "call"}, {"api_name": "main_site.models.LabSession.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "main_site.models.LabSession", "line_number": 38, "usage_type": "name"}, {"api_name": "main_site.models.LabSession.objects.get", "line_number": 44, "usage_type": "call"}, {"api_name": "main_site.models.LabSession.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "main_site.models.LabSession", "line_number": 44, "usage_type": "name"}, {"api_name": "main_site.models.LabSession.objects.get", "line_number": 49, "usage_type": "call"}, {"api_name": "main_site.models.LabSession.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "main_site.models.LabSession", "line_number": 49, "usage_type": "name"}, {"api_name": "main_site.models.Request.objects.get", "line_number": 56, "usage_type": "call"}, {"api_name": "main_site.models.Request.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "main_site.models.Request", "line_number": 56, "usage_type": "name"}, {"api_name": "main_site.models.Request.objects.get", "line_number": 63, "usage_type": "call"}, {"api_name": "main_site.models.Request.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "main_site.models.Request", "line_number": 63, "usage_type": "name"}, {"api_name": "main_site.models.Request.objects.get", "line_number": 79, "usage_type": "call"}, {"api_name": "main_site.models.Request.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "main_site.models.Request", "line_number": 79, "usage_type": "name"}, {"api_name": "main_site.models.Request.objects.get", "line_number": 80, "usage_type": "call"}, {"api_name": "main_site.models.Request.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "main_site.models.Request", "line_number": 80, "usage_type": "name"}, {"api_name": "main_site.models.Topic.objects.get", "line_number": 97, "usage_type": "call"}, {"api_name": "main_site.models.Topic.objects", "line_number": 97, "usage_type": "attribute"}, {"api_name": "main_site.models.Topic", "line_number": 97, "usage_type": "name"}, {"api_name": "main_site.models.Topic.objects.get", "line_number": 102, "usage_type": "call"}, {"api_name": "main_site.models.Topic.objects", "line_number": 102, "usage_type": "attribute"}, {"api_name": "main_site.models.Topic", "line_number": 102, "usage_type": "name"}, {"api_name": "main_site.models.Topic.objects.get", "line_number": 107, "usage_type": "call"}, {"api_name": "main_site.models.Topic.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "main_site.models.Topic", "line_number": 107, "usage_type": "name"}, {"api_name": "main_site.models.Question.objects.get", "line_number": 114, "usage_type": "call"}, {"api_name": "main_site.models.Question.objects", "line_number": 114, "usage_type": "attribute"}, {"api_name": "main_site.models.Question", "line_number": 114, "usage_type": "name"}, {"api_name": "main_site.models.Question.objects.get", "line_number": 119, "usage_type": "call"}, {"api_name": "main_site.models.Question.objects", "line_number": 119, "usage_type": "attribute"}, {"api_name": "main_site.models.Question", "line_number": 119, "usage_type": "name"}, {"api_name": "main_site.models.Question.objects.get", "line_number": 128, "usage_type": "call"}, {"api_name": "main_site.models.Question.objects", "line_number": 128, "usage_type": "attribute"}, {"api_name": "main_site.models.Question", "line_number": 128, "usage_type": "name"}, {"api_name": "main_site.models.RequestHandler.objects.get", "line_number": 135, "usage_type": "call"}, {"api_name": "main_site.models.RequestHandler.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "main_site.models.RequestHandler", "line_number": 135, "usage_type": "name"}, {"api_name": "main_site.models.RequestHandler.objects.get", "line_number": 142, "usage_type": "call"}, {"api_name": "main_site.models.RequestHandler.objects", "line_number": 142, "usage_type": "attribute"}, {"api_name": "main_site.models.RequestHandler", "line_number": 142, "usage_type": "name"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects.get", "line_number": 149, "usage_type": "call"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects", "line_number": 149, "usage_type": "attribute"}, {"api_name": "main_site.models.tempStoreUserLocationLink", "line_number": 149, "usage_type": "name"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects.get", "line_number": 154, "usage_type": "call"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "main_site.models.tempStoreUserLocationLink", "line_number": 154, "usage_type": "name"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects.get", "line_number": 163, "usage_type": "call"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects", "line_number": 163, "usage_type": "attribute"}, {"api_name": "main_site.models.tempStoreUserLocationLink", "line_number": 163, "usage_type": "name"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects.get", "line_number": 168, "usage_type": "call"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects", "line_number": 168, "usage_type": "attribute"}, {"api_name": "main_site.models.tempStoreUserLocationLink", "line_number": 168, "usage_type": "name"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects.get", "line_number": 173, "usage_type": "call"}, {"api_name": "main_site.models.tempStoreUserLocationLink.objects", "line_number": 173, "usage_type": "attribute"}, {"api_name": "main_site.models.tempStoreUserLocationLink", "line_number": 173, "usage_type": "name"}]} +{"seq_id": "147186289", "text": "from django.contrib import admin\r\n\r\nfrom modeltranslation.admin import TranslationAdmin\r\nfrom sorl.thumbnail.admin import AdminImageMixin\r\nfrom mce_filebrowser.admin import MCEFilebrowserAdmin\r\n\r\nfrom main.models import Slider, Feedback\r\n\r\n\r\nclass SliderAdmin(AdminImageMixin, TranslationAdmin, MCEFilebrowserAdmin):\r\n class Media:\r\n\t js = (\r\n\t '/static/modeltranslation/js/force_jquery.js',\r\n\t 'http://ajax.googleapis.com/ajax/libs/jqueryui/1.8.2/jquery-ui.min.js',\r\n\t '/static/modeltranslation/js/tabbed_translation_fields.js',\r\n\t )\r\n\t css = {\r\n\t 'screen': ('/static/modeltranslation/css/tabbed_translation_fields.css',),\r\n\t }\r\n fieldsets = [\r\n (None, {'fields': ['title','link', 'content', 'image']}),\r\n ]\r\n\r\n\r\nadmin.site.register(Feedback)\r\nadmin.site.register(Slider, SliderAdmin)", "sub_path": "main/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 873, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "sorl.thumbnail.admin.AdminImageMixin", "line_number": 10, "usage_type": "name"}, {"api_name": "modeltranslation.admin.TranslationAdmin", "line_number": 10, "usage_type": "name"}, {"api_name": "mce_filebrowser.admin.MCEFilebrowserAdmin", "line_number": 10, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 25, "usage_type": "call"}, {"api_name": "main.models.Feedback", "line_number": 25, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 25, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 26, "usage_type": "call"}, {"api_name": "main.models.Slider", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "359693260", "text": "# -*- coding:utf-8 -*-\nimport bs4, requests\ndef search_meteo(text):\n response = requests.post('http://meteo.ua/ua/search-forecast-by-city-name', data = {'name': text})\n b = bs4.BeautifulSoup(response.text, \"html.parser\")\n p3 = b.select('.main_cont p a')\n hrefs = p3[0]['href']\n print(hrefs)\n\n\n\n\n\n\nif __name__ == '__main__':\n search_meteo('київ')\n\n\n", "sub_path": "search.py", "file_name": "search.py", "file_ext": "py", "file_size_in_byte": 370, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "requests.post", "line_number": 4, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "620038150", "text": "# crawling data from https://www.bestplaces.net/crime/zip-code/california/los_angeles/\n\n#Parse data using BeautifulSoup\nimport urllib.request, urllib.parse, urllib.error\nfrom bs4 import BeautifulSoup\nimport re\nimport csv\n\ntable = []\ndata = open('orange_venturacountyzipcodes.txt', 'r')\nfor line in data:\n # print(line.rstrip())\n zip = re.findall('([0-9]+)', line)\n zip1 = zip[0]\n city = re.findall('\\((.*?)\\)', line)\n city1 = city[0].replace(' ', '_').lower()\n # print(zip[0], city1)\n try:\n url = 'https://www.bestplaces.net/crime/zip-code/california/' + str(city1) + '/' + str(zip1)\n html = urllib.request.urlopen(url).read()\n # print(html)\n soup = BeautifulSoup(html, 'html.parser')\n # print(soup)\n # print(tag)\n h5 = soup.find_all('h5')\n vio_sent = str(h5[0])\n # print(vio_sent)\n # print(type(vio_sent))\n vio_crime = re.findall('violent crime is ([0-9]+.[0-9])', vio_sent)\n\n prop_sent = str(h5[1])\n prop_crime = re.findall('property crime is ([0-9]+.[0-9])', prop_sent)\n result = [str(zip1), vio_crime[0], prop_crime[0]]\n table.append(result)\n print(result)\n except:\n result = [str(zip1), '0', '0']\n table.append(result)\n\n\nheader = ['zipcode', 'violent_crime_index', 'property_crime_index']\n# write into newfile:\nwith open('crime_by_zipcode_part2.csv', 'w') as csvwrite:\n writer = csv.writer(csvwrite, delimiter=',')\n writer.writerow(header)\n for i in table:\n writer.writerow(i)\n\ncsvwrite.close()\n", "sub_path": "code/scraper/crimeindex.py", "file_name": "crimeindex.py", "file_ext": "py", "file_size_in_byte": 1575, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "re.findall", "line_number": 13, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 15, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 20, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 20, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 20, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 22, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 29, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 32, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "534978672", "text": "# !/home/imyin/python_env/newspaper_python3/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nCreate on 12/27/17 4:33 PM\n\n@auther: imyin\n\n@File: compare_cars\n\"\"\"\n\nimport os\nfrom datetime import datetime\nfrom multiprocessing.dummy import Pool as ThreadPool\n\nimport pandas as pd\n\nimport constants as cons\n\nfinally_path = ''\ncars_info_path = ''\nraw_dict = ''\ncars_dict = {file_name.split('.')[0]: pd.read_csv(cars_info_path + '/' + file_name) for file_name in\n os.listdir(cars_info_path)}\n\n\ndef raw_data(path):\n return pd.read_csv(raw_dict + '/' + path, encoding='utf-8', quotechar='&')\n\n\ndef compare_it(df):\n car_4s = []\n brand = []\n addrs = []\n tels = []\n for index, item in enumerate(df['name'].values):\n car_data = cars_dict[cons.province_dict[df.loc[index, 'province']]]\n name_list = [item.strip() for item in car_data['store_name']]\n for i, w in enumerate(name_list):\n if w in item:\n car_4s.append(w)\n brand.append(car_data.loc[i, 'main_brand'])\n addrs.append(car_data.loc[i, 'address'])\n tels.append(car_data.loc[i, 'phone'])\n break\n elif i == name_length - 1:\n car_4s.append('')\n brand.append('')\n addrs.append('')\n tels.append('')\n else:\n continue\n df['store_name'] = car_4s\n df['brand'] = brand\n df['addr'] = addrs\n df['tel'] = tels\n filter_not_null = df['store_name'] != ''\n df = df[filter_not_null]\n return df\n\n\ndef run(path):\n df = compare_it(raw_data(path))\n df.to_csv(finally_path + '/' + path, encoding='utf-8', index=False)\n print('{} finished.....'.format(path))\n\n\nif __name__ == '__main__':\n time1 = datetime.now()\n print('it is : {}'.format(time1))\n raw_file = os.listdir(raw_dict)\n\n pool = ThreadPool(16)\n results = pool.map(run, raw_file)\n pool.close()\n pool.join()\n time2 = datetime.now()\n print('It cost {} sec run it'.format(time2))\n", "sub_path": "GD/compare_cars.py", "file_name": "compare_cars.py", "file_ext": "py", "file_size_in_byte": 2036, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "constants.province_dict", "line_number": 37, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 71, "usage_type": "call"}, {"api_name": "multiprocessing.dummy.Pool", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "363078407", "text": "import re\nimport os\nimport sys\nimport pandas as pd\nimport numpy as np\nimport itertools as it\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport statsmodels.stats.multitest as p_adjust\nfrom collections import Counter\nfrom pylab import text\nfrom scipy import stats\nfrom collections import deque\nfrom functools import reduce\nfrom matplotlib import colors\nfrom matplotlib.colors import LogNorm\nfrom heapq import nsmallest\nfrom Bio import SeqIO\nimport xlsxwriter\n\nall_mm = ['AG','AC','AT','CA','CG','CT','GA','GC','GT','TA','TG','TC']\n\n\ndef get_sites_list(df):\n \n sites = []\n for sample in list(set(df['sample_name'])):\n samples_df = df[df['sample_name'] == sample]\n row_gene_sites = []\n for i, row in samples_df.iterrows():\n for mm_type in eval(row['genomic_keys']):\n row_gene_sites = row_gene_sites + [row['gene_name']+';'+s for s in mm_type]\n sites = sites + list(set(row_gene_sites))\n return sites\n\n\ndef plot_editings_sites_repetitions(fig_path,names, sites, groups = [1,2,3,4,5,6,7,8,9,10]):\n col = ['b','g','salmon','y','m','tan','r','silver','yellow','crimson','teal','k','orange','brown','limegreen','navy','gold','c','olive']\n \n bars = []\n sites_counts = [list(Counter(s).values()) for s in sites]\n for s in sites_counts:\n b=[]\n for g in sorted(groups[:-1]):\n b.append(len([c for c in s if c==g]))\n b.append(len([c for c in s if c>=groups[-1]]))\n bars.append(b)\n \n # set width of bar\n barWidth = 0.2\n \n groups_labels = []\n for i in groups:\n if i < groups[-1]:\n groups_labels.append(str(i))\n groups_labels.append(str(groups[-1])+'<=')\n \n\n # Set position of bar on X axis\n rs = [np.arange(1,len(bars[0])+1)]\n r_temp = rs[0]\n for i in names[1:]:\n r_temp = [x + barWidth for x in r_temp]\n rs.append(np.array(r_temp))\n\n \n # Make the plot\n for i in range(len(bars)):\n plt.bar(list(rs[i]), bars[i], color=col[i], width=barWidth, edgecolor='white', label=names[i])\n \n # Add xticks on the middle of the group bars\n plt.xlabel('Repetitions', fontweight='bold')\n plt.ylabel('Sites', fontweight='bold')\n plt.xticks([g+0.2 for g in groups], groups_labels) \n plt.yticks(np.arange(0,20,5))\n plt.title('Discovered Editing Sites')\n \n # Create legend & Show graphic\n plt.legend()\n plt.savefig(fig_path)\n# plt.show()\n plt.close()\n\n\ndef scatter_discovered_peptides(df, name, path):\n color_markers = [('y', '+'), ('r', '*'), ('b', '>'), ('g', '<'), ('m', '^'), ('tan', 'D'), ('silver', '.'), ('yellow', 'o'), ('crimson', '<'), ('teal', '>'), ('k', 'D'), ('orange', '^'), ('brown', 'D'), ('limegreen', 'D'), ('navy', '^'), ('gold', 'o'), ('c', '.'), ('olive', '.')]\n \n tissues = Counter(list(df['tissue']))\n \n for i,t in enumerate(tissues.items()):\n print( t[0]+' '+str(t[1]))\n tissues_dfs = df[df['tissue']==t[0]]\n plt.scatter(list(tissues_dfs['total_peptides']),list(tissues_dfs['edited_peptides']), label = t[0]+' '+str(t[1]), color = color_markers[i][0], marker = color_markers[i][1])\n plt.legend(loc='center left', bbox_to_anchor=(0.8, 0.5))\n plt.title('Peptides for Samples - ' + name + ' proteom')\n plt.xlabel('total peptides')\n plt.savefig(path)\n plt.close()\n \n \nif __name__ == '__main__':\n \n quantification_methods = ['LFQ','LFQ+','unknown']\n filter_by_quantification = False\n \n\n \n \n# summ1 = 'E:/RNA_editing_Large_files/MQ/results_from_ag_finished.txt'\n# summ2 = 'E:/RNA_editing_Large_files/MQ/results_from_all_non_ag_finished.txt'\n# summ3 = 'E:/RNA_editing_Large_files/MQ/results_from_random_ag_finished.txt'\n# summ_files = [summ1,summ2,summ3]\n# \n# peps1 = 'E:/RNA_editing_Large_files/MQ/peptides_lists_from_ag_finished.txt'\n# peps2 = 'E:/RNA_editing_Large_files/MQ/peptides_lists_from_all_non_ag_finished.txt'\n# peps3 = 'E:/RNA_editing_Large_files/MQ/peptides_lists_from_random_ag_finished.txt'\n# peps_files = [peps1,peps2,peps3]\n# \n# names = ['AG','NonAG','randomAG']\n \n \n summ1 = 'E:/RNA_editing_Large_files/MQ/results_from_ag_finished.txt'\n summ2 = 'E:/RNA_editing_Large_files/MQ/results_from_all_non_ag_finished.txt'\n summ_files = [summ1,summ2]\n \n peps1 = 'E:/RNA_editing_Large_files/MQ/peptides_lists_from_ag_finished.txt'\n peps2 = 'E:/RNA_editing_Large_files/MQ/peptides_lists_from_all_non_ag_finished.txt'\n peps_files = [peps1,peps2]\n \n names = ['AG','NonAG']\n \n \n path = '/'.join(peps_files[0].split('/')[:-1]) + '/'\n \n anal_name = ''\n for n in names:\n anal_name = anal_name + '_' + n\n if filter_by_quantification:\n anal_name = anal_name[1:] + '_LFQ'\n else:\n anal_name = anal_name[1:] + '_all_quantifications'\n \n sys.stdout = open(path + anal_name + '.txt', 'w')\n \n\n \n peps_dfs_dict = {}\n writer = pd.ExcelWriter(path + 'peptides.xlsx', engine='xlsxwriter')\n for i,file in enumerate(peps_files):\n df = pd.read_csv(file,sep = '\\t', header = 0)\n for j, mm in enumerate(all_mm):\n df[mm+'sites'] = df.apply(lambda row: eval(row['genomic_keys'])[j], axis = 1)\n df.to_excel(writer, sheet_name = names[i])\n df['full_name'] = df.apply(lambda row: row['sample_name']+'_'+row['sub_name'], axis = 1)\n if filter_by_quantification:\n df = df[df['quantification'].isin(quantification_methods)]\n peps_dfs_dict.update({names[i]:df})\n writer.save()\n \n \n summaries_dfs_dict = {}\n writer = pd.ExcelWriter(path + 'sammaries.xlsx', engine='xlsxwriter')\n for i,file in enumerate(summ_files):\n df = pd.read_csv(file,sep = '\\t', header = 0)\n df.to_excel(writer, sheet_name = names[i])\n df['full_name'] = df.apply(lambda row: row['sample_name']+'_'+row['sub_name'], axis = 1)\n if filter_by_quantification:\n df = df[df['quantification'].isin(quantification_methods)]\n summaries_dfs_dict.update({names[i]:df})\n writer.save()\n \n samples_in_all = list(set.intersection(*map(set,[list(summaries_dfs_dict[n]['sample_name']) for n in names])))\n finshed_run_in_all_analyses = list(set.intersection(*map(set,[[str(i)+'_'+str(j) for i,j in zip(list(summaries_dfs_dict[n]['sample_name']),list(summaries_dfs_dict[n]['sub_name']))] for n in names])))\n for k,v in summaries_dfs_dict.items():\n summaries_dfs_dict[k] = v[v['full_name'].isin(finshed_run_in_all_analyses)]\n for k,v in peps_dfs_dict.items():\n peps_dfs_dict[k] = v[v['full_name'].isin(finshed_run_in_all_analyses)]\n \n \n \n print('\\nAnalysis for samples:')\n df = summaries_dfs_dict[names[0]]\n for s in samples_in_all:\n df_for_sample = df[df['sample_name'] == s]\n# tissue = list(df_for_sample['tissue'])[0]\n print(s)\n print('\\n')\n \n #list of sites (as a non-unique list) discovered in each file\n sites = [get_sites_list(peps_dfs_dict[n]) for n in names]\n tissues = list(set(summaries_dfs_dict[names[0]]['tissue']))\n \n plot_editings_sites_repetitions(path + anal_name + '.jpg', names, sites)\n \n for i, n in enumerate(names):\n path = '/'.join(summ_files[i].split('/')[:-1]) + '/peptides_for_samples_' + n + '.jpg' \n c = Counter(sites[i]).items()\n print('\\n\\n'+n+' Sites (' + str(len(c)) + '):')\n for key, val in Counter(sites[i]).items():\n# print(key)\n print(key+ ': ' + str(val))\n print('\\n')\n# scatter_discovered_peptides(summaries_dfs_dict[n], n, path)\n \n \n ", "sub_path": "scripts/proteomics_simulator/OLD/20190322/proteomics_simulator/additional_modules/plot_maxquant_results_for_multiple_runs.py", "file_name": "plot_maxquant_results_for_multiple_runs.py", "file_ext": "py", "file_size_in_byte": 7666, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "collections.Counter", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pandas.ExcelWriter", "line_number": 148, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 150, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 162, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 164, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 197, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 199, "usage_type": "call"}]} +{"seq_id": "395340779", "text": "# coding: utf-8\nimport argparse\nimport time\nimport math\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.optim import Adadelta,SGD\n\nimport data\nimport model\nfrom data import N_MAX_CHAR_SIZE\n\nparser = argparse.ArgumentParser(description='PyTorch Wikitext-2 RNN/LSTM Language Model')\nparser.add_argument('--data', type=str, default='./data/wikitext',\n help='location of the data corpus')\nparser.add_argument('--model', type=str, default='LSTM',\n help='type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)')\nparser.add_argument('--emsize', type=int, default=200,\n help='size of word embeddings')\nparser.add_argument('--nhid', type=int, default=400,\n help='number of hidden units per layer')\nparser.add_argument('--nlayers', type=int, default=2,\n help='number of layers')\nparser.add_argument('--lr', type=float, default=20,\n help='initial learning rate')\nparser.add_argument('--clip', type=float, default=0.25,\n help='gradient clipping')\nparser.add_argument('--epochs', type=int, default=40,\n help='upper epoch limit')\nparser.add_argument('--batch_size', type=int, default=40, metavar='N',\n help='batch size')\nparser.add_argument('--bptt', type=int, default=35,\n help='sequence length')\nparser.add_argument('--dropout', type=float, default=0.2,\n help='dropout applied to layers (0 = no dropout)')\nparser.add_argument('--tied', action='store_true',\n help='tie the word embedding and softmax weights')\nparser.add_argument('--seed', type=int, default=1111,\n help='random seed')\nparser.add_argument('--cuda', action='store_true',\n help='use CUDA')\nparser.add_argument('--log-interval', type=int, default=200, metavar='N',\n help='report interval')\nparser.add_argument('--save', type=str, default='out/model.pt',\n help='path to save the final model')\nparser.add_argument('--load_corpus', type=int, default=0,\n help='Whether load old corpus')\nparser.add_argument('--load_save', type=int, default=0,\n help='Whether load old weight')\nargs = parser.parse_args()\n\n# Set the random seed manually for reproducibility.\ntorch.manual_seed(args.seed)\nif torch.cuda.is_available():\n if not args.cuda:\n print(\"WARNING: You have a CUDA device, so you should probably run with --cuda\")\n else:\n torch.cuda.manual_seed(args.seed)\n\n###############################################################################\n# Load data\n###############################################################################\n\nif os.path.exists('out/corpus') and args.load_corpus:\n print('loading previous corpus')\n corpus = torch.load('out/corpus')\nelse:\n print('creating new corpus')\n corpus = data.Corpus(args.data)\n\nif not os.path.exists('out'):\n os.mkdir('out')\ntorch.save(corpus, 'out/corpus')\n\n# Starting from sequential data, batchify arranges the dataset into columns.\n# For instance, with the alphabet as the sequence and batch size 4, we'd get\n# ┌ a g m s ┐\n# │ b h n t │\n# │ c i o u │\n# │ d j p v │\n# │ e k q w │\n# └ f l r x ┘.\n# These columns are treated as independent by the model, which means that the\n# dependence of e. g. 'g' on 'f' can not be learned, but allows more efficient\n# batch processing.\n\ndef batchify(data, bsz):\n # Work out how cleanly we can divide the dataset into bsz parts.\n nbatch = data.size(0) // bsz\n # Trim off any extra elements that wouldn't cleanly fit (remainders).\n data = data.narrow(0, 0, nbatch * bsz)\n # Evenly divide the data across the bsz batches.\n data = data.view(bsz, -1).t().contiguous()\n if args.cuda:\n data = data.cuda()\n return data\n\neval_batch_size = args.batch_size\ntrain_data = batchify(corpus.train, args.batch_size)\nval_data = batchify(corpus.valid, eval_batch_size)\ntest_data = batchify(corpus.test, eval_batch_size)\n\n###############################################################################\n# Build the model\n###############################################################################\n\nntokens = len(corpus.dictionary)\nncharsize = len(corpus.dictionary.idx2char)\nmodel = model.RNNModel(args.model, ntokens, ncharsize, N_MAX_CHAR_SIZE,\n args.emsize, args.nhid, args.nlayers, args.dropout, args.tied)\nif os.path.exists(args.save) and args.load_save:\n print('loading from old...')\n model.load_state_dict(torch.load(args.save))\nelse:\n print('starting new...')\nif args.cuda:\n model.cuda()\n\ncriterion = nn.CrossEntropyLoss()\n\n###############################################################################\n# Training code\n###############################################################################\n\ndef repackage_hidden(h):\n \"\"\"Wraps hidden states in new Variables, to detach them from their history.\"\"\"\n if type(h) == Variable:\n return Variable(h.data)\n else:\n return tuple(repackage_hidden(v) for v in h)\n\ndef get_word_char_vecs(words: torch.LongTensor) -> torch.LongTensor:\n tmp = []\n for word in words:\n char_vectors = torch.LongTensor(corpus.dictionary.get_word_char_vec(corpus.dictionary.idx2word[word]))+1\n tmp.append(char_vectors)\n return torch.cat(tmp).view(len(words), N_MAX_CHAR_SIZE)\n\n\n# get_batch subdivides the source data into chunks of length args.bptt.\n# If source is equal to the example output of the batchify function, with\n# a bptt-limit of 2, we'd get the following two Variables for i = 0:\n# ┌ a g m s ┐ ┌ b h n t ┐\n# └ b h n t ┘ └ c i o u ┘\n# Note that despite the name of the function, the subdivison of data is not\n# done along the batch dimension (i.e. dimension 1), since that was handled\n# by the batchify function. The chunks are along dimension 0, corresponding\n# to the seq_len dimension in the LSTM.\n\ndef get_batch(source, i, evaluation=False):\n seq_len = min(args.bptt, len(source) - 1 - i)\n source_words = source[i:i+seq_len]\n source_word_vectors = []\n for batch in source_words:\n source_word_vectors.append(get_word_char_vecs(batch))\n\n source_word_vector = torch.cat(source_word_vectors).view(len(source_words), -1,\n N_MAX_CHAR_SIZE)\n if args.cuda:\n source_word_vector = source_word_vector.cuda()\n data = (Variable(source_words, volatile=evaluation), Variable(source_word_vector, volatile=evaluation))\n #print('input=', data)\n target_char_vectors = []\n for batch in source[i+1:i+1+seq_len]:\n target_char_vectors.append(get_word_char_vecs(batch))\n target_char_vectors = torch.cat(target_char_vectors).view(seq_len, -1, N_MAX_CHAR_SIZE)\n target = Variable(source[i+1:i+1+seq_len].view(-1))\n #print('expected target=', target)\n return data, target\n\ndef calculate_single_loss(output_embedding, targets):\n target_word_idx_vec, target_char_vectors = targets\n target_embedding_value = model.get_word_embedding(target_word_idx_vec, target_char_vectors)\n target_embedding = Variable(target_embedding_value.data, volatile=True)\n return criterion(output_embedding, target_embedding)\n\ndef calculate_loss(output: torch.Tensor, targets):\n return criterion(output.view(-1, ntokens), targets)\n\n #loss = calculate_single_loss(output_embedding, targets)\n # (batch, bqtt), (batch, bqtt, N_MAX_CHAR_SIZE)\n #for i in range(5):\n # random_batch_idx = random.randint(0, data_source.size(0) // args.bptt - 1) * args.bptt\n # _, rand_targets = get_batch(data_source, random_batch_idx)\n # loss -= calculate_single_loss(output_embedding, rand_targets)\n #return loss\n\n\ndef evaluate(data_source):\n # Turn on evaluation mode which disables dropout.\n model.eval()\n total_loss = 0\n ntokens = len(corpus.dictionary)\n hidden = model.init_hidden(eval_batch_size)\n for i in range(0, data_source.size(0) - 1, args.bptt):\n data, targets = get_batch(data_source, i, evaluation=True)\n output, hidden = model(data, hidden)\n cur_loss = calculate_loss(output, targets)\n total_loss += cur_loss.data[0]\n hidden = repackage_hidden(hidden)\n return total_loss / len(data_source)\n\n\ndef train():\n # Turn on training mode which enables dropout.\n total_loss = 0\n start_time = time.time()\n ntokens = len(corpus.dictionary)\n hidden = model.init_hidden(args.batch_size)\n for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):\n model.train()\n data, targets = get_batch(train_data, i)\n # Starting each batch, we detach the hidden state from how it was previously produced.\n # If we didn't, the model would try backpropagating all the way to start of the dataset.\n hidden = repackage_hidden(hidden)\n model.zero_grad()\n output, hidden = model(data, hidden)\n\n #confidence, output_max = torch.max(output.view(-1, ntokens), dim=1)\n #print('output_max=', output_max)\n #print('confidence=', confidence)\n loss = calculate_loss(output, targets)\n loss.backward()\n\n # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n # torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)\n for p in model.parameters():\n p.data.add_(-lr, p.grad.data)\n\n total_loss += loss.data[0]\n print(' || cur loss= ', loss.data[0] / args.bptt / args.batch_size)\n\n if batch % args.log_interval == 0 and batch > 0:\n cur_loss = total_loss / args.log_interval / args.bptt / args.batch_size\n elapsed = time.time() - start_time\n print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '\n 'loss {:8f} | ppl {:8f}'.format(\n epoch, batch, len(train_data) // args.bptt, lr,\n elapsed * 1000 / args.log_interval, cur_loss, math.pow(2, cur_loss)))\n total_loss = 0\n start_time = time.time()\n if batch / args.log_interval % 5 == 0:\n print('saving check point...')\n torch.save(model.state_dict(), 'out/checkpoint.t7')\n if batch / args.log_interval % 10 == 0:\n val_loss = evaluate(val_data)\n print('-' * 89)\n print('|| valid loss {:5.2f} | '\n 'valid ppl {:8.2f}'.format(val_loss, math.pow(2, val_loss)))\n print('-' * 89)\n\n# Loop over epochs.\nlr = args.lr\nbest_val_loss = None\n\n# At any point you can hit Ctrl + C to break out of training early.\ntry:\n for epoch in range(1, args.epochs+1):\n epoch_start_time = time.time()\n train()\n val_loss = evaluate(val_data)\n print('-' * 89)\n print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '\n 'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),\n val_loss, math.pow(2, val_loss)))\n print('-' * 89)\n # Save the model if the validation loss is the best we've seen so far.\n if not best_val_loss or val_loss < best_val_loss:\n torch.save(model.state_dict(), args.save)\n best_val_loss = val_loss\n else:\n # Anneal the learning rate if no improvement has been seen in the validation dataset.\n lr /= 4.0\nexcept KeyboardInterrupt:\n print('-' * 89)\n print('Exiting from training early')\n print('dumping weights...')\n torch.save(model.state_dict(), args.save)\n\n# Load the best saved model.\nmodel.load_state_dict(torch.load(args.save))\n\n# Run on test data.\ntest_loss = evaluate(test_data)\nprint('=' * 89)\nprint('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(\n test_loss, math.exp(test_loss)))\nprint('=' * 89)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 11998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 69, "usage_type": "call"}, {"api_name": "data.Corpus", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 76, "usage_type": "call"}, {"api_name": "data.size", "line_number": 92, "usage_type": "call"}, {"api_name": "data.narrow", "line_number": 94, "usage_type": "call"}, {"api_name": "data.view", "line_number": 96, "usage_type": "call"}, {"api_name": "data.cuda", "line_number": 98, "usage_type": "call"}, {"api_name": "model.RNNModel", "line_number": 112, "usage_type": "call"}, {"api_name": "data.N_MAX_CHAR_SIZE", "line_number": 112, "usage_type": "argument"}, {"api_name": "os.path.exists", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "model.load_state_dict", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 116, "usage_type": "call"}, {"api_name": "model.cuda", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 135, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 138, "usage_type": "call"}, {"api_name": "data.N_MAX_CHAR_SIZE", "line_number": 140, "usage_type": "argument"}, {"api_name": "torch.cat", "line_number": 140, "usage_type": "call"}, {"api_name": "data.N_MAX_CHAR_SIZE", "line_number": 161, "usage_type": "argument"}, {"api_name": "torch.cat", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 164, "usage_type": "call"}, {"api_name": "data.N_MAX_CHAR_SIZE", "line_number": 169, "usage_type": "argument"}, {"api_name": "torch.cat", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 170, "usage_type": "call"}, {"api_name": "model.get_word_embedding", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 180, "usage_type": "attribute"}, {"api_name": "model.eval", "line_number": 194, "usage_type": "call"}, {"api_name": "model.init_hidden", "line_number": 197, "usage_type": "call"}, {"api_name": "time.time", "line_number": 210, "usage_type": "call"}, {"api_name": "model.init_hidden", "line_number": 212, "usage_type": "call"}, {"api_name": "model.train", "line_number": 214, "usage_type": "call"}, {"api_name": "model.zero_grad", "line_number": 219, "usage_type": "call"}, {"api_name": "model.parameters", "line_number": 230, "usage_type": "call"}, {"api_name": "time.time", "line_number": 238, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 242, "usage_type": "call"}, {"api_name": "time.time", "line_number": 244, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 247, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 247, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 252, "usage_type": "call"}, {"api_name": "time.time", "line_number": 262, "usage_type": "call"}, {"api_name": "time.time", "line_number": 267, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 272, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 281, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 281, "usage_type": "call"}, {"api_name": "model.load_state_dict", "line_number": 284, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 284, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 290, "usage_type": "call"}]} +{"seq_id": "27184761", "text": "# coding=utf-8\nfrom celery import Celery\n\n# 创建celery应用对象\napp = Celery('tasks', # 模块名\n backend='redis://127.0.0.1:6379/1',\n broker='redis://127.0.0.1:6379/0')\n\n# 表示，下面的任务由app这个对象来进行管理\n@app.task\ndef my_task(a,b):\n print ('任务函数正在执行')\n return a+b\n\n", "sub_path": "tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 345, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "celery.Celery", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "21324386", "text": "#! /usr/bin/python3\n#! -*- coding: utf-8 -*-\n\nimport csv\nfrom datetime import datetime\nfrom datetime import timedelta\nimport itertools\nimport pygal\nimport os\n\n\ndef load_data(*files):\n \"\"\"Load data from CSV files. Combine data from file in one list and\n sort by date.\"\"\"\n\n #TODO: Handle czech and english table header\n #TODO: Handle duplicit data\n fieldnames = [\n 'item',\n 'posting_date',\n 'amount',\n 'bank_account_detail',\n 'execution_date',\n 'variable_symbol',\n 'cancellation',\n 'counter_account_name',\n 'constant_symbol',\n 'specific_symbol',\n 'message_for_payee',\n 'message_for_me',\n 'transaction_reference_number',\n 'client_note',\n 'payment_reference',\n 'reason_for_non_execution',\n 'warning',\n ]\n\n data = []\n\n for file_name in files:\n\n with open(file_name, newline='',\n encoding='utf-8', errors='ignore') as f:\n\n reader = csv.DictReader(f, fieldnames)\n transactions = list(reader)[1:]\n data += transactions\n\n return sorted(data, key=lambda row: row['posting_date'])\n\n\ndef account_balance(data, initial_balance=0):\n \"\"\"Return a list of days and balances for each days.\"\"\"\n\n grouped = itertools.groupby(data, lambda row: row['posting_date'])\n days = []\n balance = initial_balance\n for group in grouped:\n group_key = group[0]\n group_iterator = group[1]\n\n amount_sum = sum([float(row['amount']) for row in group_iterator])\n date = datetime.strptime(group_key, '%Y/%m/%d')\n\n balance += amount_sum\n\n days.append((date, balance))\n\n return days\n\n\ndef create_chart(balance, output_file=None):\n \"\"\"Create a SVG file from account balance data.\"\"\"\n\n if output_file is None:\n if not os.path.isdir('charts'):\n os.mkdir('charts')\n\n output_file='charts/balance.svg'\n\n datetimeline = pygal.DateTimeLine(\n title='Account balance',\n show_legend=False,\n x_label_rotation=35, truncate_label=-1,\n x_value_formatter=lambda d: d.strftime('%Y/%m/%d')\n )\n\n datetimeline.add('Balance', balance)\n datetimeline.render_to_file(output_file)\n\n\nif __name__=='__main__':\n\n import sys\n argv = sys.argv\n files = argv[1:]\n\n #TODO: Load initial balance externaly\n initial_balance = 7999.75\n final_balance = 19224.27\n\n data = load_data(*files)\n balances = account_balance(data, initial_balance)\n\n create_chart(balances)\n", "sub_path": "account_watcher.py", "file_name": "account_watcher.py", "file_ext": "py", "file_size_in_byte": 2724, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "csv.DictReader", "line_number": 45, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 77, "usage_type": "call"}, {"api_name": "pygal.DateTimeLine", "line_number": 81, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 95, "usage_type": "attribute"}]} +{"seq_id": "75862635", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 24 15:16:00 2019\n\n@author: ASUS\n\"\"\"\n\nimport pandas as pd\nfrom datetime import date, timedelta, datetime\nimport time\nfrom pymongo import MongoClient\n\n\ndef perdelta(start, end, delta):\n curr = start\n while curr < end:\n yield curr\n curr += delta\ndef date_populater():\n days = []\n for result in perdelta(date(1993, 1, 1), date(1998, 12, 20), timedelta(days=1)):\n days.append(result);\n return(days);\n \n \n \n \n \ndef accounts_importer():\n with open('trans.csv','r') as csv_file:\n lines = csv_file.readlines()\n accounts = [];\n for line in lines:\n data = line.split(';')\n accounts.append(data[1])\n del accounts[0]\n finalAccounts=[];\n for i in accounts:\n if i not in finalAccounts:\n finalAccounts.append(i)\n return(finalAccounts)\n \n \n\n\n\ndef data_frame_initielizer():\n \n \n accounts = accounts_importer()\n days = date_populater()\n zero_init =[]\n for i in range(len(accounts)):\n zero_init.append(0)\n \n data = {}\n \n for k in days:\n data[k]=zero_init\n \n df = pd.DataFrame(data,index=accounts)\n return(df)\n\n\n\n\ndef data_frame_populater():\n \n \n start = time.time()\n \n df = data_frame_initielizer()\n accounts_id = []\n dates = []\n balance = []\n initdates = []\n with open('trans.csv','r') as csv_file:\n lines = csv_file.readlines()\n for line in lines:\n data = line.split(';')\n accounts_id.append(data[1])\n initdates.append(data[2])\n balance.append(data[6])\n del initdates[0]\n del balance[0]\n del accounts_id[0]\n \n for i in initdates:\n dates.append(datetime.strptime(i, '%y%m%d').date())\n \n # populate the dataframe with true values from history of transactions\n print(len(dates))\n for i, d in enumerate(dates):\n b = balance[i]\n c = accounts_id[i]\n df.loc[c,d] = b\n if(i > 10000):\n break\n \n \n # populate the rest of the dataframe with recent values for zeros members\n \n \n for i in range(len(df.index)):\n for j in range(1,365):\n if (df.iloc[i,j]==0):\n df.iloc[i,j]=df.iloc[i,j-1]\n \n \n \n \n \n \n #populating MongoDB\n \n \n client = MongoClient('localhost',27017) # Remember your uri string\n col = client['pfe']['balance_history_test']\n df.columns = df.columns.astype(str)\n data = df.to_dict(orient='records') # Here's our added param..\n col.insert_many(data)\n \n \n \n \n \n \n \n \n end = time.time()\n print (end-start , \" : seconds \") \n print(\"-----------------------------------------\") \n print (\"Done with success\") \n print(df)\n\n \n\ndata_frame_populater()", "sub_path": "data/testif.py", "file_name": "testif.py", "file_ext": "py", "file_size_in_byte": 2874, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "datetime.date", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}, {"api_name": "time.time", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "342405501", "text": "from nltk.corpus import semcor,wordnet\nfrom nltk.corpus.reader.wordnet import Lemma\n# WordNet synset_pos : 'a', 's', 'r', 'n', 'v'\n# ADJ,ADJ_SAT,ADV,NOUN,VERB\n\ndef LookUp(INPUT='said'): #return {SYNSET:SENT}\n SYNSET_SENT={}\n for s in semcor.tagged_sents(tag='both')[:333]:\n SENT=[]\n SYNSET=0\n for t in s:\n if t.height()==2: #Function Word or Punctuation\n POS=t.label()\n WORD=t[0]\n if POS==None:POS='None'\n SENT.append(WORD)\n elif t.height()==3: #Disambiguated Word\n POS=t[0].label()\n LEMMA=t.label()\n WORD=t[0][0]\n if not isinstance(LEMMA,str): #isinstance(LEMMA,Lemma):\n# print(WORD,LEMMA.name())\n if INPUT==LEMMA.name():#WORD:\n SYNSET=LEMMA.synset()#.name()\n SENT.append(WORD)\n else: #t.height()==4: #Named Entity\n WORD='_'.join(t[0][0])\n SENT.append(WORD)\n if SYNSET:SYNSET_SENT[SYNSET]=' '.join(SENT)#[:99]\n return SYNSET_SENT\n\n#ookUp(INPUT='say')\nfor SYNSET,SENT in LookUp(INPUT='say').items():print(SYNSET,SYNSET.definition()[:22],SENT[:22])\n", "sub_path": "semcor3_tagged_sents.py", "file_name": "semcor3_tagged_sents.py", "file_ext": "py", "file_size_in_byte": 1276, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "nltk.corpus.semcor.tagged_sents", "line_number": 8, "usage_type": "call"}, {"api_name": "nltk.corpus.semcor", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "35799655", "text": "from teambuzz import view, AdminController, Controller\nfrom models import User, Project, Group\nfrom google.appengine.ext import db\nimport logging\n\nclass Index(AdminController):\n errors = {\n '1': ('success', 'Recalculation complete'),\n }\n\n @view('/admin/index.html')\n def get(self):\n return {}\n\nclass Stats(AdminController):\n @view('/admin/stats.html')\n def get(self):\n stats = {}\n stats['volunteers'] = {}\n stats['volunteers']['count'] = 0\n stats['volunteers']['count_has_group'] = 0\n stats['volunteers']['count_no_group'] = 0\n stats['volunteers']['count_has_project'] = 0\n stats['volunteers']['count_no_project'] = 0\n users = User.all()\n for user in users:\n stats['volunteers']['count'] += 1\n if user.group == None:\n stats['volunteers']['count_no_group'] += 1\n else:\n stats['volunteers']['count_has_group'] += 1\n if user.project == None:\n stats['volunteers']['count_no_project'] += 1\n else:\n stats['volunteers']['count_has_project'] += 1\n stats['groups'] = {}\n stats['groups']['count'] = Group.all().count()\n stats['groups']['total_spots'] = sum(map(lambda group: group.slots, Group.all()))\n stats['groups']['spots_taken'] = sum(map(lambda group: group.spots_taken, Group.all()))\n stats['groups']['unused_spots'] = sum(map(lambda group: group.spots_taken, Group.all()))\n stats['projects'] = {}\n stats['projects']['count'] = Project.all().count()\n stats['projects']['total_spots'] = sum(map(lambda project: project.max_volunteers, Project.all()))\n stats['projects']['spots_taken'] = sum(map(lambda project: project.spots_taken, Project.all()))\n stats['projects']['unused_spots'] = stats['projects']['total_spots'] - stats['projects']['spots_taken']\n return stats\n\nclass Init(Controller):\n def makeBasicUser(self, username):\n email = username + \"@gatech.edu\"\n password = username\n user = User.create(username, email, password)\n return user\n\n def get(self):\n \"\"\" init the datastore with some test data.\n\n assumes the datastore is clear.\n \"\"\"\n # a basic check to make sure the datastore is clear\n if Greek.all().count() > 0:\n return\n\n # Create a project\n kitten = Project()\n kitten.name = \"Kitten Rescue\"\n kitten.max_volunteers = 3\n kitten.location = \"All around Atlanta.\"\n kitten.type_of_work = \"Outdoor\"\n kitten.description = \"We will save kittens from trees all over Atlanta.\"\n kitten.put()\n\n soup = Project()\n soup.name = \"Soup Making\"\n soup.max_volunteers = 5\n soup.description = \"You will make delicious soup.\"\n soup.put()\n\n huge = Project()\n huge.name = \"Huge Project\"\n huge.max_volunteers = 20\n huge.description = \"This is a darn huge project. With 20 people what CAN'T we do?\"\n huge.put()\n\n # Make a user with a pending PC app\n u = self.makeBasicUser(\"pending\")\n pc_app = PCApplication(response=\"Here is the sample responses to the questions\")\n pc_app.put()\n u.pc_application = pc_app\n u.put()\n\n # Put a user in the kitten project\n u = self.makeBasicUser(\"kitten\")\n u.project = kitten\n u.put()\n\n # Create a PC for the soup project\n u = self.makeBasicUser(\"souppc\")\n u.project = soup\n u.is_pc = True\n u.put()\n\n # Make a group for the HUGE project\n knights_group = Group(name=\"Knights who say Ni!\", password=\"shrubbery\", project=huge, slots=5)\n knights_group.put()\n\n leader = self.makeBasicUser(\"leader\")\n leader.joinGroup(knights_group)\n leader.is_group_leader = True\n leader.is_pc = True\n leader.put()\n\n knights = [\"lancelot\", \"gawain\", \"gallahad\", \"mordred\"]\n for knight in knights:\n k = self.makeBasicUser(knight)\n k.joinGroup(knights_group)\n k.put()\n\n # Make a full project\n full = Project(name=\"Full Project\",\n max_volunteers = 5,\n description = \"This was full so quickly...\")\n full.put()\n\n alphabet = \"abcdefghijklmnopqrstuvwxyz\"\n for j in range(5):\n u = self.makeBasicUser(alphabet[j])\n u.project = full\n u.put()\n\n # Init the Greek Affliations\n for g_name in GREEK_AFFS:\n g = Greek(name=g_name)\n g.put()\n\n # Add the possible phases\n phases = [\n [\"pc_apps\", datetime.date(2014,9,5), datetime.date(2014,10,18)],\n [\"group_create\", datetime.date(2014,9,19), datetime.date(2014,10,9)],\n [\"group_join\", datetime.date(2014,9,26), datetime.date(2014,10,9)],\n [\"group_registration\", datetime.date(2014,9,19), datetime.date(2014,10,9)],\n [\"individual_registration\", datetime.date(2014,10,10), datetime.date(2014,10,21)]\n ]\n for phase_args in phases:\n phase = Phase(name=phase_args[0], start_date=phase_args[1], end_date=phase_args[2])\n phase.put()\n\n # Add a group that users can join\n nice_group = Group(name=\"A nice group for nice people\", password=\"nice!\", project=huge, slots=5)\n nice_group.put()\n\n # Make a user that has no project\n lonely_user = self.makeBasicUser(\"lonely\")\n lonely_user.put()\n\n return \"done\"\n\nclass Recalculate(Controller):\n @view(None)\n def get(self):\n # fix any references to non-existance objects\n users = User.all()\n for user in users:\n try:\n project = user.project\n except db.ReferencePropertyResolveError as e:\n logging.info('Resolving property resolve error on user.project for {}\\'s project {}'.format(user.key(), user._project))\n user.project = None\n user.put()\n\n try:\n group = user.group\n except db.ReferencePropertyResolveError as e:\n user.group = None\n user.put()\n\n groups = Group.all()\n for group in groups:\n try:\n project = group.project\n except db.ReferencePropertyResolveError as e:\n group.project = None\n group.put()\n\n projects = Project.all()\n for project in projects:\n volunteers = project.volunteers.filter('group =', None)\n num_volunteers = volunteers.count()\n groups = project.groups\n num_group_volunteers = sum([group.slots for group in groups])\n spots = num_volunteers + num_group_volunteers\n project.setSpotsTaken(spots, True)\n\n groups = Group.all()\n for group in groups:\n members = group.members\n num_members = members.count()\n group.spots_taken = num_members\n group.put()\n\n return self.redirect('/admin', {'error': '1'})\n", "sub_path": "controllers/admin/Index.py", "file_name": "Index.py", "file_ext": "py", "file_size_in_byte": 7178, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "teambuzz.AdminController", "line_number": 6, "usage_type": "name"}, {"api_name": "teambuzz.view", "line_number": 11, "usage_type": "call"}, {"api_name": "teambuzz.AdminController", "line_number": 15, "usage_type": "name"}, {"api_name": "models.User.all", "line_number": 25, "usage_type": "call"}, {"api_name": "models.User", "line_number": 25, "usage_type": "name"}, {"api_name": "models.Group.all", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Group.all", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 38, "usage_type": "name"}, {"api_name": "models.Group.all", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 39, "usage_type": "name"}, {"api_name": "models.Group.all", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 40, "usage_type": "name"}, {"api_name": "models.Project.all", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Project", "line_number": 42, "usage_type": "name"}, {"api_name": "models.Project.all", "line_number": 43, "usage_type": "call"}, {"api_name": "models.Project", "line_number": 43, "usage_type": "name"}, {"api_name": "models.Project.all", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Project", "line_number": 44, "usage_type": "name"}, {"api_name": "teambuzz.view", "line_number": 16, "usage_type": "call"}, {"api_name": "teambuzz.Controller", "line_number": 48, "usage_type": "name"}, {"api_name": "models.User.create", "line_number": 52, "usage_type": "call"}, {"api_name": "models.User", "line_number": 52, "usage_type": "name"}, {"api_name": "models.Project", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Project", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Project", "line_number": 79, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 104, "usage_type": "call"}, {"api_name": "models.Project", "line_number": 120, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 149, "usage_type": "call"}, {"api_name": "teambuzz.Controller", "line_number": 158, "usage_type": "name"}, {"api_name": "models.User.all", "line_number": 162, "usage_type": "call"}, {"api_name": "models.User", "line_number": 162, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.ReferencePropertyResolveError", "line_number": 166, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.db", "line_number": 166, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 167, "usage_type": "call"}, {"api_name": "google.appengine.ext.db.ReferencePropertyResolveError", "line_number": 173, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.db", "line_number": 173, "usage_type": "name"}, {"api_name": "models.Group.all", "line_number": 177, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 177, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.ReferencePropertyResolveError", "line_number": 181, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.db", "line_number": 181, "usage_type": "name"}, {"api_name": "models.Project.all", "line_number": 185, "usage_type": "call"}, {"api_name": "models.Project", "line_number": 185, "usage_type": "name"}, {"api_name": "models.Group.all", "line_number": 194, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 194, "usage_type": "name"}, {"api_name": "teambuzz.view", "line_number": 159, "usage_type": "call"}]} +{"seq_id": "401143855", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Nov 28 08:46:13 2020\n\n@author: Acr\n\"\"\"\nfrom pynput import mouse\nimport threading\nfrom time import sleep\n\ndef on_click(x, y, button, pressed):\n \n if button == mouse.Button.right: \n print('{} at {}'.format('Pressed Right Click' if pressed else 'Released Right Click', (x, y)))\n\ndef printit():\n threading.Timer(1.0, printit).start()\n #print (\"Hello, World!\")\n \n listener = mouse.Listener(on_click=on_click)\n\n listener.start()\n listener.join()\n\nprintit()\n", "sub_path": "robot/detect_xy.py", "file_name": "detect_xy.py", "file_ext": "py", "file_size_in_byte": 532, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pynput.mouse.Button", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pynput.mouse", "line_number": 13, "usage_type": "name"}, {"api_name": "threading.Timer", "line_number": 17, "usage_type": "call"}, {"api_name": "pynput.mouse.Listener", "line_number": 20, "usage_type": "call"}, {"api_name": "pynput.mouse", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "112404385", "text": "import os\n\nfrom flask import render_template, Blueprint, request, redirect, url_for\nfrom flask_login import login_required\nfrom werkzeug.utils import secure_filename\n\nfrom data_tools.wrappers.jobserver_control import start_job\nfrom data_tools.wrappers.sample_creation import create_sample_creation_workflow\nfrom data_tools.wrappers.sample_groups import get_sample_group, get_sample_groups, update_sample_group, \\\n delete_sample_group, \\\n create_sample_group\nfrom data_tools.wrappers.samples import get_sample, delete_sample\nfrom data_tools.template_models.entry_page import SampleGroupPageData\nfrom data_tools.template_models.form import SampleCreateFormData\nfrom data_tools.template_models.list_table import ListTableData\nfrom data_tools.util import AuthException\nfrom config.config import UPLOADDIR\nfrom helpers import handle_exception_browser, get_current_user, process_input_dict\n\nsample_groups = Blueprint('sample_groups', __name__, url_prefix='/sample_groups')\n\n\n@sample_groups.route('/', methods=['GET', 'POST'])\n@login_required\ndef render_sample_group_list():\n try:\n current_user = get_current_user()\n return render_template('pages/list.html',\n page_data=ListTableData(current_user, get_sample_groups(current_user), 'Sample Groups'))\n except Exception as e:\n return handle_exception_browser(e)\n\n\n@sample_groups.route('/', methods=['GET', 'POST', 'DELETE'])\n@login_required\ndef render_sample_group(sample_group_id=None):\n try:\n current_user = get_current_user()\n sample_group = get_sample_group(current_user, sample_group_id)\n if request.method == 'DELETE':\n samples_to_delete = [sample for sample in sample_group.samples if len(sample.sample_groups) < 2]\n delete_sample_group(current_user, sample_group)\n for sample in samples_to_delete:\n try:\n delete_sample(current_user, sample)\n except AuthException:\n pass\n return redirect(url_for('sample_groups.render_sample_group_list'))\n if request.method == 'POST':\n update_sample_group(current_user, sample_group, request.form)\n return render_template('pages/sample_group_entry.html',\n page_data=SampleGroupPageData(current_user, sample_group))\n except Exception as e:\n return handle_exception_browser(e)\n\n\n@sample_groups.route('/create', methods=['GET', 'POST'])\n@login_required\ndef render_upload_sample_group():\n try:\n current_user = get_current_user()\n if request.method == 'POST':\n files = request.files.getlist('files')\n filenames = [os.path.join(UPLOADDIR, secure_filename(file.filename)) for file in files]\n [file.save(filename) for file, filename in zip(files, filenames)]\n metadata = process_input_dict(request.form.to_dict(), True)\n workflow_data = create_sample_creation_workflow(current_user, filenames, metadata)\n metadata['samples'] = [get_sample(current_user, sample_id) for sample_id in workflow_data['output_ids']]\n sample_group = create_sample_group(current_user, metadata)\n job = start_job(workflow_data['workflow'], workflow_data['job'], current_user, 'upload')\n update_sample_group(current_user, sample_group, {'upload_job_id': job.id})\n return redirect(url_for('sample_groups.render_sample_group', sample_group_id=sample_group.id))\n return render_template('pages/create.html',\n page_data=SampleCreateFormData(current_user))\n except Exception as e:\n return handle_exception_browser(e)\n", "sub_path": "omics/omics_dashboard/blueprints/browser/sample_groups.py", "file_name": "sample_groups.py", "file_ext": "py", "file_size_in_byte": 3712, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "flask.Blueprint", "line_number": 20, "usage_type": "call"}, {"api_name": "helpers.get_current_user", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 28, "usage_type": "call"}, {"api_name": "data_tools.template_models.list_table.ListTableData", "line_number": 29, "usage_type": "call"}, {"api_name": "data_tools.wrappers.sample_groups.get_sample_groups", "line_number": 29, "usage_type": "call"}, {"api_name": "helpers.handle_exception_browser", "line_number": 31, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 24, "usage_type": "name"}, {"api_name": "helpers.get_current_user", "line_number": 38, "usage_type": "call"}, {"api_name": "data_tools.wrappers.sample_groups.get_sample_group", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "data_tools.wrappers.sample_groups.delete_sample_group", "line_number": 42, "usage_type": "call"}, {"api_name": "data_tools.wrappers.samples.delete_sample", "line_number": 45, "usage_type": "call"}, {"api_name": "data_tools.util.AuthException", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "data_tools.wrappers.sample_groups.update_sample_group", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 51, "usage_type": "call"}, {"api_name": "data_tools.template_models.entry_page.SampleGroupPageData", "line_number": 52, "usage_type": "call"}, {"api_name": "helpers.handle_exception_browser", "line_number": 54, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 35, "usage_type": "name"}, {"api_name": "helpers.get_current_user", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.request.files.getlist", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "config.config.UPLOADDIR", "line_number": 64, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 64, "usage_type": "call"}, {"api_name": "helpers.process_input_dict", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.request.form.to_dict", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 66, "usage_type": "name"}, {"api_name": "data_tools.wrappers.sample_creation.create_sample_creation_workflow", "line_number": 67, "usage_type": "call"}, {"api_name": "data_tools.wrappers.samples.get_sample", "line_number": 68, "usage_type": "call"}, {"api_name": "data_tools.wrappers.sample_groups.create_sample_group", "line_number": 69, "usage_type": "call"}, {"api_name": "data_tools.wrappers.jobserver_control.start_job", "line_number": 70, "usage_type": "call"}, {"api_name": "data_tools.wrappers.sample_groups.update_sample_group", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 73, "usage_type": "call"}, {"api_name": "data_tools.template_models.form.SampleCreateFormData", "line_number": 74, "usage_type": "call"}, {"api_name": "helpers.handle_exception_browser", "line_number": 76, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "342052961", "text": "# -*- Mode: Python -*-\n# vi:si:et:sw=4:sts=4:ts=4\nfrom twisted.internet import defer\n\nfrom feat import everything\nfrom feat.common import first\nfrom feat.test.integration import common\nfrom feat.common.text_helper import format_block\nfrom feat.agents.base import recipient, dbtools\nfrom feat.agents.common import host, raage\nfrom feat.interface.agent import Access, Address, Storage\n\n\ndef checkAllocation(test, agent, resources):\n _, allocated = agent.list_resource()\n for key in resources:\n test.assertEquals(allocated[key], resources[key], key)\n\n\ndef checkNoAllocated(test, a_id):\n test.assertEquals(a_id, None)\n\n\n@common.attr(timescale=0.1)\n@common.attr('slow')\nclass SingleHostAllocationSimulation(common.SimulationTest):\n\n timeout = 20\n\n @defer.inlineCallbacks\n def prolog(self):\n setup = format_block(\"\"\"\n load('feat.test.integration.resource')\n\n agency = spawn_agency()\n agency.disable_protocol('setup-monitoring', 'Task')\n\n host_desc = descriptor_factory('host_agent')\n req_desc = descriptor_factory('requesting_agent')\n\n host_medium = agency.start_agent(host_desc, hostdef=hostdef)\n host_agent = host_medium.get_agent()\n\n host_agent.wait_for_ready()\n host_agent.start_agent(req_desc)\n \"\"\")\n\n hostdef = host.HostDef()\n hostdef.resources = {\"host\": 1, \"epu\": 10}\n hostdef.categories = {\"access\": Access.private,\n \"address\": Address.dynamic,\n \"storage\": Storage.static}\n self.set_local(\"hostdef\", hostdef)\n\n yield self.process(setup)\n yield self.wait_for_idle(10)\n\n raage_medium = list(self.driver.iter_agents('raage_agent'))[0]\n self.raage_agent = raage_medium.get_agent()\n self.host_medium = self.get_local('host_medium')\n self.host_agent = self.get_local('host_agent')\n medium = yield self.driver.find_agent(self.get_local('req_desc'))\n self.req_agent = medium.get_agent()\n\n def testValidateProlog(self):\n self.assertEqual(1, self.count_agents('host_agent'))\n self.assertEqual(1, self.count_agents('shard_agent'))\n self.assertEqual(1, self.count_agents('raage_agent'))\n self.assertEqual(1, self.count_agents('requesting_agent'))\n\n @defer.inlineCallbacks\n def testFindHost(self):\n resources = {'host': 1}\n categories = {'access': Access.private,\n 'address': Address.none,\n 'storage': Storage.static}\n checkAllocation(self, self.host_agent, {'host': 0})\n self.info('starting test')\n allocation_id, irecipient = \\\n yield self.req_agent.request_resource(resources, categories)\n checkAllocation(self, self.host_agent, resources)\n self.assertEqual(recipient.IRecipient(self.host_medium), irecipient)\n\n @defer.inlineCallbacks\n def testNoHostFree(self):\n resources = {'host': 1}\n categories = {}\n allocation_id, irecipient = \\\n yield self.req_agent.request_resource(resources, categories)\n yield self.host_medium.wait_for_protocols_finish()\n checkAllocation(self, self.host_agent, resources)\n d = self.req_agent.request_resource(resources, categories)\n self.assertFailure(d, raage.AllocationFailedError)\n yield d\n\n @defer.inlineCallbacks\n def testBadResource(self):\n resources = {'beers': 999}\n categories = {}\n d = self.req_agent.request_resource(resources, categories)\n self.assertFailure(d, raage.AllocationFailedError)\n yield d\n\n @defer.inlineCallbacks\n def testBadCategory(self):\n resources = {'host': 1}\n categories = {'address': Address.fixed}\n d = self.req_agent.request_resource(resources, categories)\n self.assertFailure(d, raage.AllocationFailedError)\n yield d\n\n\n@common.attr(timescale=0.1)\n@common.attr('slow')\nclass MultiHostAllocationSimulation(common.SimulationTest):\n\n timeout = 20\n\n @defer.inlineCallbacks\n def prolog(self):\n setup = format_block(\"\"\"\n load('feat.test.integration.resource')\n host1_desc = descriptor_factory('host_agent')\n host2_desc = descriptor_factory('host_agent')\n host3_desc = descriptor_factory('host_agent')\n req_desc = descriptor_factory('requesting_agent')\n\n # First agency will eventually run Host, Shard, Raage and\n # Requesting agent\n agency = spawn_agency()\n agency.disable_protocol('setup-monitoring', 'Task')\n agency.start_agent(host1_desc, hostdef=hostdef)\n host = _.get_agent()\n\n wait_for_idle()\n host.start_agent(req_desc)\n\n # Second agency runs the host agent\n agency = spawn_agency()\n agency.disable_protocol('setup-monitoring', 'Task')\n agency.start_agent(host2_desc, hostdef=hostdef)\n wait_for_idle()\n\n # Third is like second\n agency = spawn_agency()\n agency.disable_protocol('setup-monitoring', 'Task')\n agency.start_agent(host3_desc, hostdef=hostdef)\n wait_for_idle()\n \"\"\")\n\n hostdef = host.HostDef()\n hostdef.resources = {\"host\": 1, \"epu\": 10}\n hostdef.categories = {\"access\": Access.private,\n \"address\": Address.dynamic,\n \"storage\": Storage.static}\n self.set_local(\"hostdef\", hostdef)\n\n yield self.process(setup)\n yield self.wait_for_idle(20)\n\n self.agents = [x.get_agent() \\\n for x in self.driver.iter_agents('host_agent')]\n req_medium = list(self.driver.iter_agents('requesting_agent'))[0]\n self.req_agent = req_medium.get_agent()\n\n @defer.inlineCallbacks\n def _waitToFinish(self, _=None):\n for x in self.driver.iter_agents():\n yield x._cancel_long_running_protocols()\n yield x.wait_for_protocols_finish()\n\n @defer.inlineCallbacks\n def _startAllocation(self, resources, categories, count, sequencial=True):\n d_list = list()\n for i in range(count):\n d = self.req_agent.request_resource(resources, categories)\n if sequencial:\n yield d\n else:\n d_list.append(d)\n if not sequencial:\n yield defer.DeferredList(d_list)\n\n def _checkAllocations(self, resources, count):\n for agent in self.agents:\n _, allocated = agent.list_resource()\n if all([allocated[name] == value \\\n for name, value in resources.iteritems()]):\n count -= 1\n self.assertEquals(count, 0)\n\n def testValidateProlog(self):\n self.assertEqual(1, self.count_agents('shard_agent'))\n self.assertEqual(1, self.count_agents('raage_agent'))\n self.assertEqual(1, self.count_agents('requesting_agent'))\n self.assertEqual(3, len(self.agents))\n\n @defer.inlineCallbacks\n def testAllocateOneHost(self):\n resources = {'host': 1}\n categories = {'access': Access.private}\n self._checkAllocations(resources, 0)\n yield self._startAllocation(resources, categories, 1)\n yield self._waitToFinish()\n self._checkAllocations(resources, 1)\n\n @defer.inlineCallbacks\n def testAllocateAllHostsSecuencially(self):\n resources = {'host': 1}\n categories = {'access': Access.private}\n self._checkAllocations(resources, 0)\n yield self._startAllocation(resources, categories, 1)\n yield self._waitToFinish()\n self._checkAllocations(resources, 1)\n\n yield self._startAllocation(resources, categories, 1)\n yield self._waitToFinish()\n self._checkAllocations(resources, 2)\n\n @defer.inlineCallbacks\n def testAllocateSomeHosts(self):\n resources = {'host': 1}\n categories = {'access': Access.private}\n self._checkAllocations(resources, 0)\n yield self._startAllocation(resources, categories, 2)\n yield self._waitToFinish()\n self._checkAllocations(resources, 2)\n\n @common.attr(timescale=0.5)\n @defer.inlineCallbacks\n def testAllocateAllHosts(self):\n resources = {'host': 1}\n categories = {'access': Access.private}\n self._checkAllocations(resources, 0)\n yield self._startAllocation(resources, categories,\n 3, sequencial=False)\n yield self._waitToFinish()\n self._checkAllocations(resources, 3)\n\n\n@common.attr(timescale=0.1)\n@common.attr('slow')\nclass ContractNestingSimulation(common.SimulationTest):\n\n timeout = 40\n\n def setUp(self):\n config = everything.shard_agent.ShardAgentConfiguration(\n doc_id = u'test-config',\n hosts_per_shard = 2)\n dbtools.initial_data(config)\n self.override_config('shard_agent', config)\n return common.SimulationTest.setUp(self)\n\n @defer.inlineCallbacks\n def prolog(self):\n setup = format_block(\"\"\"\n # Host 1 will run Raage, Host, Shard and Requesting agents\n load('feat.test.integration.resource')\n agency = spawn_agency()\n agency.disable_protocol('setup-monitoring', 'Task')\n host_desc = descriptor_factory('host_agent')\n req_desc = descriptor_factory('requesting_agent')\n agency.start_agent(host_desc, hostdef=hostdef1)\n host = _.get_agent()\n\n wait_for_idle()\n host.start_agent(req_desc)\n\n # Host 2 run only host agent\n agency = spawn_agency()\n agency.disable_protocol('setup-monitoring', 'Task')\n agency.start_agent(descriptor_factory('host_agent'), hostdef=hostdef1)\n wait_for_idle()\n\n # Host 3 will run Shard, Host and Raage\n agency = spawn_agency()\n agency.disable_protocol('setup-monitoring', 'Task')\n agency.start_agent(descriptor_factory('host_agent'), hostdef=hostdef2)\n wait_for_idle()\n\n # Host 4 will run only host agent\n agency = spawn_agency()\n agency.disable_protocol('setup-monitoring', 'Task')\n agency.start_agent(descriptor_factory('host_agent'), hostdef=hostdef2)\n \"\"\")\n\n # host definition in first shard (no space to allocate)\n hostdef1 = host.HostDef(resources=dict(host=0, epu=10, local=1))\n self.set_local(\"hostdef1\", hostdef1)\n\n # host definition in second shard (no space to allocate)\n hostdef2 = host.HostDef(resources=dict(host=1, epu=10))\n self.set_local(\"hostdef2\", hostdef2)\n\n yield self.process(setup)\n yield self.wait_for_idle(20)\n\n raage_mediums = self.driver.iter_agents('raage_agent')\n self.raage_agents = [x.get_agent() for x in raage_mediums]\n host_mediums = self.driver.iter_agents('host_agent')\n self.host_agents = [x.get_agent() for x in host_mediums]\n self.req_agent = first(\n self.driver.iter_agents('requesting_agent')).get_agent()\n\n def testValidateProlog(self):\n self.assertEqual(4, self.count_agents('host_agent'))\n self.assertEqual(2, self.count_agents('shard_agent'))\n self.assertEqual(2, self.count_agents('raage_agent'))\n\n @common.attr(timescale=0.2)\n @defer.inlineCallbacks\n def testRequestLocalResource(self):\n self.info(\"Starting test\")\n resources = dict(host=1)\n d = self.req_agent.request_local_resource(resources, {})\n self.assertFailure(d, raage.AllocationFailedError)\n yield d\n self.assert_allocated('host', 0)\n\n allocation_id, irecipient1 = \\\n yield self.req_agent.request_resource({'local': 1}, {})\n self.assert_allocated('local', 1)\n\n @common.attr(timescale=0.1)\n @defer.inlineCallbacks\n def testRequestFromOtherShard(self):\n self.info(\"Starting test\")\n resources = dict(host=1)\n allocation_id, irecipient1 = \\\n yield self.req_agent.request_resource(resources, {})\n self.assert_allocated('host', 1)\n\n allocation_id, irecipient2 = \\\n yield self.req_agent.request_resource(resources, {})\n self.assert_allocated('host', 2)\n\n shard2_hosts = map(recipient.IRecipient, self.host_agents[2:4])\n self.assertTrue(irecipient1 in shard2_hosts)\n self.assertTrue(irecipient2 in shard2_hosts)\n\n def assert_allocated(self, resource, expected):\n count = 0\n for agent in self.host_agents:\n _, allocated = agent.list_resource()\n count += allocated.get(resource, 0)\n self.assertEquals(expected, count,\n \"Expected %d allocated %s, found %d\" %\\\n (expected, resource, count, ))\n", "sub_path": "src/feat/test/integration/test_simulation_raage.py", "file_name": "test_simulation_raage.py", "file_ext": "py", "file_size_in_byte": 12730, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "feat.test.integration.common.SimulationTest", "line_number": 26, "usage_type": "attribute"}, {"api_name": "feat.test.integration.common", "line_number": 26, "usage_type": "name"}, {"api_name": "feat.common.text_helper.format_block", "line_number": 32, "usage_type": "call"}, {"api_name": "feat.agents.common.host.HostDef", "line_number": 48, "usage_type": "call"}, {"api_name": "feat.agents.common.host", "line_number": 48, "usage_type": "name"}, {"api_name": "feat.interface.agent.Access.private", "line_number": 50, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Access", "line_number": 50, "usage_type": "name"}, {"api_name": "feat.interface.agent.Address.dynamic", "line_number": 51, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Address", "line_number": 51, "usage_type": "name"}, {"api_name": "feat.interface.agent.Storage.static", "line_number": 52, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Storage", "line_number": 52, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 30, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 30, "usage_type": "name"}, {"api_name": "feat.interface.agent.Access.private", "line_number": 74, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Access", "line_number": 74, "usage_type": "name"}, {"api_name": "feat.interface.agent.Address.none", "line_number": 75, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Address", "line_number": 75, "usage_type": "name"}, {"api_name": "feat.interface.agent.Storage.static", "line_number": 76, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Storage", "line_number": 76, "usage_type": "name"}, {"api_name": "feat.agents.base.recipient.IRecipient", "line_number": 82, "usage_type": "call"}, {"api_name": "feat.agents.base.recipient", "line_number": 82, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 71, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 71, "usage_type": "name"}, {"api_name": "feat.agents.common.raage.AllocationFailedError", "line_number": 93, "usage_type": "attribute"}, {"api_name": "feat.agents.common.raage", "line_number": 93, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 84, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 84, "usage_type": "name"}, {"api_name": "feat.agents.common.raage.AllocationFailedError", "line_number": 101, "usage_type": "attribute"}, {"api_name": "feat.agents.common.raage", "line_number": 101, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 96, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 96, "usage_type": "name"}, {"api_name": "feat.interface.agent.Address.fixed", "line_number": 107, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Address", "line_number": 107, "usage_type": "name"}, {"api_name": "feat.agents.common.raage.AllocationFailedError", "line_number": 109, "usage_type": "attribute"}, {"api_name": "feat.agents.common.raage", "line_number": 109, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 104, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 104, "usage_type": "name"}, {"api_name": "feat.test.integration.common.attr", "line_number": 24, "usage_type": "call"}, {"api_name": "feat.test.integration.common", "line_number": 24, "usage_type": "name"}, {"api_name": "feat.test.integration.common.attr", "line_number": 25, "usage_type": "call"}, {"api_name": "feat.test.integration.common", "line_number": 25, "usage_type": "name"}, {"api_name": "feat.test.integration.common.SimulationTest", "line_number": 115, "usage_type": "attribute"}, {"api_name": "feat.test.integration.common", "line_number": 115, "usage_type": "name"}, {"api_name": "feat.common.text_helper.format_block", "line_number": 121, "usage_type": "call"}, {"api_name": "feat.agents.common.host.HostDef", "line_number": 151, "usage_type": "call"}, {"api_name": "feat.agents.common.host", "line_number": 151, "usage_type": "name"}, {"api_name": "feat.interface.agent.Access.private", "line_number": 153, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Access", "line_number": 153, "usage_type": "name"}, {"api_name": "feat.interface.agent.Address.dynamic", "line_number": 154, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Address", "line_number": 154, "usage_type": "name"}, {"api_name": "feat.interface.agent.Storage.static", "line_number": 155, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Storage", "line_number": 155, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 119, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 119, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 166, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 166, "usage_type": "name"}, {"api_name": "twisted.internet.defer.DeferredList", "line_number": 182, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 182, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 172, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 172, "usage_type": "name"}, {"api_name": "feat.interface.agent.Access.private", "line_number": 201, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Access", "line_number": 201, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 198, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 198, "usage_type": "name"}, {"api_name": "feat.interface.agent.Access.private", "line_number": 210, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Access", "line_number": 210, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 207, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 207, "usage_type": "name"}, {"api_name": "feat.interface.agent.Access.private", "line_number": 223, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Access", "line_number": 223, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 220, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 220, "usage_type": "name"}, {"api_name": "feat.interface.agent.Access.private", "line_number": 233, "usage_type": "attribute"}, {"api_name": "feat.interface.agent.Access", "line_number": 233, "usage_type": "name"}, {"api_name": "feat.test.integration.common.attr", "line_number": 229, "usage_type": "call"}, {"api_name": "feat.test.integration.common", "line_number": 229, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 230, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 230, "usage_type": "name"}, {"api_name": "feat.test.integration.common.attr", "line_number": 113, "usage_type": "call"}, {"api_name": "feat.test.integration.common", "line_number": 113, "usage_type": "name"}, {"api_name": "feat.test.integration.common.attr", "line_number": 114, "usage_type": "call"}, {"api_name": "feat.test.integration.common", "line_number": 114, "usage_type": "name"}, {"api_name": "feat.test.integration.common.SimulationTest", "line_number": 243, "usage_type": "attribute"}, {"api_name": "feat.test.integration.common", "line_number": 243, "usage_type": "name"}, {"api_name": "feat.everything.shard_agent.ShardAgentConfiguration", "line_number": 248, "usage_type": "call"}, {"api_name": "feat.everything.shard_agent", "line_number": 248, "usage_type": "attribute"}, {"api_name": "feat.everything", "line_number": 248, "usage_type": "name"}, {"api_name": "feat.agents.base.dbtools.initial_data", "line_number": 251, "usage_type": "call"}, {"api_name": "feat.agents.base.dbtools", "line_number": 251, "usage_type": "name"}, {"api_name": "feat.test.integration.common.SimulationTest.setUp", "line_number": 253, "usage_type": "call"}, {"api_name": "feat.test.integration.common.SimulationTest", "line_number": 253, "usage_type": "attribute"}, {"api_name": "feat.test.integration.common", "line_number": 253, "usage_type": "name"}, {"api_name": "feat.common.text_helper.format_block", "line_number": 257, "usage_type": "call"}, {"api_name": "feat.agents.common.host.HostDef", "line_number": 289, "usage_type": "call"}, {"api_name": "feat.agents.common.host", "line_number": 289, "usage_type": "name"}, {"api_name": "feat.agents.common.host.HostDef", "line_number": 293, "usage_type": "call"}, {"api_name": "feat.agents.common.host", "line_number": 293, "usage_type": "name"}, {"api_name": "feat.common.first", "line_number": 303, "usage_type": "call"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 255, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 255, "usage_type": "name"}, {"api_name": "feat.agents.common.raage.AllocationFailedError", "line_number": 317, "usage_type": "attribute"}, {"api_name": "feat.agents.common.raage", "line_number": 317, "usage_type": "name"}, {"api_name": "feat.test.integration.common.attr", "line_number": 311, "usage_type": "call"}, {"api_name": "feat.test.integration.common", "line_number": 311, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 312, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 312, "usage_type": "name"}, {"api_name": "feat.agents.base.recipient.IRecipient", "line_number": 338, "usage_type": "attribute"}, {"api_name": "feat.agents.base.recipient", "line_number": 338, "usage_type": "name"}, {"api_name": "feat.test.integration.common.attr", "line_number": 325, "usage_type": "call"}, {"api_name": "feat.test.integration.common", "line_number": 325, "usage_type": "name"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 326, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer", "line_number": 326, "usage_type": "name"}, {"api_name": "feat.test.integration.common.attr", "line_number": 241, "usage_type": "call"}, {"api_name": "feat.test.integration.common", "line_number": 241, "usage_type": "name"}, {"api_name": "feat.test.integration.common.attr", "line_number": 242, "usage_type": "call"}, {"api_name": "feat.test.integration.common", "line_number": 242, "usage_type": "name"}]} +{"seq_id": "305769777", "text": "from collections import namedtuple\nfrom struct import unpack, unpack_from, calcsize\nimport zlib\n\nCPKEntry = namedtuple(\"CPKEntry\", \"name, data\")\n\ndef get_data(resource):\n\timport os.path\n\tparts = resource.split('/')\n\tparts.insert(0, os.path.dirname(__file__))\n\tresource_name = os.path.join(*parts)\n\treturn __loader__.get_data(resource_name)\n\nstrings = frozenset(get_data(\"strings.txt\").split(b\"\\n\"))\n\ndef eld_hash(name):\n\taccum = 0\n\tfor i in name:\n\t\tif i == 0: break\n\t\ta = (i ^ 0x80) - 128 + 16 * accum\n\t\ta &= 0xffffffff\n\t\tb = a & 0xF0000000\n\t\tif b:\n\t\t\ta ^= b >> 24\n\t\taccum = ~b & a\n\treturn accum\n\ndef parse_cpk(data):\n\tif data[0:4] != b\"DCPK\":\n\t\traise Exception(\"CPK magic wrong\", data[0:4])\n\n\tnum_files, header_bytes, payload_bytes = unpack_from(\" SentinelBoxClient:\n\n if not oauth:\n oauth = self.configure_standard_box_auth(box_jwt_auth)\n\n super().__init__(oauth, **kwargs)\n self.auth_enterprise_id = self.auth._enterprise_id\n self.auth_client_id = self.auth._client_id\n self.rate_limiter = (\n sentinel_http_client.RateLimiter(rate_limit, rate_period)\n if rate_limited\n else None\n )\n\n def __repr__(self) -> AnyStr:\n return f\"\"\n\n @staticmethod\n def configure_standard_box_auth(\n box_jwt_auth: dict,\n ) -> boxsdk.JWTAuth:\n oauth = boxsdk.JWTAuth.from_settings_dictionary(\n box_jwt_auth\n )\n oauth.authenticate_instance()\n\n return oauth\n\n def make_request(\n self, method: AnyStr, url: AnyStr, **kwargs\n ) -> boxsdk.network.default_network.DefaultNetworkResponse:\n \"\"\"\n Base class override to rate limit requests\n \"\"\"\n if self.rate_limiter:\n with self.rate_limiter:\n resp = super().make_request(method, url, **kwargs)\n else:\n resp = super().make_request(method, url, **kwargs)\n\n return resp\n", "sub_path": "file_server_box_sync/box_client.py", "file_name": "box_client.py", "file_ext": "py", "file_size_in_byte": 2000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "boxsdk.Client", "line_number": 22, "usage_type": "attribute"}, {"api_name": "boxsdk.JWTAuth", "line_number": 26, "usage_type": "attribute"}, {"api_name": "file_server_box_sync.http_client.RateLimiter", "line_number": 40, "usage_type": "call"}, {"api_name": "file_server_box_sync.http_client", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 45, "usage_type": "name"}, {"api_name": "boxsdk.JWTAuth.from_settings_dictionary", "line_number": 52, "usage_type": "call"}, {"api_name": "boxsdk.JWTAuth", "line_number": 52, "usage_type": "attribute"}, {"api_name": "boxsdk.JWTAuth", "line_number": 51, "usage_type": "attribute"}, {"api_name": "typing.AnyStr", "line_number": 60, "usage_type": "name"}, {"api_name": "boxsdk.network", "line_number": 61, "usage_type": "attribute"}]} +{"seq_id": "296313359", "text": "\nimport time, cv2\nfrom matplotlib import pyplot as plt\nfrom detectors import TinyFace\nfrom PIL import Image \nfrom utils import crop_thumbnail\nimport os\n\n# load image with cv in RGB.\nIMAGE_PATH = 'selfie.jpg'\nimg = cv2.imread(IMAGE_PATH)\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\nim = Image.open(IMAGE_PATH) \n# load detector.\n\nDET = TinyFace(device='cpu')\n\n# DSFD returns bboxes.\nt = time.time()\nbboxes = DET.detect_faces(img, conf_th=0.95)\nprint('detect %d faces in %.4f seconds.' % (len(bboxes), time.time() - t))\nos.chdir('sample') \n# crop thumbnail from original image.\nresults = dict()\nt = time.time()\nfor i, bbox in enumerate(bboxes):\n thumb_img, _ = crop_thumbnail(img, bbox, padding=1, size=100)\n results[str(i)] = thumb_img\n print(bbox[1])\n \n img_save = im.crop((bbox[0],bbox[1],bbox[2],bbox[3]))\n img_save = img_save.save(str(i) + \".jpg\")\nprint('crop %d faces in %.4f seconds.' % (len(results), time.time() - t))\n\n", "sub_path": "crop.py", "file_name": "crop.py", "file_ext": "py", "file_size_in_byte": 942, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 12, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 13, "usage_type": "name"}, {"api_name": "detectors.TinyFace", "line_number": 16, "usage_type": "call"}, {"api_name": "time.time", "line_number": 19, "usage_type": "call"}, {"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "utils.crop_thumbnail", "line_number": 27, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "294131576", "text": "import sys\n\nimport pytest\n\n\ndef test_ipython(testdir):\n \"\"\"Test integration when used with IPython.\n\n - `up` used to crash due to conflicting `hidden_frames` attribute/method.\n \"\"\"\n pytest.importorskip(\"IPython\")\n child = testdir.spawn(\n \"{} -m IPython --colors=nocolor --simple-prompt\".format(\n sys.executable,\n )\n )\n child.sendline(\"%debug raise ValueError('my_value_error')\")\n child.sendline(\"up\")\n child.expect_exact(\"\\r\\nipdb> \")\n child.sendline(\"c\")\n child.expect_exact(\"\\r\\nValueError: my_value_error\\r\\n\")\n child.expect_exact(\"\\r\\nIn [2]: \")\n child.sendeof()\n child.sendline(\"y\")\n assert child.wait() == 0\n", "sub_path": "testing/test_integration.py", "file_name": "test_integration.py", "file_ext": "py", "file_size_in_byte": 684, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pytest.importorskip", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 14, "usage_type": "attribute"}]} +{"seq_id": "315333686", "text": "import os\nimport logging\n\nimport numpy as np\nimport dicom\nfrom skimage import measure, morphology, segmentation, filters\nfrom scipy import ndimage\nimport h5py\n\n\nfrom tools import filetools\n\n\nclass DicomToHdf(object):\n \"\"\"Data ingest from dicom format to HDF format.\n \"\"\"\n\n def __init__(self):\n \"\"\"Initialize\n \"\"\"\n self.min_hu = -1024 # air\n self.max_hu = 400 # bone\n self.cube_length = 448\n\n def save_to_hdf(self, images, masks, spacing, filename):\n \"\"\"Save a rescaled and resampled version of this patient information to hdf5\n\n # Arguments\n filename: The hdf file to store information\n \"\"\"\n filetools.rm_rf(filename)\n filetools.mkdir_p(os.path.dirname(filename))\n with h5py.File(filename, 'w') as f:\n f.create_dataset('images', data=images,\n dtype='int16', compression=\"gzip\")\n f.create_dataset('masks', data=masks, dtype='int8', compression=\"gzip\")\n f.create_dataset('spacing', data=spacing, compression=\"gzip\")\n\n def resample_spacing(self, images, slices, new_spacing=[1, 1, 1]):\n \"\"\"Resample the volume so that the physical distance between two consecutive\n pixels in the 3d volume of images is 1mm\n\n # Arguments\n images: 3d Numpy array of scan\n slices: the original dicom scan (necessary for thickness and pixel spacing)\n binarize: if true, will threshold to binary values\n\n # Returns\n rescaled_volume: resampled 3d Numpy array of images\n \"\"\"\n # current spacing\n spacing = map(float, ([slices[0].SliceThickness] + slices[0].PixelSpacing))\n spacing = np.array(list(spacing))\n # target spacing\n resizer = spacing / new_spacing\n new_shape = np.round(images.shape * resizer)\n resize_factor = new_shape / images.shape\n new_spacing = spacing / resize_factor\n rescaled_images = ndimage.interpolation.zoom(\n images, resize_factor, mode='nearest')\n # if hu is less than air, set it to air; if greater than bone, set it to\n # bone\n rescaled_images[rescaled_images < self.min_hu] = self.min_hu\n rescaled_images[rescaled_images > self.max_hu] = self.max_hu\n return rescaled_images, new_spacing\n\n def load_slices(self, patient_path):\n \"\"\"Read dicom files\n\n # Arguments\n patient_path: The full path of the patient files with the folder conventions as in\n Data Science Bowl 2017 input dataset\n\n # Returns\n Array of dicom slices\n \"\"\"\n slices = [dicom.read_file(patient_path + '/' + s)\n for s in os.listdir(patient_path)]\n slices.sort(key=lambda x: int(x.ImagePositionPatient[2]))\n try:\n slice_thickness = np.abs(slices[0].ImagePositionPatient[\n 2] - slices[1].ImagePositionPatient[2])\n except:\n slice_thickness = np.abs(\n slices[0].SliceLocation - slices[1].SliceLocation)\n for s in slices:\n s.SliceThickness = slice_thickness\n return slices\n\n def get_pixels_hu(self, slices):\n \"\"\"Get pixels in Hounsfield Units. From wikipedia:\n HU: air -1000, lung -500, fat -100 to -50, water 0\n\n # Arguments\n slices: The scan slices from the dicom file\n\n # Returns\n 3d Numpy array with each value representing a pixel in HU\n \"\"\"\n images = np.stack([s.pixel_array for s in slices])\n images = images.astype(np.int16)\n\n # Set outside-of-scan pixels to 0\n # The intercept is usually -1024, so air is approximately 0\n images[images == -2000] = 0\n\n # Convert to Hounsfield units (HU)\n for slice_number in range(len(slices)):\n intercept = slices[slice_number].RescaleIntercept\n slope = slices[slice_number].RescaleSlope\n if slope != 1:\n images[slice_number] = slope * images[slice_number].astype(np.float64)\n images[slice_number] = images[slice_number].astype(np.int16)\n images[slice_number] += np.int16(intercept)\n return np.array(images, dtype=np.int16)\n\n def get_lung_mask(self, image, hu_threshold):\n \"\"\"Get one or more blobs of binary segmentation of a 2d image based on\n hu threshold.\n\n # Arguments\n image: 2d Numpy array\n hu_threshold: the upper-bound for background\n\n # Returns\n 2d Numpy array with binary segmentation (tissue/no-tissue)\n \"\"\"\n # threshold and remove anything from the border\n binary_image = image < hu_threshold\n border_cleared_image = segmentation.clear_border(binary_image)\n # find the largest blobs - if more than 2 present, combine them\n labels = measure.label(border_cleared_image)\n areas = sorted([r.area for r in measure.regionprops(labels)])\n if len(areas) > 2:\n for region in measure.regionprops(labels):\n if region.area < areas[-2]:\n for coord in region.coords:\n labels[coord[0], coord[1]] = 0\n binary_image = labels > 0\n # erase 2mm of tissue (or whatever there is) from the boundary\n binary_image = morphology.binary_erosion(binary_image, morphology.disk(2))\n # keep 10mm of tissue (or whatever there is) around the boundary\n binary_image = morphology.binary_closing(binary_image, morphology.disk(10))\n # at this point, all we want are two big blobs - fill in holes in the mask\n binary_image = ndimage.binary_fill_holes(filters.roberts(binary_image))\n # zero is background so make foreground 1\n mask = binary_image != 0\n return mask\n\n def get_volume_mask(self, volume, hu_threshold=-400):\n \"\"\"Get the mask for each image in the volume stack\n\n # Arguments\n volume: 3d Numpy array with slice data\n\n # Returns\n masks: 3d Numpy array with stacked masks for each image slice\n \"\"\"\n return np.stack([self.get_lung_mask(img, hu_threshold) for img in volume])\n\n def get_masks_boundary_center(self, masks):\n # for creating boundaries, there must be more elegant solution, but for\n # now, this will do\n ax0_start = -1\n ax0_end = -1\n ax1_start = -1\n ax1_end = -1\n ax2_start = -1\n ax2_end = -1\n for i in range(np.shape(masks)[0]):\n mk = np.max(masks[i, :, :])\n if ax0_start == -1 and mk > 0:\n ax0_start = i\n if ax0_start != -1 and mk == 1:\n ax0_end = i\n for i in range(np.shape(masks)[1]):\n mk = np.max(masks[:, i, :])\n if ax1_start == -1 and mk > 0:\n ax1_start = i\n if ax1_start != -1 and mk == 1:\n ax1_end = i\n for i in range(np.shape(masks)[2]):\n mk = np.max(masks[:, :, i])\n if ax2_start == -1 and mk > 0:\n ax2_start = i\n if ax2_start != -1 and mk == 1:\n ax2_end = i\n bdr = np.int16(\n [[ax0_start, ax0_end], [ax1_start, ax1_end], [ax2_start, ax2_end]])\n # for finding center, we let scipy tell us\n vol = np.zeros(np.shape(masks))\n vol[bdr[0][0]:bdr[0][1], bdr[1][0]:bdr[1][1], bdr[2][0]:bdr[2][1]] = 1\n center = ndimage.measurements.center_of_mass(vol)\n center = np.int16(center)\n return bdr, center\n\n def standardize_to_cube(self, volume, center, empty_fill_value, new_cube_length = None):\n \"\"\"Create a cube and place volume such that the center of volume lines up\n with the center of the cube\n\n # Arguments\n volume: 3d Numpy array\n center: center of the volume\n empty_fill_value: value to put in cube prior to filling\n new_cube_length: length of new cube, defaults to cube size of 448\n \"\"\"\n if new_cube_length == None:\n new_cube_length = self.cube_length\n shift = new_cube_length / 2 - center\n vol_shp = np.int16(np.shape(volume))\n # get start/end of cube and volume\n cube_start = np.zeros(3, dtype=np.int16)\n vol_start = np.zeros(3, dtype=np.int16)\n cube_end = np.zeros(3, dtype=np.int16)\n vol_end = np.zeros(3, dtype=np.int16)\n for i in range(3):\n if shift[i] > 0:\n vol_start[i] = 0\n vol_end[i] = np.min([vol_shp[i], new_cube_length - shift[i]])\n else:\n vol_start[i] = -shift[i]\n vol_end[i] = np.min([vol_shp[i], new_cube_length + vol_start[i]])\n vol_len = vol_end[i] - vol_start[i]\n cube_start[i] = int((new_cube_length - vol_len)/2.0)\n cube_end[i] = cube_start[i] + vol_len\n\n # create new volume and copy data\n new_volume = np.full(\n (new_cube_length, new_cube_length, new_cube_length), empty_fill_value)\n new_volume[cube_start[0]:cube_end[0], cube_start[1]:cube_end[1], cube_start[2]:cube_end[\n 2]] = volume[vol_start[0]:vol_end[0], vol_start[1]:vol_end[1], vol_start[2]:vol_end[2]]\n return new_volume\n\n def rotate(self, volume, center, angle, empty_fill_value, axes=(2,1)):\n \"\"\"Rotate the volume one plane at a time to a given angle\n\n # Arguments\n volume: 3d Numpy array\n center: center of the volume\n angle: angle of rotation\n empty_fill_value: value to put in cube prior to filling\n axes: axis along which to rotate\n \"\"\"\n # pad so that when rotating no portion gets cut off\n pad_length = np.max(np.shape(volume))\n pad_length = np.int(pad_length * np.ceil(np.cbrt(3)))\n if pad_length % 2 != 0:\n pad_length += 1\n padded_image = self.standardize_to_cube(volume, center, self.min_hu, new_cube_length=pad_length)\n rotated_image = ndimage.interpolation.rotate(padded_image, angle, axes=axes, reshape=False)\n return rotated_image\n", "sub_path": "datasets/dicom_to_hdf.py", "file_name": "dicom_to_hdf.py", "file_ext": "py", "file_size_in_byte": 9188, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "tools.filetools.rm_rf", "line_number": 31, "usage_type": "call"}, {"api_name": "tools.filetools", "line_number": 31, "usage_type": "name"}, {"api_name": "tools.filetools.mkdir_p", "line_number": 32, "usage_type": "call"}, {"api_name": "tools.filetools", "line_number": 32, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 56, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation.zoom", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation", "line_number": 59, "usage_type": "attribute"}, {"api_name": "scipy.ndimage", "line_number": 59, "usage_type": "name"}, {"api_name": "dicom.read_file", "line_number": 77, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 113, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 115, "usage_type": "attribute"}, {"api_name": "skimage.segmentation.clear_border", "line_number": 130, "usage_type": "call"}, {"api_name": "skimage.segmentation", "line_number": 130, "usage_type": "name"}, {"api_name": "skimage.measure.label", "line_number": 132, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 132, "usage_type": "name"}, {"api_name": "skimage.measure.regionprops", "line_number": 133, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 133, "usage_type": "name"}, {"api_name": "skimage.measure.regionprops", "line_number": 135, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 135, "usage_type": "name"}, {"api_name": "skimage.morphology.binary_erosion", "line_number": 141, "usage_type": "call"}, {"api_name": "skimage.morphology", "line_number": 141, "usage_type": "name"}, {"api_name": "skimage.morphology.disk", "line_number": 141, "usage_type": "call"}, {"api_name": "skimage.morphology.binary_closing", "line_number": 143, "usage_type": "call"}, {"api_name": "skimage.morphology", "line_number": 143, "usage_type": "name"}, {"api_name": "skimage.morphology.disk", "line_number": 143, "usage_type": "call"}, {"api_name": "scipy.ndimage.binary_fill_holes", "line_number": 145, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 145, "usage_type": "name"}, {"api_name": "skimage.filters.roberts", "line_number": 145, "usage_type": "call"}, {"api_name": "skimage.filters", "line_number": 145, "usage_type": "name"}, {"api_name": "numpy.stack", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 191, "usage_type": "call"}, {"api_name": "scipy.ndimage.measurements.center_of_mass", "line_number": 193, "usage_type": "call"}, {"api_name": "scipy.ndimage.measurements", "line_number": 193, "usage_type": "attribute"}, {"api_name": "scipy.ndimage", "line_number": 193, "usage_type": "name"}, {"api_name": "numpy.int16", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 212, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 213, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 214, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 215, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.cbrt", "line_number": 246, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation.rotate", "line_number": 250, "usage_type": "call"}, {"api_name": "scipy.ndimage.interpolation", "line_number": 250, "usage_type": "attribute"}, {"api_name": "scipy.ndimage", "line_number": 250, "usage_type": "name"}]} +{"seq_id": "246209924", "text": "from __future__ import (absolute_import, division, print_function)\n\nfrom ansible.errors import AnsibleError\nfrom ansible.module_utils._text import to_text\nfrom ansible.plugins.action import ActionBase\n\n__metaclass__ = type\n\n\nclass ActionModule(ActionBase):\n\n def run(self, tmp=None, task_vars=None):\n if task_vars is None:\n task_vars = dict()\n result = super(ActionModule, self).run(tmp, task_vars)\n endpoint = self._task.args.get('endpoint', None)\n application_key = self._task.args.get('application_key', None)\n application_secret = self._task.args.get('application_secret', None)\n consumer_key = self._task.args.get('consumer_key', None)\n state = self._task.args.get('state', None)\n name = self._task.args.get('name', None)\n domain = self._task.args.get('domain', None)\n ip = self._task.args.get('ip', None)\n vrack = self._task.args.get('vrack', None)\n boot = self._task.args.get('boot', None)\n force_reboot = self._task.args.get('force_reboot', None)\n template = self._task.args.get('template', None)\n hostname = self._task.args.get('hostname', None)\n service = self._task.args.get('service', None)\n link_type = self._task.args.get('link_type', None)\n max_retry = self._task.args.get('max_retry', 10)\n sleep = self._task.args.get('sleep', 10)\n\n ssh_key_name = self._task.args.get('ssh_key_name', None)\n use_distrib_kernel = self._task.args.get('use_distrib_kernel', False)\n\n result['failed'] = True\n\n new_src = template\n\n credentials = ['endpoint', 'application_key', 'application_secret', 'consumer_key']\n credentials_in_args = [cred in self._task.args for cred in credentials]\n\n if name is None:\n result['msg'] = \"name is required\"\n elif service is None:\n result['msg'] = \"service is required\"\n elif any(credentials_in_args) and not all(credentials_in_args):\n result['msg'] = \"missing credentials. Either none or all the following (%s)\" % \", \".join(credentials)\n else:\n del result['failed']\n if result.get('failed'):\n return result\n\n if service == 'template':\n try:\n new_src = self._find_needle('files', template)\n except AnsibleError as e:\n result['failed'] = True\n result['msg'] = to_text(e)\n return result\n\n changed = False\n module_return = dict(changed=False)\n module_executed = False\n\n new_module_args = self._task.args.copy()\n new_module_args.update(\n dict(\n template=new_src\n )\n )\n module_return = self._execute_module(module_name='ovh', module_args=new_module_args, task_vars=task_vars)\n module_executed = True\n\n if module_return.get('failed'):\n result.update(module_return)\n return result\n if module_return.get('changed'):\n changed = True\n if module_executed:\n result.update(module_return)\n\n return result\n", "sub_path": "plugins/action/ovh.py", "file_name": "ovh.py", "file_ext": "py", "file_size_in_byte": 3154, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "ansible.plugins.action.ActionBase", "line_number": 10, "usage_type": "name"}, {"api_name": "ansible.errors.AnsibleError", "line_number": 58, "usage_type": "name"}, {"api_name": "ansible.module_utils._text.to_text", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "33671258", "text": "\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n\nfrom matplotlib import pyplot as plt\nimport seaborn as sea\nimport squarify \nimport lightgbm as lgb\nimport gc\n\n\naisles = pd.read_csv('aisles.csv')\ndepartments = pd.read_csv('departments.csv')\npriors = pd.read_csv('order_products__prior.csv')\ntrain = pd.read_csv('order_products__train.csv')\norders = pd.read_csv('orders.csv')\nproducts = pd.read_csv('products.csv')\n\nprint(\"aisles\", aisles.shape, aisles.columns)\nprint(\"departments:\", departments.shape, departments.columns)\nprint(\"priors:\", priors.shape, priors.columns)\nprint(\"train:\", train.shape, train.columns)\nprint(\"orders:\", orders.shape, orders.columns)\nprint(\"products:\", products.shape, products.columns)\n\nbest50 = priors['product_id'].value_counts()[0:50].to_frame().reset_index()\n# print(best50)\n# print((products[products['product_id']==472565]['product_name'].iloc[0]))\nname = []\nfor id in best50['index']:\n name.append(products[products['product_id']==id]['product_name'].iloc[0])\n# print(name)\n# sea.barplot(best50['product_id'][0:7],name[0:7])\nsells = pd.DataFrame({\n 'Name': np.array(name)[0:8],\n \"Volume\": best50['product_id'][0:8]\n})\nplt.figure(figsize=(16,8))\npic = sea.barplot(x='Name', y='Volume', data=sells)\npic.set_xticklabels(pic.get_xticklabels(), rotation=90)\n\n\"\"\"# 賣最好的產品，列出販賣次數前幾高的產品\"\"\"\n\n\n\"\"\"# 統計training data中商品連續被再次購買的次數(連續兩次購買相同物品，reordered即會被設為1)\"\"\"\n\nplt.figure(figsize=(10,5))\nreordered = pd.DataFrame({\n 'Reorder':['1','0'],\n 'Times':train['reordered'].value_counts()\n})\nprint(reordered)\nsea.barplot(x='Reorder',y='Times',data=reordered)\n\n\"\"\"# 商品被再次購買的比率(每次購買中)\n\n# 統計Order_dow(一週中的哪一天購買)的數量，因資料沒有特別註明數字分別代表禮拜幾，只能找出第幾天最常購買\n\"\"\"\n\nOrder_dow = orders['order_dow'].value_counts().to_frame().reset_index()\nplt.figure(figsize=(10,5))\nsea.barplot(x='index',y='order_dow',data=Order_dow)\n\n\"\"\"# 統計兩次購買間間隔幾天\n可以看出一週內再次購買的機率相當高，而最後一天30天飆高可能是超過30天購買第二次都歸類在30天\n\"\"\"\n\ntwodays = orders['days_since_prior_order'].value_counts().to_frame().reset_index()\n# print(twodays)\nplt.figure(figsize=(15,5))\nsea.barplot(x='index',y='days_since_prior_order',data=twodays)\n\n\"\"\"# 統計每次購買的時間點(幾點鐘)\n可以看出白天(約7~19)購買的機率最高\n\"\"\"\n\nhours = orders['order_hour_of_day'].value_counts().to_frame().reset_index()\nplt.figure(figsize=(15,5))\nsea.barplot(x='index',y='order_hour_of_day',data=hours)\n\n\"\"\"# 因為資料量過大，記憶體有限，將資料型態由int64轉成int8和int32(根據數據大小)。\n## Ex:orders中order_dow為0~6，故轉成int8\n## 而orders中order_id最大為3421083，故轉成int32\n\n### 印出每筆column最大範圍，決定更改型態\n\"\"\"\n\nprint('priors:order_id', max(priors.order_id))\nprint('priors:product_id', max(priors.product_id))\nprint('priors:add_to_cart_order', max(priors.add_to_cart_order))\nprint('priors:reordered', max(priors.reordered))\nprint('orders:user_id', max(orders.user_id))\nprint('orders:order_number', max(orders.order_number))\nprint('orders:order_hour_of_day', max(orders.order_hour_of_day))\nprint('orders:days_since_prior_order', max(orders.days_since_prior_order[1:]))\nprint('products:aisle_id', max(products.aisle_id))\nprint('products:department_id', max(products.department_id))\n\norders.order_dow = orders.order_dow.astype(np.int8)\norders.order_hour_of_day = orders.order_hour_of_day.astype(np.int8)\norders.order_number = orders.order_number.astype(np.int16)\norders.order_id = orders.order_id.astype(np.int32)\norders.user_id = orders.user_id.astype(np.int32)\norders.days_since_prior_order = orders.days_since_prior_order.astype(np.float32)\n\nproducts.drop(['product_name'], axis=1, inplace=True)\nproducts.aisle_id = products.aisle_id.astype(np.int8)\nproducts.department_id = products.department_id.astype(np.int8)\nproducts.product_id = products.product_id.astype(np.int32)\n\ntrain.order_id = train.order_id.astype(np.int32)\ntrain.reordered = train.reordered.astype(np.int8)\ntrain.add_to_cart_order = train.add_to_cart_order.astype(np.int16)\n\npriors.order_id = priors.order_id.astype(np.int32)\npriors.add_to_cart_order = priors.add_to_cart_order.astype(np.int16)\npriors.reordered = priors.reordered.astype(np.int8)\npriors.product_id = priors.product_id.astype(np.int32)\n\n\"\"\"# 計算先前某項產品重複購買的頻率(rate = reorders/orders)\"\"\"\n\nprods = pd.DataFrame()\nprods['orders'] = priors.groupby(priors.product_id).size().astype(np.float32)\nprods['reorders'] = priors['reordered'].groupby(priors.product_id).sum().astype(np.float32)\nprods['reorder_rate'] = (prods.reorders / prods.orders).astype(np.float32)\nproducts = products.join(prods, on='product_id') #依照product_id來排序 並把prods加進products\nproducts.set_index('product_id', drop=False, inplace=True)\ndel prods\n\nprint('add order info to priors')\norders.set_index('order_id', inplace=True, drop=False)\npriors = priors.join(orders, on='order_id', rsuffix='_new')\npriors.drop('order_id_new', inplace=True, axis=1)\n\n\"\"\"# 創建一個新的DataFrame:user紀錄每個用戶以下資訊\n1. Total_item:總共買了幾樣產品\n2. all_products_id:全部買的產品的product_id\n3. total_different_item:總共買過哪些不同的產品\n4. average_days:平均幾天買一次\n5. average_times:平均在一天的何時購買\n6. number_orders:購買的次數\n7. average_buy:平均一次購買幾樣產品\n\"\"\"\n\nusr = pd.DataFrame()\nusr['average_days'] = orders.groupby('user_id')['days_since_prior_order'].mean().astype(np.float32)\nusr['average_times'] = orders.groupby('user_id')['order_hour_of_day'].mean().astype(np.float32)\nusr['most_dow'] = orders.groupby('user_id')['order_dow'].agg(lambda x:x.value_counts().index[0]).astype(np.int8) # 利用value_counts()找出出現最多次的dow\nusr['number_orders'] = orders.groupby('user_id').size().astype(np.int16)\n\nusers = pd.DataFrame()\nusers['total_items'] = priors.groupby('user_id').size().astype(np.int16) # 計算總共買了多少數量的物品\nusers['all_products_id'] = priors.groupby('user_id')['product_id'].apply(set) # 計算買了哪些物品\nusers['total_different_item'] = (users.all_products_id.map(len)).astype(np.int16) #計算不同物品的數量\n\nusers = users.join(usr)\ndel usr\nusers['average_buy'] = (users.total_items / users.number_orders).astype(np.float32)\ngc.collect()\nprint('user f', users.shape)\n\n\npriors['user_product'] = priors.product_id + priors.user_id * 100000\n\nd= dict()\nfor row in priors.itertuples():\n z = row.user_product\n if z not in d:\n d[z] = (1, (row.order_number, row.order_id), row.add_to_cart_order)\n else:\n d[z] = (d[z][0] + 1, max(d[z][1], (row.order_number, row.order_id)), d[z][2] + row.add_to_cart_order)\nd = pd.DataFrame.from_dict(d, orient='index')\nd.columns = ['number_orders', 'last_order_id', 'sum_pos_in_cart']\nd.number_orders = d.number_orders.astype(np.int16)\nd.last_order_id = d.last_order_id.map(lambda x: x[1]).astype(np.int32)\nd.sum_pos_in_cart = d.sum_pos_in_cart.astype(np.int16)\n\nuser_product = d\nprint('user X product f', len(user_product))\n\ndel priors\n\n\n\"\"\"# 切割train/test data，透過orders的eval_set column來區分\"\"\"\n\ntest_orders = orders[orders.eval_set == 'test']\ntrain_orders = orders[orders.eval_set == 'train']\n\ntrain.set_index(['order_id', 'product_id'], inplace=True, drop=False)\n\n\n\"\"\"# 模型\"\"\"\n\ndef features(selected_orders, labels_given=False):\n print('build candidate list')\n order_list = []\n product_list = []\n labels = []\n i=0\n for row in selected_orders.itertuples():\n i+=1\n if i%10000 == 0: print('order row',i)\n order_id = row.order_id\n user_id = row.user_id\n user_products = users.all_products_id[user_id]\n product_list += user_products\n order_list += [order_id] * len(user_products)\n if labels_given:\n labels += [(order_id, product) in train.index for product in user_products]\n \n df = pd.DataFrame({'order_id':order_list, 'product_id':product_list})\n df.order_id = df.order_id.astype(np.int32)\n df.product_id = df.product_id.astype(np.int32)\n labels = np.array(labels, dtype=np.int8)\n del order_list\n del product_list\n \n df['user_id'] = df.order_id.map(orders.user_id).astype(np.int32)\n df['user_total_orders'] = df.user_id.map(users.number_orders)\n df['user_total_items'] = df.user_id.map(users.total_items)\n df['total_distinct_items'] = df.user_id.map(users.total_different_item)\n df['user_average_days_between_orders'] = df.user_id.map(users.average_days)\n df['user_average_basket'] = df.user_id.map(users.average_buy)\n df['user_average_times'] = df.user_id.map(users.average_times) #\n df['user_most_dow'] = df.user_id.map(users.most_dow)\n \n df['order_hour_of_day'] = df.order_id.map(orders.order_hour_of_day)\n df['days_since_prior_order'] = df.order_id.map(orders.days_since_prior_order)\n df['days_since_ratio'] = df.days_since_prior_order / df.user_average_days_between_orders\n \n df['aisle_id'] = df.product_id.map(products.aisle_id).astype(np.int8)\n df['department_id'] = df.product_id.map(products.department_id).astype(np.int8)\n df['product_orders'] = df.product_id.map(products.orders).astype(np.float32)\n df['product_reorders'] = df.product_id.map(products.reorders).astype(np.float32)\n df['product_reorder_rate'] = df.product_id.map(products.reorder_rate)\n\n df['z'] = df.user_id * 100000 + df.product_id\n df.drop(['user_id'], axis=1, inplace=True)\n df['UP_orders'] = df.z.map(user_product.number_orders)\n df['UP_orders_ratio'] = (df.UP_orders / df.user_total_orders).astype(np.float32)\n df['UP_last_order_id'] = df.z.map(user_product.last_order_id)\n df['UP_average_pos_in_cart'] = (df.z.map(user_product.sum_pos_in_cart) / df.UP_orders).astype(np.float32)\n df['UP_reorder_rate'] = (df.UP_orders / df.user_total_orders).astype(np.float32)\n df['UP_orders_since_last'] = df.user_total_orders - df.UP_last_order_id.map(orders.order_number)\n df['UP_delta_hour_vs_last'] = abs(df.order_hour_of_day - \\\n df.UP_last_order_id.map(orders.order_hour_of_day)).map(lambda x: min(x, 24-x)).astype(np.int8)\n\n df.drop(['UP_last_order_id', 'z'], axis=1, inplace=True)\n\n gc.collect()\n return (df, labels)\n\ndf_train, labels = features(train_orders, labels_given=True)\n\nf_to_use = ['user_total_orders', 'user_total_items', 'total_distinct_items',\n 'user_average_days_between_orders', 'user_average_basket', 'user_average_times', 'user_most_dow',\n 'order_hour_of_day', 'days_since_prior_order', 'days_since_ratio',\n 'aisle_id', 'department_id', 'product_orders', 'product_reorders',\n 'product_reorder_rate', 'UP_orders', 'UP_orders_ratio',\n 'UP_average_pos_in_cart', 'UP_reorder_rate', 'UP_orders_since_last',\n 'UP_delta_hour_vs_last']\n\n\nprint('formating for lgb')\nd_train = lgb.Dataset(df_train[f_to_use],\n label=labels,\n categorical_feature=['aisle_id', 'department_id'])\ndel df_train\ngc.collect()\n\nparams = {\n 'task': 'train',\n 'boosting_type': 'gbdt',\n 'objective': 'binary',\n 'metric': {'binary_logloss'},\n 'num_leaves': 96,\n 'feature_fraction': 0.9,\n 'bagging_fraction': 0.95,\n 'bagging_freq': 5\n}\nROUNDS = 98\n\nbst = lgb.train(params, d_train, ROUNDS)\nlgb.plot_importance(bst, figsize=(9,20))\ndel d_train\ngc.collect()\n\ndf_test, _ = features(test_orders)\npreds = bst.predict(df_test[f_to_use])\n\ndf_test['pred'] = preds\n\nTRESHOLD = 0.22 \n\nd = dict()\nfor row in df_test.itertuples():\n if row.pred > TRESHOLD:\n try:\n d[row.order_id] += ' ' + str(row.product_id)\n except:\n d[row.order_id] = str(row.product_id)\n\nfor order in test_orders.order_id:\n if order not in d:\n d[order] = 'None'\n\nsub = pd.DataFrame.from_dict(d, orient='index')\n\nsub.reset_index(inplace=True)\nsub.columns = ['order_id', 'products']\nsub.to_csv('submission.csv', index=False)", "sub_path": "dsai_hw4.py", "file_name": "dsai_hw4.py", "file_ext": "py", "file_size_in_byte": 12715, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 56, "usage_type": "call"}, {"api_name": "seaborn.barplot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 154, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 156, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 157, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 160, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 166, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 167, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 180, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 180, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 182, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 183, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 184, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 220, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 221, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 222, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 226, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 239, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 240, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 241, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 242, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 248, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 250, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 251, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 254, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 258, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 273, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 277, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 291, "usage_type": "call"}, {"api_name": "lightgbm.plot_importance", "line_number": 292, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 294, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 315, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 315, "usage_type": "attribute"}]} +{"seq_id": "618315669", "text": "from django.shortcuts import render,redirect\nfrom django.http import JsonResponse,HttpResponseRedirect\nimport time,random\nfrom .models import Wheel,nav,Mustbuy,Shop,MainShow,FoodTypes,Goods,User,Cart\nfrom django.conf import settings\nimport os\nfrom django.contrib.auth import logout\n\n# Create your views here.\ndef home(request):\n wheelsList=Wheel.objects.all()\n navList=nav.objects.all()\n mustbuyList=Mustbuy.objects.all()\n shopList=Shop.objects.all()\n shop1=shopList[0]\n shop2 = shopList[1:3]\n shop3 = shopList[3:7]\n shop4 = shopList[7:11]\n mainList=MainShow.objects.all()\n\n return render(request, 'axf/home.html',{\"title\":\"主页\",\"wheelsList\":wheelsList,\"navList\":navList,\"mustbuyList\":mustbuyList,\"shop1\":shop1,\"shop2\":shop2,\"shop3\":shop3,\"shop4\":shop4,\"mainList\":mainList})\n\ndef market(request,categoryid,cid,sortid):\n leftSlider=FoodTypes.objects.all()\n if cid == '0':\n productList=Goods.objects.filter(categoryid=categoryid)\n else:\n productList = Goods.objects.filter(categoryid=categoryid,childcid=cid)\n group=leftSlider.get(typeid=categoryid)\n childList=[]\n childnames=group.childtypenames\n arr1=childnames.split(\"#\")\n for str in arr1:\n arr2=str.split(\":\")\n obj={\"childName\":arr2[0],\"childId\":arr2[1]}\n childList.append(obj)\n if sortid==\"1\":\n productList=productList.order_by(\"productnum\")\n elif sortid==\"2\":\n pass\n elif sortid==\"3\":\n pass\n cartlist=[]\n usertoken = request.session.get(\"token\")\n if usertoken:\n user = User.objects.get(userToken=usertoken)\n cartlist=Cart.objects.filter(userAccount=user.userAccount,orderid=\"0\")\n for p in productList:\n for c in cartlist:\n if p.productid==c.productid:\n p.num=c.productnum\n\n return render(request, \"axf/market.html\",{\"title\":\"闪送超市\",\"leftSlider\":leftSlider,\"productList\":productList,\"childList\":childList,\"categoryid\":categoryid,\"cid\":cid})\n\ndef cart(request):\n usertoken=request.session.get(\"token\")\n if usertoken:\n user=User.objects.get(userToken=usertoken)\n cartslist=Cart.objects.filter(userAccount=user.userAccount,orderid=\"0\")\n\n return render(request, 'axf/cart.html', {\"title\": \"购物车\", \"cartslist\": cartslist})\n else:\n return render(request,'axf/cart.html',{\"title\":\"购物车\"})\n\ndef mine(request):\n username=request.session.get(\"username\",\"未登录\")\n return render(request,'axf/mine.html',{\"title\":\"我的\",\"username\":username})\n#注册\ndef register(request):\n if request.method==\"POST\":\n userAccount = request.POST.get(\"userAccount\")\n userPasswd = request.POST.get(\"userPasswd\")\n userName = request.POST.get(\"userName\")\n userPhone = request.POST.get(\"userPhone\")\n userAdderss = request.POST.get(\"userAdderss\")\n userRank = 0\n Token=time.time() + random.randrange(1, 100000)\n userToken =str(Token)\n f=request.FILES[\"userImg\"]\n userImg=os.path.join(settings.MDEIA_ROOT,userAccount+\".png\")\n with open (userImg,\"wb\") as fp:\n for date in f.chunks():\n fp.write(date)\n\n user=User.createuser(userAccount,userPasswd,userName,userPhone,userAdderss,userImg,userRank,userToken)\n user.save()\n request.session[\"username\"] = userName\n request.session[\"token\"] = userToken\n return redirect(\"/mine/\")\n else:\n return render(request,'axf/register.html',)\n\n#检查用户明是否可用\ndef checkuserid(request):\n userid=request.POST.get(\"userid\")\n try:\n user=User.objects.get(userAccount=userid)\n return JsonResponse({\"data\":\"改用户已经被注册\",\"status\":\"error\"})\n except User.DoesNotExist as e:\n return JsonResponse({\"data\":\"可以注册\",\"status\":\"success\"})\n\n#退出登陆\ndef quit(request):\n logout(request)\n return redirect(\"/mine/\")\n\n#登陆\nfrom .forms.login import LoginForm\nfrom django.http import HttpResponse\ndef login(request):\n if request.method == \"POST\":\n f = LoginForm(request.POST)\n if f.is_valid():\n # 信息格式没多大问题，验证账号和密码的正确性\n nameid = f.cleaned_data[\"username\"]\n pswd = f.cleaned_data[\"passwd\"]\n try:\n user = User.objects.get(userAccount = nameid)\n if user.userPasswd != pswd:\n return redirect('/login/')\n except User.DoesNotExist as e:\n return redirect('/login/')\n\n #登陆成功\n token = time.time() + random.randrange(1, 100000)\n user.userToken = str(token)\n user.save()\n request.session[\"username\"] = user.userName\n request.session[\"token\"] = user.userToken\n return redirect('/mine/')\n else:\n return render(request, 'axf/login.html', {\"title\": \"登陆\", \"form\": f,\"error\":f.errors})\n else:\n f = LoginForm()\n return render(request, 'axf/login.html', {\"title\": \"登陆\",\"form\":f})\n\ndef changecart(request,flag):\n #检查是否登陆\n usertoken =request.session.get(\"token\")\n if usertoken==None:\n return JsonResponse({\"data\":\"-1\",\"status\":\"error\"})\n user = User.objects.get(userToken=usertoken)\n productid = request.POST.get(\"productid\")\n product = Goods.objects.filter(productid=productid)\n product=product.first()\n if flag=='0':\n if product.storenums ==0:\n return JsonResponse({\"data\":\"-2\",\"status\":\"error\"})\n carts=Cart.objects.filter(userAccount=user.userAccount,orderid=\"0\")\n c=None\n if carts.count()==0:\n c=Cart.createcart(user.userAccount,productid,1,product.price,True,product.productimg,product.productlongname,False)\n c.save()\n else:\n try:\n c=carts.get(productid=productid)\n c.productnum+=1\n c.productprice = \"%.2f\" % (float(product.price) * c.productnum)\n c.save()\n except Cart.DoesNotExist as e:\n c = Cart.createcart(user.userAccount, productid, 1, product.price, True, product.productimg,product.productlongname, False)\n c.save()\n product.storenums-=1\n product.save()\n return JsonResponse({\"data\":c.productnum, \"price\":c.productprice,\"status\":\"success\"})\n if flag=='1':\n\n carts=Cart.objects.filter(userAccount=user.userAccount,orderid=\"0\")\n try:\n c=carts.get(productid=productid)\n c.productnum -= 1\n if c.productnum == 0:\n c.delete()\n return JsonResponse({\"data\": c.productnum, \"price\": c.productprice, \"status\": \"success\"})\n else:\n c.productprice = \"%.2f\" % (float(product.price) * c.productnum)\n c.save()\n product.storenums += 1\n product.save()\n return JsonResponse({\"data\": c.productnum, \"price\": c.productprice, \"status\": \"success\"})\n except Cart.DoesNotExist as e:\n return JsonResponse({\"data\": \"-2\", \"status\": \"error\"})\n\n if flag==\"3\":\n carts = Cart.objects.filter(userAccount=user.userAccount,orderid=\"0\")\n c = carts.get(productid=productid)\n c.isChose = not c.isChose\n c.save()\n str = \"\"\n if c.isChose:\n str = \"√\"\n return JsonResponse({\"data\": str, \"status\": \"success\"})\n if flag == \"4\":\n carts = Cart.objects.filter(userAccount=user.userAccount,orderid=\"0\")\n for c in carts:\n c.isChose = True\n c.save()\n str = \"√\"\n return JsonResponse({\"data\": str, \"status\": \"success\"})\n if flag==\"5\":\n carts = Cart.objects.filter(userAccount=user.userAccount,isChose=True,orderid=\"0\")\n orderid=user.userAccount+time.strftime(\"%Y%m%d%H%M%S\", time.localtime())\n # orderid=str(order)\n productList=[]\n for cart in carts:\n productList.append(cart.productid)\n cart.orderid=orderid\n cart.save()\n good=Goods.objects.filter(productid=cart.productid)\n good=good.first()\n good.productnum+=1\n good.save()\n return JsonResponse({\"data\":orderid,\"status\":\"success\",\"productList\":productList})\n\ndef order(request):\n usertoken = request.session.get(\"token\")\n if usertoken == None:\n return HttpResponseRedirect(\"/login/\")\n\n return render(request,\"axf/order.html\")\n\n\n\n\n\n\n\n", "sub_path": "axf/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "models.Wheel.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Wheel.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Wheel", "line_number": 11, "usage_type": "name"}, {"api_name": "models.nav.objects.all", "line_number": 12, "usage_type": "call"}, {"api_name": "models.nav.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.nav", "line_number": 12, "usage_type": "name"}, {"api_name": "models.Mustbuy.objects.all", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Mustbuy.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Mustbuy", "line_number": 13, "usage_type": "name"}, {"api_name": "models.Shop.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Shop.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.Shop", "line_number": 14, "usage_type": "name"}, {"api_name": "models.MainShow.objects.all", "line_number": 19, "usage_type": "call"}, {"api_name": "models.MainShow.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.MainShow", "line_number": 19, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}, {"api_name": "models.FoodTypes.objects.all", "line_number": 24, "usage_type": "call"}, {"api_name": "models.FoodTypes.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.FoodTypes", "line_number": 24, "usage_type": "name"}, {"api_name": "models.Goods.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Goods.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Goods", "line_number": 26, "usage_type": "name"}, {"api_name": "models.Goods.objects.filter", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Goods.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Goods", "line_number": 28, "usage_type": "name"}, {"api_name": "models.User.objects.get", "line_number": 46, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 46, "usage_type": "name"}, {"api_name": "models.Cart.objects.filter", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Cart.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 47, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "models.User.objects.get", "line_number": 58, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 58, "usage_type": "name"}, {"api_name": "models.Cart.objects.filter", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Cart.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 59, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 61, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 67, "usage_type": "call"}, {"api_name": "time.time", "line_number": 77, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "django.conf.settings.MDEIA_ROOT", "line_number": 80, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 80, "usage_type": "name"}, {"api_name": "models.User.createuser", "line_number": 85, "usage_type": "call"}, {"api_name": "models.User", "line_number": 85, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 91, "usage_type": "call"}, {"api_name": "models.User.objects.get", "line_number": 97, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 97, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 97, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 98, "usage_type": "call"}, {"api_name": "models.User.DoesNotExist", "line_number": 99, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 99, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 100, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 104, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 105, "usage_type": "call"}, {"api_name": "forms.login.LoginForm", "line_number": 112, "usage_type": "call"}, {"api_name": "models.User.objects.get", "line_number": 118, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 118, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 120, "usage_type": "call"}, {"api_name": "models.User.DoesNotExist", "line_number": 121, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 121, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 122, "usage_type": "call"}, {"api_name": "time.time", "line_number": 125, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 125, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 130, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 132, "usage_type": "call"}, {"api_name": "forms.login.LoginForm", "line_number": 134, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 135, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 141, "usage_type": "call"}, {"api_name": "models.User.objects.get", "line_number": 142, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 142, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 142, "usage_type": "name"}, {"api_name": "models.Goods.objects.filter", "line_number": 144, "usage_type": "call"}, {"api_name": "models.Goods.objects", "line_number": 144, "usage_type": "attribute"}, {"api_name": "models.Goods", "line_number": 144, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 148, "usage_type": "call"}, {"api_name": "models.Cart.objects.filter", "line_number": 149, "usage_type": "call"}, {"api_name": "models.Cart.objects", "line_number": 149, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 149, "usage_type": "name"}, {"api_name": "models.Cart.createcart", "line_number": 152, "usage_type": "call"}, {"api_name": "models.Cart", "line_number": 152, "usage_type": "name"}, {"api_name": "models.Cart.DoesNotExist", "line_number": 160, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 160, "usage_type": "name"}, {"api_name": "models.Cart.createcart", "line_number": 161, "usage_type": "call"}, {"api_name": "models.Cart", "line_number": 161, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 165, "usage_type": "call"}, {"api_name": "models.Cart.objects.filter", "line_number": 168, "usage_type": "call"}, {"api_name": "models.Cart.objects", "line_number": 168, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 168, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 174, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 180, "usage_type": "call"}, {"api_name": "models.Cart.DoesNotExist", "line_number": 181, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 181, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 182, "usage_type": "call"}, {"api_name": "models.Cart.objects.filter", "line_number": 185, "usage_type": "call"}, {"api_name": "models.Cart.objects", "line_number": 185, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 185, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 192, "usage_type": "call"}, {"api_name": "models.Cart.objects.filter", "line_number": 194, "usage_type": "call"}, {"api_name": "models.Cart.objects", "line_number": 194, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 194, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 199, "usage_type": "call"}, {"api_name": "models.Cart.objects.filter", "line_number": 201, "usage_type": "call"}, {"api_name": "models.Cart.objects", "line_number": 201, "usage_type": "attribute"}, {"api_name": "models.Cart", "line_number": 201, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 202, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 202, "usage_type": "call"}, {"api_name": "models.Goods.objects.filter", "line_number": 209, "usage_type": "call"}, {"api_name": "models.Goods.objects", "line_number": 209, "usage_type": "attribute"}, {"api_name": "models.Goods", "line_number": 209, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 213, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 218, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 220, "usage_type": "call"}]} +{"seq_id": "155709280", "text": "from django.conf.urls.defaults import url, patterns\n\nurlpatterns = patterns('blog.views',\n url(r'^init$', 'init', name='init'),\n\n url(r'^$', 'index', name='index'),\n url(r'^(?P\\d+)$', 'index', name='index'),\n url(r'^login$', 'login', name='login'),\n url(r'^profile$', 'profile', name='profile'),\n url(r'^dashboard$', 'dashboard', name='dashboard'),\n url(r'^dashboard/(?P\\d+)$', 'dashboard', name='dashboard'),\n url(r'^new$', 'new_post', name='new_post'),\n url(r'^edit/(?P\\d+)$', 'edit_post', name='edit_post'),\n url(r'^delete/(?P\\d+)$', 'delete_post', name='delete_post'),\n url(r'^logout$', 'logout', name='logout'),\n)\n", "sub_path": "blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 682, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.conf.urls.defaults.patterns", "line_number": 3, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 4, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "653062634", "text": "# -*- coding=utf-8 -*-\nimport attr\nimport os\nimport pip_shims\n\n\n@attr.s\nclass VCSRepository(object):\n url = attr.ib()\n name = attr.ib()\n checkout_directory = attr.ib()\n vcs_type = attr.ib()\n subdirectory = attr.ib(default=None)\n commit_sha = attr.ib(default=None)\n ref = attr.ib(default=None)\n repo_instance = attr.ib()\n\n @repo_instance.default\n def get_repo_instance(self):\n from pip_shims import VcsSupport\n VCS_SUPPORT = VcsSupport()\n backend = VCS_SUPPORT._registry.get(self.vcs_type)\n return backend(url=self.url)\n\n @property\n def is_local(self):\n url = self.url\n if '+' in url:\n url = url.split('+')[1]\n return url.startswith(\"file\")\n\n def obtain(self):\n if (os.path.exists(self.checkout_directory) and not\n self.repo_instance.is_repository_directory(self.checkout_directory)):\n self.repo_instance.unpack(self.checkout_directory)\n elif not os.path.exists(self.checkout_directory):\n self.repo_instance.obtain(self.checkout_directory)\n else:\n if self.ref:\n self.checkout_ref(self.ref)\n if not self.commit_sha:\n self.commit_sha = self.get_commit_hash()\n\n def checkout_ref(self, ref):\n if not self.repo_instance.is_commit_id_equal(\n self.checkout_directory, self.get_commit_hash()\n ) and not self.repo_instance.is_commit_id_equal(self.checkout_directory, ref):\n if not self.is_local:\n self.update(ref)\n\n def update(self, ref):\n target_ref = self.repo_instance.make_rev_options(ref)\n if pip_shims.parse_version(pip_shims.pip_version) > pip_shims.parse_version(\"18.0\"):\n self.repo_instance.update(self.checkout_directory, self.url, target_ref)\n else:\n self.repo_instance.update(self.checkout_directory, target_ref)\n self.commit_sha = self.get_commit_hash()\n\n def get_commit_hash(self, ref=None):\n return self.repo_instance.get_revision(self.checkout_directory)\n", "sub_path": "weatherenv/Lib/site-packages/pipenv/vendor/requirementslib/models/vcs.py", "file_name": "vcs.py", "file_ext": "py", "file_size_in_byte": 2079, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "attr.ib", "line_number": 9, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 10, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 11, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 12, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 13, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 14, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 15, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 16, "usage_type": "call"}, {"api_name": "pip_shims.VcsSupport", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pip_shims.parse_version", "line_number": 53, "usage_type": "call"}, {"api_name": "pip_shims.pip_version", "line_number": 53, "usage_type": "attribute"}, {"api_name": "attr.s", "line_number": 7, "usage_type": "attribute"}]} +{"seq_id": "43127439", "text": "import os, sys\nimport datetime\nimport time\nfrom schainpy.controller import Project\n\ndesc = \"USRP_test\"\nfilename = \"USRP_processing.xml\"\ncontrollerObj = Project()\ncontrollerObj.setup(id = '191', name='Test_USRP', description=desc)\n\n############## USED TO PLOT IQ VOLTAGE, POWER AND SPECTRA #############\n######PATH DE LECTURA, ESCRITURA, GRAFICOS Y ENVIO WEB#################\npath = '/home/alex/Downloads/test_rawdata'\nfigpath = '/home/alex/Downloads'\n################# RANGO DE PLOTEO######################################\ndBmin = '30'\ndBmax = '60'\nxmin = '0'\nxmax ='24'\nymin = '0'\nymax = '600'\n########################FECHA##########################################\nstr = datetime.date.today()\ntoday = str.strftime(\"%Y/%m/%d\")\nstr2 = str - datetime.timedelta(days=1)\nyesterday = str2.strftime(\"%Y/%m/%d\")\n######################## UNIDAD DE LECTURA#############################\nreadUnitConfObj = controllerObj.addReadUnit(datatype='VoltageReader',\n path=path,\n startDate=\"2020/01/01\", #\"2020/01/01\",#today,\n endDate= \"2020/12/01\", #\"2020/12/30\",#today,\n startTime='00:00:00',\n endTime='23:59:59',\n delay=0,\n #set=0,\n online=0,\n walk=1)\n\nopObj11 = readUnitConfObj.addOperation(name='printInfo')\n#opObj11 = readUnitConfObj.addOperation(name='printNumberOfBlock')\n#######################################################################\n################ OPERACIONES DOMINIO DEL TIEMPO########################\n#######################################################################\n\nprocUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId())\n'''\nopObj11 = procUnitConfObjA.addOperation(name='PulsePairVoltage', optype='other')\nopObj11.addParameter(name='n', value='256', format='int')\nopObj11.addParameter(name='removeDC', value=1, format='int')\n'''\n'''\ntype=\"power\"\nopObj10 = procUnitConfObjA.addOperation(name='ScopePlot', optype='external')\n#opObj10.addParameter(name='id', value='12')\nopObj10.addParameter(name='wintitle', value=type )\nopObj10.addParameter(name='type', value=type)\n106 32\n102 64\n99 128\n99 256s\n'''\n'''\ntype=\"WeatherPower\"\nopObj10 = procUnitConfObjA.addOperation(name='PulsepairPowerPlot', optype='external')\n#opObj10.addParameter(name='id', value='12')\nopObj10.addParameter(name='wintitle', value=type )\n\nopObj11 = procUnitConfObjA.addOperation(name='PulsepairVelocityPlot', optype='other')\nopObj11.addParameter(name='xmax', value=8)\n'''\n\ncontrollerObj.start()\n", "sub_path": "schainpy/scripts/test_sim0005.py", "file_name": "test_sim0005.py", "file_ext": "py", "file_size_in_byte": 2813, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "schainpy.controller.Project", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 23, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "108983373", "text": "import numpy as np\nimport cv2\nimport cvui\nimport os\nimport time\nfrom yolo import Yolo\nfrom configparser import ConfigParser\n\nsysPath = os.path.dirname(os.path.abspath(__file__))\n\nsettings_file = os.path.join(sysPath,\"settings.ini\")\n\n#Read config.ini file\nconfig_object = ConfigParser()\nconfig_object.read(settings_file)\n\nwin_config = config_object[\"Window\"]\nWINDOW_NAME = win_config[\"name\"]\nwin_width = int(win_config[\"width\"])\nwin_height = int(win_config[\"height\"])\n\nimage_config = config_object[\"Image\"]\nblink_every_n_frames = int(image_config[\"blink_frames\"])\nimg_alpha = [float(image_config[\"alpha\"])]\nimg_beta = [float(image_config[\"beta\"])]\nimg_gamma = [float(image_config[\"gamma\"])]\nthreshold_min = [int(image_config[\"threshold_min\"])]\nthreshold_max = [int(image_config[\"threshold_max\"])]\ncontour_area_min = [int(image_config[\"contour_area_min\"])]\ncontour_area_max = [int(image_config[\"contour_area_max\"])]\n\nyolo_config = config_object[\"YOLO\"]\nyolo_confidence = float(yolo_config[\"confidence\"])\nyolo_blob_resize = int(yolo_config[\"blob_resize\"])\nyolo_weight = yolo_config[\"weights\"]\nyolo_names = yolo_config[\"names\"]\nyolo_config = yolo_config[\"config\"]\n\n## constants that should be changed from params (future)\n\nlib_folder = \"lib\"\n\nsource_folder = \"sources\"\nsource_file_name = \"Skype_Video2.mp4\"\n\n## Window\n(win_W, win_H) = (win_width, win_height)\n\nvideoPath = os.path.join(sysPath, os.path.join(source_folder,source_file_name))\n\ncap = cv2.VideoCapture()\n\n# YOLO\nweights = os.path.join(sysPath, os.path.join(lib_folder,yolo_weight))\nconfig = os.path.join(sysPath, os.path.join(lib_folder,yolo_config))\nnames = os.path.join(sysPath, os.path.join(lib_folder,yolo_names))\n\nyo = Yolo(weights, config, names)\nyo.confidence = yolo_confidence\nyo.blobResize = yolo_blob_resize\n\n# cap/video state\nstates = {0: \"Ready for Stream\", 1: \"Streaming\", 2: \"Recognizing\"}\nstate = [states.get(0, \"\")]\n\n# recognition button states\nrecog_btn_states = {0: \"Start recognition\", 1: \"Stop recognition\"}\nrecog_btn_state = [recog_btn_states.get(0, \"\")]\n\n# recognizers state\nrecog_states = {0: \"Contours >\", 1: \"< YOLO\"}\nrecog_state = [recog_states.get(0, \"\")]\n\n# contour debug state\ncontour_states = {0: \"normal\", 1: \"thresholded\"}\ncontour_state = [contour_states.get(0, \"\")]\n\n# sub ui states\nsub_ui_states = {0: \"full screen\", 1: \"tuning\"}\nsub_ui_state = [sub_ui_states.get(0,\"\")]\n\nisBlink = [True]\nrecognize = [False]\n\n\n#\n# UI Positioning\n#\n\n# status position\nstat_l_x, stat_l_y = int(0.02 * win_W), int(0.01 * win_H)\nfps_l_x, fps_l_y = int(0.7 * win_W), int(0.01 * win_H)\n\n# capture frame position\ncap_fr1_x, cap_fr1_y, cap_fr1_w, cap_fr1_h = (\n int(0.016 * win_W),\n int(0.04 * win_H),\n int(0.48 * win_W),\n int(0.54 * win_H),\n)\ncap_fr2_x, cap_fr2_y, cap_fr2_w, cap_fr2_h = (\n int(0.51 * win_W),\n int(0.04 * win_H),\n int(0.48 * win_W),\n int(0.54 * win_H),\n)\n\n# action buttons\n(start_b_x, start_b_y, start_b_w, start_b_h) = (\n int(0.016 * win_W),\n int(0.66 * win_H),\n int(0.113 * win_W),\n int(0.113 * win_W),\n)\n(recog_b_x, recog_b_y, recog_b_w, recog_b_h) = (\n int(0.15 * win_W),\n int(0.66 * win_H),\n int(0.14 * win_W),\n int(0.113 * win_W),\n)\n(stop_all_b_x, stop_all_b_y, stop_all_b_w, stop_all_b_h) = (\n int(0.016 * win_W),\n int(0.83 * win_H),\n int(0.274 * win_W),\n int(0.16 * win_H),\n)\n(reset_crop_b_x, reset_crop_b_y, reset_crop_b_w, reset_crop_b_h) = (\n int(0.016 * win_W),\n int(0.586 * win_H),\n int(0.06 * win_W),\n int(0.06 * win_H),\n)\n\n\n# crop trackbars\n## frame_x\n(trb_X_x, trb_X_y, trb_X_w) = (\n int(0.078 * win_W),\n int(0.586 * win_H),\n int(0.195 * win_W),\n)\n## frame y\n(trb_Y_x, trb_Y_y, trb_Y_w) = (\n int(0.275 * win_W),\n int(0.586 * win_H),\n int(0.195 * win_W),\n)\n## frame width\n(trb_W_x, trb_W_y, trb_W_w) = (\n int(0.472 * win_W),\n int(0.586 * win_H),\n int(0.195 * win_W),\n)\n## frame height\n(trb_H_x, trb_H_y, trb_H_w) = (\n int(0.669 * win_W),\n int(0.586 * win_H),\n int(0.195 * win_W),\n)\n\n# recognizers setup region\n(recog_set_x, recog_set_y, recog_set_w, recog_set_h) = (\n int(0.3 * win_W),\n int(0.66 * win_H),\n int(0.33 * win_W),\n int(0.33 * win_H),\n)\n\n# recognizers setup toggle button - yolo/contour\n(recog_set_btn_x, recog_set_btn_y, recog_set_btn_w, recog_set_btn_h) = (\n int(0.305 * win_W),\n int(0.665 * win_H),\n int(0.32 * win_W),\n int(0.03 * win_H),\n)\n\n#resognizer contour normal/binary toggle button\n(cnt_state_btn_x, cnt_state_btn_y, cnt_state_btn_w, cnt_state_btn_h) = (\n int(0.88 * win_W),\n int(0.586 * win_H),\n int(0.1 * win_W),\n int(0.03 * win_H),\n)\n# recognizers trackbars\n## threshold min\n(trecog_thr_min_x, trecog_thr_min_y, trecog_thr_min_w) = (\n int(0.42 * win_W),\n int(0.7 * win_H),\n int(0.195 * win_W),\n)\n## threshold max\n(trecog_thr_max_x, trecog_thr_max_y, trecog_thr_max_w) = (\n int(0.42 * win_W),\n int(0.75 * win_H),\n int(0.195 * win_W),\n)\n## alpha\n(trecog_alpha_x, trecog_alpha_y, trecog_alpha_w) = (\n int(0.42 * win_W),\n int(0.8 * win_H),\n int(0.195 * win_W),\n)\n## beta\n(trecog_beta_x, trecog_beta_y, trecog_beta_w) = (\n int(0.42 * win_W),\n int(0.85 * win_H),\n int(0.195 * win_W),\n)\n## contour area min\n(trecog_area_min_x, trecog_area_min_y, trecog_area_min_w) = (\n int(0.42 * win_W),\n int(0.9 * win_H),\n int(0.195 * win_W),\n)\n## contour area max\n(trecog_area_max_x, trecog_area_max_y, trecog_area_max_w) = (\n int(0.42 * win_W),\n int(0.95 * win_H),\n int(0.195 * win_W),\n)\n\n\n# recognition result region\n(recog_res_x, recog_res_y, recog_res_w, recog_res_h) = (\n int(0.64 * win_W),\n int(0.66 * win_H),\n int(0.35 * win_W),\n int(0.33 * win_H),\n)\n\n# other\n(max_width, max_height) = (int(0.48 * win_W), int(0.54 * win_H))\n\ntr_vX = [0]\ntr_vY = [0]\ntr_vW = [cap_fr1_w]\ntr_vH = [cap_fr1_h]\n\n#\n# Helpers\n#\ndef setup():\n tr_vX[0] = 0\n tr_vY[0] = 0\n tr_vW[0] = cap_fr1_w\n tr_vH[0] = cap_fr1_h\n\ndef saveConfig():\n with open(settings_file, 'w') as configfile: # save\n config_object.write(configfile)\n\ndef scaleImageToMax(image, max_w, max_h):\n w = image.shape[1]\n h = image.shape[0]\n\n k_w = max_w / w\n k_max_h = h * k_w\n\n if k_max_h > max_h:\n k_h = max_h / h\n return cv2.resize(image, (int(k_h * w), int(k_h * h)))\n else:\n return cv2.resize(image, (int(k_w * w), int(k_w * h)))\n\ndef gammaCorrection(image, gamma):\n lookUpTable = np.empty((1, 256), np.uint8)\n for i in range(256):\n lookUpTable[0, i] = np.clip(pow(i / 255.0, gamma) * 255.0, 0, 255)\n\n res = cv2.LUT(image, lookUpTable)\n return res\n\n\ndef toggleRecognizers():\n if recog_state[0] == recog_states.get(1, \"\"):\n recog_state[0] = recog_states.get(0, \"\")\n ## some controls for YOLO\n else:\n recog_state[0] = recog_states.get(1, \"\")\n\n\n# YOLO\ndef detectObjectsFrom(img, frame):\n yo.detectFrom(img)\n objects = yo.objects\n # print(\"[INFO] YOLO objects: {:}\".format(len(objects)))\n\n # Visualize detected on source image\n font = cv2.FONT_HERSHEY_PLAIN\n for obj in objects:\n label = obj.name + \" {:}\".format(objects.index(obj))\n textColor = (0, 0, 0)\n boxColor = (150, 180, 20)\n cv2.rectangle(\n img,\n (obj.x, obj.y),\n (obj.x + obj.width, obj.y + obj.height),\n boxColor,\n 1,\n )\n cv2.putText(img, label, (obj.x, obj.y - 5), font, 1, textColor, 2)\n\n obj_index = 0\n for obj in objects:\n label = (\n obj.name\n + \" {:}: \".format(objects.index(obj))\n + \"{:.6f} \".format(obj.confidence)\n + \"; x: \"\n + str((obj.x + obj.width) / 2)\n + \", y: \"\n + str((obj.y + obj.height) / 2)\n )\n cvui.text(frame, recog_res_x + 20, recog_res_y + obj_index * 20 + 20, label)\n obj_index += 1\n\n\n# Contours\ndef correctedImage(img, alpha, beta, gamma):\n corrected_img = gammaCorrection(img, gamma)\n converted_img = cv2.convertScaleAbs(corrected_img, alpha=alpha, beta=beta)\n\n gray = cv2.cvtColor(converted_img, cv2.COLOR_BGR2GRAY)\n _, threshold_img = cv2.threshold(\n gray, threshold_min[0], threshold_max[0], cv2.THRESH_BINARY\n )\n threshold_img = cv2.medianBlur(threshold_img, 5)\n threshold_img = cv2.medianBlur(threshold_img, 5)\n return threshold_img\n\ndef detectContoursFrom(img, frame, alpha, beta, gamma, area_min, area_max):\n threshold_img = correctedImage(img, alpha, beta, gamma)\n\n # Detect\n contours, _ = cv2.findContours(\n threshold_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE\n )\n cntrs_cnt = 0\n good_contours = []\n for cnt in contours:\n area = cv2.contourArea(cnt)\n print(\"area: {:}\".format(area))\n # Distinguish small and big\n\n if area_min < area < area_max:\n good_contours.append(cnt)\n cntrs_cnt += 1\n\n print(\"detect contours: {:}\".format(cntrs_cnt))\n\n obj_index = 0\n\n for cnt in good_contours:\n # draw minimum area box rotated\n rect = cv2.minAreaRect(cnt)\n box = cv2.boxPoints(rect) ##\n box = np.int0(box)\n cv2.drawContours(img, [box], 0, (0, 0, 255), 2)\n ((rcx, rcy), (rw, rh), angle) = rect\n\n if rw < rh:\n angle = angle - 90\n\n print(\"bos: {:.4f}\".format(angle))\n # draw bounding contour box\n (x, y, w, h) = cv2.boundingRect(cnt)\n area = cv2.contourArea(cnt)\n # cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)\n cv2.putText(\n img, \"{:}, {}\".format(obj_index, str(area)), (x, y), 1, 1, (0, 255, 0)\n )\n\n # setup and draw result label\n label = (\n \"{:}: \".format(obj_index)\n + \"; x: \"\n + str((x + w) / 2)\n + \", y: \"\n + str((y + h) / 2)\n + \", area: {:}\".format(cv2.contourArea(cnt))\n + \", deg: {:.1f}\".format(angle)\n )\n cvui.text(frame, recog_res_x + 10, recog_res_y + obj_index * 20 + 20, label)\n obj_index += 1\n\n\n#\n# Button actions\n#\n\n#### Stream\ndef startStream():\n cap.release()\n if not cap.isOpened():\n state[0] = states.get(1, \"None\")\n cap.open(videoPath) # (\"rtsp://admin:@192.168.1.234:554\")#\n cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)\n\n\ndef stopStream():\n state[0] = states.get(0, \"None\")\n recognize[0] = False\n cap.release()\n\n\ndef resetCrop():\n setup()\n\n\n#### Recognition\ndef startRecognize():\n state[0] = states.get(2, \"None\")\n recog_btn_state[0] = recog_btn_states.get(1, \"None\")\n recognize[0] = True\n\ndef stopRecognize():\n state[0] = states.get(1, \"None\")\n recog_btn_state[0] = recog_btn_states.get(0, \"None\")\n recognize[0] = False\n\ndef contourStateToggle():\n if contour_state[0] == contour_states.get(0,\"\"):\n contour_state[0] = contour_states.get(1,\"\")\n else:\n contour_state[0] = contour_states.get(0,\"\")\n setup()\n\n#### TODO\ndef stopAll():\n stopStream()\n\n\ndef cropTrackBars(frame, cv_sh_w, cv_sh_h):\n # reset crop button\n if cvui.button(\n frame, reset_crop_b_x, reset_crop_b_y, reset_crop_b_w, reset_crop_b_h, \"Reset\"\n ):\n resetCrop()\n # x\n cvui.text(frame, trb_X_x + int(0.1 * win_W), trb_X_y - int(0.002 * win_H), \"X\", 0.5)\n if cvui.trackbar(frame, trb_X_x, trb_X_y, trb_X_w, tr_vX, 0.0, cv_sh_w):\n cr_x = cv_sh_w - int(tr_vX[0])\n if cr_x < tr_vW[0]:\n tr_vW[0] = cr_x\n # y\n cvui.text(frame, trb_Y_x + int(0.1 * win_W), trb_Y_y - int(0.002 * win_H), \"Y\", 0.5)\n if cvui.trackbar(frame, trb_Y_x, trb_Y_y, trb_Y_w, tr_vY, 0.0, cv_sh_h):\n cr_y = cv_sh_h - int(tr_vY[0])\n if cr_y < tr_vH[0]:\n tr_vH[0] = cr_y\n # w\n cvui.text(\n frame, trb_W_x + int(0.08 * win_W), trb_W_y - int(0.002 * win_H), \"Width\", 0.5\n )\n cvui.trackbar(\n frame, trb_W_x, trb_W_y, trb_W_w, tr_vW, tr_vX[0], cv_sh_w - int(tr_vX[0])\n )\n # h\n cvui.text(\n frame, trb_H_x + int(0.08 * win_W), trb_H_y - int(0.002 * win_H), \"Height\", 0.5\n )\n cvui.trackbar(\n frame, trb_H_x, trb_H_y, trb_H_w, tr_vH, tr_vY[0], cv_sh_h - int(tr_vY[0])\n )\n\n\ndef contourTrackBars(frame):\n # threshold\n cvui.text(\n frame,\n trecog_thr_min_x - int(0.1 * win_W),\n trecog_thr_min_y + int(0.015 * win_H),\n \"Threshold min\",\n )\n cvui.trackbar(\n frame,\n trecog_thr_min_x,\n trecog_thr_min_y,\n trecog_thr_min_w,\n threshold_min,\n 1,\n threshold_max[0] - 1,\n )\n\n cvui.text(\n frame,\n trecog_thr_max_x - int(0.1 * win_W),\n trecog_thr_max_y + int(0.01 * win_H),\n \"Threshold max\",\n )\n cvui.trackbar(\n frame,\n trecog_thr_max_x,\n trecog_thr_max_y,\n trecog_thr_max_w,\n threshold_max,\n threshold_min[0],\n 255,\n )\n\n # image correction\n cvui.text(\n frame,\n trecog_alpha_x - int(0.1 * win_W),\n trecog_alpha_y + int(0.015 * win_H),\n \"Correct Alpha\",\n )\n cvui.trackbar(\n frame, trecog_alpha_x, trecog_alpha_y, trecog_alpha_w, img_alpha, 1.0, 3.0, 0.1\n )\n\n cvui.text(\n frame,\n trecog_beta_x - int(0.1 * win_W),\n trecog_beta_y + int(0.015 * win_H),\n \"Correct Beta\",\n )\n cvui.trackbar(frame, trecog_beta_x, trecog_beta_y, trecog_beta_w, img_beta, 1, 100)\n\n # area\n cvui.text(\n frame,\n trecog_area_min_x - int(0.1 * win_W),\n trecog_area_min_y + int(0.015 * win_H),\n \"Area min\",\n )\n cvui.trackbar(\n frame,\n trecog_area_min_x,\n trecog_area_min_y,\n trecog_area_min_w,\n contour_area_min,\n 1.0,\n contour_area_max[0] - 1,\n )\n\n cvui.text(\n frame,\n trecog_area_max_x - int(0.1 * win_W),\n trecog_area_max_y + int(0.015 * win_H),\n \"Area max\",\n )\n cvui.trackbar(\n frame,\n trecog_area_max_x,\n trecog_area_max_y,\n trecog_area_max_w,\n contour_area_max,\n contour_area_min[0],\n 20000,\n )\n\n\ndef main():\n frame = np.zeros((win_H, win_W, 3), np.uint8)\n cvui.init(WINDOW_NAME)\n fr = 0\n while True:\n frame[:] = (0, 0, 0)\n\n objects = []\n\n if cap.isOpened():\n # main stream image\n ret, cv_frame_orig = cap.read()\n\n if ret:\n # status message blink\n fr += 1\n if fr % blink_every_n_frames == 0:\n isBlink[0] = not isBlink[0]\n if isBlink[0]:\n cvui.text(frame, stat_l_x, stat_l_y, state[0])\n\n # original stream image\n cv_frame = scaleImageToMax(cv_frame_orig, max_width, max_height)\n cv_sh_h, cv_sh_w, _ = cv_frame.shape\n cv_sh_xx = cap_fr1_x + int((max_width - cv_sh_w) / 2)\n cv_sh_yy = cap_fr1_y + int((max_height - cv_sh_h) / 2)\n cvui.image(frame, cv_sh_xx, cv_sh_yy, cv_frame)\n cv_frame_h, cv_frame_w, _ = cv_frame.shape\n\n # cropped stream image\n crop_image = cv_frame[\n int(tr_vY[0]) : (int(tr_vH[0]) + int(tr_vY[0])),\n int(tr_vX[0]) : (int(tr_vW[0]) + int(tr_vX[0])),\n ]\n crop__scaled_image = scaleImageToMax(crop_image, max_width, max_height)\n cr_sh_h, cr_sh_w, _ = crop__scaled_image.shape\n cr_sh_xx = cap_fr2_x + int((max_width - cr_sh_w) / 2)\n cr_sh_yy = cap_fr2_y + int((max_height - cr_sh_h) / 2)\n\n # crop track bars\n cropTrackBars(frame, cv_sh_w, cv_sh_h)\n\n # zooming rect\n cvui.rect(\n frame,\n cv_sh_xx + int(tr_vX[0]),\n cv_sh_yy + int(tr_vY[0]),\n min(cv_sh_w, int(tr_vW[0])),\n min(cv_sh_h, int(tr_vH[0])),\n 0x00FF00,\n )\n\n ## Setup recognition UI\n # present toggle to switch 'YOLO' and 'filters'. Toggle will drop recognition - need manual start\n # 'filter' will have adjustable trackbars for threshold (2), alpha, beta, area_min, area_max = 6 ,\n if cvui.button(\n frame,\n recog_set_btn_x,\n recog_set_btn_y,\n recog_set_btn_w,\n recog_set_btn_h,\n recog_state[0],\n ):\n stopRecognize()\n toggleRecognizers()\n\n # recognition of objects and data presenting\n if recognize[0] == True:\n start = time.time()\n if recog_state[0] == recog_states.get(0, \"\"):\n detectContoursFrom(\n crop__scaled_image,\n frame,\n img_alpha[0],\n img_beta[0],\n img_gamma[0],\n contour_area_min[0],\n contour_area_max[0],\n )\n contourTrackBars(frame)\n\n else:\n detectObjectsFrom(crop__scaled_image, frame)\n end = time.time()\n cvui.text(frame, fps_l_x, fps_l_y, \"recognition time: {:5f} sec per frame\".format(end - start))\n\n\n # draw result\n if contour_state[0] == contour_states.get(0,\"\"):\n cvui.image(frame, cr_sh_xx, cr_sh_yy, crop__scaled_image)\n else:\n colored_grey = cv2.cvtColor(correctedImage(crop__scaled_image, img_alpha[0],\n img_beta[0],\n img_gamma[0]), cv2.COLOR_GRAY2BGR)\n cvui.image(frame, cr_sh_xx, cr_sh_yy, colored_grey)\n\n else:\n cap.release()\n else:\n cvui.text(frame, stat_l_x, stat_l_y, states.get(0, \"\"))\n\n ##\n ## Static UI\n ##\n\n # frame around image\n cvui.rect(frame, cap_fr1_x, cap_fr1_y, cap_fr1_w, cap_fr1_h, 0xFFFFFF)\n cvui.rect(frame, cap_fr2_x, cap_fr2_y, cap_fr2_w, cap_fr2_h, 0xFFFFFF)\n\n # start stream button\n if cvui.button(\n frame, start_b_x, start_b_y, start_b_w, start_b_h, \"Start stream\"\n ):\n startStream()\n\n # start recognition button\n if cvui.button(\n frame, recog_b_x, recog_b_y, recog_b_w, recog_b_h, recog_btn_state[0]\n ):\n if recognize[0] == True:\n stopRecognize()\n else:\n startRecognize()\n\n # stop all button\n if cvui.button(\n frame, stop_all_b_x, stop_all_b_y, stop_all_b_w, stop_all_b_h, \"STOP!\"\n ):\n stopAll()\n\n # recognizers setup region\n cvui.rect(frame, recog_set_x, recog_set_y, recog_set_w, recog_set_h, 0x008EDF)\n\n # contour toggle\n if recognize[0] == True:\n if recog_state[0] == recog_states.get(0,\"\"):\n if cvui.button(frame, cnt_state_btn_x, cnt_state_btn_y, cnt_state_btn_w, cnt_state_btn_h, contour_state[0]):\n contourStateToggle()\n\n # recognition result region\n cvui.rect(frame, recog_res_x, recog_res_y, recog_res_w, recog_res_h, 0xFFFF00)\n\n # update all ui\n cvui.update()\n\n # Show everything on the screen\n cv2.imshow(WINDOW_NAME, frame)\n\n # Check if ESC key was pressed\n if cv2.waitKey(20) == 27:\n stopStream()\n break\n\n\nif __name__ == \"__main__\":\n main()", "sub_path": "cvui/cvui_UI-bin_filter.py", "file_name": "cvui_UI-bin_filter.py", "file_ext": "py", "file_size_in_byte": 19799, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "yolo.Yolo", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 261, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 266, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 268, "usage_type": "call"}, {"api_name": "cv2.LUT", "line_number": 270, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_PLAIN", "line_number": 289, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 294, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 301, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 314, "usage_type": "call"}, {"api_name": "cv2.convertScaleAbs", "line_number": 321, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 323, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 323, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 324, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 325, "usage_type": "attribute"}, {"api_name": "cv2.medianBlur", "line_number": 327, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 328, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 335, "usage_type": "call"}, {"api_name": "cv2.RETR_LIST", "line_number": 336, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 336, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 341, "usage_type": "call"}, {"api_name": "cv2.minAreaRect", "line_number": 355, "usage_type": "call"}, {"api_name": "cv2.boxPoints", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.int0", "line_number": 357, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 358, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 366, "usage_type": "call"}, {"api_name": "cv2.contourArea", "line_number": 367, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 369, "usage_type": "call"}, {"api_name": "cv2.contourArea", "line_number": 380, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 383, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_BUFFERSIZE", "line_number": 397, "usage_type": "attribute"}, {"api_name": "cvui.button", "line_number": 435, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 440, "usage_type": "call"}, {"api_name": "cvui.trackbar", "line_number": 441, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 446, "usage_type": "call"}, {"api_name": "cvui.trackbar", "line_number": 447, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 452, "usage_type": "call"}, {"api_name": "cvui.trackbar", "line_number": 455, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 459, "usage_type": "call"}, {"api_name": "cvui.trackbar", "line_number": 462, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 469, "usage_type": "call"}, {"api_name": "cvui.trackbar", "line_number": 475, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 485, "usage_type": "call"}, {"api_name": "cvui.trackbar", "line_number": 491, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 502, "usage_type": "call"}, {"api_name": "cvui.trackbar", "line_number": 508, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 512, "usage_type": "call"}, {"api_name": "cvui.trackbar", "line_number": 518, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 521, "usage_type": "call"}, {"api_name": "cvui.trackbar", "line_number": 527, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 537, "usage_type": "call"}, {"api_name": "cvui.trackbar", "line_number": 543, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 555, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 555, "usage_type": "attribute"}, {"api_name": "cvui.init", "line_number": 556, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 573, "usage_type": "call"}, {"api_name": "cvui.image", "line_number": 580, "usage_type": "call"}, {"api_name": "cvui.rect", "line_number": 597, "usage_type": "call"}, {"api_name": "cvui.button", "line_number": 609, "usage_type": "call"}, {"api_name": "time.time", "line_number": 622, "usage_type": "call"}, {"api_name": "time.time", "line_number": 637, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 638, "usage_type": "call"}, {"api_name": "cvui.image", "line_number": 643, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 645, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 647, "usage_type": "attribute"}, {"api_name": "cvui.image", "line_number": 648, "usage_type": "call"}, {"api_name": "cvui.text", "line_number": 653, "usage_type": "call"}, {"api_name": "cvui.rect", "line_number": 660, "usage_type": "call"}, {"api_name": "cvui.rect", "line_number": 661, "usage_type": "call"}, {"api_name": "cvui.button", "line_number": 664, "usage_type": "call"}, {"api_name": "cvui.button", "line_number": 670, "usage_type": "call"}, {"api_name": "cvui.button", "line_number": 679, "usage_type": "call"}, {"api_name": "cvui.rect", "line_number": 685, "usage_type": "call"}, {"api_name": "cvui.button", "line_number": 690, "usage_type": "call"}, {"api_name": "cvui.rect", "line_number": 694, "usage_type": "call"}, {"api_name": "cvui.update", "line_number": 697, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 700, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 703, "usage_type": "call"}]} +{"seq_id": "98570078", "text": "# -*- coding: utf-8 -*-\n\n# 自选股分析监控\n# 对自选股进行分组：上升通道，下降通道，低价股，绩优股，筹码集中\n# 对不同组的股票按需进行监控 均线，极值点，\n\nimport pandas as pd\nimport numpy as np\nimport pandas as pd\nfrom docx.shared import Cm\n\nfrom analysis_util.output_document import Doc\nfrom analysis_util.plot_k_line import save_k_line\nfrom selected_stock_analysis.stock_classification import get_up_trend_stocks\n\n\ndef output_doc(df, file_path):\n # doc文档\n doc = Doc()\n for line in df.values:\n print(line)\n doc.add_heading('，'.join(line))\n symbol = line[0]\n\n # 画出其最近100天，300天，1000天日线图\n save_k_line(symbol, 100, './resources/kline100.png')\n save_k_line(symbol, 300, './resources/kline300.png')\n save_k_line(symbol, 1000, './resources/kline1000.png')\n doc.add_picture('./resources/kline100.png', width=Cm(10))\n doc.add_picture('./resources/kline300.png', width=Cm(10))\n doc.add_picture('./resources/kline1000.png', width=Cm(10))\n\n # 保存文档\n doc.save(file_path)\n\n\nif __name__ == '__main__':\n # 获取股票池\n # file = 'stock_pool2023.txt'\n file = '自选股.csv'\n\n path = './classification/'\n\n # 先进行自选股分组\n # 上升通道股票\n get_up_trend_stocks(file)\n\n # 超短线上升通道\n df = pd.read_csv(path + '超短线上升通道%s.csv' % (file.split('.')[0]), dtype={'symbol': np.str})\n # 输出到文档\n output_doc(df, path + '超短线上升通道%s.docx' % (file.split('.')[0]))\n\n # 短线上升通道\n df = pd.read_csv(path + '短线上升通道%s.csv' % (file.split('.')[0]), dtype={'symbol': np.str})\n # 输出到文档\n output_doc(df, path + '短线上升通道%s.docx' % (file.split('.')[0]))\n\n # 中线上升通道\n df = pd.read_csv(path + '中线上升通道%s.csv' % (file.split('.')[0]), dtype={'symbol': np.str})\n # 输出到文档\n output_doc(df, path + '中线上升通道%s.docx' % (file.split('.')[0]))\n\n # 中长线上升通道\n df = pd.read_csv(path + '中长线上升通道%s.csv' % (file.split('.')[0]), dtype={'symbol': np.str})\n # 输出到文档\n output_doc(df, path + '中长线上升通道%s.docx' % (file.split('.')[0]))\n\n # 长线上升通道\n df = pd.read_csv(path + '长线上升通道%s.csv' % (file.split('.')[0]), dtype={'symbol': np.str})\n # 输出到文档\n output_doc(df, path + '长线上升通道%s.docx' % (file.split('.')[0]))\n\n # 中期反弹\n df = pd.read_csv(path + '中期反弹%s.csv' % (file.split('.')[0]), dtype={'symbol': np.str})\n # 输出到文档\n output_doc(df, path + '中期反弹%s.docx' % (file.split('.')[0]))\n", "sub_path": "selected_stock_analysis/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2756, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "analysis_util.output_document.Doc", "line_number": 19, "usage_type": "call"}, {"api_name": "analysis_util.plot_k_line.save_k_line", "line_number": 26, "usage_type": "call"}, {"api_name": "analysis_util.plot_k_line.save_k_line", "line_number": 27, "usage_type": "call"}, {"api_name": "analysis_util.plot_k_line.save_k_line", "line_number": 28, "usage_type": "call"}, {"api_name": "docx.shared.Cm", "line_number": 29, "usage_type": "call"}, {"api_name": "docx.shared.Cm", "line_number": 30, "usage_type": "call"}, {"api_name": "docx.shared.Cm", "line_number": 31, "usage_type": "call"}, {"api_name": "selected_stock_analysis.stock_classification.get_up_trend_stocks", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.str", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.str", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.str", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.str", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.str", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.str", "line_number": 74, "usage_type": "attribute"}]} +{"seq_id": "240530182", "text": "#!/usr/bin/env python\n\nimport rospy\nfrom std_msgs.msg import Header\nfrom geometry_msgs.msg import PolygonStamped, Point32, PointStamped\nimport tf \n\ndef main():\n '''uthai_control Publisher'''\n tf_listen = tf.TransformListener()\n pub_ssp = rospy.Publisher('uthai/spp', PolygonStamped, queue_size=3)\n pub_com = rospy.Publisher('uthai/com', PointStamped, queue_size=3)\n rospy.init_node('uthai_control')\n rate = rospy.Rate(10) # 10hz\n\n spp_msg = PolygonStamped()\n # spp_msg.header = Header()\n spp_msg.header.frame_id = \"r_foot_ft_link\"\n com_msg = PointStamped()\n # com_msg.header = Header()\n com_msg.header.frame_id = \"base_footprint\"\n flag = 1\n while not rospy.is_shutdown():\n \n spp_msg.header.stamp = rospy.Time.now()\n com_msg.header.stamp = rospy.Time.now()\n spp_msg.polygon.points.append(Point32(0.07, 0.07, 0))\n spp_msg.polygon.points.append(Point32(0.07, -0.07, 0))\n spp_msg.polygon.points.append(Point32(-0.07, -0.07, 0))\n spp_msg.polygon.points.append(Point32(-0.07, 0.07, 0))\n \n if flag:\n com_msg.point.x += 0.005\n else:\n com_msg.point.x -= 0.005\n if com_msg.point.x > 0.1:\n flag = 0\n elif com_msg.point.x < -0.1:\n flag = 1\n \n # rospy.loginfo(spp_msg)\n # (trans,rot) = tf_listen.lookupTransformFull('/r_foot_ft_link','/base_footprint')\n (trans,rot) = tf_listen.lookupTransform('/r_foot_ft_link','/l_foot_ft_link', rospy.Time(0))\n # rospy.loginfo(trans)\n rospy.loginfo(rot)\n # rospy.loginfo(com_msg)\n pub_ssp.publish(spp_msg)\n pub_com.publish(com_msg)\n rate.sleep()\n\n\nif __name__ == '__main__':\n try:\n main()\n except rospy.ROSInterruptException:\n pass\n", "sub_path": "uthai_control/src/uthai_control_pub.py", "file_name": "uthai_control_pub.py", "file_ext": "py", "file_size_in_byte": 1816, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "tf.TransformListener", "line_number": 10, "usage_type": "call"}, {"api_name": "rospy.Publisher", "line_number": 11, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.PolygonStamped", "line_number": 11, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 12, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.PointStamped", "line_number": 12, "usage_type": "argument"}, {"api_name": "rospy.init_node", "line_number": 13, "usage_type": "call"}, {"api_name": "rospy.Rate", "line_number": 14, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.PolygonStamped", "line_number": 16, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.PointStamped", "line_number": 19, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 23, "usage_type": "call"}, {"api_name": "rospy.Time.now", "line_number": 25, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 26, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 26, "usage_type": "attribute"}, {"api_name": "geometry_msgs.msg.Point32", "line_number": 27, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Point32", "line_number": 28, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Point32", "line_number": 29, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Point32", "line_number": 30, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 43, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 45, "usage_type": "call"}, {"api_name": "rospy.ROSInterruptException", "line_number": 55, "usage_type": "attribute"}]} +{"seq_id": "129367856", "text": "\"\"\" Player module\nThis is a template/example class for your player.\nThis is the only file you should modify.\nThe logic of your hockey robot will be implemented in this class.\nPlease implement the interface next_move().\nThe only restrictions here are:\n - to implement a class constructor with the args: paddle_pos, goal_side\n - set self.my_display_name with your team's name, max. 15 characters\n - to implement the function next_move(self, current_state),\n returnin the next position of your paddle\n\"\"\"\n\nimport copy\nimport utils\nimport random as r\n\nclass Player:\n def __init__(self, paddle_pos, goal_side):\n\n # set your team's name, max. 15 chars\n self.my_display_name = \"Random\"\n\n # these belong to my solution,\n # you may erase or change them in yours\n self.future_size = 30\n self.my_goal = goal_side\n self.my_goal_center = {}\n self.opponent_goal_center = {}\n self.my_paddle_pos = paddle_pos\n self.up= False;\n\n\n def next_move(self, current_state):\n \"\"\" Function that computes the next move of your paddle\n Implement your algorithm here. This will be the only function\n used by the GameCore. Be aware of abiding all the game rules.\n Returns:\n dict: coordinates of next position of your paddle.\n \"\"\"\n\n # update my paddle pos\n # I need to do this because GameCore moves my paddle randomly\n self.my_paddle_pos = current_state['paddle1_pos'] if self.my_goal == 'left' \\\n else current_state['paddle2_pos']\n\n \n \n # computing both goal centers\n self.my_goal_top = {'x': 100 if self.my_goal == 'left' else 800,\n 'y': (current_state['board_shape'][0]/2)+((current_state['board_shape'][0]*0.45)/2)}\n self.my_goal_down = {'x': 100 if self.my_goal == 'left' else 800,\n 'y': (current_state['board_shape'][0]/2)-((current_state['board_shape'][0]*0.45)/2)}\n \n\n\n # estimate an aiming position\n if(self.up):\n target_pos = self.my_goal_down\n self.up= False\n else: \n target_pos = self.my_goal_top\n self.up= True\n # move to target position, taking into account the max. paddle speed\n \n if target_pos != self.my_paddle_pos:\n \n direction_vector = {'x': target_pos['x'] - self.my_paddle_pos['x'],\n 'y': target_pos['y'] - self.my_paddle_pos['y']}\n \n direction_vector = {k: v / utils.vector_l2norm(direction_vector)\n for k, v in direction_vector.items()}\n \n movement_dist = min(current_state['paddle_max_speed'] * current_state['delta_t'],\n utils.distance_between_points(target_pos, self.my_paddle_pos))\n direction_vector = {k: v * movement_dist\n for k, v in direction_vector.items()}\n \n new_paddle_pos = {'x': self.my_paddle_pos['x'] + direction_vector['x'],\n 'y': self.my_paddle_pos['y'] + direction_vector['y']}\n \n \n # check if computed new position in inside board limits\n if utils.is_inside_goal_area_paddle(new_paddle_pos, current_state) is False and \\\n utils.is_out_of_boundaries_paddle(new_paddle_pos, current_state) is None:\n self.my_paddle_pos = new_paddle_pos \n return self.my_paddle_pos\n\n\n", "sub_path": "ai-airhockey/Random.py", "file_name": "Random.py", "file_ext": "py", "file_size_in_byte": 3708, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "utils.vector_l2norm", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.distance_between_points", "line_number": 74, "usage_type": "call"}, {"api_name": "utils.is_inside_goal_area_paddle", "line_number": 83, "usage_type": "call"}, {"api_name": "utils.is_out_of_boundaries_paddle", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "371117033", "text": "\"\"\"This produces a GUI that allows users to switch between segmentation\n algorithms and alter the parameters manually using a slider. It shows two images,\n one with the original image with the resulting mask and one with the original image\n with the negative of the resulting mask.\"\"\"\nimport matplotlib.pylab as plt\nimport ipywidgets as widgets\nfrom IPython.display import display, clear_output\nfrom pathlib import Path\nfrom see import Segmentors\nimport imageio\n\n\ndef showtwo(img, img2):\n \"\"\"Show two images side by side.\"\"\"\n fig = plt.figure(figsize=(20, 20))\n my_ax = fig.add_subplot(1, 2, 1)\n my_ax.imshow(img)\n my_ax = fig.add_subplot(1, 2, 2)\n my_ax.imshow(img2)\n return fig\n\ndef showthree(img, img1, img2):\n \"\"\"Show three images side by side.\"\"\"\n fig = plt.figure(figsize=(20, 20))\n my_ax = fig.add_subplot(1, 3, 1)\n my_ax.imshow(img)\n my_ax = fig.add_subplot(1, 3, 2)\n my_ax.imshow(img1)\n my_ax = fig.add_subplot(1, 3, 3)\n my_ax.imshow(img2)\n return fig\n\ndef show_segment(img, mask):\n \"\"\"Show both options for segmenting using the current mask.\n\n Keyword arguments:\n img -- original image\n mask -- resulting mask from segmentor\n\n \"\"\"\n im1 = img.copy()\n im2 = img.copy()\n im1[mask > 0, :] = 0\n im2[mask == 0, :] = 0\n fig = showtwo(im1, im2)\n return fig\n\n\ndef pickimage(folder='Image_data/Examples/'):\n #def pickimage(\n\n directory = Path(folder)\n\n allfiles = sorted(directory.glob('*'))\n\n filelist = []\n masklist = []\n for file in allfiles:\n if file.suffix ==\".jpg\" or file.suffix ==\".jpeg\" or file.suffix ==\".JPEG\" or file.suffix ==\".png\":\n if not \"_GT\" in file.name:\n filelist.append(file)\n mask = directory.glob(f\"{file.stem}_GT*\")\n for m in mask:\n masklist.append(m)\n \n w = widgets.Dropdown(\n options=filelist,\n value=filelist[0],\n description='Choose image:',\n )\n\n def update(w):\n clear_output(wait=True) # Clear output for dynamic display\n display(w)\n w.img = imageio.imread(w.value)\n index = filelist.index(w.value)\n w.mask = imageio.imread(masklist[index])\n if len(w.mask.shape) > 2:\n w.mask = w.mask[:,:,0]\n fig = showtwo(w.img, w.mask)\n print(f\"import imageio\")\n print(f\"data.img = imageio.imread(\\'{w.value}\\')\")\n print(f\"data.mask = imageio.imread(\\'{masklist[index]}\\')\")\n \n def on_change(change):\n if change['type'] == 'change' and change['name'] == 'value':\n\n update(w)\n\n w.observe(on_change)\n update(w)\n return w\n\n\ndef picksegment(algorithms):\n w = widgets.Dropdown(\n options=algorithms,\n value=algorithms[0],\n description='Choose Algorithm:',\n )\n\n def on_change(change):\n if change['type'] == 'change' and change['name'] == 'value':\n clear_output(wait=True) # Clear output for dynamic display\n display(w)\n print(Segmentors.algorithmspace[change['new']].__doc__)\n print(f\"\\nsegmentor_name=\\'{w.value}\\'\")\n w.observe(on_change)\n\n display(w)\n print(Segmentors.algorithmspace[w.value].__doc__)\n print(f\"\\nalg.value=\\'{w.value}\\'\")\n return w\n\ndef segmentwidget(img, gmask, params=None, alg=None):\n \"\"\"Generate GUI. Produce slider for each parameter for the current segmentor.\n Show both options for the masked image.\n\n Keyword arguments:\n img -- original image\n gmask -- ground truth segmentation mask for the image\n params -- list of parameter options\n alg -- algorithm to search parameters over\n\n \"\"\"\n if params:\n if alg:\n params[0] = alg;\n seg = Segmentors.algoFromParams(params)\n else:\n if alg:\n algorithm_gen = Segmentors.algorithmspace[alg]\n seg = algorithm_gen()\n else:\n seg = Segmentors.segmentor()\n\n widg = dict()\n widglist = []\n\n for ppp, ind in zip(seg.paramindexes, range(len(seg.paramindexes))):\n thislist = eval(seg.params.ranges[ppp])\n name = ppp\n current_value = seg.params[ppp]\n if not current_value in thislist:\n #TODO: We should find the min distance between current_value and this list and use that instead.\n current_value = thislist[0]\n \n thiswidg = widgets.SelectionSlider(options=tuple(thislist),\n disabled=False,\n description=name,\n value=current_value,\n continuous_update=False,\n orientation='horizontal',\n readout=True\n )\n\n widglist.append(thiswidg)\n widg[ppp] = thiswidg\n\n# algorithms = list(Segmentors.algorithmspace.keys())\n# w = widgets.Dropdown(\n# options=algorithms,\n# value=algorithms[0],\n# description='Choose Algorithm:',\n# )\n \n\n \n def func(img=img, mask=gmask, **kwargs):\n \"\"\"Find mask and fitness for current algorithm. Show masked image.\"\"\"\n print(seg.params[\"algorithm\"])\n for k in kwargs:\n seg.params[k] = kwargs[k]\n mask = seg.evaluate(img)\n fit = Segmentors.FitnessFunction(mask, gmask)\n fig = showtwo(img, mask)\n # I like the idea of printing the sharepython but it should be below the figures. \n #print(seg.sharepython(img))\n# plt.title('Fitness Value: ' + str(fit[0]))\n\n \n layout = widgets.Layout(grid_template_columns='1fr 1fr 1fr')\n u_i = widgets.GridBox(widglist, layout=layout)\n out = widgets.interactive_output(func, widg)\n display(u_i, out)\n \n return seg.params\n", "sub_path": "see/JupyterGUI.py", "file_name": "JupyterGUI.py", "file_ext": "py", "file_size_in_byte": 5889, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "matplotlib.pylab.figure", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pylab.figure", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 24, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 52, "usage_type": "call"}, {"api_name": "ipywidgets.Dropdown", "line_number": 66, "usage_type": "call"}, {"api_name": "IPython.display.clear_output", "line_number": 73, "usage_type": "call"}, {"api_name": "IPython.display.display", "line_number": 74, "usage_type": "call"}, {"api_name": "imageio.imread", "line_number": 75, "usage_type": "call"}, {"api_name": "imageio.imread", "line_number": 77, "usage_type": "call"}, {"api_name": "ipywidgets.Dropdown", "line_number": 96, "usage_type": "call"}, {"api_name": "IPython.display.clear_output", "line_number": 104, "usage_type": "call"}, {"api_name": "IPython.display.display", "line_number": 105, "usage_type": "call"}, {"api_name": "see.Segmentors.algorithmspace", "line_number": 106, "usage_type": "attribute"}, {"api_name": "see.Segmentors", "line_number": 106, "usage_type": "name"}, {"api_name": "IPython.display.display", "line_number": 110, "usage_type": "call"}, {"api_name": "see.Segmentors.algorithmspace", "line_number": 111, "usage_type": "attribute"}, {"api_name": "see.Segmentors", "line_number": 111, "usage_type": "name"}, {"api_name": "see.Segmentors.algoFromParams", "line_number": 129, "usage_type": "call"}, {"api_name": "see.Segmentors", "line_number": 129, "usage_type": "name"}, {"api_name": "see.Segmentors.algorithmspace", "line_number": 132, "usage_type": "attribute"}, {"api_name": "see.Segmentors", "line_number": 132, "usage_type": "name"}, {"api_name": "see.Segmentors.segmentor", "line_number": 135, "usage_type": "call"}, {"api_name": "see.Segmentors", "line_number": 135, "usage_type": "name"}, {"api_name": "ipywidgets.SelectionSlider", "line_number": 148, "usage_type": "call"}, {"api_name": "see.Segmentors.FitnessFunction", "line_number": 175, "usage_type": "call"}, {"api_name": "see.Segmentors", "line_number": 175, "usage_type": "name"}, {"api_name": "ipywidgets.Layout", "line_number": 182, "usage_type": "call"}, {"api_name": "ipywidgets.GridBox", "line_number": 183, "usage_type": "call"}, {"api_name": "ipywidgets.interactive_output", "line_number": 184, "usage_type": "call"}, {"api_name": "IPython.display.display", "line_number": 185, "usage_type": "call"}]} +{"seq_id": "417340240", "text": "import os\nfrom setuptools import find_packages, setup\n\nTHIS_DIR = os.path.dirname(os.path.realpath(__file__))\n\n__version__ = None\nexec(open(os.path.join(THIS_DIR, \"muffnn\", \"version.py\")).read())\n\nsetup(\n name='muffnn',\n version=__version__,\n author='Civis Analytics, Inc.',\n author_email='opensource@civisanalytics.com',\n packages=find_packages(),\n url='https://github.com/civisanalytics/muffnn',\n description=('Multilayer Feed-Forward Neural Network (MuFFNN) models with '\n 'TensorFlow and scikit-learn'),\n long_description=open(os.path.join(THIS_DIR, 'README.md')).read(),\n include_package_data=True,\n license=\"BSD-3\"\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 9, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}]} +{"seq_id": "315220577", "text": "# -*- coding: utf-8 -*-\n\nfrom bs4 import BeautifulSoup # sudo pip install beautifulsoup4\nfrom selenium import webdriver # sudo pip install selenium\nfrom selenium.webdriver.common.keys import Keys\nimport xlrd # sudo pip install xlrd\nimport xlwt # sudo pip install xlwt\nimport time\nimport os.path\nimport math\n\npathOfExcelTemplate = raw_input(\"Enter the file path of the excel sheet with the DNA Sequences:\\n\") # file path of\n# excel template that user made (contains all sequences)\ntype(pathOfExcelTemplate)\n\nnumberOfSequences = input(\"Enter number of sequences in excel sheet:\\n\") # asking for the number of sequences\n# that need to be read\ntype(numberOfSequences)\n\nnumberOfCycles = int(math.ceil(numberOfSequences/199.0)) # IDT can only read 200 sequences at a time.\n# Finding the number of loops that need to be performed to read the requested number of sequences\n# Converting input to integer and rounding integer up to account for remainder\n\nstyle0 = xlwt.easyxf('font: name Times New Roman, color-index black, bold on', num_format_str='#,##0.00')\n# setting style for excel sheet output\n\nROOT_DIR = os.path.dirname(os.path.abspath(__file__))\nCONFIG_PATH = os.path.join(ROOT_DIR, 'chromedriver') # creating a universal path for the chrome driver\n\ndriver = webdriver.Chrome(executable_path=CONFIG_PATH) # driver is assigned to the chrome driver\ndriver.get(\"https://www.idtdna.com/pages/products/custom-dna-rna/dna-oligos/custom-dna-oligos\") # open website link\ntime.sleep(3) # waiting for website to load, adjust based on internet speed...\n\nbuttonCookies = driver.find_element_by_xpath(\"//a[@class='cc-btn cc-dismiss']\").click() # close cookies dialog\n\ndriver.execute_script(\"window.scrollTo(0, 200)\") # scroll page down to get access to button\ntime.sleep(1) # wait for scroll to complete\nbuttonOrderNow = driver.find_element_by_xpath(\"//a[@class='btn btn_org']\").click() # press order now button\ntime.sleep(4) # wait for page to load\n\nsequencesNumberInTemplate = 1 # Sequence number that is being read from the excel template\n\nworkBookOutput = xlwt.Workbook() # creating workbook for output\nsheetWrite = workBookOutput.add_sheet('Sheet 1') # creating sheet in workbook, naming it 'Sheet 1'\n\n# making headers for the output excel file, using style0\nsheetWrite.write(0, 0, \"GC (%)\", style0)\nsheetWrite.write(0, 1, \"Tm\", style0)\nsheetWrite.write(0, 2, \"DeltaG (kcal/mole)\", style0)\n# end making headers\n\n# counters for variables being scraped\ncounterGC = 1 # glucose cytosine percent\ncounterTM = 1 # melting temperature\ncounterDG = 1 # delta G\n# end counter declarations\n\nfor j in range(0, numberOfCycles): # number of loops needed since IDT only takes 200 sequences at a time\n # finding and pressing blue bulk input button\n bulkInputButton = driver.find_element_by_xpath(\"//button[@class='btn btn-info btn-sm btn-block']\").click()\n time.sleep(2) # waiting for animation to end\n\n textArea = driver.find_element_by_xpath(\n \"//textarea[starts-with(@placeholder, '----------N')][@class='form-control']\")\n textArea.send_keys(Keys.TAB) # activating field by sending a tab\n textArea.clear() # clearing input field\n\n selectorItem = driver.find_element_by_id('delimiter') # defining selector\n for option in selectorItem.find_elements_by_tag_name('option'): # iterating through options in selector\n if option.text == 'Comma':\n option.click() # pressing selector that corresponds to 'Comma' when found\n break\n\n excelTemplateFile = xlrd.open_workbook(pathOfExcelTemplate) # accessing user inputted excel template of sequences\n sheet = excelTemplateFile.sheet_by_index(0) # accessing sheet 1 of excel file\n numberOfEntries = 0 # number of sequences inputted into text area\n maxLimit = 199*(j+1) # max sequence number that can be inputted in this iteration\n while sequencesNumberInTemplate < maxLimit:\n if sequencesNumberInTemplate <= numberOfSequences:\n cells = sheet.row_slice(rowx=sequencesNumberInTemplate, start_colx=1, end_colx=4)\n textArea.send_keys(sequencesNumberInTemplate, \",\")\n for cell in cells:\n textArea.send_keys(cell.value, \",\")\n textArea.send_keys(\"\\n\")\n sequencesNumberInTemplate = sequencesNumberInTemplate + 1\n numberOfEntries = numberOfEntries + 1\n else:\n break\n\n updateSequencesButton = driver.find_element_by_xpath(\"//button[@class='btn btn-primary']\").click()\n time.sleep(2)\n\n if numberOfEntries > 99: # confirm button only shows when greater than 99 entries inputted\n confirmContinueUpdateButton = driver.find_element_by_id('modal-question-component-primary-btn').click()\n\n print(\"loading... please wait approx 25 seconds...\")\n time.sleep(25)\n\n print(\"Sequences successfully imported into IDT! Looping again... wait for 'Done!' to close this program\")\n\n page = driver.page_source # getting page from web driver\n\n htmlOfThePage = BeautifulSoup(page, \"html.parser\") # html of the page\n\n # scraping GC and adding to excel sheet\n for node in htmlOfThePage.find_all(\"span\", {\"data-bind\": \"text: OligoCalcDetails.GC() + '%'\"}):\n for child in node.children:\n sheetWrite.write(counterGC, 0, child)\n counterGC = counterGC + 1\n\n # scraping melting temperature and adding to excel sheet\n for node in htmlOfThePage.find_all(\"span\", {\"data-bind\": \"text: OligoCalcDetails.TM() + 'ºC'\"}):\n for child in node.children:\n sheetWrite.write(counterTM, 1, child)\n counterTM = counterTM + 1\n\n # scraping DeltaG and adding to excel sheet\n for node in htmlOfThePage.find_all(\"span\", {\"data-bind\": \"text: OligoCalcDetails.DeltaG() + ' kcal/mole'\"}):\n for child in node.children:\n sheetWrite.write(counterDG, 2, child)\n counterDG = counterDG + 1\n\ndriver.close() # close website\nworkBookOutput.save('Scraper_IDT_GC_TM_DG.xls') # saving output into excel sheet\nprint('Done! saved as: Scraper_IDT_GC_TM_DG.xls')\n\n", "sub_path": "scraper.py", "file_name": "scraper.py", "file_ext": "py", "file_size_in_byte": 6051, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "math.ceil", "line_number": 20, "usage_type": "call"}, {"api_name": "xlwt.easyxf", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.path.dirname", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 27, "usage_type": "name"}, {"api_name": "os.path.path.abspath", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 28, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 30, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 30, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 37, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 43, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.TAB", "line_number": 65, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 65, "usage_type": "name"}, {"api_name": "xlrd.open_workbook", "line_number": 74, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 91, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 97, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "294810760", "text": "from celluloid import Camera\nfrom view import plot_board\nimport utils as u\nfrom heapq import heappush, heappop\n\n\ndef search(board: list, origin: tuple,\n target: tuple, camera: Camera = None) -> list:\n queue = [(0, [origin])]\n u.trapezoidal_dist.values = {origin: 0}\n processed = {origin}\n path = [None]\n\n while path[-1] != target:\n if not queue:\n path = None\n break\n\n path = heappop(queue)[1]\n curr = path[-1]\n if curr not in (origin, target):\n # marca como visitado\n board[curr[0]][curr[1]] = .4\n\n # Append moves\n for move in u.available_moves(board, path[-1]):\n if move not in processed:\n heappush(queue, (u.trapezoidal_dist(move, target),\n path + [move]))\n if move != target:\n # marca como tocado\n board[move[0]][move[1]] = .2\n processed.add(move)\n\n if camera is not None:\n plot_board(board)\n camera.snap()\n\n board[origin[0]][origin[1]] = u.str2n['#']\n return path\n", "sub_path": "best_first.py", "file_name": "best_first.py", "file_ext": "py", "file_size_in_byte": 1140, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "celluloid.Camera", "line_number": 8, "usage_type": "name"}, {"api_name": "utils.trapezoidal_dist", "line_number": 10, "usage_type": "attribute"}, {"api_name": "heapq.heappop", "line_number": 19, "usage_type": "call"}, {"api_name": "utils.available_moves", "line_number": 26, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 28, "usage_type": "call"}, {"api_name": "utils.trapezoidal_dist", "line_number": 28, "usage_type": "call"}, {"api_name": "view.plot_board", "line_number": 36, "usage_type": "call"}, {"api_name": "utils.str2n", "line_number": 39, "usage_type": "attribute"}]} +{"seq_id": "472075050", "text": "import pandas as pd\nimport pymysql.cursors\n\n\ndef df_builder(sql, cursor):\n cursor.execute(sql)\n result = cursor.fetchall()\n return pd.DataFrame.from_dict(result)\n\n\n# Connect to the database\nconnection = pymysql.connect(host='localhost',\n user='root',\n password='87iu0080A',\n db='lab',\n charset='utf8mb4',\n cursorclass=pymysql.cursors.DictCursor)\n\ntry:\n with connection.cursor() as cursor:\n\n df_user = df_builder(\"select * from user\", cursor)\n df_blog = df_builder(\"select * from blog\", cursor)\n df_post = df_builder(\"select * from post\", cursor)\n\n print(\" ------------------------ \")\n postByBlog = df_post[df_post.blog_id == 1]\n print(\" ------------------------ \")\n blogByUser = df_user[df_user.user_id == 1]\n print(\" ------------------------ \")\n result = pd.merge(postByBlog, blogByUser, how='left',\n on='blog_id', indicator=True)\n print(result[['user_id', 'blog_id', 'post_id', 'title', 'content']])\n\n\nfinally:\n connection.close()\n", "sub_path": "src/Pandas and Numpy and Scipy/Pandas_with_DB.py", "file_name": "Pandas_with_DB.py", "file_ext": "py", "file_size_in_byte": 1183, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pandas.DataFrame.from_dict", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pymysql.cursors.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 12, "usage_type": "name"}, {"api_name": "pymysql.cursors.cursors", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pymysql.cursors", "line_number": 17, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "28990444", "text": "# Python implementation using the multiprocessing module\n#\nfrom __future__ import division\nimport collections, resource\nimport os, sys, glob\nimport scipy\nfrom scipy.interpolate import RectBivariateSpline\nimport numpy as np\nfrom astropy import units as u\nfrom astropy import constants as const\nfrom astropy.cosmology import LambdaCDM\nimport pandas as pd\nimport h5py, pickle, pandas\nimport matplotlib as mpl\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\nsys.path.insert(0, '/cosma5/data/dp004/dc-beck3/lib/')\nimport read_hdf5\nsys.path.insert(0, '/cosma5/data/dp004/dc-beck3/StrongLensing/LensingAnalysis/lib/')\nimport lm_cfuncs as cf\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=RuntimeWarning, append=1)\nos.system(\"taskset -p 0xff %d\" % os.getpid())\nsys.settrace\n\n\ndef define_unit(simunit, hfname):\n exp = np.floor(np.log10(np.abs(simunit))).astype(int)\n if exp == 21: # simulation in [kpc]\n if hfname == 'Rockstar':\n unit = 'Mpc'\n elif hfname == 'Subfind':\n unit = 'kpc'\n elif exp == 23: #simulation in [Mpc]\n if hfname == 'Rockstar':\n unit = 'Mpc'\n elif hfname == 'Subfind':\n unit = 'kpc'\n else:\n raise Exception('Dont know this unit ->', unit)\n return unit\n\n\ndef source_selection(src_id, src_z, src_pos, halo_id):\n \"\"\"\n Find redshift of sources which are likely to be multiple imaged\n Input:\n src_id[np.array(int)] - LightCone-IDs of sources\n src_z[np.array(float)] - redshift of sources\n halo_id[int] - ID of subhalo acting as lens\n Output:\n zs[int] - redshift of source\n \"\"\"\n src_indx = np.where(src_id == halo_id)[0]\n dist = np.sqrt(src_pos[src_indx, 1]**2 + src_pos[src_indx, 2]**2)\n src_min = np.argsort(dist)\n #indx = np.argmin(dist)\n #indx = np.argmax(src_z[src_indx])\n start = 0\n end = 2\n if len(src_indx) > end:\n return src_z[src_indx[src_min[start:end]]], src_min[start:end], src_pos[src_indx[src_min[start:end]]] # indx\n else:\n return src_z[src_indx[src_min[:]]], src_min[:], src_pos[src_indx[src_min[:]]]\n\n\n\ndef sigma_crit(zLens, zSource, cosmo):\n Ds = cosmo.angular_diameter_distance(zSource)\n Dl = cosmo.angular_diameter_distance(zLens)\n Dls = cosmo.angular_diameter_distance_z1z2(zLens, zSource)\n sig_crit = (const.c**2/(4*np.pi*const.G))*Ds/(Dl*Dls)\n return sig_crit\n\n\ndef area(vs):\n \"\"\"\n Use Green's theorem to compute the area enclosed by the given contour.\n \"\"\"\n a = 0\n x0, y0 = vs[0]\n for [x1, y1] in vs[1:]:\n dy = y1 - y0\n a += x0*dy\n x0 = x1\n y0 = y1\n return a\n\n\ndef cal_lensing_signals(kap, bzz, ncc, coord):\n dsx_arc = bzz/ncc\n # deflection maps\n alpha1, alpha2 = cf.call_cal_alphas(kap, bzz, ncc)\n \n #TODO: map to finer grid\n alpha1_spline = RectBivariateSpline(coord, coord, alpha1)\n alpha2_spline = RectBivariateSpline(coord, coord, alpha2)\n lencoord = len(coord)\n centre_coord = np.linspace(coord[int(lencoord/3)],\n coord[int(2*lencoord/3)],\n 256)\n coord = np.concatenate((coord[:int(lencoord/3)],\n centre_coord,\n coord[int(2*lencoord/3+1):]))\n alpha1 = alpha1_spline(coord, coord)\n alpha2 = alpha2_spline(coord, coord)\n \n # shear maps\n al11 = 1 - np.gradient(alpha1, coord, axis=0)\n al12 = - np.gradient(alpha1, coord, axis=1)\n al21 = - np.gradient(alpha2, coord, axis=0)\n al22 = 1 - np.gradient(alpha2, coord, axis=1)\n \n detA = al11*al22 - al12*al21 # = (1-kappa0-shear0)*(1-kappa0+shear0)\n \n kappa0 = 1 - 0.5*(al11 + al22)\n shear1 = 0.5*(al11 - al22)\n shear2 = 0.5*(al21 + al12)\n shear0 = (shear1**2 + shear2**2)**0.5\n \n # magnification maps\n mu = 1/detA # = 1.0/((1.0-kappa0)**2.0-shear1*shear1-shear2*shear2)\n lambda_t = 1 - kappa0 - shear0 # tangential eigenvalue, page 115\n \n # lensing potential\n phi = cf.call_cal_phi(kap, bzz, ncc)\n\n return alpha1, alpha2, mu, phi, detA, lambda_t, coord\n\n\ndef einstein_radii(lp1, lp2, sp1, sp2 ,detA, lambda_t, cosmo, ax, method):\n \"\"\"\n Calculate Critical Curves, Caustics, and Einstein Radii\n Input:\n lp1,lp2[float] : lensing plane x,y-coordinates\n sp1,sp2[float] : source plane x,y-coordinates\n\n \"\"\"\n crit_curve = ax.contour(lp1, lp2, detA,\n levels=(0,), colors='r',\n linewidths=1.5, zorder=200)\n Ncrit = len(crit_curve.allsegs[0])\n crit_curve = crit_curve.allsegs[0]\n tan_crit_curve = ax.contour(lp1, lp2,\n lambda_t, levels=(0,), colors='r',\n linewidths=1.5, zorder=200)\n NumTCC = len(tan_crit_curve.allsegs[0])\n if NumTCC> 0:\n # Find tangential critical curve on which to base Rein\n len_tan_crit = np.zeros(NumTCC)\n for i in range(NumTCC):\n len_tan_crit[i] = len(tan_crit_curve.allsegs[0][i])\n tan_crit_curve = tan_crit_curve.allsegs[0][len_tan_crit.argmax()]\n \n # Einstein Radius\n if method == 'eqv':\n Rein = np.sqrt(np.abs(area(tan_crit_curve))/np.pi) #[arcsec]\n if method == 'med':\n dist = np.sqrt(tan_crit_curve[:, 0]**2 + tan_crit_curve[:, 1]**2)\n Rein = np.median(dist) #[arcsec]\n \n # Caustics\n for pp in range(len(tan_crit_curve)):\n c1, c2 = (cf.call_mapping_triangles([tan_crit_curve[pp, 0],\n tan_crit_curve[pp, 1]],\n sp1, sp2, lp1, lp2))\n if pp == 0:\n caustic1 = c1\n caustic2 = c2\n else:\n caustic1 = np.hstack((caustic1, c1))\n caustic2 = np.hstack((caustic2, c2))\n caustic = np.array([caustic1, caustic2]).T\n\n else:\n tan_crit_curve = np.array([])\n caustic = np.array([])\n Rein = 0\n return NumTCC, tan_crit_curve, caustic, Rein\n\n\ndef einstein_radii_proper(kappa, dsx_arc, cosmo):\n \"\"\"\n kappa:\n convergence map\n dsx_arc: [float]\n pixel size in arcsec\n \"\"\"\n #_radius = 0\n #while _kappa_enclosed < 1:\n return\n\ndef mpc2arc(SrcPosSky):\n # Source position [arcsec]\n x = SrcPosSky[0]*u.Mpc\n y = SrcPosSky[1]*u.Mpc\n z = SrcPosSky[2]*u.Mpc\n if (y == 0.) and (z == 0.):\n beta1 = 1e-3\n beta2 = 1e-3\n else:\n beta1 = ((y/x)*u.rad).to_value('arcsec')\n beta2 = ((z/x)*u.rad).to_value('arcsec')\n beta = [beta1, beta2]\n return beta\n\n\ndef timedelay_magnification(mu_map, phi_map, dsx_arc, Ncells, lp1, lp2,\n alpha1, alpha2, beta, zs, zl, cosmo):\n \"\"\"\n Input:\n mu_map: 2D magnification map\n phi_map: 2D potential map\n dsx_arc: cell size in arcsec\n Ncells: number of cells\n lp1, lp2: lens place grid coordinates\n alpha1, alpha2: 2D deflection map\n SrcPosSky: source position in Mpc\n zs: source redshift\n zl: lens redshift\n\n Output:\n len(mu): number of multiple images of supernova\n delta_t: Time it takes for photon to cover distance source-observer\n mu: luminosity magnification of source\n \"\"\"\n # Mapping light rays from image plane to source plan\n [sp1, sp2] = [lp1 - alpha1, lp2 - alpha2] #[arcsec]\n\n theta1, theta2 = cf.call_mapping_triangles([beta[0], beta[1]], \n lp1, lp2, sp1, sp2)\n # calculate magnifications of lensed Supernovae\n mu = cf.call_inverse_cic_single(mu_map, 0.0, 0.0, theta1, theta2, dsx_arc)\n # calculate time delays of lensed Supernovae in Days\n prts = cf.call_inverse_cic_single(phi_map, 0.0, 0.0, theta1, theta2, dsx_arc)\n Kc = ((1.0+zl)/const.c.to('Mpc/s') * \\\n (cosmo.angular_diameter_distance(zl) * \\\n cosmo.angular_diameter_distance(zs) / \\\n (cosmo.angular_diameter_distance(zs) - \\\n cosmo.angular_diameter_distance(zl)))).to('sday').value\n delta_t = Kc*(0.5*((theta1 - beta[0])**2.0 + \\\n (theta2 - beta[1])**2.0) - prts)/cf.apr**2\n theta = np.array([theta1, theta2]).T\n return len(mu), delta_t, mu, theta\n", "sub_path": "LensingAnalysis/lib/lenstools.py", "file_name": "lenstools.py", "file_ext": "py", "file_size_in_byte": 8332, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "matplotlib.use", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "warnings.filterwarnings", "line_number": 22, "usage_type": "call"}, {"api_name": "os.system", "line_number": 23, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.settrace", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 56, "usage_type": "call"}, {"api_name": "astropy.constants.c", "line_number": 72, "usage_type": "attribute"}, {"api_name": "astropy.constants", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 72, "usage_type": "attribute"}, {"api_name": "astropy.constants.G", "line_number": 72, "usage_type": "attribute"}, {"api_name": "lm_cfuncs.call_cal_alphas", "line_number": 93, "usage_type": "call"}, {"api_name": "scipy.interpolate.RectBivariateSpline", "line_number": 96, "usage_type": "call"}, {"api_name": "scipy.interpolate.RectBivariateSpline", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 112, "usage_type": "call"}, {"api_name": "lm_cfuncs.call_cal_phi", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 160, "usage_type": "call"}, {"api_name": "lm_cfuncs.call_mapping_triangles", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "astropy.units.Mpc", "line_number": 195, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 195, "usage_type": "name"}, {"api_name": "astropy.units.Mpc", "line_number": 196, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 196, "usage_type": "name"}, {"api_name": "astropy.units.Mpc", "line_number": 197, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 197, "usage_type": "name"}, {"api_name": "astropy.units.rad", "line_number": 202, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 202, "usage_type": "name"}, {"api_name": "astropy.units.rad", "line_number": 203, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 203, "usage_type": "name"}, {"api_name": "lm_cfuncs.call_mapping_triangles", "line_number": 230, "usage_type": "call"}, {"api_name": "lm_cfuncs.call_inverse_cic_single", "line_number": 233, "usage_type": "call"}, {"api_name": "lm_cfuncs.call_inverse_cic_single", "line_number": 235, "usage_type": "call"}, {"api_name": "astropy.constants.c.to", "line_number": 236, "usage_type": "call"}, {"api_name": "astropy.constants.c", "line_number": 236, "usage_type": "attribute"}, {"api_name": "astropy.constants", "line_number": 236, "usage_type": "name"}, {"api_name": "lm_cfuncs.apr", "line_number": 242, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 243, "usage_type": "call"}]} +{"seq_id": "225666634", "text": "# Library inmport\nimport time\nimport requests\nfrom selenium.webdriver import Chrome, ChromeOptions\nfrom selenium.webdriver.common.by import By\nfrom selenium import webdriver\nfrom selenium.webdriver.remote import file_detector\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport pandas as pd\nfrom bs4 import BeautifulSoup\n\n# deep Lに突っ込む\ndef translate(text):\n driver = webdriver.Chrome(ChromeDriverManager().install())\n\n driver.get(\"https://www.deepl.com/ja/translator\")\n time.sleep(3)\n\n driver.find_element_by_xpath(\"//*[@id='dl_translator']/div[5]/div[3]/div[1]/div[2]/div[1]/textarea\").send_keys(text)\n time.sleep(10)\n\n after = driver.find_element_by_xpath(\"//*[@id='dl_translator']/div[5]/div[3]/div[3]/div[3]/div[1]/textarea\").get_attribute('value')\n driver.quit()\n \n return after\n\n\n\n# main処理\ndef main():\n # 必要情報の入力\n search_keyword = input('Enter word:>>>')\n pages = input('Enter number how many pages do you want to get>>>')\n if pages.isdecimal()==False:\n print('Error...input number. execution is terminated.')\n exit()\n file_name = input('Enter file name>>>')\n\n # 初期設定\n col = ['title', 'link', 'publisher', 'abstract', 'abstract_ja']\n df = pd.DataFrame([], columns=col)\n page = 0\n\n # driverを起動、Google Chromeを使うこと\n driver = webdriver.Chrome(ChromeDriverManager().install())\n # Webサイトを開く\n driver.get(\"https://scholar.google.com/\")\n time.sleep(3)\n # 検索窓に入力\n driver.find_element_by_name(\"q\").send_keys(search_keyword)\n # 検索ボタンクリック\n driver.find_element_by_name(\"btnG\").click()\n time.sleep(3)\n \n # 繰り返し処理\n while page < int(pages):\n current_url = driver.current_url\n html = requests.get(current_url)\n soup = BeautifulSoup(html.text, \"lxml\")\n articles = soup.find_all(class_=\"gs_rt\")\n abst11 = soup.find_all(class_=\"gs_rs\")\n\n for article, abst in zip(articles, abst11):\n try:\n title = article.text\n link = article.find_all(\"a\")[0].get('href')\n publisher = link[8:link.find(\"/\", 8)]\n abst1 = abst.text.replace('\\n', ' ')\n # translate with deepL\n abst1_ja = translate(abst1)\n\n element = pd.Series([title, link, publisher, abst1, abst1_ja], index = col)\n df = df.append(element, ignore_index=True)\n except:\n pass\n \n # 次のページへ\n driver.find_element_by_xpath(\"//*[@id='gs_n']/center/table/tbody/tr/td[12]/a\").click()\n time.sleep(3)\n page += 1\n df.to_csv(file_name+'.csv')\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Article getter.py", "file_name": "Article getter.py", "file_ext": "py", "file_size_in_byte": 2777, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 14, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 14, "usage_type": "name"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 14, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 45, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 45, "usage_type": "name"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 58, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 72, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "92946478", "text": "from __future__ import print_function\nimport unittest\nfrom collections import OrderedDict\nimport numpy as np\nimport pytraj as pt\nfrom pytraj.utils import eq, aa_eq\n\nfrom pytraj.tools import flatten\nfrom pytraj import matrix\nfrom pytraj.compat import set\nfrom pytraj.parallel.base import _load_batch_pmap, worker_by_actlist\nfrom pytraj import c_commands\n\n\nclass TestNormalPmap(unittest.TestCase):\n\n def setUp(self):\n self.traj = pt.iterload(\"./data/Tc5b.x\", \"./data/Tc5b.top\")\n\n def test_raise(self):\n # if func is not support pmap\n self.assertRaises(ValueError, lambda: pt.pmap(pt.bfactors, self.traj))\n\n # run time: openmp vs pmap\n if 'OPENMP' in pt.compiled_info():\n self.assertRaises(RuntimeError,\n lambda: pt.pmap(pt.watershell, self.traj))\n\n # if traj is not TrajectoryIterator\n self.assertRaises(ValueError, lambda: pt.pmap(pt.radgyr, self.traj[:]))\n\n # raise if a given method does not support pmap\n def need_to_raise(traj=self.traj):\n pt.pmap(2, pt.bfactors, traj)\n\n self.assertRaises(ValueError, lambda: need_to_raise())\n\n # raise if a traj is not TrajectoryIterator\n def need_to_raise_2(traj=self.traj):\n pt.pmap(pt.bfactors, traj[:], n_cores=2)\n\n # raise if turn off pmap by setting _is_parallelizable to False\n pt.radgyr._is_parallelizable = False\n self.assertRaises(ValueError, lambda: pt.pmap(pt.radgyr, self.traj))\n pt.radgyr._is_parallelizable = True\n\n def test_general(self):\n traj = pt.iterload(\"./data/Tc5b.x\", \"./data/Tc5b.top\")\n\n # with mask\n saved_data = pt.radgyr(traj, '@CA')\n data = pt.pmap(pt.radgyr, traj, '@CA')\n data = pt.tools.dict_to_ndarray(data)\n aa_eq(saved_data, data)\n\n # with a series of functions\n func_list = [pt.radgyr, pt.molsurf, pt.rmsd]\n ref = traj[-3]\n\n for n_cores in [2, 3]:\n for func in func_list:\n if func in [pt.rmsd, ]:\n pout = pt.tools.dict_to_ndarray(pt.pmap(func=func,\n traj=traj,\n ref=ref,\n n_cores=n_cores))\n serial_out = flatten(func(traj, ref=ref))\n else:\n pout = pt.tools.dict_to_ndarray(pt.pmap(n_cores=n_cores,\n func=func,\n traj=traj))\n serial_out = flatten(func(traj))\n aa_eq(pout[0], serial_out)\n\n # test worker\n # need to test this since coverages seems not recognize partial func\n from pytraj.parallel.multiprocessing_ import worker_byfunc\n data = worker_byfunc(rank=2, n_cores=8, func=pt.radgyr, traj=traj, args=(), kwd={'mask': '@CA'}, iter_options={})\n assert data[0] == 2, 'rank must be 2'\n assert data[2] == 1, 'n_frames for rank=2 should be 1 (only 10 frames in total)'\n\n def test_different_references(self):\n traj = self.traj\n func = pt.rmsd\n for i in range(0, 8, 2):\n ref = self.traj[i]\n for n_cores in [2, 3, ]:\n pout = pt.tools.dict_to_ndarray(pt.pmap(n_cores=n_cores,\n func=func,\n traj=traj,\n ref=ref))\n serial_out = flatten(func(traj, ref=ref))\n aa_eq(pout[0], serial_out)\n\n def test_iter_options(self):\n traj = pt.iterload(\"data/tz2.ortho.nc\", \"data/tz2.ortho.parm7\")\n t0 = traj[:].autoimage().rmsfit(ref=0)\n saved_avg = pt.mean_structure(t0)\n saved_radgyr = pt.radgyr(traj, '@CA')\n\n # perform autoimage, then rms fit to 1st frame, then compute mean structure\n iter_options = {'autoimage': True, 'rmsfit': 0}\n for n_cores in [2, 3]:\n avg = pt.pmap(pt.mean_structure,\n traj,\n iter_options=iter_options,\n n_cores=n_cores)\n aa_eq(saved_avg.xyz, avg.xyz)\n radgyr_ = pt.tools.dict_to_ndarray(pt.pmap(pt.radgyr,\n traj,\n iter_options={'mask':\n '@CA'}))\n aa_eq(radgyr_[0], saved_radgyr)\n\n\nclass TestParallelMapForMatrix(unittest.TestCase):\n\n def test_matrix_module(self):\n traj = pt.iterload(\"data/tz2.nc\", \"data/tz2.parm7\")\n\n for n_cores in [2, 3]:\n for func in [matrix.dist, matrix.idea]:\n x = pt.pmap(func, traj, '@CA', n_cores=n_cores)\n aa_eq(x, func(traj, '@CA'))\n\n def test_ired_vector_and_matrix_pmap(self):\n traj = pt.iterload(\"data/tz2.nc\", \"data/tz2.parm7\")\n h = traj.top.select('@H')\n n = h - 1\n nh = list(zip(n, h))\n\n exptected_vecs, exptected_mat = pt.ired_vector_and_matrix(traj, nh)\n for n_cores in [2, 3]:\n vecs, mat = pt.pmap(pt.ired_vector_and_matrix,\n traj,\n nh,\n n_cores=n_cores)\n aa_eq(exptected_vecs, vecs, decimal=7)\n aa_eq(exptected_mat, mat, decimal=7)\n\n def test_rotation_matrix_in_rmsd_calculation(self):\n traj = pt.iterload(\"data/tz2.nc\", \"data/tz2.parm7\")\n saved_mat = pt.rotation_matrix(traj, ref=traj[3], mask='@CA')\n saved_rmsd = pt.rmsd(traj, ref=traj[3], mask='@CA')\n\n for n_cores in [2, 3]:\n out = pt.pmap(pt.rotation_matrix, traj, ref=traj[3], mask='@CA')\n out_with_rmsd = pt.pmap(pt.rotation_matrix,\n traj,\n ref=traj[3],\n mask='@CA',\n with_rmsd=True)\n mat = out[list(out.keys())[0]]\n mat2, rmsd_ = out_with_rmsd[list(out_with_rmsd.keys())[0]]\n aa_eq(saved_mat, mat)\n aa_eq(saved_mat, mat2)\n aa_eq(saved_rmsd, rmsd_)\n\n\nclass TestCpptrajCommandStyle(unittest.TestCase):\n\n def test_c_command_style(self):\n traj = pt.iterload(\"data/tz2.nc\", \"data/tz2.parm7\")\n\n angle_ = pt.angle(traj, ':3 :4 :5')\n distance_ = pt.distance(traj, '@10 @20')\n\n data = pt.pmap(['angle :3 :4 :5', 'distance @10 @20'], traj, n_cores=2)\n assert isinstance(data, OrderedDict), 'must be OrderDict'\n arr = pt.tools.dict_to_ndarray(data)\n aa_eq(angle_, arr[0])\n aa_eq(distance_, arr[1])\n\n # as whole text, case 1\n data = pt.pmap('''angle :3 :4 :5\n distance @10 @20''',\n traj,\n n_cores=2)\n assert isinstance(data, OrderedDict), 'must be OrderDict'\n arr = pt.tools.dict_to_ndarray(data)\n aa_eq(angle_, arr[0])\n aa_eq(distance_, arr[1])\n\n def test_reference(self):\n traj = pt.iterload(\"./data/tz2.nc\", \"./data/tz2.parm7\")\n\n for n_cores in [2, 3]:\n # use 4-th Frame for reference\n data = pt.pmap(['rms @CA refindex 0'],\n traj,\n ref=traj[3],\n n_cores=n_cores)\n # another way to get reference\n data2 = pt.pmap(['rms @CA reference'],\n traj,\n ref=traj[3],\n n_cores=n_cores)\n # use int for ref\n data3 = pt.pmap(pt.rmsd,\n traj,\n ref=3,\n mask='@CA',\n n_cores=n_cores)\n # use int for ref: use cpptraj's commmand style\n data4 = pt.pmap(['rms @CA reference'],\n traj,\n ref=3,\n n_cores=n_cores)\n arr = pt.tools.dict_to_ndarray(data)[0]\n arr2 = pt.tools.dict_to_ndarray(data2)[0]\n arr3 = pt.tools.dict_to_ndarray(data3)[0]\n arr4 = pt.tools.dict_to_ndarray(data4)[0]\n aa_eq(arr, pt.rmsd(traj, ref=3, mask='@CA'))\n aa_eq(arr2, pt.rmsd(traj, ref=3, mask='@CA'))\n aa_eq(arr3, pt.rmsd(traj, ref=3, mask='@CA'))\n aa_eq(arr4, pt.rmsd(traj, ref=3, mask='@CA'))\n\n # use 4-th and 5-th Frame for reference\n data = pt.pmap(\n ['rms @CA refindex 0', 'rms @CB refindex 1'],\n traj,\n ref=[traj[3], traj[4]],\n n_cores=n_cores)\n arr = pt.tools.dict_to_ndarray(data)[0]\n aa_eq(arr, pt.rmsd(traj, '@CA', 3))\n\n arr1 = pt.tools.dict_to_ndarray(data)[1]\n aa_eq(arr1, pt.rmsd(traj, ref=4, mask='@CB'))\n\n # no ref\n data = pt.pmap(['radgyr', ], traj, n_cores=n_cores)\n arr = pt.tools.dict_to_ndarray(data)[0]\n aa_eq(arr, pt.radgyr(traj))\n\n\nclass TestParallelMapForAverageStructure(unittest.TestCase):\n\n def test_pmap_average_structure(self):\n traj = pt.iterload(\"data/tz2.nc\", \"data/tz2.parm7\")\n saved_frame = pt.mean_structure(traj, '@CA')\n saved_xyz = saved_frame.xyz\n\n for n_cores in [2, 3, 4]:\n frame = pt.pmap(pt.mean_structure, traj, '@CA', n_cores=n_cores)\n aa_eq(frame.xyz, saved_xyz)\n\n\nclass TestLoadBathPmap(unittest.TestCase):\n\n def test_load_batch(self):\n '''just test ValueError\n '''\n self.assertRaises(\n ValueError,\n lambda: _load_batch_pmap(n_cores=4, lines=['autoimage'], traj=None, dtype='dict', root=0, mode='xyz', ref=None))\n\n\nclass TestFrameIndices(unittest.TestCase):\n\n def test_frame_indices(self):\n traj = pt.iterload(\"data/tz2.nc\", \"data/tz2.parm7\")\n\n # frame_indices could be a list, range\n frame_indices_list = [[0, 8, 9, 3, 2, 5], range(6)]\n\n for frame_indices in frame_indices_list:\n for n_cores in [2, 3]:\n serial_out = pt.radgyr(traj,\n '@CA',\n frame_indices=frame_indices)\n parallel_out = pt.pmap(pt.radgyr,\n traj,\n '@CA',\n frame_indices=frame_indices)\n parallel_out_c_style = pt.pmap(\n ['radgyr @CA nomax'],\n traj,\n frame_indices=frame_indices)\n aa_eq(serial_out, pt.tools.dict_to_ndarray(parallel_out))\n aa_eq(serial_out,\n pt.tools.dict_to_ndarray(parallel_out_c_style))\n\n\nclass TestCheckValidCommand(unittest.TestCase):\n\n def test_check_valid_command(self):\n from pytraj.parallel.base import check_valid_command\n assert check_valid_command(['rms', ]) == (['rms refindex 0 '], True)\n assert check_valid_command(['distrmsd', ]) == (['distrmsd refindex 0 '], True)\n assert check_valid_command(['nativecontacts', ]) == (['nativecontacts refindex 0 '], True)\n assert check_valid_command(['nastruct', ]) == (['nastruct refindex 0 '], True)\n assert check_valid_command(['symmetricrmsd', ]) == (['symmetricrmsd refindex 0 '], True)\n traj = pt.iterload(\"data/tz2.nc\", \"data/tz2.parm7\")\n\n aa_eq(pt.tools.dict_to_ndarray(\n pt.pmap(['rmsd'], traj, ref=traj[3], n_cores=3)),\n pt.rmsd(traj, ref=traj[3]))\n\n # provide refindex\n aa_eq(pt.tools.dict_to_ndarray(\n pt.pmap(['rmsd refindex 0'], traj, ref=traj[3], n_cores=3)),\n pt.rmsd(traj, ref=traj[3]))\n\n aa_eq(pt.tools.dict_to_ndarray(\n pt.pmap(['rmsd refindex 0'], traj, ref=[traj[3], traj[0]], n_cores=3)),\n pt.rmsd(traj, ref=traj[3]))\n\n # if user does not provide reference, need to give it to them\n aa_eq(pt.tools.dict_to_ndarray(\n pt.pmap(['rmsd'], traj, n_cores=3)),\n pt.rmsd(traj, ref=traj[0]))\n\n # does not support matrix\n self.assertRaises(ValueError, lambda: pt.pmap(['matrix'], traj, n_cores=2))\n\n # do not accept any c analysis command\n for word in c_commands.analysis_commands:\n self.assertRaises(ValueError, lambda: pt.pmap(word, traj, n_cores=2))\n\n\nclass TestVolmap(unittest.TestCase):\n\n def test_volmap(self):\n traj = pt.iterload(\"data/tz2.ortho.nc\", \"data/tz2.ortho.parm7\")\n\n # raise if does not provide size\n self.assertRaises(AssertionError, lambda: pt.pmap(pt.volmap, traj, mask=':WAT@O',\n grid_spacing=(0.5, 0.5, 0.5),\n n_cores=2))\n\n mask = ':WAT@O'\n grid_spacing = (0.5, 0.5, 0.5)\n\n for n_cores in [1, 2, 3]:\n for size in [(20, 20, 20), (20, 40, 60)]:\n serial_out = pt.volmap(traj, mask=mask, grid_spacing=grid_spacing, size=size)\n parallel_out = pt.pmap(pt.volmap, traj, mask=mask, grid_spacing=grid_spacing,\n size=size, n_cores=n_cores)\n self.assertEqual(serial_out.shape, tuple(2 * x for x in size))\n aa_eq(serial_out, parallel_out)\n\n\nclass TestWorker(unittest.TestCase):\n\n def testworker_by_actlist(self):\n # just want to exercise all codes\n traj = pt.iterload(\"data/tz2.nc\", \"data/tz2.parm7\")\n for ref in [None, traj[0], [traj[0], traj[1]]]:\n data = worker_by_actlist(rank=3, n_cores=8, traj=traj, lines=['radgyr @CA', 'vector :3 :7'],\n ref=ref, kwd=dict())\n\n\ndef change_10_atoms(traj):\n for frame in traj:\n frame.xyz[:10] += 1.\n yield frame\n\n\nclass TestInserNewFunction(unittest.TestCase):\n\n def test_insert_new_function(self):\n traj = pt.iterload(\"data/tz2.nc\", \"data/tz2.parm7\")\n\n # create mutable Trajectory\n t0 = traj[:]\n for frame in t0:\n frame.xyz[:10] += 1.\n\n data_parallel = pt.pmap(pt.radgyr, traj, n_cores=2, apply=change_10_atoms)\n data_serial = pt.radgyr(t0)\n aa_eq(data_parallel['RoG_00000'], data_serial)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "sub_path": "tests/test_pmap.py", "file_name": "test_pmap.py", "file_ext": "py", "file_size_in_byte": 14685, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "unittest.TestCase", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pytraj.iterload", "line_number": 18, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 22, "usage_type": "call"}, {"api_name": "pytraj.bfactors", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pytraj.compiled_info", "line_number": 25, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 27, "usage_type": "call"}, {"api_name": "pytraj.watershell", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 30, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 34, "usage_type": "call"}, {"api_name": "pytraj.bfactors", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 40, "usage_type": "call"}, {"api_name": "pytraj.bfactors", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pytraj.radgyr", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 44, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pytraj.radgyr", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pytraj.iterload", "line_number": 48, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 51, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 52, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 53, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 54, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pytraj.molsurf", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pytraj.rmsd", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pytraj.rmsd", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 63, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 63, "usage_type": "call"}, {"api_name": "pytraj.tools.flatten", "line_number": 67, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 69, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 69, "usage_type": "call"}, {"api_name": "pytraj.tools.flatten", "line_number": 72, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 73, "usage_type": "call"}, {"api_name": "pytraj.parallel.multiprocessing_.worker_byfunc", "line_number": 78, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pytraj.rmsd", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 88, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 88, "usage_type": "call"}, {"api_name": "pytraj.tools.flatten", "line_number": 92, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 93, "usage_type": "call"}, {"api_name": "pytraj.iterload", "line_number": 96, "usage_type": "call"}, {"api_name": "pytraj.mean_structure", "line_number": 98, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 99, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 104, "usage_type": "call"}, {"api_name": "pytraj.mean_structure", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 108, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 109, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 109, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 113, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pytraj.iterload", "line_number": 119, "usage_type": "call"}, {"api_name": "pytraj.matrix.dist", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pytraj.matrix", "line_number": 122, "usage_type": "name"}, {"api_name": "pytraj.matrix.idea", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 123, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 124, "usage_type": "call"}, {"api_name": "pytraj.iterload", "line_number": 127, "usage_type": "call"}, {"api_name": "pytraj.ired_vector_and_matrix", "line_number": 132, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 134, "usage_type": "call"}, {"api_name": "pytraj.ired_vector_and_matrix", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 138, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 139, "usage_type": "call"}, {"api_name": "pytraj.iterload", "line_number": 142, "usage_type": "call"}, {"api_name": "pytraj.rotation_matrix", "line_number": 143, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 144, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 147, "usage_type": "call"}, {"api_name": "pytraj.rotation_matrix", "line_number": 147, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 148, "usage_type": "call"}, {"api_name": "pytraj.rotation_matrix", "line_number": 148, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 155, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 156, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 157, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 160, "usage_type": "attribute"}, {"api_name": "pytraj.iterload", "line_number": 163, "usage_type": "call"}, {"api_name": "pytraj.angle", "line_number": 165, "usage_type": "call"}, {"api_name": "pytraj.distance", "line_number": 166, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 168, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 169, "usage_type": "argument"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 170, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 170, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 171, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 172, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 175, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 179, "usage_type": "argument"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 180, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 180, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 181, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 182, "usage_type": "call"}, {"api_name": "pytraj.iterload", "line_number": 185, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 189, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 194, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 199, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 199, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 205, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 209, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 209, "usage_type": "attribute"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 210, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 210, "usage_type": "attribute"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 211, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 211, "usage_type": "attribute"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 212, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 212, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 213, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 213, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 214, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 214, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 215, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 215, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 216, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 216, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 219, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 224, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 224, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 225, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 225, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 227, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 227, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 228, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 228, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 231, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 232, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 232, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 233, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 233, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 236, "usage_type": "attribute"}, {"api_name": "pytraj.iterload", "line_number": 239, "usage_type": "call"}, {"api_name": "pytraj.mean_structure", "line_number": 240, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 244, "usage_type": "call"}, {"api_name": "pytraj.mean_structure", "line_number": 244, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 245, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 248, "usage_type": "attribute"}, {"api_name": "pytraj.parallel.base._load_batch_pmap", "line_number": 255, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 258, "usage_type": "attribute"}, {"api_name": "pytraj.iterload", "line_number": 261, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 268, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 271, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 271, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 275, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 279, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 279, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 279, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 280, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 281, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 281, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 284, "usage_type": "attribute"}, {"api_name": "pytraj.parallel.base.check_valid_command", "line_number": 288, "usage_type": "call"}, {"api_name": "pytraj.parallel.base.check_valid_command", "line_number": 289, "usage_type": "call"}, {"api_name": "pytraj.parallel.base.check_valid_command", "line_number": 290, "usage_type": "call"}, {"api_name": "pytraj.parallel.base.check_valid_command", "line_number": 291, "usage_type": "call"}, {"api_name": "pytraj.parallel.base.check_valid_command", "line_number": 292, "usage_type": "call"}, {"api_name": "pytraj.iterload", "line_number": 293, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 295, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 295, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 295, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 296, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 297, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 300, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 300, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 300, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 301, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 302, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 304, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 304, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 304, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 305, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 306, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 309, "usage_type": "call"}, {"api_name": "pytraj.tools.dict_to_ndarray", "line_number": 309, "usage_type": "call"}, {"api_name": "pytraj.tools", "line_number": 309, "usage_type": "attribute"}, {"api_name": "pytraj.pmap", "line_number": 310, "usage_type": "call"}, {"api_name": "pytraj.rmsd", "line_number": 311, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 314, "usage_type": "call"}, {"api_name": "pytraj.c_commands.analysis_commands", "line_number": 317, "usage_type": "attribute"}, {"api_name": "pytraj.c_commands", "line_number": 317, "usage_type": "name"}, {"api_name": "pytraj.pmap", "line_number": 318, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 321, "usage_type": "attribute"}, {"api_name": "pytraj.iterload", "line_number": 324, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 327, "usage_type": "call"}, {"api_name": "pytraj.volmap", "line_number": 327, "usage_type": "attribute"}, {"api_name": "pytraj.volmap", "line_number": 336, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 337, "usage_type": "call"}, {"api_name": "pytraj.volmap", "line_number": 337, "usage_type": "attribute"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 340, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 343, "usage_type": "attribute"}, {"api_name": "pytraj.iterload", "line_number": 347, "usage_type": "call"}, {"api_name": "pytraj.parallel.base.worker_by_actlist", "line_number": 349, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 359, "usage_type": "attribute"}, {"api_name": "pytraj.iterload", "line_number": 362, "usage_type": "call"}, {"api_name": "pytraj.pmap", "line_number": 369, "usage_type": "call"}, {"api_name": "pytraj.radgyr", "line_number": 369, "usage_type": "attribute"}, {"api_name": "pytraj.radgyr", "line_number": 370, "usage_type": "call"}, {"api_name": "pytraj.utils.aa_eq", "line_number": 371, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 375, "usage_type": "call"}]} +{"seq_id": "529155519", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author: Gillett Hernandez\n# @Date: 2016-07-02 12:52:02\n# @Last Modified by: Gillett Hernandez\n# @Last Modified time: 2017-08-10 13:35:54\n\n# MARK SLOW - 4s\n\nfrom euler_funcs import timed\nimport itertools\n\n@timed\ndef main():\n\n def join(*ns):\n return int(\"\".join([str(n) for n in ns]))\n\n s = set([])\n for p in itertools.permutations([1, 2, 3, 4, 5, 6, 7, 8, 9]):\n if join(*p[:2])*join(*p[2:5]) == join(*p[5:]):\n s.add(join(*p[5:]))\n if p[0]*join(*p[1:5]) == join(*p[5:]):\n s.add(join(*p[5:]))\n print(sum(list(s)))\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Python/problem_32.py", "file_name": "problem_32.py", "file_ext": "py", "file_size_in_byte": 658, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "itertools.permutations", "line_number": 20, "usage_type": "call"}, {"api_name": "euler_funcs.timed", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "596572833", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport sys\nfrom builtins import str, input, object\nfrom past.builtins import basestring\nfrom copy import copy\nfrom datetime import datetime, date, timedelta\nfrom dateutil.relativedelta import relativedelta # for doctest\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.application import MIMEApplication\nimport errno\nfrom functools import wraps\nimport imp\nimport inspect\nimport json\nimport logging\nimport os\nimport re\nimport shutil\nimport signal\nimport six\nimport smtplib\nfrom tempfile import mkdtemp\n\nfrom alembic.config import Config\nfrom alembic import command\nfrom alembic.migration import MigrationContext\n\nfrom contextlib import contextmanager\n\nfrom sqlalchemy import event, exc\nfrom sqlalchemy.pool import Pool\n\nimport numpy as np\nfrom croniter import croniter\n\nfrom airflow import settings\nfrom airflow import configuration\n\n\nclass AirflowException(Exception):\n pass\n\n\nclass AirflowSensorTimeout(Exception):\n pass\n\n\nclass TriggerRule(object):\n ALL_SUCCESS = 'all_success'\n ALL_FAILED = 'all_failed'\n ALL_DONE = 'all_done'\n ONE_SUCCESS = 'one_success'\n ONE_FAILED = 'one_failed'\n DUMMY = 'dummy'\n\n @classmethod\n def is_valid(cls, trigger_rule):\n return trigger_rule in cls.all_triggers()\n\n @classmethod\n def all_triggers(cls):\n return [getattr(cls, attr)\n for attr in dir(cls)\n if not attr.startswith(\"__\") and not callable(getattr(cls, attr))]\n\n\nclass State(object):\n \"\"\"\n Static class with task instance states constants and color method to\n avoid hardcoding.\n \"\"\"\n QUEUED = \"queued\"\n RUNNING = \"running\"\n SUCCESS = \"success\"\n SHUTDOWN = \"shutdown\" # External request to shut down\n FAILED = \"failed\"\n UP_FOR_RETRY = \"up_for_retry\"\n UPSTREAM_FAILED = \"upstream_failed\"\n SKIPPED = \"skipped\"\n\n state_color = {\n QUEUED: 'gray',\n RUNNING: 'lime',\n SUCCESS: 'green',\n SHUTDOWN: 'blue',\n FAILED: 'red',\n UP_FOR_RETRY: 'gold',\n UPSTREAM_FAILED: 'orange',\n SKIPPED: 'pink',\n }\n\n @classmethod\n def color(cls, state):\n if state in cls.state_color:\n return cls.state_color[state]\n else:\n return 'white'\n\n @classmethod\n def color_fg(cls, state):\n color = cls.color(state)\n if color in ['green', 'red']:\n return 'white'\n else:\n return 'black'\n\n @classmethod\n def runnable(cls):\n return [\n None, cls.FAILED, cls.UP_FOR_RETRY, cls.UPSTREAM_FAILED,\n cls.SKIPPED, cls.QUEUED]\n\n\ncron_presets = {\n '@hourly': '0 * * * *',\n '@daily': '0 0 * * *',\n '@weekly': '0 0 * * 0',\n '@monthly': '0 0 1 * *',\n '@yearly': '0 0 1 1 *',\n}\n\ndef provide_session(func):\n \"\"\"\n Function decorator that provides a session if it isn't provided.\n If you want to reuse a session or run the function as part of a\n database transaction, you pass it to the function, if not this wrapper\n will create one and close it for you.\n \"\"\"\n @wraps(func)\n def wrapper(*args, **kwargs):\n needs_session = False\n if 'session' not in kwargs:\n needs_session = True\n session = settings.Session()\n kwargs['session'] = session\n result = func(*args, **kwargs)\n if needs_session:\n session.expunge_all()\n session.commit()\n session.close()\n return result\n return wrapper\n\n\ndef pessimistic_connection_handling():\n @event.listens_for(Pool, \"checkout\")\n def ping_connection(dbapi_connection, connection_record, connection_proxy):\n '''\n Disconnect Handling - Pessimistic, taken from:\n http://docs.sqlalchemy.org/en/rel_0_9/core/pooling.html\n '''\n cursor = dbapi_connection.cursor()\n try:\n cursor.execute(\"SELECT 1\")\n except:\n raise exc.DisconnectionError()\n cursor.close()\n\n@provide_session\ndef merge_conn(conn, session=None):\n from airflow import models\n C = models.Connection\n if not session.query(C).filter(C.conn_id == conn.conn_id).first():\n session.add(conn)\n session.commit()\n\n\ndef initdb():\n session = settings.Session()\n\n from airflow import models\n upgradedb()\n\n merge_conn(\n models.Connection(\n conn_id='airflow_db', conn_type='mysql',\n host='localhost', login='root', password='',\n schema='airflow'))\n merge_conn(\n models.Connection(\n conn_id='beeline_default', conn_type='beeline',\n host='localhost',\n schema='airflow'))\n merge_conn(\n models.Connection(\n conn_id='bigquery_default', conn_type='bigquery'))\n merge_conn(\n models.Connection(\n conn_id='local_mysql', conn_type='mysql',\n host='localhost', login='airflow', password='airflow',\n schema='airflow'))\n merge_conn(\n models.Connection(\n conn_id='presto_default', conn_type='presto',\n host='localhost',\n schema='hive', port=3400))\n merge_conn(\n models.Connection(\n conn_id='hive_cli_default', conn_type='hive_cli',\n schema='default',))\n merge_conn(\n models.Connection(\n conn_id='hiveserver2_default', conn_type='hiveserver2',\n host='localhost',\n schema='default', port=10000))\n merge_conn(\n models.Connection(\n conn_id='metastore_default', conn_type='hive_metastore',\n host='localhost',\n port=10001))\n merge_conn(\n models.Connection(\n conn_id='mysql_default', conn_type='mysql',\n login='root',\n host='localhost'))\n merge_conn(\n models.Connection(\n conn_id='postgres_default', conn_type='postgres',\n login='postgres',\n schema='airflow',\n host='localhost'))\n merge_conn(\n models.Connection(\n conn_id='sqlite_default', conn_type='sqlite',\n host='/tmp/sqlite_default.db'))\n merge_conn(\n models.Connection(\n conn_id='http_default', conn_type='http',\n host='https://www.google.com/'))\n merge_conn(\n models.Connection(\n conn_id='mssql_default', conn_type='mssql',\n host='localhost', port=1433))\n merge_conn(\n models.Connection(\n conn_id='vertica_default', conn_type='vertica',\n host='localhost', port=5433))\n merge_conn(\n models.Connection(\n conn_id='webhdfs_default', conn_type='hdfs',\n host='localhost', port=50070))\n merge_conn(\n models.Connection(\n conn_id='ssh_default', conn_type='ssh',\n host='localhost'))\n\n # Known event types\n KET = models.KnownEventType\n if not session.query(KET).filter(KET.know_event_type == 'Holiday').first():\n session.add(KET(know_event_type='Holiday'))\n if not session.query(KET).filter(KET.know_event_type == 'Outage').first():\n session.add(KET(know_event_type='Outage'))\n if not session.query(KET).filter(\n KET.know_event_type == 'Natural Disaster').first():\n session.add(KET(know_event_type='Natural Disaster'))\n if not session.query(KET).filter(\n KET.know_event_type == 'Marketing Campaign').first():\n session.add(KET(know_event_type='Marketing Campaign'))\n session.commit()\n\n models.DagBag(sync_to_db=True)\n\n Chart = models.Chart\n chart_label = \"Airflow task instance by type\"\n chart = session.query(Chart).filter(Chart.label == chart_label).first()\n if not chart:\n chart = Chart(\n label=chart_label,\n conn_id='airflow_db',\n chart_type='bar',\n x_is_date=False,\n sql=(\n \"SELECT state, COUNT(1) as number \"\n \"FROM task_instance \"\n \"WHERE dag_id LIKE 'example%' \"\n \"GROUP BY state\"),\n )\n session.add(chart)\n\n\ndef upgradedb():\n logging.info(\"Creating tables\")\n package_dir = os.path.abspath(os.path.dirname(__file__))\n directory = os.path.join(package_dir, 'migrations')\n config = Config(os.path.join(package_dir, 'alembic.ini'))\n config.set_main_option('script_location', directory)\n config.set_main_option('sqlalchemy.url',\n configuration.get('core', 'SQL_ALCHEMY_CONN'))\n command.upgrade(config, 'heads')\n\n\ndef resetdb():\n '''\n Clear out the database\n '''\n from airflow import models\n\n logging.info(\"Dropping tables that exist\")\n models.Base.metadata.drop_all(settings.engine)\n mc = MigrationContext.configure(settings.engine)\n if mc._version.exists(settings.engine):\n mc._version.drop(settings.engine)\n initdb()\n\n\ndef validate_key(k, max_length=250):\n if not isinstance(k, basestring):\n raise TypeError(\"The key has to be a string\")\n elif len(k) > max_length:\n raise AirflowException(\n \"The key has to be less than {0} characters\".format(max_length))\n elif not re.match(r'^[A-Za-z0-9_\\-\\.]+$', k):\n raise AirflowException(\n \"The key ({k}) has to be made of alphanumeric characters, dashes, \"\n \"dots and underscores exclusively\".format(**locals()))\n else:\n return True\n\n\ndef date_range(\n start_date,\n end_date=None,\n num=None,\n delta=None):\n \"\"\"\n Get a set of dates as a list based on a start, end and delta, delta\n can be something that can be added to ``datetime.datetime``\n or a cron expression as a ``str``\n\n :param start_date: anchor date to start the series from\n :type start_date: datetime.datetime\n :param end_date: right boundary for the date range\n :type end_date: datetime.datetime\n :param num: alternatively to end_date, you can specify the number of\n number of entries you want in the range. This number can be negative,\n output will always be sorted regardless\n :type num: int\n\n >>> date_range(datetime(2016, 1, 1), datetime(2016, 1, 3), delta=timedelta(1))\n [datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 1, 2, 0, 0), datetime.datetime(2016, 1, 3, 0, 0)]\n >>> date_range(datetime(2016, 1, 1), datetime(2016, 1, 3), delta='0 0 * * *')\n [datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 1, 2, 0, 0), datetime.datetime(2016, 1, 3, 0, 0)]\n >>> date_range(datetime(2016, 1, 1), datetime(2016, 3, 3), delta=\"0 0 0 * *\")\n [datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 2, 1, 0, 0), datetime.datetime(2016, 3, 1, 0, 0)]\n \"\"\"\n if not delta:\n return []\n if end_date and start_date > end_date:\n raise Exception(\"Wait. start_date needs to be before end_date\")\n if end_date and num:\n raise Exception(\"Wait. Either specify end_date OR num\")\n if not end_date and not num:\n end_date = datetime.now()\n\n delta_iscron = False\n if isinstance(delta, six.string_types):\n delta_iscron = True\n cron = croniter(delta, start_date)\n elif isinstance(delta, timedelta):\n delta = abs(delta)\n l = []\n if end_date:\n while start_date <= end_date:\n l.append(start_date)\n if delta_iscron:\n start_date = cron.get_next(datetime)\n else:\n start_date += delta\n else:\n for i in range(abs(num)):\n l.append(start_date)\n if delta_iscron:\n if num > 0:\n start_date = cron.get_next(datetime)\n else:\n start_date = cron.get_prev(datetime)\n else:\n if num > 0:\n start_date += delta\n else:\n start_date -= delta\n return sorted(l)\n\n\ndef json_ser(obj):\n \"\"\"\n json serializer that deals with dates\n usage: json.dumps(object, default=utils.json_ser)\n \"\"\"\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n\n\ndef alchemy_to_dict(obj):\n \"\"\"\n Transforms a SQLAlchemy model instance into a dictionary\n \"\"\"\n if not obj:\n return None\n d = {}\n for c in obj.__table__.columns:\n value = getattr(obj, c.name)\n if type(value) == datetime:\n value = value.isoformat()\n d[c.name] = value\n return d\n\n\ndef readfile(filepath):\n f = open(filepath)\n content = f.read()\n f.close()\n return content\n\n\ndef apply_defaults(func):\n \"\"\"\n Function decorator that Looks for an argument named \"default_args\", and\n fills the unspecified arguments from it.\n\n Since python2.* isn't clear about which arguments are missing when\n calling a function, and that this can be quite confusing with multi-level\n inheritance and argument defaults, this decorator also alerts with\n specific information about the missing arguments.\n \"\"\"\n @wraps(func)\n def wrapper(*args, **kwargs):\n if len(args) > 1:\n raise AirflowException(\n \"Use keyword arguments when initializing operators\")\n dag_args = {}\n dag_params = {}\n if 'dag' in kwargs and kwargs['dag']:\n dag = kwargs['dag']\n dag_args = copy(dag.default_args) or {}\n dag_params = copy(dag.params) or {}\n\n params = {}\n if 'params' in kwargs:\n params = kwargs['params']\n dag_params.update(params)\n\n default_args = {}\n if 'default_args' in kwargs:\n default_args = kwargs['default_args']\n if 'params' in default_args:\n dag_params.update(default_args['params'])\n del default_args['params']\n\n dag_args.update(default_args)\n default_args = dag_args\n arg_spec = inspect.getargspec(func)\n num_defaults = len(arg_spec.defaults) if arg_spec.defaults else 0\n non_optional_args = arg_spec.args[:-num_defaults]\n if 'self' in non_optional_args:\n non_optional_args.remove('self')\n for arg in func.__code__.co_varnames:\n if arg in default_args and arg not in kwargs:\n kwargs[arg] = default_args[arg]\n missing_args = list(set(non_optional_args) - set(kwargs))\n if missing_args:\n msg = \"Argument {0} is required\".format(missing_args)\n raise AirflowException(msg)\n\n kwargs['params'] = dag_params\n\n result = func(*args, **kwargs)\n return result\n return wrapper\n\nif 'BUILDING_AIRFLOW_DOCS' in os.environ:\n # Monkey patch hook to get good function headers while building docs\n apply_defaults = lambda x: x\n\ndef ask_yesno(question):\n yes = set(['yes', 'y'])\n no = set(['no', 'n'])\n\n done = False\n print(question)\n while not done:\n choice = input().lower()\n if choice in yes:\n return True\n elif choice in no:\n return False\n else:\n print(\"Please respond by yes or no.\")\n\n\ndef send_email(to, subject, html_content, files=None, dryrun=False):\n \"\"\"\n Send an email with html content\n\n >>> send_email('test@example.com', 'foo', 'Foo bar', ['/dev/null'], dryrun=True)\n \"\"\"\n SMTP_MAIL_FROM = configuration.get('smtp', 'SMTP_MAIL_FROM')\n\n if isinstance(to, basestring):\n if ',' in to:\n to = to.split(',')\n elif ';' in to:\n to = to.split(';')\n else:\n to = [to]\n\n msg = MIMEMultipart('alternative')\n msg['Subject'] = subject\n msg['From'] = SMTP_MAIL_FROM\n msg['To'] = \", \".join(to)\n mime_text = MIMEText(html_content, 'html')\n msg.attach(mime_text)\n\n for fname in files or []:\n basename = os.path.basename(fname)\n with open(fname, \"rb\") as f:\n msg.attach(MIMEApplication(\n f.read(),\n Content_Disposition='attachment; filename=\"%s\"' % basename,\n Name=basename\n ))\n\n send_MIME_email(SMTP_MAIL_FROM, to, msg, dryrun)\n\n\ndef send_MIME_email(e_from, e_to, mime_msg, dryrun=False):\n SMTP_HOST = configuration.get('smtp', 'SMTP_HOST')\n SMTP_PORT = configuration.getint('smtp', 'SMTP_PORT')\n SMTP_USER = configuration.get('smtp', 'SMTP_USER')\n SMTP_PASSWORD = configuration.get('smtp', 'SMTP_PASSWORD')\n SMTP_STARTTLS = configuration.getboolean('smtp', 'SMTP_STARTTLS')\n SMTP_SSL = configuration.getboolean('smtp', 'SMTP_SSL')\n\n if not dryrun:\n s = smtplib.SMTP_SSL(SMTP_HOST, SMTP_PORT) if SMTP_SSL else smtplib.SMTP(SMTP_HOST, SMTP_PORT)\n if SMTP_STARTTLS:\n s.starttls()\n if SMTP_USER and SMTP_PASSWORD:\n s.login(SMTP_USER, SMTP_PASSWORD)\n logging.info(\"Sent an alert email to \" + str(e_to))\n s.sendmail(e_from, e_to, mime_msg.as_string())\n s.quit()\n\n\ndef import_module_attrs(parent_module_globals, module_attrs_dict):\n '''\n Attempts to import a set of modules and specified attributes in the\n form of a dictionary. The attributes are copied in the parent module's\n namespace. The function returns a list of attributes names that can be\n affected to __all__.\n\n This is used in the context of ``operators`` and ``hooks`` and\n silence the import errors for when libraries are missing. It makes\n for a clean package abstracting the underlying modules and only\n brings functional operators to those namespaces.\n '''\n imported_attrs = []\n for mod, attrs in list(module_attrs_dict.items()):\n try:\n path = os.path.realpath(parent_module_globals['__file__'])\n folder = os.path.dirname(path)\n f, filename, description = imp.find_module(mod, [folder])\n module = imp.load_module(mod, f, filename, description)\n for attr in attrs:\n parent_module_globals[attr] = getattr(module, attr)\n imported_attrs += [attr]\n except Exception as err:\n logging.debug(\"Error importing module {mod}: {err}\".format(\n mod=mod, err=err))\n return imported_attrs\n\n\ndef is_in(obj, l):\n \"\"\"\n Checks whether an object is one of the item in the list.\n This is different from ``in`` because ``in`` uses __cmp__ when\n present. Here we change based on the object itself\n \"\"\"\n for item in l:\n if item is obj:\n return True\n return False\n\n\n@contextmanager\ndef TemporaryDirectory(suffix='', prefix=None, dir=None):\n name = mkdtemp(suffix=suffix, prefix=prefix, dir=dir)\n try:\n yield name\n finally:\n try:\n shutil.rmtree(name)\n except OSError as e:\n # ENOENT - no such file or directory\n if e.errno != errno.ENOENT:\n raise e\n\n\nclass AirflowTaskTimeout(Exception):\n pass\n\n\nclass timeout(object):\n \"\"\"\n To be used in a ``with`` block and timeout its content.\n \"\"\"\n def __init__(self, seconds=1, error_message='Timeout'):\n self.seconds = seconds\n self.error_message = error_message\n\n def handle_timeout(self, signum, frame):\n logging.error(\"Process timed out\")\n raise AirflowTaskTimeout(self.error_message)\n\n def __enter__(self):\n try:\n signal.signal(signal.SIGALRM, self.handle_timeout)\n signal.alarm(self.seconds)\n except ValueError as e:\n logging.warning(\"timeout can't be used in the current context\")\n logging.exception(e)\n\n def __exit__(self, type, value, traceback):\n try:\n signal.alarm(0)\n except ValueError as e:\n logging.warning(\"timeout can't be used in the current context\")\n logging.exception(e)\n\n\ndef is_container(obj):\n \"\"\"\n Test if an object is a container (iterable) but not a string\n \"\"\"\n return hasattr(obj, '__iter__') and not isinstance(obj, basestring)\n\n\ndef as_tuple(obj):\n \"\"\"\n If obj is a container, returns obj as a tuple.\n Otherwise, returns a tuple containing obj.\n \"\"\"\n if is_container(obj):\n return tuple(obj)\n else:\n return tuple([obj])\n\n\ndef round_time(dt, delta, start_date=datetime.min):\n \"\"\"\n Returns the datetime of the form start_date + i * delta\n which is closest to dt for any non-negative integer i.\n\n Note that delta may be a datetime.timedelta or a dateutil.relativedelta\n\n >>> round_time(datetime(2015, 1, 1, 6), timedelta(days=1))\n datetime.datetime(2015, 1, 1, 0, 0)\n >>> round_time(datetime(2015, 1, 2), relativedelta(months=1))\n datetime.datetime(2015, 1, 1, 0, 0)\n >>> round_time(datetime(2015, 9, 16, 0, 0), timedelta(1), datetime(2015, 9, 14, 0, 0))\n datetime.datetime(2015, 9, 16, 0, 0)\n >>> round_time(datetime(2015, 9, 15, 0, 0), timedelta(1), datetime(2015, 9, 14, 0, 0))\n datetime.datetime(2015, 9, 15, 0, 0)\n >>> round_time(datetime(2015, 9, 14, 0, 0), timedelta(1), datetime(2015, 9, 14, 0, 0))\n datetime.datetime(2015, 9, 14, 0, 0)\n >>> round_time(datetime(2015, 9, 13, 0, 0), timedelta(1), datetime(2015, 9, 14, 0, 0))\n datetime.datetime(2015, 9, 14, 0, 0)\n \"\"\"\n\n if isinstance(delta, six.string_types):\n # It's cron based, so it's easy\n cron = croniter(delta, start_date)\n prev = cron.get_prev(datetime)\n if prev == start_date:\n return start_date\n else:\n return prev\n\n # Ignore the microseconds of dt\n dt -= timedelta(microseconds = dt.microsecond)\n\n # We are looking for a datetime in the form start_date + i * delta\n # which is as close as possible to dt. Since delta could be a relative\n # delta we don't know it's exact length in seconds so we cannot rely on\n # division to find i. Instead we employ a binary search algorithm, first\n # finding an upper and lower limit and then disecting the interval until\n # we have found the closest match.\n\n # We first search an upper limit for i for which start_date + upper * delta\n # exceeds dt.\n upper = 1\n while start_date + upper*delta < dt:\n # To speed up finding an upper limit we grow this exponentially by a\n # factor of 2\n upper *= 2\n\n # Since upper is the first value for which start_date + upper * delta\n # exceeds dt, upper // 2 is below dt and therefore forms a lower limited\n # for the i we are looking for\n lower = upper // 2\n\n # We now continue to intersect the interval between\n # start_date + lower * delta and start_date + upper * delta\n # until we find the closest value\n while True:\n # Invariant: start + lower * delta < dt <= start + upper * delta\n # If start_date + (lower + 1)*delta exceeds dt, then either lower or\n # lower+1 has to be the solution we are searching for\n if start_date + (lower + 1)*delta >= dt:\n # Check if start_date + (lower + 1)*delta or\n # start_date + lower*delta is closer to dt and return the solution\n if (\n (start_date + (lower + 1) * delta) - dt <=\n dt - (start_date + lower * delta)):\n return start_date + (lower + 1)*delta\n else:\n return start_date + lower * delta\n\n # We intersect the interval and either replace the lower or upper\n # limit with the candidate\n candidate = lower + (upper - lower) // 2\n if start_date + candidate*delta >= dt:\n upper = candidate\n else:\n lower = candidate\n\n # in the special case when start_date > dt the search for upper will\n # immediately stop for upper == 1 which results in lower = upper // 2 = 0\n # and this function returns start_date.\n\n\ndef chain(*tasks):\n \"\"\"\n Given a number of tasks, builds a dependency chain.\n\n chain(task_1, task_2, task_3, task_4)\n\n is equivalent to\n\n task_1.set_downstream(task_2)\n task_2.set_downstream(task_3)\n task_3.set_downstream(task_4)\n \"\"\"\n for up_task, down_task in zip(tasks[:-1], tasks[1:]):\n up_task.set_downstream(down_task)\n\n\nclass AirflowJsonEncoder(json.JSONEncoder):\n def default(self, obj):\n # convert dates and numpy objects in a json serializable format\n if isinstance(obj, datetime):\n return obj.strftime('%Y-%m-%dT%H:%M:%SZ')\n elif isinstance(obj, date):\n return obj.strftime('%Y-%m-%d')\n elif type(obj) in [np.int_, np.intc, np.intp, np.int8, np.int16,\n np.int32, np.int64, np.uint8, np.uint16,\n np.uint32, np.uint64]:\n return int(obj)\n elif type(obj) in [np.bool_]:\n return bool(obj)\n elif type(obj) in [np.float_, np.float16, np.float32, np.float64,\n np.complex_, np.complex64, np.complex128]:\n return float(obj)\n\n # Let the base class default method raise the TypeError\n return json.JSONEncoder.default(self, obj)\n\n\nclass LoggingMixin(object):\n \"\"\"\n Convenience super-class to have a logger configured with the class name\n \"\"\"\n\n @property\n def logger(self):\n try:\n return self._logger\n except AttributeError:\n self._logger = logging.root.getChild(self.__class__.__module__ + '.' +self.__class__.__name__)\n return self._logger\n\n", "sub_path": "airflow/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 25489, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "builtins.object", "line_number": 53, "usage_type": "name"}, {"api_name": "builtins.object", "line_number": 72, "usage_type": "name"}, {"api_name": "airflow.settings.Session", "line_number": 139, "usage_type": "call"}, {"api_name": "airflow.settings", "line_number": 139, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 134, "usage_type": "call"}, {"api_name": "sqlalchemy.exc.DisconnectionError", "line_number": 161, "usage_type": "call"}, {"api_name": "sqlalchemy.exc", "line_number": 161, "usage_type": "name"}, {"api_name": "sqlalchemy.event.listens_for", "line_number": 151, "usage_type": "call"}, {"api_name": "sqlalchemy.pool.Pool", "line_number": 151, "usage_type": "argument"}, {"api_name": "sqlalchemy.event", "line_number": 151, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 167, "usage_type": "attribute"}, {"api_name": "airflow.models", "line_number": 167, "usage_type": "name"}, {"api_name": "airflow.settings.Session", "line_number": 174, "usage_type": "call"}, {"api_name": "airflow.settings", "line_number": 174, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 180, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 180, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 185, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 185, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 190, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 190, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 193, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 193, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 198, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 198, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 203, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 203, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 207, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 207, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 212, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 212, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 217, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 217, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 222, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 222, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 228, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 228, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 232, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 232, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 236, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 236, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 240, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 240, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 244, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 244, "usage_type": "name"}, {"api_name": "airflow.models.Connection", "line_number": 248, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 248, "usage_type": "name"}, {"api_name": "airflow.models.KnownEventType", "line_number": 253, "usage_type": "attribute"}, {"api_name": "airflow.models", "line_number": 253, "usage_type": "name"}, {"api_name": "airflow.models.DagBag", "line_number": 266, "usage_type": "call"}, {"api_name": "airflow.models", "line_number": 266, "usage_type": "name"}, {"api_name": "airflow.models.Chart", "line_number": 268, "usage_type": "attribute"}, {"api_name": "airflow.models", "line_number": 268, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 287, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 288, "usage_type": "call"}, {"api_name": "os.path", "line_number": 288, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 288, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "attribute"}, {"api_name": "alembic.config.Config", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path", "line_number": 290, "usage_type": "attribute"}, {"api_name": "airflow.configuration.get", "line_number": 293, "usage_type": "call"}, {"api_name": "airflow.configuration", "line_number": 293, "usage_type": "name"}, {"api_name": "alembic.command.upgrade", "line_number": 294, "usage_type": "call"}, {"api_name": "alembic.command", "line_number": 294, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 303, "usage_type": "call"}, {"api_name": "airflow.models.Base.metadata.drop_all", "line_number": 304, "usage_type": "call"}, {"api_name": "airflow.models.Base", "line_number": 304, "usage_type": "attribute"}, {"api_name": "airflow.models", "line_number": 304, "usage_type": "name"}, {"api_name": "airflow.settings.engine", "line_number": 304, "usage_type": "attribute"}, {"api_name": "airflow.settings", "line_number": 304, "usage_type": "name"}, {"api_name": "alembic.migration.MigrationContext.configure", "line_number": 305, "usage_type": "call"}, {"api_name": "alembic.migration.MigrationContext", "line_number": 305, "usage_type": "name"}, {"api_name": "airflow.settings.engine", "line_number": 305, "usage_type": "attribute"}, {"api_name": "airflow.settings", "line_number": 305, "usage_type": "name"}, {"api_name": "airflow.settings.engine", "line_number": 306, "usage_type": "attribute"}, {"api_name": "airflow.settings", "line_number": 306, "usage_type": "name"}, {"api_name": "airflow.settings.engine", "line_number": 307, "usage_type": "attribute"}, {"api_name": "airflow.settings", "line_number": 307, "usage_type": "name"}, {"api_name": "past.builtins.basestring", "line_number": 312, "usage_type": "argument"}, {"api_name": "re.match", "line_number": 317, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 358, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 358, "usage_type": "name"}, {"api_name": "six.string_types", "line_number": 361, "usage_type": "attribute"}, {"api_name": "croniter.croniter", "line_number": 363, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 364, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 371, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 379, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 381, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 395, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 395, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 408, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 440, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 441, "usage_type": "call"}, {"api_name": "inspect.getargspec", "line_number": 457, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 431, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 476, "usage_type": "attribute"}, {"api_name": "builtins.input", "line_number": 487, "usage_type": "call"}, {"api_name": "airflow.configuration.get", "line_number": 502, "usage_type": "call"}, {"api_name": "airflow.configuration", "line_number": 502, "usage_type": "name"}, {"api_name": "past.builtins.basestring", "line_number": 504, "usage_type": "argument"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 512, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 516, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 520, "usage_type": "call"}, {"api_name": "os.path", "line_number": 520, "usage_type": "attribute"}, {"api_name": "email.mime.application.MIMEApplication", "line_number": 522, "usage_type": "call"}, {"api_name": "airflow.configuration.get", "line_number": 532, "usage_type": "call"}, {"api_name": "airflow.configuration", "line_number": 532, "usage_type": "name"}, {"api_name": "airflow.configuration.getint", "line_number": 533, "usage_type": "call"}, {"api_name": "airflow.configuration", "line_number": 533, "usage_type": "name"}, {"api_name": "airflow.configuration.get", "line_number": 534, "usage_type": "call"}, {"api_name": "airflow.configuration", "line_number": 534, "usage_type": "name"}, {"api_name": "airflow.configuration.get", "line_number": 535, "usage_type": "call"}, {"api_name": "airflow.configuration", "line_number": 535, "usage_type": "name"}, {"api_name": "airflow.configuration.getboolean", "line_number": 536, "usage_type": "call"}, {"api_name": "airflow.configuration", "line_number": 536, "usage_type": "name"}, {"api_name": "airflow.configuration.getboolean", "line_number": 537, "usage_type": "call"}, {"api_name": "airflow.configuration", "line_number": 537, "usage_type": "name"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 540, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 540, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 545, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 545, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 565, "usage_type": "call"}, {"api_name": "os.path", "line_number": 565, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 566, "usage_type": "call"}, {"api_name": "os.path", "line_number": 566, "usage_type": "attribute"}, {"api_name": "imp.find_module", "line_number": 567, "usage_type": "call"}, {"api_name": "imp.load_module", "line_number": 568, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 573, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 592, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 597, "usage_type": "call"}, {"api_name": "errno.ENOENT", "line_number": 600, "usage_type": "attribute"}, {"api_name": "contextlib.contextmanager", "line_number": 590, "usage_type": "name"}, {"api_name": "builtins.object", "line_number": 608, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 617, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 622, "usage_type": "call"}, {"api_name": "signal.SIGALRM", "line_number": 622, "usage_type": "attribute"}, {"api_name": "signal.alarm", "line_number": 623, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 625, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 626, "usage_type": "call"}, {"api_name": "signal.alarm", "line_number": 630, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 632, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 633, "usage_type": "call"}, {"api_name": "past.builtins.basestring", "line_number": 640, "usage_type": "argument"}, {"api_name": "datetime.datetime.min", "line_number": 654, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 654, "usage_type": "name"}, {"api_name": "six.string_types", "line_number": 675, "usage_type": "attribute"}, {"api_name": "croniter.croniter", "line_number": 677, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 678, "usage_type": "argument"}, {"api_name": "datetime.timedelta", "line_number": 685, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 753, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 756, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 758, "usage_type": "argument"}, {"api_name": "numpy.int_", "line_number": 760, "usage_type": "attribute"}, {"api_name": "numpy.intc", "line_number": 760, "usage_type": "attribute"}, {"api_name": "numpy.intp", "line_number": 760, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 760, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 760, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 761, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 761, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 761, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 761, "usage_type": "attribute"}, {"api_name": "numpy.uint32", "line_number": 762, "usage_type": "attribute"}, {"api_name": "numpy.uint64", "line_number": 762, "usage_type": "attribute"}, {"api_name": "numpy.bool_", "line_number": 764, "usage_type": "attribute"}, {"api_name": "numpy.float_", "line_number": 766, "usage_type": "attribute"}, {"api_name": "numpy.float16", "line_number": 766, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 766, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 766, "usage_type": "attribute"}, {"api_name": "numpy.complex_", "line_number": 767, "usage_type": "attribute"}, {"api_name": "numpy.complex64", "line_number": 767, "usage_type": "attribute"}, {"api_name": "numpy.complex128", "line_number": 767, "usage_type": "attribute"}, {"api_name": "json.JSONEncoder.default", "line_number": 771, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 771, "usage_type": "attribute"}, {"api_name": "builtins.object", "line_number": 774, "usage_type": "name"}, {"api_name": "logging.root.getChild", "line_number": 784, "usage_type": "call"}, {"api_name": "logging.root", "line_number": 784, "usage_type": "attribute"}]} +{"seq_id": "573351702", "text": "import os\nimport requests\nimport hashlib\nimport pymongo\nfrom retry import retry\n\ntoken = 'F54F52381C49BB9EB4A33EB1B65604AE4B71A28AEE53518A94A2F360408B9056D57553931D15CE6DDE765562DAD286BA38E05A4CDAFC51C3D527A4959BF8E75A3B95DB7108FCEA340DDE61925616DB55118E1851E67D83EAD800460D100D6B667A4ED8EE67C8F7FB'\ndata = {\n 'token': token,\n}\nurl = 'http://open.fangjia.com/files/upload'\n\n\n# def eachFile(filepath):\n# pathDir = os.listdir(filepath)\n# for allDir in pathDir:\n# print(allDir)\n# child = os.path.join('%s\\%s' % (filepath, allDir))\n# files = {'file': open(child, 'rb')}\n# reslut = requests.post(url=url, files=files, data=data)\n# print(reslut.text)\n# break\n\n\ndef get_collection_object(host, port, db_name, collection_name):\n client = pymongo.MongoClient(host, port)\n db = client[db_name]\n collection = db[collection_name]\n return collection\n\n\nm = get_collection_object('192.168.0.235', 27017, 'image_test', 'image_test')\n\n\n@retry(tries=3)\ndef retry_(result):\n try:\n print(1 / 0)\n link = result.json()['result']['link']\n return link\n except Exception as e:\n print('重试一次-----------------------------------')\n raise\n\n\ndef start():\n for i in m.find():\n image_list = i['image_list']\n _id = i['_id']\n img_list = []\n for i in image_list:\n img = i['img']\n m1 = hashlib.md5()\n m1.update(img.encode('utf-8'))\n md5_url = m1.hexdigest()\n file_path = 'D:\\\\imagesss\\\\' + md5_url + '.png'\n try:\n files = {'file': open(file_path, 'rb')}\n result = requests.post(url=url, files=files, data=data)\n link = retry_(result)\n\n i['link'] = link\n img_list.append(i)\n\n except Exception as e:\n print('没有图片')\n\n print(img_list)\n m.update({'_id': _id}, {'$set': {'image_list': img_list}})\n\n\nif __name__ == '__main__':\n start()\n", "sub_path": "hilder_other/community_image/upload.py", "file_name": "upload.py", "file_ext": "py", "file_size_in_byte": 2035, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pymongo.MongoClient", "line_number": 26, "usage_type": "call"}, {"api_name": "retry.retry", "line_number": 35, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "458336437", "text": "import sys\nfrom PyQt5 import QtWidgets\nfrom core import Client, ServerInterchange\nfrom ui import GuiChatroomWindow, GuiMainWindow\n\n\nclass MainWindow2(QtWidgets.QMainWindow, GuiChatroomWindow):\n def __init__(self, parent=None):\n super(MainWindow2, self).__init__(parent)\n chatroom_name = self.listWidget.selectedIndexes()[0].data()\n nickname = self.lineEdit.text()\n joined_chatroom = ServerInterchange.join_chat(chatroom_name, nickname, client.rcv.port)\n self.setup_chatroom_ui(self.window, joined_chatroom, client, chatroom_name, nickname)\n self.pushButton_2.clicked.connect(self.openFirst)\n self.pushButton_2.clicked.connect(self.hide)\n\n def openFirst(self):\n self.FW = MainWindow()\n self.FW.show()\n\n\nclass MainWindow(QtWidgets.QMainWindow, GuiMainWindow):\n def __init__(self, parent=None):\n super(MainWindow, self).__init__(parent)\n self.setup_ui(self)\n self.pushButton.clicked.connect(self.openSecond)\n self.pushButton.clicked.connect(self.hide)\n\n def openSecond(self):\n self.SW = MainWindow2()\n self.SW.show()\n\n\nif __name__ == \"__main__\":\n client = Client()\n client.start()\n\n rooms = ServerInterchange.list_rooms()\n\n app = QtWidgets.QApplication(sys.argv)\n main_window = QtWidgets.QMainWindow()\n ui = GuiMainWindow()\n ui.setup_ui(main_window, rooms)\n main_window.show()\n app.exec_()\n", "sub_path": "client/gui.py", "file_name": "gui.py", "file_ext": "py", "file_size_in_byte": 1431, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 7, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 7, "usage_type": "name"}, {"api_name": "ui.GuiChatroomWindow", "line_number": 7, "usage_type": "name"}, {"api_name": "core.ServerInterchange.join_chat", "line_number": 12, "usage_type": "call"}, {"api_name": "core.ServerInterchange", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 22, "usage_type": "name"}, {"api_name": "ui.GuiMainWindow", "line_number": 22, "usage_type": "name"}, {"api_name": "core.Client", "line_number": 35, "usage_type": "call"}, {"api_name": "core.ServerInterchange.list_rooms", "line_number": 38, "usage_type": "call"}, {"api_name": "core.ServerInterchange", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 40, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 41, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 41, "usage_type": "name"}, {"api_name": "ui.GuiMainWindow", "line_number": 42, "usage_type": "call"}, {"api_name": "ui.setup_ui", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "386532488", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2010 - 2021, Fraunhofer-Gesellschaft zur Foerderung der angewandten Forschung e.V.\n# All rights reserved.\n#\n# SPDX-License-Identifier: BSD-3-Clause\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#\n# 1. Redistributions of source code must retain the above copyright notice, this\n# list of conditions and the following disclaimer.\n#\n# 2. Redistributions in binary form must reproduce the above copyright notice,\n# this list of conditions and the following disclaimer in the documentation\n# and/or other materials provided with the distribution.\n#\n# 3. Neither the name of the copyright holder nor the names of its\n# contributors may be used to endorse or promote products derived from\n# this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n#\n# We kindly request you to use one or more of the following phrases to refer to\n# foxBMS in your hardware, software, documentation or advertising materials:\n#\n# - \"This product uses parts of foxBMS®\"\n# - \"This product includes parts of foxBMS®\"\n# - \"This product is derived from foxBMS®\"\n\n\"\"\"Template for Python scripts\"\"\"\n\nimport logging\nimport argparse\n\n\ndef main():\n \"\"\"This script does this and that\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"-v\",\n \"--verbosity\",\n dest=\"verbosity\",\n action=\"count\",\n default=0,\n help=\"set verbosity level\",\n )\n args = parser.parse_args()\n\n if args.verbosity == 1:\n logging.basicConfig(level=logging.INFO)\n elif args.verbosity > 1:\n logging.basicConfig(level=logging.DEBUG)\n else:\n logging.basicConfig(level=logging.ERROR)\n logging.debug(args)\n\n with open(\"multiplexed_cellVoltages_for_sym_file.txt\", \"w\") as f:\n # create .sym file messages for 54 * 4 cell voltages\n for i in range(0, 54):\n f.write(\"[foxBMS_CellVoltage]\\n\")\n if i == 0:\n f.write(\"ID=240h\\n\")\n f.write(\"DLC=8\\n\")\n f.write(\n \"Mux=mux_cellVoltage_\"\n + str(i * 4)\n + \"_\"\n + str((i * 4) + 3)\n + \" 0,8 \"\n + hex(i)[2:].upper()\n + \"h -m\\n\"\n )\n f.write(\"Var=cellVoltage_\" + str(i * 4) + \"_invalidFlag unsigned 11,1 -m\\n\")\n f.write(\n \"Var=cellVoltage_\"\n + str((i * 4) + 1)\n + \"_invalidFlag unsigned 10,1 -m\\n\"\n )\n f.write(\n \"Var=cellVoltage_\" + str((i * 4) + 2) + \"_invalidFlag unsigned 9,1 -m\\n\"\n )\n f.write(\n \"Var=cellVoltage_\" + str((i * 4) + 3) + \"_invalidFlag unsigned 8,1 -m\\n\"\n )\n f.write(\"Var=cellVoltage_\" + str(i * 4) + \" unsigned 12,13 -m /u:mV\\n\")\n f.write(\n \"Var=cell_voltage_\" + str((i * 4) + 1) + \" unsigned 25,13 -m /u:mV\\n\"\n )\n f.write(\n \"Var=cell_voltage_\" + str((i * 4) + 2) + \" unsigned 38,13 -m /u:mV\\n\"\n )\n f.write(\n \"Var=cell_voltage_\" + str((i * 4) + 3) + \" unsigned 51,13 -m /u:mV\\n\"\n )\n f.write(\"\\n\")\n\n with open(\"multiplexed_cellTemperatures_for_sym_file.txt\", \"w\") as f:\n # create .sym file messages for 30 * 6 cell temperatures\n for i in range(0, 30):\n f.write(\"[foxBMS_CellTemperature]\\n\")\n if i == 0:\n f.write(\"ID=250h\\n\")\n f.write(\"DLC=8\\n\")\n f.write(\n \"Mux=mux_cellTemperature_\"\n + str(i * 6)\n + \"_\"\n + str((i * 6) + 5)\n + \" 0,8 \"\n + hex(i)[2:].upper()\n + \"h -m\\n\"\n )\n f.write(\n \"Var=cellTemperature_\" + str(i * 6) + \"_invalidFlag unsigned 15,1 -m\\n\"\n )\n f.write(\n \"Var=cellTemperature_\"\n + str((i * 6) + 1)\n + \"_invalidFlag unsigned 14,1 -m\\n\"\n )\n f.write(\n \"Var=cellTemperature_\"\n + str((i * 6) + 2)\n + \"_invalidFlag unsigned 13,1 -m\\n\"\n )\n f.write(\n \"Var=cellTemperature_\"\n + str((i * 6) + 3)\n + \"_invalidFlag unsigned 12,1 -m\\n\"\n )\n f.write(\n \"Var=cellTemperature_\"\n + str((i * 6) + 4)\n + \"_invalidFlag unsigned 11,1 -m\\n\"\n )\n f.write(\n \"Var=cellTemperature_\"\n + str((i * 6) + 5)\n + \"_invalidFlag unsigned 10,1 -m\\n\"\n )\n f.write(\"Var=cellTemperature_\" + str(i * 6) + \" signed 16,8 -m /u:degC\\n\")\n f.write(\n \"Var=cellTemperature_\" + str((i * 6) + 1) + \" signed 24,8 -m /u:degC\\n\"\n )\n f.write(\n \"Var=cellTemperature_\" + str((i * 6) + 2) + \" signed 32,8 -m /u:degC\\n\"\n )\n f.write(\n \"Var=cellTemperature_\" + str((i * 6) + 3) + \" signed 40,8 -m /u:degC\\n\"\n )\n f.write(\n \"Var=cellTemperature_\" + str((i * 6) + 4) + \" signed 48,8 -m /u:degC\\n\"\n )\n f.write(\n \"Var=cellTemperature_\" + str((i * 6) + 5) + \" signed 56,8 -m /u:degC\\n\"\n )\n f.write(\"\\n\")\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "tools/dbc/symbol_creator.py", "file_name": "symbol_creator.py", "file_ext": "py", "file_size_in_byte": 6379, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 61, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 61, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 63, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 65, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 65, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "433901585", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Aug 30 10:54:21 2017\r\n\r\n@author: ishort\r\n\"\"\"\r\n\r\n#plotting:\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\n#%matplotlib inline\r\nimport pylab\r\n\r\nfrom functools import reduce\r\nimport subprocess\r\nimport os\r\nimport sys\r\n\r\n#General file for printing ad hoc quantities\r\n#dbgHandle = open(\"debug.out\", 'w')\r\n\r\nthisOS = \"unknown\" #default\r\nmyOS= \"\"\r\n#returns 'posix' form unix-like OSes and 'nt' for Windows??\r\nthisOS = os.name\r\nprint(\"\")\r\nprint(\"Running on OS: \", thisOS)\r\nprint(\"\")\r\n\r\nabsPath0 = \"./\" #default\r\n\r\nif thisOS == \"nt\": \r\n #windows\r\n absPath0 = subprocess.check_output(\"cd\", shell=True) \r\n backSpace = 2\r\nelif thisOS == \"posix\":\r\n absPath0 = subprocess.check_output(\"pwd\", shell=True)\r\n backSpace = 1\r\n \r\nabsPath0 = bytes.decode(absPath0)\r\n\r\n#remove OS_dependent trailing characters 'r\\n'\r\nnCharsPath = len(absPath0)\r\nnCharsPath -= backSpace\r\nabsPath0 = absPath0[0: nCharsPath]\r\n\r\nslashIndex = absPath0.find('\\\\') #The first backslash is the escape character!\r\nwhile slashIndex != -1:\r\n #python strings are immutable:\r\n absPathCopy = absPath0[0: slashIndex]\r\n absPathCopy += '/'\r\n absPathCopy += absPath0[slashIndex+1: len(absPath0)]\r\n absPath0 = absPathCopy\r\n #print(absPathCopy, absPath0)\r\n slashIndex = absPath0.find('\\\\')\r\n \r\nabsPath = absPath0 + '/'\r\n\r\n\r\n#Now get the synthetic spectrum pre-computed with ChromaStarPy\r\nmodelPath = absPath + \"Outputs/\"\r\n#outPath = absPath + \"Outputs/\"\r\n\r\n\r\nproject = \"Project\"\r\nrunVers = \"RunGas\"\r\nteff = 3600.0\r\nlogg = 1.0\r\nlog10ZScale = 0.0 \r\nlambdaStart = 695.0 \r\nlambdaStop = 700.0 \r\nfileStem = project + \"-\"\\\r\n + str(round(teff, 7)) + \"-\" + str(round(logg, 3)) + \"-\" + str(round(log10ZScale, 3))\\\r\n + \"-\" + str(round(lambdaStart, 5)) + \"-\" + str(round(lambdaStop, 5))\\\r\n + \"-\" + runVers \r\ninFile = modelPath + fileStem + \".ppress.txt\"\r\n\r\n\r\n#whichSpec = \"Ca+\"\r\n#whichSpec = [\"C\", \"N\", \"O\", \"Na\", \"Mg\", \"Si\", \"S\", \"K\", \"Ca\", \"Fe\"]\r\n#colrSpec = [\"black\", \"brown\", \"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"indigo\", \"violet\", \"gray\"]\r\n#whichIon = [\"Na+\", \"Mg+\", \"Si+\", \"S+\", \"K+\", \"Ca+\", \"Fe+\"]\r\n#colrIon = [\"orange\", \"yellow\", \"green\", \"blue\", \"indigo\", \"violet\", \"gray\"]\r\nwhichSpec = [\"H2\", \"C2\", \"O2\", \"N2\", \"CO\", \"OH\", \"CN\", \"TiO\", \"CaH\", \"CaO\"]\r\ncolrSpec = [\"black\", \"brown\", \"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"indigo\", \"violet\", \"gray\"]\r\nwhichIon = [\"H2+\", \"H2O\", \"CaOH\"]\r\ncolrIon = [\"black\", \"brown\", \"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"indigo\", \"violet\", \"gray\"]\r\nthisSpec = 0 #default initialization (H)\r\n\r\nnumSampleDepths = 48 \r\n#numSampleDepths = 2 #debug\r\nnumSpecies = 105\r\n#numSpecies = 3 #debug\r\n\r\n#numStr = fields[0].strip() #first field is number of following records\r\n#num = int(numStr)\r\nspecies = [0.0 for i in range(numSpecies)]\r\nlogTau = [0.0 for i in range(numSampleDepths)]\r\nlogTkin = [0.0 for i in range(numSampleDepths)]\r\nlogPGas = [0.0 for i in range(numSampleDepths)]\r\nlogPe = [0.0 for i in range(numSampleDepths)]\r\nlogPP = [ [ 0.0 for j in range(numSpecies) ] for i in range(numSampleDepths)]\r\n\r\n\r\nfileTeff = 0.0\r\nfileLogg = 0.0\r\nfileLogZ = 0.0\r\n\r\n\r\nwith open(inFile, 'r') as inputHandle: \r\n \r\n #Expects number of records on first lines, then white space delimited columns of\r\n #wavelengths in nm and continuum rectified fluxes\r\n inLine = inputHandle.readline() #line of header\r\n print(inLine)\r\n fields = inLine.split()\r\n fileTeff = float(fields[1].strip())\r\n fileLogg = float(fields[3].strip())\r\n fileZ = float(fields[5].strip())\r\n if ( (fileTeff != teff) or\r\n (fileLogg != logg) or\r\n (fileLogZ != log10ZScale) ):\r\n print(\" \")\r\n print(\" !!!!!!!!!!!!!!!!!!!!!!\")\r\n print(\" \")\r\n print(\"Mismatch between input file name and stellar paramters in input file!\")\r\n print(\" \")\r\n print(\" !!!!!!!!!!!!!!!!!!!!!!\")\r\n print(\" \") \r\n\r\n#Header line \r\n inLine = inputHandle.readline()\r\n print(inLine)\r\n \r\n \r\n #Get the synthetic spectrum\r\n for i in range(numSampleDepths):\r\n #Begin reading data - each depthwise record is two lines:\r\n #line 1 has depth and environmental paramters\r\n #line 2 has specieswise partial pressures\r\n inLine1 = inputHandle.readline()\r\n #print(inLine1)\r\n fields = inLine1.split()\r\n logTau[i] = float(fields[1].strip())\r\n logTkin[i] = float(fields[3].strip())\r\n logPGas[i] = float(fields[6].strip())\r\n logPe[i] = float(fields[9].strip()) \r\n #Relative to total gas pressure for plot:\r\n logPe[i] = logPe[i] - logPGas[i]\r\n \r\n inLine2 = inputHandle.readline()\r\n #print(inLine2)\r\n fields = inLine2.split()\r\n for j in range(numSpecies):\r\n species[j] = fields[2*j].strip()\r\n #if (species[j] == whichSpec):\r\n # thisSpec = j\r\n logPP[i][j] = float(fields[(2*j) + 1].strip())\r\n #Relative to total gas pressure for plot:\r\n logPP[i][j] = logPP[i][j] - logPGas[i]\r\n #print(\"j \", j, \" 2*j \", 2*j, \" 2*j+1 \", (2*j)+1, \" species \", species[j], \" pp \", logPP[i][j])\r\n \r\n \r\n#plot some partial pressures \r\n#plt.title('Synthetic spectrum')\r\nplt.figure()\r\nplt.subplot(1, 1, 1) \r\n#plt.ylabel(r'$\\log P$ dynes cm$^{\\rm -2}$', fontsize=14)\r\nplt.ylabel(r'$\\log_{10} (P/P_{\\rm H})$', fontsize=14)\r\nplt.xlabel(r'$\\log_{10}\\tau_{\\rm Ros}$') \r\nxMin = min(logTau)\r\nxMax = max(logTau)\r\npylab.xlim(xMin, xMax)\r\npylab.ylim(-12.0, 0.0) \r\n#thisSpec = 3\r\ncolr = 0\r\n\r\nfor wS in whichSpec:\r\n \r\n for i in range(numSpecies):\r\n if (species[i] == wS):\r\n thisSpec = i\r\n print(\"Species: \", species[thisSpec])\r\n\r\n #print(\"At plot:\")\r\n #print (\"logPP \", [logPP[i][0] for i in range(numSampleDepths)]) \r\n # skip first depth point [i=0] - upper boundary condition:\r\n pylab.plot( [logTau[i] for i in range(1, numSampleDepths)],\\\r\n [logPP[i][thisSpec] for i in range(1, numSampleDepths)],\\\r\n color=colrSpec[colr], linewidth=2)\r\n pylab.text(logTau[4], logPP[4][thisSpec], species[thisSpec],\\\r\n color=colrSpec[colr], fontsize=13, weight='bold')\r\n colr+=1\r\n \r\n#pylab.plot(logTau, logPe, 'o', color='black')\r\n#pylab.text(logTau[numSampleDepths-3], logPe[numSampleDepths-3], 'Pe',\\\r\n# color='black', fontsize=13, weight='bold')\r\n\r\ncolr = 0 \r\nfor wI in whichIon:\r\n \r\n for i in range(numSpecies):\r\n if (species[i] == wI):\r\n thisSpec = i\r\n print(\"Species: \", species[thisSpec])\r\n \r\n pylab.plot([logTau[i] for i in range(1, numSampleDepths)],\\\r\n [logPP[i][thisSpec] for i in range(1, numSampleDepths)],\\\r\n '--', color=colrIon[colr], linewidth=2)\r\n pylab.text(logTau[numSampleDepths-8], logPP[numSampleDepths-8][thisSpec],\\\r\n species[thisSpec], color=colrIon[colr], fontsize=13, weight='bold')\r\n colr+=1\r\n\r\n#Save as encapsulated postscript (eps) for LaTex\r\nepsName = fileStem + \"Mols.eps\"\r\nplt.savefig(epsName, format='eps', dpi=1000)", "sub_path": "PPressPlotMols.py", "file_name": "PPressPlotMols.py", "file_ext": "py", "file_size_in_byte": 7157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.name", "line_number": 25, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 34, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "pylab.xlim", "line_number": 173, "usage_type": "call"}, {"api_name": "pylab.ylim", "line_number": 174, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 188, "usage_type": "call"}, {"api_name": "pylab.text", "line_number": 191, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 207, "usage_type": "call"}, {"api_name": "pylab.text", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 216, "usage_type": "name"}]} +{"seq_id": "52616368", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nimport sys\nfrom PyQt5 import QtGui, QtWidgets, QtCore\nimport random\nimport time\nimport pandas as pd\nfrom datetime import datetime\n\nFIELDS = [\"id\", \"condition\", \"mode\", \"run\", \"pressed_key\",\n \"pressed_correct_key\", \"reaction_time_in_sec\",\n \"time_waited_in_sec\", \"time_stamp\"]\n\n\nclass SpaceRecorder(QtWidgets.QWidget):\n\n def __init__(self, isDarkmode, id):\n super().__init__()\n self.id = id\n self.black = QtGui.QColor(0, 0, 0)\n self.white = QtGui.QColor(255, 255, 255)\n self.width = 1200\n self.height = 800\n self.minRectWidth = 40\n self.maxRectWidth = 200\n self.rectAppeared = False\n self.isDarkmode = isDarkmode\n self.initUI()\n self.timer = QtCore.QTimer()\n self.round = 1\n self.timerStarted = False\n self.color = self.white if self.isDarkmode else self.black\n self.df = pd.DataFrame(columns=FIELDS)\n self.circleAppeared = False\n self.timeWaited = 0\n self.timeWaitedFalse = 0\n\n def showRect(self):\n self.update()\n self.timerStarted = False\n\n def initUI(self):\n # set the text property of the widget we are inheriting\n self.setGeometry(100, 100, self.width, self.height)\n self.setWindowTitle('Darkmode vs Lightmode Test 2')\n # widget should accept focus by click and tab key\n self.setFocusPolicy(QtCore.Qt.StrongFocus)\n self.__setBackgroundColor()\n self.show()\n\n def keyPressEvent(self, ev):\n if ev.key() == QtCore.Qt.Key_Space:\n if self.round <= 20:\n if not self.timerStarted:\n self.__modeOne()\n\n def paintEvent(self, event):\n if not self.timerStarted:\n self.__paintRectOrText(event)\n\n def drawText(self, event, qp):\n if self.rectAppeared:\n self.rectAppeared = False\n elif self.circleAppeared:\n self.circleAppeared = False\n qp.setPen(self.color)\n qp.setFont(QtGui.QFont('Decorative', 32))\n if self.round == 1:\n self.text = \"\"\"Press 'Space' to start. \\nPress 'Space'\n only if a RECTANGLE appears\"\"\"\n if self.round > 1:\n self.text = f'Press \"Space\" to start round {str(self.round)}'\n if self.round == 21:\n self.text = \"You have finished the first test. \\nThank you!\"\n self.df = self.df.to_csv(\n f'/home/erik/assignments/assignment-03-bs/test2_{self.id}.csv',\n index=False)\n qp.drawText(event.rect(), QtCore.Qt.AlignCenter, self.text)\n\n def drawRect(self, event, qp):\n randNum = random.randint(0, 1)\n qp.setBrush(self.color)\n rect = self.__getRandomRect()\n if randNum == 0:\n qp.drawRect(rect)\n self.rectAppeared = True\n else:\n qp.drawEllipse(rect)\n self.circleAppeared = True\n self.timer.singleShot(3000, lambda: self.__drawAgain())\n\n def __getRandomRect(self):\n xPos = random.randint(0, self.width - self.maxRectWidth)\n yPos = random.randint(0, self.height - self.maxRectWidth)\n height = random.randint(self.minRectWidth, self.maxRectWidth)\n return QtCore.QRect(xPos, yPos, height, height)\n\n def __drawAgain(self):\n self.circleAppeared = False\n self.update()\n\n def __setColorScheme(self):\n if self.round == 10:\n self.__changeColorTheme()\n\n def __changeColorTheme(self):\n self.isDarkmode = not self.isDarkmode\n self.color = self.white if self.isDarkmode else self.black\n self.__setBackgroundColor()\n\n def __setBackgroundColor(self):\n if self.isDarkmode:\n self.setStyleSheet('background-color: black')\n else:\n self.setStyleSheet('background-color: white')\n\n def __modeOne(self):\n if not self.rectAppeared and not self.circleAppeared:\n self.timerStarted = True\n self.update()\n self.timeWaited = random.randint(1, 6)\n self.timer.singleShot(self.timeWaited*1000,\n lambda: self.showRect())\n else:\n # catch reation time here\n self.__addRow()\n self.__setColorScheme()\n self.round += 1\n self.update()\n\n def __paintRectOrText(self, event):\n if not self.rectAppeared and not self.circleAppeared:\n qp = QtGui.QPainter()\n qp.begin(self)\n self.drawRect(event, qp)\n qp.end()\n self.startTime = time.time()\n else:\n qp = QtGui.QPainter()\n qp.begin(self)\n self.drawText(event, qp)\n qp.end()\n\n def __addRow(self):\n condition = \"dark\" if self.isDarkmode else \"light\"\n reactionTime = time.time() - self.startTime\n timeStamp = datetime.now()\n run = self.round if self.round <= 10 else self.round-10\n d = {\n 'id': self.id,\n 'condition': condition,\n 'mode': 2,\n 'run': run,\n 'pressed_key': 'space',\n 'pressed_correct_key': True if self.rectAppeared else False,\n 'reaction_time_in_sec': reactionTime,\n 'time_waited_in_sec': self.timeWaited,\n 'time_stamp': timeStamp\n }\n print(d)\n self.df = self.df.append(d, ignore_index=True)\n\n\ndef main():\n isDarkmode = False\n if len(sys.argv) == 3:\n if sys.argv[1] in ('True', 'False'):\n if sys.argv[1] == 'True':\n isDarkmode = True\n else:\n print(\"Argument has to be 'True' or 'False'\")\n sys.exit()\n else:\n print(\"\"\"Set second argument to 'True'\n to start with dark mode or 'False' to start\n with light mode and third argument should be the participantID\"\"\")\n sys.exit()\n app = QtWidgets.QApplication(sys.argv)\n # variable is never used, class automatically\n # registers itself for Qt main loop:\n space = SpaceRecorder(isDarkmode, sys.argv[2])\n sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "reaction_test2.py", "file_name": "reaction_test2.py", "file_ext": "py", "file_size_in_byte": 6197, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 16, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 21, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 21, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 30, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 48, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 48, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 53, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 68, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 79, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 79, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 82, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 94, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 95, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 96, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 97, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 97, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 122, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 134, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 134, "usage_type": "name"}, {"api_name": "time.time", "line_number": 138, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 140, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 140, "usage_type": "name"}, {"api_name": "time.time", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 148, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 148, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 167, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 168, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 169, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 173, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 178, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 179, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 179, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 179, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 182, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 183, "usage_type": "call"}]} +{"seq_id": "236154932", "text": "# image blending\n\nimport cv2\nimport numpy\n\n\n# remember that size of both the images need to be same\nimg1 = cv2.imread('me.jpg')\nimg1 = cv2.resize(img1, (700, 500))\nimg2 = cv2.imread('PM.jpg')\nimg2 = cv2.resize(img2, (700, 500))\n\nval1 = cv2.add(img1, img2)\nval2 = cv2.addWeighted(img1, 0.7, img2, 0.7, 0)\n\ncv2.imshow('add_image1', val1)\ncv2.imshow('add_image2', val2)\ncv2.waitKey(0)\ncv2.destroyAllWindows()", "sub_path": "adding_Images.py", "file_name": "adding_Images.py", "file_ext": "py", "file_size_in_byte": 405, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "cv2.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.add", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "372249445", "text": "#/usr/bin python\nimport datetime\nfrom dateutil.relativedelta import relativedelta\n\nstart='2020-10-21'\nend='2021-01-21'\n\ndatestart=datetime.datetime.strptime(start,'%Y-%m-%d')\ndateend=datetime.datetime.strptime(end,'%Y-%m-%d')\n\n#table_name=\"accounting_journal\"\ntable_name=['sub_ledger_entry','general_ledger_entry','application_journal','adjust_sub_ledger_entry','adjust_ledger_entry','accounting_journal_register','accounting_journal_entry','accounting_journal','adjust_journal_entry']\n\nfor table in table_name:\n start='2019-09-01'\n end='2022-02-01'\n\n datestart=datetime.datetime.strptime(start,'%Y-%m-%d')\n dateend=datetime.datetime.strptime(end,'%Y-%m-%d')\n\n # cteate default partition\n #print(\"CREATE TABLE {0}_default_partition PARTITION OF {0} DEFAULT;\".format(table))\n\n # cteate min partition\n print(\"CREATE TABLE {0}_p201909_before PARTITION OF {0} FOR VALUES FROM (MINVALUE) TO ('2019-09-01 00:00:00'::timestamp);;\".format(table))\n\n # create dayliy partition\n while datestart num_runs:\n raise ValueError()\n if dataframe.shape[1] != len(relevant_parameters) + 1: # plus 1 for y data\n raise ValueError()\n\n dataframe = dataframe.reindex(sorted(dataframe.columns), axis=1)\n\n return dataframe\n\n\ndef get_X_y_from_openml(task_id, flow_id, num_runs, relevant_parameters, cache_directory):\n\n dataframe = get_dataframe_from_openml(task_id, flow_id, num_runs, relevant_parameters, cache_directory)\n\n categorical_columns = set(dataframe.columns) - set(dataframe._get_numeric_data().columns)\n categorical_indices = {dataframe.columns.get_loc(col_name) for col_name in categorical_columns}\n\n y = np.array(dataframe['y'], dtype=np.float)\n\n dataframe.drop('y', 1, inplace=True)\n return dataframe.as_matrix(), y, dataframe.columns.values, categorical_indices\n", "sub_path": "activetesting/utils/connect.py", "file_name": "connect.py", "file_ext": "py", "file_size_in_byte": 4472, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.makedirs", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "openml.evaluations.list_evaluations", "line_number": 23, "usage_type": "call"}, {"api_name": "openml.evaluations", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 28, "usage_type": "call"}, {"api_name": "openmlcontrib.setups.obtain_setups_by_ids", "line_number": 34, "usage_type": "call"}, {"api_name": "openmlcontrib.setups", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 37, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 40, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 110, "usage_type": "attribute"}]} +{"seq_id": "304403426", "text": "import urllib.request as urllib2\nimport pytest\nimport numpy as np\n\nfrom hqbot import answerbot\n\nSIMPLIFY_QUES_DATA = [\n # question, clean_question, neg\n (\n \"Which of these countries is NOT in Scandinavia?\",\n \"countries scandinavia?\",\n True),\n (\n \"The hottest multiplayer video game this year was designed by which famous gamer?\",\n \"hottest multiplayer video game year was designed famous gamer?\",\n False\n ),\n (\n \"Which of these state names is NOT a Native American word or phrase?\",\n \"state names native american word or phrase?\",\n True\n ),\n (\n \"Which pathogen is identifed by abnormally folded, \\\n transmissible proteins causing rare and fatal diseases?\",\n \"pathogen identifed abnormally folded, transmissible proteins causing rare fatal diseases?\",\n False\n )\n]\n\n\ndef test_screen_grab():\n assert isinstance(answerbot.screen_grab(save=False, location=None), np.ndarray)\n\n with pytest.raises(TypeError):\n answerbot.screen_grab(save=False, location=5)\n\n\ndef test_preprocess_img():\n img = answerbot.screen_grab(save=False, location=None)\n assert isinstance(answerbot.preprocess_img(img), np.ndarray)\n\n\ndef test_preprocess_img_exception():\n with pytest.raises(TypeError):\n answerbot.preprocess_img(img=None)\n\n\ndef test_read_screen():\n assert isinstance(answerbot.read_screen(), str)\n\n\ndef test_parse_question():\n question, options = answerbot.parse_question(save=False)\n assert isinstance(question, str)\n assert isinstance(options, list)\n\n\n@pytest.mark.parametrize(\"question,clean_question,neg\", SIMPLIFY_QUES_DATA)\ndef test_simplify_ques(question, clean_question, neg):\n processed_question, actual_neg = answerbot.simplify_ques(question, debug=True)\n assert processed_question == clean_question and actual_neg == neg\n\n with pytest.raises(TypeError):\n answerbot.simplify_ques(question=[])\n\n\n@pytest.mark.parametrize(\"link\", [\n \"https://wikipedia.org/\",\n \"https://en.wikipedia.org/wiki/HQ_Trivia\",\n \"https://google.com/\"\n])\ndef test_get_page(link):\n assert isinstance(answerbot.get_page(link), bytes)\n\n with pytest.raises((urllib2.URLError, urllib2.HTTPError, ValueError)):\n answerbot.get_page(\"4584\")\n", "sub_path": "tests/test_answerbot.py", "file_name": "test_answerbot.py", "file_ext": "py", "file_size_in_byte": 2295, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "hqbot.answerbot.screen_grab", "line_number": 33, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 35, "usage_type": "call"}, {"api_name": "hqbot.answerbot.screen_grab", "line_number": 36, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 36, "usage_type": "name"}, {"api_name": "hqbot.answerbot.screen_grab", "line_number": 40, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 40, "usage_type": "name"}, {"api_name": "hqbot.answerbot.preprocess_img", "line_number": 41, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 45, "usage_type": "call"}, {"api_name": "hqbot.answerbot.preprocess_img", "line_number": 46, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 46, "usage_type": "name"}, {"api_name": "hqbot.answerbot.read_screen", "line_number": 50, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 50, "usage_type": "name"}, {"api_name": "hqbot.answerbot.parse_question", "line_number": 54, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 54, "usage_type": "name"}, {"api_name": "hqbot.answerbot.simplify_ques", "line_number": 61, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 61, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 64, "usage_type": "call"}, {"api_name": "hqbot.answerbot.simplify_ques", "line_number": 65, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 65, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 59, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 59, "usage_type": "attribute"}, {"api_name": "hqbot.answerbot.get_page", "line_number": 74, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 74, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 76, "usage_type": "call"}, {"api_name": "urllib.request.URLError", "line_number": 76, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 76, "usage_type": "name"}, {"api_name": "urllib.request.HTTPError", "line_number": 76, "usage_type": "attribute"}, {"api_name": "hqbot.answerbot.get_page", "line_number": 77, "usage_type": "call"}, {"api_name": "hqbot.answerbot", "line_number": 77, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 68, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 68, "usage_type": "attribute"}]} +{"seq_id": "566864705", "text": "#!/usr/bin/env python\n#-*-coding:utf-8\n\n### Reference ###\n# Author information: Xiaodi Yang, China Agricultural University, email: xiao_di_yang@163.com.\n# Cititation: Yamg,X. et al. (2021) Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction. Bioinformatics.\n# Part of the code was modified from https://github.com/muhaochen/seq_ppi and the corresponding reference is: \n# Chen,M. et al. (2019) Multifaceted protein-protein interaction prediction based on Siamese residual RCNN. Bioinformatics, 35, i305–i314.\n\nfrom __future__ import division\nimport time\nfrom numpy.random import seed\nseed(2066)\nfrom tensorflow import set_random_seed\nset_random_seed(2066)\n\nstart=time.ctime()\nimport os, sys\nif '../embeddings' not in sys.path:\n sys.path.append('../embeddings')\n\nfrom seq2pssm import s2p\n\nimport keras\n\nfrom keras.models import Model\nfrom keras.layers import Input, Dense, Activation, Dropout, merge\nfrom keras.layers.merge import concatenate\nfrom keras.layers.convolutional import Conv1D\nfrom keras.layers.pooling import MaxPooling1D, GlobalMaxPooling1D\nfrom keras.optimizers import Adam\n\nimport numpy as np\nfrom numpy import linalg as LA\nimport scipy\nimport sklearn\nfrom sklearn.metrics import roc_auc_score, average_precision_score\n\nimport tensorflow as tf\nimport keras.backend.tensorflow_backend as KTF\n\n# Set GPU memory fraction\ngpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.80)\nsession=tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\nKTF.set_session(session)\n\n\n\n# Parameter setting\nid1_index = 0\nid2_index = 1\nlabel_index = 2\n\ntest_file, test_id2seq_file, train_virus, test_virus, batch_size, hidden_dim, dense_dim, n_epochs = sys.argv[1:]\nn_epochs = int(n_epochs)\n\nseq_size = 2000\n# batch_size = 64\n# hidden_dim = 64\n# dense_dim = 512\n# n_epochs = 100\nlr = 0.0001\nkernel_size = 3\npooling_size = 2\n\nid2seqindex = {}\nseqs = []\nseqindex = 0\nprint(test_id2seq_file)\nfor line in open(test_id2seq_file):\n line = line.strip().split('\\t')\n id2seqindex[line[0]] = seqindex\n seqs.append(line[1])\n seqindex += 1\n\nfrom tqdm import tqdm\nseq2p = s2p()\nindex2id1,index2id2={},{}\nraw_data = []\nid_array = []\nseq_array = []\nid2index = {}\nindex = 0\nfor line in tqdm(open(test_file)):\n line = line.strip().split('\\t')\n if line[id1_index] not in id2index:\n id2index[line[id1_index]] = index\n index += 1\n id_array.append(line[id1_index])\n seq_array.append(seqs[id2seqindex[line[id1_index]]])\n id1=line[id1_index]\n line[id1_index] = id2index[line[id1_index]]\n index1=line[id1_index]\n index2id1[index1]=id1\n if line[id2_index] not in id2index:\n id2index[line[id2_index]] = index\n index += 1\n id_array.append(line[id2_index])\n seq_array.append(seqs[id2seqindex[line[id2_index]]])\n id2=line[id2_index]\n line[id2_index] = id2index[line[id2_index]]\n index2=line[id2_index]\n index2id2[index2]=id2\n raw_data.append(line)\nprint(len(raw_data))\n\ndim = 20\nseq_tensor = np.array([seq2p.pssm_normalized(id, line, seq_size) for id, line in zip(tqdm(id_array),tqdm(seq_array))], dtype='int32')\ndel id_array\ndel seq_array\n\nseq_index1 = np.array([line[id1_index] for line in tqdm(raw_data)])\nseq_index2 = np.array([line[id2_index] for line in tqdm(raw_data)])\n\nclass_map = {'0':1,'1':0}\nclass_labels = np.zeros((len(raw_data), 2))\nfor i in range(len(raw_data)):\n class_labels[i][class_map[raw_data[i][label_index]]] = 1.\nprint(class_labels)\n\nfrom sklearn.model_selection import KFold, StratifiedKFold, ShuffleSplit\nkf = StratifiedKFold(n_splits=5, random_state=2066, shuffle=True)\ntrain_test = []\nprint(class_labels[:,0])\nfor train, test in kf.split(class_labels[:,0],class_labels[:,0]):\n print(np.sum(class_labels[train], 0)[0],np.sum(class_labels[train], 0)[1])\n train_test.append((train, test))\n\nprint (len(train_test))\n\n# initialization\nn_model = 0\nn_hit = 0\nn_total = 0\nn_pos = 0\nn_true_pos = 0\nn_false_pos = 0\nn_true_neg = 0\nn_false_neg = 0\n\nfrom keras.utils import plot_model\nfrom collections import Counter\nfrom keras.models import load_model\n\ntrain_file='../results/'+train_virus+'_cnnpssm.txt'\nfor train, test in train_test:\n n_model+=1\n # Load model of train dataset\n merge_model=load_model(train_file+str(n_model)+'.h5')\n print(seq_index1[train][0])\n print(merge_model.get_weights()[0][0][0])\n print(class_labels[train][:,0])\n # Set class weight for samples\n counter = Counter(class_labels[train][:,0])\n majority = max(counter.values())\n class_weight = {cls: float(majority / count) for cls, count in counter.items()}\n print(class_weight)\n # Test the model\n pred = merge_model.predict([seq_tensor[seq_index1[test]], seq_tensor[seq_index2[test]]])\n # Output prediction scores (Format: 'label\\tscore_t\\tscore_f\\tid1\\tid2\\n')\n w = open(test_virus+'_'+train_virus+'_cross_viral_test_score'+str(n_model),'w')\n for i in range(len(class_labels[test])):\n n_total += 1\n w.write(str(class_labels[test][i][0])+'\\t'+str(pred[i][0])+'\\t'+str(pred[i][1])+'\\t'+str(index2id1[seq_index1[test][i]])+'\\t'+str(index2id2[seq_index2[test][i]])+'\\n')\n if np.argmax(class_labels[test][i]) == np.argmax(pred[i]):\n n_hit += 1\n if class_labels[test][i][0] > 0.:\n n_pos += 1.\n if pred[i][0] > pred[i][1]:\n n_true_pos += 1\n else:\n n_false_neg += 1\n else:\n if pred[i][0] > pred[i][1]:\n n_false_pos += 1\n else:\n n_true_neg += 1\n w.close()\n\n# Calculate metrics\naccuracy = n_hit / n_total\nprec = n_true_pos / (n_true_pos + n_false_pos)\nrecall = n_true_pos / n_pos\nspec = n_true_neg / (n_true_neg + n_false_pos)\nf1 = 2. * prec * recall / (prec + recall)\nprint (accuracy, prec, recall, f1)\n\nresult_file = '../results/'+test_virus+'_'+train_virus+'_cross_viral_test.txt'\nbasename = test_virus+'_'+train_virus+'_cross_viral_test_score'\nos.system('cat '+basename+'1 '+basename+'2 '+basename+'3 '+basename+'4 '+basename+'5 > '+ result_file)\ndata=np.genfromtxt(result_file, dtype=str)\ny = data[:,0]\nx = data[:,1]\ny = y.astype(float)\nx = x.astype(float)\nauc = roc_auc_score(y,x)\nauprc = average_precision_score(y,x)\n\nend=time.ctime()\nw = open('../Run_result.txt','a')\nif os.popen(\"grep $'Source' ../Run_result.txt\").read():pass\nelse:w.write('Source\\tTarget\\tMethod\\tBatch_size\\tSequence_size\\tn_epochs\\tlearning_rate\\tAUC\\tAUPRC\\tAccuracy\\tPrecision\\tRecall\\tSpecificity\\tF1\\tStart\\tEnd\\n')\nw.write('Human-' + train_virus + '\\tHuman-' + test_virus + '\\tCross viral test\\t' + str(batch_size) + '\\t' + str(seq_size) + '\\t' + str(n_epochs) + '\\t' + str(lr) + '\\t%.3f'%auc + '\\t%.3f'%auprc + '\\t%.3f'%accuracy + '\\t%.3f'%prec + '\\t%.3f'%recall + '\\t%.3f'%spec + '\\t%.3f'%f1 + '\\t'+str(start) + '\\t' + str(end) + '\\n')\n", "sub_path": "script/cnnpssm_cross_viruses.py", "file_name": "cnnpssm_cross_viruses.py", "file_ext": "py", "file_size_in_byte": 6872, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.random.seed", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.set_random_seed", "line_number": 15, "usage_type": "call"}, {"api_name": "time.ctime", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.GPUOptions", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend.set_session", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend", "line_number": 45, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 54, "usage_type": "attribute"}, {"api_name": "seq2pssm.s2p", "line_number": 77, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 116, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 126, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 149, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 165, "usage_type": "call"}, {"api_name": "os.system", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 191, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 196, "usage_type": "call"}, {"api_name": "sklearn.metrics.average_precision_score", "line_number": 197, "usage_type": "call"}, {"api_name": "time.ctime", "line_number": 199, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "269992604", "text": "# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT License.\n\nimport sys\nfrom pathlib import Path\n\nimport qlib\nimport fire\nimport pandas as pd\nimport ruamel.yaml as yaml\nfrom qlib.utils import init_instance_by_config, flatten_dict\nfrom qlib.workflow import R\nfrom qlib.workflow.record_temp import SignalRecord\n\n\n# worflow handler function\ndef workflow(config_path, experiment_name=\"workflow\"):\n with open(config_path) as fp:\n config = yaml.load(fp, Loader=yaml.Loader)\n\n provider_uri = config.get(\"provider_uri\")\n region = config.get(\"region\")\n qlib.init(provider_uri=provider_uri, region=region)\n\n # model initiaiton\n model = init_instance_by_config(config.get(\"task\")[\"model\"])\n dataset = init_instance_by_config(config.get(\"task\")[\"dataset\"])\n\n # start exp\n with R.start(experiment_name=experiment_name):\n # train model\n R.log_params(**flatten_dict(config.get(\"task\")))\n model.fit(dataset)\n recorder = R.get_recorder()\n\n # generate records: prediction, backtest, and analysis\n for record in config.get(\"task\")[\"record\"]:\n if record[\"class\"] == SignalRecord.__name__:\n srconf = {\"model\": model, \"dataset\": dataset, \"recorder\": recorder}\n record[\"kwargs\"].update(srconf)\n sr = init_instance_by_config(record)\n sr.generate()\n else:\n rconf = {\"recorder\": recorder}\n record[\"kwargs\"].update(rconf)\n ar = init_instance_by_config(record)\n ar.generate()\n\n\n# function to run worklflow by config\ndef run():\n fire.Fire(workflow)\n\n\nif __name__ == \"__main__\":\n run()\n", "sub_path": "qlib/workflow/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 1695, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "ruamel.yaml.load", "line_number": 19, "usage_type": "call"}, {"api_name": "ruamel.yaml", "line_number": 19, "usage_type": "name"}, {"api_name": "ruamel.yaml.Loader", "line_number": 19, "usage_type": "attribute"}, {"api_name": "qlib.init", "line_number": 23, "usage_type": "call"}, {"api_name": "qlib.utils.init_instance_by_config", "line_number": 26, "usage_type": "call"}, {"api_name": "qlib.utils.init_instance_by_config", "line_number": 27, "usage_type": "call"}, {"api_name": "qlib.workflow.R.start", "line_number": 30, "usage_type": "call"}, {"api_name": "qlib.workflow.R", "line_number": 30, "usage_type": "name"}, {"api_name": "qlib.workflow.R.log_params", "line_number": 32, "usage_type": "call"}, {"api_name": "qlib.workflow.R", "line_number": 32, "usage_type": "name"}, {"api_name": "qlib.utils.flatten_dict", "line_number": 32, "usage_type": "call"}, {"api_name": "qlib.workflow.R.get_recorder", "line_number": 34, "usage_type": "call"}, {"api_name": "qlib.workflow.R", "line_number": 34, "usage_type": "name"}, {"api_name": "qlib.workflow.record_temp.SignalRecord.__name__", "line_number": 38, "usage_type": "attribute"}, {"api_name": "qlib.workflow.record_temp.SignalRecord", "line_number": 38, "usage_type": "name"}, {"api_name": "qlib.utils.init_instance_by_config", "line_number": 41, "usage_type": "call"}, {"api_name": "qlib.utils.init_instance_by_config", "line_number": 46, "usage_type": "call"}, {"api_name": "fire.Fire", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "115038458", "text": "import re\nimport warnings\nfrom collections import namedtuple\n\nfrom url_parser.public_suffix_list import PublicSuffixList\n\nUrlObject = namedtuple(\n 'UrlObject', [\n 'protocol',\n 'www',\n 'sub_domain',\n 'domain',\n 'top_domain',\n 'path',\n 'dir',\n 'file',\n 'fragment',\n 'query'\n ])\n\n\ndef _split_query_group(query_groups: list) -> dict:\n result = dict()\n\n for query_group in query_groups:\n query = query_group.split('=')\n\n if len(query) == 1:\n result[query[0]] = None\n continue\n\n result[query[0]] = query[1]\n\n return result\n\n\ndef _parse_url_with_top_domain(url, top_domain):\n regex = r\"^(?:(?P[\\w\\d]+)(?:\\:\\/\\/))?\" \\\n r\"(?P\" \\\n r\"(?P(?:www)?)(?:\\.?)\" \\\n r\"(?:(?:[\\w\\d-]+|\\.)*?)?\" \\\n r\")(?:\\.?)\" \\\n r\"(?P[^./]+(?=\\.))\\.\" \\\n r\"(?P\" + re.escape(top_domain) + r\"(?![^/?#]))\" \\\n r\"(?P\" \\\n r\"(?P\\/(?:[^/\\r\\n]+(?:/))+)?\" \\\n r\"(?:\\/?)(?P[^?#\\r\\n]+)?\" \\\n r\")?\" \\\n r\"(?:\\#(?P[^#?\\r\\n]*))?\" \\\n r\"(?:\\?(?P.*(?=$)))*$\"\n\n dict_data = {\n 'protocol': None,\n 'www': None,\n 'sub_domain': None,\n 'domain': None,\n 'top_domain': None,\n 'path': None,\n 'dir': None,\n 'file': None,\n 'fragment': None,\n 'query': None,\n }\n\n match = re.search(regex, url)\n\n dict_data['protocol'] = match.group('protocol') if match.group('protocol') else None\n dict_data['www'] = match.group('www') if match.group('www') else None\n dict_data['sub_domain'] = match.group('sub_domain') if match.group('sub_domain') else None\n dict_data['domain'] = match.group('domain')\n dict_data['top_domain'] = top_domain\n dict_data['path'] = match.group('path') if match.group('path') else None\n dict_data['dir'] = match.group('dir') if match.group('dir') else None\n dict_data['file'] = match.group('file') if match.group('file') else None\n dict_data['fragment'] = match.group('fragment') if match.group('fragment') else None\n\n query = match.group('query') if match.group('query') else None\n\n if query is not None:\n query_groups = query.split('&')\n query = _split_query_group(query_groups)\n dict_data['query'] = query\n\n return dict_data\n\n\ndef _parse_url_with_public_suffix(url):\n public_suffix = PublicSuffixList.get_list()\n public_suffix.sort()\n\n domain_regex = r\"(?:^|\\/)(?P[^:/#?]+)(?:[/#?]|$)\"\n match = re.search(domain_regex, url)\n domain = match.group('domain')\n domain_parts = domain.split('.')\n\n top_domain = None\n\n for i in range(len(domain_parts)):\n tail_gram = domain_parts[i:len(domain_parts)]\n tail_gram = '.'.join(tail_gram)\n\n if tail_gram in public_suffix:\n top_domain = tail_gram\n break\n\n data = _parse_url_with_top_domain(url, top_domain)\n\n return data\n\n\ndef get_base_url(url: str) -> str:\n url = get_url(url)\n protocol = str(url.protocol) + '://' if url.protocol is not None else 'http://'\n www = 'www.' if url.www is not None else ''\n sub_domain = str(url.sub_domain) + '.' if url.sub_domain is not None and url.sub_domain != 'www.' else ''\n return protocol + www + sub_domain + url.domain + '.' + url.top_domain\n\n\ndef get_url(url: str) -> UrlObject:\n data = _parse_url_with_public_suffix(url)\n\n object_data = UrlObject(\n protocol=data['protocol'],\n www=data['www'],\n sub_domain=data['sub_domain'],\n domain=data['domain'],\n top_domain=data['top_domain'],\n path=data['path'],\n dir=data['dir'],\n file=data['file'],\n fragment=data['fragment'],\n query=data['query'],\n )\n\n return object_data\n\n\ndef parse_url(url: str) -> dict:\n warnings.warn(\n \"parse_url is deprecated, use get_url instead\",\n DeprecationWarning\n )\n\n data = get_url(url)\n return data._asdict()\n", "sub_path": "url_parser/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 4163, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "collections.namedtuple", "line_number": 7, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 44, "usage_type": "call"}, {"api_name": "re.search", "line_number": 65, "usage_type": "call"}, {"api_name": "url_parser.public_suffix_list.PublicSuffixList.get_list", "line_number": 88, "usage_type": "call"}, {"api_name": "url_parser.public_suffix_list.PublicSuffixList", "line_number": 88, "usage_type": "name"}, {"api_name": "re.search", "line_number": 92, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "308524263", "text": "import os, json, requests\nimport pandas as pd\nimport numpy as np\nimport jieba\nimport keras\nfrom sklearn.preprocessing import LabelEncoder\nfrom keras import backend as K\nfrom flask import Flask, request\napp = Flask(__name__)\n\ntf_serving_api = \"http://127.0.0.1:8501/v1/models/VarietyPredictionZh:predict\"\nbase_dir=\"/Users/wxf/Documents/GitHub/FL-WINE-PROJECT/\"\n\njieba_dict_file = base_dir + \"dataset/jieba-dict/dict.txt\"\njieba.load_userdict(jieba_dict_file)\n\ndata = pd.read_csv(base_dir + \"dataset/wine-review/winemag-data-130k-v2-zh-resampled.csv\")\nall_description = data['desc_zh_cut'][:]\n\nencoder = LabelEncoder()\nencoder.fit(data['variety'][:])\n\nvocab_size = 12000 # 词袋数量\ntokenize = keras.preprocessing.text.Tokenizer(num_words=vocab_size, char_level=False)\ntokenize.fit_on_texts(all_description)\n\nmax_seq_length = 170\n\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n # if request.method == \"POST\":\n # request param\n desc = request.values.get(\"desc\", \"\")\n desc = jieba_cut(desc)\n price = float(request.values.get(\"price\", \"0.0\"))\n print(desc, price, type(desc), type(price))\n\n desc = pd.Series([desc])\n price = pd.Series([price])\n\n description_bow_test = tokenize.texts_to_matrix(desc)\n test_embed = tokenize.texts_to_sequences(desc)\n test_embed = keras.preprocessing.sequence.pad_sequences(test_embed, maxlen=max_seq_length, padding=\"post\")\n\n res = get_predicted(description_bow_test, price, test_embed)\n rindex = np.argmax(res)\n variety_predicted = encoder.inverse_transform(rindex)\n K.clear_session()\n return _ret(data={\"variety_predicted\": variety_predicted})\n\ndef get_predicted(description_bow_test, price, test_embed):\n # print(description_bow_test.shape, price.shape, test_embed.shape)\n description_bow_test = description_bow_test.flatten()\n # print(description_bow_test.shape)\n # price = price.values.reshape(1, 1)\n test_embed = test_embed.flatten()\n\n payload = {\n \"instances\": [{\"input_bow\": description_bow_test.tolist(),\n \"input_price\": price.tolist(),\n \"input_embed\": test_embed.tolist()}]\n }\n\n # print(payload)\n r = requests.post(tf_serving_api, json=payload)\n print(r.text)\n rdict = json.loads(r.content.decode(\"utf-8\"))\n return rdict[\"predictions\"]\n\ndef jieba_cut(input):\n res = jieba.cut(input, cut_all=False, HMM=True)\n res = \" \".join(res)\n return res\n\ndef _ret(msg=\"\", errcode=0, data={}):\n ret = {\n \"msg\": msg,\n \"code\": errcode,\n \"data\": data\n }\n return json.dumps(ret)\n", "sub_path": "VarietyPredictionTFServingUwsgi.py", "file_name": "VarietyPredictionTFServingUwsgi.py", "file_ext": "py", "file_size_in_byte": 2584, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "jieba.load_userdict", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.preprocessing.text.Tokenizer", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.preprocessing", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request.values.get", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.values.get", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.preprocessing", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.backend.clear_session", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 50, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 67, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 69, "usage_type": "call"}, {"api_name": "jieba.cut", "line_number": 73, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "129827056", "text": "import network, machine, time, math\nimport ujson\n\nprint('Starting boot(try to store wifi).py')\n\n# ssid = 'KVN'\n# ssidpss = 'cccccccc'\nssid = ''\nssidpss = ''\n\ntry:\n # with open('config.txt') as config_file:\n # config_text = config_file.read()\n # config_parced = ujson.loads(config_text)\n # ssid = config_parced['ssid']\n # ssidpss = config_parced['ssidpss']\n file = open('config.txt')\n text = file.read()\n config_parced = ujson.loads(text)\n ssid = config_parced['ssid']\n print('DBG got SSID {}'.format(ssid))\n ssidpss = config_parced['ssidpss']\n print('DBG got ssidpss {}'.format(ssidpss))\n\nexcept:\n print('DBG error reading settings')\n\n\n# >>> file = open('config.txt')\n# >>> text = file.read()\n# >>> text\n# '{\"ssid\":\"KVN\",\"ssidpss\":\"cccccccc\",\"trick\":\"demo\"}'\n# >>> parced = ujson.loads(text)\n# >>> parced\n# {'ssid': 'KVN', 'ssidpss': 'cccccccc', 'trick': 'demo'}\n# >>> parced['ssid']\n\n\n# ssid = 't4m'\n# ssidpss = ''\n\ns_timeout = 200\nl_timeout = 2000\n\nnetworkpin = machine.Pin(2, machine.Pin.OUT)\nnetworkpin.on()\ntime.sleep_ms(s_timeout)\nnetworkpin.off()\ntime.sleep_ms(s_timeout)\nnetworkpin.on()\ntime.sleep_ms(s_timeout)\nnetworkpin.off()\ntime.sleep_ms(s_timeout)\nnetworkpin.on()\ntime.sleep_ms(s_timeout)\nnetworkpin.off()\ntime.sleep_ms(s_timeout)\nnetworkpin.on()\ntime.sleep_ms(s_timeout)\n\nsta_if = network.WLAN(network.STA_IF)\nap_if = network.WLAN(network.AP_IF)\n\nprint(ap_if.ifconfig())\nprint(ssid, ssidpss)\n\nsta_if.active(True)\nsta_if.connect(ssid, ssidpss)\n\ntime.sleep_ms(l_timeout)\ntime.sleep_ms(l_timeout) # 4 s\ntime.sleep_ms(l_timeout) # 6 S\n\nprint('AP:')\nprint(ap_if.ifconfig())\nprint('STATION:')\nprint(sta_if.ifconfig())\n\nnetworkpin.off()\n", "sub_path": "demo-app/demo-app-010/boot(try to store wifi).py", "file_name": "boot(try to store wifi).py", "file_ext": "py", "file_size_in_byte": 1702, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "ujson.loads", "line_number": 19, "usage_type": "call"}, {"api_name": "machine.Pin", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep_ms", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep_ms", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep_ms", "line_number": 51, "usage_type": "call"}, {"api_name": "time.sleep_ms", "line_number": 53, "usage_type": "call"}, {"api_name": "time.sleep_ms", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep_ms", "line_number": 57, "usage_type": "call"}, {"api_name": "time.sleep_ms", "line_number": 59, "usage_type": "call"}, {"api_name": "network.WLAN", "line_number": 61, "usage_type": "call"}, {"api_name": "network.STA_IF", "line_number": 61, "usage_type": "attribute"}, {"api_name": "network.WLAN", "line_number": 62, "usage_type": "call"}, {"api_name": "network.AP_IF", "line_number": 62, "usage_type": "attribute"}, {"api_name": "time.sleep_ms", "line_number": 70, "usage_type": "call"}, {"api_name": "time.sleep_ms", "line_number": 71, "usage_type": "call"}, {"api_name": "time.sleep_ms", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "456067429", "text": "##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n## Created by: Hang Zhang\n## Email: zhanghang0704@gmail.com\n## Copyright (c) 2020\n##\n## LICENSE file in the root directory of this source tree \n##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\"\"\"ResNeSt models\"\"\"\n\nimport torch\nfrom resnet_new import ResNet, Bottleneck\n#from .resnet_new import ResNet, Bottleneck\nimport torch.nn as nn\nfrom Config import *\nfrom Weight import Weight\nimport mmd\n\n__all__ = ['resnest50', 'resnest101', 'resnest200', 'resnest269']\nfrom build import RESNEST_MODELS_REGISTRY\n\n_url_format = 'https://github.com/zhanghang1989/ResNeSt/releases/download/weights_step1/{}-{}.pth'\n\n_model_sha256 = {name: checksum for checksum, name in [\n ('528c19ca', 'resnest50'),\n ('22405ba7', 'resnest101'),\n ('75117900', 'resnest200'),\n ('0cc87c48', 'resnest269'),\n ]}\n\ndef short_hash(name):\n if name not in _model_sha256:\n raise ValueError('Pretrained model for {name} is not available.'.format(name=name))\n return _model_sha256[name][:8]\n\nresnest_model_urls = {name: _url_format.format(name, short_hash(name)) for\n name in _model_sha256.keys()\n}\n\nclass DSAN(nn.Module):\n # 实例化DSAN时，执行此操作。\n def __init__(self, num_classes=10):\n super(DSAN, self).__init__()\n # 输入参数为true表示加载利用ImageNet预训练好的resnet50模型\n\t\t# 之前此处调用的参数一直为true，即是用训练好的域训练模型，后续可以尝试使用没有预训练的模型进行试验。\n self.feature_layers = resnest50(False)\n self.num_classes = num_classes\n if bottle_neck:\n self.bottle = nn.Linear(2048, 256)\n self.cls_fc = nn.Linear(256, num_classes)\n else:\n self.cls_fc = nn.Linear(2048, num_classes)\n\n #当把DSAN当作一个方法来调用的时候，执行此操作。\n def forward(self, source, target, s_label):\n source = self.feature_layers(source)\n if bottle_neck:\n source = self.bottle(source)\n s_pred = self.cls_fc(source)\n if self.training ==True:\n target = self.feature_layers(target)\n if bottle_neck:\n target = self.bottle(target)\n t_label = self.cls_fc(target)\n #原始的lmmd\n loss = mmd.lmmd(source, target, s_label, torch.nn.functional.softmax(t_label, dim=1),num_classes = self.num_classes)\n #混合核lmmd\n #loss = mklmmd.mix_poly_rbf(source, target, s_label, torch.nn.functional.softmax(t_label, dim=1))\n else:\n loss = 0\n return s_pred, loss\n\n@RESNEST_MODELS_REGISTRY.register()\ndef resnest50(pretrained=False, root='~/.encoding/models', **kwargs):\n model = ResNet(Bottleneck, [3, 4, 6, 3],\n radix=2, groups=1, bottleneck_width=64,\n deep_stem=True, stem_width=32, avg_down=True,\n avd=True, avd_first=False, **kwargs)\n if pretrained:\n model.load_state_dict(torch.hub.load_state_dict_from_url(\n resnest_model_urls['resnest50'], progress=True, check_hash=True))\n return model\n\n@RESNEST_MODELS_REGISTRY.register()\ndef resnest101(pretrained=False, root='~/.encoding/models', **kwargs):\n model = ResNet(Bottleneck, [3, 4, 23, 3],\n radix=2, groups=1, bottleneck_width=64,\n deep_stem=True, stem_width=64, avg_down=True,\n avd=True, avd_first=False, **kwargs)\n if pretrained:\n model.load_state_dict(torch.hub.load_state_dict_from_url(\n resnest_model_urls['resnest101'], progress=True, check_hash=True))\n return model\n\n@RESNEST_MODELS_REGISTRY.register()\ndef resnest200(pretrained=False, root='~/.encoding/models', **kwargs):\n model = ResNet(Bottleneck, [3, 24, 36, 3],\n radix=2, groups=1, bottleneck_width=64,\n deep_stem=True, stem_width=64, avg_down=True,\n avd=True, avd_first=False, **kwargs)\n if pretrained:\n model.load_state_dict(torch.hub.load_state_dict_from_url(\n resnest_model_urls['resnest200'], progress=True, check_hash=True))\n return model\n\n@RESNEST_MODELS_REGISTRY.register()\ndef resnest269(pretrained=False, root='~/.encoding/models', **kwargs):\n model = ResNet(Bottleneck, [3, 30, 48, 8],\n radix=2, groups=1, bottleneck_width=64,\n deep_stem=True, stem_width=64, avg_down=True,\n avd=True, avd_first=False, **kwargs)\n if pretrained:\n model.load_state_dict(torch.hub.load_state_dict_from_url(\n resnest_model_urls['resnest269'], progress=True, check_hash=True))\n return model\n", "sub_path": "20210623resnest+matrix/resnest.py", "file_name": "resnest.py", "file_ext": "py", "file_size_in_byte": 4733, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torch.nn.Module", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "mmd.lmmd", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "attribute"}, {"api_name": "resnet_new.ResNet", "line_number": 74, "usage_type": "call"}, {"api_name": "resnet_new.Bottleneck", "line_number": 74, "usage_type": "argument"}, {"api_name": "torch.hub.load_state_dict_from_url", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.hub", "line_number": 79, "usage_type": "attribute"}, {"api_name": "build.RESNEST_MODELS_REGISTRY.register", "line_number": 72, "usage_type": "call"}, {"api_name": "build.RESNEST_MODELS_REGISTRY", "line_number": 72, "usage_type": "name"}, {"api_name": "resnet_new.ResNet", "line_number": 85, "usage_type": "call"}, {"api_name": "resnet_new.Bottleneck", "line_number": 85, "usage_type": "argument"}, {"api_name": "torch.hub.load_state_dict_from_url", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.hub", "line_number": 90, "usage_type": "attribute"}, {"api_name": "build.RESNEST_MODELS_REGISTRY.register", "line_number": 83, "usage_type": "call"}, {"api_name": "build.RESNEST_MODELS_REGISTRY", "line_number": 83, "usage_type": "name"}, {"api_name": "resnet_new.ResNet", "line_number": 96, "usage_type": "call"}, {"api_name": "resnet_new.Bottleneck", "line_number": 96, "usage_type": "argument"}, {"api_name": "torch.hub.load_state_dict_from_url", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.hub", "line_number": 101, "usage_type": "attribute"}, {"api_name": "build.RESNEST_MODELS_REGISTRY.register", "line_number": 94, "usage_type": "call"}, {"api_name": "build.RESNEST_MODELS_REGISTRY", "line_number": 94, "usage_type": "name"}, {"api_name": "resnet_new.ResNet", "line_number": 107, "usage_type": "call"}, {"api_name": "resnet_new.Bottleneck", "line_number": 107, "usage_type": "argument"}, {"api_name": "torch.hub.load_state_dict_from_url", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.hub", "line_number": 112, "usage_type": "attribute"}, {"api_name": "build.RESNEST_MODELS_REGISTRY.register", "line_number": 105, "usage_type": "call"}, {"api_name": "build.RESNEST_MODELS_REGISTRY", "line_number": 105, "usage_type": "name"}]} +{"seq_id": "649184258", "text": "from sklearn import metrics\nimport pandas as pd\nimport numpy as np\nfrom gerador_modelo import get_model\n\nmodel, X_train, X_test, y_train, y_test = get_model(['temp_max', 'chuva', 'fds'])\nmodel_2, X2_train, X2_test, y2_train, y2_test = get_model(['temp_media', 'chuva', 'fds'])\n\n# Obtendo coeficiente de determinação (R²)\n# Medida resumida que diz o quanto a linha de regressão se ajusta aos dados. È um valor entre 0 e 1.\n# Quanto mais próximo de 1, melhor\nprint(f'R² Temp. Máxima = {model.score(X_train, y_train).round(2)}')\nprint(f'R² Temp. Média = {model_2.score(X2_train, y2_train).round(2)}\\n')\n\n# Gerando previsões\ny_preview = model.predict(X_test)\ny2_preview = model_2.predict(X2_test)\n\n# Obtendo R² da previsão\nR2 = metrics.r2_score(y_test, y_preview).round(2)\nR2_2 = metrics.r2_score(y2_test, y2_preview).round(2)\nprint('R² da previsão:')\nprint(f'R² Temp. Máxima = {R2}')\nprint(f'R² Temp. Média = {R2_2}\\n')\n\n# Utilizando outras métricas de comparação\n# EQM = Erro quadrático médio\n# REQM = Raiz quadrática média\n\nEQM = metrics.mean_squared_error(y_test, y_preview)\nREQM = np.sqrt(EQM).round(2)\n\nEQM_2 = metrics.mean_squared_error(y2_test, y2_preview)\nREQM_2 = np.sqrt(EQM_2).round(2)\n\n# R² - Quanto mais próximo de 1, melhor (mais adequado ao modelo)\n# EQM e REQM, quando menor, melhor (menos erros)\nmetrics_temp_max = pd.DataFrame([EQM, REQM, R2], ['EQM', 'REQM', 'R²'], columns=['Métricas - Temp Máxima'])\nmetrics_temp_media = pd.DataFrame([EQM_2, REQM_2, R2_2], ['EQM', 'REQM', 'R²'], columns=['Métricas - Temp Média'])\n\nprint(pd.concat([metrics_temp_max, metrics_temp_media], axis=1))\nprint('\\n')\n\n# Visualizando os coeficientes da regressão\n# Intercepto: Mantendo as variáveis explicativas = 0, o efeito médio no consumo de cerveja seria 5951 litros\n# X1, X2, X3: Mantendo os outros coeficientes constantes, o acréscimo de 1 unidade em Xi gera uma variação média\n# no consumo de cerveja de X1 = 684 litros, X2 = -60 litros, X3 = 5401 litros\nindex = ['Intercepto', 'Temperatura Máxima', 'Chuva (mm)', 'Final de Semana']\ncoeficientes = pd.DataFrame(data=np.append(model.intercept_, model.coef_), index=index, columns=['Parâmetros'])\nprint(coeficientes)\n\n# Gerando previsão pontual\nX_input = X_test[0:1]\nprint(f'Consumo previsto: {model.predict(X_input)[0].round(2)} litros')\n\n# Simulando dados\ntemp_max = 40\nchuva = 0\nfds = 1\nX_input = [[temp_max, chuva, fds]]\nprint(f'Consumo previsto simulação: {model.predict(X_input)[0].round(2)} litros\\n')", "sub_path": "comparando_modelos.py", "file_name": "comparando_modelos.py", "file_ext": "py", "file_size_in_byte": 2503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "gerador_modelo.get_model", "line_number": 6, "usage_type": "call"}, {"api_name": "gerador_modelo.get_model", "line_number": 7, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 20, "usage_type": "name"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 21, "usage_type": "name"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "413329143", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# pylint: disable=missing-docstring, line-too-long, bare-except, invalid-name\n\nimport re\nimport locale\n\nimport sys\nimport getopt\n\nimport requests\nfrom requests.adapters import HTTPAdapter\nfrom urllib3.util import Retry\n\nimport pywikibot\nfrom pywikibot import pagegenerators as pg\n\n\ndef requests_retry_session(\n retries=5,\n backoff_factor=0.3,\n status_forcelist=(500, 502, 504),\n session=None,\n):\n session = session or requests.Session()\n retry = Retry(\n total=retries,\n read=retries,\n connect=retries,\n backoff_factor=backoff_factor,\n status_forcelist=status_forcelist,\n )\n adapter = HTTPAdapter(max_retries=retry)\n session.mount('http://', adapter)\n session.mount('https://', adapter)\n return session\n\n\nwikidata_site = pywikibot.Site('wikidata', 'wikidata')\nrepo = wikidata_site.data_repository()\n\ndiocese = False\ndioid = False\nposition = False\n\n\ndef create_claim_pit(year):\n cal_model = None\n if year < 1584:\n cal_model = 'http://www.wikidata.org/entity/Q1985786'\n\n my_wbtime = pywikibot.WbTime(year=year,\n month=1, day=1,\n precision=9, before=0, after=0,\n timezone=0, calendarmodel=cal_model)\n\n claim = pywikibot.Claim(repo, 'P585')\n claim.setTarget(my_wbtime)\n return claim\n\n\ndef main(argv):\n opts, args = getopt.getopt(argv, \"hd:i:\", [\"diocese=\", 'id='])\n ret = {}\n for opt, arg in opts:\n if opt == '-h':\n print('check_bishopstart_on_chorg.py -d -i ')\n sys.exit()\n elif opt in (\"-d\", \"--diocese\"):\n diocese = arg\n if diocese.isnumeric():\n diocese = 'Q' + diocese\n ret['diocese'] = diocese\n elif opt in (\"-i\", \"--id\"):\n dioid = arg\n ret['dioid'] = dioid\n return ret\n\n\nif __name__ == \"__main__\":\n mainret = main(sys.argv[1:])\n if 'diocese' in mainret:\n diocese = mainret['diocese']\n if 'dioid' in mainret:\n dioid = mainret['dioid']\n\n\nif diocese == False and dioid == False:\n print('!! NO DIOCESE GIVEN. I quit.')\n\nitemdetails = False\ntarget_page = False\n\nif diocese:\n target_page = pywikibot.ItemPage(repo, diocese)\n itemdetails = target_page.get(get_redirect=True)\n list_currentdioids = itemdetails['claims']['P1866']\n if len(list_currentdioids) == 1:\n diocese = target_page.id\n print('-- Diocese found: https://www.wikidata.org/wiki/{}'.format(diocese))\n dioid = list_currentdioids[0].getTarget()\n else:\n print('! Length of P1866 (Claim for Catholic Hierarchy diocese ID) !=1')\n exit()\nelif not diocese and dioid:\n SPARQL = \"SELECT ?item WHERE { ?item wdt:P1866 \\\"\" + dioid + \"\\\". }\"\n my_generator = pg.WikidataSPARQLPageGenerator(SPARQL, site=wikidata_site)\n my_generator = list(my_generator)\n if len(my_generator) != 1:\n print('!!!- Found {} Results for DioId=\"' + dioid + '\" that works only with an exact match.'.format(len(my_generator)))\n exit()\n else:\n target_page = my_generator[0]\n itemdetails = target_page.get(get_redirect=True)\n list_currentdioids = itemdetails['claims']['P1866']\n if len(list_currentdioids) == 1:\n diocese = target_page.id\n print('-- Diocese found: https://www.wikidata.org/wiki/{}'.format(diocese))\n else:\n print('! Length of P1866 (Claim for Catholic Hierarchy diocese ID) !=1')\n\nchorgurl = 'http://www.catholic-hierarchy.org/diocese/d' + dioid + '.html'\n\n\ntry:\n r = requests_retry_session().get(chorgurl)\nexcept:\n print('!!! Could not request url:' + chorgurl)\n exit()\n\nif r.status_code != 200:\n print('### ERROR ON http-call for : ' + chorgurl)\nif r.status_code == 200:\n print('>> Catholic-Hierarchy-DioId: ' + dioid)\n print('-- URL: ' + chorgurl)\n\n\nbytes_regex = str.encode('

Statistics<\\/a><\\/h2>

\\n()')\nstat_table = re.findall(bytes_regex, r.content)\n\nif stat_table:\n print('--- Stat-Table found.')\nelse:\n print('!!- Stat-Table not found.')\n exit()\n\nif not itemdetails:\n print('!!- Could not load WD-Item')\n exit()\n\nbytes_regex = str.encode('(.*<\\/th>)<\\/tr>')\nstat_html_headlines = re.findall(bytes_regex, stat_table[0])\n\nif not stat_html_headlines:\n print('!!- No Headlines found')\n exit()\n\n\nbytes_regex = str.encode('([\\w ]+)<\\/th>')\nlist_headlines = re.findall(bytes_regex, stat_html_headlines[0])\n\nif not list_headlines:\n print('!!- No Plaintext Headlines found')\n exit()\n\n# print(list_headlines)\n\nyearpos = False\nmemberpos = False\n\n\ntry:\n yearpos = list_headlines.index(str.encode('Year'))\nexcept:\n print('!-- Year-Column not found')\n exit()\n\ntry:\n memberpos = list_headlines.index(str.encode('Catholics'))\nexcept:\n print('!-- Catholics-Column not found')\n exit()\n\nbytes_regex = str.encode(']*>(.*)<\\/tr>')\nstat_html_rows = re.findall(bytes_regex, stat_table[0])\n\na_yearstat_ch = {}\n\nlocale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\nfor html_row in stat_html_rows:\n bytes_regex = str.encode('.*<\\/th>')\n stat_th_in = re.findall(bytes_regex, html_row)\n if stat_th_in:\n continue\n\n bytes_regex = str.encode('([^<]*)')\n td_in = re.findall(bytes_regex, html_row)\n if not len(td_in) == len(list_headlines):\n continue\n\n try:\n my_year = int(td_in[yearpos])\n my_members = int(locale.atoi(td_in[memberpos].decode('utf-8')))\n if my_members == 0:\n continue\n a_yearstat_ch[str(my_year)] = my_members\n except:\n continue\n\n\ntry:\n known_stats = itemdetails['claims']['P2124']\nexcept KeyError:\n known_stats = []\n\n\na_yearstat_wd = {}\nfor known_stat in known_stats:\n trgt_number = known_stat.getTarget()\n print(trgt_number)\n a_qualis = known_stat.qualifiers\n if 'P585' not in a_qualis:\n print('-- Point in time unknown. I skip that one')\n continue\n\n my_year = a_qualis['P585'][0].getTarget().year\n my_members = trgt_number.amount\n a_yearstat_wd[str(my_year)] = int(my_members)\n\na_todo_stat = {}\n\nfor key in a_yearstat_ch.keys():\n if key not in a_yearstat_wd:\n a_todo_stat[key] = a_yearstat_ch[key]\n\n\nfor key in a_todo_stat:\n claim_membernumber = pywikibot.Claim(repo, 'P2124')\n amount_membernumber = pywikibot.WbQuantity(a_todo_stat[key], site=wikidata_site)\n claim_membernumber.setTarget(amount_membernumber)\n quali_pit = create_claim_pit(int(key))\n claim_membernumber.addQualifier(quali_pit)\n\n source_claim = pywikibot.Claim(repo, 'P1866')\n source_claim.setTarget(dioid)\n claim_membernumber.addSources([source_claim])\n target_page.addClaim(claim_membernumber, summary='added member amount')\n\nprint('Done!')\n", "sub_path": "set_memberstats.py", "file_name": "set_memberstats.py", "file_ext": "py", "file_size_in_byte": 6901, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "requests.Session", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib3.util.Retry", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.adapters.HTTPAdapter", "line_number": 33, "usage_type": "call"}, {"api_name": "pywikibot.Site", "line_number": 39, "usage_type": "call"}, {"api_name": "pywikibot.WbTime", "line_number": 52, "usage_type": "call"}, {"api_name": "pywikibot.Claim", "line_number": 57, "usage_type": "call"}, {"api_name": "getopt.getopt", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pywikibot.ItemPage", "line_number": 95, "usage_type": "call"}, {"api_name": "pywikibot.pagegenerators.WikidataSPARQLPageGenerator", "line_number": 107, "usage_type": "call"}, {"api_name": "pywikibot.pagegenerators", "line_number": 107, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 139, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 152, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 160, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 185, "usage_type": "call"}, {"api_name": "locale.setlocale", "line_number": 189, "usage_type": "call"}, {"api_name": "locale.LC_ALL", "line_number": 189, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 192, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 197, "usage_type": "call"}, {"api_name": "locale.atoi", "line_number": 203, "usage_type": "call"}, {"api_name": "pywikibot.Claim", "line_number": 238, "usage_type": "call"}, {"api_name": "pywikibot.WbQuantity", "line_number": 239, "usage_type": "call"}, {"api_name": "pywikibot.Claim", "line_number": 244, "usage_type": "call"}]} +{"seq_id": "351688131", "text": "# Copyright 2021 Huawei Technologies Co., Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ============================================================================\n\nimport numpy as np\nimport pytest\n\nimport mindspore.context as context\nimport mindspore.nn as nn\nfrom mindspore import Tensor, Parameter\nimport mindspore.common.dtype as mstype\nimport mindspore.ops as ops\n\ncontext.set_context(mode=context.GRAPH_MODE, device_target=\"GPU\")\n\nfunc_map = {\n \"update\": ops.ScatterNdUpdate,\n \"add\": ops.ScatterNdAdd,\n \"sub\": ops.ScatterNdSub,\n \"mul\": ops.ScatterNdMul,\n \"div\": ops.ScatterNdDiv,\n \"max\": ops.ScatterNdMax,\n \"min\": ops.ScatterNdMin,\n}\n\nnp_func_map = {\n \"update\": lambda a, b: b,\n \"add\": lambda a, b: a + b,\n \"sub\": lambda a, b: a - b,\n \"mul\": lambda a, b: a * b,\n \"div\": lambda a, b: a / b,\n \"max\": np.maximum,\n \"min\": np.minimum,\n}\n\n\nclass TestScatterNdFuncNet(nn.Cell):\n def __init__(self, func, lock, inputx, indices, updates):\n super(TestScatterNdFuncNet, self).__init__()\n\n self.scatter_func = func_map[func](use_locking=lock)\n self.inputx = Parameter(inputx, name=\"inputx\")\n self.indices = Parameter(indices, name=\"indices\")\n self.updates = Parameter(updates, name=\"updates\")\n\n def construct(self):\n out = self.scatter_func(self.inputx, self.indices, self.updates)\n return out\n\n\ndef scatter_nd_func_np(func, inputx, indices, updates):\n result = inputx.asnumpy().copy()\n updates_np = updates.asnumpy()\n\n f = np_func_map[func]\n\n for idx, _ in np.ndenumerate(np.zeros(indices.shape[:-1])):\n out_index = indices[idx]\n result[out_index] = f(result[out_index], updates_np[idx])\n\n return result\n\n\ndef compare_scatter_nd_func(func, lock, inputx, indices, updates):\n output = TestScatterNdFuncNet(func, lock, inputx, indices, updates)()\n expected = scatter_nd_func_np(func, inputx, indices, updates)\n np.testing.assert_array_almost_equal(output.asnumpy(), expected)\n\n\n@pytest.mark.level0\n@pytest.mark.platform_x86_gpu_training\n@pytest.mark.env_onecard\n@pytest.mark.parametrize('lock', [True, False])\n@pytest.mark.parametrize('func', ['update', 'add', 'sub', 'div', 'mul', 'max', 'min'])\n@pytest.mark.parametrize('data_type',\n [mstype.uint8, mstype.int8, mstype.int16, mstype.int32, mstype.float16, mstype.float32,\n mstype.float64])\n@pytest.mark.parametrize('index_type', [mstype.int32])\ndef test_scatter_nd_func_small(lock, func, data_type, index_type):\n \"\"\"\n Feature: ALL To ALL\n Description: test cases for small input of ScatterNd* like functions\n Expectation: the result match to numpy implementation\n \"\"\"\n inputx = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), data_type)\n indices = Tensor(np.array([[0, 0], [1, 1]]), index_type)\n updates = Tensor(np.array([1.0, 2.2]), data_type)\n\n compare_scatter_nd_func(func, lock, inputx, indices, updates)\n\n\n@pytest.mark.level0\n@pytest.mark.platform_x86_gpu_training\n@pytest.mark.env_onecard\n@pytest.mark.parametrize('lock', [True, False])\ndef test_scatter_nd_func_small_update(lock):\n \"\"\"\n Feature: ALL To ALL\n Description: test cases for bool input of ScatterNdUpdate\n Expectation: the result match to numpy implementation\n \"\"\"\n inputx = Tensor(np.array([True, False, True, False, True, True, False, True]), mstype.bool_)\n indices = Tensor(np.array([[False], [True], [False], [True]]), mstype.int32)\n updates = Tensor(np.array([9, 10, 11, 12]), mstype.bool_)\n\n compare_scatter_nd_func(\"update\", lock, inputx, indices, updates)\n\n\n@pytest.mark.level0\n@pytest.mark.platform_x86_gpu_training\n@pytest.mark.env_onecard\n@pytest.mark.parametrize('lock', [True, False])\n@pytest.mark.parametrize('func', ['update', 'add', 'sub', 'div', 'mul', 'max', 'min'])\n@pytest.mark.parametrize('data_type',\n [mstype.uint8, mstype.int8, mstype.int16, mstype.int32, mstype.float16, mstype.float32,\n mstype.float64])\n@pytest.mark.parametrize('index_type', [mstype.int32])\ndef test_scatter_nd_func_small_int(lock, func, data_type, index_type):\n \"\"\"\n Feature: ALL To ALL\n Description: test cases for int input of ScatterNd* like functions\n Expectation: the result match to numpy implementation\n \"\"\"\n inputx = Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), data_type)\n indices = Tensor(np.array([[4], [3], [1], [7]]), index_type)\n updates = Tensor(np.array([9, 10, 11, 12]), data_type)\n\n compare_scatter_nd_func(func, lock, inputx, indices, updates)\n\n\n@pytest.mark.level0\n@pytest.mark.platform_x86_gpu_training\n@pytest.mark.env_onecard\n@pytest.mark.parametrize('lock', [True, False])\n@pytest.mark.parametrize('func', ['update', 'add', 'sub', 'div', 'mul', 'max', 'min'])\n@pytest.mark.parametrize('data_type',\n [mstype.uint8, mstype.int8, mstype.int16, mstype.int32, mstype.float16, mstype.float32,\n mstype.float64])\n@pytest.mark.parametrize('index_type', [mstype.int32])\ndef test_scatter_nd_func_small_negative(lock, func, data_type, index_type):\n \"\"\"\n Feature: ALL To ALL\n Description: test cases for negative input of ScatterNd* like functions\n Expectation: the result match to numpy implementation\n \"\"\"\n inputx = Tensor(np.array([-1, -2, -3, -4, -5, -6, -7, -8]), data_type)\n indices = Tensor(np.array([[4], [3], [1], [7]]), index_type)\n updates = Tensor(np.array([9, -10, 11, -12]), data_type)\n\n compare_scatter_nd_func(func, lock, inputx, indices, updates)\n\n\n@pytest.mark.level0\n@pytest.mark.platform_x86_gpu_training\n@pytest.mark.env_onecard\n@pytest.mark.parametrize('lock', [True, False])\n@pytest.mark.parametrize('func', ['update', 'add', 'sub', 'div', 'mul', 'max', 'min'])\n@pytest.mark.parametrize('data_type',\n [mstype.uint8, mstype.int8, mstype.int16, mstype.int32, mstype.float16, mstype.float32,\n mstype.float64])\n@pytest.mark.parametrize('index_type', [mstype.int32])\ndef test_scatter_nd_func_multi_dims(lock, func, data_type, index_type):\n \"\"\"\n Feature: ALL To ALL\n Description: test cases for multi-dims input of ScatterNd* like functions\n Expectation: the result match to numpy implementation\n \"\"\"\n inputx = Tensor(np.zeros((4, 4, 4)), data_type)\n indices = Tensor(np.array([[0], [2]]), index_type)\n updates = Tensor(\n np.array(\n [\n [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],\n [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],\n ]\n ),\n data_type,\n )\n\n compare_scatter_nd_func(func, lock, inputx, indices, updates)\n\n\n@pytest.mark.level0\n@pytest.mark.platform_x86_gpu_training\n@pytest.mark.env_onecard\n@pytest.mark.parametrize('lock', [True, False])\n@pytest.mark.parametrize('func', ['update', 'add', 'sub', 'div', 'mul', 'max', 'min'])\n@pytest.mark.parametrize('data_type',\n [mstype.uint8, mstype.int8, mstype.int16, mstype.int32, mstype.float16, mstype.float32,\n mstype.float64])\n@pytest.mark.parametrize('index_type', [mstype.int32])\ndef test_scatter_nd_func_one_value(lock, func, data_type, index_type):\n \"\"\"\n Feature: ALL To ALL\n Description: test cases for one value modification of ScatterNd* like functions\n Expectation: the result match to numpy implementation\n \"\"\"\n inputx = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), data_type)\n indices = Tensor(np.array([[0, 1]]), index_type)\n updates = Tensor(np.array([1.0]), data_type)\n\n compare_scatter_nd_func(func, lock, inputx, indices, updates)\n", "sub_path": "tests/st/ops/gpu/test_scatter_nd_func_op.py", "file_name": "test_scatter_nd_func_op.py", "file_ext": "py", "file_size_in_byte": 8196, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "mindspore.context.set_context", "line_number": 25, "usage_type": "call"}, {"api_name": "mindspore.context", "line_number": 25, "usage_type": "name"}, {"api_name": "mindspore.context.GRAPH_MODE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "mindspore.ops.ScatterNdUpdate", "line_number": 28, "usage_type": "attribute"}, {"api_name": "mindspore.ops", "line_number": 28, "usage_type": "name"}, {"api_name": "mindspore.ops.ScatterNdAdd", "line_number": 29, "usage_type": "attribute"}, {"api_name": "mindspore.ops", "line_number": 29, "usage_type": "name"}, {"api_name": "mindspore.ops.ScatterNdSub", "line_number": 30, "usage_type": "attribute"}, {"api_name": "mindspore.ops", "line_number": 30, "usage_type": "name"}, {"api_name": "mindspore.ops.ScatterNdMul", "line_number": 31, "usage_type": "attribute"}, {"api_name": "mindspore.ops", "line_number": 31, "usage_type": "name"}, {"api_name": "mindspore.ops.ScatterNdDiv", "line_number": 32, "usage_type": "attribute"}, {"api_name": "mindspore.ops", "line_number": 32, "usage_type": "name"}, {"api_name": "mindspore.ops.ScatterNdMax", "line_number": 33, "usage_type": "attribute"}, {"api_name": "mindspore.ops", "line_number": 33, "usage_type": "name"}, {"api_name": "mindspore.ops.ScatterNdMin", "line_number": 34, "usage_type": "attribute"}, {"api_name": "mindspore.ops", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.maximum", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.minimum", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mindspore.nn.Cell", "line_number": 48, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "mindspore.Parameter", "line_number": 53, "usage_type": "call"}, {"api_name": "mindspore.Parameter", "line_number": 54, "usage_type": "call"}, {"api_name": "mindspore.Parameter", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.ndenumerate", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_almost_equal", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 78, "usage_type": "attribute"}, {"api_name": "mindspore.Tensor", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 96, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 84, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 85, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 86, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 86, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.uint8", "line_number": 87, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 87, "usage_type": "name"}, {"api_name": "mindspore.common.dtype.int8", "line_number": 87, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int16", "line_number": 87, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float16", "line_number": 87, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float64", "line_number": 88, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 88, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 89, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 89, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 89, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 89, "usage_type": "name"}, {"api_name": "mindspore.Tensor", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "mindspore.common.dtype.bool_", "line_number": 113, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 113, "usage_type": "name"}, {"api_name": "mindspore.Tensor", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 114, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 114, "usage_type": "name"}, {"api_name": "mindspore.Tensor", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "mindspore.common.dtype.bool_", "line_number": 115, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 115, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 106, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 106, "usage_type": "attribute"}, {"api_name": "mindspore.Tensor", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 137, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 123, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 124, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 125, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 125, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.uint8", "line_number": 126, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 126, "usage_type": "name"}, {"api_name": "mindspore.common.dtype.int8", "line_number": 126, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int16", "line_number": 126, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 126, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float16", "line_number": 126, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float32", "line_number": 126, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float64", "line_number": 127, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 127, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 128, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 128, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 128, "usage_type": "name"}, {"api_name": "mindspore.Tensor", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 157, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 158, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 142, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 144, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 145, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 145, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 146, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 147, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 147, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.uint8", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 148, "usage_type": "name"}, {"api_name": "mindspore.common.dtype.int8", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int16", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float16", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float32", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float64", "line_number": 149, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 149, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 150, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 150, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 150, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 150, "usage_type": "name"}, {"api_name": "mindspore.Tensor", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 180, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 182, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 167, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 168, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 169, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 169, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.uint8", "line_number": 170, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 170, "usage_type": "name"}, {"api_name": "mindspore.common.dtype.int8", "line_number": 170, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int16", "line_number": 170, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 170, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float16", "line_number": 170, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float32", "line_number": 170, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float64", "line_number": 171, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 171, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 172, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 172, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 172, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 172, "usage_type": "name"}, {"api_name": "mindspore.Tensor", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 209, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 210, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 211, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 194, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 195, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 197, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 198, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 198, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 199, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 199, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.uint8", "line_number": 200, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 200, "usage_type": "name"}, {"api_name": "mindspore.common.dtype.int8", "line_number": 200, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int16", "line_number": 200, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 200, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float16", "line_number": 200, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float32", "line_number": 200, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.float64", "line_number": 201, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 201, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 202, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 202, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype.int32", "line_number": 202, "usage_type": "attribute"}, {"api_name": "mindspore.common.dtype", "line_number": 202, "usage_type": "name"}]} +{"seq_id": "121510213", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Feb 19 11:32:57 2019\r\n\r\n@author: Pedro Augusto\r\n\"\"\"\r\n\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\ndef request(url):\r\n headers = {'User-Agent': 'Mozilla/5.0'}\r\n source = requests.get('https://old.reddit.com/r/' + url + '/', headers=headers)\r\n return source\r\n\r\n#%% Wrapper\r\nif __name__ == \"__main__\": \r\n input_ = input(\"Entre com os subreddits separados por ponto-e-vírgula: \")\r\n url = input_.split(';')\r\n #url = ['askreddit', 'worldnews', 'cats']\r\n \r\n for u in url:\r\n source = request(u)\r\n soup = BeautifulSoup(source.text, 'html.parser')\r\n \r\n#%% Navegação pelas tags \r\n subreddit = '/r/' + u\r\n upvotes, title, link, comments_link = [],[],[],[]\r\n \r\n for paragraph in soup.find_all('div', class_='score unvoted'):\r\n if paragraph.string != \"•\":\r\n upvotes.append(paragraph.get('title'))\r\n else:\r\n upvotes.append('0')\r\n \r\n for paragraph in soup.find_all('a', class_='title'):\r\n title.append(str(paragraph.text))\r\n if paragraph.get('href')[0] == '/':\r\n link.append('https://old.reddit.com' + paragraph.get('href'))\r\n else:\r\n link.append(paragraph.get('href'))\r\n \r\n for url in soup.find_all('a', class_={'bylink comments empty may-blank', 'bylink comments may-blank'}):\r\n comments_link.append(url.get('href'))\r\n \r\n if len(upvotes) == 0:\r\n print(\"Subreddit não encontrado: {0}\".format(subreddit))\r\n continue\r\n \r\n # Remove a thread referente à propagando, caso exista \r\n if len(comments_link) != len(link):\r\n upvotes.pop(0)\r\n title.pop(0)\r\n link.pop(0)\r\n\r\n#%% Desconsiderar as threads com menos de 5000 upvotes \r\n i, k = 0, 0\r\n j = len(upvotes) \r\n while k < j:\r\n if int(upvotes[i]) < 5000:\r\n upvotes.pop(i)\r\n title.pop(i)\r\n link.pop(i)\r\n comments_link.pop(i)\r\n else:\r\n i = i + 1 \r\n k = k + 1 \r\n \r\n if len(upvotes) == 0:\r\n print(\"Nenhuma thread relevante para o subreddit: {0}\".format(u))\r\n \r\n#%% Impressão das informações\r\n for i in range(0, len(upvotes)): \r\n print(\"Subreddit: {0}\".format(subreddit))\r\n print(\"Título da thread: {0}\".format(title[i]))\r\n print(\"Número de upvotes: {0}\".format(upvotes[i])) \r\n print(\"Link para os comentários da thread: {0}\".format(comments_link[i]))\r\n print(\"Link da thread: {0}\\n\".format(link[i]))\r\n \r\n print(\"\\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\\n\\n\")", "sub_path": "crawlers/crawlers_idwall.py", "file_name": "crawlers_idwall.py", "file_ext": "py", "file_size_in_byte": 2967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "403858323", "text": "\"\"\"\r\nAuthors: Kovid, Tharun, Vishal, Anh, Dhriti, Rinku\r\nLast Edited By: Kovid\r\nLast Edited On: 9/22/2019\r\nClass Description: Class to Extract Features from images\r\n\"\"\"\r\n# Import statements\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.image as mpimg\r\nimport matplotlib.pyplot as plt\r\nimport glob\r\nimport numpy as np\r\nfrom scipy.stats import skew\r\nfrom PostgresDB import PostgresDB\r\nimport tqdm\r\nimport os\r\nimport cv2\r\nfrom skimage import feature\r\nfrom skimage.transform import downscale_local_mean\r\nfrom scipy.linalg import svd\r\nfrom scipy.sparse.linalg import svds\r\n# import time\r\nimport math\r\nimport joblib\r\n\r\n# Task 3 4 5\r\nimport csv\r\nimport matplotlib.pyplot as plt\r\nimport copy\r\nimport os\r\n\r\nDATABASE_NAME = 'mwdb'\r\nTABLE_NAME = 'images_demo'\r\nPASSWORD = \"password\"\r\n# dirpath='/home/anhnguyen/ASU/CSE-515/Project/Phase 1/Project - Phase 2/Data/testset1/'\r\n# ext='*.jpg'\r\ncsvFile = \"HandInfo.csv\"\r\n\r\n\r\n\r\n\r\nclass imageProcess:\r\n def __init__(self, dirpath, ext='*.jpg'):\r\n self.dirpath = dirpath\r\n self.ext = ext\r\n\r\n # Method to fetch images as pixels\r\n def fetchImagesAsPix(self, filename):\r\n image = cv2.imread(filename)\r\n size = image.shape\r\n img_yuv = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)\r\n return img_yuv, size\r\n\r\n # Method to calculate the moments\r\n def calMommets(self, calc):\r\n calc = np.array([x for y in calc for x in y])\r\n mean = np.mean(calc, axis=0)\r\n sd = np.std(calc, axis=0)\r\n skw = skew(calc, axis=0)\r\n mom = [mean.tolist(), sd.tolist(), skw.tolist()]\r\n mom = [x for y in mom for x in y]\r\n return mom\r\n\r\n # Method to split image into 100*100 blocks\r\n def imageMoments(self, image, size, x=100, y=100):\r\n momments = []\r\n for idx1 in range(0, size[0], x):\r\n for idx2 in range(0, size[1], y):\r\n window = image[idx1:idx1 + x, idx2:idx2 + y]\r\n momments.append(self.calMommets(window.tolist()))\r\n return momments\r\n\r\n # Function to calculate the N SIFT feature vectors for each image\r\n def sift_features(self, filepath):\r\n img = cv2.imread(filepath)\r\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n sift = cv2.xfeatures2d.SIFT_create()\r\n kp, des = sift.detectAndCompute(gray, None)\r\n return des\r\n\r\n # Function to Calculate the HOG of an image\r\n def hog_process(self, filename):\r\n image = cv2.imread(filename)\r\n img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n dsimg = downscale_local_mean(img, (10, 10))\r\n (H, hogImage) = feature.hog(dsimg, orientations=9, pixels_per_cell=(8, 8),\r\n cells_per_block=(2, 2), block_norm=\"L2-Hys\",\r\n visualize=True)\r\n return H\r\n\r\n # Function to calculate the local binary pattern of the window\r\n def calculate_lbp(self, curr_window):\r\n eps = 1e-7\r\n hist = []\r\n # Initializing LBP settings - radius and number of points\r\n radius = 3\r\n num_of_points = 8 * radius\r\n # Checking for uniform patterns\r\n window_lbp = feature.local_binary_pattern(curr_window, num_of_points, radius, method='uniform')\r\n # Generating the histogram\r\n (histogram, temp) = np.histogram(window_lbp.ravel(),\r\n bins=np.arange(0, num_of_points + 3),\r\n range=(0, num_of_points + 2))\r\n # Typecasting histogram type to float\r\n histogram = histogram.astype('float')\r\n # Normalizing the histogram such that sum = 1\r\n histogram /= (histogram.sum() + eps)\r\n hist.append(histogram)\r\n return hist\r\n\r\n # Function to pre-process images into grayscale and form windows of 100X100 to be fed to calculate_lbp\r\n def lbp_preprocess(self, filename):\r\n local_binary_pattern = []\r\n # Converting the BGR image to Grayscale\r\n img = cv2.imread(filename)\r\n gray = cv2.cvtColor(img, cv2.cv2.COLOR_BGR2GRAY)\r\n for i in range(0, gray.shape[0], 100):\r\n j = 0\r\n while j < gray.shape[1]:\r\n current_window = gray[i:i + 99, j:j + 99]\r\n temp_lbp = self.calculate_lbp(current_window)\r\n local_binary_pattern.extend(temp_lbp)\r\n j = j + 100\r\n\r\n local_binary_pattern = [x for y in local_binary_pattern for x in y]\r\n local_binary_pattern = np.asarray(local_binary_pattern, dtype=float).tolist()\r\n\r\n return local_binary_pattern\r\n\r\n \"\"\"\r\n Method to Save feature data to Postgres Database\r\n 1. Sift: imagedata_s(imageid, data)\r\n 2. Moments: imagedata_m(imageid, data)\r\n 3. Hog: imagedata_h(imageid, data)\r\n 4. LBP: imagedata_l(imageid, data) \r\n \"\"\"\r\n def dbSave(self, conn, model):\r\n # Count the number of files in the directory\r\n filecnt = len(glob.glob(self.dirpath + self.ext))\r\n pbar = tqdm.tqdm(total=filecnt)\r\n # Read images from the directory\r\n for filename in glob.glob(self.dirpath + self.ext):\r\n if model == 'm':\r\n pixels, size = self.fetchImagesAsPix(filename)\r\n momments = self.imageMoments(pixels, size)\r\n # Convert to string to insert into DB as an array\r\n values_st = str(np.asarray(momments).tolist())\r\n # values_st = str(momments).replace('[', '{')\r\n # values_st = values_st.replace(']', '}')\r\n dbname = 'imagedata_m'\r\n elif model == 's':\r\n des = self.sift_features(filename)\r\n values_st = str(np.asarray(des).tolist())\r\n # values_st = str(des.tolist()).replace('[', '{')\r\n # values_st = values_st.replace(']', '}')\r\n dbname = 'imagedata_s'\r\n elif model == 'h':\r\n h_val = self.hog_process(filename)\r\n values_st = str(np.asarray(h_val).tolist())\r\n # values_st = str(h_val.tolist()).replace('[', '{')\r\n # values_st = values_st.replace(']', '}')\r\n dbname = 'imagedata_h'\r\n elif model == 'l':\r\n lbp_val = self.lbp_preprocess(filename)\r\n values_st = str(np.asarray(lbp_val).tolist())\r\n # values_st = str(lbp_val.tolist()).replace('[', '{')\r\n # values_st = values_st.replace(']', '}')\r\n dbname = 'imagedata_l'\r\n else:\r\n print('Incorrect value for Model provided')\r\n exit()\r\n sql = \"CREATE TABLE IF NOT EXISTS {db} (imageid TEXT NOT NULL, imagedata TEXT, PRIMARY KEY (imageid))\".format(db=dbname)\r\n cur = conn.cursor()\r\n cur.execute(sql)\r\n name = os.path.basename(filename)\r\n name = os.path.splitext(name)[0]\r\n # create a cursor\r\n sql = \"SELECT {field} FROM {db} WHERE {field} = '{condition}';\".format(field=\"imageid\",db=dbname,condition=name)\r\n # print(\"SQL Check Exist - HOG: \", sql)\r\n cur.execute(sql)\r\n\r\n # cur.execute(sql)\r\n if cur.fetchone() is None:\r\n print(\"Insert\")\r\n # print(\"Not Exist HOG - Insert\")\r\n sql = \"INSERT INTO {db} VALUES('{x}', '{y}');\".format(x=name,y=values_st, db=dbname)\r\n else:\r\n print(\"Update\")\r\n # print(\"Exist HOG - Update\")\r\n # column = \"HOG\"\r\n \r\n sql = \"UPDATE {db} SET imagedata ='{y}' WHERE IMAGEID = '{id}'\".format(y=values_st, db=dbname, id=name)\r\n \r\n cur.execute(sql)\r\n conn.commit()\r\n # close cursor\r\n cur.close()\r\n pbar.update(1)\r\n\r\n # Method to fetch data from Database\r\n def dbFetch(self, conn, dbname, condition = \"\"):\r\n # Create cursor\r\n cur = conn.cursor()\r\n # if model == 's':\r\n # dbname = 'imagedata_sift'\r\n # elif model == 'm':\r\n # dbname = 'imagedata_moments'\r\n # elif model == 'h':\r\n # dbname = 'imagedata_hog'\r\n # elif model == 'l':\r\n # dbname = 'imagedata_lbp'\r\n # dbname = 'imagedata_' + model\r\n # if condition:\r\n # dbname += \"_\" + technique\r\n sql = \"SELECT * FROM {db} {condition}\".format(db=dbname, condition=condition)\r\n # print (sql)\r\n # print(\"before\")\r\n cur.execute(sql)\r\n # print(\"here\")\r\n recs = cur.fetchall()\r\n return recs\r\n\r\n # Method to access the database\r\n def dbProcess(self, password, process='s', model='s', host='localhost',\r\n database='mwdb', user='postgres', port=5432, dbase = 'imagedata_l'):\r\n # Connect to the database\r\n db = PostgresDB(password=PASSWORD, host=host,\r\n database=DATABASE_NAME, user=user, port=port)\r\n conn = db.connect()\r\n if process == 's':\r\n self.dbSave(conn, model)\r\n print('Data saved successfully to the Database!')\r\n elif process == 'f':\r\n recs = self.dbFetch(conn,dbase)\r\n recs_flt = []\r\n # Flatten the data structure and \r\n for rec in recs:\r\n recs_flt.append((rec[0],np.asarray(eval(rec[1]))))\r\n # if model == 'm':\r\n # print(recs)\r\n # for rec in recs:\r\n # recs_flt.append(np.asarray(eval(rec[1])))\r\n # recs_flt.append((rec[0], [float(x) for y in rec[1] for x in y]))\r\n # elif model == 's':\r\n # for rec in recs:\r\n # recs_flt.append((rec[0], [[float(x) for x in y] for y in rec[1]]))\r\n # elif model == 'l' or model == 'h':\r\n # for rec in recs:\r\n # recs_flt.append((rec[0], [float(x) for x in rec[1]]))\r\n return recs_flt\r\n\r\n # Method to calculate the Cosine Similarity\r\n def cosine_sim(self, vec1, vec2):\r\n dot_product = np.dot(vec1, vec2)\r\n norm_a = np.linalg.norm(vec1)\r\n norm_b = np.linalg.norm(vec2)\r\n cos = 1 - dot_product / (norm_a * norm_b)\r\n return cos\r\n # return 1 - spatial.distance.cosine(vec1, vec2)\r\n\r\n # method to calculate Manhattan distance\r\n def man_dist(self, vec1, vec2):\r\n dist = [abs(x - y) for x,y in zip(vec1, vec2)]\r\n return sum(dist)\r\n\r\n # Calculate the L2 distance\r\n def l2Dist(self, d1, d2):\r\n d1 = np.array(d1, dtype=np.float32)\r\n d2 = np.array(d2, dtype=np.float32)\r\n dist = cv2.norm(d1, d2, cv2.NORM_L2)\r\n return dist\r\n\r\n # Calculate the Euclidean distance\r\n def euclidean_distance(self, imageA, imageB):\r\n # d=math.sqrt(np.sum([((a-b) ** 2) for (a,b) in zip(imageA,imageB)]))\r\n # return d\r\n return np.sqrt(np.sum((imageA - imageB) ** 2, axis=0))\r\n\r\n # Calculate the vector matches\r\n def knnMatch(self, d1, d2, k=2):\r\n distances = []\r\n for d in d1:\r\n dis = sorted([self.l2Dist(d, x) for x in d2])\r\n distances.append(dis[0:k])\r\n return distances\r\n\r\n # Method to calculate Similarity for SIFT vectors\r\n def sift_sim(self, d1, d2):\r\n matches = self.knnMatch(d1, d2, k=2)\r\n good = []\r\n all = []\r\n d1 = np.array(d1, dtype=np.float32)\r\n for m, n in matches:\r\n all.append(m)\r\n if m < 0.8 * n:\r\n good.append(m)\r\n return len(good) / d1.shape[0]\r\n\r\n # Method to calculate Similarity\r\n def SimCalc(self, img, recs, imgmodel='m', k=5):\r\n # Calculate the Similarity matrix for Moments model\r\n rec_dict = dict((x, y) for x, y in recs)\r\n img_vec = rec_dict[img]\r\n if imgmodel == 'm':\r\n sim_matrix = sorted([(rec[0], self.cosine_sim(img_vec, rec[1])) for rec in recs\r\n if rec[0] != img], key=lambda x: x[1])\r\n if imgmodel == 's':\r\n sim_matrix = sorted([(rec[0], self.sift_sim(img_vec, rec[1])) for rec in recs\r\n if rec[0] != img], key=lambda x: x[1], reverse=True)\r\n return sim_matrix[0:k]\r\n\r\n\r\n def queryImageNotLabel(self, image_data, feature, technique, label):\r\n print(\"Not Same Label\")\r\n # cursor.execute(\"SELECT * FROM imagedata_{0}_{1} WHERE imageid = '{2}'\".format(feature,technique,image))\r\n # image_data = cursor.fetchall()\r\n # print(image_data)\r\n image_data = np.asarray(eval(image_data[0][1]))\r\n path = os.path.normpath(os.getcwd() + os.sep + os.pardir + os.sep + 'Models' +os.sep)\r\n\r\n model = joblib.load(path + os.sep + \"{0}_{1}_{2}.joblib\".format(feature, technique, label))\r\n latent = np.asarray(model.components_)\r\n \r\n if feature == 's':\r\n kmeans = joblib.load(path + os.sep + 'kmeans_{0}_{1}.joblib'.format(latent.shape[1], label))\r\n histo = np.zeros(latent.shape[1])\r\n nkp = np.size(image_data)\r\n for d in image_data:\r\n idx = kmeans.predict([d])\r\n histo[idx] += 1/nkp\r\n print(np.asarray((model.components_)).shape)\r\n image_data = np.asarray(histo).dot(latent.T)\r\n return image_data\r\n \r\n def similarity (self, feature, technique, dbase, k, image, label = \"\"):\r\n db = PostgresDB(password = \"mynhandepg\", database = \"mwdb\")\r\n conn = db.connect()\r\n if conn is None:\r\n print(\"Can not connect to database\")\r\n exit()\r\n cursor = conn.cursor()\r\n cursor.execute(\"SELECT * FROM \" + dbase)\r\n data = cursor.fetchall()\r\n image_id = [rec[0] for rec in data]\r\n similarity = {}\r\n if image in image_id:\r\n image_index = image_id.index(image)\r\n print(image_index)\r\n image_data = np.asarray(eval(data[image_index][1]))\r\n else:\r\n print(\"Not Same Label\")\r\n dbase = 'imagedata_' + feature\r\n label = label.replace(\" \", \"_\")\r\n image_data = self.dbFetch(conn,dbase, \"WHERE imageid = '{0}'\".format(image))\r\n image_data = self.queryImageNotLabel(image_data, feature, technique, label)\r\n similarity[image] = self.euclidean_distance(image_data,image_data)\r\n \r\n # print (image_id)\r\n for i in range(len(image_id)):\r\n image_cmp = np.asarray(eval(data[i][1]))\r\n # if self.metrics:\r\n # # similarity[row[0]] = 1- self.cosine_similarity(image, result)\r\n # similarity[image_id[i]] = 1 - st.pearsonr(image,image_cmp)[0]\r\n # # similarity[row[0]] = mean_squared_error(image,result)\r\n # # similarity[row[0]] = 0 - self.psnr(image,result)\r\n # else:\r\n similarity[image_id[i]] = self.euclidean_distance(image_data,image_cmp)\r\n similarity = sorted(similarity.items(), key = lambda x : x[1], reverse=False)\r\n print(similarity)\r\n self.dispImages(similarity,feature, technique, 11, k)\r\n\r\n # Method to display images\r\n def dispImages(self, similarity, feature, technique, no_images, k):\r\n columns = 4\r\n rows = no_images // columns\r\n if no_images % columns != 0:\r\n rows += 1\r\n ax = []\r\n fig=plt.figure(figsize=(30, 20))\r\n fig.canvas.set_window_title('Task 3 - Images Similarity')\r\n fig.suptitle(str(no_images - 1) + ' Similar Images of ' + similarity[0][0] + ' based on ' + feature + \", \"+ str(k) + \" latent semantics and \" + technique)\r\n # plt.title(str(no_images - 1) + ' Similar Images of ' + similarity[0][0] + ' based on ' + type,y=-0.01)\r\n plt.axis('off')\r\n # fig.title(str(k) + 'Similar Images of ' + similarity[0][0] + ' based on ' + type)\r\n f= open(\"../Outputs/task3_result.txt\",\"w+\")\r\n f.write(\"Task 2 - Matching Score \" + str(no_images) + \" images with \" + similarity[0][0] + ' based on ' + feature + \", \"+ str(k) + \" latent semantics and \" + technique + \":\\n\")\r\n for i in range(no_images):\r\n f.write(similarity[i][0] + \": \" + str(similarity[i][1]) + \"\\n\")\r\n img = mpimg.imread(self.dirpath + self.ext.replace('*', similarity[i][0]))\r\n # create subplot and append to ax\r\n ax.append( fig.add_subplot(rows, columns, i+1))\r\n if i == 0:\r\n ax[-1].set_title(\"Given Image: \" +similarity[i][0] ) # set title\r\n else:\r\n ax[-1].set_title(\"Image \"+str(i) + \": \" +similarity[i][0] ) # set title\r\n ax[-1].axis('off')\r\n plt.imshow(img)\r\n plt.savefig('../Outputs/task3_result.png')\r\n f.close()\r\n plt.show()\r\n plt.close()\r\n\r\n # Method to write to a file\r\n def writeFile(self, content, path):\r\n with open(path, 'w+') as file:\r\n file.write(str(content))\r\n\r\n # Convert list to string\r\n def list2string(self, lst):\r\n values_st = str(lst).replace('[[', '(')\r\n values_st = values_st.replace('[', '(')\r\n values_st = values_st.replace(']]', ']')\r\n values_st = values_st.replace(']', ')')\r\n return values_st\r\n \r\n def createInsertMeta(self, conn):\r\n # Read the metadata file\r\n metafile = self.readMetaData()\r\n # Create cursor\r\n cur = conn.cursor()\r\n # Create the meta table\r\n sqlc = \"CREATE TABLE IF NOT EXISTS \" \\\r\n \"img_meta(subjectid TEXT, image_id TEXT, gender TEXT, aspect TEXT, orient TEXT, accessories TEXT)\"\r\n cur.execute(sqlc)\r\n conn.commit()\r\n # Insert the meta data into the database table\r\n values_st = self.list2string(metafile)\r\n sqli = \"INSERT INTO img_meta VALUES {x}\".format(x=values_st)\r\n cur.execute(sqli)\r\n conn.commit()\r\n print('Meta Data saved into Database!')\r\n cur.close()\r\n \r\n \r\n def readMetaData(self):\r\n with open(self.dirpath + csvFile, 'r') as file:\r\n csv_reader = csv.reader(file)\r\n meta_file = []\r\n for idx, row in enumerate(csv_reader):\r\n if idx == 0:\r\n continue\r\n sub_id = row[0]\r\n id = row[7].split('.')[0]\r\n gender = row[2]\r\n orientation = row[6].split(' ')\r\n accessories = row[4]\r\n meta_file.append([sub_id, id, gender, orientation[0], orientation[1], accessories])\r\n return meta_file\r\n\r\n def CSV(self, label = \"\"):\r\n label = label.lower()\r\n if label in (\"dorsal\", \"palmar\", \"left\", \"right\"):\r\n index = \"aspectOfHand\"\r\n elif label in (\"male\", \"female\"):\r\n index = \"gender\"\r\n elif label in (\"with accessories\", \"without accessories\"):\r\n index = \"accessories\"\r\n else:\r\n index = \"\"\r\n\r\n with open(self.dirpath + csvFile, 'r', newline='') as f:\r\n reader = csv.reader(f, delimiter=',')\r\n # next(cr) gets the header row (row[0])\r\n header = next(reader)\r\n i = header.index(index)\r\n id = header.index(\"imageName\")\r\n # print(i,index)\r\n # list comprehension through remaining cr iterables\r\n if index in (\"aspectOfHand\", \"gender\"):\r\n filteredImage = [row[id][:len(row[id]) - 4] for row in reader if row[i].find(label) != -1]\r\n elif label == \"with accessories\":\r\n filteredImage = [row[id][:len(row[id]) - 4] for row in reader if int(row[i]) == 0]\r\n elif label == \"without accessories\":\r\n filteredImage = [row[id][:len(row[id]) - 4] for row in reader if int(row[i]) == 1]\r\n # else:\r\n # return data, header\r\n # print (filteredImage)\r\n return filteredImage\r\n\r\n # def plotImage(self, data, path):\r\n # for i,k in enumerate(data):\r\n # print(i, k[0])\r\n # # break\r\n\r\n\r\n\r\ndef cosine_similarity(imageA, imageB):\r\n # print(imageA)\r\n # print(imageB)\r\n return np.dot(imageA, imageB)/(np.sqrt(np.sum(imageA ** 2, axis=0))*np.sqrt(np.sum(imageB ** 2, axis=0)))\r\n\r\n", "sub_path": "imageProcess.py", "file_name": "imageProcess.py", "file_ext": "py", "file_size_in_byte": 20282, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "cv2.imread", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2YUV", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 58, "usage_type": "call"}, {"api_name": "scipy.stats.skew", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.xfeatures2d.SIFT_create", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.xfeatures2d", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 84, "usage_type": "attribute"}, {"api_name": "skimage.transform.downscale_local_mean", "line_number": 85, "usage_type": "call"}, {"api_name": "skimage.feature.hog", "line_number": 86, "usage_type": "call"}, {"api_name": "skimage.feature", "line_number": 86, "usage_type": "name"}, {"api_name": "skimage.feature.local_binary_pattern", "line_number": 99, "usage_type": "call"}, {"api_name": "skimage.feature", "line_number": 99, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 126, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 139, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 140, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "PostgresDB.PostgresDB", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 255, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 256, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 268, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 269, "usage_type": "attribute"}, {"api_name": "cv2.norm", "line_number": 270, "usage_type": "call"}, {"api_name": "cv2.NORM_L2", "line_number": 270, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 292, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 318, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 319, "usage_type": "call"}, {"api_name": "os.path", "line_number": 319, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 319, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 319, "usage_type": "attribute"}, {"api_name": "os.pardir", "line_number": 319, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 321, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 321, "usage_type": "attribute"}, {"api_name": "skimage.feature", "line_number": 321, "usage_type": "argument"}, {"api_name": "numpy.asarray", "line_number": 322, "usage_type": "call"}, {"api_name": "skimage.feature", "line_number": 324, "usage_type": "name"}, {"api_name": "joblib.load", "line_number": 325, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 325, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 332, "usage_type": "call"}, {"api_name": "PostgresDB.PostgresDB", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 349, "usage_type": "call"}, {"api_name": "skimage.feature", "line_number": 352, "usage_type": "name"}, {"api_name": "skimage.feature", "line_number": 355, "usage_type": "argument"}, {"api_name": "numpy.asarray", "line_number": 360, "usage_type": "call"}, {"api_name": "skimage.feature", "line_number": 370, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 379, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 379, "usage_type": "name"}, {"api_name": "skimage.feature", "line_number": 381, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 383, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 383, "usage_type": "name"}, {"api_name": "skimage.feature", "line_number": 386, "usage_type": "name"}, {"api_name": "matplotlib.image.imread", "line_number": 389, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 389, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 397, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 397, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 398, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 398, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 400, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 400, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 401, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 401, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 437, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 462, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 490, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 490, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 490, "usage_type": "call"}]} +{"seq_id": "418960824", "text": "import base64\nfrom django.http import JsonResponse\nfrom django.contrib.auth import authenticate, login\nfrom django.contrib.auth import logout\nfrom django.shortcuts import render\nfrom django.db.models import Avg, Sum\nfrom django.core.files import File\nfrom .models import *\nfrom django.views.decorators.csrf import csrf_exempt\nimport json,os\n\n\ndef logout_view(req):\n ctx = {}\n logout(req)\n return render(req, 'ecosmart/index.tpl', ctx)\n\n# Create your views here.\ndef index(req):\n ctx = {}\n if req.method == 'POST':\n user = authenticate(username=req.POST['user'], password=req.POST['pwd'])\n if user is not None and user.is_active:\n login(req, user)\n if req.user.is_authenticated():\n return render(req, 'ecosmart/frontend.tpl', ctx)\n else:\n return render(req, 'ecosmart/index.tpl', ctx)\n\n\ndef api_trashcans(req):\n ctx = {}\n ctx['trashcans'] = [ { 'id': tc.id, 'address': tc.address, 'trash_type': tc.trash_type_id, 'max_kg': tc.max_kg } for tc in\n TrashCan.objects.all().order_by('trash_type') ]\n return JsonResponse(ctx, safe=False)\n\n\ndef api_trashcan(req, id=None):\n if req.method == 'GET':\n if id == None:\n return api_trashcans(req)\n tc = TrashCan.objects.get(id = id)\n return JsonResponse( { 'id': tc.id, 'address': tc.address, 'trash_type': tc.trash_type_id, 'max_kg': tc.max_kg } )\n\n\ndef api_hhsummary(req):\n if req.method != 'GET':\n return\n ctx = {}\n # get for current household, linked through currently logged in user\n tes = TrashEvent.objects.filter(household__householduser__user = req.user)\n\n ctx['events'] = [ { 'id': te.id, 'date': te.date, 'kg': te.kg, 'trash_can': te.trash_can_id, 'flags': te.flags } for te in tes ]\n ctx['tr_labels'] = [ format_date(ev['date']) for ev in ctx['events'] ]\n ctx['tr_values'] = [ ev['kg'] for ev in ctx['events'] ]\n\n ctx['trash_types'] = [ {'id': tt.id, 'name': tt.name, 'cost_kg': tt.cost_kg } for tt in TrashType.objects.all().order_by('name') ]\n ctx['tt_labels'] = [ tt['name'] for tt in ctx['trash_types'] ]\n ctx['tt_values'] = [ TrashEvent.objects.filter(trash_can__trash_type_id = tt['id']).aggregate(Sum('kg'))['kg__sum'] for tt in ctx['trash_types'] ]\n\n return JsonResponse(ctx, safe=False)\n\n\ndef api_rfid_known(req, rfid_code):\n try:\n hh = Household.objects.get(rfid_code = rfid_code)\n return JsonResponse({ 'ok': True, 'household_id': hh.id })\n except Household.DoesNotExist:\n return JsonResponse({ 'ok': False, 'message': 'Household.DoesNotExist' })\n\n\n\"\"\"\n trash_can = models.ForeignKey(TrashCan)\n date = models.DateTimeField(auto_now_add = True)\n kg = models.FloatField()\n household = models.ForeignKey(Household)\n trash_pic = models.FileField()\n flags = models.IntegerField(default=0)\n \"\"\"\n\n\n@csrf_exempt\ndef api_trash_event(req):\n # Data in POST body\n data = json.loads(req.body.decode('utf-8'))\n te = TrashEvent(trash_can_id = data['trash_can_id'], kg = data['kg'], household_id = data['household_id'])\n f = open('/tmp/pic.jpg', 'wb+')\n bd = base64.b64decode(data['trash_pic'])\n f.write(bd)\n f.seek(0, 0)\n te.trash_pic.save('trash.jpg', File(f))\n te.save()\n return JsonResponse({ 'ok': True, 'id': te.id })\n\n\ndef format_date(d):\n return d.strftime('%Y-%m-%d %H:%M')\n\n", "sub_path": "web/ecosmart/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3439, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.contrib.auth.logout", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 35, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 59, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 61, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 67, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 69, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 85, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 88, "usage_type": "call"}, {"api_name": "django.core.files.File", "line_number": 91, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 93, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 82, "usage_type": "name"}]} +{"seq_id": "177346511", "text": "#!/usr/local/bin/python3.7\n# encoding: utf-8\n\"\"\"\nController for a process with many steps forming a directed graph with cycles.\n\nold.constructor.host_constructor is a description\n\nIt defines classes_and_methods\n\n@author: Jonathan Gossage\n\n@copyright: 2018 Jonathan Gossage All rights reserved.\n\n@license: Apache2\n\n@contact: jgossage at gmail.com\n@deffield updated: Updated\n\"\"\"\n\nimport sys\nimport os\n\nimport argparse\nfrom getpass import getuser\nimport logging\nfrom pathlib import Path\nimport shutil\nfrom typing import Tuple, Sequence, List\nfrom warnings import warn\n\nfrom ruamel.yaml import YAML\n\nimport utilities.arg_parser\nfrom utilities.setup_logging import capture_sys_output\nfrom utilities.yaml_loader import YamlLoader\n\n__all__ = []\n__version__ = '0.1.0'\n__date__ = '2018-08-27'\n__updated__ = '2018-08-27'\n\n\nclass MakeWorkspace(argparse.Action):\n \"\"\"Handle creation of a non-temporary workspace\"\"\"\n def __init__(self, option_strings: Sequence[str], dest: str, **kwargs) -> None:\n super().__init__(option_strings, dest, **kwargs)\n\n def __call__(self, parser: argparse.ArgumentParser, namespace: argparse.Namespace, values: List[Path], # @UnusedVariable\n option_string: Sequence[str]=None) -> None: # @UnusedVariable\n if values.exists(): # Workspace exists\n if values.is_dir():\n shutil.rmtree(str(values)) # Remove the old copy\n else:\n values.rm()\n values.mkdir() # Create the workspace\n setattr(namespace, self.dest, values)\n\n\ndef toPath(path: str) -> Path:\n \"\"\"\"Convert string to Path\"\"\"\n p = Path(path)\n return p\n\n\ndef getConfig() -> List[Path]:\n \"\"\"\n Gets the standard list of configuration files.\n The first file found is considered the base file and the others will update the base file in the order they are encountered.\n We start with the files specified in /etc/WiseOldBird/stdcfglist.yaml.\n None of the files needs to exist.\n \"\"\"\n cfg = []\n yaml = YAML(typ='unsafe')\n p = Path('/etc/WiseOldBird/stdcfglist.yaml')\n if p.is_file():\n cfglist = None\n with p.open(mode='r') as fp:\n cfglist = yaml.load(fp)\n if cfglist is not None and len(cfglist) > 0:\n for c in cfglist:\n c = Path(c)\n if c.is_file():\n cfg.append(c)\n return cfg\n\n\nclass JobManager():\n def __init__(self, argv: Sequence[str]=None, prog: str=None) -> None:\n self._logger = logging.getLogger() # Start with the root logger in default configuration\n self._logger.setLevel(logging.DEBUG)\n self._user = getuser()\n self._home = os.environ['HOME']\n self._args, self._program_name = self._handleArguments(argv, prog)\n self._logger.setLevel(logging.DEBUG if self._args.debug is not None else logging.INFO)\n stdcfg = getConfig() # Get the standard list of configuration files\n # Add the optional configuration files specified on the command line\n if self._args.add is not None:\n stdcfg.extend(self._args.add)\n elif self._args.replace is not None:\n stdcfg = self._args.replace # Only use the configuration files specified on the command line\n # Build the configuration by updating the base configuration file with all the update files found\n self._config = YamlLoader(stdcfg)()\n\n def __call__(self) -> int:\n try:\n if self._args.debug is not None:\n raise RuntimeError('Raised at the end of execution in debug mode to verify that uncaught exceptions are logged')\n return 0\n\n except KeyboardInterrupt:\n # handle keyboard interrupt\n return 0\n except BaseException as e: # pylint: disable=broad-except\n self._logger.exception('JOB MANAGER Error', exc_info=e)\n return 2\n\n def _handleArguments(self, # IGNORE:C0111 @DontTrace\n argv: Sequence=None,\n prog=None) -> Tuple:\n \"\"\"Command line options.\"\"\"\n # Setup the arguments\n if argv is None: # Running production - use arguments from command line\n argv = sys.argv[1:]\n\n # Setup the information needed by the argument parser\n program_version = \"v{}\".format(__version__)\n program_build_date = str(__updated__)\n if prog is not None: # Unit testing - simplify results for easier checking\n program_name = prog\n # Setup argument parser in debug mode\n parser = utilities.arg_parser.ArgParser(prog=program_name, usage='')\n else:\n program_name = os.path.basename(sys.argv[0])\n program_shortdesc = __import__('__main__').__doc__.split(\"\\n\")[1]\n program_license = \"\"\"{}\n\n Created by Jonathan Gossage on {}.\n Copyright 2018 Jonathan Gossage. All rights reserved.\n\n Licensed under the Apache License 2.0\n http://www.apache.org/licenses/LICENSE-2.0\n\n Distributed on an \"AS IS\" basis without warranties\n or conditions of any kind, either express or implied.\n\n USAGE\n \"\"\".format(program_shortdesc, str(__date__))\n # Setup argument parser in production mode\n parser = utilities.arg_parser.ArgParser(description=program_license,\n formatter_class=argparse.RawDescriptionHelpFormatter)\n\n program_version_message = '{} {} ({})'.format(program_name, program_version, program_build_date)\n\n # Add the acceptable arguments\n parser.add_argument('-d', '--debug', dest='debug', type=toPath, action=MakeWorkspace,\n help='debugging path to retained workspace')\n parser.add_argument('-a', '--add', dest='add', type=toPath, action='store', nargs='*',\n help='use this/these configuration file[s] in addition to the standard files')\n parser.add_argument('-r', '--replace', dest='replace', type=toPath, action='store', nargs='*',\n help='use this/these configuration file[s] in place of the standard files')\n parser.add_argument('-v', '--version', action='version', version=program_version_message)\n parser.add_argument(dest='job', help='name of the table for the job to be run')\n\n # Process arguments\n args = parser.parse_args(argv)\n\n # Check for conflicting arguments\n if args.replace is not None:\n if args.add is not None:\n warn('Task Controller: The arguments --replace and --add conflict - using --replace')\n args.add = None\n return (args, program_name)\n\n\nif __name__ == \"__main__\":\n logging.captureWarnings(True)\n with capture_sys_output():\n sys.exit(JobManager()())\n", "sub_path": "ConstructDevelopmentSystem/src/manager/job_manager.py", "file_name": "job_manager.py", "file_ext": "py", "file_size_in_byte": 6883, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "argparse.Action", "line_number": 43, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 45, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 48, "usage_type": "attribute"}, {"api_name": "argparse.Namespace", "line_number": 48, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 48, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 49, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 52, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 61, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 59, "usage_type": "name"}, {"api_name": "ruamel.yaml.YAML", "line_number": 73, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 74, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 81, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 65, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 88, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 89, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 90, "usage_type": "attribute"}, {"api_name": "getpass.getuser", "line_number": 91, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 92, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 94, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 94, "usage_type": "attribute"}, {"api_name": "utilities.yaml_loader.YamlLoader", "line_number": 102, "usage_type": "call"}, {"api_name": "typing.Sequence", "line_number": 118, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 123, "usage_type": "attribute"}, {"api_name": "utilities.arg_parser.arg_parser.ArgParser", "line_number": 131, "usage_type": "call"}, {"api_name": "utilities.arg_parser.arg_parser", "line_number": 131, "usage_type": "attribute"}, {"api_name": "utilities.arg_parser", "line_number": 131, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 133, "usage_type": "attribute"}, {"api_name": "utilities.arg_parser.arg_parser.ArgParser", "line_number": 149, "usage_type": "call"}, {"api_name": "utilities.arg_parser.arg_parser", "line_number": 149, "usage_type": "attribute"}, {"api_name": "utilities.arg_parser", "line_number": 149, "usage_type": "name"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 150, "usage_type": "attribute"}, {"api_name": "warnings.warn", "line_number": 170, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 119, "usage_type": "name"}, {"api_name": "logging.captureWarnings", "line_number": 176, "usage_type": "call"}, {"api_name": "utilities.setup_logging.capture_sys_output", "line_number": 177, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 178, "usage_type": "call"}]} +{"seq_id": "373602356", "text": "#%matplotlib inline\n#%config InlineBackend.figure_format = 'retina'\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch import optim\nimport torch.nn.functional as F\nimport torchvision\nfrom torchvision import datasets, transforms, models\nfrom torch.optim import lr_scheduler\nimport matplotlib.pyplot as plt\nimport time\nimport copy\nfrom sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, precision_recall_fscore_support, auc, precision_recall_curve\nimport sys\nimport os\nfrom collections import Counter\nimport math\nimport random\nfrom PIL import Image\nfrom torch.autograd import Variable\nimport pandas\nimport scipy\nfrom prg import prg\n\ntransforms_ = transforms.Compose(\n [\n transforms.Resize((255)),\n #transforms.Resize((227,227)),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(\n mean=[0.485, 0.456, 0.406], \n std=[0.229, 0.224, 0.225]\n )\n ]\n)\n\none_shot_examples = {\n 'badminton': '/evaluation_dataset/one_shot_data/event_img/badminton/Easy_Mid_badminton_98.jpg',\n 'bocce': '/evaluation_dataset/one_shot_data/event_img/bocce/Medium_Mid_bocce_78.jpg',\n 'croquet': '/evaluation_dataset/one_shot_data/event_img/croquet/Easy_Mid_croquet_2.jpg',\n 'polo': '/evaluation_dataset/one_shot_data/event_img/polo/Easy_Mid_polo_96.jpg',\n 'RockClimbing': '/evaluation_dataset/one_shot_data/event_img/RockClimbing/Easy_Mid_RockClimbing_90.jpg',\n 'rowing': '/evaluation_dataset/one_shot_data/event_img/rowing/Easy_Mid_Rowing_5.jpg',\n 'sailing': '/evaluation_dataset/one_shot_data/event_img/sailing/Easy_Mid_sailing_83.jpg',\n 'snowboarding': '/evaluation_dataset/one_shot_data/event_img/snowboarding/Easy_Mid_snowboarding_33.jpg'\n}\n\n# A simple hook class that returns the input and output of a layer during forward/backward pass\nclass Hook():\n def __init__(self, module, backward=False):\n if backward==False:\n self.hook = module.register_forward_hook(self.hook_fn)\n else:\n self.hook = module.register_backward_hook(self.hook_fn)\n def hook_fn(self, module, input, output):\n self.input = input\n self.output = output\n def close(self):\n self.hook.remove()\n\nclass ImageFoldersWithPaths(datasets.ImageFolder):\n def __getitem__(self, index):\n original_tuple = super(ImageFoldersWithPaths, self).__getitem__(index)\n path = self.imgs[index][0]\n tuple_with_path = (original_tuple + (path,))\n return tuple_with_path\n\ndef accuracy(y_truth, y_pred):\n '''\n Returns accuracy score \n\n y_truth - list of true labels same size as y_pred\n y_pred - list of predicted labels same size as y_truth\n '''\n return float(sum([x==y for x, y in zip(y_truth, y_pred)]))/len(y_truth)\n\n\ndef load_data(datadir, is_valid_file_func= lambda x:True):\n '''\n Returns a pytorch Dataloader\n\n datadir - directory where each sub directory contains images for a specific class\n is_valid_file_func - function that takes in a file_path and determines if is a valid file for loading with a Dataset object\n '''\n data = datasets.ImageFolder(\n root=datadir,\n transform=transforms_,\n is_valid_file=is_valid_file_func \n )\n \n dataloader = torch.utils.data.DataLoader(\n data,\n batch_size=1024,\n num_workers=10,\n pin_memory=True\n )\n return dataloader\n\ndef load_data_with_paths(datadir, is_valid_file_func= lambda x:True):\n '''\n Returns a pytorch Dataloader, at each iteraction the dataloader returns:(feature_activations, labels, paths)\n\n datadir - directory where each sub directory contains images for a specific class\n is_valid_file_func - function that takes in a file_path and determines if is a valid file for loading with a Dataset object\n '''\n data = ImageFoldersWithPaths(\n root=datadir,\n transform=transforms_,\n is_valid_file=is_valid_file_func \n )\n \n dataloader = torch.utils.data.DataLoader(\n data,\n batch_size=1024,\n num_workers=10,\n pin_memory=True\n )\n return dataloader\n\ndef evaluate(W, data):\n '''\n Returns label predictions and softmax scores after applying weight matrix (W) to data\n W - class X weights matrix\n data - example X features\n '''\n return np.argmax(\n np.matmul(\n W,\n data.transpose()\n ),\n 0\n )\n\ndef oneshot_update(K, train_features, novel_features):\n '''\n Builds a new linear classifier based on original train_features and novel features. A new weight matrix (W) is returned\n\n K - matrix of labels for train features [n_classes, n_train_features] where 1 indicates class label\n train_features - example X features\n novel_features - features for one class [examples, features]\n '''\n features = np.hstack([train_features, novel_features])\n\n num_samples = train_features.shape[1] + novel_features.shape[1]\n\n num_classes = train_features.shape[1] + 1\n\n K_prime = np.zeros((num_classes,num_samples))\n K_prime[0:K.shape[0], 0:K.shape[1]] = K\n for i in range(train_features.shape[1], num_samples):\n K_prime[num_classes - 1,i] = 1\n\n W = np.matmul( K_prime, np.linalg.pinv(features) )\n\n return W \n\n\ndef get_features(device, model, test_dataloader):\n '''\n Gets pre-classification features from a resnet model. Returns true_classnames (determined by test_dataloader), labels, activation features.\n\n device - pytorch device\n model - pytorch model\n test_dataloader - pytorch dataloader\n '''\n truth_classnames = test_dataloader.dataset.classes\n avgPoolF = Hook(model.avgpool)\n features_arr = []\n labels_arr = []\n with torch.no_grad():\n for i, (inputs, labels) in enumerate(test_dataloader):\n inputs = inputs.to(device)\n outputs = model(inputs)\n print(avgPoolF.output.shape)\n avgPoolF_npy = avgPoolF.output.detach().cpu().reshape( labels.size()[0], -1).numpy()\n features_arr.append(avgPoolF_npy)\n labels_arr.append(labels.detach().cpu().numpy())\n \n features = np.vstack(features_arr)\n labels = np.hstack(labels_arr)\n return truth_classnames, labels, features\n\ndef get_features_with_paths(device, model, test_dataloader):\n '''\n Gets pre-classification features from a resnet model. Returns true_classnames (determined by test_dataloader), labels, activation features, image_path.\n\n device - pytorch device\n model - pytorch model\n test_dataloader - pytorch dataloader\n '''\n truth_classnames = test_dataloader.dataset.classes\n avgPoolF = Hook(model.avgpool)\n features_arr = []\n labels_arr = []\n image_paths = []\n with torch.no_grad():\n for i, (inputs, labels, paths) in enumerate(test_dataloader):\n inputs = inputs.to(device)\n outputs = model(inputs)\n print(avgPoolF.output.shape)\n avgPoolF_npy = avgPoolF.output.detach().cpu().reshape( labels.size()[0], -1).numpy()\n features_arr.append(avgPoolF_npy)\n labels_arr.append(labels.detach().cpu().numpy())\n image_paths += paths\n\n features = np.vstack(features_arr)\n labels = np.hstack(labels_arr)\n return truth_classnames, labels, features, image_paths\n\npreextracted_data_path = '/export/u10/jfaschin_ad/places_features.npz'\n\nmodel_path = '/export/u10/users/jfaschin/places.365.resnet18.pth'\n\ndef image_loader(image_name):\n \"\"\"load image, returns cuda tensor\"\"\"\n image = Image.open(image_name)\n image = transforms_(image).float()\n image = Variable(image, requires_grad=True)\n image = image.unsqueeze(0) #this is for VGG, may not be needed for ResNet\n return image.cuda() #assumes that you're using GPU\n\n\ndef compute_statistics(W_new, gt_label, new_labels, new_classes_features, original_labels, original_features):\n '''\n Computes classification related statistics based on the supplied W_new weight matrix.\n\n Arguments:\n W_new - linear classifier\n gt_label - integer label for the new class\n new_labels - labels for unseen classes\n new_classes_features - examples X features for unseen classes\n original_labels - labels for original training set\n original_features - examples X features for original training set\n\n Returns:\n acc_on_whole_set - accuracy on the new_class images + original training set images\n p_new - precision on new class\n r_new - recall on new class\n f1_new - F1 on new class\n acc_on_new_class - accuracy on the new_class\n pg - precision gain on new class\n rg - recall gain on new class\n '''\n y_pred = evaluate(W_new, new_classes_features)\n y_pred_arr = np.array(y_pred)\n\n y_pred_original = evaluate(W_new, original_features)\n\n #Y_pred_total = np.vstack((Y_pred_original, Y_pred[new_labels==gt_label,:]))\n\n N_original = len(y_pred_original)\n N_new = sum(new_labels == gt_label)\n\n y_pred_total_arr = np.concatenate((np.array(y_pred_original), y_pred_arr[new_labels == gt_label]))\n\n y_truth_total_arr = np.concatenate((original_labels, np.array([365] * N_new)))\n\n v = y_truth_total_arr == 365\n # np.set_printoptions(threshold=sys.maxsize)\n # prg_curve = prg.create_prg_curve(y_truth_total_arr == 365, Y_pred_total[:,365])\n # pr_xx, re_xx, _ = precision_recall_curve(y_truth_total_arr,\n # Y_pred_total[:,365],pos_label=365 )\n #plt.plot(re_xx, pr_xx)\n\n #print(prg_curve['recall_gain'])\n #print( prg_curve['precision_gain'])\n #plt.plot(prg_curve['recall_gain'], prg_curve['precision_gain'])\n #auprg = prg.calc_auprg(prg_curve)\n #print(auprg)\n \n tp = float(sum(y_pred_total_arr[-N_new:] == [365]*N_new))\n fp = float(sum(y_pred_total_arr[:N_original] == [365]*N_original))\n tn = float(sum(y_pred_total_arr[:N_original] != [365]*N_original))\n fn = float(sum(y_pred_total_arr[-N_new:] != [365]*N_new))\n '''\n print(f'tp:{tp}')\n print(f'fp:{fp}')\n print(f'tn:{tn}')\n print(f'fN:{fn}')\n\n\n tpr = tp / (tp + fn)\n fpr = fp / (fp + tn) \n '''\n pg = prg.precision_gain(tp, fn, fp, tn)\n rg = prg.recall_gain(tp, fn, fp, tn)\n\n acc_on_whole_set = accuracy(y_truth_total_arr, y_pred_total_arr)\n precision, recall, f1, _ = precision_recall_fscore_support(\n y_truth_total_arr,\n y_pred_total_arr,\n labels=range(366),\n zero_division=0\n )\n p_new = precision[365]\n r_new = recall[365]\n f1_new = f1[365]\n acc_on_new_class = accuracy(y_truth_total_arr == 365, y_pred_total_arr == 365)\n\n return acc_on_whole_set, p_new, r_new, f1_new, acc_on_new_class, pg, rg\n # return acc_on_whole_set, p_new, r_new, f1_new, acc_on_new_class, tpr, fpr\n\n\ndef main():\n experiment_name = '11232020.codecleanup'\n\n # Load up pretrained model\n num_classes = 365\n model = models.resnet18(num_classes=num_classes)\n model.load_state_dict(torch.load(model_path)['state_dict'])\n model.eval()\n\n device = torch.device(\n \"cuda\" if torch.cuda.is_available() \n else \"cpu\"\n )\n\n model.to(device)\n\n # Recover the weight matrix for the output layer to initialize one-shot detection\n W = model.fc.weight.detach().cpu().numpy()\n b = model.fc.bias.detach().cpu().numpy()\n\n # Compute the pseudo-inverse to recover features necessary for the one-shot update\n prior_features = np.linalg.pinv(W)\n\n # Load pre-extracted activation features for both the original places validation set and the images from new_classes\n preextracted_data = np.load(preextracted_data_path)\n \n original_classnames = preextracted_data['original_classnames']\n original_labels = preextracted_data['original_labels']\n original_features = preextracted_data['original_feature']\n\n new_classnames = preextracted_data['new_classnames']\n new_classes_labels = preextracted_data['new_classes_labels']\n new_classes_features = preextracted_data['new_classes_features']\n\n # Setting up data frames for analysis\n df = pandas.DataFrame(\n columns=[\n 'new_class',\n 'mod',\n 'value',\n 'filter',\n 'acc_on_all_classes',\n 'acc_on_new_class',\n 'p_new',\n 'r_new',\n 'f1_new'\n ]\n )\n df_summary = pandas.DataFrame(\n columns=[\n 'new_class',\n 'filter',\n 'aupg',\n 'aupr'\n ]\n )\n\n for new_class, example_path in one_shot_examples.items():\n print(new_class)\n \n # Loading up the oneshot example image for that class\n training_image_path = example_path\n\n training_image = image_loader(training_image_path)\n\n avgPoolF = Hook(model.avgpool)\n\n # Recovering the activations\n output = model(training_image)\n\n avgPoolF_npy = avgPoolF.output.detach().cpu().numpy()\n #active_srt = [y for _, y in sorted(zip(avgPoolF_npy[0,:,:,0], range(512)),reverse=True)]\n #print(active_srt[:20])\n\n # Determine new weight matrix W_new for linear classification\n W_new = oneshot_update( np.eye(365), prior_features, avgPoolF_npy[0,:,:,0])\n\n # Rescale W_new\n scale_factor_sum = 0\n for x in range(365):\n numerator = np.sqrt(np.mean(np.square(W_new[x, :])))\n denomonator = np.sqrt(np.mean(np.square(W_new[365, :])))\n scale_factor_sum += numerator/denomonator\n scale_factor = scale_factor_sum / 365\n W_new[365, :] *= scale_factor\n\n # Save new weight matrix for further investigation\n np.savez(f'{new_class}_w_new.{experiment_name}', W_new)\n \n # Predict using new weight matrix\n y_pred, _ = evaluate(W_new, new_classes_features)\n \n # Determine the index assigned to the new class amongst the other new classes\n gt_label = list(new_classnames).index(new_class)\n \n # Create numpy arrays for easier syntax\n y_truth_arr = np.array(new_classes_labels)\n y_pred_arr = np.array(y_pred)\n\n # Count number of false negatives from the new class\n misclassified = np.logical_and(y_truth_arr == gt_label, y_pred_arr < 365)\n\n print(f'Number of false negatives for new class: {sum(misclassified)}')\n \n # Print out which of the original classes, examples of the new class are confused with\n print(f'Confused classes:')\n print(f'-----------------')\n for class_name, count in Counter([original_classnames[x] for x in y_pred_arr[misclassified]]).most_common():\n print(f'{class_name}:{count}')\n print(f'-----------------')\n \n # Print out the number from the new class that are correct\n n_correct = sum(np.logical_and(y_truth_arr == gt_label, y_pred_arr == 365))\n print(n_correct)\n W_new_base = np.copy(W_new)\n\n top_positive_weights = []\n top_negative_weights = []\n\n new_class_most_positive_filters = [x for _, x in sorted(zip(W_new[365, :], range(512)), reverse=True)][:20]\n new_class_most_negative_filters = [x for _, x in sorted(zip(W_new[365, :], range(512)))][:20]\n\n filters_to_try = new_class_most_positive_filters + new_class_most_negative_filters\n \n for filter_index in filters_to_try:\n pg_l = []\n rg_l = []\n p_l = []\n r_l = []\n for magnitude in range(-5, 6, 2):\n W_new = np.copy(W_new_base)\n\n # Set the weight value to be the highest weight value or lowest weight value for the weight vector depending on the sign of the original weight value for that filter\n weight_value = W_new[365, new_class_most_positive_filters[0]] if W_new[365, filter_index] > 0 else W_new[365, new_class_most_negative_filters[0]]\n W_new[365, filter_index] = magnitude * weight_value\n # W_new[365, filter_index] = magnitude * W_new[365, filter_index]\n tic = time.perf_counter()\n\n # Compute statistics based on the modified W_new matrix\n acc_on_whole_set, p_new, r_new, f1_new, acc_on_new_class, pg, rg = compute_statistics(W_new, gt_label, y_truth_arr, new_classes_features, original_labels, original_features)\n print(f'pg:{pg} rg:{rg}')\n pg_l.append(pg)\n rg_l.append(rg)\n p_l.append(p_new)\n r_l.append(r_new)\n toc = time.perf_counter()\n print(f'Stats time: {toc-tic:.2f}')\n df = df.append(\n {\n 'new_class': new_class, \n 'mod': 'multiply',\n 'filter': filter_index,\n 'value': magnitude,\n 'acc_on_all_classes': acc_on_whole_set,\n 'acc_on_new_class': acc_on_new_class,\n 'p_new': p_new,\n 'r_new': r_new,\n 'f1_new': f1_new\n },\n ignore_index=True\n )\n # print(acc_on_whole_set)\n # print(f'Precision on new class: {p_new}')\n # print(f'Recall on new class: {r_new}')\n # print(f'F1 on new class: {f1_new}')\n # print(f'Accuracy on new class: {acc_on_new_class}')\n # print(df)\n sorted_pr_l = sorted(zip(r_l, p_l))\n p_l = [x for _, x in sorted_pr_l]\n r_l = [y for y, _ in sorted_pr_l]\n\n sorted_prg_l = sorted(zip(rg_l, pg_l))\n pg_l = [x for _, x in sorted_prg_l]\n rg_l = [y for y, _ in sorted_prg_l]\n df_summary = df_summary.append(\n {\n 'new_class': new_class,\n 'filter': filter_index,\n 'aupg': prg.calc_auprg(\n {\n 'precision_gain': pg_l,\n 'recall_gain': rg_l\n }\n ),\n 'aupr': auc(r_l, p_l)\n },\n ignore_index=True\n )\n print('WRITE SUMMARY')\n df_summary.to_pickle(f\"./results.summary.{experiment_name}.pkl\")\n\n df.to_pickle(f\"./results.{experiment_name}.pkl\")\n\n\nif __name__==\"__main__\":\n main()\n", "sub_path": "utilitiesService/service/one_shot/oneshot_places365.py", "file_name": "oneshot_places365.py", "file_ext": "py", "file_size_in_byte": 18573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torchvision.transforms.Compose", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 27, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 29, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 29, "usage_type": "name"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torchvision.datasets", "line_number": 64, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 88, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 210, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 219, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 219, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 259, "usage_type": "call"}, {"api_name": "prg.prg.precision_gain", "line_number": 288, "usage_type": "call"}, {"api_name": "prg.prg", "line_number": 288, "usage_type": "name"}, {"api_name": "prg.prg.recall_gain", "line_number": 289, "usage_type": "call"}, {"api_name": "prg.prg", "line_number": 289, "usage_type": "name"}, {"api_name": "sklearn.metrics.precision_recall_fscore_support", "line_number": 292, "usage_type": "call"}, {"api_name": "torchvision.models.resnet18", "line_number": 312, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 312, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 313, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 316, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 317, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 317, "usage_type": "attribute"}, {"api_name": "numpy.linalg.pinv", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 328, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 331, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 342, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 407, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 419, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 421, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 437, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 443, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 452, "usage_type": "call"}, {"api_name": "prg.prg.calc_auprg", "line_number": 485, "usage_type": "call"}, {"api_name": "prg.prg", "line_number": 485, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 491, "usage_type": "call"}]} +{"seq_id": "473623748", "text": "from common import cnx\n\n\nclass Table:\n\n def __init__(self, sql_read_statement, params=None):\n self.headers, self.data = self._fetch_table_contents(sql_read_statement, params)\n self.table = (self.headers, *self.data)\n\n def __repr__(self):\n column_lengths = [max([len(str(i[x])) for i in self.table]) for x in range(len(self.data[0]))]\n divider_text, header_text = \"+\", \"\"\n\n for i, header in enumerate(self.headers):\n padding_left = round((column_lengths[i] - len(header))/2 + 0.5) + 1\n padding_right = round((column_lengths[i] - len(header))/2 - 0.5) + 1\n header_text += \"|\" + \" \" * padding_left + str(header) + \" \" * padding_right\n divider_text += \"-\" * (padding_left + padding_right + len(header)) + \"+\"\n header_text += \"|\"\n full_text = divider_text + \"\\n\" + header_text + \"\\n\" + divider_text + \"\\n\"\n\n for record in self.data:\n for i, field in enumerate(record):\n padding_left = 1\n padding_right = (column_lengths[i] - len(str(field))) + 1\n full_text += \"|\" + \" \" * padding_left + str(field) + \" \" * padding_right\n full_text += \"|\\n\"\n full_text += divider_text\n return full_text\n\n @staticmethod\n @cnx.connection_handler()\n def _fetch_table_contents(connection, cursor, statement, params=None):\n cursor.execute(statement, params)\n data = cursor.fetchall()\n return tuple(i[0] for i in cursor.description), data\n\n def sort_by(self, column, asc=False):\n self.data = sorted(self.data, key=lambda x: x[column], reverse=asc)\n", "sub_path": "classes/Table.py", "file_name": "Table.py", "file_ext": "py", "file_size_in_byte": 1646, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "common.cnx.connection_handler", "line_number": 32, "usage_type": "call"}, {"api_name": "common.cnx", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "275169616", "text": "import uuid\n\nfrom rift.data.handler import get_handler\n\nJOB_COLLECTION = \"jobs\"\n\n\nclass Tenant(object):\n def __init__(self, tenant_id, name=None, targets=None):\n self.tenant_id = tenant_id\n self.name = name\n self.targets = targets if targets is not None else list()\n\n def as_dict(self):\n return {\n \"tenant_id\": self.tenant_id,\n \"name\": self.name,\n \"targets\": [target.as_dict() for target in self.targets]\n }\n\n\nclass Job(object):\n def __init__(self, tenant_id, name, actions, job_id=None):\n self.tenant_id = tenant_id\n self.name = name\n self.actions = actions\n self.job_id = job_id if job_id is not None else uuid.uuid4()\n\n def as_dict(self):\n return {\n \"job_id\": self.job_id,\n \"tenant_id\": self.tenant_id,\n \"name\": self.name,\n \"actions\": [action.as_dict() for action in self.actions]\n }\n\n\nclass Action(object):\n def __init__(self, targets, action_type, parameters=None):\n self.targets = targets\n self.action_type = action_type\n self.parameters = parameters if parameters is not None else dict()\n\n def as_dict(self):\n return {\n \"targets\": [target.as_dict() for target in self.targets],\n \"action_type\": self.action_type,\n \"parameters\": self.parameters\n }\n\n\nclass Target(object):\n \"\"\"\n Represents a target node to execute actions against\n \"\"\"\n def __init__(self, name, public_ip, private_ip):\n self.name = name\n self.public_ip = public_ip\n self.private_ip = private_ip\n\n def as_dict(self):\n return {\n \"name\": self.name,\n \"public_ip\": self.public_ip,\n \"private_ip\": self.private_ip\n }\n\n\ndef build_job_from_dict(job_dict):\n tenant_id = job_dict[\"tenant_id\"]\n name = job_dict[\"name\"]\n actions = [\n _build_action_from_dict(action_dict)\n for action_dict in job_dict[\"actions\"]]\n job_id = job_dict[\"job_id\"]\n\n return Job(tenant_id=tenant_id, name=name, actions=actions, job_id=job_id)\n\n\ndef _build_action_from_dict(action_dict):\n targets = [\n _build_target_from_dict(target_dict)\n for target_dict in action_dict[\"targets\"]]\n action_type = action_dict[\"action_type\"]\n parameters = action_dict[\"parameters\"]\n return Action(\n targets=targets,\n action_type=action_type,\n parameters=parameters\n )\n\n\ndef _build_target_from_dict(target_dict):\n return Target(**target_dict)\n\n\ndef save_job(job):\n db_handler = get_handler()\n db_handler.insert_document(\n object_name=JOB_COLLECTION, document=job.as_dict()\n )\n\n\ndef update_job(job):\n db_handler = get_handler()\n db_handler.update_document(\n object_name=JOB_COLLECTION,\n document=job.as_dict(),\n query_filter={\"job_id\": job.job_id}\n )\n\ndef get_job(job_id):\n db_handler = get_handler()\n job_dict = db_handler.get_document(\n object_name=JOB_COLLECTION,\n query_filter={\"job_id\": job_id})\n\n return build_job_from_dict(job_dict)\n\ndef get_jobs(tenant_id):\n db_handler = get_handler()\n jobs_dict = db_handler.get_documents(\n object_name=JOB_COLLECTION,\n query_filter={\"tenant_id\": tenant_id})\n\n return [build_job_from_dict(job) for job in jobs_dict]\n\ndef delete_job(job_id):\n db_handler = get_handler()\n db_handler.delete_document(\n object_name=JOB_COLLECTION,\n query_filter={\"job_id\": job_id}\n )\n", "sub_path": "rift/data/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 3539, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "uuid.uuid4", "line_number": 27, "usage_type": "call"}, {"api_name": "rift.data.handler.get_handler", "line_number": 98, "usage_type": "call"}, {"api_name": "rift.data.handler.get_handler", "line_number": 105, "usage_type": "call"}, {"api_name": "rift.data.handler.get_handler", "line_number": 113, "usage_type": "call"}, {"api_name": "rift.data.handler.get_handler", "line_number": 121, "usage_type": "call"}, {"api_name": "rift.data.handler.get_handler", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "154261647", "text": "\"\"\"\nTa koda kopira vse notebooke spletne knjige v spletno stran in pripravi stran,\nki jih poveže\n\"\"\"\nimport os\nimport nbformat\nimport shutil\n\nEXCLUDE_NOTEBOOKS = ['NM2016.ipynb',\n 'PiNM2016-17.ipynb',\n 'Predavanje 10b - Taylorjeve vrste.ipynb',\n 'working.ipynb']\nPAGEFILE = \"\"\"title: {title}\nurl:\nsave_as: {htmlfile}\nTemplate: {template}\n\n{{% notebook notebooks/{notebook_file} cells[{cells}] %}}\n\"\"\"\n\nINTRO_TEXT = \"\"\"Ta domača stran je pripravljena na podlagi spletnega učbenika \n[Programiranje in numerične metode v ekosistemu Pythona](https://github.com/jankoslavic/pypinm), \nki ga je pripravil Janko Slavič v obliki Jupyter notebookov.\n\"\"\"\n\n\ndef abspath_from_here(*args):\n here = os.path.dirname(__file__)\n path = os.path.join(here, *args)\n return os.path.abspath(path)\n\nNB_SOURCE_DIR = abspath_from_here('..')\nNB_DEST_DIR = abspath_from_here('content', 'notebooks')\nPAGE_DEST_DIR = abspath_from_here('content', 'pages')\n\n\ndef copy_notebooks():\n nblist = sorted(nb for nb in os.listdir(NB_SOURCE_DIR)\n if nb.endswith('.ipynb') and nb not in EXCLUDE_NOTEBOOKS)\n name_map = {nb: nb.rsplit('.', 1)[0].lower() + '.html'\n for nb in nblist}\n\n figsource = abspath_from_here('..', 'fig')\n figdest = abspath_from_here('content', 'fig')\n\n if os.path.exists(figdest):\n shutil.rmtree(figdest)\n shutil.copytree(figsource, figdest)\n\n figurelist = os.listdir(abspath_from_here('content', 'fig'))\n figure_map = {os.path.join('fig', fig) : os.path.join('/pypinm/fig', fig)\n for fig in figurelist}\n\n for nb in nblist:\n base, ext = os.path.splitext(nb)\n print('-', nb)\n\n content = nbformat.read(os.path.join(NB_SOURCE_DIR, nb),\n as_version=4)\n\n if nb == 'NM2016.ipynb':\n cells = '1:'\n template = 'page'\n title = 'Numerične metode 2016/17'\n content.cells[2].source = INTRO_TEXT\n else:\n cells = '2:'\n template = 'booksection'\n title = content.cells[0].source.split('')[1].split('')[0]\n if title == '':\n #if not title.startswith('') or len(title.splitlines()) > 1:\n raise ValueError('title not found in first cell')\n title = title.lstrip('#').strip()\n\n # put nav below title\n content.cells[0], content.cells[1], content.cells[2] = content.cells[2], content.cells[0], content.cells[1]\n\n # Replace internal URLs and figure links in notebook\n for cell in content.cells:\n if cell.cell_type == 'markdown':\n for nbname, htmlname in name_map.items():\n if nbname in cell.source:\n cell.source = cell.source.replace(nbname, htmlname)\n for figname, newfigname in figure_map.items():\n if figname in cell.source:\n cell.source = cell.source.replace(figname, newfigname)\n\n nb_no_spaces = nb.replace(' ', '_')\n nbformat.write(content, os.path.join(NB_DEST_DIR, nb_no_spaces))\n\n pagefile = os.path.join(PAGE_DEST_DIR, base + '.md')\n htmlfile = base.lower() + '.html'\n with open(pagefile, 'w', encoding='utf-8') as f:\n f.write(PAGEFILE.format(title=title,\n htmlfile=htmlfile,\n notebook_file=nb_no_spaces,\n template=template,\n cells=cells))\n\nif __name__ == '__main__':\n copy_notebooks()", "sub_path": "book/copy_notebooks.py", "file_name": "copy_notebooks.py", "file_ext": "py", "file_size_in_byte": 3706, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.dirname", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 47, "usage_type": "call"}, {"api_name": "shutil.copytree", "line_number": 48, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "nbformat.read", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "nbformat.write", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}]} +{"seq_id": "315901735", "text": "from django.conf.urls import url, include\nfrom rest_framework import routers\n\nfrom vpmotree.views import *\n\n# Imports related to Djangular API's\n# https://github.com/pyaf/djangular/blob/master/djangular/urls.py\nfrom django.contrib import admin\nfrom django.views.generic import TemplateView\nfrom django.views.generic import RedirectView\n# End of API Imports\n\nrouter = routers.DefaultRouter()\n# router.register(r'users', UserViewSet)\n\nurlpatterns = (\n url(r'^api/organisations/$', AllTeamsView.as_view()),\n url(r'^api/filtered_organisations/$', FilteredTeamsView.as_view(), name=\"filtered_teams\"),\n url(r'^api/projects/$', AllProjectsView.as_view()),\n url(r'^api/projects/add/$', CreateProjectView.as_view()),\n url(r'^api/projects/(?P.+)/$', UpdateProjectView.as_view()),\n url(r'^api/teams/add/$', CreateTeamView.as_view()),\n url(r'^api/deliverable/add/$', CreateDeliverableView.as_view()),\n\n url(r\"^api/update_project/(?P<_id>.+)/$\", UpdateProjectView.as_view(), name=\"update_project\"),\n\n url(r\"^api/teams_tree/(?P.+)/$\", TeamTreeView.as_view(), name=\"team_tree_view\"),\n url(r\"^api/project_tree/(?P.+)/$\", ProjectTreeView.as_view(), name=\"project_tree_view\"),\n\n url(r'^api/messages/(?P.+)/$', MessageListView.as_view(), name=\"message_list\"),\n\n url(r'^(?P.*\\..*)/$', RedirectView.as_view(url='/static/%(path)s')),\n url(r'^', TemplateView.as_view(template_name='angular/index.html')),\n)", "sub_path": "vpmotree/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1471, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 13, "usage_type": "call"}, {"api_name": "rest_framework.routers", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "django.views.generic.RedirectView.as_view", "line_number": 32, "usage_type": "call"}, {"api_name": "django.views.generic.RedirectView", "line_number": 32, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView.as_view", "line_number": 33, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "330415930", "text": "import os\nimport re\nimport tempfile\n\nfrom commands.AbstractCommand import AbstractCommand\nfrom config.config import API_TOKEN\nfrom models.Location import Location\n\n\nclass AddCommand(AbstractCommand):\n STATE_START = 0\n STATE_NAME = 1\n STATE_IMAGE = 2\n STATE_COORDINATES = 3\n\n def __init__(self, bot, connection):\n super().__init__(bot, connection)\n self.__previous_state = None\n self.__location = None\n self.reload()\n\n def process(self, message):\n self.process_next_state(message)\n\n def process_next_state(self, message):\n result_message = ''\n message_info = self.get_message_info(message)\n\n if self.__previous_state is None:\n self.increment_step()\n\n self.get_location().set_user_id(message_info.get_user().get_id())\n\n result_message = \"\"\"\n You want add new location. Please write location name:\n \"\"\"\n elif self.__previous_state == self.STATE_START:\n self.increment_step()\n self.get_location().set_name(message_info.get_text())\n\n result_message = \"\"\"Please add location photo or use: \\n{}\"\"\".format(self.get_cancel_step_commands_text())\n\n elif self.__previous_state == self.STATE_NAME:\n self.increment_step()\n if message_info.get_file_id() is not None:\n file_info = self.bot.get_file(message_info.get_file_id())\n downloaded_file = self.bot.download_file(file_info.file_path)\n\n if downloaded_file != '':\n file_url = os.path.join(tempfile.gettempdir(), '{}.jpg'.format(message_info.get_file_id()))\n with open(file_url, 'wb') as new_file:\n new_file.write(downloaded_file)\n\n self.get_location().set_photo_url(file_url)\n\n result_message = \"\"\"Please add coordinates in format (50.45466, 30.5238) without brackets, where:\n 50.45466 - latitude\n 30.5238 - longitude\n \\n{}\"\"\".format(self.get_cancel_step_commands_text())\n elif self.__previous_state == self.STATE_IMAGE:\n location_coordinates = message_info.get_text()\n if self.is_coordinates(location_coordinates) is True:\n latitude, longtitude = location_coordinates.split(',')\n self.get_location().set_latitude(latitude.strip())\n self.get_location().set_longtitude(longtitude.strip())\n\n self.save(message)\n\n if result_message != '':\n self.bot.send_message(message_info.get_chat().get_id(), result_message)\n\n def is_coordinates(self, text):\n result = re.search(r\"([0-9]{1,2}).([0-9]+),([\\s]{0,})([0-9]{1,2}).([0-9]+)\", text)\n return result is not None\n\n def save(self, message):\n connection = self.connection.get_connection()\n c = connection.cursor()\n c.execute(self.get_location().get_insert_query())\n connection.commit()\n self.connection.close_connection()\n\n self.bot.send_message(self.get_message_info(message).get_chat().get_id(), 'Location successfully saved')\n self.reload()\n\n def reload(self):\n self.__previous_state = None\n self.__location = Location()\n\n def increment_step(self):\n if self.__previous_state is None:\n self.__previous_state = self.STATE_START\n elif self.__previous_state == self.STATE_START:\n self.__previous_state = self.STATE_NAME\n elif self.__previous_state == self.STATE_NAME:\n self.__previous_state = self.STATE_IMAGE\n elif self.__previous_state == self.STATE_IMAGE:\n self.__previous_state = self.STATE_COORDINATES\n else:\n self.reload()\n\n def get_location(self) -> Location:\n return self.__location\n\n def get_cancel_step_commands_text(self):\n return \"\"\"\n /next_step - got to next step of current command\n /cancel - for cancel add new location\n \"\"\"", "sub_path": "commands/AddCommand.py", "file_name": "AddCommand.py", "file_ext": "py", "file_size_in_byte": 4032, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "commands.AbstractCommand.AbstractCommand", "line_number": 10, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 50, "usage_type": "call"}, {"api_name": "re.search", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Location.Location", "line_number": 88, "usage_type": "call"}, {"api_name": "models.Location.Location", "line_number": 102, "usage_type": "name"}]} +{"seq_id": "300475776", "text": "from django.urls import path\nfrom banner import views\n\napp_name = 'banner'\n\nurlpatterns = [\n path('save/', views.save_banner, name='save'),\n path('show/', views.show_banner, name='show'),\n path('edit/', views.edit_banner, name='edit'),\n]\n", "sub_path": "banner/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 247, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "banner.views.save_banner", "line_number": 7, "usage_type": "attribute"}, {"api_name": "banner.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "banner.views.show_banner", "line_number": 8, "usage_type": "attribute"}, {"api_name": "banner.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "banner.views.edit_banner", "line_number": 9, "usage_type": "attribute"}, {"api_name": "banner.views", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "336767333", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport random\nimport time\n\n\nTICKS = 1024\nHARMONICS = 8\nFREQUENCY = 1200\nSTEP = FREQUENCY / HARMONICS\n\nclass Harmonic:\n def __init__(self, frequency, ticks):\n self.amplitude = random.uniform(0.0, 1.0)\n self.phase = random.uniform(0.0, 2*np.pi)\n current_xs = [0 for _ in range(ticks)]\n current_xs = [self.amplitude*np.sin(frequency*t+self.phase) for t in range(ticks)]\n self.xs = current_xs\n\nclass Signal:\n def __init__(self, harmonic_amount, ticks, step):\n current_sum = [0 for _ in range(ticks)]\n start = time.time()\n for i in range(1, harmonic_amount+1):\n current_harmonic = Harmonic(step*i, ticks)\n current_sum = [current_sum[t]+current_harmonic.xs[t] for t in range(ticks)]\n self.elapsed_time = time.time() - start\n self.xss = current_sum\n self.ts = [t for t in range(ticks)]\n\ndef generate_signal(harmonic_amount, ticks, step):\n return Signal(harmonic_amount, ticks, step)\n\ndef calc_mean(signal, ticks):\n mean = sum(signal.xss) / ticks\n print(\"Mean = {}\".format(mean))\n return mean\n\ndef calc_dispertion(signal, mean, ticks):\n dispertion = sum(((x-mean)*(x-mean)) for x in signal.xss) / (ticks-1)\n print(\"Dispertion = {}\".format(dispertion))\n print('----------------------------------------------------------')\n\ndef calc_autocorrelation(signal, mean, ticks):\n autocorr_sum = [sum([(signal.xss[t]-mean)*(signal.xss[t+tau]-mean) \\\n for t in range(ticks)]) for tau in range(ticks)]\n autocorr_sum = np.divide(autocorr_sum, (ticks-1))\n return autocorr_sum\n\ndef calc_correlation(signal_1, signal_2, mean_1, mean_2, ticks):\n corr_sum = [sum([(signal_1.xss[t]-mean_1)*(signal_2.xss[t+tau]-mean_2) \\\n for t in range(ticks)]) for tau in range(ticks)]\n corr_sum = np.divide(corr_sum, (ticks-1))\n return corr_sum\n\ndef calc_complexity(num):\n times = [generate_signal(HARMONICS, TICKS*i, STEP).elapsed_time for i in range(1, num+1)]\n nums = [TICKS*i for i in range(1, num+1)]\n return times, nums\n\nsignal_x = generate_signal(HARMONICS, TICKS*2, STEP)\nsignal_y = generate_signal(HARMONICS, TICKS*2, STEP)\n\nmean_x = calc_mean(signal_x, TICKS*2)\ncalc_dispertion(signal_x, mean_x, TICKS*2)\n\nmean_y = calc_mean(signal_y, TICKS*2)\ncalc_dispertion(signal_y, mean_y, TICKS*2)\n\nautocorr_x = calc_autocorrelation(signal_x, mean_x, TICKS)\nautocorr_y = calc_autocorrelation(signal_y, mean_y, TICKS)\ncorr_xy = calc_correlation(signal_x, signal_y, mean_x, mean_y, TICKS)\n\ncomplexity_t, complexity_N = calc_complexity(10)\n\nplt.subplot(3, 2, 1)\nplt.plot(signal_x.ts, signal_x.xss, 'm')\nplt.ylabel('x(t)')\nplt.grid(True)\n\nplt.subplot(3, 2, 3)\nplt.plot(signal_y.ts, signal_y.xss, 'b')\nplt.xlabel('t')\nplt.ylabel('y(t)')\nplt.grid(True)\n\nplt.subplot(3, 2, 5)\nplt.plot(complexity_N, complexity_t, 'k')\nplt.xlabel('N')\nplt.ylabel('t')\nplt.grid(True)\n\nttau = np.arange(0, TICKS, 1)\n\nplt.subplot(3, 2, 2)\nplt.plot(ttau, autocorr_x, 'm')\nplt.ylabel('Rxx')\nplt.grid(True)\n\nplt.subplot(3, 2, 4)\nplt.plot(ttau, autocorr_y, 'b')\nplt.ylabel('Ryy')\nplt.grid(True)\n\nplt.subplot(3, 2, 6)\nplt.plot(ttau, corr_xy, 'c')\nplt.xlabel('tau')\nplt.ylabel('Rxy')\nplt.grid(True)\n\nplt.savefig('fig2.png')\nplt.show()\n", "sub_path": "Anna_Doroshenko_IO52/lab02.py", "file_name": "lab02.py", "file_ext": "py", "file_size_in_byte": 3284, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "random.uniform", "line_number": 14, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}]} +{"seq_id": "331584660", "text": "import numpy as np\nimport numba\nfrom .fast_interp import interp1d\n\nclass FunctionGenerator(object):\n \"\"\"\n This class provides a simple way to construct a fast \"function evaluator\"\n For 1-D functions defined on an interval\n \"\"\"\n def __init__(self, f, a, b, tol=1e-10, n=1000, k=5):\n \"\"\"\n f: function to create evaluator for\n a: lower bound of evaluation interval\n b: upper bound of evaluation interval\n tol: accuracy to recreate function to\n n: number of points used in interpolations\n k: degree of polynomial used (1, 3, 5, or 7)\n \"\"\"\n self.f = f\n self.a = float(a)\n self.b = float(b)\n self.tol = tol\n self.n = n\n self.k = k\n self.lbs = []\n self.ubs = []\n self.hs = []\n self.fs = []\n self._fit(self.a, self.b)\n self.lbs = np.array(self.lbs)\n self.ubs = np.array(self.ubs)\n self.hs = np.array(self.hs)\n self.fs = np.row_stack(self.fs)\n def __call__(self, x, out=None):\n \"\"\"\n Evaluate function at input x\n \"\"\"\n if isinstance(x, np.ndarray):\n xr = x.ravel()\n outr = np.zeros_like(xr) if out is None else out.ravel()\n _evaluates[self.k](self.fs, xr, outr, self.lbs, self.ubs, self.hs, self.n)\n return outr.reshape(x.shape)\n else:\n return _evaluate1s[self.k](self.fs, x, self.lbs, self.ubs, self.hs, self.n)\n def _fit(self, a, b):\n x, h = np.linspace(a, b, self.n, retstep=True)\n interp = interp1d(a, b, h, self.f(x), self.k, c=True)\n check_x = x[:-1] + h/2.0\n check_f = self.f(check_x)\n estim_f = interp(check_x)\n reg = np.abs(check_f)\n reg[reg < 1] = 1.0\n err = np.abs((check_f-estim_f)/reg).max()\n if err < self.tol:\n self.lbs.append(a)\n self.ubs.append(b)\n self.fs.append(interp._f)\n self.hs.append(h)\n else:\n m = a + (b-a)/2\n self._fit(a, m)\n self._fit(m, b)\n\n@numba.njit\ndef _single_interp_1d_k1(f, x, a, h, n):\n xx = x - a\n ix = min(int(xx//h), n-2)\n ratx = xx/h - (ix+0.5)\n asx = np.empty(2)\n asx[0] = 0.5 - ratx\n asx[1] = 0.5 + ratx\n fout = 0.0\n for i in range(2):\n fout += f[ix+i]*asx[i]\n return fout\n@numba.njit\ndef _single_interp_1d_k3(f, x, a, h, n):\n xx = x - a\n ix = min(int(xx//h), n-2)\n ratx = xx/h - (ix+0.5)\n asx = np.empty(4)\n asx[0] = -1/16 + ratx*( 1/24 + ratx*( 1/4 - ratx/6))\n asx[1] = 9/16 + ratx*( -9/8 + ratx*(-1/4 + ratx/2))\n asx[2] = 9/16 + ratx*( 9/8 + ratx*(-1/4 - ratx/2))\n asx[3] = -1/16 + ratx*(-1/24 + ratx*( 1/4 + ratx/6))\n fout = 0.0\n for i in range(4):\n fout += f[ix+i]*asx[i]\n return fout\n@numba.njit\ndef _single_interp_1d_k5(f, x, a, h, n):\n xx = x - a\n ix = min(int(xx//h), n-2)\n ratx = xx/h - (ix+0.5)\n asx = np.empty(6)\n asx[0] = 3/256 + ratx*( -9/1920 + ratx*( -5/48/2 + ratx*( 1/8/6 + ratx*( 1/2/24 - 1/8/120*ratx))))\n asx[1] = -25/256 + ratx*( 125/1920 + ratx*( 39/48/2 + ratx*(-13/8/6 + ratx*(-3/2/24 + 5/8/120*ratx))))\n asx[2] = 150/256 + ratx*(-2250/1920 + ratx*(-34/48/2 + ratx*( 34/8/6 + ratx*( 2/2/24 - 10/8/120*ratx))))\n asx[3] = 150/256 + ratx*( 2250/1920 + ratx*(-34/48/2 + ratx*(-34/8/6 + ratx*( 2/2/24 + 10/8/120*ratx))))\n asx[4] = -25/256 + ratx*( -125/1920 + ratx*( 39/48/2 + ratx*( 13/8/6 + ratx*(-3/2/24 - 5/8/120*ratx))))\n asx[5] = 3/256 + ratx*( 9/1920 + ratx*( -5/48/2 + ratx*( -1/8/6 + ratx*( 1/2/24 + 1/8/120*ratx))))\n fout = 0.0\n for i in range(6):\n fout += f[ix+i]*asx[i]\n return fout\n@numba.njit\ndef _single_interp_1d_k7(f, x, a, h, n):\n xx = x - a\n ix = min(int(xx//h), n-2)\n ratx = xx/h - (ix+0.5)\n asx = np.empty(8)\n asx[0] = -5/2048 + ratx*( 75/107520 + ratx*( 259/11520/2 + ratx*( -37/1920/6 + ratx*( -7/48/24 + ratx*( 5/24/120 + ratx*( 1/2/720 - 1/5040*ratx))))))\n asx[1] = 49/2048 + ratx*( -1029/107520 + ratx*(-2495/11520/2 + ratx*( 499/1920/6 + ratx*( 59/48/24 + ratx*( -59/24/120 + ratx*(-5/2/720 + 7/5040*ratx))))))\n asx[2] = -245/2048 + ratx*( 8575/107520 + ratx*(11691/11520/2 + ratx*(-3897/1920/6 + ratx*(-135/48/24 + ratx*( 225/24/120 + ratx*( 9/2/720 - 21/5040*ratx))))))\n asx[3] = 1225/2048 + ratx*(-128625/107520 + ratx*(-9455/11520/2 + ratx*( 9455/1920/6 + ratx*( 83/48/24 + ratx*(-415/24/120 + ratx*(-5/2/720 + 35/5040*ratx))))))\n asx[4] = 1225/2048 + ratx*( 128625/107520 + ratx*(-9455/11520/2 + ratx*(-9455/1920/6 + ratx*( 83/48/24 + ratx*( 415/24/120 + ratx*(-5/2/720 - 35/5040*ratx))))))\n asx[5] = -245/2048 + ratx*( -8575/107520 + ratx*(11691/11520/2 + ratx*( 3897/1920/6 + ratx*(-135/48/24 + ratx*(-225/24/120 + ratx*( 9/2/720 + 21/5040*ratx))))))\n asx[6] = 49/2048 + ratx*( 1029/107520 + ratx*(-2495/11520/2 + ratx*( -499/1920/6 + ratx*( 59/48/24 + ratx*( 59/24/120 + ratx*(-5/2/720 - 7/5040*ratx))))))\n asx[7] = -5/2048 + ratx*( -75/107520 + ratx*( 259/11520/2 + ratx*( 37/1920/6 + ratx*( -7/48/24 + ratx*( -5/24/120 + ratx*( 1/2/720 + 1/5040*ratx))))))\n fout = 0.0\n for i in range(8):\n fout += f[ix+i]*asx[i]\n return fout\n\n@numba.njit\ndef _get_ind(x, ubs):\n ind = 0\n while(x > ubs[ind]):\n ind += 1\n return ind\n\n@numba.njit(fastmath=True)\ndef _evaluate1_1(fs, x, lbs, ubs, hs, n):\n ind = _get_ind(x, ubs)\n return _single_interp_1d_k1(fs[ind], x, lbs[ind], hs[ind], n)\n@numba.njit(fastmath=True)\ndef _evaluate1_3(fs, x, lbs, ubs, hs, n):\n ind = _get_ind(x, ubs)\n return _single_interp_1d_k3(fs[ind], x, lbs[ind], hs[ind], n)\n@numba.njit(fastmath=True)\ndef _evaluate1_5(fs, x, lbs, ubs, hs, n):\n ind = _get_ind(x, ubs)\n return _single_interp_1d_k5(fs[ind], x, lbs[ind], hs[ind], n)\n@numba.njit(fastmath=True)\ndef _evaluate1_7(fs, x, lbs, ubs, hs, n):\n ind = _get_ind(x, ubs)\n return _single_interp_1d_k7(fs[ind], x, lbs[ind], hs[ind], n)\n\n@numba.njit(parallel=True, fastmath=True)\ndef _evaluate_1(fs, xs, out, lbs, ubs, hs, n):\n m = xs.shape[0]\n for i in numba.prange(m):\n out[i] = _evaluate1_1(fs, xs[i], lbs, ubs, hs, n)\n@numba.njit(parallel=True, fastmath=True)\ndef _evaluate_3(fs, xs, out, lbs, ubs, hs, n):\n m = xs.shape[0]\n for i in numba.prange(m):\n out[i] = _evaluate1_3(fs, xs[i], lbs, ubs, hs, n)\n@numba.njit(parallel=True, fastmath=True)\ndef _evaluate_5(fs, xs, out, lbs, ubs, hs, n):\n m = xs.shape[0]\n fplop = np.empty((6, m), dtype=np.float64)\n asxs = np.empty((6, m), dtype=np.float64)\n ixs = np.empty(m, dtype=np.int32)\n ratxs = np.empty(m, dtype=np.float64)\n inds = np.empty(m, dtype=np.int32)\n for k in numba.prange(m):\n x = xs[k]\n ind = 0\n while(x > ubs[ind]):\n ind += 1\n a = lbs[ind]\n h = hs[ind]\n xx = x - a\n ix = min(int(xx//h), n-2)\n ratxs[k] = xx/h - (ix+0.5)\n ixs[k] = ix\n inds[k] = ind\n for k in numba.prange(m):\n ratx = ratxs[k]\n asxs[0, k] = 3/256 + ratx*( -9/1920 + ratx*( -5/48/2 + ratx*( 1/8/6 + ratx*( 1/2/24 - 1/8/120*ratx))))\n asxs[1, k] = -25/256 + ratx*( 125/1920 + ratx*( 39/48/2 + ratx*(-13/8/6 + ratx*(-3/2/24 + 5/8/120*ratx))))\n asxs[2, k] = 150/256 + ratx*(-2250/1920 + ratx*(-34/48/2 + ratx*( 34/8/6 + ratx*( 2/2/24 - 10/8/120*ratx))))\n asxs[3, k] = 150/256 + ratx*( 2250/1920 + ratx*(-34/48/2 + ratx*(-34/8/6 + ratx*( 2/2/24 + 10/8/120*ratx))))\n asxs[4, k] = -25/256 + ratx*( -125/1920 + ratx*( 39/48/2 + ratx*( 13/8/6 + ratx*(-3/2/24 - 5/8/120*ratx))))\n asxs[5, k] = 3/256 + ratx*( 9/1920 + ratx*( -5/48/2 + ratx*( -1/8/6 + ratx*( 1/2/24 + 1/8/120*ratx))))\n for k in numba.prange(m):\n ix = ixs[k]\n ind = inds[k]\n for i in range(6):\n fplop[i, k] = fs[ind, ix+i]\n for k in numba.prange(m):\n out[k] = 0.0\n for i in range(6):\n out[k] += fplop[i,k]*asxs[i,k]\n@numba.njit(parallel=True, fastmath=True)\ndef _evaluate_7(fs, xs, out, lbs, ubs, hs, n):\n m = xs.shape[0]\n for i in numba.prange(m):\n out[i] = _evaluate1_7(fs, xs[i], lbs, ubs, hs, n)\n\n_evaluate1s = [None, _evaluate1_1, None, _evaluate1_3, None, _evaluate1_5, None, _evaluate1_7]\n_evaluates = [None, _evaluate_1, None, _evaluate_3, None, _evaluate_5, None, _evaluate_7 ]\n\n", "sub_path": "fast_interp/function_generator.py", "file_name": "function_generator.py", "file_ext": "py", "file_size_in_byte": 8434, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.row_stack", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 46, "usage_type": "call"}, {"api_name": "fast_interp.interp1d", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 69, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 81, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 76, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 95, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 111, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numba.njit", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numba.njit", "line_number": 132, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 136, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 140, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 144, "usage_type": "call"}, {"api_name": "numba.prange", "line_number": 152, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 149, "usage_type": "call"}, {"api_name": "numba.prange", "line_number": 157, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 164, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 165, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 166, "usage_type": "attribute"}, {"api_name": "numba.prange", "line_number": 167, "usage_type": "call"}, {"api_name": "numba.prange", "line_number": 179, "usage_type": "call"}, {"api_name": "numba.prange", "line_number": 187, "usage_type": "call"}, {"api_name": "numba.prange", "line_number": 192, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 159, "usage_type": "call"}, {"api_name": "numba.prange", "line_number": 199, "usage_type": "call"}, {"api_name": "numba.njit", "line_number": 196, "usage_type": "call"}]} +{"seq_id": "173692468", "text": "import numpy as np\nimport numpy.linalg as la\nimport scipy.sparse as spp\n\n\nclass FastDiagonalisationStokesSolver:\n def __init__(\n self,\n grid_size_r,\n grid_size_z,\n dx,\n real_dtype=np.float64,\n bc_type=\"homogenous_neumann_along_z_and_r\",\n ):\n self.dx = dx\n self.grid_size_r = grid_size_r\n self.grid_size_z = grid_size_z\n self.real_dtype = real_dtype\n self.bc_type = bc_type\n self.radial_coord = (\n np.linspace(dx / 2, grid_size_r * dx - dx / 2, grid_size_r)\n .astype(real_dtype)\n .reshape(grid_size_r, 1)\n )\n\n (\n poisson_matrix_z,\n poisson_matrix_r,\n derivative_matrix_r,\n ) = self.construct_fdm_matrices()\n self.apply_boundary_conds_to_poisson_matrices(\n poisson_matrix_z, poisson_matrix_r, derivative_matrix_r\n )\n self.compute_spectral_decomp_of_poisson_matrices(\n poisson_matrix_z, poisson_matrix_r, derivative_matrix_r\n )\n\n # allocate buffer for spectral field manipulation\n self.spectral_field_buffer = np.zeros_like(self.inv_eig_val_matrix)\n\n def construct_fdm_matrices(self):\n \"\"\"\n Construct the finite difference matrices\n \"\"\"\n inv_dx2 = self.real_dtype(1 / self.dx / self.dx)\n inv_2dx = self.real_dtype(1 / 2 / self.dx)\n poisson_matrix_z = inv_dx2 * spp.diags(\n [-1, 2, -1],\n [-1, 0, 1],\n shape=(self.grid_size_z, self.grid_size_z),\n format=\"csr\",\n )\n poisson_matrix_z = poisson_matrix_z.toarray().astype(self.real_dtype)\n poisson_matrix_r = inv_dx2 * spp.diags(\n [-1, 2, -1],\n [-1, 0, 1],\n shape=(self.grid_size_r, self.grid_size_r),\n format=\"csr\",\n )\n poisson_matrix_r = poisson_matrix_r.toarray().astype(self.real_dtype)\n derivative_matrix_r = inv_2dx * spp.diags(\n [1, -1], [-1, 1], shape=(self.grid_size_r, self.grid_size_r), format=\"csr\"\n )\n derivative_matrix_r = derivative_matrix_r.toarray().astype(self.real_dtype)\n derivative_matrix_r[...] = derivative_matrix_r / self.radial_coord\n\n return poisson_matrix_z, poisson_matrix_r, derivative_matrix_r\n\n def apply_boundary_conds_to_poisson_matrices(\n self,\n poisson_matrix_z,\n poisson_matrix_r,\n derivative_matrix_r,\n ):\n \"\"\"\n Apply boundary conditions to matrices\n \"\"\"\n inv_dx2 = self.real_dtype(1 / self.dx / self.dx)\n if self.bc_type == \"homogenous_neumann_along_z_and_r\":\n # neumann at z=0 and r/z=L, but the modification below operates on\n # nodes at z=dx/2 and r/z=L-dx/2, because of the grid shift in sims.\n poisson_matrix_z[0, 0] = inv_dx2\n poisson_matrix_z[-1, -1] = inv_dx2\n poisson_matrix_r[-1, -1] = inv_dx2\n # neumann at R_max\n derivative_matrix_r[-1, -2] = 0\n\n elif self.bc_type == \"homogenous_neumann_along_r_and_periodic_along_z\":\n poisson_matrix_z[0, -1] = poisson_matrix_z[0, 1]\n poisson_matrix_z[-1, 0] = poisson_matrix_z[-1, -2]\n poisson_matrix_r[-1, -1] = inv_dx2\n # neumann at R_max\n derivative_matrix_r[-1, -2] = 0\n\n elif self.bc_type == \"homogenous_dirichlet_along_r_and_periodic_along_z\":\n poisson_matrix_z[0, -1] = poisson_matrix_z[0, 1]\n poisson_matrix_z[-1, 0] = poisson_matrix_z[-1, -2]\n\n def compute_spectral_decomp_of_poisson_matrices(\n self,\n poisson_matrix_z,\n poisson_matrix_r,\n derivative_matrix_r,\n ):\n \"\"\"\n Compute spectral decomposition (eigenvalue and vectors) of the\n Poisson matrices\n \"\"\"\n eig_vals_r, eig_vecs_r = la.eig(poisson_matrix_r - derivative_matrix_r)\n # sort eigenvalues in decreasing order\n idx = eig_vals_r.argsort()[::-1]\n eig_vals_r[...] = eig_vals_r[idx]\n eig_vecs_r[...] = eig_vecs_r[:, idx]\n self.eig_vecs_r = eig_vecs_r\n self.inv_of_eig_vecs_r = la.inv(eig_vecs_r)\n\n eig_vals_z, eig_vecs_z = la.eig(poisson_matrix_z)\n # sort eigenvalues in decreasing order\n idx = eig_vals_z.argsort()[::-1]\n eig_vals_z[...] = eig_vals_z[idx]\n eig_vecs_z[...] = eig_vecs_z[:, idx]\n self.tranpose_of_eig_vecs_z = np.transpose(eig_vecs_z)\n self.tranpose_of_inv_of_eig_vecs_z = np.transpose(la.inv(eig_vecs_z))\n\n eig_val_matrix = np.tile(\n eig_vals_z.reshape(1, self.grid_size_z), reps=(self.grid_size_r, 1)\n ) + np.tile(eig_vals_r.reshape(self.grid_size_r, 1), reps=(1, self.grid_size_z))\n self.inv_eig_val_matrix = self.real_dtype(1) / eig_val_matrix\n\n def solve(self, solution_field, rhs_field):\n \"\"\"\n solves the Stokes stream function pseudo Poisson:\n -d^2 solution_field / dr^2 - d^2 solution_field / dx^2\n + d solution_field / dr / r = rhs_field\n \"\"\"\n # transform to spectral space (\"forward transform\")\n la.multi_dot(\n [\n self.inv_of_eig_vecs_r,\n np.multiply(rhs_field, self.radial_coord),\n self.tranpose_of_inv_of_eig_vecs_z,\n ],\n out=self.spectral_field_buffer,\n )\n\n # convolution (elementwise) in spectral space\n np.multiply(\n self.spectral_field_buffer,\n self.inv_eig_val_matrix,\n out=self.spectral_field_buffer,\n )\n\n # transform to physical space (\"backward transform\")\n solution_field[...] = la.multi_dot(\n [self.eig_vecs_r, self.spectral_field_buffer, self.tranpose_of_eig_vecs_z],\n )\n", "sub_path": "pyaxisymflow/kernels/FastDiagonalisationStokesSolver.py", "file_name": "FastDiagonalisationStokesSolver.py", "file_ext": "py", "file_size_in_byte": 5809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.float64", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 39, "usage_type": "call"}, {"api_name": "scipy.sparse.diags", "line_number": 47, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 47, "usage_type": "name"}, {"api_name": "scipy.sparse.diags", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 54, "usage_type": "name"}, {"api_name": "scipy.sparse.diags", "line_number": 61, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.linalg.eig", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 109, "usage_type": "name"}, {"api_name": "numpy.linalg.inv", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 115, "usage_type": "name"}, {"api_name": "numpy.linalg.eig", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 117, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 123, "usage_type": "name"}, {"api_name": "numpy.tile", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 137, "usage_type": "name"}, {"api_name": "numpy.multiply", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 154, "usage_type": "name"}]} +{"seq_id": "572352554", "text": "#!/usr/bin/env python3\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree.\nimport inspect\nimport logging\nimport os\nfrom pathlib import Path\nfrom urllib.parse import urlparse\n\nimport classy_vision\nimport torch\nfrom classy_vision.generic.opts import check_generic_args, get_parser\nfrom classy_vision.generic.registry_utils import import_all_packages_from_directory\nfrom classy_vision.generic.util import load_checkpoint, load_json\nfrom classy_vision.hooks import (\n CheckpointHook,\n LossLrMeterLoggingHook,\n ModelComplexityHook,\n ProfilerHook,\n TimeMetricsHook,\n)\nfrom classy_vision.tasks import FineTuningTask, build_task\nfrom classy_vision.trainer.elastic_trainer import ElasticTrainer\nfrom torch.distributed import Backend\nfrom torchelastic.p2p import CoordinatorP2P\nfrom torchvision import set_video_backend\n\n\nlog = logging.getLogger(__name__)\nlogging.basicConfig(\n level=logging.INFO, format=\"[%(levelname)s] %(asctime)s %(module)s: %(message)s\"\n)\n\n\n# local_rank == host local rank assigned and passed by torch.multiprocessing\ndef main(local_rank, c10d_backend, rdzv_init_url, max_world_size, classy_args):\n torch.manual_seed(0)\n set_video_backend(classy_args.video_backend)\n\n # Loads config, sets up task\n config = load_json(classy_args.config_file)\n\n task = build_task(config)\n\n # Load checkpoint, if available\n checkpoint = load_checkpoint(classy_args.checkpoint_folder, classy_args.device)\n task.set_checkpoint(checkpoint)\n\n pretrained_checkpoint = load_checkpoint(\n classy_args.pretrained_checkpoint_folder, classy_args.device\n )\n if pretrained_checkpoint is not None:\n assert isinstance(\n task, FineTuningTask\n ), \"Can only use a pretrained checkpoint for fine tuning tasks\"\n task.set_pretrained_checkpoint(pretrained_checkpoint)\n\n hooks = [\n LossLrMeterLoggingHook(classy_args.log_freq),\n ModelComplexityHook(),\n TimeMetricsHook(),\n ]\n\n if classy_args.checkpoint_folder != \"\":\n args_dict = vars(classy_args)\n args_dict[\"config\"] = config\n hooks.append(\n CheckpointHook(\n classy_args.checkpoint_folder,\n args_dict,\n checkpoint_period=classy_args.checkpoint_period,\n )\n )\n if classy_args.profiler:\n hooks.append(ProfilerHook())\n\n task.set_hooks(hooks)\n\n assert c10d_backend == Backend.NCCL or c10d_backend == Backend.GLOO\n if c10d_backend == torch.distributed.Backend.NCCL:\n # needed to enable NCCL error handling\n os.environ[\"NCCL_BLOCKING_WAIT\"] = \"1\"\n\n coordinator = CoordinatorP2P(\n c10d_backend=c10d_backend,\n init_method=rdzv_init_url,\n max_num_trainers=max_world_size,\n process_group_timeout=60000,\n )\n trainer = ElasticTrainer(\n use_gpu=classy_args.device == \"gpu\",\n num_dataloader_workers=classy_args.num_workers,\n local_rank=local_rank,\n elastic_coordinator=coordinator,\n input_args={},\n )\n trainer.train(task)\n\n\ndef parse_classy_args():\n \"\"\"\n parses default classy args from sys.argv adding some nice-to-have\n decorations (e.g. automatically set --device depending on the host type)\n \"\"\"\n parser = get_parser()\n args = parser.parse_args()\n\n args.config_file = to_abs_path(args.config_file)\n args.device = \"gpu\" if torch.cuda.is_available() else \"cpu\"\n check_generic_args(args)\n return args\n\n\n# TODO we may want to upstream this to classy_vision utils\ndef to_abs_path(config_path_url):\n \"\"\"\n Returns the absolute file path to the classy config file\n\n Get config relative to classy's module\n to_abs_path(\"classy-vision://config/resnet_50.json\")\n -- or --\n\n Get config relative to this script\n to_abs_path(\"my_config_dir/resnet_50.json\")\n -- or --\n\n Get config from absolute path\n to_abs_path(\"/absolute/config/dir/path/resnet_50.json\")\n \"\"\"\n config_url = urlparse(config_path_url)\n if config_url.scheme == \"classy-vision\":\n # read relative to classy_vision module\n classy_path = Path(inspect.getfile(classy_vision)).parent\n classy_config_file = os.path.join(\n classy_path, f\"{config_url.netloc}{config_url.path}\"\n )\n else:\n # read relative to script if not absolute path\n if os.path.isabs(config_url.path):\n classy_config_file = config_url.path\n else:\n classy_config_file = os.path.join(\n os.path.dirname(__file__), config_url.path\n )\n return classy_config_file\n\n\ndef default_local_world_size():\n \"\"\"\n If CUDA is available, returns the number of GPU devices on the host.\n Otherwise returns 1.\n \"\"\"\n if torch.cuda.is_available():\n return torch.cuda.device_count()\n else:\n return 1\n\n\nif __name__ == \"__main__\":\n # num_nodes == number of hosts participating on this job\n # assumes homogeneous hosts\n # local_world_size = number of workers to run per node\n # world_size = total number of workers\n num_nodes = os.environ.get(\"SIZE\", 1)\n min_num_nodes = os.environ.get(\"MIN_SIZE\", num_nodes)\n max_num_nodes = os.environ.get(\"MAX_SIZE\", num_nodes)\n\n local_world_size = default_local_world_size()\n min_world_size = local_world_size * min_num_nodes\n max_world_size = local_world_size * max_num_nodes\n\n if torch.cuda.is_available():\n if not local_world_size:\n num_gpus = torch.cuda.device_count()\n log.info(f\"Found {num_gpus} gpus on this host\")\n local_world_size = num_gpus\n else:\n if not local_world_size:\n local_world_size = 1\n\n world_size = local_world_size * num_nodes\n log.info(f\"Running {local_world_size}/{world_size} workers on this host\")\n\n rdzv_endpoint = os.environ.get(\"RDZV_ENDPOINT\", \"localhost:2379\")\n job_id = os.environ.get(\"JOB_ID\", \"torchelastic_classy_vision_example\")\n rdzv_init_method = (\n f\"etcd://{rdzv_endpoint}/{job_id}\"\n f\"?min_workers={min_world_size}\"\n f\"&max_workers={max_world_size}\"\n f\"&last_call_timeout=5\"\n )\n log.info(f\"rdzv init method={rdzv_init_method}\")\n\n c10d_backend = os.environ.get(\"TORCH_DISTRIBUTED_BACKEND\", Backend.GLOO).lower()\n\n file_root = Path(__file__).parent\n import_all_packages_from_directory(file_root)\n\n if local_world_size == 1:\n local_rank = 0\n main(\n local_rank,\n c10d_backend,\n rdzv_init_method,\n max_world_size,\n parse_classy_args(),\n )\n else:\n torch.multiprocessing.spawn(\n fn=main,\n args=(c10d_backend, rdzv_init_method, max_world_size, parse_classy_args()),\n nprocs=local_world_size,\n join=True,\n )\n", "sub_path": "examples/classy_vision/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6960, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.getLogger", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 41, "usage_type": "call"}, {"api_name": "torchvision.set_video_backend", "line_number": 42, "usage_type": "call"}, {"api_name": "classy_vision.generic.util.load_json", "line_number": 45, "usage_type": "call"}, {"api_name": "classy_vision.tasks.build_task", "line_number": 47, "usage_type": "call"}, {"api_name": "classy_vision.generic.util.load_checkpoint", "line_number": 50, "usage_type": "call"}, {"api_name": "classy_vision.generic.util.load_checkpoint", "line_number": 53, "usage_type": "call"}, {"api_name": "classy_vision.tasks.FineTuningTask", "line_number": 58, "usage_type": "argument"}, {"api_name": "classy_vision.hooks.LossLrMeterLoggingHook", "line_number": 63, "usage_type": "call"}, {"api_name": "classy_vision.hooks.ModelComplexityHook", "line_number": 64, "usage_type": "call"}, {"api_name": "classy_vision.hooks.TimeMetricsHook", "line_number": 65, "usage_type": "call"}, {"api_name": "classy_vision.hooks.CheckpointHook", "line_number": 72, "usage_type": "call"}, {"api_name": "classy_vision.hooks.ProfilerHook", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.distributed.Backend.NCCL", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.distributed.Backend", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.distributed.Backend.GLOO", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.distributed", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torchelastic.p2p.CoordinatorP2P", "line_number": 88, "usage_type": "call"}, {"api_name": "classy_vision.trainer.elastic_trainer.ElasticTrainer", "line_number": 94, "usage_type": "call"}, {"api_name": "classy_vision.generic.opts.get_parser", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 113, "usage_type": "attribute"}, {"api_name": "classy_vision.generic.opts.check_generic_args", "line_number": 114, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 134, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 137, "usage_type": "call"}, {"api_name": "inspect.getfile", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.path.isabs", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 157, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 168, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 168, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 169, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 170, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 170, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 176, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 188, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 189, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 198, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 198, "usage_type": "attribute"}, {"api_name": "torch.distributed.Backend.GLOO", "line_number": 198, "usage_type": "attribute"}, {"api_name": "torch.distributed.Backend", "line_number": 198, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 200, "usage_type": "call"}, {"api_name": "classy_vision.generic.registry_utils.import_all_packages_from_directory", "line_number": 201, "usage_type": "call"}, {"api_name": "torch.multiprocessing.spawn", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.multiprocessing", "line_number": 213, "usage_type": "attribute"}]} +{"seq_id": "368637152", "text": "import json\nimport pytest\nfrom jsonschema import FormatChecker, Draft4Validator, ValidationError\n\nfrom app import app\n\n\nfile_content = 'Welcome,\\r\\n\\r\\nyou are connected to an FTP or SFTP server used for testing purposes by Rebex FTP/SSL' \\\n ' or Rebex SFTP sample code.\\r\\nOnly read access is allowed and the FTP download speed is limited to ' \\\n '16KBps.\\r\\n\\r\\nFor infomation about Rebex FTP/SSL, Rebex SFTP and other Rebex .NET components, please' \\\n ' visit our website at http://www.rebex.net/\\r\\n\\r\\nFor feedback and support, contact ' \\\n 'support@rebex.net\\r\\n\\r\\nThanks!\\r\\n'\nschema_200 = {\n 'type': 'object',\n 'properties': {\n 'ip': {'type': 'string', 'format': 'ipv4', 'enum': ['195.144.107.198']},\n 'hostname': {'type': 'string', 'format': 'hostname', 'enum': ['test.rebex.net']},\n 'path': {'type': 'string', 'enum': ['/readme.txt']},\n 'content': {'type': 'string', 'enum': [file_content]}\n },\n 'additionalProperties': False,\n 'required': ['ip', 'hostname', 'path', 'content']\n}\n\n\n@pytest.fixture\ndef client(request):\n return app.test_client()\n\n\ndef test_successful(client):\n response = client.get('/sftp/api/v1.0/get-file?ip=195.144.107.198&path=/readme.txt')\n response_json = json.loads(response.data)\n assert response.status_code == 200\n validator = Draft4Validator(schema_200, format_checker=FormatChecker(('ipv4', 'hostname')))\n validation_errors = [error.message for error in validator.iter_errors(response_json)]\n if validation_errors:\n raise ValidationError(validation_errors)\n\n\ndef test_unknown_ip(client):\n response = client.get('/sftp/api/v1.0/get-file?ip=127.0.0.1&path=/readme.txt')\n response_json = json.loads(response.data)\n assert response.status_code == 400\n assert response_json == {'error': 'Specified ip address is not registered in service!'}\n\n\ndef test_incorrect_ip(client):\n response = client.get('/sftp/api/v1.0/get-file?ip=a.b.c.d&path=/readme.txt')\n response_json = json.loads(response.data)\n assert response.status_code == 400\n assert response_json == {'error': [\"'a.b.c.d' is not a 'ipv4'\"]}\n\n\ndef test_ip_is_absent(client):\n response = client.get('/sftp/api/v1.0/get-file?path=/readme.txt')\n response_json = json.loads(response.data)\n assert response.status_code == 400\n assert response_json == {'error': [\"'ip' is a required property\"]}\n\n\ndef test_path_is_absent(client):\n response = client.get('/sftp/api/v1.0/get-file?ip=195.144.107.198')\n response_json = json.loads(response.data)\n assert response.status_code == 400\n assert response_json == {'error': [\"'path' is a required property\"]}\n\n\ndef test_path_is_empty(client):\n response = client.get('/sftp/api/v1.0/get-file?ip=195.144.107.198&path=')\n response_json = json.loads(response.data)\n assert response.status_code == 400\n assert response_json == {'error': [\"'' is too short\"]}\n\n\ndef test_path_is_not_exists(client):\n response = client.get('/sftp/api/v1.0/get-file?ip=195.144.107.198&path=/unknown.txt')\n response_json = json.loads(response.data)\n assert response.status_code == 404\n assert response_json == {'error': 'File not found!'}\n\n\ndef test_incorrect_request(client):\n response = client.get('/sftp/api/v1.0/get-file')\n response_json = json.loads(response.data)\n assert response.status_code == 400\n assert response_json == {'error': [\"'ip' is a required property\", \"'path' is a required property\"]}\n\n\ndef test_unexpected_post(client):\n response = client.post('/sftp/api/v1.0/get-file?ip=195.144.107.198&path=/readme.txt')\n assert response.status_code == 405\n", "sub_path": "tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 3679, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "app.app.test_client", "line_number": 28, "usage_type": "call"}, {"api_name": "app.app", "line_number": 28, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 26, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "jsonschema.Draft4Validator", "line_number": 35, "usage_type": "call"}, {"api_name": "jsonschema.FormatChecker", "line_number": 35, "usage_type": "call"}, {"api_name": "jsonschema.ValidationError", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 43, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 50, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 57, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 64, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 71, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 78, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "555618539", "text": "import glob\nimport os\nfrom pathlib import Path\n\nfrom strictdoc.backend.dsl.errors.document_tree_error import DocumentTreeError\nfrom strictdoc.backend.source_file_syntax.reader import (\n SourceFileTraceabilityReader,\n)\nfrom strictdoc.cli.cli_arg_parser import ExportCommandConfig\nfrom strictdoc.core.document_finder import DocumentFinder\nfrom strictdoc.core.finders.source_files_finder import (\n SourceFilesFinder,\n SourceFile,\n)\nfrom strictdoc.core.source_tree import SourceTree\nfrom strictdoc.core.traceability_index import TraceabilityIndex\nfrom strictdoc.core.traceability_index_builder import TraceabilityIndexBuilder\nfrom strictdoc.export.excel.excel_generator import ExcelGenerator\nfrom strictdoc.export.html.html_generator import HTMLGenerator\nfrom strictdoc.export.rst.document_rst_generator import DocumentRSTGenerator\nfrom strictdoc.helpers.file_modification_time import get_file_modification_time\nfrom strictdoc.helpers.timing import timing_decorator\n\n\nclass ExportAction:\n @staticmethod\n @timing_decorator(\"Export\")\n def export(config: ExportCommandConfig, parallelizer):\n assert parallelizer\n cwd = os.getcwd()\n strict_own_files = glob.iglob(\n \"{}/strictdoc/**/*\".format(config.strictdoc_root_path),\n recursive=True,\n )\n strict_own_files = [\n f\n for f in strict_own_files\n if f.endswith(\".html\") or f.endswith(\".py\")\n ]\n latest_strictdoc_own_file = max(strict_own_files, key=os.path.getctime)\n strictdoc_last_update = get_file_modification_time(\n latest_strictdoc_own_file\n )\n\n assert isinstance(config.formats, list)\n\n path_to_single_file_or_doc_root = config.input_paths\n if isinstance(path_to_single_file_or_doc_root, str):\n path_to_single_file_or_doc_root = [config.input_paths]\n output_dir = config.output_dir if config.output_dir else \"output\"\n\n if not os.path.isabs(output_dir):\n output_dir = os.path.join(cwd, output_dir)\n\n output_html_root = \"{}/html\".format(output_dir)\n\n document_tree, asset_dirs = DocumentFinder.find_sdoc_content(\n path_to_single_file_or_doc_root, output_html_root, parallelizer\n )\n\n try:\n traceability_index: TraceabilityIndex = (\n TraceabilityIndexBuilder.create(document_tree)\n )\n except DocumentTreeError as exc:\n print(exc.to_print_message())\n exit(1)\n\n if config.experimental_enable_file_traceability:\n source_tree: SourceTree = SourceFilesFinder.find_source_files(\n output_html_root, document_tree\n )\n source_files = source_tree.source_files\n source_file: SourceFile\n for source_file in source_files:\n traceability_reader = SourceFileTraceabilityReader()\n traceability_info = traceability_reader.read_from_file(\n source_file.full_path\n )\n if traceability_info:\n traceability_index.attach_traceability_info(\n source_file.in_doctree_source_file_rel_path,\n traceability_info,\n )\n document_tree.attach_source_tree(source_tree)\n\n if \"html\" in config.formats or \"html-standalone\" in config.formats:\n Path(output_html_root).mkdir(parents=True, exist_ok=True)\n HTMLGenerator.export_tree(\n config,\n document_tree,\n traceability_index,\n output_html_root,\n strictdoc_last_update,\n asset_dirs,\n parallelizer,\n )\n\n if \"rst\" in config.formats:\n output_rst_root = \"{}/rst\".format(output_dir)\n Path(output_rst_root).mkdir(parents=True, exist_ok=True)\n DocumentRSTGenerator.export_tree(\n document_tree, traceability_index, output_rst_root\n )\n\n if \"excel\" in config.formats:\n output_excel_root = \"{}/excel\".format(output_dir)\n ExcelGenerator.export_tree(\n document_tree,\n traceability_index,\n output_excel_root,\n config.fields,\n )\n", "sub_path": "strictdoc/core/actions/export_action.py", "file_name": "export_action.py", "file_ext": "py", "file_size_in_byte": 4334, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "strictdoc.cli.cli_arg_parser.ExportCommandConfig", "line_number": 28, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 30, "usage_type": "call"}, {"api_name": "glob.iglob", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "strictdoc.helpers.file_modification_time.get_file_modification_time", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.isabs", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "strictdoc.core.document_finder.DocumentFinder.find_sdoc_content", "line_number": 57, "usage_type": "call"}, {"api_name": "strictdoc.core.document_finder.DocumentFinder", "line_number": 57, "usage_type": "name"}, {"api_name": "strictdoc.core.traceability_index.TraceabilityIndex", "line_number": 62, "usage_type": "name"}, {"api_name": "strictdoc.core.traceability_index_builder.TraceabilityIndexBuilder.create", "line_number": 63, "usage_type": "call"}, {"api_name": "strictdoc.core.traceability_index_builder.TraceabilityIndexBuilder", "line_number": 63, "usage_type": "name"}, {"api_name": "strictdoc.backend.dsl.errors.document_tree_error.DocumentTreeError", "line_number": 65, "usage_type": "name"}, {"api_name": "strictdoc.core.source_tree.SourceTree", "line_number": 70, "usage_type": "name"}, {"api_name": "strictdoc.core.finders.source_files_finder.SourceFilesFinder.find_source_files", "line_number": 70, "usage_type": "call"}, {"api_name": "strictdoc.core.finders.source_files_finder.SourceFilesFinder", "line_number": 70, "usage_type": "name"}, {"api_name": "strictdoc.core.finders.source_files_finder.SourceFile", "line_number": 74, "usage_type": "name"}, {"api_name": "strictdoc.backend.source_file_syntax.reader.SourceFileTraceabilityReader", "line_number": 76, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 88, "usage_type": "call"}, {"api_name": "strictdoc.export.html.html_generator.HTMLGenerator.export_tree", "line_number": 89, "usage_type": "call"}, {"api_name": "strictdoc.export.html.html_generator.HTMLGenerator", "line_number": 89, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 101, "usage_type": "call"}, {"api_name": "strictdoc.export.rst.document_rst_generator.DocumentRSTGenerator.export_tree", "line_number": 102, "usage_type": "call"}, {"api_name": "strictdoc.export.rst.document_rst_generator.DocumentRSTGenerator", "line_number": 102, "usage_type": "name"}, {"api_name": "strictdoc.export.excel.excel_generator.ExcelGenerator.export_tree", "line_number": 108, "usage_type": "call"}, {"api_name": "strictdoc.export.excel.excel_generator.ExcelGenerator", "line_number": 108, "usage_type": "name"}, {"api_name": "strictdoc.helpers.timing.timing_decorator", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "584416209", "text": "#!/usr/bin/env python\n#\n# Copyright 2012 Jordon Mears (http://www.finefrog.com/)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport tornado.ioloop\nimport tornado.web\n\nimport handlers\n\napplication = tornado.web.Application([\n (r'/', handlers.IndexHandler),\n (r'/q/?', handlers.QueryHandler),\n (r'/query/?', handlers.QueryHandler),\n (r'/query-browser.html', handlers.QueryBrowserHandler),\n (r'/api/(.*)', tornado.web.StaticFileHandler, {'path' : './api'}),\n (r'/static/(.*)', tornado.web.StaticFileHandler, {'path' : './static'}),\n (r'/(crossdomain\\.xml)', tornado.web.StaticFileHandler, {'path' : './'})\n])\n\nif __name__ == '__main__':\n application.listen(8888)\n tornado.ioloop.IOLoop.instance().start()", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1231, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "tornado.ioloop.web.Application", "line_number": 22, "usage_type": "call"}, {"api_name": "tornado.ioloop.web", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 22, "usage_type": "name"}, {"api_name": "handlers.IndexHandler", "line_number": 23, "usage_type": "attribute"}, {"api_name": "handlers.QueryHandler", "line_number": 24, "usage_type": "attribute"}, {"api_name": "handlers.QueryHandler", "line_number": 25, "usage_type": "attribute"}, {"api_name": "handlers.QueryBrowserHandler", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tornado.ioloop.web", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 27, "usage_type": "name"}, {"api_name": "tornado.ioloop.web", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 28, "usage_type": "name"}, {"api_name": "tornado.ioloop.web", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 29, "usage_type": "name"}, {"api_name": "tornado.ioloop.ioloop.IOLoop.instance", "line_number": 34, "usage_type": "call"}, {"api_name": "tornado.ioloop.ioloop", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "521494279", "text": "from setuptools import find_packages\nfrom setuptools import setup\n\nimport os\n\n\nlong_description = (\n open(os.path.join(\"src\", \"hexagonit\", \"socialbutton\", \"docs\", \"README.rst\")).read() + \"\\n\" +\n open(os.path.join(\"src\", \"hexagonit\", \"socialbutton\", \"docs\", \"HISTORY.rst\")).read() + \"\\n\" +\n open(os.path.join(\"src\", \"hexagonit\", \"socialbutton\", \"docs\", \"CONTRIBUTORS.rst\")).read())\n\n\nsetup(\n name='hexagonit.socialbutton',\n version='0.11',\n description=\"Adds viewlets for embedding codes such as social buttons for Plone.\",\n long_description=long_description,\n classifiers=[\n \"Framework :: Plone\",\n \"Framework :: Plone :: 4.3\",\n \"License :: OSI Approved :: BSD License\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 2.7\"],\n keywords='',\n author='Hexagon IT',\n author_email='oss@hexagonit.fi',\n url='http://www.hexagonit.fi',\n license='BSD',\n packages=find_packages('src', exclude=['ez_setup']),\n package_dir={'': 'src'},\n namespace_packages=['hexagonit'],\n include_package_data=True,\n zip_safe=False,\n install_requires=[\n 'plone.stringinterp >= 1.0.11',\n 'setuptools'],\n extras_require={'test': ['hexagonit.testing']},\n entry_points=\"\"\"\n # -*- Entry points: -*-\n\n [z3c.autoinclude.plugin]\n target = plone\n \"\"\")\n", "sub_path": "pypi_install_script/hexagonit.socialbutton-0.11/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1360, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 13, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "303532314", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Volin',\n fields=[\n ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n ('author', models.CharField(max_length=20, verbose_name='auth.user')),\n ('title', models.CharField(max_length=100, verbose_name='Nazwa zadania')),\n ('destination', models.CharField(blank=True, max_length=50, verbose_name='Miejsce ')),\n ('description', models.TextField(verbose_name='Opis')),\n ('publication_date', models.DateTimeField(blank=True, null=True, verbose_name='Data publikacji')),\n ],\n options={\n 'ordering': ['-id'],\n },\n ),\n ]\n", "sub_path": "Volontario/Ind/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 963, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "536635814", "text": "\"\"\"Decorators for API resources.\"\"\"\nimport functools\n\nfrom flask import request\n\nfrom api.exception import BadRequestException\n\n\ndef expect_required_fields(func):\n \"\"\"Examines an HTTP request to verify thit it contains all required fields.\n\n Raises:\n BadRequestException: If any any required fields are missing.\n \"\"\"\n @functools.wraps(func)\n def decorated(instance, *args, **kwargs):\n request_data = request.get_json(force=True, silent=True)\n missing_fields = []\n given_fields = request_data.keys()\n for required_field in instance.__model__.required_fields():\n if required_field not in given_fields:\n missing_fields.append(required_field)\n if len(missing_fields) > 0:\n message = f'Missing required fields: [{\", \".join(missing_fields)}]'\n raise BadRequestException(message)\n return func(instance, *args, **kwargs)\n return decorated\n\ndef refuse_unknown_fields(func):\n \"\"\"Examines an HTTP request to verify thit it does not contain any\n unknown fields.\n\n Raises:\n BadRequestException: If any any unknown fields are discovered.\n \"\"\"\n @functools.wraps(func)\n def decorated(instance, *args, **kwargs):\n request_data = request.get_json(force=True, silent=True)\n model = instance.__model__\n unknown_fields = []\n known_fields = model.required_fields() + model.optional_fields() + ['id']\n for given_field in request_data:\n if given_field not in known_fields:\n unknown_fields.append(given_field)\n if len(unknown_fields) > 0:\n message = f'Unknown fields: [{\", \".join(unknown_fields)}]'\n raise BadRequestException(message)\n return func(instance, *args, **kwargs)\n return decorated\n\n#def audit_request(func):\n# \"\"\"Ensures that we have a valid request from the client.\n#\n# Raises:\n# BadRequestException: If no data was received with the request.\n# \"\"\"\n# @functools.wraps(func)\n# def decorated(instance, *args, **kwargs):\n# request_data = request.get_json(force=True, silent=True)\n# if not request_data:\n# raise BadRequestException('No data received with the request.')\n", "sub_path": "02_trivia_api/backend/api/resources/decorators.py", "file_name": "decorators.py", "file_ext": "py", "file_size_in_byte": 2241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.request.get_json", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "api.exception.BadRequestException", "line_number": 25, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "api.exception.BadRequestException", "line_number": 47, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "399699029", "text": "import torch.nn as nn \nimport torch\n\n\nclass one_conv(nn.Module):\n def __init__(self, inchanels, growth_rate, kernel_size = 3):\n super(one_conv, self).__init__()\n self.conv = nn.Conv2d(inchanels, growth_rate, kernel_size=kernel_size, padding=kernel_size>>1, stride=1)\n self.relu = nn.ReLU()\n def forward(self, x):\n output = self.relu(self.conv(x))\n return torch.cat((x, output), 1)\n\nclass RDB(nn.Module):\n def __init__(self, G0, C, G, kernel_size = 3):\n super(RDB, self).__init__()\n convs = []\n for i in range(C):\n convs.append(one_conv(G0+i*G, G))\n self.conv = nn.Sequential(*convs)\n #local_feature_fusion\n self.LFF = nn.Conv2d(G0+C*G, G0, kernel_size=1, padding=0, stride=1)\n def forward(self, x):\n out = self.conv(x)\n lff = self.LFF(out)\n #local residual learning\n return lff + x\n\nclass Network(nn.Module):\n def __init__(self):\n super(Network, self).__init__()\n '''\n D: RDB number\n C: the number of conv layer in RDB\n G: the growth rate\n G0:local and global feature fusion layers\n '''\n self.D = 20\n self.C = 6\n self.G = 32\n self.G0 = 64\n kernel_size = 3\n\n # shallow feature extraction \n self.SFE = nn.Conv2d(3, self.G0, kernel_size=kernel_size, padding=kernel_size>>1, stride=1)\n # feature extraction \n self.FE = nn.Conv2d(self.G0, self.G0, kernel_size=kernel_size, padding=kernel_size>>1, stride=1)\n # RDB for paper we have D RDB block\n self.RDBS = nn.ModuleList()\n for d in range(self.D):\n self.RDBS.append(RDB(self.G0, self.C, self.G, kernel_size))\n # Global feature fusion\n self.GFF = nn.Sequential(\n nn.Conv2d(self.D*self.G0, self.G0, kernel_size=1, padding=0 , stride=1), \n nn.Conv2d(self.G0, self.G0, kernel_size=kernel_size, padding=kernel_size>>1, stride=1), \n )\n # feature reconstruction\n self.FR = nn.Conv2d(self.G0, 3, kernel_size=kernel_size, padding=kernel_size>>1, stride=1)\n #init\n for para in self.modules():\n if isinstance(para, nn.Conv2d):\n nn.init.kaiming_normal_(para.weight)\n if para.bias is not None:\n para.bias.data.zero_()\n\n def forward(self, x):\n f_1 = self.SFE(x)\n out = self.FE(f_1)\n RDB_outs = []\n for i in range(self.D):\n out = self.RDBS[i](out)\n RDB_outs.append(out)\n out = torch.cat(RDB_outs, 1)\n out = self.GFF(out)\n out = f_1 + out \n\n out = self.FR(out)\n out = x + out\n return out\n", "sub_path": "model/rdnp/netmodel.py", "file_name": "netmodel.py", "file_ext": "py", "file_size_in_byte": 2726, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "148332508", "text": "import pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nMydataset = pd.read_csv('path4.csv')\r\nelevation = Mydataset.iloc[:,:].values\r\n\r\nz = Mydataset.iloc[:,3].values\r\nx = np.linspace(0,54,1025)\r\nx_resize = np.linspace(0,54,256)\r\nN_elevation= len(z)\r\nZ_512 = np.zeros([256])\r\n\r\ndef AverageStep(InputData,OutputData):\r\n InputData_calc = InputData.copy()\r\n OutputData_calc = OutputData.copy()\r\n \r\n na = len(InputData_calc)\r\n nb = len(OutputData_calc)\r\n step = int(na/nb)\r\n\r\n for i in range(0,na-step,step):\r\n contain=0\r\n for j in range(step):\r\n contain = contain + InputData_calc[i+j]\r\n average = contain/step\r\n pointer = int(i/step+1)\r\n OutputData_calc[pointer-1]=average\r\n \r\n return OutputData_calc\r\n \r\nZ_resize = AverageStep(z,Z_512)\r\n \r\nplt.plot(x_resize,Z_resize)\r\n#plt.plot(x,z)\r\nplt.grid(True)\r\nplt.plot ", "sub_path": "csv.py", "file_name": "csv.py", "file_ext": "py", "file_size_in_byte": 908, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 37, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "593986124", "text": "from math import ceil\nimport torch\nfrom torchvision import transforms, datasets\nfrom torch.utils.data.sampler import SubsetRandomSampler\nimport numpy as np\nimport torchvision\n\n\ndef data_processing(filepath, batch_size=4):\n \"\"\"\n Prepare data for the network. We transform the images\n and return training, validation and test sets.\n Args:\n filepath (str): path to the folder with all data images.\n batch_size (int): size of each batch.\n Returns:\n tuple: (train_loader, validation_loader, test_loader)\n \"\"\"\n data_transform = transforms.Compose([\n transforms.Resize([256,342]),\n transforms.ToTensor(),\n transforms.Normalize(mean=(0.485, 0.456, 0.406),\n std=(0.229, 0.224, 0.225))\n ])\n music_dataset = datasets.ImageFolder(root=filepath, transform=data_transform)\n batch_size = 4\n train_data, validation_data, test_data = torch.utils.data.random_split(\n music_dataset, [ceil(0.6*len(music_dataset)),\n ceil(0.2*len(music_dataset)),\n len(music_dataset)-(ceil(0.6*len(music_dataset))+ceil(0.2*len(music_dataset)))])\n train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,shuffle=True)\n validation_loader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size,shuffle=True)\n test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size,shuffle=True)\n return train_loader, validation_loader, test_loader\n", "sub_path": "data_processing.py", "file_name": "data_processing.py", "file_ext": "py", "file_size_in_byte": 1506, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torchvision.transforms.Compose", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 19, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 20, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 21, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 25, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.utils.data.random_split", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 27, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 28, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 29, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 33, "usage_type": "attribute"}]} +{"seq_id": "26157043", "text": "from django.shortcuts import render, redirect\nfrom django.http import HttpResponseRedirect\nfrom .forms import DebitEntryForm, TransactionsForms, SecondPageForm, CreditEntryForm, OverviewForm\nfrom .models import CreditEntry, SecondPage, DebitEntry, Overview\nfrom random import randrange\nfrom django.contrib import messages\n\ncompanyIdDict = {'2909': ['Verizon Wireless', '141 Industrial Parkway', 'Branchburg', '1720010'], '1644': ['ATT', 'New York City', 'New York', '70817081'], '60':['Pierre', '23 WestVille Avenue','Caldwell', '70817081']}\n\nlastSecondPage = SecondPage.objects.last() \nlastDebitEntry = DebitEntry.objects.last()\nlastCreditEntry = CreditEntry.objects.last()\n\ncarrierNumber = lastSecondPage.accountBottom\nfiscal = lastSecondPage.postDate.year\ncompCode = lastSecondPage.companyCode\ncurrency = lastSecondPage.currency\nglAcc = lastDebitEntry.accountBottom\ndifferenceDebitCredit = lastDebitEntry.amount - lastCreditEntry.amount\n\ntry:\n carrierDetails = companyIdDict[carrierNumber]\nexcept KeyError:\n carrierDetails = \"carrier not found for Account Number {accNum} that was entered in last page. Go back to correct it.\".format(accNum=carrierNumber)\ncompanyInfo = [carrierNumber, carrierDetails]\n\ndef docNumGen():\n return '1600000' + str(randrange(0,999))\n\ndef landPage(request):\n if request.method == 'POST':\n form = TransactionsForms(request.POST)\n if form.is_valid():\n transactionCode = form.cleaned_data['transactionCode']\n print(transactionCode)\n if transactionCode.lower() == 'vf04':\n return redirect(secondPage)\n else:\n form = TransactionsForms()\n return render(request, \"main/landingPage.html\", {'form': form})\n\ndef secondPage(request):\n try:\n if request.session['docNum']:\n documentNumber = request.session['docNum']\n print(documentNumber)\n messages.add_message(request, 20, 'Document Number is {docNum}'.format(docNum=documentNumber))\n print(request.session.get_expiry_age())\n except KeyError:\n pass\n if request.method == 'POST':\n form = SecondPageForm(request.POST)\n if form.is_valid():\n secondPageData = form.save()\n return redirect(debitEntry)\n else: \n form = SecondPageForm()\n return render(request, 'main/secondPage.html', {'form': form})\n\ndef debitEntry(request):\n lastSecondPage = SecondPage.objects.last() \n carrierNumber = lastSecondPage.accountBottom\n try:\n carrierDetails = companyIdDict[carrierNumber]\n except KeyError:\n carrierDetails = \"carrier not found for Account Number {accNum} that was entered in last page. Go back to correct it.\".format(accNum=carrierNumber)\n companyInfo = [carrierNumber, carrierDetails]\n if request.method == 'POST':\n form = DebitEntryForm(request.POST)\n if form.is_valid():\n debitEntryData = form.save()\n return redirect(creditEntry)\n else: \n form = DebitEntryForm()\n return render(request, 'main/debitEntry.html', {'form': form, 'companyInfo': companyInfo, 'lastSecondPage': lastSecondPage})\n\ndef creditEntry(request):\n lastSecondPage = SecondPage.objects.last() \n lastDebitEntry = DebitEntry.objects.last() \n compCode = lastSecondPage.companyCode\n currency = lastSecondPage.currency\n glAcc = lastDebitEntry.accountBottom\n listForHtml = [compCode, glAcc, currency]\n if request.method == 'POST':\n form = CreditEntryForm(request.POST)\n print(listForHtml[2])\n if form.is_valid():\n creditEntrydata = form.save()\n return redirect(overview)\n else: \n form = CreditEntryForm()\n print(listForHtml[2])\n return render(request, 'main/creditEntry.html', {'listForHtml': listForHtml, 'form': form})\n\ndef overview(request):\n lastSecondPage = SecondPage.objects.last() \n lastDebitEntry = DebitEntry.objects.last()\n lastCreditEntry = CreditEntry.objects.last()\n fiscal = lastSecondPage.postDate.year\n try:\n carrierDetails = companyIdDict[carrierNumber]\n except KeyError:\n carrierDetails = \"carrier not found for Account Number {accNum} that was entered in last page. Go back to correct it.\".format(accNum=carrierNumber)\n companyInfo = [carrierNumber, carrierDetails]\n differenceDebitCredit = lastDebitEntry.amount - lastCreditEntry.amount\n if request.method == 'POST':\n form = OverviewForm(request.POST)\n if form.is_valid():\n overviewData = form.save()\n cleanedForm = form.cleaned_data\n lastSecondPage.reference = cleanedForm['reference']\n lastCreditEntry.trdgPartBA = cleanedForm['trdgPartBA']\n lastSecondPage.docHeader = cleanedForm['docHeader']\n lastSecondPage.save()\n lastCreditEntry.save()\n request.session['docNum'] = docNumGen()\n return redirect(secondPage)\n else: \n form = OverviewForm()\n return render(request, 'main/overview.html', {'lastSecondPage': lastSecondPage, 'lastDebitEntry': lastDebitEntry, 'lastCreditEntry': lastCreditEntry, 'fiscal': fiscal, 'form': form, 'companyInfo': companyInfo, 'differenceDebitCredit': differenceDebitCredit})\n\ndef docHeaderPage(request):\n return render(request, 'main/docHeaderPage.html')\n\ndef fastDataEntry(request):\n return render(request, 'main/fastDataEntry.html')\n", "sub_path": "main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5408, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "models.SecondPage.objects.last", "line_number": 10, "usage_type": "call"}, {"api_name": "models.SecondPage.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.SecondPage", "line_number": 10, "usage_type": "name"}, {"api_name": "models.DebitEntry.objects.last", "line_number": 11, "usage_type": "call"}, {"api_name": "models.DebitEntry.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.DebitEntry", "line_number": 11, "usage_type": "name"}, {"api_name": "models.CreditEntry.objects.last", "line_number": 12, "usage_type": "call"}, {"api_name": "models.CreditEntry.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.CreditEntry", "line_number": 12, "usage_type": "name"}, {"api_name": "random.randrange", "line_number": 28, "usage_type": "call"}, {"api_name": "forms.TransactionsForms", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 37, "usage_type": "call"}, {"api_name": "forms.TransactionsForms", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "django.contrib.messages.add_message", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 47, "usage_type": "name"}, {"api_name": "forms.SecondPageForm", "line_number": 52, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 55, "usage_type": "call"}, {"api_name": "forms.SecondPageForm", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 58, "usage_type": "call"}, {"api_name": "models.SecondPage.objects.last", "line_number": 61, "usage_type": "call"}, {"api_name": "models.SecondPage.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.SecondPage", "line_number": 61, "usage_type": "name"}, {"api_name": "forms.DebitEntryForm", "line_number": 69, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 72, "usage_type": "call"}, {"api_name": "forms.DebitEntryForm", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 75, "usage_type": "call"}, {"api_name": "models.SecondPage.objects.last", "line_number": 78, "usage_type": "call"}, {"api_name": "models.SecondPage.objects", "line_number": 78, "usage_type": "attribute"}, {"api_name": "models.SecondPage", "line_number": 78, "usage_type": "name"}, {"api_name": "models.DebitEntry.objects.last", "line_number": 79, "usage_type": "call"}, {"api_name": "models.DebitEntry.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "models.DebitEntry", "line_number": 79, "usage_type": "name"}, {"api_name": "forms.CreditEntryForm", "line_number": 85, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "forms.CreditEntryForm", "line_number": 91, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 93, "usage_type": "call"}, {"api_name": "models.SecondPage.objects.last", "line_number": 96, "usage_type": "call"}, {"api_name": "models.SecondPage.objects", "line_number": 96, "usage_type": "attribute"}, {"api_name": "models.SecondPage", "line_number": 96, "usage_type": "name"}, {"api_name": "models.DebitEntry.objects.last", "line_number": 97, "usage_type": "call"}, {"api_name": "models.DebitEntry.objects", "line_number": 97, "usage_type": "attribute"}, {"api_name": "models.DebitEntry", "line_number": 97, "usage_type": "name"}, {"api_name": "models.CreditEntry.objects.last", "line_number": 98, "usage_type": "call"}, {"api_name": "models.CreditEntry.objects", "line_number": 98, "usage_type": "attribute"}, {"api_name": "models.CreditEntry", "line_number": 98, "usage_type": "name"}, {"api_name": "forms.OverviewForm", "line_number": 107, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 117, "usage_type": "call"}, {"api_name": "forms.OverviewForm", "line_number": 119, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 120, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 123, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "309060526", "text": "# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom sqlalchemy import func\nfrom sqlalchemy import sql\n\nfrom placement.db.sqlalchemy import models\nfrom placement import db_api\n\n\nclass Usage(object):\n\n def __init__(self, resource_class=None, usage=0):\n self.resource_class = resource_class\n self.usage = int(usage)\n\n\ndef get_all_by_resource_provider_uuid(context, rp_uuid):\n \"\"\"Get a list of Usage objects filtered by one resource provider.\"\"\"\n usage_list = _get_all_by_resource_provider_uuid(context, rp_uuid)\n return [Usage(**db_item) for db_item in usage_list]\n\n\ndef get_all_by_project_user(context, project_id, user_id=None):\n \"\"\"Get a list of Usage objects filtered by project and (optional) user.\"\"\"\n usage_list = _get_all_by_project_user(context, project_id,\n user_id=user_id)\n return [Usage(**db_item) for db_item in usage_list]\n\n\n@db_api.placement_context_manager.reader\ndef _get_all_by_resource_provider_uuid(context, rp_uuid):\n query = (context.session.query(models.Inventory.resource_class_id,\n func.coalesce(func.sum(models.Allocation.used), 0))\n .join(models.ResourceProvider,\n models.Inventory.resource_provider_id ==\n models.ResourceProvider.id)\n .outerjoin(models.Allocation,\n sql.and_(models.Inventory.resource_provider_id ==\n models.Allocation.resource_provider_id,\n models.Inventory.resource_class_id ==\n models.Allocation.resource_class_id))\n .filter(models.ResourceProvider.uuid == rp_uuid)\n .group_by(models.Inventory.resource_class_id))\n result = [dict(resource_class=context.rc_cache.string_from_id(item[0]),\n usage=item[1])\n for item in query.all()]\n return result\n\n\n@db_api.placement_context_manager.reader\ndef _get_all_by_project_user(context, project_id, user_id=None):\n query = (context.session.query(models.Allocation.resource_class_id,\n func.coalesce(func.sum(models.Allocation.used), 0))\n .join(models.Consumer,\n models.Allocation.consumer_id == models.Consumer.uuid)\n .join(models.Project,\n models.Consumer.project_id == models.Project.id)\n .filter(models.Project.external_id == project_id))\n if user_id:\n query = query.join(models.User,\n models.Consumer.user_id == models.User.id)\n query = query.filter(models.User.external_id == user_id)\n query = query.group_by(models.Allocation.resource_class_id)\n result = [dict(resource_class=context.rc_cache.string_from_id(item[0]),\n usage=item[1])\n for item in query.all()]\n return result\n", "sub_path": "placement/objects/usage.py", "file_name": "usage.py", "file_ext": "py", "file_size_in_byte": 3390, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "placement.db.sqlalchemy.models.Inventory", "line_number": 42, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 42, "usage_type": "name"}, {"api_name": "sqlalchemy.func.coalesce", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 43, "usage_type": "name"}, {"api_name": "sqlalchemy.func.sum", "line_number": 43, "usage_type": "call"}, {"api_name": "placement.db.sqlalchemy.models.Allocation", "line_number": 43, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 43, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.ResourceProvider", "line_number": 44, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 44, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Inventory", "line_number": 45, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 45, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.ResourceProvider", "line_number": 46, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 46, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Allocation", "line_number": 47, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 47, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.and_", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.sql", "line_number": 48, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Inventory", "line_number": 48, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 48, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Allocation", "line_number": 49, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 49, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Inventory", "line_number": 50, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 50, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Allocation", "line_number": 51, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 51, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.ResourceProvider", "line_number": 52, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 52, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Inventory", "line_number": 53, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 53, "usage_type": "name"}, {"api_name": "placement.db_api.placement_context_manager", "line_number": 40, "usage_type": "attribute"}, {"api_name": "placement.db_api", "line_number": 40, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Allocation", "line_number": 62, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 62, "usage_type": "name"}, {"api_name": "sqlalchemy.func.coalesce", "line_number": 63, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 63, "usage_type": "name"}, {"api_name": "sqlalchemy.func.sum", "line_number": 63, "usage_type": "call"}, {"api_name": "placement.db.sqlalchemy.models.Allocation", "line_number": 63, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 63, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Consumer", "line_number": 64, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 64, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Allocation", "line_number": 65, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 65, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Consumer", "line_number": 65, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models.Project", "line_number": 66, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 66, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Consumer", "line_number": 67, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 67, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Project", "line_number": 67, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models.Project", "line_number": 68, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 68, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.User", "line_number": 70, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 70, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Consumer", "line_number": 71, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 71, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.User", "line_number": 71, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models.User", "line_number": 72, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 72, "usage_type": "name"}, {"api_name": "placement.db.sqlalchemy.models.Allocation", "line_number": 73, "usage_type": "attribute"}, {"api_name": "placement.db.sqlalchemy.models", "line_number": 73, "usage_type": "name"}, {"api_name": "placement.db_api.placement_context_manager", "line_number": 60, "usage_type": "attribute"}, {"api_name": "placement.db_api", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "230978398", "text": "from django import forms\nfrom sio.models import *\n\nclass CreateStudentForm(forms.ModelForm):\n class Meta:\n model = Student\n exclude = ()\n\n def clean_andrew_id(self):\n andrew_id = self.cleaned_data.get('andrew_id')\n if Student.objects.filter(andrew_id__exact=andrew_id):\n raise forms.ValidationError(\"AndrewID is already taken.\")\n return andrew_id\n\n\nclass CreateCourseForm(forms.ModelForm):\n class Meta:\n model = Course\n exclude = ('students', ) \n\n def clean_course_number(self):\n course_number = self.cleaned_data.get('course_number')\n if Course.objects.filter(course_number__exact=course_number):\n raise forms.ValidationError(\"Course %s already exists.\" % \n course_number)\n return course_number\n\n", "sub_path": "in-class/2016-09-28 ModelForms/hand-out code/sio/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 824, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.forms.ModelForm", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 4, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "18933372", "text": "import numpy as np\nfrom sklearn.manifold import MDS\nfrom sklearn.metrics import euclidean_distances\nimport scipy\nfrom matplotlib.offsetbox import OffsetImage, AnnotationBbox\nimport os\nfrom matplotlib.image import BboxImage\nfrom matplotlib.transforms import Bbox, TransformedBbox\nfrom pycocotools.coco import COCO\nfrom annotation_scatter import annotate_scatter\nimport shutil\n\ndef getImage(path):\n return OffsetImage(plt.imread(path, 0), zoom=0.1)\n\n\n\n\n\n# Generate a list of tags\n# possible_tags = pickle.load(open('possible_tags.pkl', 'rb'))\n\n# tags = []\n# logging.info('Testing: get embedding of all possible tags')\n# for tag in possible_tags:\n# tags.append(tag)\n\n\n\nfrom itertools import zip_longest\nimport matplotlib.pyplot as plt\nimport matplotlib\n\ndef grouper(n, iterable, fillvalue=None):\n \"grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx\"\n args = [iter(iterable)] * n\n return zip_longest(fillvalue=fillvalue, *args)\n\n\nf = open('/home/ubuntu/CCA-images-text/main/i2t_results.txt', 'r')\n# Array of top 5 tags for each image\nX = [np.array([line1, line2.replace(\" \", \"\").split(',')], dtype=object) for line1, line2 in grouper(2, f)]\n\n# Generate annotation tag for each image\n# ann_dict = {'kitchen_counter': ['kitchen', 'counter'], 'kitchen_refrigerator': ['kitchen', 'refrigerator']}\n# ann_dict = {'kitchen_counter': ['kitchen', 'counter']}\n# ann_dict = {'kitchen_refrigerator': ['kitchen', 'refrigerator']}\n# ann_dict = {'kitchen': ['kitchen'], 'bedroom': ['bedroom'], 'bathroom': ['bathroom'], 'living': ['living']}\n# ann_dict = {'kitchen': ['kitchen', 'island']}\n# ann_dict = {'living': ['living', 'fireplace']}\nann_dict = {'bathroom': ['bathroom']}\n# annot_list, indices_list = annotate_scatter(X, ann_list = [\"bathroom\"])\n# annot_list, indices_list = annotate_scatter(X, ann_dict = ann_dict)\nannot_list, indices_list = annotate_scatter(X, ann_dict = ann_dict)\n\n# annot_list, indices_list = annotate_scatter(X, [\"dog\", \"cat\"])\n# print(annot_list)\n# print(len(annot_list))\nprint(len(indices_list))\n\n\ndef gen_scatter_multi_tag(annot_list, indices_list):\n # Load score matrix\n scores_obj = np.load('/newvolume/score_matrix.npz')\n scores = scores_obj['scores']\n\n # Slice out the scores relating to the images tags with the relevant tags\n score_subset = list(map(scores.__getitem__, indices_list))\n\n # Generate MDS object\n mds = MDS(n_components=2, dissimilarity=\"precomputed\")\n\n # Calculate euclidean distance between each image word vector\n similarities = euclidean_distances(score_subset)\n\n pos = mds.fit(similarities).embedding_\n\n # label_list = ['kitchen counter', 'kitchen refrigerator']\n # label_list = ['kitchen refrigerator']\n # label_list = ['kitchen island', 'kitchen']\n label_list = ['bathroom']\n\n group = np.array(annot_list)\n\n # colors = {'kitchen counter':'red', 'kitchen refrigerator': 'blue'}\n # colors = {'kitchen island':'black', 'kitchen': 'red'}\n # colors = {'fireplace': 'black', 'living': 'yellow'}\n colors = {'bathroom': 'green'}\n\n col_list = [c for c in map(lambda x: colors[x],annot_list)]\n\n fig, ax = plt.subplots()\n\n scatter_x = np.array(pos[:,0])\n scatter_y = np.array(pos[:,1])\n\n################################################################################\n# # Uncomment to add coloured dots instead of images to scatter plot #############\n for g in np.unique(group):\n ix = np.where(group == g)\n ax.scatter(scatter_x[ix], scatter_y[ix], c = colors[g], label = g)\n ax.legend(loc='lower right')\n################################################################################\n\n################################################################################\n# Uncomment section below to add images instead of dots as points of scatter plot\n # Plot image instead of point\n # obtain file paths for each image\n annFile = '/newvolume/annotations/instances_val2014.json'\n coco_val = COCO(annFile)\n ids = coco_val.getAnnIds()\n annotations = coco_val.loadAnns(ids)\n\n img_info = {}\n for ann in annotations:\n image_id = ann['image_id']\n if image_id not in img_info:\n img_info[image_id] = coco_val.imgs[image_id]\n\n img_path_list = []\n for image_id, info in img_info.items():\n file_name = info['file_name']\n img = '/newvolume/val2014/' + file_name\n img_path_list.append(img)\n\n # # Slice out the relevant images\n img_subset = list(map(img_path_list.__getitem__, indices_list))\n\n print(len(img_subset))\n # dest = '/newvolume/kitchen_island'\n dest = '/newvolume/bathroom'\n # dest_super = '/newvolume/kitchen'\n dest_super = '/newvolume/bathroom'\n print(\"annot_list = \", annot_list)\n # dest = '/newvolume/mds_results'\n for g, path in zip(annot_list, img_subset):\n print(g)\n if g == 'living fireplace':\n shutil.copy(path, dest)\n elif g == 'bathroom':\n shutil.copy(path, dest_super)\n else:\n continue\n\n\n # for x0, y0, path in zip(scatter_x, scatter_y,img_subset):\n # print(path)\n # shutil.copy(path, dest)\n # ab = AnnotationBbox(getImage(path), (x0, y0), frameon=False)\n # ax.add_artist(ab)\n # plt.scatter(pos[:, 0], pos[:, 1], c= col_list)\n################################################################################\n # return ax\n\n plt.show()\n\n plt.savefig('/newvolume/images_bathroom.pdf')\n\ngen_scatter_multi_tag(annot_list, indices_list)\n\n\n\n\ndef gen_scatter_single_tag(annot_list, indices_list, ax = None):\n # Load score matrix\n scores_obj = np.load('/newvolume/score_matrix.npz')\n scores = scores_obj['scores']\n\n print(len(scores))\n\n # Slice out the scores relating to the images tags with the relevant tags\n score_subset = list(map(scores.__getitem__, indices_list))\n\n # Generate MDS object\n mds = MDS(n_components=2, dissimilarity=\"precomputed\")\n\n # Calculate euclidean distance between each image word vector\n similarities = euclidean_distances(score_subset)\n\n pos = mds.fit(similarities).embedding_\n print(len(pos))\n\n # fig = plt.figure(figsize=(12,10))\n\n # colors = ['red','blue','green','orange', 'black']\n # label_list = ['kitchen', 'bedroom', 'bathroom', 'living room']\n label_list = ['kitchen']\n # label_list = ['living_room']\n # label_list = ['bathroom']\n # label_list = ['dog', 'cat']\n\n group = np.array(annot_list)\n\n # colors = {'kitchen':'red', 'bedroom':'blue', 'bathroom':'green', 'living':'orange'}\n # colors = {'kitchen':'red'}\n colors = {'living':'yellow'}\n # colors = {'dog':'red', 'cat':'blue'}\n col_list = [c for c in map(lambda x: colors[x],annot_list)]\n print(len(col_list))\n print(col_list)\n if ax == None:\n fig, ax = plt.subplots()\n\n scatter_x = np.array(pos[:,0])\n scatter_y = np.array(pos[:,1])\n for g in np.unique(group):\n ix = np.where(group == g)\n ax.scatter(scatter_x[ix], scatter_y[ix], c = colors[g], label = g)\n\n################################################################################\n# Uncomment section below to add images instead of dots as points of scatter plot\n # Plot image instead of point\n # obtaine file paths for each image\n # annFile = '/newvolume/annotations/instances_val2014.json'\n # coco_val = COCO(annFile)\n # ids = coco_val.getAnnIds()\n # annotations = coco_val.loadAnns(ids)\n\n # img_info = {}\n # for ann in annotations:\n # image_id = ann['image_id']\n # if image_id not in img_info:\n # img_info[image_id] = coco_val.imgs[image_id]\n\n # img_path_list = []\n # for image_id, info in img_info.items():\n # file_name = info['file_name']\n # img = '/newvolume/val2014/' + file_name\n # img_path_list.append(img)\n\n # # Slice out the relevant images\n # img_subset = list(map(img_path_list.__getitem__, indices_list))\n\n\n # dest = '/newvolume/bathroom'\n # for x0, y0, path in zip(scatter_x, scatter_y,img_subset):\n # print(path)\n # # shutil.copy(path, dest)\n # ab = AnnotationBbox(getImage(path), (x0, y0), frameon=False)\n # ax.add_artist(ab)\n ################################################################################\n\n ax.legend(loc='lower right')\n # colors = {'kitchen':'red', 'bedroom':'blue', 'bathroom':'green', 'washroom':'black', 'tarmac': 'orange', 'notlabelled': 'white'}\n\n # col_list = [c for c in map(lambda x: colors[x],annot_list)]\n # plt.scatter(pos[:, 0], pos[:, 1], c= col_list)\n\n # col_list = [c for c in map(lambda x: colors[x],annot_list)]\n # plt.scatter(pos[:, 0], pos[:, 1], c= col_list)\n plt.show()\n\n # plt.savefig('/newvolume/images_room_type.pdf')\n plt.savefig('/newvolume/images_kitchens_and_kitchen_islands.pdf')\n\n#gen_scatter_single_tag(annot_superset, indices_key_superset, ax = ax_obj)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# import chart_studio.plotly as py\n# from plotly.offline import plot\n# import plotly.graph_objs as go\n\n# import numpy as np\n\n# from sklearn import manifold\n# from sklearn.metrics import euclidean_distances\n# from sklearn.decomposition import PCA\n# import matplotlib.pyplot as plt\n\n# n_samples = 20\n# seed = np.random.RandomState(seed=3)\n# X_true = seed.randint(0, 20, 2 * n_samples).astype(np.float)\n# X_true = X_true.reshape((n_samples, 2))\n# # Center the data\n# X_true -= X_true.mean()\n\n# similarities = euclidean_distances(X_true)\n\n# # Add noise to the similarities\n# noise = np.random.rand(n_samples, n_samples)\n# noise = noise + noise.T\n# noise[np.arange(noise.shape[0]), np.arange(noise.shape[0])] = 0\n# similarities += noise\n\n# mds = manifold.MDS(n_components=2, max_iter=3000, eps=1e-9, random_state=seed,\n# dissimilarity=\"precomputed\", n_jobs=1)\n# pos = mds.fit(similarities).embedding_\n\n# print(pos)\n\n# pos *= np.sqrt((X_true ** 2).sum()) / np.sqrt((pos ** 2).sum())\n# print(pos)\n\n# # Rotate the data\n# clf = PCA(n_components=2)\n# X_true = clf.fit_transform(X_true)\n\n# pos = clf.fit_transform(pos)\n\n\n\n# fig = plt.figure(figsize=(12,10))\n\n# plt.scatter(pos[:, 0], pos[:, 1])\n# plt.scatter(X_true[:, 0], X_true[:, 1])\n\n# plt.show()\n\n# data = []\n# p1 = go.Scatter(x=X_true[:, 0], y=X_true[:, 1],\n# mode='markers+lines',\n# marker=dict(color='navy', size=10),\n# line=dict(width=1),\n# name='True Position')\n# data.append(p1)\n# p2 = go.Scatter(x=pos[:, 0], y=pos[:, 1],\n# mode='markers+lines',\n# marker=dict(color='turquoise', size=10),\n# line=dict(width=1),\n# name='MDS')\n# data.append(p2)\n\n\n\n\n\n# layout = go.Layout(xaxis=dict(zeroline=False, showgrid=False,\n# ticks='', showticklabels=False),\n# yaxis=dict(zeroline=False, showgrid=False,\n# ticks='', showticklabels=False),\n", "sub_path": "main/mds_pca.py", "file_name": "mds_pca.py", "file_ext": "py", "file_size_in_byte": 10896, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "matplotlib.offsetbox.OffsetImage", "line_number": 14, "usage_type": "call"}, {"api_name": "itertools.zip_longest", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "annotation_scatter.annotate_scatter", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.manifold.MDS", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.metrics.euclidean_distances", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 100, "usage_type": "call"}, {"api_name": "pycocotools.coco.COCO", "line_number": 110, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 139, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 166, "usage_type": "call"}, {"api_name": "sklearn.manifold.MDS", "line_number": 175, "usage_type": "call"}, {"api_name": "sklearn.metrics.euclidean_distances", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 254, "usage_type": "name"}]} +{"seq_id": "7530512", "text": "\"\"\"\nModule designed to perform file and directory ops\n\"\"\"\n\nfrom io import BytesIO\nimport os\nimport shutil\nfrom zipfile import ZipFile\n\nUPLOADS_DIR = '/tmp/toUpload'\nXML_UNZIPPED_FOLDER = '/tmp/unzipped_xml'\nJSON_FOLDER = '/tmp/toUpload/json'\nAVRO_FOLDER = '/tmp/toUpload/avro'\nZIPPED_FILES = '/tmp/toUpload/zips'\n\n\ndef clean_tmp_dir():\n \"\"\"\n Clean temporal directory\n \"\"\"\n\n if os.path.exists(XML_UNZIPPED_FOLDER):\n shutil.rmtree(XML_UNZIPPED_FOLDER, ignore_errors=True)\n\n os.mkdir(XML_UNZIPPED_FOLDER)\n\n if os.path.exists(UPLOADS_DIR):\n shutil.rmtree(UPLOADS_DIR, ignore_errors=True)\n\n os.mkdir(UPLOADS_DIR)\n os.mkdir(JSON_FOLDER)\n os.mkdir(AVRO_FOLDER)\n\n\ndef get_signals_zipped_file(s3_client, file_path, landing_bucket):\n \"\"\"\n Get zipped file which contains signals in xml format\n :param s3_client: s3 client\n :param file_path: target file path\n :param landing_bucket: landing bucket name\n :return: a ZipFile which contains signals\n \"\"\"\n stream_body = (s3_client.Bucket(landing_bucket).Object(file_path).get())['Body'].read()\n in_memory = BytesIO(stream_body)\n return ZipFile(in_memory, \"r\")\n\n\ndef unzip_signals_file(zipped_file):\n \"\"\"\n Unzip signals zip file\n :param zipped_file: signals zip file\n :return: a signals list\n \"\"\"\n zipped_file.extractall(os.path.join(XML_UNZIPPED_FOLDER))\n zipped_file.close()\n return [os.path.join(XML_UNZIPPED_FOLDER, f) for f in os.listdir(XML_UNZIPPED_FOLDER) if\n os.path.isfile(os.path.join(XML_UNZIPPED_FOLDER, f))]\n\n\ndef get_signal_list(s3_client, bucket, key):\n \"\"\"\n Receive a bucket name and key and returns a signals list.\n :param s3_client:\n :param bucket:\n :param key:\n :return:\n \"\"\"\n zipped_file = get_signals_zipped_file(s3_client, key, bucket)\n signal_file_list = unzip_signals_file(zipped_file)\n return signal_file_list\n", "sub_path": "src/signal-preprocessor/file_handler.py", "file_name": "file_handler.py", "file_ext": "py", "file_size_in_byte": 1905, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 23, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 28, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 31, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 32, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 44, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "355483941", "text": "import logging\nimport torch.nn as nn\nfrom alphabet import Alphabet\nfrom options import opt\nimport norm_utils\nfrom data import build_pretrain_embedding, my_tokenize, load_data_fda\nfrom my_utils import random_embedding, freeze_net\nimport torch\nfrom torch.utils.data import DataLoader, Dataset\nimport numpy as np\nimport torch.optim as optim\nimport time\nimport os\nfrom data_structure import Entity\nimport torch.nn.functional as functional\nimport math\nimport numpy as np\nfrom stopword import stop_word\n\nclass DotAttentionLayer(nn.Module):\n def __init__(self, hidden_size):\n super(DotAttentionLayer, self).__init__()\n self.hidden_size = hidden_size\n self.W = nn.Linear(hidden_size, 1, bias=False)\n\n def forward(self, input):\n \"\"\"\n input: (unpacked_padded_output: batch_size x seq_len x hidden_size, lengths: batch_size)\n \"\"\"\n inputs, lengths = input\n batch_size, max_len, _ = inputs.size()\n flat_input = inputs.contiguous().view(-1, self.hidden_size)\n logits = self.W(flat_input).view(batch_size, max_len)\n alphas = functional.softmax(logits, dim=1)\n\n # computing mask\n idxes = torch.arange(0, max_len, out=torch.LongTensor(max_len)).unsqueeze(0)\n if opt.gpu >= 0 and torch.cuda.is_available():\n idxes = idxes.cuda(opt.gpu)\n mask = (idxes= 0 and torch.cuda.is_available():\n self.word_embedding = self.word_embedding.cuda(self.gpu)\n self.attn = self.attn.cuda(self.gpu)\n self.linear = self.linear.cuda(self.gpu)\n\n\n def forward(self, x, lengths):\n # length = x.size(1)\n x = self.word_embedding(x)\n x = self.word_drop(x)\n\n x = self.attn((x, lengths))\n # x = x.unsqueeze_(1)\n # x = functional.avg_pool2d(x, (length, 1))\n # x = x.squeeze_(1).squeeze_(1)\n\n x = self.linear(x)\n\n return x\n\n def loss(self, y_pred, y_gold):\n\n return self.criterion(y_pred, y_gold)\n\n\n\n def normalize(self, y_pred):\n\n return functional.softmax(y_pred, dim=1)\n\n def process_one_doc(self, doc, entities, dictionary, dictionary_reverse, isMeddra_dict):\n\n if isMeddra_dict:\n Xs, Ys = generate_instances(entities, self.word_alphabet, self.dict_alphabet)\n else:\n Xs, Ys = generate_instances_ehr(entities, self.word_alphabet, self.dict_alphabet, dictionary_reverse)\n\n data_loader = DataLoader(MyDataset(Xs, Ys), opt.batch_size, shuffle=False, collate_fn=my_collate)\n data_iter = iter(data_loader)\n num_iter = len(data_loader)\n\n entity_start = 0\n\n for i in range(num_iter):\n\n x, lengths, _ = next(data_iter)\n\n y_pred = self.forward(x, lengths)\n\n y_pred = self.normalize(y_pred)\n\n values, indices = torch.max(y_pred, 1)\n\n actual_batch_size = lengths.size(0)\n\n for batch_idx in range(actual_batch_size):\n entity = entities[entity_start+batch_idx]\n norm_id = norm_utils.get_dict_name(self.dict_alphabet, indices[batch_idx].item())\n if isMeddra_dict:\n name = dictionary[norm_id]\n entity.norm_ids.append(norm_id)\n entity.norm_names.append(name)\n else:\n concept = dictionary[norm_id]\n entity.norm_ids.append(norm_id)\n entity.norm_names.append(concept.names)\n\n if opt.ensemble == 'sum':\n entity.norm_confidences.append(y_pred[batch_idx].detach().cpu().numpy())\n else:\n entity.norm_confidences.append(values[batch_idx].item())\n\n entity.neural_id = norm_id\n\n entity_start += actual_batch_size\n\n\n\n\nclass MyDataset(Dataset):\n\n def __init__(self, X, Y):\n self.X = X\n self.Y = Y\n\n assert len(self.X) == len(self.Y), 'X and Y have different lengths'\n\n def __len__(self):\n return len(self.Y)\n\n def __getitem__(self, idx):\n return (self.X[idx], self.Y[idx])\n\ndef generate_instances(entities, word_alphabet, dict_alphabet):\n Xs = []\n Ys = []\n\n for entity in entities:\n if len(entity.norm_ids) > 0:\n Y = norm_utils.get_dict_index(dict_alphabet, entity.norm_ids[0]) # use the first id to generate instance\n if Y >= 0 and Y < norm_utils.get_dict_size(dict_alphabet): # for tac, can be none or oov ID\n Ys.append(Y)\n else:\n continue\n else:\n Ys.append(0)\n\n\n tokens = my_tokenize(entity.name)\n word_ids = []\n for token in tokens:\n if token in stop_word:\n continue\n token = norm_utils.word_preprocess(token)\n word_id = word_alphabet.get_index(token)\n word_ids.append(word_id)\n\n Xs.append(word_ids)\n\n return Xs, Ys\n\ndef generate_instances_ehr(entities, word_alphabet, dict_alphabet, dictionary_reverse):\n Xs = []\n Ys = []\n\n for entity in entities:\n if len(entity.norm_ids) > 0:\n if entity.norm_ids[0] in dictionary_reverse:\n cui_list = dictionary_reverse[entity.norm_ids[0]]\n Y = norm_utils.get_dict_index(dict_alphabet, cui_list[0]) # use the first id to generate instance\n if Y >= 0 and Y < norm_utils.get_dict_size(dict_alphabet):\n Ys.append(Y)\n else:\n raise RuntimeError(\"entity {}, {}, cui not in dict_alphabet\".format(entity.id, entity.name))\n else:\n logging.debug(\"entity {}, {}, can't map to umls, ignored\".format(entity.id, entity.name))\n continue\n else:\n Ys.append(0)\n\n\n tokens = my_tokenize(entity.name)\n word_ids = []\n for token in tokens:\n if token in stop_word:\n continue\n token = norm_utils.word_preprocess(token)\n word_id = word_alphabet.get_index(token)\n word_ids.append(word_id)\n\n Xs.append(word_ids)\n\n return Xs, Ys\n\ndef my_collate(batch):\n x, y = zip(*batch)\n\n x, lengths, y = pad(x, y)\n\n if opt.gpu >= 0 and torch.cuda.is_available():\n x = x.cuda(opt.gpu)\n lengths = lengths.cuda(opt.gpu)\n y = y.cuda(opt.gpu)\n return x, lengths, y\n\ndef pad(x, y):\n tokens = x\n\n lengths = [len(row) for row in tokens]\n max_len = max(lengths)\n\n tokens = pad_sequence(tokens, max_len)\n lengths = torch.LongTensor(lengths)\n\n y = torch.LongTensor(y).view(-1)\n\n\n return tokens, lengths, y\n\n\ndef pad_sequence(x, max_len):\n\n padded_x = np.zeros((len(x), max_len), dtype=np.int)\n for i, row in enumerate(x):\n padded_x[i][:len(row)] = row\n\n padded_x = torch.LongTensor(padded_x)\n\n return padded_x\n\ndef generate_dict_instances(dictionary, dict_alphabet, word_alphabet, isMeddra_dict):\n Xs = []\n Ys = []\n\n if isMeddra_dict:\n for concept_id, concept_name in dictionary.items():\n\n Y = norm_utils.get_dict_index(dict_alphabet, concept_id)\n if Y >= 0 and Y < norm_utils.get_dict_size(dict_alphabet):\n Ys.append(Y)\n else:\n continue\n\n\n tokens = my_tokenize(concept_name)\n word_ids = []\n for token in tokens:\n if token in stop_word:\n continue\n token = norm_utils.word_preprocess(token)\n word_id = word_alphabet.get_index(token)\n word_ids.append(word_id)\n\n Xs.append(word_ids)\n else :\n for concept_id, concept in dictionary.items():\n Y = norm_utils.get_dict_index(dict_alphabet, concept_id)\n if Y >= 0 and Y < norm_utils.get_dict_size(dict_alphabet):\n pass\n else:\n continue\n\n # for concept_name in concept.names:\n #\n # tokens = my_tokenize(concept_name)\n # word_ids = []\n # for token in tokens:\n # token = norm_utils.word_preprocess(token)\n # word_id = word_alphabet.get_index(token)\n # word_ids.append(word_id)\n #\n # Ys.append(Y)\n # Xs.append(word_ids)\n\n\n tokens = my_tokenize(concept.names[0])\n word_ids = []\n for token in tokens:\n if token in stop_word:\n continue\n token = norm_utils.word_preprocess(token)\n word_id = word_alphabet.get_index(token)\n word_ids.append(word_id)\n\n Ys.append(Y)\n Xs.append(word_ids)\n\n\n return Xs, Ys\n\n\ndef dict_pretrain(dictionary, dictionary_reverse, d, isMeddra_dict, optimizer, neural_model):\n logging.info('use dict pretrain ...')\n\n dict_Xs, dict_Ys = generate_dict_instances(dictionary, neural_model.dict_alphabet, neural_model.word_alphabet, isMeddra_dict)\n\n data_loader = DataLoader(MyDataset(dict_Xs, dict_Ys), opt.batch_size, shuffle=True, collate_fn=my_collate)\n\n expected_accuracy = int(d.config['norm_neural_pretrain_accuracy'])\n\n logging.info(\"start dict pretraining ...\")\n\n bad_counter = 0\n best_accuracy = 0\n\n for idx in range(9999):\n epoch_start = time.time()\n\n neural_model.train()\n\n correct, total = 0, 0\n\n sum_loss = 0\n\n train_iter = iter(data_loader)\n num_iter = len(data_loader)\n\n for i in range(num_iter):\n\n x, lengths, y = next(train_iter)\n\n y_pred = neural_model.forward(x, lengths)\n\n l = neural_model.loss(y_pred, y)\n\n sum_loss += l.item()\n\n l.backward()\n\n if opt.gradient_clip > 0:\n torch.nn.utils.clip_grad_norm_(neural_model.parameters(), opt.gradient_clip)\n optimizer.step()\n neural_model.zero_grad()\n\n total += y.size(0)\n _, pred = torch.max(y_pred, 1)\n correct += (pred == y).sum().item()\n\n epoch_finish = time.time()\n accuracy = 100.0 * correct / total\n logging.info(\"epoch: %s pretraining finished. Time: %.2fs. loss: %.4f Accuracy %.2f\" % (\n idx, epoch_finish - epoch_start, sum_loss / num_iter, accuracy))\n\n\n if accuracy > expected_accuracy:\n logging.info(\"Exceed {}% training accuracy, breaking ... \".format(expected_accuracy))\n break\n\n if accuracy > best_accuracy:\n best_accuracy = accuracy\n bad_counter = 0\n else:\n bad_counter += 1\n\n if bad_counter >= opt.patience:\n logging.info('Early Stop!')\n break\n\n return\n\n\ndef train(train_data, dev_data, test_data, d, dictionary, dictionary_reverse, opt, fold_idx, isMeddra_dict):\n logging.info(\"train the neural-based normalization model ...\")\n\n external_train_data = []\n if d.config.get('norm_ext_corpus') is not None:\n for k, v in d.config['norm_ext_corpus'].items():\n if k == 'tac':\n external_train_data.extend(load_data_fda(v['path'], True, v.get('types'), v.get('types'), False, True))\n else:\n raise RuntimeError(\"not support external corpus\")\n if len(external_train_data) != 0:\n train_data.extend(external_train_data)\n\n logging.info(\"build alphabet ...\")\n word_alphabet = Alphabet('word')\n norm_utils.build_alphabet_from_dict(word_alphabet, dictionary, isMeddra_dict)\n norm_utils.build_alphabet(word_alphabet, train_data)\n if opt.dev_file:\n norm_utils.build_alphabet(word_alphabet, dev_data)\n if opt.test_file:\n norm_utils.build_alphabet(word_alphabet, test_data)\n norm_utils.fix_alphabet(word_alphabet)\n logging.info(\"alphabet size {}\".format(word_alphabet.size()))\n\n\n if d.config.get('norm_emb') is not None:\n logging.info(\"load pretrained word embedding ...\")\n pretrain_word_embedding, word_emb_dim = build_pretrain_embedding(d.config.get('norm_emb'),\n word_alphabet,\n opt.word_emb_dim, False)\n word_embedding = nn.Embedding(word_alphabet.size(), word_emb_dim, padding_idx=0)\n word_embedding.weight.data.copy_(torch.from_numpy(pretrain_word_embedding))\n embedding_dim = word_emb_dim\n else:\n logging.info(\"randomly initialize word embedding ...\")\n word_embedding = nn.Embedding(word_alphabet.size(), d.word_emb_dim, padding_idx=0)\n word_embedding.weight.data.copy_(\n torch.from_numpy(random_embedding(word_alphabet.size(), d.word_emb_dim)))\n embedding_dim = d.word_emb_dim\n\n\n\n dict_alphabet = Alphabet('dict')\n norm_utils.init_dict_alphabet(dict_alphabet, dictionary)\n norm_utils.fix_alphabet(dict_alphabet)\n\n neural_model = NeuralNormer(word_alphabet, word_embedding, embedding_dim, dict_alphabet)\n\n train_X = []\n train_Y = []\n for doc in train_data:\n\n if isMeddra_dict:\n temp_X, temp_Y = generate_instances(doc.entities, word_alphabet, dict_alphabet)\n else:\n temp_X, temp_Y = generate_instances_ehr(doc.entities, word_alphabet, dict_alphabet, dictionary_reverse)\n train_X.extend(temp_X)\n train_Y.extend(temp_Y)\n\n\n train_loader = DataLoader(MyDataset(train_X, train_Y), opt.batch_size, shuffle=True, collate_fn=my_collate)\n\n optimizer = optim.Adam(neural_model.parameters(), lr=opt.lr, weight_decay=opt.l2)\n\n if opt.tune_wordemb == False:\n freeze_net(neural_model.word_embedding)\n\n if d.config['norm_neural_pretrain'] == '1':\n dict_pretrain(dictionary, dictionary_reverse, d, isMeddra_dict, optimizer, neural_model)\n\n\n best_dev_f = -10\n best_dev_p = -10\n best_dev_r = -10\n\n bad_counter = 0\n\n logging.info(\"start training ...\")\n\n for idx in range(opt.iter):\n epoch_start = time.time()\n\n neural_model.train()\n\n train_iter = iter(train_loader)\n num_iter = len(train_loader)\n\n sum_loss = 0\n\n correct, total = 0, 0\n\n for i in range(num_iter):\n\n x, lengths, y = next(train_iter)\n\n y_pred = neural_model.forward(x, lengths)\n\n l = neural_model.loss(y_pred, y)\n # debug feili\n # if np.any(np.isnan(l.item())):\n # logging.info(\"loss: {}\".format(l.item()))\n # exit()\n\n sum_loss += l.item()\n\n l.backward()\n\n if opt.gradient_clip > 0:\n torch.nn.utils.clip_grad_norm_(neural_model.parameters(), opt.gradient_clip)\n optimizer.step()\n neural_model.zero_grad()\n\n total += y.size(0)\n _, pred = torch.max(y_pred, 1)\n correct += (pred == y).sum().item()\n\n epoch_finish = time.time()\n accuracy = 100.0 * correct / total\n logging.info(\"epoch: %s training finished. Time: %.2fs. loss: %.4f Accuracy %.2f\" % (\n idx, epoch_finish - epoch_start, sum_loss / num_iter, accuracy))\n\n if opt.dev_file:\n p, r, f = norm_utils.evaluate(dev_data, dictionary, dictionary_reverse, None, neural_model, None, d, isMeddra_dict)\n logging.info(\"Dev: p: %.4f, r: %.4f, f: %.4f\" % (p, r, f))\n else:\n f = best_dev_f\n\n if f > best_dev_f:\n logging.info(\"Exceed previous best f score on dev: %.4f\" % (best_dev_f))\n\n if fold_idx is None:\n torch.save(neural_model, os.path.join(opt.output, \"norm_neural.pkl\"))\n else:\n torch.save(neural_model, os.path.join(opt.output, \"norm_neural_{}.pkl\".format(fold_idx+1)))\n\n best_dev_f = f\n best_dev_p = p\n best_dev_r = r\n\n bad_counter = 0\n else:\n bad_counter += 1\n\n if len(opt.dev_file) != 0 and bad_counter >= opt.patience:\n logging.info('Early Stop!')\n break\n\n logging.info(\"train finished\")\n\n if len(opt.dev_file) == 0:\n torch.save(neural_model, os.path.join(opt.output, \"norm_neural.pkl\"))\n\n return best_dev_p, best_dev_r, best_dev_f\n\n\n\n", "sub_path": "norm_neural.py", "file_name": "norm_neural.py", "file_ext": "py", "file_size_in_byte": 17153, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torch.nn.Module", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.arange", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 37, "usage_type": "call"}, {"api_name": "options.opt.gpu", "line_number": 38, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 38, "usage_type": "attribute"}, {"api_name": "options.opt.gpu", "line_number": 39, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.sum", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "options.opt.gpu", "line_number": 56, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "options.opt.dropout", "line_number": 57, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "norm_utils.get_dict_size", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "options.opt.gpu", "line_number": 62, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.softmax", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 99, "usage_type": "call"}, {"api_name": "options.opt.batch_size", "line_number": 99, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 113, "usage_type": "call"}, {"api_name": "norm_utils.get_dict_name", "line_number": 119, "usage_type": "call"}, {"api_name": "options.opt.ensemble", "line_number": 129, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 129, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 141, "usage_type": "name"}, {"api_name": "norm_utils.get_dict_index", "line_number": 161, "usage_type": "call"}, {"api_name": "norm_utils.get_dict_size", "line_number": 162, "usage_type": "call"}, {"api_name": "data.my_tokenize", "line_number": 170, "usage_type": "call"}, {"api_name": "stopword.stop_word", "line_number": 173, "usage_type": "name"}, {"api_name": "norm_utils.word_preprocess", "line_number": 175, "usage_type": "call"}, {"api_name": "norm_utils.get_dict_index", "line_number": 191, "usage_type": "call"}, {"api_name": "norm_utils.get_dict_size", "line_number": 192, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 197, "usage_type": "call"}, {"api_name": "data.my_tokenize", "line_number": 203, "usage_type": "call"}, {"api_name": "stopword.stop_word", "line_number": 206, "usage_type": "name"}, {"api_name": "norm_utils.word_preprocess", "line_number": 208, "usage_type": "call"}, {"api_name": "options.opt.gpu", "line_number": 221, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 221, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 221, "usage_type": "attribute"}, {"api_name": "options.opt.gpu", "line_number": 222, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 222, "usage_type": "name"}, {"api_name": "options.opt.gpu", "line_number": 223, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 223, "usage_type": "name"}, {"api_name": "options.opt.gpu", "line_number": 224, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 224, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 234, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 244, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 248, "usage_type": "call"}, {"api_name": "norm_utils.get_dict_index", "line_number": 259, "usage_type": "call"}, {"api_name": "norm_utils.get_dict_size", "line_number": 260, "usage_type": "call"}, {"api_name": "data.my_tokenize", "line_number": 266, "usage_type": "call"}, {"api_name": "stopword.stop_word", "line_number": 269, "usage_type": "name"}, {"api_name": "norm_utils.word_preprocess", "line_number": 271, "usage_type": "call"}, {"api_name": "norm_utils.get_dict_index", "line_number": 278, "usage_type": "call"}, {"api_name": "norm_utils.get_dict_size", "line_number": 279, "usage_type": "call"}, {"api_name": "data.my_tokenize", "line_number": 297, "usage_type": "call"}, {"api_name": "stopword.stop_word", "line_number": 300, "usage_type": "name"}, {"api_name": "norm_utils.word_preprocess", "line_number": 302, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 318, "usage_type": "call"}, {"api_name": "options.opt.batch_size", "line_number": 318, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 318, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 322, "usage_type": "call"}, {"api_name": "time.time", "line_number": 328, "usage_type": "call"}, {"api_name": "options.opt.gradient_clip", "line_number": 351, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 351, "usage_type": "name"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 352, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 352, "usage_type": "attribute"}, {"api_name": "options.opt.gradient_clip", "line_number": 352, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 352, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 357, "usage_type": "call"}, {"api_name": "time.time", "line_number": 360, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 362, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 367, "usage_type": "call"}, {"api_name": "options.opt.patience", "line_number": 376, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 376, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 377, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 384, "usage_type": "call"}, {"api_name": "data.load_data_fda", "line_number": 390, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 396, "usage_type": "call"}, {"api_name": "alphabet.Alphabet", "line_number": 397, "usage_type": "call"}, {"api_name": "norm_utils.build_alphabet_from_dict", "line_number": 398, "usage_type": "call"}, {"api_name": "norm_utils.build_alphabet", "line_number": 399, "usage_type": "call"}, {"api_name": "options.opt.dev_file", "line_number": 400, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 400, "usage_type": "name"}, {"api_name": "norm_utils.build_alphabet", "line_number": 401, "usage_type": "call"}, {"api_name": "options.opt.test_file", "line_number": 402, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 402, "usage_type": "name"}, {"api_name": "norm_utils.build_alphabet", "line_number": 403, "usage_type": "call"}, {"api_name": "norm_utils.fix_alphabet", "line_number": 404, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 405, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 409, "usage_type": "call"}, {"api_name": "data.build_pretrain_embedding", "line_number": 410, "usage_type": "call"}, {"api_name": "options.opt.word_emb_dim", "line_number": 412, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 412, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 413, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 413, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 414, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 417, "usage_type": "call"}, {"api_name": "torch.nn.Embedding", "line_number": 418, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 418, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 420, "usage_type": "call"}, {"api_name": "my_utils.random_embedding", "line_number": 420, "usage_type": "call"}, {"api_name": "alphabet.Alphabet", "line_number": 425, "usage_type": "call"}, {"api_name": "norm_utils.init_dict_alphabet", "line_number": 426, "usage_type": "call"}, {"api_name": "norm_utils.fix_alphabet", "line_number": 427, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 443, "usage_type": "call"}, {"api_name": "options.opt.batch_size", "line_number": 443, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 443, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 445, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 445, "usage_type": "name"}, {"api_name": "options.opt.lr", "line_number": 445, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 445, "usage_type": "name"}, {"api_name": "options.opt.l2", "line_number": 445, "usage_type": "attribute"}, {"api_name": "options.opt.tune_wordemb", "line_number": 447, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 447, "usage_type": "name"}, {"api_name": "my_utils.freeze_net", "line_number": 448, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 460, "usage_type": "call"}, {"api_name": "options.opt.iter", "line_number": 462, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 462, "usage_type": "name"}, {"api_name": "time.time", "line_number": 463, "usage_type": "call"}, {"api_name": "options.opt.gradient_clip", "line_number": 490, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 490, "usage_type": "name"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 491, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 491, "usage_type": "attribute"}, {"api_name": "options.opt.gradient_clip", "line_number": 491, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 491, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 496, "usage_type": "call"}, {"api_name": "time.time", "line_number": 499, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 501, "usage_type": "call"}, {"api_name": "options.opt.dev_file", "line_number": 504, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 504, "usage_type": "name"}, {"api_name": "norm_utils.evaluate", "line_number": 505, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 506, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 511, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 514, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 514, "usage_type": "call"}, {"api_name": "os.path", "line_number": 514, "usage_type": "attribute"}, {"api_name": "options.opt.output", "line_number": 514, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 514, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 516, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 516, "usage_type": "call"}, {"api_name": "os.path", "line_number": 516, "usage_type": "attribute"}, {"api_name": "options.opt.output", "line_number": 516, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 516, "usage_type": "name"}, {"api_name": "options.opt.dev_file", "line_number": 526, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 526, "usage_type": "name"}, {"api_name": "options.opt.patience", "line_number": 526, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 527, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 530, "usage_type": "call"}, {"api_name": "options.opt.dev_file", "line_number": 532, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 532, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 533, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 533, "usage_type": "call"}, {"api_name": "os.path", "line_number": 533, "usage_type": "attribute"}, {"api_name": "options.opt.output", "line_number": 533, "usage_type": "attribute"}, {"api_name": "options.opt", "line_number": 533, "usage_type": "name"}]} +{"seq_id": "506156129", "text": "#!/usr/bin/env python3\n\n# Copyright (C) 2017-2019 sunborn23@github.com\n# Copyright (C) 2019 CDMIUB@github.com\n\nimport configparser\nimport datetime\nimport email\nimport email.header\nimport email.mime.text\nimport email.mime.multipart\nimport imaplib\nimport os\nimport re\nimport smtplib\nimport sys\nfrom _socket import gaierror\n\nconfig = None\nconfig_file_path = \"autoresponder.config.ini\"\nincoming_mail_server = None\noutgoing_mail_server = None\nstatistics = {\n \"start_time\": datetime.datetime.now(),\n \"mails_loading_error\": 0,\n \"mails_total\": 0,\n \"mails_processed\": 0,\n \"mails_in_trash\": 0,\n \"mails_wrong_sender\": 0\n}\n\n\ndef run():\n get_config_file_path()\n initialize_configuration()\n connect_to_mail_servers()\n check_folder_names()\n check_local_path()\n mails = fetch_emails()\n for mail in mails:\n process_email(mail)\n log_statistics()\n shutdown(0)\n\n\ndef get_config_file_path():\n if \"--help\" in sys.argv or \"-h\" in sys.argv:\n display_help_text()\n if \"--config-path\" in sys.argv and len(sys.argv) >= 3:\n global config_file_path\n config_file_path = sys.argv[2]\n if not os.path.isfile(config_file_path):\n shutdown_with_error(\"Configuration file not found. Expected it at '\" + config_file_path + \"'.\")\n\n\ndef initialize_configuration():\n try:\n config_file = configparser.ConfigParser()\n config_file.read(config_file_path, encoding=\"UTF-8\")\n global config\n config = {\n 'in.user': cast(config_file[\"login credentials\"][\"mailserver.incoming.username\"], str),\n 'in.pw': cast(config_file[\"login credentials\"][\"mailserver.incoming.password\"], str),\n 'out.user': cast(config_file[\"login credentials\"][\"mailserver.outgoing.username\"], str),\n 'out.pw': cast(config_file[\"login credentials\"][\"mailserver.outgoing.password\"], str),\n 'display.name': cast(config_file[\"login credentials\"][\"mailserver.outgoing.display.name\"], str),\n 'display.mail': cast(config_file[\"login credentials\"][\"mailserver.outgoing.display.mail\"], str),\n 'in.host': cast(config_file[\"mail server settings\"][\"mailserver.incoming.imap.host\"], str),\n 'in.port': cast(config_file[\"mail server settings\"][\"mailserver.incoming.imap.port.ssl\"], str),\n 'out.host': cast(config_file[\"mail server settings\"][\"mailserver.outgoing.smtp.host\"], str),\n 'out.port': cast(config_file[\"mail server settings\"][\"mailserver.outgoing.smtp.port.tls\"], str),\n 'folders.inbox': cast(config_file[\"mail server settings\"][\"mailserver.incoming.folders.inbox.name\"], str),\n 'folders.trash': cast(config_file[\"mail server settings\"][\"mailserver.incoming.folders.trash.name\"], str),\n 'request.from': cast(config_file[\"mail content settings\"][\"mail.request.from\"], str),\n 'reply.subject': cast(config_file[\"mail content settings\"][\"mail.reply.subject\"], str).strip(),\n 'reply.body': cast(config_file[\"mail content settings\"][\"mail.reply.body\"], str).strip(),\n }\n except KeyError as e:\n shutdown_with_error(\"Configuration file is invalid! (Key not found: \" + str(e) + \")\")\n depends = {\n 'nothing': None,\n 'delete': None,\n 'forward': 'post.address',\n 'move': 'post.folder',\n 'download': 'post.path',\n }\n try:\n config['post.action'] = cast(config_file[\"post-reply action settings\"][\"post.action\"], str).strip()\n if config['post.action'] not in depends:\n shutdown_with_error(\"Post-reply action {} is invalid!\".format(config['post.action']))\n except KeyError:\n config['post.action'] = 'nothing'\n \n dkey=depends[config['post.action']]\n if dkey is not None:\n try:\n config[dkey]= cast(config_file[\"post-reply action settings\"][dkey], str).strip()\n except KeyError as e:\n shutdown_with_error(\"Configuration file is invalid! (post.action = \"+config['post.action']+\" reqires \"+dkey)\n\n\ndef connect_to_mail_servers():\n connect_to_imap()\n connect_to_smtp()\n\n\ndef check_folder_names():\n global incoming_mail_server\n global outgoing_mail_server\n (retcode, msg_count) = incoming_mail_server.select(config['folders.inbox'])\n if retcode != \"OK\" or re.match('[^0-9]',msg_count[0].decode()):\n shutdown_with_error(\"Inbox folder does not exist: \" + config['folders.inbox'])\n (retcode, msg_count) = incoming_mail_server.select(config['folders.trash'])\n if retcode != \"OK\" or re.match('[^0-9]',msg_count[0].decode()):\n shutdown_with_error(\"Trash folder does not exist: \" + config['folders.trash'])\n if 'post.folder' not in config: \n return()\n (retcode, msg_count) = incoming_mail_server.select(config['post.folder'])\n if retcode != \"OK\" or re.match('[^0-9]',msg_count[0].decode()):\n shutdown_with_error(\"Destination folder does not exist: \" + config['post.folder'])\n\n\ndef connect_to_imap():\n global incoming_mail_server\n try:\n do_connect_to_imap()\n except gaierror:\n shutdown_with_error(\"IMAP connection failed! Specified host not found.\")\n except imaplib.IMAP4_SSL.error as e:\n shutdown_with_error(\"IMAP login failed! Reason: '\" + cast(e.args[0], str, 'UTF-8') + \"'.\")\n except Exception as e:\n shutdown_with_error(\"IMAP connection/login failed! Reason: '\" + cast(e, str) + \"'.\")\n\n\ndef do_connect_to_imap():\n global incoming_mail_server\n incoming_mail_server = imaplib.IMAP4_SSL(config['in.host'], config['in.port'])\n (retcode, capabilities) = incoming_mail_server.login(config['in.user'], config['in.pw'])\n if retcode != \"OK\":\n shutdown_with_error(\"IMAP login failed! Return code: '\" + cast(retcode, str) + \"'.\")\n\n\ndef connect_to_smtp():\n global outgoing_mail_server\n try:\n do_connect_to_smtp()\n except gaierror:\n shutdown_with_error(\"SMTP connection failed! Specified host not found.\")\n except smtplib.SMTPAuthenticationError as e:\n shutdown_with_error(\"SMTP login failed! Reason: '\" + cast(e.smtp_error, str, 'UTF-8') + \"'.\")\n except Exception as e:\n shutdown_with_error(\"SMTP connection/login failed! Reason: '\" + cast(e, str) + \"'.\")\n\n\ndef do_connect_to_smtp():\n global outgoing_mail_server\n outgoing_mail_server = smtplib.SMTP(config['out.host'], config['out.port'])\n outgoing_mail_server.starttls()\n (retcode, capabilities) = outgoing_mail_server.login(config['out.user'], config['out.pw'])\n if not (retcode == 235 or retcode == 250):\n shutdown_with_error(\"SMTP login failed! Return code: '\" + str(retcode) + \"'.\")\n\n\ndef fetch_emails():\n global incoming_mail_server\n global outgoing_mail_server\n # get the message ids from the inbox folder\n incoming_mail_server.select(config['folders.inbox'])\n (retcode, message_indices) = incoming_mail_server.search(None, 'ALL')\n if retcode == 'OK':\n messages = []\n for message_index in message_indices[0].split():\n # get the actual message for the current index\n (retcode, data) = incoming_mail_server.fetch(message_index, '(RFC822)')\n if retcode == 'OK':\n # parse the message into a useful format\n message = email.message_from_string(data[0][1].decode('utf-8'))\n (retcode, data) = incoming_mail_server.fetch(message_index, \"(UID)\")\n if retcode == 'OK':\n mail_uid = parse_uid(cast(data[0], str, 'UTF-8'))\n message['mailserver_email_uid'] = mail_uid\n messages.append(message)\n else:\n statistics['mails_loading_error'] += 1\n log_warning(\"Failed to get UID for email with index '\" + message_index + \"'.\")\n else:\n statistics['mails_loading_error'] += 1\n log_warning(\"Failed to get email with index '\" + message_index + \"'.\")\n statistics['mails_total'] = len(messages)\n return messages\n else:\n return []\n\n\ndef process_email(mail):\n# try:\n mail_from = email.header.decode_header(mail['From'])\n mail_sender = mail_from[-1]\n mail_sender = cast(mail_sender[0], str, 'UTF-8')\n if config['request.from'] in mail_sender or config['request.from'] == '':\n reply_to_email(mail)\n if config['post.action'] == 'delete':\n delete_email(mail)\n elif config['post.action'] == 'forward':\n forward_email(mail)\n elif config['post.action'] == 'move':\n move_email(mail)\n elif config['post.action'] == 'download':\n download_email(mail)\n else:\n pass\n else:\n statistics['mails_wrong_sender'] += 1\n statistics['mails_processed'] += 1\n# except Exception as e:\n# log_warning(\"Unexpected error while processing email: '\" + str(e) + \"'.\")\n\n\ndef reply_to_email(mail):\n global outgoing_mail_server\n try:\n receiver_emails = email.header.decode_header(mail['Reply-To'])\n except TypeError:\n receiver_emails = email.header.decode_header(mail['From'])\n #get actual email adress, in case field entry is in form \"John Doe \" \n for x,e in receiver_emails:\n e = 'utf-8' if e is None else e\n y = x.decode(e) if isinstance(x,bytes) else x\n if '@' in y:\n receiver_email = y\n break\n message = email.mime.text.MIMEText(config['reply.body'])\n message['Subject'] = config['reply.subject']\n message['To'] = receiver_email\n message['From'] = email.utils.formataddr((\n cast(email.header.Header(config['display.name'], 'utf-8'), str), config['display.mail']))\n outgoing_mail_server.sendmail(config['display.mail'], receiver_email, message.as_string())\n\ndef forward_email(mail):\n global outgoing_mail_server\n sender = email.header.decode_header(mail['From'])\n parts = []\n for x,e in sender :\n e = 'utf-8' if e is None else e\n y = x.decode(e) if isinstance(x,bytes) else x\n parts.append(y)\n subject = mail['Subject']\n prefix = '{} (from {})'.format(subject,' '.join(parts)) \n receiver_email = config['post.address']\n message = mail #email.message_from_string(mail.as_string())\n message.replace_header('Subject', prefix)\n message.replace_header(\"To\", receiver_email)\n message.replace_header(\"From\", email.utils.formataddr((\n cast(email.header.Header(config['display.name'], 'utf-8'), str), config['display.mail'])))\n outgoing_mail_server.sendmail(config['display.mail'], receiver_email, message.as_string().encode('utf-8'))\n delete_email(mail)\n\ndef move_email(mail):\n global incoming_mail_server\n mail_uid=mail['mailserver_email_uid']\n retcode,_ = incoming_mail_server.uid('COPY', mail_uid, config['post.folder'])\n if retcode != \"OK\":\n shutdown_with_error(\"Failed moving message to folder: \" + config['post.folder'])\n else:\n delete_email(mail)\n\ndef check_local_path():\n if not 'post.path' in config:\n return()\n path = config['post.path']\n if not os.path.isdir(path):\n shutdown_with_error(\"Local directory does not exist: \"+path)\n if not os.access(path, os.W_OK):\n shutdown_with_error(\"Cannot write to local directory: \"+path)\n \ndef download_email(mail):\n subject = email.header.decode_header(mail['Subject'])\n parts = []\n for x,e in subject :\n e = 'utf-8' if e is None else e\n y = x.decode(e) if isinstance(x,bytes) else x\n parts.append(y)\n short='_'.join(parts[0:min(len(y),5)])\n mail_uid=mail['mailserver_email_uid']\n filename = '{}_{}.txt'.format(mail_uid,short)\n path=os.path.join(config['post.path'],filename)\n with open(path,'wb') as f:\n f.write(mail.as_string().encode('utf-8'))\n delete_email(mail)\n\ndef delete_email(mail):\n global incoming_mail_server\n result = incoming_mail_server.uid('COPY', mail['mailserver_email_uid'], config['folders.trash'])\n if result[0] == \"OK\":\n statistics['mails_in_trash'] += 1\n else:\n log_warning(\"Copying email to trash failed. Reason: \" + str(result))\n incoming_mail_server.uid('STORE', mail['mailserver_email_uid'], '+FLAGS', '(\\Deleted)')\n incoming_mail_server.expunge()\n\n\ndef parse_uid(data):\n pattern_uid = re.compile('\\d+ \\(UID (?P\\d+)\\)')\n match = pattern_uid.match(data)\n return match.group('uid')\n\n\ndef cast(obj, to_type, options=None):\n try:\n if options is None:\n return to_type(obj)\n else:\n return to_type(obj, options)\n except ValueError and TypeError:\n return obj\n\n\ndef shutdown_with_error(message):\n message = \"Error! \" + str(message)\n message += \"\\nCurrent configuration file path: '\" + str(config_file_path) + \"'.\"\n if config is not None:\n message += \"\\nCurrent configuration: \" + str(config)\n print(message)\n shutdown(1)\n\n\ndef log_warning(message):\n print(\"Warning! \" + message)\n\n\ndef log_statistics():\n run_time = datetime.datetime.now() - statistics['start_time']\n total_mails = statistics['mails_total']\n loading_errors = statistics['mails_loading_error']\n wrong_sender_count = statistics['mails_wrong_sender']\n processing_errors = total_mails - statistics['mails_processed']\n moving_errors = statistics['mails_processed'] - statistics['mails_in_trash'] - statistics['mails_wrong_sender']\n total_warnings = loading_errors + processing_errors + moving_errors\n message = \"Executed \"\n message += \"without warnings \" if total_warnings is 0 else \"with \" + str(total_warnings) + \" warnings \"\n message += \"in \" + str(run_time.total_seconds()) + \" seconds. \"\n message += \"Found \" + str(total_mails) + \" emails in inbox\"\n message += \". \" if wrong_sender_count is 0 else \" with \" + str(wrong_sender_count) + \" emails from wrong senders. \"\n message += \"Processed \" + str(statistics['mails_processed']) + \\\n \" emails, replied to \" + str(total_mails - wrong_sender_count) + \" emails. \"\n if total_warnings is not 0:\n message += \"Encountered \" + str(loading_errors) + \" errors while loading emails, \" + \\\n str(processing_errors) + \" errors while processing emails and \" + \\\n str(moving_errors) + \" errors while moving emails to trash.\"\n print(message)\n\n\ndef display_help_text():\n print(\"Options:\")\n print(\"\\t--help: Display this help information\")\n print(\"\\t--config-path : \"\n \"Override path to config file (defaults to same directory as the script is)\")\n exit(0)\n\n\ndef shutdown(error_code):\n if incoming_mail_server is not None:\n try:\n incoming_mail_server.close()\n except Exception:\n pass\n try:\n incoming_mail_server.logout()\n except Exception:\n pass\n if outgoing_mail_server is not None:\n try:\n outgoing_mail_server.quit()\n except Exception:\n pass\n if error_code != 0:\n raise SystemExit\n\n\nrun()\n", "sub_path": "run_autoresponder.py", "file_name": "run_autoresponder.py", "file_ext": "py", "file_size_in_byte": 15089, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 58, "usage_type": "call"}, {"api_name": "re.match", "line_number": 111, "usage_type": "call"}, {"api_name": "re.match", "line_number": 114, "usage_type": "call"}, {"api_name": "re.match", "line_number": 119, "usage_type": "call"}, {"api_name": "_socket.gaierror", "line_number": 127, "usage_type": "name"}, {"api_name": "imaplib.IMAP4_SSL", "line_number": 129, "usage_type": "attribute"}, {"api_name": "imaplib.IMAP4_SSL", "line_number": 137, "usage_type": "call"}, {"api_name": "_socket.gaierror", "line_number": 147, "usage_type": "name"}, {"api_name": "smtplib.SMTPAuthenticationError", "line_number": 149, "usage_type": "attribute"}, {"api_name": "smtplib.SMTP", "line_number": 157, "usage_type": "call"}, {"api_name": "email.message_from_string", "line_number": 177, "usage_type": "call"}, {"api_name": "email.header.decode_header", "line_number": 197, "usage_type": "call"}, {"api_name": "email.header", "line_number": 197, "usage_type": "attribute"}, {"api_name": "email.header.decode_header", "line_number": 222, "usage_type": "call"}, {"api_name": "email.header", "line_number": 222, "usage_type": "attribute"}, {"api_name": "email.header.decode_header", "line_number": 224, "usage_type": "call"}, {"api_name": "email.header", "line_number": 224, "usage_type": "attribute"}, {"api_name": "email.mime.text.MIMEText", "line_number": 232, "usage_type": "call"}, {"api_name": "email.mime", "line_number": 232, "usage_type": "attribute"}, {"api_name": "email.utils.formataddr", "line_number": 235, "usage_type": "call"}, {"api_name": "email.utils", "line_number": 235, "usage_type": "attribute"}, {"api_name": "email.header.Header", "line_number": 236, "usage_type": "call"}, {"api_name": "email.header", "line_number": 236, "usage_type": "attribute"}, {"api_name": "email.header.decode_header", "line_number": 241, "usage_type": "call"}, {"api_name": "email.header", "line_number": 241, "usage_type": "attribute"}, {"api_name": "email.utils.formataddr", "line_number": 253, "usage_type": "call"}, {"api_name": "email.utils", "line_number": 253, "usage_type": "attribute"}, {"api_name": "email.header.Header", "line_number": 254, "usage_type": "call"}, {"api_name": "email.header", "line_number": 254, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 271, "usage_type": "call"}, {"api_name": "os.path", "line_number": 271, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 273, "usage_type": "call"}, {"api_name": "os.W_OK", "line_number": 273, "usage_type": "attribute"}, {"api_name": "email.header.decode_header", "line_number": 277, "usage_type": "call"}, {"api_name": "email.header", "line_number": 277, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 286, "usage_type": "call"}, {"api_name": "os.path", "line_number": 286, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 303, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 332, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 332, "usage_type": "attribute"}]} +{"seq_id": "226683490", "text": "#!/usr/bin/python3\n\"\"\"create a route /status on the object app_views that returns a JSON.\"\"\"\nfrom flask import Flask, jsonify, abort, request, make_response\nfrom api.v1.views import app_views\nfrom models import storage\nfrom models.place import Place\nfrom models.city import City\nfrom models.user import User\n\n\n@app_views.route('/cities//places', strict_slashes=False)\ndef all_places(city_id):\n \"\"\"Retrieves the list of all place objects.\"\"\"\n new_dict = []\n if not storage.get(City, city_id):\n abort(404)\n for plc in storage.all('Place').values():\n if city_id == plc.to_dict()['city_id']:\n new_dict.append(plc.to_dict())\n return jsonify(new_dict)\n\n\n@app_views.route('/places/', strict_slashes=False,\n methods=['GET'])\ndef get_place(place_id):\n \"\"\"GET the list of all place objects.\"\"\"\n try:\n plc = jsonify(storage.get(Place, place_id).to_dict())\n return plc\n except BaseException:\n abort(404)\n\n\n@app_views.route('/places/', strict_slashes=False,\n methods=['DELETE'])\ndef delete_place(place_id):\n \"\"\"GET the list of all place objects.\"\"\"\n plc = storage.get(Place, place_id)\n if plc:\n plc.delete(), storage.save()\n return {}\n else:\n abort(404)\n\n\n@app_views.route('cities//places', methods=['POST'],\n strict_slashes=False)\ndef create_place(city_id):\n \"\"\"POST the list of all place objects.\"\"\"\n plc = request.get_json()\n if not storage.get(City, city_id):\n abort(404)\n if type(plc) is not dict:\n abort(400, {'Not a JSON'})\n elif 'user_id' not in plc:\n abort(400, {'Missing user_id'})\n elif 'name' not in plc:\n abort(400, {'Missing name'})\n elif not storage.get(User, plc['user_id']):\n abort(404)\n else:\n plc['city_id'] = city_id\n new_plc = Place(**plc)\n storage.new(new_plc)\n storage.save()\n return make_response(jsonify(new_plc.to_dict()), 201)\n\n\n@app_views.route('/places/', strict_slashes=False,\n methods=['PUT'])\ndef update_place(place_id):\n \"\"\"PUT the list of all place objects.\"\"\"\n update_plc = request.get_json()\n if type(update_plc) is not dict:\n abort(400, {'Not a JSON'})\n plc = storage.get(Place, place_id)\n if not plc:\n abort(404)\n else:\n for key, value in update_plc.items():\n if key not in ['id', 'user_id', 'city_id', 'created_at',\n 'updated_at']:\n setattr(plc, key, value)\n storage.save()\n return jsonify(plc.to_dict())\n", "sub_path": "api/v1/views/places.py", "file_name": "places.py", "file_ext": "py", "file_size_in_byte": 2645, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "models.storage.get", "line_number": 15, "usage_type": "call"}, {"api_name": "models.city.City", "line_number": 15, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 16, "usage_type": "call"}, {"api_name": "models.storage.all", "line_number": 17, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 20, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 11, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 28, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 28, "usage_type": "call"}, {"api_name": "models.place.Place", "line_number": 28, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 31, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 23, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 23, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 38, "usage_type": "call"}, {"api_name": "models.place.Place", "line_number": 38, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 38, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 40, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 43, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 34, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 51, "usage_type": "call"}, {"api_name": "models.city.City", "line_number": 51, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 58, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 59, "usage_type": "call"}, {"api_name": "models.user.User", "line_number": 59, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 60, "usage_type": "call"}, {"api_name": "models.place.Place", "line_number": 63, "usage_type": "call"}, {"api_name": "models.storage.new", "line_number": 64, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 64, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 65, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 66, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 46, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 75, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 76, "usage_type": "call"}, {"api_name": "models.place.Place", "line_number": 76, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 78, "usage_type": "call"}, {"api_name": "models.storage.save", "line_number": 84, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 85, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 69, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 69, "usage_type": "name"}]} +{"seq_id": "627997668", "text": "from .base_page import BasePage\nfrom .locators import ProductPageLocators\nfrom selenium.common.exceptions import NoAlertPresentException\nimport math\n\nclass ProductPage(BasePage):\n def add_to_card(self):\n buttun = self.browser.find_element(*ProductPageLocators.BUTTON_ADD_CARD)\n buttun.click()\n\n def solve_quiz_and_get_code(self):\n alert = self.browser.switch_to.alert\n x = alert.text.split(\" \")[2]\n answer = str(math.log(abs((12 * math.sin(float(x))))))\n alert.send_keys(answer)\n alert.accept()\n try:\n alert = self.browser.switch_to.alert\n print(\"Your code: {}\".format(alert.text))\n alert.accept()\n except NoAlertPresentException:\n print(\"No second alert presented\")\n\n def should_be_message(self):\n message = self.browser.find_element(*ProductPageLocators.PRODUCT_ADD_CARD).text\n product = self.browser.find_element(*ProductPageLocators.PRODUCT).text\n assert message == product, \"message incorect\"\n\n def should_be_cost(self):\n message_cost = self.browser.find_element(*ProductPageLocators.COST_ADD_CARD).text\n product_cost = self.browser.find_element(*ProductPageLocators.PRODUCT_COST).text\n assert product_cost == message_cost, \"cost incorect\"\n\n def should_not_be_success_message(self):\n assert self.is_not_element_present(*ProductPageLocators.SUCCESS_MESSAGE), \\\n \"Success message is presented, but should not be\"\n\n def should_be_disappeared(self):\n assert self.is_disappeared(*ProductPageLocators.SUCCESS_MESSAGE), \\\n \"Success message is presented, but should not be\"", "sub_path": "pages/product_page.py", "file_name": "product_page.py", "file_ext": "py", "file_size_in_byte": 1673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "base_page.BasePage", "line_number": 6, "usage_type": "name"}, {"api_name": "locators.ProductPageLocators.BUTTON_ADD_CARD", "line_number": 8, "usage_type": "attribute"}, {"api_name": "locators.ProductPageLocators", "line_number": 8, "usage_type": "name"}, {"api_name": "math.log", "line_number": 14, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 14, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoAlertPresentException", "line_number": 21, "usage_type": "name"}, {"api_name": "locators.ProductPageLocators.PRODUCT_ADD_CARD", "line_number": 25, "usage_type": "attribute"}, {"api_name": "locators.ProductPageLocators", "line_number": 25, "usage_type": "name"}, {"api_name": "locators.ProductPageLocators.PRODUCT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "locators.ProductPageLocators", "line_number": 26, "usage_type": "name"}, {"api_name": "locators.ProductPageLocators.COST_ADD_CARD", "line_number": 30, "usage_type": "attribute"}, {"api_name": "locators.ProductPageLocators", "line_number": 30, "usage_type": "name"}, {"api_name": "locators.ProductPageLocators.PRODUCT_COST", "line_number": 31, "usage_type": "attribute"}, {"api_name": "locators.ProductPageLocators", "line_number": 31, "usage_type": "name"}, {"api_name": "locators.ProductPageLocators.SUCCESS_MESSAGE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "locators.ProductPageLocators", "line_number": 35, "usage_type": "name"}, {"api_name": "locators.ProductPageLocators.SUCCESS_MESSAGE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "locators.ProductPageLocators", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "133437070", "text": "'''\n1111111111111\n\n\n\n使用前pip 一下transformers 保证版本最新.\n'''\n\n\n\n\n\n\n\n# https://www.ctolib.com/amp/brightmart-albert_zh.html\n\nfrom transformers import *\nimport torch\nfrom torch.nn.functional import softmax\n\n\n\n\nfrom transformers import *\n\n\n\n\n\npretrained = 'voidful/albert_chinese_xxlarge'\ntokenizer = BertTokenizer.from_pretrained(pretrained) # 主要这里面的tokenizer是bert的.\n\n\n\nquestion, text = \"Who was Jim Henson?\", \"Jim Henson was a nice puppet\"\n# 看看这个函数怎么用\ntokenizer.encode_plus(question, text,) # 就是编码成 albert的输入格式, 一个input(里面是2句话做好了sep,并且有token,表示句子顺序的,还有attentionmask用于设置padding的.\n\n\n\n\n\nmodel = AlbertForSequenceClassification.from_pretrained(pretrained)\n\ninputtext = \"今天[MASK]情很好\" # 编码后第一个位置是cls,所以msk的索引是3\n#计算mask所在的索引位置,\n\nAutoModelWithLMHead\n\n\n'''\n正规运行的模型一共有4个:\nAlbertForMaskedLM --------输入一个mask文本,来返回maks的真正内容.\n\n\nAlbertForSequenceClassification \"\"\"Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. \"\"\",\n \n \nAlbertForTokenClassification @add_start_docstrings(\n \"\"\"Albert Model with a token classification head on top (a linear layer on top of\n the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. \"\"\",\n ALBERT_START_DOCSTRING,\n)\n\n\n\nAlbertForQuestionAnswering \"\"\"Albert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of\n the hidden-states output to compute `span start logits` and `span end logits`). \"\"\",\n'''\n\n\n\n\n\n\n\n\nmodel = AlbertForMaskedLM.from_pretrained(pretrained)\nmodel = AlbertForMaskedLM.from_pretrained(pretrained)\nmodel = AlbertForMaskedLM.from_pretrained(pretrained)\nmodel = AlbertForMaskedLM.from_pretrained(pretrained)\nmodel = AlbertForMaskedLM.from_pretrained(pretrained)\nmodel = AlbertForMaskedLM.from_pretrained(pretrained)\nmodel = AlbertForMaskedLM.from_pretrained(pretrained)\nmodel = AlbertForMaskedLM.from_pretrained(pretrained)\nmodel = AlbertForMaskedLM.from_pretrained(pretrained)\n\n\n\nmaskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103)\n\ninput_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0) # Batch size 1\noutputs = model(input_ids, masked_lm_labels=input_ids)\nloss, prediction_scores = outputs[:2]\nlogit_prob = softmax(prediction_scores[0, maskpos]).data.tolist()\npredicted_index = torch.argmax(prediction_scores[0, maskpos]).item()\npredicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]\nprint(predicted_token,logit_prob[predicted_index])\n\n\n\n\n'''\n下面进行使用hugging face 进行模型训练.\n'''\nmodel.num_parameters()\n# model.train()\n\n\nfrom transformers import LineByLineTextDataset\n\ndataset = LineByLineTextDataset(\n tokenizer=tokenizer,\n file_path=\"./lunyu.txt\",\n block_size=256,\n)\nfrom transformers import DataCollatorForLanguageModeling\n\ndata_collator = DataCollatorForLanguageModeling(\n tokenizer=tokenizer, mlm=True, mlm_probability=0.15\n)\n\n\nfrom transformers import Trainer, TrainingArguments\n\ntraining_args = TrainingArguments(\n output_dir=\"./lunyuAlbert\",\n overwrite_output_dir=True,\n num_train_epochs=20,\n per_gpu_train_batch_size=16,\n save_steps=2000,\n save_total_limit=2,\n)\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n data_collator=data_collator,\n train_dataset=dataset,\n prediction_loss_only=True,\n)\n\n# %%time\ntrainer.train()\n\n\n\n\n\n\n\n'''\n模型名\tMODEL_NAME\nalbert_tiny_google_zh\tvoidful/albert_chinese_tiny\nalbert_small_google_zh\tvoidful/albert_chinese_small\nalbert_base_zh (from google)\tvoidful/albert_chinese_base\nalbert_large_zh (from google)\tvoidful/albert_chinese_large\nalbert_xlarge_zh (from google)\tvoidful/albert_chinese_xlarge\nalbert_xxlarge_zh (from google)\tvoidful/albert_chinese_xxlarge\n'''\n\n\n\n\nprint(1)\n\n\n\n'''\n调用简单:去https://huggingface.co/voidful/albert_chinese_xxlarge\n上面搜索模型,然后\n'''\n\nfrom transformers import AlbertTokenizer, AlbertForSequenceClassification\nimport torch\n\ntokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')\nmodel = AlbertForSequenceClassification.from_pretrained('albert-base-v2')\ninput_ids = torch.tensor(tokenizer.encode(\"Hello, my dog is cute\")).unsqueeze(0) # Batch size 1\nlabels = torch.tensor([1]).unsqueeze(0) # Batch size 1\noutputs = model(input_ids, labels=labels)\nloss, logits = outputs[:2]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "albert使用/22222albert.py", "file_name": "22222albert.py", "file_ext": "py", "file_size_in_byte": 4660, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torch.tensor", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 97, "usage_type": "call"}, {"api_name": "transformers.LineByLineTextDataset", "line_number": 113, "usage_type": "call"}, {"api_name": "transformers.DataCollatorForLanguageModeling", "line_number": 120, "usage_type": "call"}, {"api_name": "transformers.TrainingArguments", "line_number": 127, "usage_type": "call"}, {"api_name": "transformers.Trainer", "line_number": 136, "usage_type": "call"}, {"api_name": "transformers.AlbertTokenizer.from_pretrained", "line_number": 178, "usage_type": "call"}, {"api_name": "transformers.AlbertTokenizer", "line_number": 178, "usage_type": "name"}, {"api_name": "transformers.AlbertForSequenceClassification.from_pretrained", "line_number": 179, "usage_type": "call"}, {"api_name": "transformers.AlbertForSequenceClassification", "line_number": 179, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 181, "usage_type": "call"}]} +{"seq_id": "589645801", "text": "# -*- coding: utf-8 -*-\n\"\"\"Test generation of bandwidth measurements document (v3bw)\"\"\"\nimport json\nimport os.path\n\nfrom sbws import __version__ as version\nfrom sbws.globals import SPEC_VERSION, SBWS_SCALING, TORFLOW_SCALING\nfrom sbws.lib.resultdump import Result, load_result_file\nfrom sbws.lib.v3bwfile import (V3BWHeader, V3BWLine, TERMINATOR, LINE_SEP,\n KEYVALUE_SEP_V110, num_results_of_type,\n V3BWFile)\nfrom sbws.util.timestamp import now_fname\n\ntimestamp = 1523974147\ntimestamp_l = str(timestamp)\nversion_l = KEYVALUE_SEP_V110.join(['version', SPEC_VERSION])\nsoftware_l = KEYVALUE_SEP_V110.join(['software', 'sbws'])\nsoftware_version_l = KEYVALUE_SEP_V110.join(['software_version', version])\nfile_created = '2018-04-25T13:10:57'\nfile_created_l = KEYVALUE_SEP_V110.join(['file_created', file_created])\nlatest_bandwidth = '2018-04-17T14:09:07'\nlatest_bandwidth_l = KEYVALUE_SEP_V110.join(['latest_bandwidth',\n latest_bandwidth])\nheader_ls = [timestamp_l, version_l, file_created_l, latest_bandwidth_l,\n software_l, software_version_l, TERMINATOR]\nheader_str = LINE_SEP.join(header_ls) + LINE_SEP\nearliest_bandwidth = '2018-04-16T14:09:07'\nearliest_bandwidth_l = KEYVALUE_SEP_V110.join(['earliest_bandwidth',\n earliest_bandwidth])\ngenerator_started = '2018-04-16T14:09:05'\ngenerator_started_l = KEYVALUE_SEP_V110.join(['generator_started',\n generator_started])\nheader_extra_ls = [timestamp_l, version_l,\n earliest_bandwidth_l, file_created_l, generator_started_l,\n latest_bandwidth_l,\n software_l, software_version_l, TERMINATOR]\nheader_extra_str = LINE_SEP.join(header_extra_ls) + LINE_SEP\n\nbwl_str = \"bw=56 bw_bs_mean=61423 bw_bs_median=55656 \"\\\n \"desc_avg_bw_bs=1000000000 desc_obs_bw_bs_last=524288 \"\\\n \"desc_obs_bw_bs_mean=524288 error_circ=0 error_misc=0 error_stream=1 \" \\\n \"master_key_ed25519=g+Shk00y9Md0hg1S6ptnuc/wWKbADBgdjT0Kg+TSF3s \" \\\n \"nick=A \" \\\n \"node_id=$AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA rtt=456 success=1 \" \\\n \"time=2018-04-17T14:09:07\\n\"\n\nv3bw_str = header_extra_str + bwl_str\n\n\ndef test_v3bwheader_str():\n \"\"\"Test header str\"\"\"\n header = V3BWHeader(timestamp_l, file_created=file_created)\n assert header_str == str(header)\n\n\ndef test_v3bwheader_extra_str():\n \"\"\"Test header str with additional headers\"\"\"\n header = V3BWHeader(timestamp_l,\n file_created=file_created,\n generator_started=generator_started,\n earliest_bandwidth=earliest_bandwidth)\n assert header_extra_str == str(header)\n\n\ndef test_v3bwheader_from_lines():\n \"\"\"\"\"\"\n header_obj = V3BWHeader(timestamp_l,\n file_created=file_created,\n generator_started=generator_started,\n earliest_bandwidth=earliest_bandwidth)\n header, _ = V3BWHeader.from_lines_v110(header_extra_ls)\n assert str(header_obj) == str(header)\n\n\ndef test_v3bwheader_from_text():\n \"\"\"\"\"\"\n header_obj = V3BWHeader(timestamp_l,\n file_created=file_created,\n generator_started=generator_started,\n earliest_bandwidth=earliest_bandwidth)\n header, _ = V3BWHeader.from_text_v110(header_extra_str)\n assert str(header_obj) == str(header)\n\n\ndef test_num_results_of_type(result_success, result_error_stream):\n assert num_results_of_type([result_success], 'success') == 1\n assert num_results_of_type([result_error_stream], 'success') == 0\n assert num_results_of_type([result_success], 'error-stream') == 0\n assert num_results_of_type([result_error_stream], 'error-stream') == 1\n\n\ndef test_v3bwline_from_results_file(datadir):\n lines = datadir.readlines('results.txt')\n d = dict()\n for line in lines:\n r = Result.from_dict(json.loads(line.strip()))\n fp = r.fingerprint\n if fp not in d:\n d[fp] = []\n d[fp].append(r)\n bwl = V3BWLine.from_data(d, fp)\n # bw store now B, not KB\n bwl.bw = round(bwl.bw / 1000)\n assert bwl_str == str(bwl)\n\n\ndef test_from_results_read(datadir, tmpdir, conf, args):\n results = load_result_file(str(datadir.join(\"results.txt\")))\n expected_header = V3BWHeader(timestamp_l,\n earliest_bandwidth=earliest_bandwidth,\n latest_bandwidth=latest_bandwidth)\n expected_bwls = [V3BWLine.from_results(results[fp]) for fp in results]\n # bw store now B, not KB\n expected_bwls[0].bw = round(expected_bwls[0].bw / 1000)\n expected_f = V3BWFile(expected_header, expected_bwls)\n # This way is going to convert bw to KB\n v3bwfile = V3BWFile.from_results(results)\n assert str(expected_f)[1:] == str(v3bwfile)[1:]\n output = os.path.join(args.output, now_fname())\n v3bwfile.write(output)\n\n\ndef test_from_arg_results_write(datadir, tmpdir, conf, args):\n results = load_result_file(str(datadir.join(\"results.txt\")))\n v3bwfile = V3BWFile.from_results(results)\n output = os.path.join(args.output, now_fname())\n v3bwfile.write(output)\n assert os.path.isfile(output)\n\n\ndef test_from_arg_results_write_read(datadir, tmpdir, conf, args):\n results = load_result_file(str(datadir.join(\"results.txt\")))\n v3bwfile = V3BWFile.from_results(results)\n output = os.path.join(args.output, now_fname())\n v3bwfile.write(output)\n with open(output) as fd:\n v3bw = fd.read()\n assert v3bw == str(v3bwfile)\n\n\ndef test_sbws_scale(datadir):\n results = load_result_file(str(datadir.join(\"results.txt\")))\n v3bwfile = V3BWFile.from_results(results, scaling_method=SBWS_SCALING)\n assert v3bwfile.bw_lines[0].bw == 8\n\n\ndef test_torflow_scale(datadir):\n results = load_result_file(str(datadir.join(\"results.txt\")))\n v3bwfile = V3BWFile.from_results(results, scaling_method=TORFLOW_SCALING)\n assert v3bwfile.bw_lines[0].bw == 1000\n v3bwfile = V3BWFile.from_results(results, scaling_method=TORFLOW_SCALING,\n torflow_cap=0.0001)\n assert v3bwfile.bw_lines[0].bw == 1000\n v3bwfile = V3BWFile.from_results(results, scaling_method=TORFLOW_SCALING,\n torflow_cap=1, torflow_round_digs=0)\n assert v3bwfile.bw_lines[0].bw == 524\n", "sub_path": "tests/unit/lib/test_v3bwfile.py", "file_name": "test_v3bwfile.py", "file_ext": "py", "file_size_in_byte": 6492, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110.join", "line_number": 16, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110", "line_number": 16, "usage_type": "name"}, {"api_name": "sbws.globals.SPEC_VERSION", "line_number": 16, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110.join", "line_number": 17, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110", "line_number": 17, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110.join", "line_number": 18, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110", "line_number": 18, "usage_type": "name"}, {"api_name": "sbws.__version__", "line_number": 18, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110.join", "line_number": 20, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110", "line_number": 20, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110.join", "line_number": 22, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110", "line_number": 22, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.TERMINATOR", "line_number": 25, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.LINE_SEP.join", "line_number": 26, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.LINE_SEP", "line_number": 26, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110.join", "line_number": 28, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110", "line_number": 28, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110.join", "line_number": 31, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.KEYVALUE_SEP_V110", "line_number": 31, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.TERMINATOR", "line_number": 36, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.LINE_SEP.join", "line_number": 37, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.LINE_SEP", "line_number": 37, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.V3BWHeader", "line_number": 52, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWHeader", "line_number": 58, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWHeader", "line_number": 67, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWHeader.from_lines_v110", "line_number": 71, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWHeader", "line_number": 71, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.V3BWHeader", "line_number": 77, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWHeader.from_text_v110", "line_number": 81, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWHeader", "line_number": 81, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.num_results_of_type", "line_number": 86, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.num_results_of_type", "line_number": 87, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.num_results_of_type", "line_number": 88, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.num_results_of_type", "line_number": 89, "usage_type": "call"}, {"api_name": "sbws.lib.resultdump.Result.from_dict", "line_number": 96, "usage_type": "call"}, {"api_name": "sbws.lib.resultdump.Result", "line_number": 96, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 96, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWLine.from_data", "line_number": 101, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWLine", "line_number": 101, "usage_type": "name"}, {"api_name": "sbws.lib.resultdump.load_result_file", "line_number": 108, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWHeader", "line_number": 109, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWLine.from_results", "line_number": 112, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWLine", "line_number": 112, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile", "line_number": 115, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile.from_results", "line_number": 117, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile", "line_number": 117, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 119, "usage_type": "name"}, {"api_name": "sbws.util.timestamp.now_fname", "line_number": 119, "usage_type": "call"}, {"api_name": "sbws.lib.resultdump.load_result_file", "line_number": 124, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile.from_results", "line_number": 125, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile", "line_number": 125, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 126, "usage_type": "name"}, {"api_name": "sbws.util.timestamp.now_fname", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 128, "usage_type": "name"}, {"api_name": "sbws.lib.resultdump.load_result_file", "line_number": 132, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile.from_results", "line_number": 133, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile", "line_number": 133, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 134, "usage_type": "name"}, {"api_name": "sbws.util.timestamp.now_fname", "line_number": 134, "usage_type": "call"}, {"api_name": "sbws.lib.resultdump.load_result_file", "line_number": 142, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile.from_results", "line_number": 143, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile", "line_number": 143, "usage_type": "name"}, {"api_name": "sbws.globals.SBWS_SCALING", "line_number": 143, "usage_type": "name"}, {"api_name": "sbws.lib.resultdump.load_result_file", "line_number": 148, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile.from_results", "line_number": 149, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile", "line_number": 149, "usage_type": "name"}, {"api_name": "sbws.globals.TORFLOW_SCALING", "line_number": 149, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile.from_results", "line_number": 151, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile", "line_number": 151, "usage_type": "name"}, {"api_name": "sbws.globals.TORFLOW_SCALING", "line_number": 151, "usage_type": "name"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile.from_results", "line_number": 154, "usage_type": "call"}, {"api_name": "sbws.lib.v3bwfile.V3BWFile", "line_number": 154, "usage_type": "name"}, {"api_name": "sbws.globals.TORFLOW_SCALING", "line_number": 154, "usage_type": "name"}]} +{"seq_id": "181514840", "text": "#!/usr/bin/python\n\n# A script to produce name labels for each pupil, on a per-class basis. Uses reportLab to output printable PDF documents sized\n# to fit Avery L7160 label sheets.\n\n# Standard libraries.\nimport os\nimport sys\n\n# PIL - the Python Image Library, used for bitmap image manipulation.\nimport PIL\nimport PIL.ImageFont\nimport PIL.ImageDraw\n\n# ReportLab - used for PDF document generation.\nimport reportlab.lib.units\nimport reportlab.lib.utils\nimport reportlab.lib.colors\nimport reportlab.pdfgen.canvas\nimport reportlab.lib.pagesizes\nimport reportlab.graphics.renderPM\n\n# Data-handling.\nimport pandas\n\n# Our own library.\nimport dataLib\n\n# Load the config file.\nconfig = dataLib.loadConfig([\"dataFolder\"])\n\n# Make sure the output folder exists.\nlabelsRoot = config[\"dataFolder\"] + os.sep + \"Labels\"\nformLabelsRoot = labelsRoot + os.sep + \"Form Labels\"\nspineLabelsRoot = labelsRoot + os.sep + \"Spine Labels\"\nos.makedirs(formLabelsRoot, exist_ok=True)\nos.makedirs(spineLabelsRoot, exist_ok=True)\n\n# We are printing on Avery L7160 labels (A4, 7 rows of 3 labels) - set the page size and borders, in mm.\npageWidth = 210\npageHeight = 297\nlabelsX = 3\nlabelsY = 7\nlabelWidth = 63.5\nlabelHeight = 38.1\nlabelBorder = 40\nlabelHorizontalGap = 3\nlineSpacing = 30\ninitialFontSize = 132\nfontSizeStep = 4\nleftBorder = (pageWidth - ((labelWidth * labelsX) + (labelHorizontalGap * 2))) / 2\ntopBorder = (pageHeight - (labelHeight * labelsY)) / 2\n\n# Splits a string into two as-even-as-possible strings, split by space.\ndef evenlySplitString(theString):\n\ttheString = theString.strip()\n\tif theString.find(\" \") == -1:\n\t\treturn(theString, \"\")\n\tstringSplit = theString.split(\" \")\n\tif len(stringSplit) == 2:\n\t\treturn(stringSplit[0], stringSplit[1])\n\tresult1 = \"\"\n\tresult2 = \"\"\n\tlowestDiff = 999\n\tfor pl in range(1, len(stringSplit)):\n\t\ttempResult1 = \" \".join(stringSplit[:pl])\n\t\ttempResult2 = \" \".join(stringSplit[pl:])\n\t\ttempDiff = abs(len(tempResult1)-len(tempResult2))\n\t\tif tempDiff < lowestDiff:\n\t\t\tresult1 = tempResult1\n\t\t\tresult2 = tempResult2\n\t\t\tlowestDiff = tempDiff\n\treturn(result1, result2)\n\n# Set up a bunch of different font sizes for use with name labels.\nfonts = {}\nfor fontSize in range(4, 129, 4):\n\tfonts[fontSize] = PIL.ImageFont.truetype(\"DejaVuSerif.ttf\", fontSize)\n\nprint(\"Writing per-form PDF Stickers...\")\npupils = pandas.read_csv(config[\"dataFolder\"] + os.sep + \"pupils.csv\", header=0)\n\nforms = {}\nfor pupilsIndex, pupilsValue in pupils.iterrows():\n\tforms[pupilsValue[\"Form\"]] = 1\n\nfor form in forms.keys():\n\t# Create the blank PDF document to start drawing page elements on.\n\tpdfCanvas = reportlab.pdfgen.canvas.Canvas(formLabelsRoot + os.sep + form + \".pdf\")\n\tlabelCount = 0\n\tfor pupilsIndex, pupilsValue in pupils.iterrows():\n\t\tif form == pupilsValue[\"Form\"]:\n\t\t\tlabelX = labelCount % labelsX\n\t\t\tlabelY = ((labelCount - labelX) / labelsX) % labelsY\n\t\t\t\n\t\t\t# Create a blank image to place the label details on.\n\t\t\tlabelImageWidth = int(labelWidth*10)\n\t\t\tlabelImageHeight = int(labelHeight*10)\n\t\t\tlabelImage = PIL.Image.new(\"RGB\", (labelImageWidth,labelImageHeight), (255, 255, 255))\n\t\t\t\n\t\t\t# Draw the pupil's full name on the label image, centred, 20 pixels down from the top.\n\t\t\tfontSize = initialFontSize\n\t\t\tline1Width = labelImageWidth\n\t\t\tline1Height = labelImageHeight\n\t\t\tline2Width = labelImageWidth\n\t\t\tline2Height = labelImageHeight\n\t\t\ttextDrawer = PIL.ImageDraw.Draw(labelImage)\n\t\t\tline1Text, line2Text = evenlySplitString(pupilsValue[\"GivenName\"] + \" \" + pupilsValue[\"FamilyName\"])\n\t\t\twhile line1Width >= (labelImageWidth-labelBorder) or line2Width >= (labelImageWidth-labelBorder) or (line1Height + lineSpacing + line2Height) >= labelImageHeight:\n\t\t\t\tfontSize = fontSize - fontSizeStep\n\t\t\t\tline1Width, line1Height = textDrawer.textsize(line1Text, font=fonts[fontSize])\n\t\t\t\tline2Width, line2Height = textDrawer.textsize(line2Text, font=fonts[fontSize])\n\t\t\ttextDrawer.text((int((labelImageWidth-line1Width)/2), (labelBorder / 2)), line1Text, fill=\"black\", font=fonts[fontSize])\n\t\t\ttextDrawer.text((int((labelImageWidth-line2Width)/2), line1Height+lineSpacing), line2Text, fill=\"black\", font=fonts[fontSize])\n \n\t\t\t# Place the label image on the PDF document.\n\t\t\tpdfCanvas.drawInlineImage(labelImage, (leftBorder+(labelX*(labelWidth+labelHorizontalGap)))*reportlab.lib.units.mm, (pageHeight-(topBorder+((labelY+1)*labelHeight)))*reportlab.lib.units.mm, labelWidth*reportlab.lib.units.mm, labelHeight*reportlab.lib.units.mm)\n\t\t\t\n\t\t\tlabelCount = labelCount + 1\n\t# Save the PDF document.\n\tpdfCanvas.save()\n\t\nfor form in forms.keys():\n\t# Create the blank PDF document to start drawing page elements on.\n\tpdfCanvas = reportlab.pdfgen.canvas.Canvas(spineLabelsRoot + os.sep + form + \".pdf\")\n\tlabelCount = 0\n\tfor pupilsIndex, pupilsValue in pupils.iterrows():\n\t\tif form == pupilsValue[\"Form\"]:\n\t\t\tlabelX = labelCount % labelsX\n\t\t\tlabelY = ((labelCount - labelX) / labelsX) % labelsY\n\t\t\t\n\t\t\t# Create a blank image to place the label details on.\n\t\t\tlabelImageWidth = int(labelWidth*10)\n\t\t\tlabelImageHeight = int((labelHeight/3)*10)\n\t\t\tlabelImage = PIL.Image.new(\"RGB\", (labelImageWidth,labelImageHeight), (255, 255, 255))\n\t\t\t\n\t\t\t# Draw the pupil's given name.\n\t\t\tfontSize = initialFontSize\n\t\t\tlineWidth = labelImageWidth\n\t\t\tlineHeight = labelImageHeight / 3\n\t\t\ttextDrawer = PIL.ImageDraw.Draw(labelImage)\n\t\t\twhile lineWidth >= (labelImageWidth-labelBorder) or lineHeight >= labelImageHeight:\n\t\t\t\tfontSize = fontSize - fontSizeStep\n\t\t\t\tlineWidth, lineHeight = textDrawer.textsize(pupilsValue[\"GivenName\"], font=fonts[fontSize])\n\t\t\ttextDrawer.text((int((labelImageWidth-lineWidth)/2), (labelBorder / 2)), pupilsValue[\"GivenName\"], fill=\"black\", font=fonts[fontSize])\n \n\t\t\t# Place the label image on the PDF document.\n\t\t\tpdfCanvas.drawInlineImage(labelImage, (leftBorder+(labelX*(labelWidth+labelHorizontalGap)))*reportlab.lib.units.mm, (pageHeight-(topBorder+((labelY+1)*labelHeight)))*reportlab.lib.units.mm, labelWidth*reportlab.lib.units.mm, labelHeight*reportlab.lib.units.mm)\n\t\t\t\n\t\t\tlabelCount = labelCount + 1\n\t# Save the PDF document.\n\tpdfCanvas.save()\n", "sub_path": "generateClassNameStickers.py", "file_name": "generateClassNameStickers.py", "file_ext": "py", "file_size_in_byte": 6084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "dataLib.loadConfig", "line_number": 30, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 36, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 78, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 81, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 81, "usage_type": "attribute"}, {"api_name": "reportlab.lib.units.pdfgen.canvas.Canvas", "line_number": 89, "usage_type": "call"}, {"api_name": "reportlab.lib.units.pdfgen", "line_number": 89, "usage_type": "attribute"}, {"api_name": "reportlab.lib.units", "line_number": 89, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 89, "usage_type": "attribute"}, {"api_name": "PIL.Image.new", "line_number": 99, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 99, "usage_type": "attribute"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 107, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 107, "usage_type": "attribute"}, {"api_name": "reportlab.lib.units.lib", "line_number": 117, "usage_type": "attribute"}, {"api_name": "reportlab.lib.units", "line_number": 117, "usage_type": "name"}, {"api_name": "reportlab.lib.units.pdfgen.canvas.Canvas", "line_number": 125, "usage_type": "call"}, {"api_name": "reportlab.lib.units.pdfgen", "line_number": 125, "usage_type": "attribute"}, {"api_name": "reportlab.lib.units", "line_number": 125, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 125, "usage_type": "attribute"}, {"api_name": "PIL.Image.new", "line_number": 135, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 135, "usage_type": "attribute"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 141, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 141, "usage_type": "attribute"}, {"api_name": "reportlab.lib.units.lib", "line_number": 148, "usage_type": "attribute"}, {"api_name": "reportlab.lib.units", "line_number": 148, "usage_type": "name"}]} +{"seq_id": "629894706", "text": "from .models import Question, Answer, Subject\nfrom io import BytesIO\nfrom reportlab.pdfgen import *\nfrom reportlab.lib.pagesizes import *\nfrom reportlab.lib import utils\n\nfrom reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image, PageBreak, Indenter\nfrom reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle\n\nimport random\n\n\nclass PDFWriter():\n\tdef __init__(self, buffer, page_size):\n\t\tself.buffer = buffer\n\t\tif page_size == 'A4':\n\t\t\tself.page_size = A4\n\t\telif page_size == 'Letter':\n\t\t\tself.page_size = letter\n\t\tself.width, self.height = self.page_size\n\n\tdef generate_exam(self, title, question_ids):\n\t\tdocument = SimpleDocTemplate(self.buffer, rightMargin = 72, leftMargin = 72, topMargin = 30, bottomMargin = 72, pageSize = self.page_size)\n\t\tstyles = getSampleStyleSheet()\n\t\tdata = []\n\t\tcorrect_answers = []\n\t\tdata.append(Paragraph(\"Name:__________________________\", styles['Normal']))\n\t\tdata.append(Spacer(self.width, self.height/48))\n\t\tdata.append(Paragraph(title, styles['Title']))\n\t\tdata.append(Spacer(self.width, self.height/36))\n\t\tquestion_number = 1;\n\t\tfor q_id in question_ids:\n\t\t\tquestion = Question.objects.get(pk = q_id)\n\t\t\timages = question.image_set.all()\n\n\t\t\t\n\t\t\tfor image in images:\n\t\t\t\tif(image.image_field):\n\t\t\t\t\timg = utils.ImageReader(image.image_field)\n\t\t\t\t\twidth, height = img.getSize()\n\n\t\t\t\t\tif width > self.width or height > self.height:\n\t\t\t\t\t\taspect = height/float(width)\n\t\t\t\t\t\twidth = self.width*0.9\n\t\t\t\t\t\theight = aspect*width\n\n\n\n\t\t\t\t\tprint(image.image_field)\n\t\t\t\t\ti = Image(image.image_field)\n\t\t\t\t\ti.drawWidth = width\n\t\t\t\t\ti.drawHeight = height\n\t\t\t\t\tdata.append(i)\n\n\t\t\tanswers = list(question.answer_set.all())\n\t\t\tdata.append(Paragraph(str(question_number) + \". \"+ question.question_field, styles['Normal']))\n\t\t\tletters = ['a) ', 'b) ', 'c) ', 'd) ', 'f) ', 'g) ']\n\t\t\tcounter = 0\n\n\t\t\trandom.shuffle(answers)\n\t\t\tfor answer in answers:\n\t\t\t\tif answer.correct_answer_field == True:\n\t\t\t\t\tcorrect = str(question_number) + \". \" + letters[counter]\n\t\t\t\t\tcorrect_answers.append(correct.strip(\")\"))\n\t\t\t\tdata.append(Indenter(left = 0.2*inch))\n\t\t\t\tdata.append(Paragraph(letters[counter] + \" \" + answer.answer_field, styles['Normal']))\n\t\t\t\tdata.append(Indenter(left = -0.2*inch))\n\t\t\t\tcounter = counter+1\n\n\t\t\tquestion_number=question_number+1\n\t\t\tdata.append(Spacer(self.width, self.height/30))\n\n\t\tdata.append(PageBreak())\n\t\tdata.append(Paragraph(\"Answer Key\", styles['Title']))\n\t\tfor q in correct_answers:\n\t\t\tdata.append(Paragraph(q, styles['Normal']))\n\n\t\tdocument.build(data)\n\t\tpdf = self.buffer.getvalue()\n\t\tself.buffer.close()\n\t\treturn pdf\n \n\n", "sub_path": "exammaker/exams/PDFWriter.py", "file_name": "PDFWriter.py", "file_ext": "py", "file_size_in_byte": 2595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "reportlab.platypus.SimpleDocTemplate", "line_number": 23, "usage_type": "call"}, {"api_name": "reportlab.lib.styles.getSampleStyleSheet", "line_number": 24, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 27, "usage_type": "call"}, {"api_name": "reportlab.platypus.Spacer", "line_number": 28, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 29, "usage_type": "call"}, {"api_name": "reportlab.platypus.Spacer", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Question.objects.get", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Question.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Question", "line_number": 33, "usage_type": "name"}, {"api_name": "reportlab.lib.utils.ImageReader", "line_number": 39, "usage_type": "call"}, {"api_name": "reportlab.lib.utils", "line_number": 39, "usage_type": "name"}, {"api_name": "reportlab.platypus.Image", "line_number": 50, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 56, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 60, "usage_type": "call"}, {"api_name": "reportlab.platypus.Indenter", "line_number": 65, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 66, "usage_type": "call"}, {"api_name": "reportlab.platypus.Indenter", "line_number": 67, "usage_type": "call"}, {"api_name": "reportlab.platypus.Spacer", "line_number": 71, "usage_type": "call"}, {"api_name": "reportlab.platypus.PageBreak", "line_number": 73, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 74, "usage_type": "call"}, {"api_name": "reportlab.platypus.Paragraph", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "425438894", "text": "import backFunc\nfrom kivy.app import App\nfrom kivy.lang import Builder\nfrom kivy.uix.screenmanager import Screen\nfrom kivy.uix.button import ButtonBehavior\nfrom kivy.uix.image import Image\nimport subprocess\nimport mysql.connector\nfrom threading import Thread\nfrom kivy.uix.dropdown import DropDown\nfrom kivy.uix.boxlayout import BoxLayout\n\n\nclass HomeScreen(Screen):\n pass\n\n\nclass SchoolIdCreate(Screen):\n pass\n\n\nclass SchoolIdCreate2(Screen):\n pass\n\n\nclass SchoolIdCreate3(Screen):\n pass\n\n\nclass TeacherIdCheck(Screen):\n pass\n\n\nclass TeacherIdCreate(Screen):\n pass\n\n\nclass IndexScreen(Screen):\n pass\n\n\nclass DisplayTimeTable(Screen):\n pass\n\n\n# class for functions\nclass ImageButton(ButtonBehavior, Image):\n pass\n\n\nGUI = Builder.load_file('main.kv')\n\n\nclass MainApp(App):\n def build(self):\n return GUI\n\n def change_screen(self, screen_name):\n global screen_manager\n screen_manager = self.root.ids['screen_manager']\n screen_manager.current = screen_name\n\n def check_school_id(self):\n MainApp.change_screen(self, 'HomeScreen')\n school_id = self.root.ids['HomeScreen'].ids[\"school_id\"].text\n school_pa = self.root.ids['HomeScreen'].ids[\"school_pa\"].text\n school_check = backFunc.school_id_check(school_id, school_pa)\n if school_check == True:\n MainApp.change_screen(self, \"TeacherIdCheck\")\n else:\n self.root.ids['HomeScreen'].ids[\"school_id\"].text = \"\"\n self.root.ids['HomeScreen'].ids[\"school_pa\"].text = \"\"\n\n def lower_grade(self, grade_recived_lower):\n global school_lower_grade\n school_lower_grade = grade_recived_lower\n print(school_lower_grade)\n\n def upper_grade(self, grade_recived_upper):\n global school_upper_grade\n school_upper_grade = grade_recived_upper\n print(school_upper_grade)\n\n def School_id_create(self):\n school_id = self.root.ids['SchoolIdCreate'].ids[\"school_id\"].text\n school_pa = self.root.ids['SchoolIdCreate'].ids[\"school_pa\"].text\n if self.root.ids['SchoolIdCreate'].ids[\"school_periods_day\"].text in \"1234567890\":\n school_periods_day = self.root.ids['SchoolIdCreate'].ids[\"school_periods_day\"].text\n try:\n backFunc.school_id_create1(school_id, school_pa, school_periods_day, school_lower_grade, school_upper_grade)\n MainApp.School_id_create2(self)\n except NameError:\n self.root.ids['SchoolIdCreate'].ids[\"submit\"].text = \"Form Incomplete\"\n except:\n self.root.ids['SchoolIdCreate'].ids[\"school_id\"].text = \"\"\n self.root.ids['SchoolIdCreate'].ids[\"school_id\"].hint_text = \"This id is taken, please try another one\"\n else:\n self.root.ids['SchoolIdCreate'].ids[\"school_periods_day\"].text = \"\"\n self.root.ids['SchoolIdCreate'].ids[\"school_periods_day\"].hint_text = \"Enter a Numeric Value\"\n\n def School_id_create2(self):\n MainApp.change_screen(self, \"SchoolIdCreate2\")\n global grades_school\n grades_school = []\n for i in range(int(school_lower_grade), int(school_upper_grade) + 1):\n grades_school.append(i)\n MainApp.count = 0\n MainApp.School_id_create3(self)\n\n count = 0\n\n # rotate through each grade in the school asking for the number of sections\n def School_id_create3(self):\n if MainApp.count < len(grades_school):\n grade = grades_school[MainApp.count]\n print(grade)\n self.root.ids['SchoolIdCreate2'].ids[\"label_message\"].text = f\"How many sections does grade {grade} has\"\n MainApp.change_screen(self, \"SchoolIdCreate2\")\n global sections_in_class\n sections_in_class = self.root.ids['SchoolIdCreate2'].ids[\"number_sections\"].text\n MainApp.count += 1\n MainApp.count2 = 0\n else:\n if sections_in_class in \"1234567890\":\n MainApp.check_school_id(self)\n else:\n self.root.ids['SchoolIdCreate2'].ids[\"number_sections\"].text = \"\"\n self.root.ids['SchoolIdCreate2'].ids[\"number_sections\"].hint_text = \"Enter a Numeric Value\"\n\n count2 = 0\n\n def sub1(self, subject):\n global subject1\n subject1 = subject\n\n def sub2(self, subject):\n global subject2\n subject2 = subject\n\n def sub3(self, subject):\n global subject3\n subject3 = subject\n\n def sub4(self, subject):\n global subject4\n subject4 = subject\n\n def sub5(self, subject):\n global subject5\n subject5 = subject\n\n def sub6(self, subject):\n global subject6\n subject6 = subject\n\n def sub7(self, subject):\n global subject7\n subject7 = subject\n\n def sub8(self, subject):\n global subject8\n subject8 = subject\n\n def sub9(self, subject):\n global subject9\n subject9 = subject\n\n def sub10(self, subject):\n global subject10\n subject10 = subject\n\n def School_id_create4(self):\n if self.root.ids['SchoolIdCreate2'].ids[\"number_sections\"].text in \"1234567890\":\n if MainApp.count2 < int(self.root.ids['SchoolIdCreate2'].ids[\"number_sections\"].text):\n self.root.ids['SchoolIdCreate3'].ids[\"grade_name\"].text = str(\n MainApp.count + school_lower_grade - 1) + chr(\n MainApp.count2 + 65)\n MainApp.change_screen(self, \"SchoolIdCreate3\")\n else:\n MainApp.School_id_create3(self)\n else:\n self.root.ids['SchoolIdCreate2'].ids[\"number_sections\"].text = \"\"\n self.root.ids['SchoolIdCreate2'].ids[\"number_sections\"].hint_text = \"Enter a Numeric Value\"\n\n def subject_assigning(self):\n school_id = self.root.ids['SchoolIdCreate'].ids[\"school_id\"].text\n try:\n subject_list = (subject1, subject2, subject3, subject4, subject5, subject6, subject7, subject8, subject9, subject10)\n backFunc.school_id_create2(school_id, chr(MainApp.count2 + 65), str(MainApp.count + school_lower_grade - 1), subject1, subject2, subject3, subject4, subject5, subject6, subject7, subject8, subject9, subject10)\n MainApp.count2 += 1\n MainApp.School_id_create4(self)\n except NameError:\n self.root.ids['SchoolIdCreate3'].ids[\"submit\"].text = \"Form Incomplete\"\n\n def check_teacher_id(self):\n school_id = self.root.ids['HomeScreen'].ids[\"school_id\"].text\n teacher_id = self.root.ids['TeacherIdCheck'].ids['teacher_id'].text\n teacher_pa = self.root.ids['TeacherIdCheck'].ids['teacher_pa'].text\n teacher_check = backFunc.teacher_id_check(school_id, teacher_id, teacher_pa)\n if teacher_check == True:\n self.root.ids['IndexScreen'].ids['username'].text = self.root.ids['TeacherIdCheck'].ids['teacher_id'].text\n MainApp.change_screen(self, \"IndexScreen\")\n else:\n self.root.ids['TeacherIdCheck'].ids[\"teacher_id\"].text = \"\"\n self.root.ids['TeacherIdCheck'].ids[\"teacher_pa\"].text = \"\"\n\n def subject_of_teacher(self, subject_recived):\n global subject_teacher\n subject_teacher = subject_recived\n print(subject_teacher)\n\n def type_of_teacher(self, type_recived):\n global type_teacher\n type_teacher = type_recived\n print(type_teacher)\n\n def grade_of_teacher(self, grade_recived):\n global grade_teacher\n grade_teacher = grade_recived\n print(grade_teacher)\n\n def grade_of_teacher2(self, grade_recived2):\n global grade_teacher2\n grade_teacher2 = grade_recived2\n print(grade_teacher2)\n\n def teacher_id_create(self):\n school_id = self.root.ids['HomeScreen'].ids[\"school_id\"].text\n teacher_id = self.root.ids['TeacherIdCreate'].ids[\"teacher_id\"].text\n teacher_pa = self.root.ids['TeacherIdCreate'].ids[\"teacher_pa\"].text\n try:\n backFunc.teacher_id_create(school_id, teacher_id, teacher_pa, subject_teacher, type_teacher, grade_teacher,\n grade_teacher2)\n self.root.ids['TeacherIdCreate'].ids[\"teacher_id\"].text = \"\"\n self.root.ids['TeacherIdCreate'].ids[\"teacher_pa\"].text = \"\"\n MainApp.check_teacher_id(self)\n MainApp.change_screen(self, \"TeacherIdCheck\")\n except NameError:\n self.root.ids['TeacherIdCreate'].ids[\"submit\"].text = \"Form Incomplete\"\n except:\n teacher_id = self.root.ids['TeacherIdCreate'].ids[\"teacher_id\"].text = \"\"\n teacher_id = self.root.ids['TeacherIdCreate'].ids[\n \"teacher_id\"].hint_text = \"This id is taken, please try another one\"\n\n def index_create_time_table(self):\n school_id = self.root.ids['HomeScreen'].ids[\"school_id\"].text\n teacher_id = self.root.ids['TeacherIdCheck'].ids['teacher_id'].text\n mydb = mysql.connector.connect(username=\"doadmin\",password=\"aiyherpvx760tdng\",host=\"db-mysql-blr1-16639-do-user-7263481-0.a.db.ondigitalocean.com\",port=\"25060\",database=school_id)\n mycursor = mydb.cursor()\n mycursor.execute(f\"SELECT teacher_type FROM teacher_general_record WHERE teacher_id = '{teacher_id}'\")\n for i in mycursor:\n teacher_type = i[0]\n if teacher_type == \"teacher\":\n self.root.ids[\"IndexScreen\"].ids[\"create_button\"].text = \"You don\\'t have the write to create a time table\"\n else:\n try:\n backFunc.table_droper(school_id)\n backFunc.teacher_assign(school_id)\n backFunc.create_tables_for_classes(school_id)\n backFunc.create_time_table(school_id)\n except:\n self.root.ids[\"IndexScreen\"].ids[\"create_button\"].text = \"insufficient teachers\"\n\n def index_update_time_table(self):\n school_id = self.root.ids['HomeScreen'].ids[\"school_id\"].text\n teacher_id = self.root.ids['TeacherIdCheck'].ids['teacher_id'].text\n mydb = mysql.connector.connect(username=\"doadmin\",password=\"aiyherpvx760tdng\",host=\"db-mysql-blr1-16639-do-user-7263481-0.a.db.ondigitalocean.com\",port=\"25060\",database=school_id)\n mycursor = mydb.cursor()\n mycursor.execute(f\"SELECT teacher_type FROM teacher_general_record WHERE teacher_id = '{teacher_id}'\")\n for i in mycursor:\n teacher_type = i[0]\n if teacher_type == \"teacher\":\n self.root.ids[\"IndexScreen\"].ids[\n \"update_button\"].text = \"You don\\'t have the write to update the time table\"\n else:\n try:\n backFunc.create_time_table(school_id)\n except:\n self.root.ids[\"IndexScreen\"].ids[\"update_button\"].text = \"First click the create time table button\"\n\n def display_time_table(self):\n school_id = self.root.ids['HomeScreen'].ids[\"school_id\"].text\n teacher_id = self.root.ids['TeacherIdCheck'].ids[\"teacher_id\"].text\n\n mydb = mysql.connector.connect(\n username=\"doadmin\",\n password=\"aiyherpvx760tdng\",\n host=\"db-mysql-blr1-16639-do-user-7263481-0.a.db.ondigitalocean.com\",\n port=\"25060\",\n database=school_id)\n mycursor = mydb.cursor()\n\n mycursor.execute(f\"SELECT * FROM {teacher_id}\")\n for i in mycursor:\n day = str(i[0])\n self.root.ids['DisplayTimeTable'].ids[f\"p{day}\"].text = f\"Period {str(i[0])}\"\n self.root.ids['DisplayTimeTable'].ids[f\"p{day}\"].color = 0, 0, 0, 1\n self.root.ids['DisplayTimeTable'].ids[f\"mon{day}\"].text = i[1]\n self.root.ids['DisplayTimeTable'].ids[f\"mon{day}\"].color = 0, 0, 0, 1\n self.root.ids['DisplayTimeTable'].ids[f\"tue{day}\"].text = i[2]\n self.root.ids['DisplayTimeTable'].ids[f\"tue{day}\"].color = 0, 0, 0, 1\n self.root.ids['DisplayTimeTable'].ids[f\"wen{day}\"].text = i[3]\n self.root.ids['DisplayTimeTable'].ids[f\"wen{day}\"].color = 0, 0, 0, 1\n self.root.ids['DisplayTimeTable'].ids[f\"thr{day}\"].text = i[4]\n self.root.ids['DisplayTimeTable'].ids[f\"thr{day}\"].color = 0, 0, 0, 1\n self.root.ids['DisplayTimeTable'].ids[f\"fri{day}\"].text = i[5]\n self.root.ids['DisplayTimeTable'].ids[f\"fri{day}\"].color = 0, 0, 0, 1\n\n MainApp.change_screen(self, \"DisplayTimeTable\")\n\n\nMainApp().run()\n\n# find a place for this thing\n# while True:\n# Thread(target = MainApp().chatRecive(\"Left\")).start()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 12583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "kivy.uix.screenmanager.Screen", "line_number": 14, "usage_type": "name"}, {"api_name": "kivy.uix.screenmanager.Screen", "line_number": 18, "usage_type": "name"}, {"api_name": "kivy.uix.screenmanager.Screen", "line_number": 22, "usage_type": "name"}, {"api_name": "kivy.uix.screenmanager.Screen", "line_number": 26, "usage_type": "name"}, {"api_name": "kivy.uix.screenmanager.Screen", "line_number": 30, "usage_type": "name"}, {"api_name": "kivy.uix.screenmanager.Screen", "line_number": 34, "usage_type": "name"}, {"api_name": "kivy.uix.screenmanager.Screen", "line_number": 38, "usage_type": "name"}, {"api_name": "kivy.uix.screenmanager.Screen", "line_number": 42, "usage_type": "name"}, {"api_name": "kivy.uix.button.ButtonBehavior", "line_number": 47, "usage_type": "name"}, {"api_name": "kivy.uix.image.Image", "line_number": 47, "usage_type": "name"}, {"api_name": "kivy.lang.Builder.load_file", "line_number": 51, "usage_type": "call"}, {"api_name": "kivy.lang.Builder", "line_number": 51, "usage_type": "name"}, {"api_name": "kivy.app.App", "line_number": 54, "usage_type": "name"}, {"api_name": "backFunc.school_id_check", "line_number": 67, "usage_type": "call"}, {"api_name": "backFunc.school_id_create1", "line_number": 90, "usage_type": "call"}, {"api_name": "backFunc.school_id_create2", "line_number": 189, "usage_type": "call"}, {"api_name": "backFunc.teacher_id_check", "line_number": 199, "usage_type": "call"}, {"api_name": "backFunc.teacher_id_create", "line_number": 232, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 248, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 248, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 248, "usage_type": "name"}, {"api_name": "backFunc.table_droper", "line_number": 257, "usage_type": "call"}, {"api_name": "backFunc.teacher_assign", "line_number": 258, "usage_type": "call"}, {"api_name": "backFunc.create_tables_for_classes", "line_number": 259, "usage_type": "call"}, {"api_name": "backFunc.create_time_table", "line_number": 260, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 267, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 267, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 267, "usage_type": "name"}, {"api_name": "backFunc.create_time_table", "line_number": 277, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 285, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 285, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 285, "usage_type": "name"}]} +{"seq_id": "512954076", "text": "from django.shortcuts import render\nfrom django.views.generic.base import View\nfrom database.models import Post\nfrom django.http import HttpResponseRedirect\nfrom django.conf import settings\nimport time\n\nfrom . import runTSPY_Online\n\n#from TSPY_Online.py import runTSPY_Online\n\nclass HomePage(View):\n def get(self, request):\n # code to query the database goes here!\n posts = Post.objects.all()[::-1]\n context = {}\n context[\"posts\"] = posts\n context[\"MEDIA_URL\"] = settings.MEDIA_URL\n return render(request, 'index.html', context)\n\nclass CreatePost(View):\n def post(self,request):\n name = request.POST.get(\"name\")\n if name == \"\":\n name = \"Anonymous\"\n date = time.strftime(\"%x\")\n name += (\" on %s\" % date)\n url = request.POST.get(\"imgURL\")\n caption = request.POST.get(\"caption\")\n if caption == \"\":\n caption = \"Untitled\"\n brightness = request.POST.get(\"brightness\")\n if brightness == \"\":\n brightness = \"1.5\"\n contrast = request.POST.get(\"contrast\")\n if contrast == \"\":\n contrast = \"1.5\"\n opacity = request.POST.get(\"opacity\")\n if opacity == \"\":\n opacity = \"0.75\"\n\n post = Post(imgURL=url, caption=caption, postedBy=name)\n post.save()\n postID = post.id\n\n mediaPath = settings.MEDIA_ROOT\n runTSPY_Online.runTSPY_Online(url,postID,mediaPath,brightness,contrast,opacity)\n return HttpResponseRedirect(\"/post/%s\" % postID)\n\nclass PostPage(View):\n def get(self, request, postID):\n post = Post.objects.get(id=postID)\n context = {}\n context[\"post\"] = post\n context[\"MEDIA_URL\"] = settings.MEDIA_URL\n context[\"TSP_Url\"] = settings.MEDIA_URL + \"TSPY_Online_Post\" + postID + \".png\"\n return render(request, 'post.html', context)\n\nclass GalleryPage(View):\n def get(self,request):\n posts = Post.objects.all()[::-1]\n context = {}\n context[\"posts\"] = posts\n context[\"MEDIA_URL\"] = settings.MEDIA_URL\n return render(request,'gallery.html',context)\n\nclass HelpPage(View):\n def get(self,request):\n return render(request,'help.html')\n\nclass AboutPage(View):\n def get(self,request):\n return render(request,'about.html')", "sub_path": "TSPY_Online/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2332, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.views.generic.base.View", "line_number": 12, "usage_type": "name"}, {"api_name": "database.models.Post.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "database.models.Post.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "database.models.Post", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.views.generic.base.View", "line_number": 21, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 26, "usage_type": "call"}, {"api_name": "database.models.Post", "line_number": 42, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 46, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 48, "usage_type": "call"}, {"api_name": "django.views.generic.base.View", "line_number": 50, "usage_type": "name"}, {"api_name": "database.models.Post.objects.get", "line_number": 52, "usage_type": "call"}, {"api_name": "database.models.Post.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "database.models.Post", "line_number": 52, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 55, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 55, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 56, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 57, "usage_type": "call"}, {"api_name": "django.views.generic.base.View", "line_number": 59, "usage_type": "name"}, {"api_name": "database.models.Post.objects.all", "line_number": 61, "usage_type": "call"}, {"api_name": "database.models.Post.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "database.models.Post", "line_number": 61, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 64, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 64, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 65, "usage_type": "call"}, {"api_name": "django.views.generic.base.View", "line_number": 67, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 69, "usage_type": "call"}, {"api_name": "django.views.generic.base.View", "line_number": 71, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "646129832", "text": "\n\n# [1,1,4,2,1,3]\n# Move 4, 1, 3 => [1,1,1,2,3,4]\n# pull 1 and put at end of lowest\n# 3)\n\n# => create a set\n# get counts for each\n# create the ideal set\n# find the number of changes\n# [1,1,4,2,1,3]\n# [1,1,1,2,3,4]\n\nfrom collections import deque\nclass Solution:\n\n # For every letter in name\n # if there exists in typed the same letter with at least as many pressed\n # then true\n\n\n def is_long_pressed_name(self, name: str, typed: str) -> bool:\n\n len_n = len(name)\n len_t = len(typed)\n if len_t < len_t:\n return False\n\n name_q = deque(name)\n typed_q = deque(typed)\n\n i = 0\n name_letter_counts = {}\n typed_letter_counts = {}\n\n num_cur_ltr = 0\n cur_ltr = name_q.popleft()\n counter = 1\n while name_q:\n name_letter_counts[str(num_cur_ltr) + cur_ltr] = counter\n\n next_letter = name_q.popleft()\n if next_letter == cur_ltr:\n counter = counter + 1\n else:\n counter, num_cur_ltr = 1, num_cur_ltr + 1\n cur_ltr = next_letter\n\n # capture the last letter\n name_letter_counts[str(num_cur_ltr) + cur_ltr] = counter\n\n # now get counts from typed name\n num_cur_ltr = 0\n cur_ltr = typed_q.popleft()\n counter = 1\n while typed_q:\n typed_letter_counts[str(num_cur_ltr) + cur_ltr] = counter\n\n next_letter = typed_q.popleft()\n if next_letter == cur_ltr:\n counter = counter + 1\n else:\n counter, num_cur_ltr = 1, num_cur_ltr + 1\n cur_ltr = next_letter\n\n # capture the last letter\n typed_letter_counts[str(num_cur_ltr) + cur_ltr] = counter\n\n # must have same letters in each\n if list(name_letter_counts.keys()) != list(typed_letter_counts.keys()):\n return False\n\n for key in name_letter_counts.keys():\n if typed_letter_counts.get(key) < name_letter_counts[key]:\n return False\n\n return True\n\n\n\n\n\n\n\n\n", "sub_path": "is_long_pressed_name/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 2082, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "collections.deque", "line_number": 30, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "585322194", "text": "import logging\nimport pytest\n\nfrom conftest import DUT_VTEP_IP\nfrom tests.common.utilities import wait\nfrom tests.common.helpers.assertions import pytest_assert as pt_assert\n\n\npytestmark = [\n pytest.mark.topology('t0'),\n pytest.mark.device_type('vs')\n]\n\n\nclass Test_EVPN_Config():\n @pytest.fixture(scope=\"class\")\n def setup_dut(self, evpn_env):\n evpn_env.setup_dut_base()\n yield\n evpn_env.teardown_dut_base()\n\n @pytest.fixture(scope=\"class\")\n def vrf_vni_map_set(self, duthost, setup_dut):\n duthost.shell(\"config vrf add Vrf1\")\n duthost.shell(\"config vrf add_vrf_vni_map Vrf1 10000\")\n yield\n duthost.shell(\"config vrf del_vrf_vni_map Vrf1\")\n duthost.shell(\"config vrf del Vrf1\")\n wait(3)\n duthost.shell(\"vtysh -c 'configure' -c 'no vrf {}'\".format(\"Vrf1\"))\n\n def test_vlan_vni_map_configuration(self, duthost, setup_dut):\n # vtep\n res = duthost.shell(\"redis-cli -n 4 -c hgetall 'VXLAN_TUNNEL|vtep'\")\n res_list = res['stdout_lines']\n pt_assert(res_list[0] == 'src_ip')\n pt_assert(res_list[1] == DUT_VTEP_IP)\n\n res = duthost.shell(\"redis-cli -n 0 -c hgetall 'VXLAN_TUNNEL_TABLE:vtep'\")\n res_list = res['stdout_lines']\n pt_assert(res_list[0] == 'src_ip')\n pt_assert(res_list[1] == DUT_VTEP_IP)\n\n # evpnnvo\n res = duthost.shell(\"redis-cli -n 4 -c hgetall 'VXLAN_EVPN_NVO|evpnnvo1'\")\n res_list = res['stdout_lines']\n pt_assert(res_list[0] == 'source_vtep')\n pt_assert(res_list[1] == 'vtep')\n\n res = duthost.shell(\"redis-cli -n 0 -c hgetall 'VXLAN_EVPN_NVO_TABLE:evpnnvo1'\")\n res_list = res['stdout_lines']\n pt_assert(res_list[0] == 'source_vtep')\n pt_assert(res_list[1] == 'vtep')\n\n # map\n res = duthost.shell(\"redis-cli -n 4 -c hgetall 'VXLAN_TUNNEL_MAP|vtep|map_10000_Vlan1000'\")\n res_list = res['stdout_lines']\n pt_assert(res_list[1] == '10000')\n pt_assert(res_list[3] == 'Vlan1000')\n\n res = duthost.shell(\"redis-cli -n 0 -c hgetall 'VXLAN_TUNNEL_MAP_TABLE:vtep:map_10000_Vlan1000'\")\n res_list = res['stdout_lines']\n logging.info(res_list)\n pt_assert(res_list[1] == '10000')\n pt_assert(res_list[3] == 'Vlan1000')\n\n def test_vrf_vni_map_configuration(self, duthost, vrf_vni_map_set):\n # vrf\n res = duthost.shell(\"redis-cli -n 4 -c hgetall 'VRF|Vrf1'\")\n res_list = res['stdout_lines']\n pt_assert('vni' in res_list)\n pt_assert('10000' in res_list)\n\n res = duthost.shell(\"redis-cli -n 0 -c hgetall 'VRF_TABLE:Vrf1'\")\n res_list = res['stdout_lines']\n pt_assert('vni' in res_list)\n pt_assert('10000' in res_list)\n\n res = duthost.shell(\"redis-cli -n 0 -c hgetall 'VXLAN_VRF_TABLE:vtep:evpn_map_10000_Vrf1'\")\n res_list = res['stdout_lines']\n pt_assert('10000' in res_list)\n pt_assert('Vrf1' in res_list)", "sub_path": "tests/evpn/test_evpn_config.py", "file_name": "test_evpn_config.py", "file_ext": "py", "file_size_in_byte": 2974, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pytest.mark.topology", "line_number": 10, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pytest.mark.device_type", "line_number": 11, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 16, "usage_type": "call"}, {"api_name": "tests.common.utilities.wait", "line_number": 29, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 22, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 36, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 37, "usage_type": "call"}, {"api_name": "conftest.DUT_VTEP_IP", "line_number": 37, "usage_type": "name"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 41, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 42, "usage_type": "call"}, {"api_name": "conftest.DUT_VTEP_IP", "line_number": 42, "usage_type": "name"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 47, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 48, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 52, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 53, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 58, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 63, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 64, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 65, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 71, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 72, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 76, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 77, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 81, "usage_type": "call"}, {"api_name": "tests.common.helpers.assertions.pytest_assert", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "326904141", "text": "from flask import Flask, render_template, session, redirect, url_for, escape, request, json, g, flash, jsonify\nfrom flask.ext.sqlalchemy import SQLAlchemy\nfrom flask.ext.openid import OpenID\nfrom flask_oauth import OAuth\nimport urllib\nimport re\nimport os\nfrom database import init_db, db_session, Base\nfrom models import User as User\n\napp = Flask(__name__)\napp.config.from_object(__name__)\n\napp.config.update(dict(\n\tSECRET_KEY='development key',\n\tDEBUG=True,\n\tSTEAM_API_KEY=os.environ['STEAM_API_KEY'],\n\tTWITTER_CONSUMER_KEY=os.environ['TWITTER_CONSUMER_KEY'],\n\tTWITTER_CONSUMER_SECRET=os.environ['TWITTER_CONSUMER_SECRET'],\n))\n\noid = OpenID(app)\n\noauth = OAuth()\n\n_steam_id_re = re.compile('steamcommunity.com/openid/id/(.*?)$')\n\ninit_db()\n\n@app.teardown_appcontext\ndef shutdown_session(exception=None):\n\tdb_session.remove()\n\n# bridge steam api\ndef get_steam_userinfo(steam_id):\n\toptions = {\n\t\t'key': app.config['STEAM_API_KEY'],\n\t\t'steamids': steam_id\n\t}\n\turl = 'http://api.steampowered.com/ISteamUser/GetPlayerSummaries/v0001/?%s' % urllib.urlencode(options)\n\tresponse = json.load(urllib.urlopen(url))\n\tresponse_dict = {\n\t\t'avatar': response['response']['players']['player'][0]['avatar'] or '',\n\t\t'personaname': response['response']['players']['player'][0]['personaname'] or '',\n\t} \n\treturn response_dict\n\n@app.route('/')\ndef home_handler():\n\tif g.user:\n\t\treturn redirect('/dashboard')\n\telse:\n\t\treturn render_template('index.html')\n\n@app.route('/about')\ndef about_handler():\n\tif g.user:\n\t\treturn render_template('about.html', logged=True)\n\telse:\n\t\treturn render_template('about.html', logged=False)\n\n@app.route('/contact')\ndef contact_hanlder():\n\tif g.user:\n\t\treturn render_template('contact.html', logged=True)\n\telse:\n\t\treturn render_template('contact.html', logged=False)\n\n@app.route('/dashboard')\ndef dashboard_handler():\n\tif g.user:\n\t\tsession['tweet_format_string'] = g.user.tweet_format_string\n\t\tsession['tweet_twitch_username'] = g.user.tweet_twitch_username\n\t\tsession['tweet_custom_win'] = g.user.tweet_custom_win\n\t\tsession['tweet_custom_loss'] = g.user.tweet_custom_loss\n\n\t\treturn render_template('dashboard.html', logged=True)\n\telse:\n\t\treturn redirect('/')\n\n@app.route('/dashboard', methods=['POST'])\ndef dashboard_post_handler():\n\tg.user = User.query.get(session['user_id'])\n\n\tg.user.tweet_format_string = request.form['tweet-format']\n\tg.user.tweet_twitch_username = request.form['twitch']\n\tg.user.tweet_custom_win = request.form['win']\n\tg.user.tweet_custom_loss = request.form['lose']\n\tdb_session.commit()\n\n\tsession['tweet_format_string'] = g.user.tweet_format_string\n\tsession['tweet_twitch_username'] = g.user.tweet_twitch_username\n\tsession['tweet_custom_win'] = g.user.tweet_custom_win\n\tsession['tweet_custom_loss'] = g.user.tweet_custom_loss\n\n\treturn jsonify({\n\t\t\t'success': True, # can be False if something went wrong\n\t\t\t'message': \"Successfully saved settings\",\n\t\t}\n\t)\n\n@app.route('/dashboard/filters', methods=['POST'])\ndef dashboard_filters_post_handler():\n\tg.user = User.query.get(session['user_id'])\n\n\treturn jsonify({\n\t\t\t'success': True, # can be False if something went wrong\n\t\t\t'message': \"Successfully added filter\"\n\t\t}\n\t)\n\n@app.route('/settings')\ndef settings_handler():\n\tif g.user:\n\t\treturn render_template('settings.html', logged=True)\n\telse:\n\t\treturn redirect('/')\n\n# steam login\n\n@app.route('/login')\n@oid.loginhandler\ndef login_handler():\n\tif g.user is not None:\n\t\treturn redirect(oid.get_next_url())\n\treturn oid.try_login('http://steamcommunity.com/openid')\n\n@oid.after_login\ndef create_or_login(resp):\n\tmatch = _steam_id_re.search(resp.identity_url)\n\tg.user = User.get_or_create(match.group(1), db_session)\n\tsteamdata = get_steam_userinfo(g.user.steam_id)\n\tg.user.steam_nickname = steamdata['personaname']\n\tg.user.steam_avatar = steamdata['avatar']\n\tdb_session.commit()\n\t\n\tsession['user_id'] = g.user.id\n\tsession['nickname'] = g.user.steam_nickname\n\tsession['avatar'] = g.user.steam_avatar\n\tif g.user.twitter_nickname != '':\n\t\tsession['twitter_user'] = g.user.twitter_nickname\n\n\tflash('You are logged in as %s' % g.user.steam_nickname)\n\n\treturn redirect(oid.get_next_url())\n\n@app.before_request\ndef before_request():\n\tg.user = None\n\tif 'user_id' in session:\n\t\tg.user = User.query.get(session['user_id'])\n\n@app.route('/logout')\ndef logout():\n\tsession.clear()\n\treturn redirect(oid.get_next_url())\n\n# twitter login\n\ntwitter = oauth.remote_app('twitter',\n\tbase_url='https://api.twitter.com/1.1/',\n\trequest_token_url='https://api.twitter.com/oauth/request_token',\n\taccess_token_url='https://api.twitter.com/oauth/access_token',\n\tauthorize_url='https://api.twitter.com/oauth/authorize',\n\tconsumer_key=app.config['TWITTER_CONSUMER_KEY'],\n\tconsumer_secret=app.config['TWITTER_CONSUMER_SECRET']\n)\n\n@twitter.tokengetter\ndef get_twitter_token(token=None):\n\treturn session.get('twitter_token')\n\n@app.route('/twitter_login')\ndef twitter_login_handler():\n\treturn twitter.authorize(callback=url_for('oauth_authorized', next=request.args.get('next') or request.referrer or None))\n\n@app.route('/twitter_logout')\ndef twitter_logout_handler():\n\tg.user = User.query.get(session['user_id'])\n\n\tg.user.twitter_access_token = ''\n\tg.user.twitter_access_token_secret = ''\n\tg.user.twitter_nickname = ''\n\tdb_session.commit()\n\n\tsession.pop('twitter_user', None)\n\n\tflash('You were signed out of Twitter.')\n\n\treturn redirect('/settings')\n\n@app.route('/oauth_authorized')\n@twitter.authorized_handler\ndef oauth_authorized(resp):\n\tnext_url = request.args.get('next') or url_for('dashboard')\n\tif resp is None:\n\t\tflash(u'You denied the request to sign in.')\n\t\treturn redirect(next_url)\n\n\tg.user = User.query.get(session['user_id'])\n\n\tg.user.twitter_access_token = resp['oauth_token']\n\tg.user.twitter_access_token_secret = resp['oauth_token_secret']\n\tg.user.twitter_nickname = resp['screen_name']\n\tdb_session.commit()\n\n\tsession['twitter_user'] = resp['screen_name']\n\t\n\tflash('You were signed in as %s' % resp['screen_name'])\n\n\treturn redirect(next_url)\n\n# start server\n\nif __name__ == '__main__':\n\tapp.run(port=38165, debug=True)\n", "sub_path": "dotabrag.py", "file_name": "dotabrag.py", "file_ext": "py", "file_size_in_byte": 6046, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.ext.openid.OpenID", "line_number": 22, "usage_type": "call"}, {"api_name": "flask_oauth.OAuth", "line_number": 24, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 26, "usage_type": "call"}, {"api_name": "database.init_db", "line_number": 28, "usage_type": "call"}, {"api_name": "database.db_session.remove", "line_number": 32, "usage_type": "call"}, {"api_name": "database.db_session", "line_number": 32, "usage_type": "name"}, {"api_name": "urllib.urlencode", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.json.load", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 41, "usage_type": "name"}, {"api_name": "urllib.urlopen", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 83, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 83, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 83, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 87, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 87, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 87, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 87, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 88, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 88, "usage_type": "name"}, {"api_name": "database.db_session.commit", "line_number": 89, "usage_type": "call"}, {"api_name": "database.db_session", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 104, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 104, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 104, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 104, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 114, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 114, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 124, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 131, "usage_type": "name"}, {"api_name": "models.User.get_or_create", "line_number": 131, "usage_type": "call"}, {"api_name": "database.db_session", "line_number": 131, "usage_type": "argument"}, {"api_name": "models.User", "line_number": 131, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 132, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 133, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 134, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 134, "usage_type": "name"}, {"api_name": "database.db_session.commit", "line_number": 135, "usage_type": "call"}, {"api_name": "database.db_session", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 137, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 137, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 137, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 138, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 138, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 138, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 139, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 140, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 140, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 141, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 143, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 149, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 149, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 151, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 151, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 151, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 151, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 151, "usage_type": "name"}, {"api_name": "flask.session.clear", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 155, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 171, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 171, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 175, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 175, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 175, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 175, "usage_type": "name"}, {"api_name": "flask.request.referrer", "line_number": 175, "usage_type": "attribute"}, {"api_name": "flask.g.user", "line_number": 179, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 179, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 179, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 179, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 179, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 179, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 181, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 181, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 182, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 182, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 183, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 183, "usage_type": "name"}, {"api_name": "database.db_session.commit", "line_number": 184, "usage_type": "call"}, {"api_name": "database.db_session", "line_number": 184, "usage_type": "name"}, {"api_name": "flask.session.pop", "line_number": 186, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 186, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 188, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 190, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 195, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 195, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 195, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 195, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 197, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 198, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 200, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 200, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 200, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 200, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 200, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 200, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 202, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 202, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 203, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 203, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 204, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 204, "usage_type": "name"}, {"api_name": "database.db_session.commit", "line_number": 205, "usage_type": "call"}, {"api_name": "database.db_session", "line_number": 205, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 207, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 209, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 211, "usage_type": "call"}]} +{"seq_id": "23336810", "text": "import cv2 as cv\nimport numpy as np\nimport imageio\nimport scipy.ndimage\nfrom skimage.measure import _structural_similarity\n\ndef dodge(front,back):\n result=front*255/(255-back+1)\n result[result>255]=255\n result[back==255]=255\n return result.astype('uint8')\n\ndef gray(bgr):\n bgr[:,:,0] = bgr[:,:,0] * 0.1114\n bgr[:,:,1] = bgr[:,:,1] * 0.587\n bgr[:,:,2] = bgr[:,:,2] * 0.299\n return bgr\n\nssim = _structural_similarity.compare_ssim\npath = '/home/cheeze/PycharmProjects/KJW/capstone_project/human2animal/transfer_network/image_transfer/test_transfer/'\ncategory = ['cat', 'dog', 'pig'] # 1. cat, 2. dog, 3. pig\n\ncat_path = path + category[0]\ndog_path = path + category[1]\npig_path = path + category[2]\nfor i in range(1,12):\n pic_human = '/home/cheeze/PycharmProjects/KJW/capstone_project/human2animal/transfer_network/image_transfer/test_human/human_%04d.jpg'%(i)\n pic_cat = cat_path + '/human_cat_%04d.jpg'%(i)\n pic_dog = dog_path + '/human_dog_%04d.jpg'%(i)\n pic_pig = pig_path + '/human_pig_%04d.jpg'%(i)\n\n # Read Image\n img_human = cv.imread(pic_human)\n img_human = cv.resize(img_human, (256, 256))\n img_cat = cv.imread(pic_cat)\n img_dog = cv.imread(pic_dog)\n img_pig = cv.imread(pic_pig)\n\n # Canny algorithm\n #human_gray = cv.cvtColor(img_human, cv.COLOR_BGR2GRAY)\n #cat_gray = cv.cvtColor(img_cat, cv.COLOR_BGR2GRAY)\n #dog_gray = cv.cvtColor(img_dog, cv.COLOR_BGR2GRAY)\n #pig_gray = cv.cvtColor(img_pig, cv.COLOR_BGR2GRAY)\n\n human_gray = gray(img_human)\n cat_gray = gray(img_cat)\n dog_gray = gray(img_dog)\n pig_gray = gray(img_pig)\n\n human_gray2 = 255 - human_gray\n cat_gray2 = 255 - cat_gray\n dog_gray2 = 255 - dog_gray\n pig_gray2 = 255 - pig_gray\n\n #human_edges = cv.Canny(human_gray, 170, 190)\n #cat_edges = cv.Canny(cat_gray, 170, 190)\n #dog_edges = cv.Canny(dog_gray, 170, 190)\n #pig_edges = cv.Canny(pig_gray, 170, 190)\n\n # Gaussian Blurring\n human_gauss = cv.GaussianBlur(human_gray2, (5,5), 150)\n cat_gauss = cv.GaussianBlur(cat_gray2, (5,5), 150)\n dog_gauss = cv.GaussianBlur(dog_gray2, (5,5), 150)\n pig_gauss = cv.GaussianBlur(pig_gray2, (5,5), 150)\n\n # Dodge processing\n human_dodge = dodge(human_gauss, human_gray)\n cat_dodge = dodge(cat_gauss, cat_gray)\n dog_dodge = dodge(dog_gauss, dog_gray)\n pig_dodge = dodge(pig_gauss, pig_gray)\n\n cv.imwrite('/home/cheeze/PycharmProjects/KJW/capstone_project/human2animal/transfer_network/image_transfer/edge_result/cat/cat_%04d.jpg'%(i), cat_dodge)\n cv.imwrite('/home/cheeze/PycharmProjects/KJW/capstone_project/human2animal/transfer_network/image_transfer/edge_result/dog/dog_%04d.jpg'%(i), dog_dodge)\n cv.imwrite('/home/cheeze/PycharmProjects/KJW/capstone_project/human2animal/transfer_network/image_transfer/edge_result/pig/pig_%04d.jpg'%(i), pig_dodge)\n cv.imwrite('/home/cheeze/PycharmProjects/KJW/capstone_project/human2animal/transfer_network/image_transfer/edge_result/human/human_%04d.jpg'%(i), human_dodge)\n\n human = np.asarray(human_gauss)\n cat = np.asarray(cat_gauss)\n dog = np.asarray(dog_gauss)\n pig = np.asarray(pig_gauss)\n\n cat_err = np.sum((human_dodge.astype(\"float\") - cat_dodge.astype(\"float\"))**2)\n cat_err /= float(human_gauss.shape[0] * human_gauss.shape[1])\n cat_ssim = ssim(human_dodge, cat_dodge)\n\n dog_err = np.sum((human_dodge.astype(\"float\") - dog_dodge.astype(\"float\"))**2)\n dog_err /= float(human_gauss.shape[0] * human_gauss.shape[1])\n dog_ssim = ssim(human_dodge, dog_dodge)\n\n pig_err = np.sum((human_dodge.astype(\"float\") - pig_dodge.astype(\"float\"))**2)\n pig_err /= float(human_gauss.shape[0] * human_gauss.shape[1])\n pig_ssim = ssim(human_dodge, pig_dodge)\n\n\n print(\"The MSE of %04dth cat is : %f\"%(i, cat_err))\n print(\"The MSE of %04dth dog is : %f\"%(i, dog_err))\n print(\"The MSE of %04dth pig is : %f\\n\"%(i, pig_err))\n print(\"The SSIM of %04dth cat is : %f\"%(i, cat_ssim))\n print(\"The SSIM of %04dth dog is : %f\"%(i, dog_ssim))\n print(\"The SSIM of %04dth pig is : %f\\n\\n\\n\"%(i, pig_ssim))\n\n\n", "sub_path": "1. capstone/3. Classifier/edge_and_similarity.py", "file_name": "edge_and_similarity.py", "file_ext": "py", "file_size_in_byte": 4087, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "skimage.measure._structural_similarity.compare_ssim", "line_number": 19, "usage_type": "attribute"}, {"api_name": "skimage.measure._structural_similarity", "line_number": 19, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "373178572", "text": "from PIL import Image, ImageDraw #Подключим необходимые библиотеки.\r\nimport random\r\nimport io\r\nclass kartinka:\r\n def __init__(self,img):\r\n self.image = Image.open(io.BytesIO(img))#Image.open(img)\r\n #self.image = img\r\n self.draw = ImageDraw.Draw(self.image) # Создаем инструмент для рисования\r\n self.width = self.image.size[0] # Определяем ширину\r\n self.height = self.image.size[1] # Определяем высоту\r\n self.pix = self.image.load() # Выгружаем значения пикселей\r\n\r\n def image_to_bytes(self):\r\n b=io.BytesIO()\r\n self.image.save(b, 'JPEG')\r\n image_bytes = b.getvalue()\r\n return image_bytes\r\n\r\n def negative(self):\r\n for i in range(self.width):\r\n for j in range(self.height):\r\n a = self.pix[i, j][0]\r\n b = self.pix[i, j][1]\r\n c = self.pix[i, j][2]\r\n self.draw.point((i, j), (255 - a, 255 - b, 255 - c))\r\n\r\n def shum(self,factor):#добавляем на картинку шум\r\n for i in range(self.width):\r\n for j in range(self.height):\r\n rand = random.randint(-factor, factor)\r\n a = self.pix[i, j][0] + rand\r\n b = self.pix[i, j][1] + rand\r\n c = self.pix[i, j][2] + rand\r\n if (a < 0):\r\n a = 0\r\n if (b < 0):\r\n b = 0\r\n if (c < 0):\r\n c = 0\r\n if (a > 255):\r\n a = 255\r\n if (b > 255):\r\n b = 255\r\n if (c > 255):\r\n c = 255\r\n self.draw.point((i, j), (a, b, c))", "sub_path": "BotTelegram/kartinka.py", "file_name": "kartinka.py", "file_ext": "py", "file_size_in_byte": 1827, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "PIL.Image.open", "line_number": 6, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 6, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 6, "usage_type": "call"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 8, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 8, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 14, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "19247294", "text": "# Copyright 2020 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom pants.backend.python.rules.pex import Pex\nfrom pants.backend.python.rules.pex_from_targets import PexFromTargetsRequest\nfrom pants.backend.python.rules.python_sources import PythonSourceFiles, PythonSourceFilesRequest\nfrom pants.backend.python.subsystems.ipython import IPython\nfrom pants.backend.python.target_types import PythonSources\nfrom pants.core.goals.repl import ReplImplementation, ReplRequest\nfrom pants.engine.fs import Digest, MergeDigests\nfrom pants.engine.rules import Get, MultiGet, collect_rules, rule\nfrom pants.engine.unions import UnionRule\n\n\nclass PythonRepl(ReplImplementation):\n name = \"python\"\n required_fields = (PythonSources,)\n\n\n@rule\nasync def create_python_repl_request(repl: PythonRepl) -> ReplRequest:\n pex_request = Get(\n Pex,\n PexFromTargetsRequest(\n (tgt.address for tgt in repl.targets),\n output_filename=\"python.pex\",\n include_source_files=False,\n ),\n )\n sources_request = Get(PythonSourceFiles, PythonSourceFilesRequest(repl.targets))\n pex, sources = await MultiGet(pex_request, sources_request)\n merged_digest = await Get(\n Digest, MergeDigests((pex.digest, sources.source_files.snapshot.digest))\n )\n return ReplRequest(\n digest=merged_digest,\n binary_name=pex.output_filename,\n env={\"PEX_EXTRA_SYS_PATH\": \":\".join(sources.source_roots)},\n )\n\n\nclass IPythonRepl(ReplImplementation):\n name = \"ipython\"\n required_fields = (PythonSources,)\n\n\n@rule\nasync def create_ipython_repl_request(repl: IPythonRepl, ipython: IPython) -> ReplRequest:\n pex_request = Get(\n Pex,\n PexFromTargetsRequest(\n (tgt.address for tgt in repl.targets),\n output_filename=\"ipython.pex\",\n entry_point=ipython.entry_point,\n additional_requirements=ipython.all_requirements,\n include_source_files=True,\n ),\n )\n sources_request = Get(PythonSourceFiles, PythonSourceFilesRequest(repl.targets))\n pex, sources = await MultiGet(pex_request, sources_request)\n merged_digest = await Get(\n Digest, MergeDigests((pex.digest, sources.source_files.snapshot.digest))\n )\n return ReplRequest(\n digest=merged_digest,\n binary_name=pex.output_filename,\n env={\"PEX_EXTRA_SYS_PATH\": \":\".join(sources.source_roots)},\n )\n\n\ndef rules():\n return [\n *collect_rules(),\n UnionRule(ReplImplementation, PythonRepl),\n UnionRule(ReplImplementation, IPythonRepl),\n ]\n", "sub_path": "src/python/pants/backend/python/rules/repl.py", "file_name": "repl.py", "file_ext": "py", "file_size_in_byte": 2652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pants.core.goals.repl.ReplImplementation", "line_number": 15, "usage_type": "name"}, {"api_name": "pants.backend.python.target_types.PythonSources", "line_number": 17, "usage_type": "name"}, {"api_name": "pants.engine.rules.Get", "line_number": 22, "usage_type": "call"}, {"api_name": "pants.backend.python.rules.pex.Pex", "line_number": 23, "usage_type": "argument"}, {"api_name": "pants.backend.python.rules.pex_from_targets.PexFromTargetsRequest", "line_number": 24, "usage_type": "call"}, {"api_name": "pants.engine.rules.Get", "line_number": 30, "usage_type": "call"}, {"api_name": "pants.backend.python.rules.python_sources.PythonSourceFiles", "line_number": 30, "usage_type": "argument"}, {"api_name": "pants.backend.python.rules.python_sources.PythonSourceFilesRequest", "line_number": 30, "usage_type": "call"}, {"api_name": "pants.engine.rules.MultiGet", "line_number": 31, "usage_type": "call"}, {"api_name": "pants.engine.rules.Get", "line_number": 32, "usage_type": "call"}, {"api_name": "pants.engine.fs.Digest", "line_number": 33, "usage_type": "argument"}, {"api_name": "pants.engine.fs.MergeDigests", "line_number": 33, "usage_type": "call"}, {"api_name": "pants.core.goals.repl.ReplRequest", "line_number": 35, "usage_type": "call"}, {"api_name": "pants.engine.rules.rule", "line_number": 20, "usage_type": "name"}, {"api_name": "pants.core.goals.repl.ReplRequest", "line_number": 21, "usage_type": "name"}, {"api_name": "pants.core.goals.repl.ReplImplementation", "line_number": 42, "usage_type": "name"}, {"api_name": "pants.backend.python.target_types.PythonSources", "line_number": 44, "usage_type": "name"}, {"api_name": "pants.backend.python.subsystems.ipython.IPython", "line_number": 48, "usage_type": "name"}, {"api_name": "pants.engine.rules.Get", "line_number": 49, "usage_type": "call"}, {"api_name": "pants.backend.python.rules.pex.Pex", "line_number": 50, "usage_type": "argument"}, {"api_name": "pants.backend.python.rules.pex_from_targets.PexFromTargetsRequest", "line_number": 51, "usage_type": "call"}, {"api_name": "pants.engine.rules.Get", "line_number": 59, "usage_type": "call"}, {"api_name": "pants.backend.python.rules.python_sources.PythonSourceFiles", "line_number": 59, "usage_type": "argument"}, {"api_name": "pants.backend.python.rules.python_sources.PythonSourceFilesRequest", "line_number": 59, "usage_type": "call"}, {"api_name": "pants.engine.rules.MultiGet", "line_number": 60, "usage_type": "call"}, {"api_name": "pants.engine.rules.Get", "line_number": 61, "usage_type": "call"}, {"api_name": "pants.engine.fs.Digest", "line_number": 62, "usage_type": "argument"}, {"api_name": "pants.engine.fs.MergeDigests", "line_number": 62, "usage_type": "call"}, {"api_name": "pants.core.goals.repl.ReplRequest", "line_number": 64, "usage_type": "call"}, {"api_name": "pants.engine.rules.rule", "line_number": 47, "usage_type": "name"}, {"api_name": "pants.core.goals.repl.ReplRequest", "line_number": 48, "usage_type": "name"}, {"api_name": "pants.engine.rules.collect_rules", "line_number": 73, "usage_type": "call"}, {"api_name": "pants.engine.unions.UnionRule", "line_number": 74, "usage_type": "call"}, {"api_name": "pants.core.goals.repl.ReplImplementation", "line_number": 74, "usage_type": "argument"}, {"api_name": "pants.engine.unions.UnionRule", "line_number": 75, "usage_type": "call"}, {"api_name": "pants.core.goals.repl.ReplImplementation", "line_number": 75, "usage_type": "argument"}]} +{"seq_id": "169656142", "text": "import pygame\nfrom network import Network\nimport threading\nimport time\nimport json\nimport random\n\nusername = input(\"Nome do jogador: \")\nusers = (username,)\nnetwork_received = None\n\n\n# Tamanho da tela\nWIDTH = 1200 # Largura da tela.\nHEIGHT = 600 # Altura da tela.\nMARGIN = 5 # Margem entre as celulas.\n# Tela\ntela = pygame.display.set_mode([WIDTH, HEIGHT])\n# Cores\ncor_branca = (255, 255, 255)\ncor_cinza = (105, 105, 105)\ncor_cinza_fosco = (108, 123, 139)\ncor_cinza_claro = (220, 220, 220)\ncor_vermelho = (250, 128, 114)\ncor_vermelho_escuro = (165, 42, 42)\ncor_verde_escuro = (60, 179, 113)\ncor_verde = (152, 251, 152)\ncor_preta = (0,0,0)\n# Superficies\nsup = pygame.Surface((WIDTH/4, HEIGHT))\nsup2 = pygame.Surface((WIDTH/4, HEIGHT))\nsup3 = pygame.Surface((175,HEIGHT))\nsup.fill(cor_verde)\nsup2.fill(cor_vermelho)\nsup3.fill(cor_cinza_fosco)\n\n# Retangulos\nretangulo = pygame.Rect(0,0, 175, 100)\nrect_acertos = pygame.Rect(175,0, 300, 100)\nrect_erros = pygame.Rect(900,0, 300, 100)\n\n# Fontes\npygame.font.init()\nfont_padrao = pygame.font.get_default_font()\nfonte_jogo = pygame.font.SysFont(font_padrao, 30)\n\n# Textos Padrão\nacertos = fonte_jogo.render('Acertos: ', 1, (cor_branca))\nerros = fonte_jogo.render('Erros: ', 1, (cor_branca))\njogadores = fonte_jogo.render('Jogadores: ', 1, (cor_branca))\n# Tempo da rodada\ntempo_rodada = 120\n\n# Dados\ndado0 = ['R', 'I', 'F', 'O', 'B', 'X']\ndado1 = ['I', 'F', 'E', 'H', 'E', 'Y']\ndado2 = ['D', 'E', 'N', 'O', 'W', 'S']\ndado3 = ['U', 'T', 'O', 'K', 'N', 'D']\ndado4 = ['H', 'M', 'S', 'R', 'A', 'O']\ndado5 = ['L', 'U', 'P', 'E', 'T', 'S']\ndado6 = ['A', 'C', 'I', 'T', 'O', 'A']\ndado7 = ['Y', 'L', 'G', 'K', 'U', 'E']\ndado8 = ['Q', 'B', 'M', 'J', 'O', 'A']\ndado9 = ['E', 'H', 'I', 'S', 'P', 'N']\ndado10 = ['V', 'E', 'T', 'I', 'G', 'N']\ndado11 = ['B', 'A', 'L', 'I', 'Y', 'T']\ndado12 = ['E', 'Z', 'A', 'V', 'N', 'D']\ndado13 = ['R', 'A', 'L', 'E', 'S', 'C']\ndado14 = ['U', 'W', 'I', 'L', 'R', 'G']\ndado15 = ['P', 'A', 'C', 'E', 'M', 'D']\n\nlista_dados = [dado0,dado1,dado2,dado3,dado4,dado5,dado6,dado7,dado8,dado9,dado10,dado11,dado12,dado13,dado14,dado15]\n\n#### Inicio dos metodos estaticos ####\ndef quitGame():\n pygame.quit()\n quit()\n\ndef enviar():\n print('enviado!')\n pass\ndef pontoJogadores(p):\n pontos = p\n if pontos > 0:\n pt = fonte_jogo.render(str(pontos), 0, cor_branca)\n tela.blit(pt, (140, 103))\ndef players(u):\n plays = u.__len__()\n h = 103\n w = 30\n if u.__len__()>0:\n while plays > 0:\n p = fonte_jogo.render(u[plays-1], 1, cor_branca)\n tela.blit(p, (w,h))\n h = h + 20\n plays -= 1\n\n\ndef certo_errado(err, corr):\n cor = corr.__len__()\n er = err.__len__()\n w1 = 925\n w2 = 200\n he = 103\n hc = 103\n if err.__len__()> 0:\n while er > 0:\n e = fonte_jogo.render(err[er-1], 1, cor_vermelho_escuro)\n tela.blit(e, (w1, he))\n he = he+20\n er -=1\n if he > 580:\n w1 = w1 + 145\n he = 103\n if corr.__len__()> 0:\n while cor > 0:\n e = fonte_jogo.render(corr[cor-1], 1, cor_verde_escuro)\n tela.blit(e, (w2, hc))\n hc = hc+20\n cor -=1\n if hc > 580:\n w2 = w2 + 145\n hc = 103\n\ndef text_objects(text, font):\n textSurface = font.render(text, True, cor_preta)\n return textSurface, textSurface.get_rect()\n\ndef button(msg, x, y, w, h, i, a, action=None):\n # Eventos do mouse nos botoes\n mouse = pygame.mouse.get_pos()\n click = pygame.mouse.get_pressed()\n if x + w > mouse[0] > x and y + h > mouse[1] > y:\n pygame.draw.rect(tela, a, (x,y,w,h))\n if click[0] == 1 and action != None:\n action()\n else:\n pygame.draw.rect(tela, i, (x,y,w,h))\n textSuperfice, textRect = text_objects(msg,fonte_jogo)\n textRect.center = ((x + (w/2)),(y +(h/2)))\n tela.blit(textSuperfice, textRect)\n\n#### Fim dos metodos estaticos ####\nclass Game():\n def __init__(self):\n pygame.init()\n # network\n self.network = Network()\n self.network.connect()\n self.network.login(username)\n netThread = threading.Thread(target=self.update_network)\n netThread.start()\n pygame.display.set_caption(\"Boogle\")\n relogio = pygame.time.Clock()\n\n arq = open('palavras.txt', 'r')\n lista_palavras = arq.readlines()\n\n sair = False\n b1 = random.randint(0, 5)\n b2 = random.randint(0, 5)\n b3 = random.randint(0, 5)\n b4 = random.randint(0, 5)\n b5 = random.randint(0, 5)\n b6 = random.randint(0, 5)\n b7 = random.randint(0, 5)\n b8 = random.randint(0, 5)\n b9 = random.randint(0, 5)\n b10 = random.randint(0, 5)\n b11 = random.randint(0, 5)\n b12 = random.randint(0, 5)\n b13 = random.randint(0, 5)\n b14 = random.randint(0, 5)\n b15 = random.randint(0, 5)\n b16 = random.randint(0, 5)\n pontos = 0\n lista_clicados = []\n lista_digitados = []\n lista_erradas = []\n lista_corretas = []\n\n clicado1 = False\n clicado2 = False\n clicado3 = False\n clicado4 = False\n clicado5 = False\n clicado6 = False\n clicado7 = False\n clicado8 = False\n clicado9 = False\n clicado10 = False\n clicado11 = False\n clicado12 = False\n clicado13 = False\n clicado14 = False\n clicado15 = False\n clicado16 = False\n while sair != True:\n for event in pygame.event.get():\n # Eventos\n if event.type == pygame.QUIT:\n sair = True\n if event.type == pygame.MOUSEBUTTONDOWN:\n # User clicks the mouse. Get the position\n pos = pygame.mouse.get_pos()\n print(\"Click \", pos)\n mouse = pygame.mouse.get_pos()\n\n if 480 + 100 > mouse[0] > 480 and 40 + 100 > mouse[1] > 40:\n if clicado1 == False:\n # print (dado0[b1])\n lista_clicados.append(dado0[b1])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado1 = True\n else:\n print(lista_clicados.__len__())\n print(dado0[b1])\n\n elif 585 + 100 > mouse[0] > 585 and 40 + 100 > mouse[1] > 40:\n if clicado2 == False:\n # print (dado1[b2])\n lista_clicados.append(dado1[b2])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado2 = True\n else:\n print(lista_clicados.__len__())\n print(dado1[b2])\n elif 690 + 100 > mouse[0] > 690 and 40 + 100 > mouse[1] > 40:\n if clicado3 == False:\n # print (dado2[b3])\n lista_clicados.append(dado2[b3])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado3 = True\n else:\n print(lista_clicados.__len__())\n print(dado2[b3])\n elif 795 + 100 > mouse[0] > 795 and 40 + 100 > mouse[1] > 40:\n if clicado4 == False:\n # print (dado3[b4])\n lista_clicados.append(dado3[b4])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado4 = True\n else:\n print(lista_clicados.__len__())\n print(dado3[b4])\n elif 480 + 100 > mouse[0] > 480 and 145 + 100 > mouse[1] > 145:\n if clicado5 == False:\n # print (dado4[b5])\n lista_clicados.append(dado4[b5])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado5 = True\n else:\n print(lista_clicados.__len__())\n print(dado4[b5])\n elif 585 + 100 > mouse[0] > 585 and 145 + 100 > mouse[1] > 145:\n if clicado6 == False:\n # print (dado5[b6])\n lista_clicados.append(dado5[b6])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado6 = True\n else:\n print(lista_clicados.__len__())\n print(dado5[b6])\n elif 690 + 100 > mouse[0] > 690 and 145 + 100 > mouse[1] > 145:\n if clicado7 == False:\n # print (dado6[b7])\n lista_clicados.append(dado6[b7])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado7 = True\n else:\n print(lista_clicados.__len__())\n print(dado6[b7])\n elif 795 + 100 > mouse[0] > 795 and 145 + 100 > mouse[1] > 145:\n if clicado8 == False:\n # print(dado7[b8])\n lista_clicados.append(dado7[b8])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado8 = True\n else:\n print(lista_clicados.__len__())\n print(dado7[b8])\n elif 480 + 100 > mouse[0] > 480 and 250 + 100 > mouse[1] > 250:\n if clicado9 == False:\n # print (dado8[b9])\n lista_clicados.append(dado8[b9])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado9 = True\n else:\n print(lista_clicados.__len__())\n print(dado8[b9])\n elif 585 + 100 > mouse[0] > 585 and 250 + 100 > mouse[1] > 250:\n if clicado10 == False:\n # print (dado9[b10])\n lista_clicados.append(dado9[b10])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado10 = True\n else:\n print(lista_clicados.__len__())\n print(dado9[b10])\n elif 690 + 100 > mouse[0] > 690 and 250 + 100 > mouse[1] > 250:\n if clicado11 == False:\n # print (dado10[b11])\n lista_clicados.append(dado10[b11])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado11 = True\n else:\n print(lista_clicados.__len__())\n print(dado10[b11])\n elif 795 + 100 > mouse[0] > 795 and 250 + 100 > mouse[1] > 250:\n if clicado12 == False:\n # print (dado11[b12])\n lista_clicados.append(dado11[b12])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado12 = True\n else:\n print(lista_clicados.__len__())\n print(dado11[b12])\n elif 480 + 100 > mouse[0] > 480 and 355 + 100 > mouse[1] > 355:\n if clicado13 == False:\n # print (dado12[b13])\n lista_clicados.append(dado12[b13])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado13 = True\n else:\n print(lista_clicados.__len__())\n print(dado12[b13])\n elif 585 + 100 > mouse[0] > 585 and 355 + 100 > mouse[1] > 355:\n if clicado14 == False:\n # print (dado13[b14])\n lista_clicados.append(dado13[b14])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado14 = True\n else:\n print(lista_clicados.__len__())\n print(dado13[b14])\n elif 690 + 100 > mouse[0] > 690 and 355 + 100 > mouse[1] > 355:\n if clicado15 == False:\n # print (dado14[b15])\n lista_clicados.append(dado14[b15])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado15 = True\n else:\n print(lista_clicados.__len__())\n print(dado14[b15])\n elif 795 + 100 > mouse[0] > 795 and 355 + 100 > mouse[1] > 355:\n if clicado16 == False:\n # print (dado15[b16])\n lista_clicados.append(dado15[b16])\n print(lista_clicados.__len__())\n print(lista_clicados[0])\n clicado16 = True\n else:\n print(lista_clicados.__len__())\n print(dado15[b16])\n elif 515 + 130 > mouse[0] > 425 and 530 + 50 > mouse[1] > 530:\n lista_clicados.clear()\n clicado1 = False\n clicado2 = False\n clicado3 = False\n clicado4 = False\n clicado5 = False\n clicado6 = False\n clicado7 = False\n clicado8 = False\n clicado9 = False\n clicado10 = False\n clicado11 = False\n clicado12 = False\n clicado13 = False\n clicado14 = False\n clicado15 = False\n clicado16 = False\n # print((\"\").join(lista_palavras))\n if lista_corretas.__len__() > 0:\n print('corretas:' + str(lista_corretas))\n if lista_erradas.__len__() > 0:\n print('erradas:' + str(lista_erradas))\n\n elif 740 + 130 > mouse[0] > 425 and 530 + 50 > mouse[1] > 530:\n if click_list.__len__() > 0:\n a = ('').join(click_list)\n if a not in lista_digitados:\n lista_digitados.append(('').join(click_list))\n if a + '\\n' not in (lista_palavras):\n lista_erradas.append(a)\n print(\"erradas: \" + str(lista_erradas))\n else:\n lista_corretas.append(a)\n print(\"corretas: \" + str(lista_corretas))\n pontos = pontos + 10\n lista_clicados.clear()\n clicado1 = False\n clicado2 = False\n clicado3 = False\n clicado4 = False\n clicado5 = False\n clicado6 = False\n clicado7 = False\n clicado8 = False\n clicado9 = False\n clicado10 = False\n clicado11 = False\n clicado12 = False\n clicado13 = False\n clicado14 = False\n clicado15 = False\n clicado16 = False\n else:\n lista_clicados.clear()\n clicado1 = False\n clicado2 = False\n clicado3 = False\n clicado4 = False\n clicado5 = False\n clicado6 = False\n clicado7 = False\n clicado8 = False\n clicado9 = False\n clicado10 = False\n clicado11 = False\n clicado12 = False\n clicado13 = False\n clicado14 = False\n clicado15 = False\n clicado16 = False\n for item in lista_digitados:\n print(item)\n else:\n lista_clicados.clear()\n for item in lista_digitados:\n print(item)\n relogio.tick(20)\n # Criando os elementos de tela\n tela.fill(cor_cinza_fosco)\n tela.blit(sup, [175, 0])\n tela.blit(sup2, [WIDTH * 3 / 4, 0])\n tela.blit(sup3, [0, 30])\n pygame.draw.rect(tela, cor_cinza, retangulo)\n pygame.draw.rect(tela, cor_verde_escuro, rect_acertos)\n pygame.draw.rect(tela, cor_vermelho_escuro, rect_erros)\n # mostrar jogadores na tela\n players(users)\n # mostrar pontos na tela\n pontoJogadores(pontos)\n # mostrar erros e acertos na tela\n certo_errado(lista_erradas, lista_corretas)\n # Eventos e criação do butao\n button('Limpar', 515, 530, 130, 50, cor_cinza_claro, cor_cinza, None)\n button('Enviar', 740, 530, 130, 50, cor_cinza_claro, cor_cinza, enviar)\n button(dado0[b1], 480, 40, 100, 100, cor_branca, cor_verde, None)\n button(dado1[b2], 585, 40, 100, 100, cor_branca, cor_verde, None)\n button(dado2[b3], 690, 40, 100, 100, cor_branca, cor_verde, None)\n button(dado3[b4], 795, 40, 100, 100, cor_branca, cor_verde, None)\n button(dado4[b5], 480, 145, 100, 100, cor_branca, cor_verde, None)\n button(dado5[b6], 585, 145, 100, 100, cor_branca, cor_verde, None)\n button(dado6[b7], 690, 145, 100, 100, cor_branca, cor_verde, None)\n button(dado7[b8], 795, 145, 100, 100, cor_branca, cor_verde, None)\n button(dado8[b9], 480, 250, 100, 100, cor_branca, cor_verde, None)\n button(dado9[b10], 585, 250, 100, 100, cor_branca, cor_verde, None)\n button(dado10[b11], 690, 250, 100, 100, cor_branca, cor_verde, None)\n button(dado11[b12], 795, 250, 100, 100, cor_branca, cor_verde, None)\n button(dado12[b13], 480, 355, 100, 100, cor_branca, cor_verde, None)\n button(dado13[b14], 585, 355, 100, 100, cor_branca, cor_verde, None)\n button(dado14[b15], 690, 355, 100, 100, cor_branca, cor_verde, None)\n button(dado15[b16], 795, 355, 100, 100, cor_branca, cor_verde, None)\n\n # Textos na tela\n tela.blit(acertos, (195, 35))\n tela.blit(erros, (925, 35))\n tela.blit(jogadores, (20, 35))\n\n\n # Mostrar letras digitadas\n click_list = lista_clicados\n i = 0\n while i != click_list.__len__():\n digitados = fonte_jogo.render(('').join(click_list), 0, cor_branca)\n i = i + 1\n tela.blit(digitados, (585, 480))\n\n # Timer\n segundos = pygame.time.get_ticks() / 1000\n resto = int(segundos)\n timer = tempo_rodada\n timer -= resto\n contador = fonte_jogo.render('Tempo: ' + str(timer), 0, cor_branca)\n tela.blit(contador, (630, 3))\n\n # Atualiza a tela\n pygame.display.update()\n if timer == 0:\n sair = True\n pygame.quit()\n\n def update(self):\n\n pass\n\n \n def update_network(self):\n global network_received\n while True:\n data = {'action': 'update', 'id': username}\n network_received = json.loads(self.network.update(json.dumps(data)))\n time.sleep(0.05)\n\nGame()\n\n\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 21736, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pygame.display.set_mode", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.font.init", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.font.get_default_font", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 131, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 131, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 132, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 134, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 138, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 146, "usage_type": "call"}, {"api_name": "network.Network", "line_number": 148, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 151, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 153, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 154, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 154, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 160, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 161, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 162, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 163, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 164, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 165, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 166, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 167, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 168, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 169, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 170, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 171, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 172, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 173, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 174, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 175, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 199, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 199, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 203, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 205, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 205, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 207, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 207, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 453, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 453, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 454, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 454, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 455, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 455, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 497, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 497, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 505, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 505, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 508, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 519, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 519, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 520, "usage_type": "call"}]} +{"seq_id": "308642131", "text": "import requests\nimport json\nfrom argparse import ArgumentParser\nimport netifaces as ni\n\ndef post_to_slack(url, message):\n if url:\n requests.post(url, headers={'content-type': 'application/json'}, data=json.dumps({'text': message}))\n\ndef get_ip(interface):\n return ni.ifaddresses(interface)[ni.AF_INET][0]['addr']\n\ndef main():\n parser = ArgumentParser()\n parser.add_argument('interface')\n parser.add_argument('-s', '--slack')\n parser.add_argument('--debug', action='store_true', default=False)\n args = parser.parse_args()\n\n ip = get_ip(args.interface)\n msg_list = [f'[INFO] IP address: {ip}']\n\n if msg_list:\n post_to_slack(args.slack, '\\n'.join(msg_list))\n\nif __name__ == '__main__':\n main()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 737, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "requests.post", "line_number": 8, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 8, "usage_type": "call"}, {"api_name": "netifaces.ifaddresses", "line_number": 11, "usage_type": "call"}, {"api_name": "netifaces.AF_INET", "line_number": 11, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "367152716", "text": "# 이 코드에서는 Python으로 손쉽게 Computer Vision API를 호출하는 방법을 소개해 드립니다.\n# Comupter Vision 이미지 분석 API method에 대한 문서는 이곳에서 참고하세요.\n# https://westus.dev.cognitive.microsoft.com/docs/services/5adf991815e1060e6355ad44/operations/56f91f2e778daf14a499e1fa\n\n# SQLER 강좌의 내용 https://www.sqler.com/board_CSharp/1095782 을 참조하세요.\n\n# requests 라이브러리를 사용하여 Python에서 간단하게 REST API 호출을 진행합니다.\nimport requests\n\n# 웹 서비스의 응답(Response)를 처리하려면 json 라이브러리가 필요합니다.\nimport json\n\n# SUBSCRIPTION_KEY를 자신의 Computer Vision 서비스의 키로 수정하세요.\nSUBSCRIPTION_KEY = \"xxxxxxxxxxxxxxxxxxxxxxxxxxx\"\n\n# 아래 Vision_service_address를 자신에게 할당된 Computer Vision API 서비스의 주소로 수정해야 합니다. \n# 유료 가입 계정과 7일 체험 계정의 endpoint가 다를 수 있습니다. 맨 뒤의 \"/v2.0/\"을 확인하세요.\nvision_service_address = \"https://westcentralus.api.cognitive.microsoft.com/vision/v2.0/\" \n\n# 호출하려는 API 함수의 이름을 주소에 추가합니다.\naddress = vision_service_address + \"analyze\"\n\n# analyze image 함수의 문서에 따르면 세 가지의 Optional(선택적) 파라미터가 있습니다 : language, details, visualFeatures 파라미터\nparameters = {'visualFeatures':'Description,Color',\n 'language':'en'}\n\n# 분석할 이미지가 포함된 파일을 열어서 파일 오브젝트로 가져옵니다.\nimage_path = \"./TestImages/PolarBear.jpg\"\nimage_data = open(image_path, \"rb\").read()\n\n# analyze image 함수 문서에서 기술한대로, HTTP 헤더에 구독 키와 content-type을 지정합니다.\n# content-type 값은 \"application/octet-stream\" 입니다.\nheaders = {'Content-Type': 'application/octet-stream',\n 'Ocp-Apim-Subscription-Key': SUBSCRIPTION_KEY}\n\n# analyze image 함수 문서에서 가이드 하는 것처럼, HTTP POST 방식으로 함수를 호출합니다.\nresponse = requests.post(address, headers=headers, params=parameters, data=image_data)\n\n# HTTP 호출에서 오류가 생기면, 예외를 발생 시킵니다.\nresponse.raise_for_status()\n\n# 리턴 받은 JSON 결과를 출력합니다.\nresults = response.json()\nprint(json.dumps(results))\n\n\n# description에 있는 모든 태그를 인쇄합니다.\nprint()\nprint('all tags')\nfor item in results['description']['tags']:\n print(item)\n\n# description의 첫 번째 태그를 인쇄합니다.\nprint()\nprint('first_tag')\nprint(results['description']['tags'][0])\n\n\n", "sub_path": "python-for-beginners/17 - JSON/read_key_pair_list.py", "file_name": "read_key_pair_list.py", "file_ext": "py", "file_size_in_byte": 2654, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "requests.post", "line_number": 37, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "378694801", "text": "## ConcentrationPlotter returns a map of the difference between two concentration plots for the same area, latitude and longitude\r\n## Can be run standalone (use main to pass arguments if so desired) or use a python script to pass through values to generate multiple plots\r\n\r\nimport numpy as np\r\nimport os\r\nimport rpnpy.librmn.all as rmn\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.basemap import Basemap\r\nfrom math import cos, radians\r\nimport errno\r\nimport logging\r\n\r\n## constants\r\ncurrentDir = os.path.dirname(os.path.abspath(__file__))\r\ndefaultPathStnd = os.path.join(currentDir,'FST_files/GEMMACH_dev_rev_67537156/20100710000000/model/')\r\ndefaultPathAlt = os.path.join(currentDir,'FST_files/adomKPPKPPB_adomYB/20100710000000/model/')\r\ndefaultSave = currentDir\r\n# defaultFile is the name of the FST files to be compared\r\ndefaultFile = '2010071000_001'\r\ndefaultIp1=76696048\r\n# defaultSpc not in list format since this function only takes one species\r\ndefaultSpc='TO3'\r\ndefaultDiffCmap = 'RdBu'\r\ndefaultConcCmap = 'spectral'\r\ndefaultRev = True\r\ndefaultExt = 'neither'\r\ndefaultProjection = 'stere'\r\ndefaultBins = None\r\n# defaultClear removes a set of x/y coordinates from the data\r\ndefaultClear = 0\r\n# buff indicates the degress of extra buffer space to be added to the map dimensions. May be needed if the map does not display all of the data\r\nbuff = 2\r\nR=6400E3 #radius of earth\r\n# cmaps is a list of all possible default colormaps. Currently the list is incomplete, though maps not in this list may still be plotted\r\ncmaps = ['RdBu', 'Greys','cubehelix','jet','spectral']\r\n\r\ndef getConc(filePath, level, spc):\r\n \"\"\" Get the concentration data for an FST file.\r\n\r\n Returns the data in an array, the datakey as a key and the fileID of the FST file.\r\n \"\"\"\r\n\r\n try:\r\n fileID = rmn.fstopenall(filePath,rmn.FST_RO)\r\n dataKey = rmn.fstinf(fileID,nomvar=spc,ip1=level)['key']\r\n dataRec = rmn.fstluk(dataKey)\r\n concData = dataRec['d']\r\n print ('File {} recorded'.format(filePath))\r\n return {'concData':concData, 'dataKey':dataKey, 'fileID':fileID}\r\n except TypeError:\r\n print('Unable to record file {}. Please see log for details'.format(filePath))\r\n # log an error into the log file\r\n logging.warning('nomvar {} and ip1 {} could not be found for file {}.'.format(fileID, spc, level, filePath))\r\n pass\r\n\r\ndef concDiff(concDataStnd, concDataAlt):\r\n \"\"\" Get the differences between two sets of concentration data.\r\n\r\n Returns the difference between two concentration data sets in an array.\r\n \"\"\"\r\n\r\n # Get the dimensions of the difference data, assume both concentration data sets are the same size\r\n dim1 = concDataStnd.shape[0]\r\n dim2 = concDataStnd.shape[1]\r\n # Initialize an array of zeros.\r\n diffData = np.zeros((dim1,dim2))\r\n # Replace the zeros with differences between the concentrations.\r\n # Note that the difference should be Model-Base\r\n for i in range(dim1):\r\n for j in range(dim2):\r\n diffVal = concDataAlt[i][j]-concDataStnd[i][j]\r\n diffData[i][j] = diffVal\r\n return diffData\r\n\r\ndef getGrid(dataKey, fileID):\r\n \"\"\" Get the grid details in form of lon/lat of the FST file. \"\"\"\r\n\r\n # Get the file metadata, then add on the keypair for 'iunit'\r\n fileMeta = rmn.fstprm(dataKey)\r\n fileMeta['iunit'] = fileID\r\n # Get the grid data and decode it\r\n gridData = rmn.ezqkdef(fileMeta)\r\n gridDecode = rmn.decodeGrid(gridData)\r\n llGridData = rmn.gdll(gridData)\r\n latData = llGridData['lat']\r\n lonData = llGridData['lon']\r\n return {'gridll':llGridData,'gridID':gridData, 'latData':latData,'lonData':lonData}\r\n\r\ndef closeFST(fileID):\r\n \"\"\" Closes the FST file once relevant data has been saved. \"\"\"\r\n\r\n rmn.fstcloseall(fileID)\r\n print ('File has been closed.')\r\n\r\ndef gridCheck(gridStnd, gridAlt):\r\n \"\"\" Does a sanity check in case the grids are different for the FST files. \"\"\"\r\n\r\n if (np.array_equiv(gridStnd['lat'],gridAlt['lat'])) and (np.array_equiv(gridStnd['lon'],gridAlt['lon'])):\r\n True\r\n else:\r\n print('The area of the files do not match each other. Program may not function as expected. See log for more details.')\r\n logging.warning('Grids for current run do not match up. The differences are : \\n' + str(np.setdiff1d(gridStnd,gridAlt)))\r\n\r\ndef maxDimensions(lowLon,highLon,lowLat,highLat,buff):\r\n \"\"\" Get the maximum dimensions of the map.\r\n\r\n Note that this function is still underdevelopment and may not represent the ideal dimensions for the resulting map.\r\n \"\"\"\r\n\r\n lonDiff = radians(highLon - lowLon)\r\n latDiff = radians(highLat - lowLat)\r\n radBuffer = 2*(radians(buff))\r\n yDist = R*(latDiff + radBuffer)\r\n Rad1 = R*cos(lowLat)\r\n Rad2 = R*cos(highLat)\r\n xDist1 = Rad1*(lonDiff + radBuffer)\r\n xDist2 = Rad2*(lonDiff + radBuffer)\r\n xDist = (xDist1+xDist2)/2\r\n return {'xDist':xDist, 'yDist':yDist}\r\n\r\ndef midLatLon(lonData, latData):\r\n \"\"\" Provides an alternate manner in which to calculate the central lon/lat values. \"\"\"\r\n\r\n ni = len(lonData)\r\n nj = len(lonData[0])\r\n #find the middle value between these\r\n if ni%2==0:\r\n if nj%2==0:\r\n midLon=(lonData[ni//2-1,nj//2]+lonData[ni//2,nj//2])/2\r\n midLat=(latData[ni//2,nj//2-1]+latData[ni//2,nj//2])/2\r\n else:\r\n midLon=(lonData[ni//2-1,nj//2]+lonData[ni//2,nj//2])/2\r\n midlat=(latData[ni//2,nj//2-1]+latData[ni//2,nj//2])/2\r\n else:\r\n if nj%2==0:\r\n midLon=(lonData[ni//2,nj//2]+lonData[ni//2,nj//2+1])/2\r\n midLat=(latData[ni//2,nj//2]+latData[ni//2,nj//2+1])/2\r\n else:\r\n midLon=lonData[ni//2,nj//2]\r\n midLat=latData[ni//2,nj//2]\r\n return {'midLat':midLat, 'midLon':midLon}\r\n\r\ndef reverseName(mapType, reverse):\r\n \"\"\" Returns the reverse mapType if reverse=True, otherwise returns mapType. \"\"\"\r\n\r\n if reverse == True:\r\n mapType = mapType + '_r'\r\n return mapType\r\n else:\r\n return mapType\r\n\r\ndef isCmap(mapType):\r\n \"\"\" Checks to see if the mapType is in the list of default colormaps. \"\"\"\r\n\r\n if mapType in cmaps:\r\n return True\r\n else:\r\n print('Warning: Type mapType is not in the list of cmaps, may resort to default cmap.')\r\n # TODO: If cmap is invalid, set cmap = default cmap\r\n # note that this behaviour might be default! May set to cmap 'jet' though\r\n\r\ndef cmapType(cmap,reverse):\r\n \"\"\" Returns map details. \"\"\"\r\n\r\n isCmap(cmap)\r\n mapType = reverseName(cmap,reverse)\r\n return mapType\r\n\r\ndef makeDir(path):\r\n \"\"\" Creates a directory if it doesn't exist.\r\n\r\n Existence errors will be ignored. All other errors will be raised.\r\n \"\"\"\r\n\r\n try:\r\n os.makedirs(path)\r\n except OSError as exc:\r\n if exc.errno == errno.EEXIST and os.path.isdir(path):\r\n pass\r\n else:\r\n raise\r\n\r\ndef plotConc(concData, lonData, latData, saveLoc, modelRun, prtype, ip1, spc, cmapType, bins, minVal, maxVal, extension, name, removed,buff):\r\n \"\"\" Plots and saves concentration data.\r\n\r\n Will plot difference data if difference data was passed.\r\n \"\"\"\r\n\r\n # useful things for determining projection details\r\n lowLon = np.amin(lonData)\r\n lowLat = np.amin(latData)\r\n highLon = np.amax(lonData)\r\n highLat = np.amax(latData)\r\n maxDim = maxDimensions(lowLon,highLon,lowLat,highLat,buff)\r\n midLon = (highLon+lowLon)/2\r\n midLat = (highLat + lowLat)/2\r\n # Uncomment the following lines for an alternative midLatLon calculation\r\n #mids = midLatLon(lonData,latData)\r\n #midLon = mids['midLon']\r\n #midLat = mids['midLat']\r\n max_width = maxDim['xDist']\r\n max_height = maxDim['yDist']\r\n # Initialize the figure\r\n fig = plt.figure(figsize=(8,8))\r\n\r\n # Create the map based on projection type\r\n if (prtype=='ortho') or (prtype == 'nsper') or (prtype == 'laea') or (prtype == 'aeqd') or (prtype == 'gnom') or (prtype == 'lcc'):\r\n concMap = Basemap(projection=prtype, resolution = 'c', lon_0=midLon, lat_0=midLat, width=max_width, height=max_height)\r\n elif prtype == 'stere':\r\n concMap = Basemap(projection=prtype, lon_0=midLon,lat_0=midLat,width=max_width,height=max_height)\r\n elif (prtype == 'cyl') or (prtype == 'merc'):\r\n concMap = Basemap(projection=prtype, resolution='c', llcrnrlat=lowLat, urcrnrlat=highLat, llcrnrlon=lowLon, urcrnrlon=highLon)\r\n elif (prtype == 'aea') or (prtype == 'eqdc'):\r\n concMap = Basemap(projection = prtype, lon_0=midLon, lat_0=midLat, llcrnrlat=lowLat, urcrnrlat=highLat, llcrnrlon=lowLon, urcrnrlon=highLon)\r\n else:\r\n print('Error: Could not generate map. Try a different projection.')\r\n # Can check the available basemap types and add to existing if statements\r\n\r\n # Add in other map details\r\n mapColor = cmapType\r\n mapBins = bins\r\n # if stripping the borders of the data....\r\n if removed != 0:\r\n ni = len(lonData)\r\n nj = len(lonData[0])\r\n n_pil = removed\r\n x, y = concMap(lonData[n_pil:ni-n_pil,n_pil:nj-n_pil], latData[n_pil:ni-n_pil,n_pil:nj-n_pil])\r\n concMap.pcolormesh(x,y,concData[n_pil:ni-n_pil,n_pil:nj-n_pil],cmap=plt.cm.get_cmap(mapColor,mapBins))\r\n else:\r\n x, y = concMap(lonData, latData)\r\n concMap.pcolormesh(x, y, concData, cmap=plt.cm.get_cmap(mapColor, mapBins))\r\n concMap.drawcoastlines(color='lightgray')\r\n concMap.drawcountries(color='gray')\r\n concMap.drawstates(color='gray')\r\n # Comment out the following to remove lonlat lines\r\n concMap.drawmeridians(np.arange(0,360,10), labels=[0,0,0,1], fontsize=6)\r\n concMap.drawparallels(np.arange(-180,180,10), labels=[1,0,0,0],fontsize=6)\r\n\r\n # Add colorbar and details\r\n #TODO: Fix the set label option for the colorbar, right now the colorbar doesn't have a title\r\n cbar = plt.colorbar(extend = extension, shrink=0.5)\r\n cbar.set_label=('Concentration: '+spc)\r\n plt.clim(minVal, maxVal)\r\n\r\n # Name and save the figure\r\n hy = ((os.popen('r.ip1 {}'.format(ip1))).read()).lstrip()\r\n plt.title('{}, {} \\n hy: {}, Spc: {}'.format(name,modelRun, hy,spc))\r\n fig.savefig(os.path.join(saveLoc, modelRun) + '_' + spc + '_' + name + '.png', dpi=300, bbox_inches='tight', pad_inches=0.3)\r\n\r\n plt.close('all')\r\n\r\ndef diffPlot(fileRun=defaultFile, baseFile=defaultPathStnd+defaultFile, modelFile=defaultPathStnd+defaultFile, savePath=defaultSave, level=defaultIp1, species=defaultSpc, buffering = buff, mapType=defaultDiffCmap, reverse=False, extension='neither', projType='stere', vmin=None, vmax=None, totalBins=defaultBins,partName='test',removed=defaultClear):\r\n \"\"\" Execute difference plot creation. \"\"\"\r\n\r\n logging.basicConfig(filename='ConcentrationPlotter.log', level=logging.DEBUG,format='%(asctime)s %(message)s')\r\n\r\n # print the min number of messages produced by librmn\r\n rmn.fstopt(rmn.FSTOP_MSGLVL,rmn.FSTOPI_MSG_CATAST)\r\n # get the data for the standard first, close file to continue\r\n concDataStnd = getConc(baseFile,level,species)\r\n if concDataStnd == None:\r\n return None\r\n else:\r\n gridStnd = getGrid(concDataStnd['dataKey'],concDataStnd['fileID'])\r\n closeFST(concDataStnd['fileID'])\r\n # get the data for the alternative, close file to continue\r\n concDataAlt = getConc(modelFile,level,species)\r\n if concDataAlt == None:\r\n return None\r\n else:\r\n gridAlt = getGrid(concDataAlt['dataKey'],concDataAlt['fileID'])\r\n closeFST(concDataAlt['fileID'])\r\n # get the difference between the datasets\r\n diffData = concDiff(concDataStnd['concData'],concDataAlt['concData'])\r\n # check that the grid sizes make sense\r\n gridCheck(gridStnd['gridll'],gridAlt['gridll'])\r\n # minor cleanup\r\n cmapDetails = cmapType(mapType,reverse)\r\n makeDir(savePath)\r\n # plot and save the figure\r\n plotConc(diffData, gridStnd['lonData'], gridStnd['latData'], savePath, fileRun, projType, level, species,cmapDetails, totalBins, vmin, vmax, extension, partName,removed,buffering)\r\n\r\ndef concPlot(fileRun=defaultFile, modelFile=defaultPathStnd+defaultFile, savePath=defaultSave, level=defaultIp1, species=defaultSpc, buffering=buff, mapType=defaultConcCmap, reverse=False, extension='neither', projType='stere', vmin=None, vmax=None, totalBins=defaultBins, partName='test', removed=defaultClear):\r\n \"\"\" Execute the concentration plot creation. \"\"\"\r\n\r\n logging.basicConfig(filename='ConcentrationPlotter.log', level=logging.DEBUG,format='%(asctime)s %(message)s')\r\n\r\n # print the min number of messages produced by librmn\r\n rmn.fstopt(rmn.FSTOP_MSGLVL,rmn.FSTOPI_MSG_CATAST)\r\n # get concentration and grid data, then close file\r\n concData = getConc(modelFile,level,species)\r\n if concData == None:\r\n return None\r\n else:\r\n gridData = getGrid(concData['dataKey'],concData['fileID'])\r\n closeFST(concData['fileID'])\r\n # minor cleanup\r\n cmapDetails =cmapType(mapType, reverse)\r\n makeDir(savePath)\r\n # plot and save the figure\r\n plotConc(concData['concData'], gridData['lonData'], gridData['latData'], savePath, fileRun, projType, level, species, cmapDetails, totalBins, vmin, vmax, extension, partName, removed, buffering)\r\n\r\n# To run this program alone, uncomment one of the following and include desired parameters\r\n# Otherwise the defaults will run (and I can't guarantee it'll work if the files have been moved!)\r\n\r\n# diffPlot()\r\n# concPlot()\r\n", "sub_path": "ConcentrationCrop/ConcPlotter Zoom/ConcentrationPlotter.py", "file_name": "ConcentrationPlotter.py", "file_ext": "py", "file_size_in_byte": 13619, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "rpnpy.librmn.all.fstopenall", "line_number": 44, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all", "line_number": 44, "usage_type": "name"}, {"api_name": "rpnpy.librmn.all.FST_RO", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rpnpy.librmn.all.fstinf", "line_number": 45, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all", "line_number": 45, "usage_type": "name"}, {"api_name": "rpnpy.librmn.all.fstluk", "line_number": 46, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all", "line_number": 46, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all.fstprm", "line_number": 79, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all", "line_number": 79, "usage_type": "name"}, {"api_name": "rpnpy.librmn.all.ezqkdef", "line_number": 82, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all", "line_number": 82, "usage_type": "name"}, {"api_name": "rpnpy.librmn.all.decodeGrid", "line_number": 83, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all", "line_number": 83, "usage_type": "name"}, {"api_name": "rpnpy.librmn.all.gdll", "line_number": 84, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all", "line_number": 84, "usage_type": "name"}, {"api_name": "rpnpy.librmn.all.fstcloseall", "line_number": 92, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.array_equiv", "line_number": 98, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.setdiff1d", "line_number": 102, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 110, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 111, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 112, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 114, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 115, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 176, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "numpy.amin", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 208, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 210, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 212, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm.get_cmap", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 228, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.get_cmap", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 231, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clim", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "os.popen", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path", "line_number": 248, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 250, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 255, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 255, "usage_type": "attribute"}, {"api_name": "rpnpy.librmn.all.fstopt", "line_number": 258, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all", "line_number": 258, "usage_type": "name"}, {"api_name": "rpnpy.librmn.all.FSTOP_MSGLVL", "line_number": 258, "usage_type": "attribute"}, {"api_name": "rpnpy.librmn.all.FSTOPI_MSG_CATAST", "line_number": 258, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 286, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 286, "usage_type": "attribute"}, {"api_name": "rpnpy.librmn.all.fstopt", "line_number": 289, "usage_type": "call"}, {"api_name": "rpnpy.librmn.all", "line_number": 289, "usage_type": "name"}, {"api_name": "rpnpy.librmn.all.FSTOP_MSGLVL", "line_number": 289, "usage_type": "attribute"}, {"api_name": "rpnpy.librmn.all.FSTOPI_MSG_CATAST", "line_number": 289, "usage_type": "attribute"}]} +{"seq_id": "255000560", "text": "from __future__ import division\nfrom __future__ import print_function\n\nimport time\nimport os\nimport sys\n\nimport tensorflow as tf\nimport numpy as np\nimport scipy.sparse as sp\nimport scipy.stats as stats\n\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import average_precision_score\nfrom sklearn.preprocessing import normalize\n\nfrom sklearn import manifold\nfrom scipy.special import expit\n\nfrom optimizer import OptimizerVAE\nfrom input_data import *\n\nfrom preprocessing import *\nfrom utils.visualizer import visualize_reconstruct, visualize_traverse, find_latent\nfrom utils.utils import LossesLogger\nfrom collections import defaultdict\nfrom utils.evaluation import generation_evaluation, disentangle_evaluation, reconstruct_evaluation\n\n\n\ndef sigmoid(x):\n return 1 / (1 + np.exp(-x))\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\nconfig = tf.ConfigProto()\nconfig.gpu_options.per_process_gpu_memory_fraction = 1.0\nconfig.gpu_options.allow_growth = True\n\nflags = tf.app.flags\nFLAGS = flags.FLAGS\n \nflags.DEFINE_integer('spatial_conv_layers', 3, 'Number of spatial_conv_layers.')\nflags.DEFINE_list('s_channel', [10,10,20], 'Number of channles in spatial convolution.')\nflags.DEFINE_list('s_kernel_size', [5,5,5], 'size of kernel in each spatial conv layer.')\nflags.DEFINE_list('s_strides', [1,1,1], 'Number of strides in each spatial conv layer.')\nflags.DEFINE_integer('s_hidden_size', 100, 'length of the hidden layer of spatial.')\nflags.DEFINE_integer('s_latent_size', 100, 'length of the latent representation of spatial.')\n#graph encoder\nflags.DEFINE_integer('graph_conv_layers', 2, 'Number of graph_conv_layers.')\nflags.DEFINE_list('g_conv_hidden', [10,20], 'Number of strides in each spatial conv layer.')\nflags.DEFINE_integer('g_hidden_size', 100, 'length of the hidden layer of spatial.')\nflags.DEFINE_integer('g_latent_size', 100, 'length of the latent representation of graph.')\n#spatial graph encoder\nflags.DEFINE_integer('spatial_graph_conv_layers', 2, 'Number of spatial-graph_conv_layers.')\nflags.DEFINE_list('sg_conv_hidden', [[20,20,20],[50,50,50]], 'length of hidden size in each spatial-graph conv layer.')\nflags.DEFINE_integer('sg_hidden_size', 200, 'length of the hidden layer of spatial-graph.')\nflags.DEFINE_integer('sg_latent_size', 200, 'length of the latent representation of sptial graph.')\n#spatial decoder\nflags.DEFINE_integer('spatial_deconv_layers', 3, 'Number of spatial_deconv_layers.')\nflags.DEFINE_list('s_d_channel', [50,20,10], 'Number of channles in spatial deconvolution.')\nflags.DEFINE_list('s_d_kernel_size', [5,5,5], 'size of kernel in each spatial deconv layer.')\nflags.DEFINE_list('s_d_strides', [1,1,1], 'Number of strides in each spatial deconv layer.')\n#graph decoder\nflags.DEFINE_integer('graph_deconv_layers', 2, 'Number of graph_deconv_layers.')\nflags.DEFINE_list('n_d_channel', [50,20,10], 'Number of channles in node deconvolution.')\nflags.DEFINE_list('n_d_kernel_size', [5,5,5], 'size of kernel in each node deconv layer.')\nflags.DEFINE_list('n_d_strides', [1,1,1], 'Number of strides in each node deconv layer.')\nflags.DEFINE_integer('d_hidden_size', 20, 'length of the hidden layer of graph deconv.')\nflags.DEFINE_list('e_d_hidden', [50,20,10], 'Number of channles in sedge deconvolution.')\n\nflags.DEFINE_integer('node_h_size', 20, 'node latent size in decoder part.')\nflags.DEFINE_string('model_type', 'disentangled', 'base, disentangled, disentangled_C,NED-VAE-IP,beta-TCVAE, geoGCN, posGCN')\n\n#training paramters\nflags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')\nflags.DEFINE_integer('epochs', 2000, 'Number of epochs to train.')\nflags.DEFINE_float('dropout', 1, 'keep probability.')\nflags.DEFINE_integer('batch_size', 2, 'Number of samples in a batch.')\nflags.DEFINE_integer('decoder_batch_size',2, 'Number of samples in a batch.')\nflags.DEFINE_integer('sg_batch_size', 5, 'Number of samples in a batch.')\nflags.DEFINE_integer('sg_decoder_batch_size',5, 'Number of samples in a batch.')\nflags.DEFINE_string('dataset_path', '../dataset/', 'Number of samples in a batch.')\nflags.DEFINE_integer('num_feature', 1, 'Number of features.')\nflags.DEFINE_integer('spatial_dim', 2, 'The dimension of spatial information.')\nflags.DEFINE_integer('verbose', 1, 'Output all epoch data')\nflags.DEFINE_integer('test_count', 10, 'batch of tests')\nflags.DEFINE_string('model', 'feedback', 'Model string.')\nflags.DEFINE_integer('seeded', 1, 'Set numpy random seed')\nflags.DEFINE_integer('connected_split', 0, 'use split with training set always connected')\nflags.DEFINE_string('type', 'test_reconstruct', 'train or test')\nflags.DEFINE_integer('if_traverse', 1, 'varying the z to see the generated graphs')\nflags.DEFINE_integer('visualize_length', 5, 'varying the z to see the generated graphs')\nflags.DEFINE_string('dataset', 'synthetic2', 'synthetic1 or synthetic2')\n\nflags.DEFINE_float('C_max', 100, 'capacity parameter(C) of bottleneck channel')\nflags.DEFINE_float('C_stop_iter', 1e2, 'when to stop increasing the capacity')\nflags.DEFINE_float('gamma', 100, 'gamma parameter for KL-term in understanding beta-VAE')\nflags.DEFINE_float('C_step', 20, 'every c_step epoch the C changes')\n\nflags.DEFINE_integer('sampling_num', 10, 'sampling ten times')\n\nflags.DEFINE_integer('dim', None, 'dim for traverse')\nflags.DEFINE_string('group_type', None, 'group type for traverse')\n\nif FLAGS.model_type=='base':\n from model_joint import *\nelse:\n from model import *\n\ndef ZscoreNormalization(x, mean_, std_):\n \"\"\"Z-score normaliaztion\"\"\"\n x = (x - mean_) / std_\n return x\n\n\ndef main(beta, type_model):\n if 'vae_type' in list(flags.FLAGS):\n delattr(flags.FLAGS,'vae_type')\n flags.DEFINE_string('vae_type', type_model, 'local or global or local_global')\n\n if FLAGS.type =='test_disentangle':\n FLAGS.batch_size=FLAGS.visualize_length*(FLAGS.s_latent_size+FLAGS.g_latent_size+FLAGS.sg_latent_size)\n FLAGS.decoder_batch_size=FLAGS.batch_size\n if FLAGS.seeded:\n np.random.seed(1)\n\n # Load data\n if FLAGS.dataset == 'synthetic1':\n dataset_path=FLAGS.dataset_path+'spatial_network_correlated1/25'\n if True:\n feature,spatial, adj, rel, factor, adj_truth = load_data_syn(FLAGS.type, dataset_path)\n adj = adj.reshape(-1,adj.shape[-2],adj.shape[-1])\n print (adj.shape)\n else:\n feature,spatial, adj, rel, factor= load_data_syn(FLAGS.type, dataset_path)\n FLAGS.spatial_conv_layers=3\n FLAGS.s_channel=[10,10,20]\n FLAGS.s_kernel_size=[5,5,5]\n FLAGS.s_strides=[1,1,1]\n FLAGS.s_hidden_size=100\n FLAGS.s_latent_size=100\n #graph encoder\n FLAGS.graph_conv_layers= 2\n FLAGS.g_conv_hidden=[10,20]\n FLAGS.g_hidden_size=100\n FLAGS.g_latent_size=100\n #spatial graph encoder\n FLAGS.spatial_graph_conv_layers=2\n FLAGS.sg_conv_hidden=[[20,20,20],[50,50,50]]\n FLAGS.sg_hidden_size=500\n FLAGS.sg_latent_size=500\n #spatial decoder\n FLAGS.spatial_deconv_layers=3\n FLAGS.s_d_channel=[50,20,10]\n FLAGS.s_d_kernel_size=[5,5,5]\n FLAGS.s_d_strides=[1,1,1]\n #graph decoder\n FLAGS.graph_deconv_layers=2\n FLAGS.n_d_channel=[50,20,10]\n FLAGS.n_d_kernel_size=[5,5,5]\n FLAGS.n_d_strides=[1,1,1]\n FLAGS.d_hidden_size=20\n FLAGS.e_d_hidden=[50,20,10]\n FLAGS.node_h_size=50\n #training paramters\n FLAGS.learning_rate=0.001\n FLAGS.epochs=1000\n FLAGS.dropout=1\n FLAGS.batch_size=10\n FLAGS.decoder_batch_size=10\n FLAGS.sg_batch_size=10\n FLAGS.sg_decoder_batch_size=10\n elif FLAGS.dataset == 'synthetic2':\n dataset_path=FLAGS.dataset_path+'spatial_network_correlated2/25'\n if True:\n feature,spatial, adj, rel, factor, adj_truth = load_data_syn(FLAGS.type, dataset_path)\n adj = adj.reshape(-1,adj.shape[-2],adj.shape[-1])\n print (adj.shape)\n else:\n feature,spatial, adj, rel, factor= load_data_syn(FLAGS.type, dataset_path)\n FLAGS.spatial_conv_layers=3\n FLAGS.s_channel=[10,10,20]\n FLAGS.s_kernel_size=[5,5,5]\n FLAGS.s_strides=[1,1,1]\n FLAGS.s_hidden_size=100\n FLAGS.s_latent_size=100\n #graph encoder\n FLAGS.graph_conv_layers= 2\n FLAGS.g_conv_hidden=[10,20]\n FLAGS.g_hidden_size=100\n FLAGS.g_latent_size=100\n #spatial graph encoder\n FLAGS.spatial_graph_conv_layers=2\n FLAGS.sg_conv_hidden=[[20,20,20],[50,50,50]]\n FLAGS.sg_hidden_size=100\n FLAGS.sg_latent_size=100\n #spatial decoder\n FLAGS.spatial_deconv_layers=3\n FLAGS.s_d_channel=[50,20,10]\n FLAGS.s_d_kernel_size=[5,5,5]\n FLAGS.s_d_strides=[1,1,1]\n #graph decoder\n FLAGS.graph_deconv_layers=2\n FLAGS.n_d_channel=[50,20,10]\n FLAGS.n_d_kernel_size=[5,5,5]\n FLAGS.n_d_strides=[1,1,1]\n FLAGS.d_hidden_size=20\n FLAGS.e_d_hidden=[50,20,10]\n FLAGS.node_h_size=20\n #training paramters\n FLAGS.learning_rate=0.0008\n FLAGS.epochs=1000\n FLAGS.dropout=1\n FLAGS.batch_size=10\n FLAGS.decoder_batch_size=10\n FLAGS.sg_batch_size=10\n FLAGS.sg_decoder_batch_size=10\n elif FLAGS.dataset == 'protein':\n FLAGS.spatial_dim = 3\n dataset_path=FLAGS.dataset_path+'protein'\n feature,spatial, adj, rel, factor, adj_truth = load_data_protein(FLAGS.type, dataset_path)\n adj = adj.reshape(-1,adj.shape[-2],adj.shape[-1])\n FLAGS.sg_conv_hidden = [[10,10,10,10],[20,20,20,20]]\n FLAGS.sg_hidden_size=50\n FLAGS.sg_latent_size=50\n FLAGS.s_hidden_size=5\n FLAGS.s_latent_size=5 \n FLAGS.g_hidden_size=5\n FLAGS.g_latent_size=5 \n FLAGS.node_h_size=5 \n FLAGS.s_channel = [10,10,20]\n FLAGS.s_kernel_size=[5,5,5]\n FLAGS.batch_size=50\n FLAGS.decoder_batch_size=50\n FLAGS.sg_batch_size=50\n FLAGS.sg_decoder_batch_size=50\n elif FLAGS.dataset == 'mnist':\n FLAGS.spatial_dim = 3\n dataset_path=FLAGS.dataset_path+'3D_mesh'\n feature,spatial, adj, rel = load_data_mnist(FLAGS.type, dataset_path)\n FLAGS.sg_conv_hidden = [[20,20,20,20],[50,50,50,50]]\n\n num_nodes = adj.shape[1]\n\n num_features = FLAGS.num_feature\n pos_weight = float(adj.shape[0] *adj.shape[1] * adj.shape[1] - adj.sum()) / adj.sum()\n norm = adj.shape[0] *adj.shape[1] * adj.shape[1] / float((adj.shape[0] *adj.shape[1] * adj.shape[1] - adj.sum()) * 2)\n\n feature=feature.reshape([-1,num_nodes,num_features])\n rel=rel.reshape([-1,num_nodes,num_nodes,1])\n\n if True:\n placeholders = {\n 'features': tf.placeholder(tf.float32,[FLAGS.batch_size*FLAGS.sampling_num,num_nodes,num_features]),\n 'spatial': tf.placeholder(tf.float32,[FLAGS.batch_size*FLAGS.sampling_num,num_nodes,FLAGS.spatial_dim]),\n 'adj': tf.placeholder(tf.float32,[FLAGS.batch_size*FLAGS.sampling_num,adj.shape[1],adj.shape[2]]),\n 'adj_truth': tf.placeholder(tf.float32,[FLAGS.batch_size,adj.shape[1],adj.shape[2]]),\n 'feature_truth': tf.placeholder(tf.float32,[FLAGS.batch_size,num_nodes,num_features]),\n 'spatial_truth': tf.placeholder(tf.float32,[FLAGS.batch_size,num_nodes,FLAGS.spatial_dim]),\n 'rel_truth': tf.placeholder(tf.float32,[FLAGS.batch_size,adj.shape[1],adj.shape[2], 1]),\n 'rel': tf.placeholder(tf.float32,[FLAGS.batch_size*FLAGS.sampling_num,adj.shape[1],adj.shape[2], 1]),\n 'dropout': tf.placeholder_with_default(0., shape=()),\n 'global_iter': tf.placeholder_with_default(0., shape=()),\n }\n else:\n placeholders = {\n 'features': tf.placeholder(tf.float32,[FLAGS.batch_size,num_nodes,num_features]),\n 'spatial': tf.placeholder(tf.float32,[FLAGS.batch_size,num_nodes,FLAGS.spatial_dim]),\n 'adj': tf.placeholder(tf.float32,[FLAGS.batch_size,adj.shape[1],adj.shape[2]]),\n 'rel': tf.placeholder(tf.float32,[FLAGS.batch_size,adj.shape[1],adj.shape[2], 1]),\n 'dropout': tf.placeholder_with_default(0., shape=()),\n 'global_iter': tf.placeholder_with_default(0., shape=()),\n }\n\n if FLAGS.type != 'test_disentangle':\n model = SGCNModelVAE(placeholders, num_features, num_nodes)\n\n TRAIN_LOSSES_LOGFILE='./train_loss_'+FLAGS.dataset+'_'+FLAGS.model_type+'.txt'\n\n losses_logger = LossesLogger(os.path.join(TRAIN_LOSSES_LOGFILE))\n\n\n if FLAGS.type=='train':\n with tf.name_scope('optimizer'):\n opt = OptimizerVAE(preds_edge=model.generated_adj_prob,\n preds_node=model.generated_node_feat,\n preds_spatial=model.generated_spatial,\n labels_edge=placeholders['adj_truth'],\n labels_node=placeholders['feature_truth'],\n labels_spatial=placeholders['spatial_truth'],\n labels_rel=placeholders['rel_truth'],\n global_iter=placeholders['global_iter'],\n model=model, num_nodes=num_nodes,\n pos_weight=pos_weight,\n norm=norm,\n beta=beta)\n\n if FLAGS.type != 'test_disentangle':\n saver = tf.train.Saver()\n if FLAGS.type=='train':\n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n # Train model\n feature_truth = feature \n spatial_truth = spatial \n rel_truth = rel\n feature = np.tile(feature, (FLAGS.sampling_num,1,1))\n spatial = np.tile(spatial, (FLAGS.sampling_num,1,1))\n rel = np.tile(rel, (FLAGS.sampling_num,1,1,1))\n for epoch in range(FLAGS.epochs):\n storer = defaultdict(list)\n batch_num=int(adj.shape[0]/(FLAGS.batch_size*FLAGS.sampling_num))\n check=[]\n epoch_time = time.time()\n for i in range(batch_num):\n adj_batch=adj[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n feature_batch=feature[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n spatial_batch=spatial[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n rel_batch=rel[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n adj_truth_batch=adj_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n feature_truth_batch=feature_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n spatial_truth_batch=spatial_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n rel_truth_batch=rel_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n\n t = time.time()\n # Construct feed dictionary\n feed_dict = construct_feed_dict_train(feature_batch, spatial_batch, adj_batch, rel_batch, adj_truth_batch, feature_truth_batch, spatial_truth_batch, rel_truth_batch, placeholders)\n feed_dict.update({placeholders['dropout']: FLAGS.dropout})\n feed_dict.update({placeholders['global_iter']: epoch})\n # Run single weight update\n outs = sess.run([opt.opt_op, opt.overall_loss, model.generated_adj], feed_dict=feed_dict)\n # Compute average loss\n overall_loss=outs[1]\n acc=sum(sum(sum(outs[2]==adj_truth_batch)))/(FLAGS.batch_size*num_nodes*num_nodes)\n storer['loss'].append(overall_loss[0])\n storer['spatial_loss'].append(overall_loss[1])\n storer['adj_loss'].append(overall_loss[2])\n storer['adj_acc'].append(acc)\n storer['node_loss'].append(overall_loss[3])\n if FLAGS.model_type in ['disentangled','disentangled_C','NED-VAE-IP','beta-TCVAE']:\n storer['graph_kl'].append(overall_loss[4])\n storer['spatial_kl'].append(overall_loss[5])\n storer['sg_kl'].append(overall_loss[6])\n else:\n storer['sg_kl'].append(overall_loss[4])\n check.append(outs[2])\n\n print(\"Epoch:\", '%04d' % (epoch + 1), \"loss=\", \"{:.5f}\".format(overall_loss[0]),\n \"time=\", \"{:.5f}\".format(time.time() - t))\n print (\"epoch time=\", \"{:.5f}\".format(time.time()-epoch_time))\n if epoch%100==0:\n save_path = saver.save(sess, \"/home/ydu6/generation_eff_latent_sg/src/tmp/\"+FLAGS.dataset+'_'+FLAGS.model_type+\"/model_dgt_global_\"+str(epoch)+\".ckpt\")\n losses_logger.log(epoch, storer)\n\n print(\"Optimization Finished!\")\n return np.array(check), adj\n\n def generate_new_train(feed_dic):\n feed_dict = feed_dic\n feed_dict.update({placeholders['dropout']: 1.0})\n z_s,z_sg,z_g, adj, spatial, node = sess.run([model.z_mean_s,model.z_mean_sg,model.z_mean_g, model.generated_adj, model.generated_spatial, model.generated_node_feat], feed_dict=feed_dict)\n return z_s,z_sg,z_g, adj, spatial, node\n\n def generate_new(feature_batch, spatial_batch, adj_batch,rel_batch):\n feed_dict = construct_feed_dict(feature_batch, spatial_batch, adj_batch, rel_batch, placeholders)\n feed_dict.update({placeholders['dropout']: 1.0})\n if FLAGS.model_type == \"base\":\n z_sg, adj, node, spatial = sess.run([model.z_mean_sg, model.generated_adj,model.generated_node_feat,model.generated_spatial], feed_dict=feed_dict)\n return z_sg, adj, spatial, node\n else:\n z_s,z_sg,z_g, adj, spatial, node = sess.run([model.z_mean_s,model.z_mean_sg,model.z_mean_g, model.generated_adj, model.generated_spatial, model.generated_node_feat], feed_dict=feed_dict)\n return z_s,z_sg,z_g, adj, spatial, node\n\n if FLAGS.type =='test_reconstruct':\n with tf.Session() as sess:\n saver.restore(sess, \"/home/ydu6/generation_eff_latent_sg/src/tmp/\"+FLAGS.dataset+'_'+FLAGS.model_type+\"/model_dgt_global_\"+str(480)+\".ckpt\")\n print(\"Model restored.\")\n generated_adj=[]\n generated_nodes=[]\n generated_spatial=[]\n batch_num=int(adj.shape[0]/(FLAGS.batch_size*FLAGS.sampling_num))\n feature_truth = feature \n spatial_truth = spatial \n rel_truth = rel\n feature = np.tile(feature, (FLAGS.sampling_num,1,1))\n spatial = np.tile(spatial, (FLAGS.sampling_num,1,1))\n rel = np.tile(rel, (FLAGS.sampling_num,1,1,1))\n #generated_adj_prob=[]\n z_s=[]\n z_sg=[]\n z_g=[]\n for i in range(batch_num):\n adj_batch=adj[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n feature_batch=feature[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n spatial_batch=spatial[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n rel_batch=rel[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n adj_truth_batch=adj_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n feature_truth_batch=feature_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n spatial_truth_batch=spatial_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n rel_truth_batch=rel_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n feed_dict = construct_feed_dict_train(feature_batch, spatial_batch, adj_batch, rel_batch, adj_truth_batch, feature_truth_batch, spatial_truth_batch, rel_truth_batch, placeholders)\n z_s_batch,z_sg_batch, z_g_batch, generated_adj_, generated_spatial_, generated_node_= generate_new_train(feed_dict)\n generated_adj.append(generated_adj_)\n generated_nodes.append(generated_node_)\n generated_spatial.append(generated_spatial_)\n z_s.append(z_s_batch.reshape((FLAGS.batch_size,-1)))\n z_sg.append(z_sg_batch.reshape((FLAGS.batch_size,FLAGS.sampling_num,-1)).mean(axis=1))\n z_g.append(z_g_batch.reshape((FLAGS.batch_size,-1)))\n\n\n if FLAGS.model_type == \"base\":\n np.save('./qualitative_evaluation/'+str(FLAGS.dataset)+\"/\"+FLAGS.vae_type+'_z_sg.npy',np.array(z_sg))\n else:\n np.save('./qualitative_evaluation/'+str(FLAGS.dataset)+\"/\"+FLAGS.vae_type+'_z_s.npy',np.array(z_s))\n np.save('./qualitative_evaluation/'+str(FLAGS.dataset)+\"/\"+FLAGS.vae_type+'_z_sg.npy',np.array(z_sg))\n np.save('./qualitative_evaluation/'+str(FLAGS.dataset)+\"/\"+FLAGS.vae_type+'_z_g.npy',np.array(z_g))\n print (len(z_s))\n print (z_s[0].shape)\n generated_adj=np.array(generated_adj).reshape(-1,num_nodes,num_nodes)\n generated_nodes=np.array(generated_nodes).reshape(-1,num_nodes,num_features)\n generated_spatial=np.array(generated_spatial).reshape(-1,num_nodes,FLAGS.spatial_dim)\n #visualize_reconstruct(5, adj_batch, feature_batch*120, spatial_batch*600, generated_adj, generated_nodes*120, generated_spatial*600)\n evaluate_results=reconstruct_evaluation(generated_adj,generated_nodes,generated_spatial, adj_truth, feature_truth, spatial_truth, FLAGS.dataset)\n disentangle_results=disentangle_evaluation(z_s, z_g, z_sg, factor, FLAGS.dataset)\n print (evaluate_results,disentangle_results)\n return disentangle_results\n\n if FLAGS.type =='test_generation':\n with tf.Session() as sess:\n saver.restore(sess, \"/home/ydu6/generation_eff_latent_sg/src/tmp/\"+FLAGS.dataset+'_'+FLAGS.model_type+\"/model_dgt_global_\"+str(480)+\".ckpt\")\n print(\"Model restored.\")\n generated_adj=[]\n generated_nodes=[]\n generated_spatial=[]\n batch_num=int(adj.shape[0]/(FLAGS.batch_size*FLAGS.sampling_num))\n feature_truth = feature \n spatial_truth = spatial \n rel_truth = rel\n feature = np.tile(feature, (FLAGS.sampling_num,1,1))\n spatial = np.tile(spatial, (FLAGS.sampling_num,1,1))\n rel = np.tile(rel, (FLAGS.sampling_num,1,1,1))\n z_s=[]\n z_sg=[]\n z_g=[]\n for i in range(batch_num):\n adj_batch=adj[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n feature_batch=feature[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n spatial_batch=spatial[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n rel_batch=rel[i*FLAGS.batch_size*FLAGS.sampling_num:i*FLAGS.batch_size*FLAGS.sampling_num+FLAGS.batch_size*FLAGS.sampling_num]\n adj_truth_batch=adj_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n feature_truth_batch=feature_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n spatial_truth_batch=spatial_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n rel_truth_batch=rel_truth[i*FLAGS.batch_size:i*FLAGS.batch_size+FLAGS.batch_size]\n feed_dict = construct_feed_dict_train(feature_batch, spatial_batch, adj_batch, rel_batch, adj_truth_batch, feature_truth_batch, spatial_truth_batch, rel_truth_batch, placeholders)\n z_s_batch,z_sg_batch, z_g_batch, generated_adj_, generated_spatial_, generated_node_= generate_new_train(feed_dict)\n generated_adj.append(generated_adj_)\n generated_nodes.append(generated_node_)\n generated_spatial.append(generated_spatial_)\n z_s.append(z_s_batch.reshape((FLAGS.batch_size,-1)))\n z_sg.append(z_sg_batch.reshape((FLAGS.batch_size,FLAGS.sampling_num,-1)).mean(axis=1))\n z_g.append(z_g_batch.reshape((FLAGS.batch_size,-1)))\n\n generated_adj=np.array(generated_adj).reshape(-1,num_nodes,num_nodes)\n generated_nodes=np.array(generated_nodes).reshape(-1,num_nodes,num_features)\n generated_spatial=np.array(generated_spatial).reshape(-1,num_nodes,FLAGS.spatial_dim)\n #visualize_reconstruct(5, adj_batch, feature_batch*120, spatial_batch*600, generated_adj, generated_nodes*120, generated_spatial*600)\n evaluate_results=generation_evaluation(generated_adj,generated_nodes,generated_spatial, adj, feature, spatial, FLAGS.dataset)\n print (evaluate_results)\n return evaluate_results\n\n\n\n if FLAGS.type== 'test_disentangle':\n with tf.Session() as sess:\n generated_adj=[]\n generated_nodes=[]\n generated_spatial=[]\n adj_batch=adj[:FLAGS.batch_size]\n feature_batch=feature[:FLAGS.batch_size]\n spatial_batch=spatial[:FLAGS.batch_size]\n rel_batch=rel[:FLAGS.batch_size]\n model = SGCNModelVAE(placeholders, num_features, num_nodes, group_type=FLAGS.group_type, dim=FLAGS.dim, dim_a=77, dim_b=48, dim_c=171)\n saver = tf.train.Saver()\n saver.restore(sess, \"/home/ydu6/generation/src/tmp/\"+FLAGS.dataset+'_'+FLAGS.model_type+\"/model_dgt_global_\"+str(300)+\".ckpt\")\n print(\"Model restored.\")\n\n z_s,z_sg, z_g, generated_adj, generated_spatial, generated_nodes = generate_new(feature_batch, spatial_batch, adj_batch, rel_batch)\n generated_adj=np.array(generated_adj).reshape([-1, num_nodes, num_nodes])\n generated_nodes=np.array(generated_nodes).reshape([-1, num_nodes, num_features])\n generated_spatial=np.array(generated_spatial).reshape([-1, num_nodes,FLAGS.spatial_dim])\n print (generated_adj.shape, generated_nodes.shape, generated_spatial.shape)\n min_n, max_n = np.min(generated_nodes[FLAGS.visualize_length:FLAGS.visualize_length*2]*120), np.max(generated_nodes[FLAGS.visualize_length:FLAGS.visualize_length*2]*120)\n generated_nodes[FLAGS.visualize_length:FLAGS.visualize_length*2] = (generated_nodes[FLAGS.visualize_length:FLAGS.visualize_length*2]*120-min_n)/(max_n-min_n)\n # print (np.min(generated_nodes[FLAGS.visualize_length:FLAGS.visualize_length*2]),np.max(generated_nodes[FLAGS.visualize_length:FLAGS.visualize_length*2]))\n print (np.min(generated_spatial[:FLAGS.visualize_length]*600), np.max(generated_spatial[:FLAGS.visualize_length]*600))\n print (np.min(generated_spatial)*600, np.max(generated_spatial)*600)\n visualize_traverse(generated_adj, generated_nodes*120, generated_spatial*600,1,FLAGS.visualize_length,FLAGS.dataset)\n # print('spatial:'+str(a))\n # print('graph:'+str(b))\n # print('joint:'+str(c))\n\nif __name__ == '__main__':\n np_load_old = np.load\n np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)\n models=['disentangled'] #'posGCN','geoGCN','disentangled_C''NED-VAE-IP','beta-TCVAE','disentangled','base',,,,,,'InfoVAE',,, ,'HFVAE'],,,,'InfoVAE''FactorVAE''InfoVAE','DIP-VAE''FactorVAE','HFVAE'\n types= ['train','test_reconstruct','test_generation']\n generation_results={}\n reconstruct_results={}\n for type_ in types:\n FLAGS.type=type_\n for t in models:\n tf.reset_default_graph()\n FLAGS.model_type=t\n if FLAGS.type =='train':\n main(1,t)\n elif FLAGS.type =='test_reconstruct':\n reconstruct_result=main(1,t)\n reconstruct_results[t]=reconstruct_result\n elif FLAGS.type =='test_generation':\n generation_result=main(1,t)\n generation_results[t]=generation_result\n else:\n main(1,t)\n print(generation_results)\n print(reconstruct_results)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 29381, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.exp", "line_number": 32, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 125, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 254, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 254, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 255, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 255, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 256, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 256, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 257, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 257, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 258, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 258, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 259, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 259, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 260, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 260, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 261, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 261, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder_with_default", "line_number": 262, "usage_type": "call"}, {"api_name": "tensorflow.placeholder_with_default", "line_number": 263, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 267, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 267, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 268, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 268, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 269, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 269, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 270, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 270, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder_with_default", "line_number": 271, "usage_type": "call"}, {"api_name": "tensorflow.placeholder_with_default", "line_number": 272, "usage_type": "call"}, {"api_name": "utils.utils.LossesLogger", "line_number": 280, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 280, "usage_type": "call"}, {"api_name": "os.path", "line_number": 280, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 284, "usage_type": "call"}, {"api_name": "optimizer.OptimizerVAE", "line_number": 285, "usage_type": "call"}, {"api_name": "model.generated_adj_prob", "line_number": 285, "usage_type": "attribute"}, {"api_name": "model.generated_node_feat", "line_number": 286, "usage_type": "attribute"}, {"api_name": "model.generated_spatial", "line_number": 287, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 299, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 299, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 301, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 309, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 311, "usage_type": "call"}, {"api_name": "time.time", "line_number": 314, "usage_type": "call"}, {"api_name": "time.time", "line_number": 325, "usage_type": "call"}, {"api_name": "model.generated_adj", "line_number": 331, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 349, "usage_type": "call"}, {"api_name": "time.time", "line_number": 350, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 356, "usage_type": "call"}, {"api_name": "model.z_mean_s", "line_number": 361, "usage_type": "attribute"}, {"api_name": "model.z_mean_sg", "line_number": 361, "usage_type": "attribute"}, {"api_name": "model.z_mean_g", "line_number": 361, "usage_type": "attribute"}, {"api_name": "model.generated_adj", "line_number": 361, "usage_type": "attribute"}, {"api_name": "model.generated_spatial", "line_number": 361, "usage_type": "attribute"}, {"api_name": "model.generated_node_feat", "line_number": 361, "usage_type": "attribute"}, {"api_name": "model.z_mean_sg", "line_number": 368, "usage_type": "attribute"}, {"api_name": "model.generated_adj", "line_number": 368, "usage_type": "attribute"}, {"api_name": "model.generated_node_feat", "line_number": 368, "usage_type": "attribute"}, {"api_name": "model.generated_spatial", "line_number": 368, "usage_type": "attribute"}, {"api_name": "model.z_mean_s", "line_number": 371, "usage_type": "attribute"}, {"api_name": "model.z_mean_sg", "line_number": 371, "usage_type": "attribute"}, {"api_name": "model.z_mean_g", "line_number": 371, "usage_type": "attribute"}, {"api_name": "model.generated_adj", "line_number": 371, "usage_type": "attribute"}, {"api_name": "model.generated_spatial", "line_number": 371, "usage_type": "attribute"}, {"api_name": "model.generated_node_feat", "line_number": 371, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 419, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 420, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 421, "usage_type": "call"}, {"api_name": "utils.evaluation.reconstruct_evaluation", "line_number": 423, "usage_type": "call"}, {"api_name": "utils.evaluation.disentangle_evaluation", "line_number": 424, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 429, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 439, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 441, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 463, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 465, "usage_type": "call"}, {"api_name": "utils.evaluation.generation_evaluation", "line_number": 467, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 474, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 483, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 483, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 488, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 489, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 490, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 495, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 495, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 496, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 496, "usage_type": "call"}, {"api_name": "utils.visualizer.visualize_traverse", "line_number": 497, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 503, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 504, "usage_type": "attribute"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 512, "usage_type": "call"}]} +{"seq_id": "472609251", "text": "# Import necessary modules \r\nfrom sklearn.neighbors import KNeighborsClassifier \r\n#from sklearn.model_selection import train_test_split \r\n#from scapy.all import rdpcap\r\n\r\nimport joblib as joblib\r\nimport Read_pcap_scapy\r\n\r\n# Loading data test data\r\nfile_name = \"Thesis-git\\Vagrant_Network\\server\\server_traffic_5m.pcap\"\r\n#file_name = \"Thesis-git\\DTU_server\\dtu_server_nordvpn_25m.pcap\"\r\ndata = Read_pcap_scapy.FindFeaturesInLargeFiles(file_name)\r\n#print(data)\r\n## Create feature and target arrays \r\nX = data[:,1:]\r\ny = data[:,0] \r\n\r\nknn = KNeighborsClassifier(n_neighbors=7)\r\nknn.fit(X, y)\r\n\r\n## Save the model as a pickle in a file \r\njoblib.dump(knn, 'knnModel_7neighbor_5m.pkl') ", "sub_path": "KNN_training.py", "file_name": "KNN_training.py", "file_ext": "py", "file_size_in_byte": 680, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "Read_pcap_scapy.FindFeaturesInLargeFiles", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 18, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "547689868", "text": "from django.shortcuts import render\nfrom .models import *\nfrom django.http import JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom hashlib import md5\nfrom django.utils import timezone\nimport random\nimport os\nfrom .sendVerCode import codeSender\nfrom cv import faceDet\nfrom cv.faceMerge import faceMerge\nimport json\nfrom django.forms.models import model_to_dict\nimport time\n\ndef exceptError(func):\n \"错误记录装饰器\"\n def wrap(*args, **kwargs):\n try:\n return func(*args, **kwargs)\n except Exception as e:\n \"在errorRecord文件中记录错误\"\n with open(\"errorRecord\", 'a') as f:\n f.write(f\"{e}\\n\")\n return JsonResponse(Auth.res(\"未知错误\", \"\"))\n return wrap\n\n@csrf_exempt\n@exceptError\ndef uploadFaceImg(request):\n \"上传人脸图片\"\n res = {\n \"mes\": \"\",\n \"data\": \"\"\n }\n uploadImgDir = os.path.join(\"static\", \"FaceImg\")\n # 读取post中的图片\n fd = request.FILES['pic'].read()\n userId = request.POST.get(\"id\")\n name = md5(fd).hexdigest()\n # 查询是否存在相应用户\n try:\n user = UserInfo.objects.get(id=userId)\n except UserInfo.DoesNotExist:\n return JsonResponse(Auth.error(1))\n\n # 查询图片是否已经存在\n try:\n img = FaceImg.objects.get(md5=name)\n except FaceImg.DoesNotExist:\n filePath = os.path.join(uploadImgDir, f\"{name}.jpg\")\n # 将图片字节流写入文件\n with open(filePath, \"wb\") as f:\n f.write(fd)\n \n # 查询图片对象是否存在\n try:\n img = FaceImg.objects.get(user=user)\n except FaceImg.DoesNotExist:\n img = FaceImg(md5=name, path=filePath, user=user)\n img.save()\n else:\n img.path=filePath\n img.save()\n \n else:\n filePath = img.path\n\n\n # 调用人脸检测器检测是否由人脸\n if not faceDet.hasFace(filePath):\n res['mes'] = \"该图片未包含人脸，请重新上传\"\n else:\n # windows系统path处理\n if \"\\\\\" in filePath:\n filePath = filePath.replace(\"\\\\\", \"/\")\n res['data'] = f\"/{filePath}\"\n return JsonResponse(res)\n\n\ndef faceMerge_(request):\n model = request.GET.get('modelId')\n userList = request.GET.get(\"userList\")\n try:\n model = ModelImg.objects.get(id=model)\n dstList = [UserInfo.objects.get(id=int(i)).faceImg.path for i in userList.split(\",\")]\n except ModelImg.DoesNotExist:\n return JsonResponse(Auth.error(1))\n \n outImg = f\"static/outImg/{str(int(time.time()*1000))}.jpg\"\n faceMerge(modelImg=model.path.replace(\"\\\\\", '/'), \n dstList=[i.replace(\"\\\\\", '/') for i in dstList], \n faces=json.loads(model.faces),\n outImg=outImg)\n return JsonResponse(Auth.res(\"\", outImg))\n\n\ndef getModels(request):\n models = [{\"id\": i.id, \"path\": i.path} for i in ModelImg.objects.all()]\n return JsonResponse(Auth.res(\"\", models))\n\n\n@exceptError\ndef getAllClass(request):\n res = []\n for class_ in Class.objects.all():\n res.append({\n \"id\": class_.id,\n \"name\": class_.name, \n })\n return JsonResponse(Auth.res(\"\", res))\n\n@exceptError\ndef getUserByClass(request):\n classId = request.GET.get(\"classId\")\n res = []\n mes = \"\"\n if classId:\n try:\n class_ = Class.objects.get(id=classId)\n except Class.DoesNotExist:\n return JsonResponse(Auth.error(1))\n for stu in class_.students.all():\n try:\n faceimg = \"/\" + stu.faceImg.path.replace(\"\\\\\", \"/\")\n except UserInfo.faceImg.RelatedObjectDoesNotExist:\n faceimg = None\n res.append({\n \"id\": stu.id,\n \"name\": stu.username,\n \"faceImg\": faceimg,\n })\n else:\n mes = \"参数错误\"\n return JsonResponse(Auth.res(mes, res))\n\nclass Auth:\n\n sender = codeSender()\n\n @classmethod\n def userSet(cls, request):\n username = request.GET.get(\"username\")\n phoneNumber = request.GET.get(\"phoneNumber\")\n userId = request.GET.get(\"id\")\n\n \n # 都不为空\n if username and phoneNumber:\n try:\n user = UserInfo.objects.get(id=userId)\n except UserInfo.DoesNotExist:\n return JsonResponse(cls.error(1))\n user.username = username\n user.phoneNumber = phoneNumber\n user.save()\n return JsonResponse(cls.res(\"\", \"修改成功\"))\n return JsonResponse(cls.error(1))\n\n @classmethod\n @csrf_exempt\n def passwordSet(cls, request):\n curPass = request.POST.get(\"curPass\")\n Pass = request.POST.get(\"Pass\")\n userId = request.POST.get(\"id\")\n\n try:\n user = UserInfo.objects.get(id=userId).user\n except:\n return JsonResponse(cls.error(1))\n if user.check_password(curPass):\n user.set_password(Pass)\n user.save()\n return JsonResponse(cls.res(\"\", \"修改成功\"))\n return JsonResponse(cls.res(\"当前密码错误\", \"\"))\n\n @staticmethod\n def detVerCode(verCode, email):\n res = \"\"\n try:\n code = VerCode.objects.get(email=email)\n if str(code.code) == str(verCode):\n res = ''\n else:\n res = \"验证码错误\"\n except VerCode.DoesNotExist:\n res = \"请先获取验证码\"\n return res\n\n @staticmethod\n def res(mes, data=\"\"):\n return {\"mes\": mes, \"data\": data}\n\n @classmethod\n def error(cls, mesId):\n mes = \"\"\n # 错误分类\n if mesId == 0:\n mes = \"数据库链接错误\"\n elif mesId == 1:\n mes = \"参数错误\"\n else:\n mes = \"未知错误\"\n return cls.res(mes, \"\")\n \n @classmethod\n def senVerCode(cls, request):\n \"发送验证码\"\n email = request.GET.get(\"email\")\n try:\n u = User.objects.filter(email=email)\n assert u.count() > 0\n return JsonResponse(cls.res(\"该邮箱已注册\"))\n except AssertionError:\n pass\n\n # 卡发送时间\n try:\n eCode = VerCode.objects.get(email=email)\n if (timezone.now() - eCode.time).seconds <= 60:\n return JsonResponse(cls.res(\"发送间隔太小，请等会再尝试\"))\n except VerCode.DoesNotExist:\n pass\n \n # 随机生成验证码\n code = ''.join([str(random.randint(0,9)) for i in range(6)])\n # 调用发送模块\n res = cls.sender.send(email, code)\n if \"成功\" in res:\n try:\n v = VerCode.objects.get(email=email)\n v.code = code\n v.time = timezone.now()\n v.save()\n except VerCode.DoesNotExist:\n VerCode(email=email, code=code, time=timezone.now()).save()\n return JsonResponse(cls.res(\"\", res))\n\n @classmethod\n @csrf_exempt\n def monitorLogin(cls, request):\n \"管理员登录\"\n email = request.POST.get(\"username\")\n password = request.POST.get(\"password\")\n \n # 判断用户是否存在\n try:\n user = User.objects.get(username=email)\n except User.DoesNotExist:\n return JsonResponse(cls.res(\"用户不存在\", \"\"))\n\n if user.check_password(password):\n if user.userinfo.Class.monitor == user.userinfo:\n userinfo = UserInfo.objects.get(user=user)\n return JsonResponse(cls.res(\"\", {\n \"user\": userinfo.id,\n \"class\": userinfo.Class.id\n }))\n else:\n return JsonResponse(cls.res(\"非管理员用户不允许登录\", \"\"))\n else:\n return JsonResponse(cls.res(\"用户名或密码错误\", \"\"))\n\n\n\n @classmethod\n def getUserInfo(cls, request):\n userId = request.GET.get(\"id\")\n if userId:\n try:\n userInfo = UserInfo.objects.get(id=userId)\n except User.DoesNotExist:\n return JsonResponse(cls.error(1))\n else:\n try:\n faceimg = \"/\" + userInfo.faceImg.path.replace(\"\\\\\", \"/\")\n except UserInfo.faceImg.RelatedObjectDoesNotExist:\n faceimg = None\n user = userInfo.user\n res = {\n \"username\": userInfo.username,\n \"email\": user.username,\n \"phoneNumber\": userInfo.phoneNumber,\n \"class\": userInfo.Class.name,\n \"faceImg\": faceimg\n }\n return JsonResponse(cls.res(\"\", res))\n else:\n return JsonResponse(cls.error(1))\n\n @classmethod\n @csrf_exempt\n @exceptError\n def login(cls, request):\n email = request.POST.get(\"username\")\n password = request.POST.get(\"password\")\n \n try:\n user = User.objects.get(username=email)\n except User.DoesNotExist:\n return JsonResponse(cls.res(\"用户不存在\", \"\"))\n if user.check_password(password):\n userinfo = UserInfo.objects.get(user=user)\n return JsonResponse(cls.res(\"\", userinfo.id))\n else:\n return JsonResponse(cls.res(\"用户名或密码错误\", \"\"))\n\n @classmethod\n @csrf_exempt # 使此次请求忽略csrf校验\n @exceptError\n def register(cls, request):\n \"注册\"\n res = {}\n username = request.POST.get(\"username\")\n email = request.POST.get(\"email\")\n password = request.POST.get(\"password\")\n phoneNumber = request.POST.get(\"phoneNumber\")\n verCode = request.POST.get(\"verCode\")\n\n # 验证验证码的正确性\n verCodeRes = cls.detVerCode(verCode, email)\n if verCodeRes:\n return JsonResponse(cls.res(verCode))\n # 前端传来的班级\n _class = request.POST.get(\"class\")\n\n \n\n\n classExist = True\n\n # 创建用户\n try:\n User.objects.create_user(email, email, password)\n except Exception as a:\n print(a)\n return JsonResponse(cls.error(0)) \n \n # 如果班级不存在就创建班级\n try:\n class_ = Class.objects.get(name=_class)\n except Class.DoesNotExist:\n # 后端班级对象\n classExist = False\n class_ = Class(name=_class)\n class_.save()\n \n try:\n user = User.objects.get(username=email)\n userInfo = UserInfo(user=user, username=username , Class=class_, phoneNumber=phoneNumber)\n userInfo.save()\n except Exception as a:\n print(a)\n return JsonResponse(cls.error(0)) \n \n if not classExist:\n class_.monitor = userInfo\n class_.save()\n\n return JsonResponse(cls.res(\"\", \"注册成功\"))", "sub_path": "backend/common/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 11182, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.http.JsonResponse", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "hashlib.md5", "line_number": 40, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv.faceDet.hasFace", "line_number": 71, "usage_type": "call"}, {"api_name": "cv.faceDet", "line_number": 71, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 78, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 28, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 88, "usage_type": "call"}, {"api_name": "time.time", "line_number": 90, "usage_type": "call"}, {"api_name": "cv.faceMerge.faceMerge", "line_number": 91, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 93, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 95, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 100, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 111, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 122, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 135, "usage_type": "call"}, {"api_name": "sendVerCode.codeSender", "line_number": 139, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 153, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 157, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 158, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 170, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 174, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 175, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 161, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 213, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 220, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 220, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 221, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 226, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 233, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 233, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 236, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 236, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 237, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 250, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 255, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 260, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 262, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 240, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 273, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 287, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 289, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 301, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 304, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 306, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 292, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 323, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 337, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 354, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 360, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 309, "usage_type": "name"}]} +{"seq_id": "199925325", "text": "\"\"\"\nexport PYTHONPATH=$PYTHONPATH:~/Desktop/projects/m251:~/Desktop/projects/del8\n\npython3 m251/exp_groups/paper/nlp/intermediate/results/merge_results.py\n\n\"\"\"\nimport collections\nimport csv\nimport json\nimport os\n\nimport numpy as np\n\nfrom del8.core.experiment import experiment\nfrom del8.core.storage.storage import RunState\nfrom del8.core.utils.type_util import hashabledict\n\nfrom m251.data.processing.constants import NUM_GLUE_TRAIN_EXAMPLES\nfrom m251.fisher.execs import merging_execs\nfrom m251.exp_groups.paper.results import utils as result_utils\n\nfrom m251.exp_groups.paper.nlp.intermediate import defs\n\nget_single_score = result_utils.get_single_score\nresult_file = result_utils.result_file\n\nBAD_FINETUNE_RUN_UUIDS = defs.BAD_FINETUNE_RUN_UUIDS\n\n\nMERGE_PAIRS_JSON = result_file(\"nlp/intermediate/merge_pairs.json\")\n\n\ndef _is_bad_cola(mtm, res):\n return mtm.task == \"cola\" and not get_single_score(res.results)\n\n\ndef _is_bad_mrpc(mtm, res):\n return mtm.task == \"mrpc\" and get_single_score(res.results) < 75\n\n\ndef _is_bad_qqp(mtm, res):\n return (\n mtm.task == \"qqp\"\n and mtm.model_checkpoint_uuid == \"536880b57b0248718b8f9748f8b2e847\"\n )\n\n\ndef create_json(merge_exp):\n with merge_exp.get_storage() as storage:\n exps_data = storage.retrieve_storage_data(experiment_uuid=[merge_exp.uuid])\n\n merge_run_ids = exps_data.get_finished_runs_ids(experiment_uuid=merge_exp.uuid)\n\n items = []\n for run_id in merge_run_ids:\n merge_run = exps_data.get_run_data(run_id)\n\n params = merge_run.get_single_item_by_class(merge_exp.params_cls)\n reses = merge_run.get_items_by_class(merging_execs.MergingEvaluationResults)\n\n # print([(r.weighting[0], get_single_score(r.results)) for r in reses])\n\n res = max(reses, key=lambda r: get_single_score(r.results))\n og_res = max(reses, key=lambda r: r.weighting[0])\n donor_body_res = max(reses, key=lambda r: r.weighting[1])\n\n assert og_res.weighting[0] == 1.0\n assert donor_body_res.weighting[1] == 1.0\n\n target_mtm, donor_mtm = params.models_to_merge\n\n if target_mtm.fisher_run_uuid in BAD_FINETUNE_RUN_UUIDS:\n continue\n elif donor_mtm.fisher_run_uuid in BAD_FINETUNE_RUN_UUIDS:\n continue\n\n items.append(\n {\n \"target_task\": target_mtm.task,\n \"donor_task\": donor_mtm.task,\n \"trial_index\": params.trial_index,\n \"original_score\": og_res.results,\n \"merged_score\": res.results,\n \"donor_body_score\": donor_body_res.results,\n \"weighting\": res.weighting[0],\n }\n )\n\n return items\n\n\ndef create_csv_table(filepath, round_digits=1):\n items = result_utils.load_json(filepath)\n\n row_groups = collections.defaultdict(list)\n for item in items:\n group_key = hashabledict(\n {\n \"target_task\": item[\"target_task\"],\n \"donor_task\": item[\"donor_task\"],\n }\n )\n row_groups[group_key].append(item)\n\n header = [\n \"task\",\n \"donor\",\n \"merged score\",\n \"stddev\",\n \"orig score\",\n \"stddev\",\n \"mean boost\",\n \"stddev\",\n \"max boost\",\n \"min boost\",\n \"num trials\",\n ]\n body = []\n for hp, row_items in row_groups.items():\n og_scores = np.array(\n [get_single_score(item[\"original_score\"]) for item in row_items]\n )\n merged_scores = np.array(\n [get_single_score(item[\"merged_score\"]) for item in row_items]\n )\n row = [\n hp[\"target_task\"],\n hp[\"donor_task\"],\n round(np.mean(merged_scores), round_digits),\n round(np.std(merged_scores), round_digits),\n #\n round(np.mean(og_scores), round_digits),\n round(np.std(og_scores), round_digits),\n #\n round(np.mean(merged_scores - og_scores), round_digits),\n round(np.std(merged_scores - og_scores), round_digits),\n #\n round(np.max(merged_scores - og_scores), round_digits),\n round(np.min(merged_scores - og_scores), round_digits),\n len(row_items),\n ]\n body.append(row)\n\n body = sorted(body, key=lambda r: r[:2])\n\n rows = [header] + body\n\n return result_utils.csv_to_str(rows)\n\n\nif __name__ == \"__main__\":\n from m251.exp_groups.paper.nlp.intermediate import fisher\n from m251.exp_groups.paper.nlp.intermediate import merge\n\n ###########################################################################\n\n merge_exp = merge.Merge_Pairs_Normalized_LastCkpt\n summary = create_json(merge_exp)\n # s = json.dumps(summary, indent=2)\n # print(s)\n\n filepath = MERGE_PAIRS_JSON\n with open(filepath, \"w\") as f:\n json.dump(summary, f, indent=2)\n\n t = create_csv_table(filepath)\n print(t)\n", "sub_path": "m251/exp_groups/paper/nlp/intermediate/results/merge_results.py", "file_name": "merge_results.py", "file_ext": "py", "file_size_in_byte": 4940, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "m251.exp_groups.paper.results.utils.get_single_score", "line_number": 24, "usage_type": "attribute"}, {"api_name": "m251.exp_groups.paper.results.utils", "line_number": 24, "usage_type": "name"}, {"api_name": "m251.exp_groups.paper.results.utils.result_file", "line_number": 25, "usage_type": "attribute"}, {"api_name": "m251.exp_groups.paper.results.utils", "line_number": 25, "usage_type": "name"}, {"api_name": "m251.exp_groups.paper.nlp.intermediate.defs.BAD_FINETUNE_RUN_UUIDS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "m251.exp_groups.paper.nlp.intermediate.defs", "line_number": 27, "usage_type": "name"}, {"api_name": "m251.fisher.execs.merging_execs.MergingEvaluationResults", "line_number": 59, "usage_type": "attribute"}, {"api_name": "m251.fisher.execs.merging_execs", "line_number": 59, "usage_type": "name"}, {"api_name": "m251.exp_groups.paper.results.utils.load_json", "line_number": 93, "usage_type": "call"}, {"api_name": "m251.exp_groups.paper.results.utils", "line_number": 93, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 95, "usage_type": "call"}, {"api_name": "del8.core.utils.type_util.hashabledict", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 139, "usage_type": "call"}, {"api_name": "m251.exp_groups.paper.results.utils.csv_to_str", "line_number": 148, "usage_type": "call"}, {"api_name": "m251.exp_groups.paper.results.utils", "line_number": 148, "usage_type": "name"}, {"api_name": "m251.exp_groups.paper.nlp.intermediate.merge.Merge_Pairs_Normalized_LastCkpt", "line_number": 157, "usage_type": "attribute"}, {"api_name": "m251.exp_groups.paper.nlp.intermediate.merge", "line_number": 157, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 164, "usage_type": "call"}]} +{"seq_id": "223933667", "text": "\"\"\"Module for analysing feeding buzz bouts\"\"\"\n\nimport time\nfrom collections import defaultdict\n\nfrom helper.write_data import write_array\nfrom sonochiro_dataset_creation import load_sonochiro_file\n\n\ndef transect_entries(sonochiro_array):\n \"\"\"Return a dictionary with for each transect sorted timestamps of all recordings\"\"\"\n activity_times = defaultdict(list)\n for row in sonochiro_array:\n transect, sec_time = row[1], row[5]\n activity_times[transect].append(sec_time)\n for tr in activity_times: # sort all entry times\n activity_times[tr] = sorted(activity_times[tr])\n return activity_times\n\n\ndef find_activity_gaps(sonochiro_array, gap_sec):\n \"\"\"Return a dictionary with for each transect the end time of each\n activity gap same as or larger than specified gap_sec\"\"\"\n activity_gaps = defaultdict(list)\n activity_times = transect_entries(sonochiro_array)\n for transect in activity_times:\n last_activity = 0 # ensure first recording of transect is included\n for activity in activity_times[transect]:\n if activity - last_activity >= gap_sec: # if it is the end time of a gap longer than gap_sec seconds\n activity_gaps[transect].append(activity)\n last_activity = activity\n return activity_gaps\n\n\ndef find_time_since_end_activity_gap(activity_gap_dict, transect, recording_time):\n \"\"\"Return time as int since last activity gap for a given transect and recording time\"\"\"\n for i, gap_end_time in enumerate(activity_gap_dict[transect]):\n if recording_time < gap_end_time:\n return recording_time - activity_gap_dict[transect][i-1]\n elif recording_time == gap_end_time:\n return 0\n else: # the entry is after the last gap_end_time so will not be found with above for loop\n return recording_time - activity_gap_dict[transect][-1]\n\n\ndef create_bout_analysis_dataset(sonochiro_array, gap_sec, only_pp=True, buzz_index=2):\n \"\"\"Create and return a sonochiro array with additional information per file on time since last gap\n and whether a buzz index was higher or the same as specified buzz_index. Can filter out everything\n that is not identified as a Pippistrellus pippistrellus.\n \"\"\"\n if only_pp: # filter out entries other than Pippistrellus pippistrellus\n edited_array = [row for row in sonochiro_array if row[8] == \"PippiT\"]\n else:\n edited_array = sonochiro_array[:] # create copy to avoid editing original array\n activity_gaps = find_activity_gaps(edited_array, gap_sec)\n for i, row in enumerate(edited_array):\n transect, time_in_sec = row[1], row[5]\n gap_dt = find_time_since_end_activity_gap(activity_gaps, transect, time_in_sec)\n row.extend([gap_dt, 0])\n if row[19] >= buzz_index:\n row[-1] = 1\n edited_array[i] = row\n column_names = ['filename', 'transect', 'site', 'colour', 'night', 'total_time_sec', 'detector', 'comp_fl',\n 'final_id', 'contact', 'group', 'group_index', 'species', 'species_index',\n 'nb_calls', 'med_freq', 'med_int', 'i_qual', 'i_sc', 'i_buzz', 'gap_dt', 'buzz']\n return edited_array, column_names\n\n\nif __name__ == \"__main__\":\n gap = 30 # which amount of seconds is defined as a gap\n file_to_write = \"dataset_bout_analysis_with_{}_second_gaps.csv\".format(gap)\n\n start = time.time()\n sc = load_sonochiro_file()[0]\n print(\"loaded sc file in {:.3f} seconds\".format(time.time()-start))\n start2 = time.time()\n bout_array, col_names = create_bout_analysis_dataset(sc, gap)\n print(\"created bout array in {:.3f} seconds\".format(time.time() - start2))\n write_array(bout_array, col_names, file_to_write)\n", "sub_path": "feed_buzz_bout_analysis.py", "file_name": "feed_buzz_bout_analysis.py", "file_ext": "py", "file_size_in_byte": 3730, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "collections.defaultdict", "line_number": 12, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 73, "usage_type": "call"}, {"api_name": "sonochiro_dataset_creation.load_sonochiro_file", "line_number": 74, "usage_type": "call"}, {"api_name": "time.time", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 76, "usage_type": "call"}, {"api_name": "time.time", "line_number": 78, "usage_type": "call"}, {"api_name": "helper.write_data.write_array", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "281001087", "text": "from flask import request, url_for, Blueprint\nfrom flask_restplus import Resource, Api\nfrom ..models import Note, Tag\nfrom flask_restplus import fields\n\nblueprint = Blueprint('api', __name__)\n\napi = Api(blueprint,\n title=\"Note App\",\n version='1.0',\n description=\"API documentation for my graph notes app\")\n\n\n# Models\nnote_ns = api.namespace('notes',\n description=\"Operations for retrieving \"\n \"Notes, Child Notes and Parent Notes.\")\n\nnote_content = api.model('Note Content', {\n \"content\": fields.String\n })\n\npaginated_notes_meta = api.model('Paginated Notes Meta', {\n \"currentPage\": fields.Integer,\n \"itemsPerPage\": fields.Integer,\n})\n\n\npaginated_notes_links = api.model('Paginated Notes Links', {\n \"currentPageEndpoint\": fields.String,\n \"nextPageEndpoint\": fields.String,\n \"prevPageEndpoint\": fields.String\n})\n\nnote_links = api.model('Note Links', {\n \"currentNoteEndpoint\": fields.String,\n \"parentNoteEndpoint\": fields.String,\n \"childNoteEndpoint\": fields.String,\n})\n\nnote_model = api.model('Note Model', {\n 'id': fields.Integer,\n 'uid': fields.String,\n 'content': fields.String,\n 'createdAt': fields.DateTime,\n 'archived': fields.Boolean,\n 'tags': fields.List(fields.String),\n '_links': fields.Nested(note_links)\n})\n\npaginated_notes_model = api.model('Paginated Notes Model', {\n 'data': fields.List(fields.Nested(note_model)),\n '_meta': fields.Nested(paginated_notes_meta),\n '_links': fields.Nested(paginated_notes_links)\n})\n\n\n# Routes\n@note_ns.route('/')\nclass Notes(Resource):\n \"\"\" Main Note routes.\n \"\"\"\n @api.marshal_with(paginated_notes_model)\n @api.response(200, 'Successfully read notes')\n @api.param('page', 'Number of the page to get')\n @api.param('per_page', 'Number of notes per page')\n @api.param('tag', \"Get notes matching this tag\")\n @api.param('start_date', \"Date to match after eg. 1970-10-10\")\n @api.param('end_date', \"Date to match before eg. 1970-10-15\")\n @api.param('search', \"Get notes with content containing this string\")\n def get(self):\n\n \"\"\" Get outstanding notes\n\n Begins with the base clause and adds additional query clauses.\n \"\"\"\n\n # Query parameters\n params = {}\n\n # Base Query\n query = \"\"\"\n MATCH (n: Note)\n \"\"\"\n\n # Parse query parameters\n params['tag'] = request.args.get('tag')\n params['search'] = request.args.get(\"search\")\n params['start_date'] = request.args.get(\"start_date\")\n params['end_date'] = request.args.get('end_date')\n params['page'] = request.args.get('page', 1, type=int)\n params['per_page'] = request.args.get('per_page', 5, type=int)\n\n # Pagination variables\n params['skip'] = (params['page'] * params['per_page']) - params['per_page']\n params['limit'] = (params['per_page']) + 1\n\n if params['tag']:\n tag_clause = \"<-[:TAGGED]-(t: Tag)\"\n query = \"\".join((query, tag_clause))\n\n # Add query clauses.\n archived_clause = \"WHERE n.archived = False\"\n query = \" \".join((query, archived_clause))\n\n if params['tag']:\n tag_clause = \"AND t.text = $tag\"\n query = \" \".join((query, tag_clause))\n if params['search']:\n search_clause = \"AND toLower(n.content) CONTAINS toLower($search) \"\n query = \" \".join((query, search_clause))\n if params['start_date']:\n start_date_clause = \"AND n.createdAt > $start_date\"\n query = \" \".join((query, start_date_clause))\n if params['end_date']:\n end_date_clause = \"AND n.createdAt < $end_date\"\n query = \" \".join((query, end_date_clause))\n\n # Return clause with pagination\n return_clause = \"\"\"\n RETURN n\n ORDER BY n.createdAt DESC\n \"\"\"\n pagination_clause = \"SKIP $skip LIMIT $limit\"\n query = \" \".join((query, return_clause, pagination_clause))\n\n # Get paginated Note collection\n data = Note.to_collection_dict(query,\n params,\n 'api.notes_notes',\n search=params['search'],\n start_date=params['start_date'],\n end_date=params['end_date'],\n per_page=params['per_page'],\n tag=params['tag'])\n return data\n\n @api.marshal_with(note_model)\n @api.response(201, 'Successfully created new note')\n @api.expect(note_content)\n def post(self):\n \"\"\" Create a new parent note\n\n Returns the newly created parent note.\n \"\"\"\n\n # Parse content\n data = request.get_json()\n content = data[\"content\"]\n\n if content:\n note = Note(content=content)\n note.save_note()\n return note.to_dict()\n # TODO return error message\n\n\n@note_ns.route('/')\nclass NotesNote(Resource):\n\n \"\"\" Individual Notes\n \"\"\"\n\n @api.marshal_with(note_model)\n @api.response(200, 'Successfully read note')\n def get(self, uid):\n\n \"\"\" Get a single note\n\n Allows the user to get a single note from\n the database according to the id.\n \"\"\"\n\n note = Note.nodes.get_or_none(uid=uid)\n if note:\n return note.to_dict()\n # else...\n\n\n@note_ns.route('//parent')\nclass NoteParent(Resource):\n\n \"\"\" For finding parent Notes.\n \"\"\"\n\n @api.marshal_with(note_model)\n @api.response(200, 'Successfully read note')\n def get(self, uid):\n\n \"\"\" Get parent of note by id\n\n Get a parent note from the database\n according to the child's id\"\"\"\n\n child = Note.nodes.get_or_none(uid=uid)\n if child:\n parent = child.parent.get_or_none()\n if parent:\n return parent.to_dict()\n # else....\n # else ...\n\n\n@note_ns.route('//child')\nclass NoteChild(Resource):\n\n \"\"\" Finding Child Notes.\n \"\"\"\n\n @api.marshal_with(note_model)\n @api.response(200, 'Successfully read note')\n def get(self, uid):\n\n \"\"\" Get child of note by id\n\n Get a child note from the database\n according to the parent's id\"\"\"\n \n parent = Note.nodes.get_or_none(uid=uid)\n if parent:\n child = parent.child.get_or_none()\n if child:\n return child.to_dict()\n # else....\n # else ...\n\n @api.marshal_with(note_model)\n @api.response(201, 'Successfully created child note')\n @api.expect(note_content)\n def post(self, uid):\n\n \"\"\" Create a child of note by id\n\n Create and return a child note of the note according\n to id and archive the parent note \"\"\"\n\n parent = Note.nodes.get_or_none(uid=uid)\n if parent:\n # Parse content\n data = request.get_json()\n content = data.get(\"content\")\n # Create child\n child = Note(content=content)\n child.save()\n # Connect parent and child\n parent.child.connect(child)\n child.parent.connect(parent) # TODO test this\n parent.archived = True\n parent.save()\n child.save()\n return child.to_dict()\n # else...\n\n\n@note_ns.route(\"//archive\")\nclass NoteArchive(Resource):\n \"\"\"\n Archive a note\n \"\"\"\n @api.marshal_with(note_model)\n @api.response(200, 'Successfully archived note')\n def post(self, uid):\n \"\"\" Archive a note\n \"\"\"\n note = Note.nodes.get_or_none(uid=uid)\n if note:\n note.archived = True\n note.save()\n return note.to_dict()\n # else...\n", "sub_path": "app/api/api_routes.py", "file_name": "api_routes.py", "file_ext": "py", "file_size_in_byte": 8044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.Blueprint", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_restplus.Api", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_restplus.fields.String", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 20, "usage_type": "name"}, {"api_name": "flask_restplus.fields.Integer", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 24, "usage_type": "name"}, {"api_name": "flask_restplus.fields.Integer", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 25, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 31, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 32, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 36, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 37, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 38, "usage_type": "name"}, {"api_name": "flask_restplus.fields.Integer", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 42, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 43, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 44, "usage_type": "name"}, {"api_name": "flask_restplus.fields.DateTime", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 45, "usage_type": "name"}, {"api_name": "flask_restplus.fields.Boolean", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields", "line_number": 46, "usage_type": "name"}, {"api_name": "flask_restplus.fields.List", "line_number": 47, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 47, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask_restplus.fields.Nested", "line_number": 48, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 48, "usage_type": "name"}, {"api_name": "flask_restplus.fields.List", "line_number": 52, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 52, "usage_type": "name"}, {"api_name": "flask_restplus.fields.Nested", "line_number": 52, "usage_type": "call"}, {"api_name": "flask_restplus.fields.Nested", "line_number": 53, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 53, "usage_type": "name"}, {"api_name": "flask_restplus.fields.Nested", "line_number": 54, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 54, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 87, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 87, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 88, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 89, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "models.Note.to_collection_dict", "line_number": 128, "usage_type": "call"}, {"api_name": "models.Note", "line_number": 128, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 148, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 148, "usage_type": "name"}, {"api_name": "models.Note", "line_number": 152, "usage_type": "call"}, {"api_name": "flask_restplus.Resource", "line_number": 159, "usage_type": "name"}, {"api_name": "models.Note.nodes.get_or_none", "line_number": 174, "usage_type": "call"}, {"api_name": "models.Note.nodes", "line_number": 174, "usage_type": "attribute"}, {"api_name": "models.Note", "line_number": 174, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 181, "usage_type": "name"}, {"api_name": "models.Note.nodes.get_or_none", "line_number": 195, "usage_type": "call"}, {"api_name": "models.Note.nodes", "line_number": 195, "usage_type": "attribute"}, {"api_name": "models.Note", "line_number": 195, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 205, "usage_type": "name"}, {"api_name": "models.Note.nodes.get_or_none", "line_number": 219, "usage_type": "call"}, {"api_name": "models.Note.nodes", "line_number": 219, "usage_type": "attribute"}, {"api_name": "models.Note", "line_number": 219, "usage_type": "name"}, {"api_name": "models.Note.nodes.get_or_none", "line_number": 237, "usage_type": "call"}, {"api_name": "models.Note.nodes", "line_number": 237, "usage_type": "attribute"}, {"api_name": "models.Note", "line_number": 237, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 240, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 240, "usage_type": "name"}, {"api_name": "models.Note", "line_number": 243, "usage_type": "call"}, {"api_name": "flask_restplus.Resource", "line_number": 256, "usage_type": "name"}, {"api_name": "models.Note.nodes.get_or_none", "line_number": 265, "usage_type": "call"}, {"api_name": "models.Note.nodes", "line_number": 265, "usage_type": "attribute"}, {"api_name": "models.Note", "line_number": 265, "usage_type": "name"}]} +{"seq_id": "500879214", "text": "from flask import Flask\nfrom flask import Flask, request, jsonify\nimport json\nfrom flask_restful import Resource,Api\n\nfrom Prerdiction import Prediction\nfrom Productivity import Productivity\n\napp = Flask(__name__)\napi = Api(app)\n\nclass Api(Resource):\n #get request to call the json objects sent to the backend\n def get(self):\n request_data = request.data\n request_data = json.loads(request_data.decode('utf-8'))\n return request_data\n #post request to get the data from front end to do processing\n def post(self):\n request_data = request.data\n request_data = json.loads(request_data.decode('utf-8'))\n date = request_data['date']\n startTime = request_data['startTime']\n endTime = request_data['endTime']\n capacity = request_data['capacity']\n p1 = Prediction(date, startTime,endTime)\n irr = p1.getIrradiance()\n e1 = Productivity(irr,1,capacity)\n pro = e1.getUnits()\n print(\"Productivity : \",pro)\n return round(pro,2)\napi.add_resource(Api,\"/api\")\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n", "sub_path": "Hosting_backend/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1113, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_restful.Api", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "Prerdiction.Prediction", "line_number": 26, "usage_type": "call"}, {"api_name": "Productivity.Productivity", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "175612411", "text": "from fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom . import routes as handlers\nfrom .database import Base, engine\n\norigins = [\"http://localhost\", \"http://localhost:3000\", \"https://parky.ml\"]\n\n\ndef create_app():\n app = FastAPI()\n\n app.add_middleware(\n CORSMiddleware,\n allow_origins=origins,\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n )\n\n # Initialize DB connection\n Base.metadata.create_all(bind=engine)\n\n # Register handlers\n app.get(\"/\")(handlers.handle_status)\n\n app.post(\"/user\")(handlers.handle_signup)\n app.post(\"/user/signin\")(handlers.handle_signin)\n\n app.get(\"/vehicle\")(handlers.handle_get_vehicles)\n app.get(\"/vehicle/{number}\")(handlers.handle_find_vehicle)\n app.post(\"/vehicle\")(handlers.handle_register_vehicle)\n\n app.post(\"/v2d/auth/{uid}/{vid}\")(handlers.handle_start_session)\n app.post(\"/v2d/auth/vehicle\")(handlers.handle_auth_vehicle)\n app.post(\"/v2d/auth/client\")(handlers.handle_auth_client)\n\n app.get(\"/parking\")(handlers.handle_get_all_parking_lot)\n app.get(\"/parking/{parking_id}/{user_id}\")(handlers.handle_reserve)\n app.get(\"/parking/{parking_id}/{vehicle_number}\")(handlers.handle_income_car)\n app.get(\"/parking/{vehicle_number}\")(handlers.handle_go_out_car)\n\n return app\n", "sub_path": "backend/parky/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1355, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "fastapi.FastAPI", "line_number": 11, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 14, "usage_type": "argument"}, {"api_name": "database.Base.metadata.create_all", "line_number": 22, "usage_type": "call"}, {"api_name": "database.Base.metadata", "line_number": 22, "usage_type": "attribute"}, {"api_name": "database.Base", "line_number": 22, "usage_type": "name"}, {"api_name": "database.engine", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "347571128", "text": "import re\nfrom collections import OrderedDict\n\nfrom django import forms\nfrom django.conf import settings\nfrom govuk_forms.forms import GOVUKForm\n\nfrom datetime import date, datetime, timedelta\n\nfrom ..fields import CustomSplitDateFieldNameAndAddressHistory\n\n\nclass PreviousAddressEntryForm(GOVUKForm):\n\n ERROR_MESSAGE_POSTCODE_NOT_ENTERED = 'Please enter your postcode'\n\n field_label_classes = 'form-label-bold'\n auto_replace_widgets = True\n error_summary_title = 'There was a problem'\n\n postcode = forms.CharField(label='Postcode', error_messages={'required': 'Please enter your postcode'})\n\n\nclass PreviousAddressSelectForm(GOVUKForm):\n # Address validation messages\n ERROR_MESSAGE_ADDRESS_BLANK = 'Please select your address'\n\n # Moved in/out date validation messages\n ERROR_MESSAGE_DATE_BLANK = 'Enter the full date, including the day, month and year'\n ERROR_MESSAGE_DAY_OUT_OF_RANGE = 'Day must be between 1 and 31'\n ERROR_MESSAGE_MONTH_OUT_OF_RANGE = 'Month must be between 1 and 12'\n ERROR_MESSAGE_MOVED_IN_YEAR_BEFORE_1900 = 'Date moved in must be after 1900'\n ERROR_MESSAGE_MOVED_OUT_YEAR_BEFORE_1900 = 'Date you moved out must be after 1900'\n ERROR_MESSAGE_YEAR_LESS_THAN_4_DIGITS = 'Enter the whole year (4 digits)'\n ERROR_MESSAGE_INVALID_DATE = 'Enter a real date'\n ERROR_MESSAGE_NON_NUMERIC = 'Use numbers for the date'\n\n ERROR_MESSAGE_MOVED_IN_DATE_AFTER_CURRENT_DATE = 'Enter the full date, including day, month and year. This cannot be in the future'\n ERROR_MESSAGE_MOVED_IN_DATE_AFTER_MOVED_OUT_DATE = 'Date you moved in must be before date you moved out'\n\n ERROR_MESSAGE_MOVED_OUT_DATE_AFTER_CURRENT_DATE = 'Enter the full date, including day, month and year. This cannot be in the future'\n ERROR_MESSAGE_MOVED_OUT_DATE_BEFORE_MOVED_IN_DATE = 'Date you moved out must be after the date you moved in'\n\n ERROR_MESSAGE_MOVED_OUT_DATE_OVER_FIVE_YEARS_AGO = 'Date you moved out must be less than five years ago'\n\n auto_replace_widgets = True\n field_label_classes = 'form-label-bold'\n error_summary_title = 'There was a problem'\n\n address = forms.ChoiceField(\n label='Select address',\n required=True,\n error_messages={'required': ERROR_MESSAGE_ADDRESS_BLANK}\n )\n moved_in_date = CustomSplitDateFieldNameAndAddressHistory(\n label='Date you moved in',\n required=True,\n help_text='For example, 31 03 1980',\n min_value=None,\n max_value=CustomSplitDateFieldNameAndAddressHistory.TODAY,\n allow_short_year=False,\n error_messages={'required': ERROR_MESSAGE_DATE_BLANK,\n 'incomplete': ERROR_MESSAGE_DATE_BLANK,\n 'max_today': ERROR_MESSAGE_MOVED_IN_DATE_AFTER_CURRENT_DATE,\n 'invalid': ERROR_MESSAGE_INVALID_DATE},\n day_error_messages={'min_value': ERROR_MESSAGE_DAY_OUT_OF_RANGE,\n 'max_value': ERROR_MESSAGE_DAY_OUT_OF_RANGE,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC},\n month_error_messages={'min_value': ERROR_MESSAGE_MONTH_OUT_OF_RANGE,\n 'max_value': ERROR_MESSAGE_MONTH_OUT_OF_RANGE,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC},\n year_min_value=1900,\n year_max_value=None,\n year_error_messages={'min_value': ERROR_MESSAGE_MOVED_IN_YEAR_BEFORE_1900,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC,\n 'short_year': ERROR_MESSAGE_YEAR_LESS_THAN_4_DIGITS},\n )\n moved_out_date = CustomSplitDateFieldNameAndAddressHistory(\n label='Date you moved out',\n required=True,\n help_text='For example, 31 03 1980',\n min_value=None,\n max_value=CustomSplitDateFieldNameAndAddressHistory.TODAY,\n allow_short_year=False,\n error_messages={'required': ERROR_MESSAGE_DATE_BLANK,\n 'incomplete': ERROR_MESSAGE_DATE_BLANK,\n 'max_today': ERROR_MESSAGE_MOVED_OUT_DATE_AFTER_CURRENT_DATE,\n 'invalid': ERROR_MESSAGE_INVALID_DATE},\n day_error_messages={'min_value': ERROR_MESSAGE_DAY_OUT_OF_RANGE,\n 'max_value': ERROR_MESSAGE_DAY_OUT_OF_RANGE,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC},\n month_error_messages={'min_value': ERROR_MESSAGE_MONTH_OUT_OF_RANGE,\n 'max_value': ERROR_MESSAGE_MONTH_OUT_OF_RANGE,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC},\n year_min_value=1900,\n year_max_value=None,\n year_error_messages={'min_value': ERROR_MESSAGE_MOVED_OUT_YEAR_BEFORE_1900,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC,\n 'short_year': ERROR_MESSAGE_YEAR_LESS_THAN_4_DIGITS},\n )\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Configure available address choices via passed kwarg.\n \"\"\"\n self.choices = kwargs.pop('choices')\n super(PreviousAddressSelectForm, self).__init__(*args, **kwargs)\n self.fields['address'].choices = self.choices\n\n def clean(self):\n super().clean()\n\n # check start date is after end date\n start_date = self.cleaned_data.get('moved_in_date', None)\n end_date = self.cleaned_data.get('moved_out_date', None)\n if start_date and end_date and end_date <= start_date:\n self.add_error('moved_in_date', self.ERROR_MESSAGE_MOVED_IN_DATE_AFTER_MOVED_OUT_DATE)\n self.add_error('moved_out_date', self.ERROR_MESSAGE_MOVED_OUT_DATE_BEFORE_MOVED_IN_DATE)\n\n end_date = self.cleaned_data.get('moved_out_date', None)\n five_years_ago = subtract_years(datetime.today().date(), 5)\n if end_date and five_years_ago > end_date:\n self.add_error('moved_out_date', self.ERROR_MESSAGE_MOVED_OUT_DATE_OVER_FIVE_YEARS_AGO)\n\n # de-duplicate error messages for each field\n for field, errors in self.errors.items():\n dedup = OrderedDict([(k, None) for k in errors])\n self.errors[field] = list(dedup.keys())\n\n\nclass PreviousAddressManualForm(GOVUKForm):\n \"\"\"\n Form for adding previous addresses to childminder/nanny applicants, people in the home, children etc.\n \"\"\"\n\n # Manual address entry field validation messages\n ERROR_MESSAGE_STREET_LINE_1_BLANK = 'Please enter the first line of your address'\n ERROR_MESSAGE_STREET_LINE_1_TOO_LONG = 'The first line of your address must be under 50 characters long'\n ERROR_MESSAGE_STREET_LINE_2_TOO_LONG = 'The second line of your address must be under 50 characters long'\n ERROR_MESSAGE_TOWN_BLANK = 'Please enter the name of the town or city'\n ERROR_MESSAGE_TOWN_INVALID = 'Spell out the name of the town or city using letters'\n ERROR_MESSAGE_TOWN_TOO_LONG = 'The name of the town or city must be under 50 characters long'\n ERROR_MESSAGE_COUNTY_INVALID = 'Spell out the name of the county using letters'\n ERROR_MESSAGE_COUNTY_TOO_LONG = 'The name of the county must be under 50 characters long'\n ERROR_MESSAGE_COUNTRY_BLANK = 'Please enter the name of the country'\n ERROR_MESSAGE_COUNTRY_INVALID = 'Spell out the name of the country using letters'\n ERROR_MESSAGE_COUNTRY_TOO_LONG = 'The name of the country must be under 50 characters long'\n ERROR_MESSAGE_POSTCODE_BLANK = 'Please enter your postcode'\n ERROR_MESSAGE_POSTCODE_INVALID = 'Please enter a valid postcode'\n ERROR_MESSAGE_POSTCODE_INVALID_UK_ADDRESS = 'Placeholder - Altered in view for ID'\n\n # Moved in/out date validation messages\n ERROR_MESSAGE_DATE_BLANK = 'Enter the full date, including the day, month and year'\n ERROR_MESSAGE_DAY_OUT_OF_RANGE = 'Day must be between 1 and 31'\n ERROR_MESSAGE_MONTH_OUT_OF_RANGE = 'Month must be between 1 and 12'\n ERROR_MESSAGE_MOVED_IN_YEAR_BEFORE_1900 = 'Date moved in must be after 1900'\n ERROR_MESSAGE_MOVED_OUT_YEAR_BEFORE_1900 = 'Date you moved out must be after 1900'\n ERROR_MESSAGE_YEAR_LESS_THAN_4_DIGITS = 'Enter the whole year (4 digits)'\n ERROR_MESSAGE_INVALID_DATE = 'Enter a real date'\n ERROR_MESSAGE_NON_NUMERIC = 'Use numbers for the date'\n\n ERROR_MESSAGE_MOVED_IN_DATE_AFTER_CURRENT_DATE = 'Enter the full date, including day, month and year. This cannot be in the future'\n ERROR_MESSAGE_MOVED_IN_DATE_AFTER_MOVED_OUT_DATE = 'Date you moved in must be before date you moved out'\n\n ERROR_MESSAGE_MOVED_OUT_DATE_AFTER_CURRENT_DATE = 'Enter the full date, including day, month and year. This cannot be in the future'\n ERROR_MESSAGE_MOVED_OUT_DATE_BEFORE_MOVED_IN_DATE = 'Date you moved out must be after the date you moved in'\n\n ERROR_MESSAGE_MOVED_OUT_DATE_OVER_FIVE_YEARS_AGO = 'Date you moved out must be less than five years ago'\n\n auto_replace_widgets = True\n field_label_classes = 'form-label-bold'\n error_summary_title = 'There was a problem'\n\n street_line1 = forms.CharField(\n label='Address line 1',\n required=True,\n error_messages={'required': ERROR_MESSAGE_STREET_LINE_1_BLANK}\n )\n\n street_line2 = forms.CharField(\n label='Address line 2',\n required=False\n )\n\n town = forms.CharField(\n label='Town or city',\n required=True,\n error_messages={'required': ERROR_MESSAGE_TOWN_BLANK}\n )\n\n county = forms.CharField(\n label='County (optional)',\n required=False\n )\n\n country = forms.CharField(\n label='Country',\n required=True,\n error_messages={'required': ERROR_MESSAGE_COUNTRY_BLANK}\n )\n\n postcode = forms.CharField(\n label='Postcode',\n required=True,\n error_messages={'required': ERROR_MESSAGE_POSTCODE_BLANK}\n )\n\n moved_in_date = CustomSplitDateFieldNameAndAddressHistory(\n label='Date you moved in',\n required=True,\n help_text='For example, 31 03 2012',\n min_value=None,\n max_value=CustomSplitDateFieldNameAndAddressHistory.TODAY,\n allow_short_year=False,\n error_messages={'required': ERROR_MESSAGE_DATE_BLANK,\n 'incomplete': ERROR_MESSAGE_DATE_BLANK,\n 'max_today': ERROR_MESSAGE_MOVED_IN_DATE_AFTER_CURRENT_DATE,\n 'invalid': ERROR_MESSAGE_INVALID_DATE},\n day_error_messages={'min_value': ERROR_MESSAGE_DAY_OUT_OF_RANGE,\n 'max_value': ERROR_MESSAGE_DAY_OUT_OF_RANGE,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC},\n month_error_messages={'min_value': ERROR_MESSAGE_MONTH_OUT_OF_RANGE,\n 'max_value': ERROR_MESSAGE_MONTH_OUT_OF_RANGE,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC},\n year_min_value=1900,\n year_max_value=None,\n year_error_messages={'min_value': ERROR_MESSAGE_MOVED_IN_YEAR_BEFORE_1900,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC,\n 'short_year': ERROR_MESSAGE_YEAR_LESS_THAN_4_DIGITS},\n )\n moved_out_date = CustomSplitDateFieldNameAndAddressHistory(\n label='Date you moved out',\n required=True,\n help_text='For example, 31 03 2012',\n min_value=None,\n max_value=CustomSplitDateFieldNameAndAddressHistory.TODAY,\n allow_short_year=False,\n error_messages={'required': ERROR_MESSAGE_DATE_BLANK,\n 'incomplete': ERROR_MESSAGE_DATE_BLANK,\n 'max_today': ERROR_MESSAGE_MOVED_OUT_DATE_AFTER_CURRENT_DATE,\n 'invalid': ERROR_MESSAGE_INVALID_DATE},\n day_error_messages={'min_value': ERROR_MESSAGE_DAY_OUT_OF_RANGE,\n 'max_value': ERROR_MESSAGE_DAY_OUT_OF_RANGE,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC},\n month_error_messages={'min_value': ERROR_MESSAGE_MONTH_OUT_OF_RANGE,\n 'max_value': ERROR_MESSAGE_MONTH_OUT_OF_RANGE,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC},\n year_min_value=1900,\n year_max_value=None,\n year_error_messages={'min_value': ERROR_MESSAGE_MOVED_OUT_YEAR_BEFORE_1900,\n 'invalid': ERROR_MESSAGE_NON_NUMERIC,\n 'short_year': ERROR_MESSAGE_YEAR_LESS_THAN_4_DIGITS},\n )\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Setup initial data for the manual form\n \"\"\"\n record = kwargs.pop('record', None)\n moved_in_date = kwargs.pop('moved_in_date', None)\n moved_out_date = kwargs.pop('moved_out_date', None)\n lived_abroad = kwargs.pop('lived_abroad', None)\n super().__init__(*args, **kwargs)\n\n if record:\n self.fields['street_line1'].initial = record.street_line1\n self.fields['street_line2'].initial = record.street_line2\n self.fields['town'].initial = record.town\n self.fields['county'].initial = record.county\n self.fields['country'].initial = record.country\n self.fields['postcode'].initial = record.postcode\n self.fields['moved_in_date'].initial = moved_in_date\n self.fields['moved_out_date'].initial = moved_out_date\n\n if not lived_abroad:\n self.base_fields['country'].required = False\n self.base_fields['postcode'].required = True\n self.base_fields['country'].label = 'Country (optional)'\n self.base_fields['postcode'].label = 'Postcode'\n if lived_abroad:\n self.base_fields['postcode'].required = False\n self.base_fields['country'].required = True\n self.base_fields['country'].label = 'Country'\n self.base_fields['postcode'].label = 'Postcode (optional)'\n\n def clean(self):\n super().clean()\n\n # check start date is after end date\n start_date = self.cleaned_data.get('moved_in_date', None)\n end_date = self.cleaned_data.get('moved_out_date', None)\n if start_date and end_date and end_date <= start_date:\n self.add_error('moved_in_date', self.ERROR_MESSAGE_MOVED_IN_DATE_AFTER_MOVED_OUT_DATE)\n self.add_error('moved_out_date', self.ERROR_MESSAGE_MOVED_OUT_DATE_BEFORE_MOVED_IN_DATE)\n\n end_date = self.cleaned_data.get('moved_out_date', None)\n five_years_ago = subtract_years(datetime.today().date(), 5)\n if end_date and five_years_ago > end_date:\n self.add_error('moved_out_date', self.ERROR_MESSAGE_MOVED_OUT_DATE_OVER_FIVE_YEARS_AGO)\n\n def clean_street_line1(self):\n \"\"\"\n Street name and number validation\n :return: string\n \"\"\"\n street_line1 = self.cleaned_data['street_line1']\n if len(street_line1) > 50:\n raise forms.ValidationError(self.ERROR_MESSAGE_STREET_LINE_1_TOO_LONG)\n return street_line1\n\n def clean_street_line2(self):\n \"\"\"\n Street name and number line 2 validation\n :return: string\n \"\"\"\n street_line2 = self.cleaned_data['street_line2']\n if len(street_line2) > 50:\n raise forms.ValidationError(self.ERROR_MESSAGE_STREET_LINE_2_TOO_LONG)\n return street_line2\n\n def clean_town(self):\n \"\"\"\n Town validation\n :return: string\n \"\"\"\n town = self.cleaned_data['town']\n if re.match(settings.REGEX['TOWN'], town) is None:\n raise forms.ValidationError(self.ERROR_MESSAGE_TOWN_INVALID)\n if len(town) > 50:\n raise forms.ValidationError(self.ERROR_MESSAGE_TOWN_TOO_LONG)\n return town\n\n def clean_county(self):\n \"\"\"\n County validation\n :return: string\n \"\"\"\n county = self.cleaned_data['county']\n if county != '':\n if re.match(settings.REGEX['COUNTY'], county) is None:\n raise forms.ValidationError(self.ERROR_MESSAGE_COUNTY_INVALID)\n if len(county) > 50:\n raise forms.ValidationError(self.ERROR_MESSAGE_COUNTY_TOO_LONG)\n return county\n\n def clean_country(self):\n \"\"\"\n Country validation\n :return: string\n \"\"\"\n country = self.cleaned_data['country']\n if country != '':\n if re.match(settings.REGEX['COUNTRY'], country) is None:\n raise forms.ValidationError(self.ERROR_MESSAGE_COUNTRY_INVALID)\n if len(country) > 50:\n raise forms.ValidationError(self.ERROR_MESSAGE_COUNTRY_TOO_LONG)\n return country\n\n def clean_postcode(self):\n \"\"\"\n Postcode validation\n :return: string\n \"\"\"\n postcode = self.cleaned_data['postcode']\n if postcode != '':\n postcode_no_space = postcode.replace(\" \", \"\")\n postcode_uppercase = postcode_no_space.upper()\n if not self.base_fields['postcode'].required:\n if re.match(settings.REGEX['POSTCODE_MANUAL'], postcode_uppercase) is None:\n raise forms.ValidationError(self.ERROR_MESSAGE_POSTCODE_INVALID)\n else:\n if re.match(settings.REGEX['POSTCODE_UPPERCASE'], postcode_uppercase) is None:\n raise forms.ValidationError(self.ERROR_MESSAGE_POSTCODE_INVALID_UK_ADDRESS)\n return postcode\n\n\ndef subtract_years(dt, years):\n \"\"\"\n Method returning the date submitted minus the years specified\n :param dt: a datetime object\n :param years: the number of years to remove\n :return: a datetime object\n \"\"\"\n try:\n dt = dt.replace(year=dt.year-years)\n except ValueError:\n dt = dt.replace(year=dt.year-years, day=dt.day-1)\n return dt", "sub_path": "application/forms/other_person_health_check/previous_addresses.py", "file_name": "previous_addresses.py", "file_ext": "py", "file_size_in_byte": 17707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "govuk_forms.forms.GOVUKForm", "line_number": 13, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 21, "usage_type": "name"}, {"api_name": "govuk_forms.forms.GOVUKForm", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 50, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 50, "usage_type": "name"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory", "line_number": 55, "usage_type": "call"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory.TODAY", "line_number": 60, "usage_type": "attribute"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory", "line_number": 60, "usage_type": "name"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory", "line_number": 78, "usage_type": "call"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory.TODAY", "line_number": 83, "usage_type": "attribute"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory", "line_number": 83, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 121, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 127, "usage_type": "call"}, {"api_name": "govuk_forms.forms.GOVUKForm", "line_number": 131, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 174, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 174, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 180, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 180, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 185, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 185, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 191, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 191, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 196, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 196, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 202, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 202, "usage_type": "name"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory", "line_number": 208, "usage_type": "call"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory.TODAY", "line_number": 213, "usage_type": "attribute"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory", "line_number": 213, "usage_type": "name"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory", "line_number": 231, "usage_type": "call"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory.TODAY", "line_number": 236, "usage_type": "attribute"}, {"api_name": "fields.CustomSplitDateFieldNameAndAddressHistory", "line_number": 236, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 297, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 297, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 308, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 308, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 318, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 318, "usage_type": "name"}, {"api_name": "re.match", "line_number": 327, "usage_type": "call"}, {"api_name": "django.conf.settings.REGEX", "line_number": 327, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 327, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 328, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 328, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 330, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 330, "usage_type": "name"}, {"api_name": "re.match", "line_number": 340, "usage_type": "call"}, {"api_name": "django.conf.settings.REGEX", "line_number": 340, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 340, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 341, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 341, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 343, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 343, "usage_type": "name"}, {"api_name": "re.match", "line_number": 353, "usage_type": "call"}, {"api_name": "django.conf.settings.REGEX", "line_number": 353, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 353, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 354, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 354, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 356, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 356, "usage_type": "name"}, {"api_name": "re.match", "line_number": 369, "usage_type": "call"}, {"api_name": "django.conf.settings.REGEX", "line_number": 369, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 369, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 370, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 370, "usage_type": "name"}, {"api_name": "re.match", "line_number": 372, "usage_type": "call"}, {"api_name": "django.conf.settings.REGEX", "line_number": 372, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 372, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 373, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 373, "usage_type": "name"}]} +{"seq_id": "133084788", "text": "import sqlite3\ndb=sqlite3.connect(\"kutuphane.db\")\nimlec=db.cursor()\n\nmenu=\"\"\"\n [1] Kitap Ara\n [2] Yazar Ara\n\"\"\"\n\nprint(menu)\nislem=input(\"İşleminiz: \")\nif islem==\"1\":\n isim=input(\"Kitap Adı:\")\n sorgu=(\"select * from kitaplar where kitap='{}'\".format(isim))\n imlec.execute(sorgu)\n veriler=imlec.fetchall()\n for veri in veriler:\n print(veri)\nelif islem==\"2\":\n yazar=input(\"Yazar Adı Giriniz: \")\n sorgu=(\"select * from kitaplar where yazar='{}'\".format(yazar))\n imlec.execute(sorgu)\n veriler=imlec.fetchall()\n for veri in veriler:\n print(veri)\nelse:\n print(\"Yanlış Seçim!\")\n\ndb.close()", "sub_path": "kutuphane_uygulaması.py", "file_name": "kutuphane_uygulaması.py", "file_ext": "py", "file_size_in_byte": 642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "sqlite3.connect", "line_number": 2, "usage_type": "call"}]} +{"seq_id": "445495933", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.shortcuts import render\nfrom .models import Recipe, Step, Ingredient, Category\n\n\n#def index(request):\n# recipes = Recipe.objects.all()\n# return render(request, \"main/index.html\", {'recipes': recipes})\n\ndef recipe_list(request):\n object_list = Recipe.objects.all()\n\n paginator = Paginator(list(object_list), 2) # 5 рецептов на каждой странице\n page_number = request.GET.get('page')\n page_obj = paginator.get_page(page_number)\n try:\n Recipes = paginator.page(page_number)\n except PageNotAnInteger:\n Recipes = paginator.page(1)\n except EmptyPage:\n Recipes = paginator.page(paginator.num_pages)\n print(page_number)\n return render(request, 'main/index.html', {'page_obj': page_obj, 'recipes': Recipes})\n\ndef recipe_category(request, slug):\n category = {'meat':'Мясо',\n 'bird':'Птица',\n 'fish':'Рыба',\n 'bakery':'Выпечка',\n 'salat':'Салаты',\n 'cereals':'Крупы',\n 'vegetables':'Овощи',\n 'cold':'Холодное',\n 'sweet':'Сладкое'}\n this_category = category[slug]\n object_list = Recipe.objects.all().filter(category = Category.objects.get(name=this_category) )\n\n paginator = Paginator(list(object_list), 2) # 5 рецептов на каждой странице\n page_number = request.GET.get('page')\n page_obj = paginator.get_page(page_number)\n try:\n Recipes = paginator.page(page_number)\n except PageNotAnInteger:\n Recipes = paginator.page(1)\n except EmptyPage:\n Recipes = paginator.page(paginator.num_pages)\n print(page_number)\n return render(request, 'main/index.html', {'page_obj': page_obj, 'recipes': Recipes})\n\ndef recipe_detail(request, id):\n object = Recipe.objects.get(id=id)\n\n ingredients = Ingredient.objects.all().filter(recipe_id=id)\n\n steps = Step.objects.all().filter(recipe_id=id)\n\n # print(ing_list[0]['ing'])\n context = {'recipe': object,\n 'ingredients': ingredients,\n 'steps': steps}\n\n return render(request, 'main/recipe.html', context=context)", "sub_path": "FoodForStudent/main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2361, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "models.Recipe.objects.all", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Recipe.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Recipe", "line_number": 13, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 15, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 20, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 22, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Recipe.objects.all", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Recipe.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Recipe", "line_number": 38, "usage_type": "name"}, {"api_name": "models.Category.objects.get", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 38, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 40, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 45, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 47, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Recipe.objects.get", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Recipe.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.Recipe", "line_number": 53, "usage_type": "name"}, {"api_name": "models.Ingredient.objects.all", "line_number": 55, "usage_type": "call"}, {"api_name": "models.Ingredient.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.Ingredient", "line_number": 55, "usage_type": "name"}, {"api_name": "models.Step.objects.all", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Step.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "models.Step", "line_number": 57, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "176159110", "text": "# pylint: disable=no-self-argument\nfrom wtforms.fields import (\n BooleanField, DateField,\n StringField, SubmitField,\n TextAreaField, TimeField\n)\nfrom flask_wtf import FlaskForm\nfrom wtforms.validators import DataRequired, ValidationError\nfrom wtforms.widgets.html5 import DateInput, TimeInput\nfrom datetime import datetime\n\n\nclass AppointmentForm(FlaskForm):\n name= StringField('Name',validators=[DataRequired()])\n start_date= DateField('Start Date',validators=[DataRequired()], widget=DateInput())\n start_time= TimeField('Start Time',validators=[DataRequired()], widget=TimeInput())\n end_date= DateField('End Date',validators=[DataRequired()], widget=DateInput())\n end_time= TimeField('End Time',validators=[DataRequired()], widget=TimeInput())\n description= TextAreaField('Description',validators=[DataRequired()])\n private= BooleanField('Private')\n submit= SubmitField('Create Appointment')\n\n def validate_end_date(form, field): \n start = datetime.combine(form.start_date.data, form.start_time.data)\n # end = datetime.combine(field.data, form.end_time.data)\n print(field)\n end = datetime.combine(form.end_date.data, form.end_time.data)\n if start >= end:\n msg = \"End date/time must come after start date/time\"\n raise ValidationError(msg)", "sub_path": "app/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1332, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "flask_wtf.FlaskForm", "line_number": 13, "usage_type": "name"}, {"api_name": "wtforms.fields.StringField", "line_number": 14, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 14, "usage_type": "call"}, {"api_name": "wtforms.fields.DateField", "line_number": 15, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 15, "usage_type": "call"}, {"api_name": "wtforms.widgets.html5.DateInput", "line_number": 15, "usage_type": "call"}, {"api_name": "wtforms.fields.TimeField", "line_number": 16, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 16, "usage_type": "call"}, {"api_name": "wtforms.widgets.html5.TimeInput", "line_number": 16, "usage_type": "call"}, {"api_name": "wtforms.fields.DateField", "line_number": 17, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 17, "usage_type": "call"}, {"api_name": "wtforms.widgets.html5.DateInput", "line_number": 17, "usage_type": "call"}, {"api_name": "wtforms.fields.TimeField", "line_number": 18, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 18, "usage_type": "call"}, {"api_name": "wtforms.widgets.html5.TimeInput", "line_number": 18, "usage_type": "call"}, {"api_name": "wtforms.fields.TextAreaField", "line_number": 19, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 19, "usage_type": "call"}, {"api_name": "wtforms.fields.BooleanField", "line_number": 20, "usage_type": "call"}, {"api_name": "wtforms.fields.SubmitField", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.combine", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "wtforms.validators.ValidationError", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "515965570", "text": "from django.urls import path\nfrom django.conf import settings\nfrom django.conf.urls.static import static\nfrom .views import (IndexView, ProductView, ProductCreateView, ProductDeleteView, ProductUpdateView, ReviewCreate, ReviewDeleteView, ReviewUpdateView, ReviewListView)\n\n\nurlpatterns = [\n path('', IndexView.as_view(), name='index'),\n path('/product', ProductView.as_view(), name='product'),\n path('add/', ProductCreateView.as_view(), name='product-add'),\n path('/update', ProductUpdateView.as_view(), name='product-update'),\n path('/delete', ProductDeleteView.as_view(), name='product-delete'),\n path('/review/add', ReviewCreate.as_view(), name='review-add'),\n path('/review/delete', ReviewDeleteView.as_view(), name='review-delete'),\n path('/review/update', ReviewUpdateView.as_view(), name='review-update'),\n path('reviews-list', ReviewListView.as_view(), name='review-list')\n]", "sub_path": "source/webapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 957, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.IndexView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "views.IndexView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.ProductView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "views.ProductView", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.ProductCreateView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "views.ProductCreateView", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.ProductUpdateView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.ProductUpdateView", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.ProductDeleteView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "views.ProductDeleteView", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "views.ReviewCreate.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "views.ReviewCreate", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "views.ReviewDeleteView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "views.ReviewDeleteView", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "views.ReviewUpdateView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "views.ReviewUpdateView", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "views.ReviewListView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "views.ReviewListView", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "378139780", "text": "import asyncio\nimport logging\nimport socket\nfrom collections import namedtuple\n\nimport netfilterqueue\nfrom dpkt import icmp, ip\nfrom netfilter import TCPEndPoint\nfrom utility import Net\n\n\nclass ContainerProxy:\n\n def __init__(self, container_mgr, config): \n self.endpoint = TCPEndPoint(config.firewall.get_interface_ip(), config.firewall.proxy_port)\n self.container_mgr = container_mgr\n self.config = config\n\n def start(self, loop):\n '''\n Starts the proxy server on the specified loop\n '''\n logging.info('Starting the container proxy server on %s', self.endpoint.port)\n server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n # This option allows multiple processes to listen on the same port\n # server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)\n server_socket.bind(('0.0.0.0', int(self.endpoint.port)))\n return asyncio.start_server(self.client_connected,\n sock=server_socket,\n loop=loop)\n\n async def proxy(self, reader, writer, start_data, container_name):\n '''\n Proxies data between the client connected to the proxy and the container\n specified by container name\n '''\n read_buffer = self.config.firewall.read_buffer\n\n try:\n while True:\n if start_data is not None:\n data = start_data\n start_data = None\n else:\n data = await reader.read(read_buffer)\n if not data:\n break\n # Update last seen so that the idle monitor can determine\n # if the container has not received network IO\n await self.container_mgr.update_container(container_name)\n writer.write(data)\n await writer.drain()\n except ConnectionResetError:\n logging.debug('Connection reset writer %s', container_name)\n except Exception as ex:\n logging.error('proxy - %s', ex)\n finally:\n await self.close_stream_writer(writer)\n\n async def close_stream_writer(self, writer):\n ''' \n Safely closes the specified stream writer\n '''\n try:\n # If the call to open_connection failed, the writer\n # will be null1\n if not writer is None:\n writer.close()\n await writer.wait_closed()\n except Exception as ex:\n # Most likely connection reset from client sending a RST\n logging.debug('Closing writer %s', ex)\n\n async def client_connected(self, client_reader, client_writer):\n\n start_data = None\n remote_writer = None\n \n # Read a bit of data to see if this is just a scanner\n try:\n source_addr = client_writer.get_extra_info('peername')\n source_ip = Net.ipstr_to_int(source_addr[0])\n source_port = source_addr[1]\n container_addr = self.container_mgr.connections.get(source_ip, source_port)\n # This can happen if someone tries to connect directly to tcp:5996\n if container_addr is None:\n await self.close_stream_writer(client_writer)\n return\n\n # Some TCP discovery scanners will not send any data but SSH clients\n # send a client banner.\n if self.config.firewall.read_client:\n start_data = await client_reader.read(self.config.firewall.read_buffer)\n await client_reader.drain()\n except ConnectionResetError:\n # Client sent a RST pkt no need to clean up writer\n return\n\n # If a scanner is doing a connect scan for discovery it will not send any\n # data. Setting read_client to true during a discovery scan will prevent\n # containers from starting up and overloading the system\n if self.config.firewall.read_client and (start_data is None or len(start_data) == 0):\n await self.close_stream_writer(client_writer)\n return\n\n start_data = None\n host = container_addr[0]\n port = container_addr[1]\n\n # Start the container for the specified address\n container = await self.container_mgr.start_if_not_running(host, port, 6)\n\n if container is None:\n await self.close_stream_writer(client_writer)\n return\n\n host = Net.ipint_to_str(host)\n\n # It might take a couple of tries to hit the container until it\n # fully spins up\n for retry in range(1, container.start_retry_count):\n try:\n logging.debug('Attempt %s to connect to %s %s:%s',\n retry, container.name, host, port)\n remote_reader, remote_writer = await asyncio.open_connection(host, port)\n except Exception as err:\n await asyncio.sleep(container.start_delay)\n logging.debug(err)\n continue\n\n # Pass the initial data that was received above plus the container\n # name so that we know what container to update in the\n # container manager\n asyncio.ensure_future(self.proxy(\n client_reader, remote_writer, start_data, container.name))\n asyncio.ensure_future(self.proxy(\n remote_reader, client_writer, None, container.name))\n return\n\n # If there was never a connection made the and remote writer\n # will be null\n if not remote_writer is None:\n await self.close_stream_writer(remote_writer)\n\n await self.close_stream_writer(client_writer)\n", "sub_path": "proxies.py", "file_name": "proxies.py", "file_ext": "py", "file_size_in_byte": 5805, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "netfilter.TCPEndPoint", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 23, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 24, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 24, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 24, "usage_type": "attribute"}, {"api_name": "asyncio.start_server", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 54, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 72, "usage_type": "call"}, {"api_name": "utility.Net.ipstr_to_int", "line_number": 82, "usage_type": "call"}, {"api_name": "utility.Net", "line_number": 82, "usage_type": "name"}, {"api_name": "utility.Net.ipint_to_str", "line_number": 117, "usage_type": "call"}, {"api_name": "utility.Net", "line_number": 117, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 123, "usage_type": "call"}, {"api_name": "asyncio.open_connection", "line_number": 125, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 127, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 128, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 134, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "149037609", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2020 Dremio\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\"\"\"Tests for a GA version config for the `nessiedemo` package.\"\"\"\nimport os\nimport sys\nfrom subprocess import run # noqa: S404\n\nimport pytest\nfrom pytest import fixture, skip\n\nfrom nessiedemo.demo import setup_demo\nfrom .util import demo_setup_fixture_for_tests, expect_error\n\n\n__anything_done: bool = False\n\n\n@fixture(scope=\"function\", autouse=True)\ndef before_all(tmpdir_factory, request) -> None: # noqa: ANN001\n \"\"\"Sets up env-vars to use a pytest temp-dir and use assets from the source-tree.\"\"\"\n global __anything_done\n\n demo_setup_fixture_for_tests(tmpdir_factory, request)\n\n def __dispose_spark() -> None:\n if __anything_done:\n # Should not do this import, which would import pyspark, which fails, if we're running the wrong Python version\n from nessiedemo.delta_spark import delta_spark_dispose\n\n print(\"TEST-TEARDOWN: Disposing SparkContext...\")\n delta_spark_dispose()\n\n request.addfinalizer(__dispose_spark)\n\n\nclass TestWithDelta:\n \"\"\"Test NessieDemo with Deltlake.\"\"\"\n\n @staticmethod\n def __test_with_delta(versions_yaml: str, spark_version: int, required_envs: list) -> None:\n \"\"\"Test NessieDemo plus NessieDemoDelta.\"\"\"\n global __anything_done\n\n if False in [e in os.environ for e in required_envs]:\n skip(\n \"Missing mandatory environment variable(s) {} for in-development-yaml-test, skipping test\".format(\n \", \".join([\"{}={}\".format(e, os.environ[e] if e in os.environ else \"\") for e in required_envs])\n )\n )\n\n __anything_done = True\n\n demo = setup_demo(versions_yaml)\n\n print(\"Nessie version: {}\".format(demo.get_nessie_version()))\n\n # Same with notebooks: must NOT import nessiedemo.spark BEFORE the demo's setup has \"pip-install-ed\" the spark dependencies\n from nessiedemo.delta_spark import delta_spark_for_demo\n\n spark, sc, jvm, demo_delta = delta_spark_for_demo(demo, spark_version=spark_version)\n assert spark.conf.get(\"spark.hadoop.nessie.ref\") == \"main\"\n assert spark.conf.get(\"spark.hadoop.nessie.url\") == demo.get_nessie_api_uri()\n assert spark.conf.get(\"spark.jars.packages\") == \"org.projectnessie:nessie-deltalake-spark{}:{}\".format(\n spark_version, demo.get_nessie_version()\n )\n assert sc is not None\n assert jvm is not None\n\n run([\"nessie\", \"branch\", \"dev\"]) # noqa: S603 S607\n demo_delta.change_ref(\"dev\")\n\n dataset = demo.fetch_dataset(\"region-nation\")\n\n region_path = demo_delta.table_path(\"testing/region\")\n\n region_df = spark.read.load(dataset[\"region.parquet\"])\n region_df.write.format(\"delta\").mode(\"overwrite\").option(\"hadoop.nessie.ref\", \"dev\").save(region_path)\n\n spark.sql(\"CREATE TABLE region USING delta LOCATION '{}'\".format(region_path))\n\n assert spark.sql(\"SELECT COUNT(*) FROM region\").collect()[0][0] == 5\n\n # Verify that the table does not exist on the main branch\n demo_delta.change_ref(\"main\")\n # TODO the following fails with Delta 0.6!\n expect_error(\"pyspark.sql.utils.AnalysisException\", lambda: spark.sql(\"SELECT COUNT(*) FROM region\"))\n\n run([\"nessie\", \"merge\", \"dev\", \"-b\", \"main\", \"--force\"]) # noqa: S603 S607\n\n demo_delta.change_ref(\"main\")\n assert spark.sql(\"SELECT COUNT(*) FROM region\").collect()[0][0] == 5\n\n @pytest.mark.skip(\"Skipped until necessary Nessie PR is in\")\n @pytest.mark.forked\n def test_with_delta_spark3(self: object) -> None:\n \"\"\"Test NessieDemo+Spark against Nessie 0.6.\"\"\"\n TestWithDelta.__test_with_delta(\"in-development/nessie-0.6-delta-spark3.yml\", 3, [])\n\n # TODO figure out why the 'expect_error' checking that the 'region' table does not exist on the main branch fails,\n # the table seems to exist on the main branch although it's created on the dev branch\n @pytest.mark.skip(\"Delta/Spark2 behaves differently\")\n @pytest.mark.forked\n def test_with_delta_spark2(self: object) -> None:\n \"\"\"Test NessieDemo+Spark against Nessie 0.6.\"\"\"\n if sys.version_info >= (3, 8):\n skip(\"The necessary configuration requires pyspark==2.4.x, which does not work with Python > 3.7\")\n TestWithDelta.__test_with_delta(\"in-development/nessie-0.6-delta-spark2.yml\", 2, [])\n", "sub_path": "pydemolib/tests/test_with_delta_spark.py", "file_name": "test_with_delta_spark.py", "file_ext": "py", "file_size_in_byte": 5010, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "util.demo_setup_fixture_for_tests", "line_number": 38, "usage_type": "call"}, {"api_name": "nessiedemo.delta_spark.delta_spark_dispose", "line_number": 46, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 33, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pytest.skip", "line_number": 60, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 62, "usage_type": "attribute"}, {"api_name": "nessiedemo.demo.setup_demo", "line_number": 68, "usage_type": "call"}, {"api_name": "nessiedemo.delta_spark.delta_spark_for_demo", "line_number": 75, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 84, "usage_type": "call"}, {"api_name": "util.expect_error", "line_number": 101, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 103, "usage_type": "call"}, {"api_name": "{'delta_spark_for_demo': 'nessiedemo.delta_spark.delta_spark_for_demo'}.__test_with_delta", "line_number": 112, "usage_type": "call"}, {"api_name": "pytest.mark.skip", "line_number": 108, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 109, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pytest.skip", "line_number": 121, "usage_type": "call"}, {"api_name": "{'delta_spark_for_demo': 'nessiedemo.delta_spark.delta_spark_for_demo'}.__test_with_delta", "line_number": 122, "usage_type": "call"}, {"api_name": "pytest.mark.skip", "line_number": 116, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 117, "usage_type": "attribute"}]} +{"seq_id": "65081029", "text": "import sys\nimport numpy as np\nfrom astropy.io import fits\nfrom astropy.wcs import WCS\nfrom reproject import reproject_interp, reproject_from_healpix\nfrom iminuit import Minuit\nimport time\n\n\nclass linmap_chi2:\n def __init__(self, map1, maps, maskmap, errormap):\n self.map1 = map1\n self.maps = maps\n self.maskmap = maskmap\n self.errormap = errormap\n\n def model(self, pars):\n return np.sum(pars[:-1] * np.moveaxis(self.maps, 0, -1), axis=2) + pars[-1]\n\n def residuals(self, pars):\n return self.map1 - self.model(pars)\n\n def __call__(self, *pars):\n pars = np.array(pars)\n model = self.model(pars)\n residuals = self.map1 - model\n residuals /= self.errormap\n residuals *= self.maskmap\n chi2 = np.sum(np.power(residuals, 2))\n return chi2\n\n\nclass dustmap_residuals:\n def __init__(self, dustmap, colname, inmaps, scale=1., errorname='None'):\n \"\"\"\n Constructor for class to create extinction residuals from extinction cube and set of\n gas maps for given distance range.\n :param dustmap: `string`\n FITS file with dust map\n :param colname: `string`\n name of column containing dust map\n :param inmaps: `list`\n FITS file with WCS map in first HDU (gas maps)\n :param scale: `float`\n scaling to apply to the extinction map (so that fitting coeff are O(1))\n :param scale: `string`\n name of column containing error map, 'None' for no error\n \"\"\"\n\n # read in dust map\n self.dustmap = fits.open(dustmap)[1]\n self.colname = colname\n self.scale = scale\n self.errorname = errorname\n\n # read gas maps\n self.gasmaps = []\n self.nreg = len(inmaps)\n self.region = []\n for s, region in enumerate(inmaps):\n for filename in region:\n self.region.append(s)\n self.gasmaps.append(fits.open(filename)[0])\n\n def reproject_dustmap(self, outheader):\n \"\"\"\n Reprojects dust map (and errors) on the desired WCS\n :param outheader: `~astropy.io.fits.header.Header`\n output map header\n :return: outmap: `~numpy.ndarray`\n output map\n :return: errormap: `~numpy.ndarray`\n erromap\n \"\"\"\n\n # load properties of healpix grid\n coordsys = self.dustmap.header['COORDSYS']\n if coordsys == 'C':\n coordsys = 'ICRS'\n elif coordsys == 'E':\n coordsys = 'Ecliptic'\n elif coordsys == 'G':\n coordsys = 'Galactic'\n else:\n print('coordinate system of input dust map unknown:', coordsys)\n\n nested = self.dustmap.header['ORDERING']\n if nested == 'NESTED':\n nested = True\n elif nested == 'RING':\n nested = False\n else:\n print('ordering of input dust map unknown:', nested)\n\n # dust map\n outmap, footprint = reproject_from_healpix((self.dustmap.data[self.colname], coordsys),\n outheader, nested=nested)\n\n # error map\n if self.errorname == 'None':\n errormap = np.ones(np.shape(outmap))\n else:\n errormap, footprint = reproject_from_healpix(\n (self.dustmap.data[self.errorname], coordsys),\n outheader, nested=nested)\n\n outmap *= self.scale\n errormap *= self.scale\n\n return outmap, errormap\n\n def reproject_gasmaps(self, outheader):\n \"\"\"\n Reproject gas maps onto desired output WCS\n :param outheader: `~astropy.io.fits.header.Header`\n output map header\n :return: gasmaps: `~numpy.ndarray`\n input gas maps reprojected onto the output WCS as 3D array (#, lon, lat)\n \"\"\"\n gasmaps = np.zeros([len(self.gasmaps), outheader['NAXIS2'], outheader['NAXIS1']])\n for s, inmap in enumerate(self.gasmaps):\n repromap, footrpint = reproject_interp(inmap, outheader)\n gasmaps[s] = repromap\n\n return gasmaps\n\n def fit(self, extmap, gasmaps, maskmap, errormap, outfilename='fit', outdir='./',\n split=True):\n\n chi2 = linmap_chi2(extmap, gasmaps, maskmap, errormap)\n\n # define params tuple, initial values, limits, etc\n ptup = ()\n kwdarg = {}\n # map coefficients\n for n in range(len(gasmaps)):\n ptup = ptup + ('A_' + str(n),)\n kwdarg['A_' + str(n)] = 1.\n kwdarg['error_A_' + str(n)] = 0.01\n kwdarg['limit_A_' + str(n)] = (0., 1.e4)\n # constant\n ptup = ptup + ('C',)\n kwdarg['C'] = 0.\n kwdarg['error_C'] = 0.01\n kwdarg['limit_C'] = (-1.e4, 1.e4)\n\n # fitting\n m = Minuit(chi2, forced_parameters=ptup, errordef=1, **kwdarg)\n fitres = m.migrad()[0]\n\n # save results\n saveout = sys.stdout\n file = open(outdir + outfilename + '.log', 'w')\n sys.stdout = file\n print('parameters')\n for n in range(len(gasmaps)):\n print(m.values['A_' + str(n)], m.errors['A_' + str(n)])\n print(m.values['C'], m.errors['C'])\n print('FCN', m.fval, 'dof', extmap.size - len(m.args))\n print('Minuit output')\n print(fitres)\n sys.stdout = saveout\n file.close()\n\n # calculate residuals\n residuals = chi2.residuals(np.array(m.args))\n\n # calculate weights of each region\n parvals = np.array(m.args)\n parvals[-1] = 0 # remove constant\n total_model = chi2.model(parvals)\n weights = []\n for s in range(self.nreg):\n parvals = np.array(m.args)\n for k in range(len(self.region)):\n if self.region[k] == s:\n pass\n else:\n parvals[k] = 0.\n model_reg = chi2.model(parvals)\n weight = model_reg / total_model\n weight[total_model < 0.1] = 0.\n weights.append(weight)\n\n return fitres, residuals, weights\n\n def make(self, lmin, lmax, bmin, bmax, pixsize, outfilename, names, outdir='./',\n mask='None', name='L. Tibaldo', email='luigi.tibaldo@irap.omp.eu'):\n \"\"\"\n Make residual maps over a sky region\n :param lmin: `float`\n minimum longitude (deg)\n :param lmax: `float`\n maximum longitude (deg)\n :param bmin: `float`\n minimum latitude (deg)\n :param bmax: `float`\n maximum latitude (deg)\n :param pixsize: `float`\n pixel size (deg)\n :param outfilename: `str`\n root for the output file names\n :param outdir: `str`\n output directory\n :param mask: `str`\n conditions to use a pixel at latitude lat and longitude lon in fit (passed to Python eval),\n default 'None' to accept all pixels\n :param name:\n :param email:\n :return:\n \"\"\"\n\n # create output WCS\n outwcs = WCS(naxis=2) # wcs class\n npix = (np.array([lmax - lmin, bmax - bmin]) / pixsize).astype(int)\n outwcs.wcs.crpix = [int(1 + npix[0] / 2) + 0.5, int(1. - bmin / pixsize) + 0.5]\n outwcs.wcs.cdelt = [-pixsize, pixsize]\n outwcs.wcs.crval = [(lmax + lmin) / 2, 0.]\n outwcs.wcs.ctype = ['GLON-CAR', 'GLAT-CAR']\n outwcs.wcs.cunit = ['deg', 'deg']\n # create output header\n outheader = outwcs.to_header()\n outheader['NAXIS'] = 2\n outheader['NAXIS1'] = int((lmax - lmin) / pixsize) + 1\n outheader['NAXIS2'] = int((bmax - bmin) / pixsize) + 1\n\n # reproject input map onto required grid\n dustmap, errormap = self.reproject_dustmap(outheader)\n\n # reproject gas maps onto output map footprint\n gasmaps = self.reproject_gasmaps(outheader)\n\n # create mask map\n maskmap = np.ones(np.shape(dustmap))\n if mask == 'None':\n pass\n else:\n for ll in range(outheader['NAXIS1']):\n for bb in range(outheader['NAXIS2']):\n lon = outwcs.wcs.crval[0] + outwcs.wcs.cdelt[0] * (\n ll - outwcs.wcs.crpix[0])\n lat = outwcs.wcs.crval[1] + outwcs.wcs.cdelt[1] * (\n bb - outwcs.wcs.crpix[1])\n if eval(mask):\n pass\n else:\n maskmap[bb, ll] = 0\n\n # set NaNs to zero\n dustmap = np.nan_to_num(dustmap)\n gasmaps = np.nan_to_num(gasmaps)\n errormap = np.nan_to_num(errormap)\n # set to 1 error if == 0\n errormap[errormap == 0.] = 1.\n\n print('Finished reprojecting maps, starting fit')\n\n # model fitting\n fitres, residuals, weights = self.fit(dustmap, gasmaps, maskmap, errormap, outfilename,\n outdir, split=True)\n\n # save total residual map\n hdu = fits.PrimaryHDU(header=outheader, data=residuals)\n # add history cards\n hdu.header.add_history('map generated by {}, {}'.format(name, email))\n hdu.header.add_history('on ' + time.ctime() + ' ' + time.tzname[1])\n hdu.writeto(outdir + outfilename + '.fits')\n\n # save splitted residuals\n for s in range(self.nreg):\n split_residuals = residuals * weights[s]\n split_residuals[split_residuals < 0] = 0.\n hdu = fits.PrimaryHDU(header=outheader, data=split_residuals)\n # add history cards\n hdu.header.add_history('map generated by {}, {}'.format(name, email))\n hdu.header.add_history('on ' + time.ctime() + ' ' + time.tzname[1])\n hdu.writeto(outdir + outfilename + '_{}.fits'.format(names[s]))\n", "sub_path": "ISM/dustmap_residuals.py", "file_name": "dustmap_residuals.py", "file_ext": "py", "file_size_in_byte": 9785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "numpy.sum", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.moveaxis", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 29, "usage_type": "call"}, {"api_name": "astropy.io.fits.open", "line_number": 51, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 51, "usage_type": "name"}, {"api_name": "astropy.io.fits.open", "line_number": 63, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 63, "usage_type": "name"}, {"api_name": "reproject.reproject_from_healpix", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 101, "usage_type": "call"}, {"api_name": "reproject.reproject_from_healpix", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "reproject.reproject_interp", "line_number": 122, "usage_type": "call"}, {"api_name": "iminuit.Minuit", "line_number": 148, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 152, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 154, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "astropy.wcs.WCS", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 252, "usage_type": "call"}, {"api_name": "astropy.io.fits.PrimaryHDU", "line_number": 263, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 263, "usage_type": "name"}, {"api_name": "time.ctime", "line_number": 266, "usage_type": "call"}, {"api_name": "time.tzname", "line_number": 266, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.PrimaryHDU", "line_number": 273, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 273, "usage_type": "name"}, {"api_name": "time.ctime", "line_number": 276, "usage_type": "call"}, {"api_name": "time.tzname", "line_number": 276, "usage_type": "attribute"}]} +{"seq_id": "308226586", "text": "#!/usr/bin/python\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom email.header import Header\nfrom email_credentials import email_credentials\nfrom sqlite3 import connect\nfrom os.path import dirname, abspath\nfrom dateutil.parser import parse\n\ndef send_mail(data):\n mailhost, fromaddr, toaddrs, subject, credentials = email_credentials()\n username, password = credentials\n subject = 'seanweather weekly report'\n body = 'Here are some interesting results, my good-looking friend:\\n\\n' + data\n msg = MIMEText(body, _charset=\"UTF-8\")\n msg['Subject'] = Header(subject, \"utf-8\")\n server = smtplib.SMTP(mailhost)\n server.starttls()\n server.login(username, password)\n server.sendmail(fromaddr, toaddrs, msg.as_string())\n server.quit()\n\ndef gather_data(cur):\n data = \"Last week's lookups\\n\"\n query = 'select zipcode, count(*) as c from lookup group by zipcode order by c desc'\n data += '\\n'.join('{}{:>3}'.format(*result) for result in cur.execute(query))\n data += '\\n\\nComments\\n'\n query = 'select date, text from comment'\n results = ((parse(date), text) for date, text in cur.execute(query))\n data += u'\\n'.join(u'{:>12} -- {}'.format(date.strftime('%m/%d %H:%M'), text) for date, text in results)\n return data\n\ndef clean_up(cur):\n cur.execute('delete from lookup')\n cur.execute('delete from comment')\n cur.execute('update location set cache=\"\" where julianday(last_updated) < julianday()-1')\n\nif __name__ == '__main__':\n directory = dirname(abspath(__file__))\n conn = connect(directory + '/db.db')\n cur = conn.cursor()\n send_mail(gather_data(cur))\n clean_up(cur)\n conn.commit()\n conn.close()\n", "sub_path": "email_report.py", "file_name": "email_report.py", "file_ext": "py", "file_size_in_byte": 1680, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "email_credentials.email_credentials", "line_number": 11, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 15, "usage_type": "call"}, {"api_name": "email.header.Header", "line_number": 16, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 17, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "65269076", "text": "\nfrom django.shortcuts import render, redirect\nfrom .forms import RegisterForm\nfrom django.core.mail import send_mail\n\ndef profile(request):\n if request.user.is_authenticated:\n return render(request, 'accounts/profile.html')\n else:\n return redirect('index')\n\ndef SignUp(request):\n if request.method == 'POST':\n form = RegisterForm(request.POST)\n if form.is_valid():\n email_address = form.cleaned_data['email']\n form.save()\n try:\n send_mail('Alert!', \"Someone new has signed up for the website. Verify and group them quickly.\", 'noreply@villamachine.com', ['mattv@villamachine.com'])\n send_mail('Welcome!', \"Thanks for signing up with us at Villa Machine! You will be verified within the next business day, although usually it's less than 10 minutes.\", 'noreply@villamachine.com', [email_address])\n except:\n pass\n return redirect('login')\n else:\n ##form = RegisterForm()\n return render(request, 'accounts/signup.html', { 'form': form })\n else:\n form = RegisterForm()\n return render(request, 'accounts/signup.html', { 'form': form })\n", "sub_path": "mysite/accounts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1216, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.shortcuts.render", "line_number": 8, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 10, "usage_type": "call"}, {"api_name": "forms.RegisterForm", "line_number": 14, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 19, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "forms.RegisterForm", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "434198033", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport collections\nimport statistics \nimport os\nimport time\nimport multiprocessing \nimport random\nfrom sklearn import svm\nimport sys\nimport copy\nimport pickle\nimport bisect \nimport numba \n\n# This file handles the creation of the training and test sets for Experiment 2\n\ndef mean_feature(data):\n return statistics.mean(data)\n\n@numba.jit(nopython=True)\ndef upper_feature(data):\n '''\n Takes in a set of data, assumed to be sorted. \n Returns item at the start of the top 20%\n '''\n upper_index = len(data) // 5\n return data[upper_index]\n \n@numba.jit(nopython=True)\ndef calculateMagnitudes(flow_list, frame_index, debugging = False):\n '''\n Calculate the magnitudes for each optical flow value\n Return magnitudes as a sorted list\n '''\n step = 1\n #debugging\n if debugging:\n step = 2\n magnitudes = numba.typed.List() \n\n for x in range(0, len((flow_list[frame_index][0])), step):\n for y in range(0, len((flow_list[frame_index])), step):\n magnitude = float(np.linalg.norm(flow_list[frame_index][y][x]))\n\n magnitudes.append(magnitude)\n\n sorted_magnitudes = sorted(magnitudes)\n return sorted_magnitudes\n\n@numba.jit(nopython=True)\ndef calculateRelatives(dif_fm_upper, dif_fm_mean, dif_fm_sd, average_flow):\n '''\n Calculate relative features by dividing each item by mean\n '''\n rel_dif_fm_mean = numba.typed.List() \n rel_dif_fm_upper = numba.typed.List() \n rel_dif_fm_sd = numba.typed.List()\n\n for item in dif_fm_upper:\n rel_dif_fm_upper.append(item / average_flow)\n\n for item in dif_fm_mean:\n rel_dif_fm_mean.append(item / average_flow)\n\n for item in dif_fm_sd:\n rel_dif_fm_sd.append(item / average_flow)\n\n return rel_dif_fm_upper, rel_dif_fm_mean, rel_dif_fm_sd\n \n\n\ndef processVideo(file, load_folder, return_dict, relative = False, debugging = False):\n '''\n Convert optical flow of a clip to features\n '''\n flow_list = pickle.load( open( os.path.join(load_folder, file), \"rb\" ))\n\n typed_flow_list = numba.typed.List()\n [typed_flow_list.append(np.array(x)) for x in flow_list] \n flow_list = typed_flow_list\n\n mean_list = []\n median_list =[]\n sd_list = []\n upper_mark = []\n dif_fm_upper = numba.typed.List()\n dif_fm_mean = numba.typed.List()\n dif_fm_sd = numba.typed.List()\n rel_mean_pos = numba.typed.List()\n\n if len(file.split(\".\"))> 1:\n name = file.split(\".\")\n else:\n name = file\n\n for frame_index in range(len(flow_list)):\n #get optical flow from 2 frames and save changes in magnitudes\n \n if len(flow_list[frame_index]) < 1:\n continue\n \n magnitudes = calculateMagnitudes(flow_list, frame_index, debugging)\n\n\n mean = statistics.mean(magnitudes)\n mean_list.append(mean) # mean of whole flow\n median_list.append(statistics.median(magnitudes)) #median of all flow\n sd_list.append(statistics.pstdev(magnitudes)) # sd of all flow\n\n \n if len(mean_list)>1:\n dif_fm_mean.append(abs(mean_list[-1] - mean_list[-2])) # Difference between this fmames mean val and last frame's mean val\n if len(sd_list)>1:\n dif_fm_sd.append(abs(sd_list[-1] - sd_list[-2])) # Difference between this frames sd val and last frame's sd val\n\n upper_mark.append(upper_feature(np.array(magnitudes)))\n if len(upper_mark)>1:\n dif_fm_upper.append(abs(upper_mark[-1] - upper_mark[-2])) # Difference between this frames max val and last frame's max val\n \n if not sd_list[-1] == 0:\n rel_mean_pos.append(mean / (max(magnitudes) - min(magnitudes)))\n\n\n # For each max frame difference calculate the distance relative to mean of whole video\n average_flow = mean_feature(mean_list)\n \n\n #Are We testing relative? If so do the following. If not leave commented.\n if relative:\n dif_fm_upper, dif_fm_mean, dif_fm_sd = calculateRelatives(dif_fm_upper, dif_fm_mean, dif_fm_sd, average_flow)\n \n # Rate of change of mean & sd:\n mean_change_rate = []\n sd_change_rate = []\n for index in range(1, len(dif_fm_mean)):\n mean_change_rate.append(abs(dif_fm_mean[index] - dif_fm_mean[index-1]))\n sd_change_rate.append(abs(dif_fm_sd[index] - dif_fm_sd[index-1]))\n\n\n video_windspeed = name.split(\"-\")[-1]\n video_features = {} \n \n video_features[\"category\"] = video_windspeed\n video_features[\"mean\"] = average_flow\n video_features[\"median\"] = mean_feature(median_list)\n video_features[\"sd\"] = mean_feature(sd_list)\n video_features[\"mean_dif_fm_upper\"] = mean_feature(dif_fm_upper)\n video_features[\"max_dif_fm_upper\"] = upper_feature(np.sort(dif_fm_upper))\n video_features[\"mean_dif_fm_mean\"] = mean_feature(dif_fm_mean)\n video_features[\"max_dif_fm_mean\"] = upper_feature(np.sort(dif_fm_mean))\n video_features[\"mean_dif_fm_sd\"] = mean_feature(dif_fm_sd)\n video_features[\"max_dif_fm_sd\"] = upper_feature(np.sort(dif_fm_sd))\n video_features[\"mean_rate_of_mean_change\"] = mean_feature( mean_change_rate)\n video_features[\"mean_rate_of_sd_change\"] = mean_feature(sd_change_rate)\n video_features[\"mean_relative_position\"] = mean_feature(rel_mean_pos)\n\n return_dict[name] = video_features\n\n\ndef evalSet(video_set, directory, flowtype, start_time):\n '''\n Send every data item off for processing and format returned features\n '''\n # Extract the features of each video in its own thread.\n # A maximum of 12 threads run at a time (for optimal performance on development platform) \n set_results = {}\n count = 0\n total_folders = len(video_set)\n threads = []\n manager = multiprocessing.Manager()\n return_dict = manager.dict()\n for video in video_set:\n save_folder = os.path.join(directory, video)\n load_folder = os.path.join(save_folder, flowtype)\n\n for file in os.listdir(load_folder):\n while len(threads) >= 10:\n for thread in threads:\n if not thread.is_alive():\n threads.remove(thread)\n # Return and save each feature from the video using a thread-safe dictionary \n for key,value in return_dict.items():\n for feat_key, feat_val in value.items():\n if not feat_key in set_results.keys():\n set_results[feat_key] = []\n set_results[feat_key].append(feat_val) \n del return_dict[key]\n\n p = multiprocessing.Process(target=processVideo, args=(file, load_folder, return_dict, debugging))\n threads.append(p)\n p.start()\n\n\n count +=1 \n if count % 10 == 0:\n print(\"Completed {} out of {} folders\".format(count, total_folders))\n \n current_time = time.time()\n time_taken = current_time - start_time\n print(\"Taken {} seconds so far, or approximately {} minutes.\".format(time_taken, time_taken//60))\n \n\n\n # Wait for any remaining threads to finish before continuing\n for thread in threads:\n thread.join()\n for key,value in return_dict.items():\n for feat_key, feat_val in value.items():\n set_results[feat_key].append(feat_val) \n del return_dict[key]\n \n print(len(set_results))\n print(len(set_results[\"category\"]))\n\n return set_results\n \n\nif __name__ == '__main__':\n\n flowtype = \"DenseFlow\"# dense\n relative = False\n debugging = False \n if len(sys.argv)>1:\n if sys.argv[1].lower() == \"dense\":\n flowtype = \"DenseFlow\"\n elif sys.argv[1].lower() == \"points\":\n flowtype = \"PointsFlow\"\n else:\n print(sys.argv[1])\n raise Exception(\"Bad argument; argument 1\")\n\n if len(sys.argv)>2:\n if sys.argv[2].lower() == \"true\":\n print(\"Relative = True\")\n relative = True\n elif sys.argv[2].lower() == \"false\":\n relative = False\n else:\n print(sys.argv[1])\n raise Exception(\"Bad argument; argument 2\")\n\n start = time.time()\n \n load_directory = os.path.join(os.path.split(os.path.abspath(os.curdir))[0], \"OpticalFlow\")\n Save_directory = os.path.join(os.path.split(os.path.abspath(os.curdir))[0], os.path.join(\"DataSets\", flowtype))\n\n # New method: Store per wind category in lists of every feature from each clip.\n\n # Separate into training and test datasets\n training_video_sets = []\n test_video_sets = []\n \n # Get 5 different test and training configurations \n for group in range(3):\n #Order into wind force categories\n videos_by_force = [[] for x in range(13)]\n for wind_force in range(0, 13):\n for video in os.listdir(load_directory):\n ending = \"-\"+str(wind_force)\n if video.endswith(ending):\n videos_by_force[wind_force].append(video)\n \n # take 20% of the video of each wind force and separate them into a test set.\n training_video_sets.append([])\n test_video_sets.append([])\n for wind_force in range(0, 13):\n total = len(videos_by_force[wind_force])\n if group == 0:\n print(total, \"videos of force\", str(wind_force))\n test_set_size = int(total/5) # 20% of each force for testing\n for video in range(test_set_size):\n choice = random.choice(videos_by_force[wind_force])\n test_video_sets[group].append(choice)\n videos_by_force[wind_force].remove(choice)\n\n for remainder in videos_by_force[wind_force]:\n training_video_sets[group].append(remainder)\n \n\n total_test_videos = len(test_video_sets[0])\n\n print(\"Finished establishing data sets\")\n\n for set_index in range(len(training_video_sets)):\n \n training_features = evalSet(training_video_sets[set_index], load_directory, flowtype, start)\n\n test_features = evalSet(test_video_sets[set_index], load_directory, flowtype, start)\n\n\n if not os.path.exists(os.path.join(Save_directory,str(set_index))):\n os.mkdir(os.path.join(Save_directory,str(set_index)))\n print(\"\\n\\nSaving\")\n with open(Save_directory+\"\\\\\"+str(set_index)+\"\\\\TrainingSet\"+str(set_index), 'wb') as out:\n pickle.dump(training_features, out)\n with open(Save_directory+\"\\\\\"+str(set_index)+\"\\\\TestSet\"+str(set_index), 'wb') as out:\n pickle.dump(test_features, out)\n\n \n end=time.time()\n print(\"Moment of truth...\")\n print(\"threading global averages time:\")\n print(str(end - start))\n\n", "sub_path": "3rd year/evaluateDataSetsV2.py", "file_name": "evaluateDataSetsV2.py", "file_ext": "py", "file_size_in_byte": 10898, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "statistics.mean", "line_number": 19, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 21, "usage_type": "call"}, {"api_name": "numba.typed.List", "line_number": 40, "usage_type": "call"}, {"api_name": "numba.typed", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numba.jit", "line_number": 30, "usage_type": "call"}, {"api_name": "numba.typed.List", "line_number": 56, "usage_type": "call"}, {"api_name": "numba.typed", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numba.typed.List", "line_number": 57, "usage_type": "call"}, {"api_name": "numba.typed", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numba.typed.List", "line_number": 58, "usage_type": "call"}, {"api_name": "numba.typed", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numba.jit", "line_number": 51, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numba.typed.List", "line_number": 79, "usage_type": "call"}, {"api_name": "numba.typed", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numba.typed.List", "line_number": 87, "usage_type": "call"}, {"api_name": "numba.typed", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numba.typed.List", "line_number": 88, "usage_type": "call"}, {"api_name": "numba.typed", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numba.typed.List", "line_number": 89, "usage_type": "call"}, {"api_name": "numba.typed", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numba.typed.List", "line_number": 90, "usage_type": "call"}, {"api_name": "numba.typed", "line_number": 90, "usage_type": "attribute"}, {"api_name": "statistics.mean", "line_number": 106, "usage_type": "call"}, {"api_name": "statistics.median", "line_number": 108, "usage_type": "call"}, {"api_name": "statistics.pstdev", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 153, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 177, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 190, "usage_type": "call"}, {"api_name": "time.time", "line_number": 199, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 224, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 225, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 227, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 230, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 233, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 234, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 237, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 240, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path", "line_number": 245, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 245, "usage_type": "call"}, {"api_name": "os.curdir", "line_number": 245, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path", "line_number": 246, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 246, "usage_type": "call"}, {"api_name": "os.curdir", "line_number": 246, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 259, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 273, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 292, "usage_type": "call"}, {"api_name": "os.path", "line_number": 292, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 292, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 293, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 293, "usage_type": "call"}, {"api_name": "os.path", "line_number": 293, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 296, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 298, "usage_type": "call"}, {"api_name": "time.time", "line_number": 301, "usage_type": "call"}]} +{"seq_id": "401175289", "text": "from django.core.exceptions import ValidationError\nfrom django.test import TestCase\n\nfrom accounting.models import Associate, PurchaseItem, SaleItem\nfrom inventory.models import Item\nfrom inventory.tests.item_factory import ItemFactory\nfrom .accounting_factory import AssociateFactory, PurchaseFactory, \\\n PurchaseItemFactory, SaleFactory, \\\n SaleItemFactory, PaymentFactory, ItemReturnFactory\n\n\nclass AssociateMethodTests(TestCase):\n def test_associate_model_accepts_numbers_for_tin_and_contact_number(self):\n associate = Associate.objects.create(TIN='123456789',\n company_name='green apple',\n contact_person='jobs',\n contact_number='123456', type='C')\n self.assertIsInstance(associate, Associate)\n\n def test_associate_model_does_not_accept_invalid_data(self):\n with self.assertRaises(ValidationError):\n Associate.objects.create(company_name='greenapple',\n contact_person='jobs',\n contact_number='123456', type='C')\n with self.assertRaises(ValidationError):\n Associate.objects.create(TIN='123456789as0',\n company_name='apple',\n contact_person='jobs',\n contact_number='123a456', type='C')\n\n def test_associate_should_return_proper_str(self):\n associate = AssociateFactory()\n self.assertEqual(str(associate), str(associate.company_name))\n\n\nclass PurchaseMethodTests(TestCase):\n def test_purchase_model_str_method(self):\n purchase = PurchaseFactory()\n self.assertEqual(str(purchase),\n purchase.shipment_delivery_receipt_number)\n\n def test_purchase_model_save_method_should_fail_supplier_field_is_not_a_supplier(\n self):\n client = AssociateFactory(type='C')\n with self.assertRaises(ValidationError):\n purchase = PurchaseFactory(supplier=client)\n\n\nclass PurchaseItemMethodTests(TestCase):\n def setUp(self):\n self.item = ItemFactory()\n self.purchase = PurchaseFactory()\n self.purchaseitem = PurchaseItem(purchase=self.purchase, item=self.item,\n quantity=1,\n purchase_unit_cost=3)\n self.purchaseitem.save()\n\n def test_buy_item_should_be_triggered_on_purchase_item_post_save(self):\n item = Item.objects.get(id=self.item.id)\n self.assertEqual(item.id, self.purchaseitem.item.id) # sanity check\n self.assertNotEqual(item.quantity, self.purchaseitem.item.quantity)\n self.assertNotEqual(item.unit_price, self.purchaseitem.item.unit_price)\n\n def test_delete_item_should_be_triggered_on_purchase_item_post_delete(self):\n item = Item.objects.get(id=self.item.id)\n self.purchaseitem.delete()\n self.assertEqual(item.id, self.purchaseitem.item.id) # sanity check\n self.assertNotEqual(item.quantity, self.purchaseitem.item.quantity)\n self.assertNotEqual(item.unit_price, self.purchaseitem.item.unit_price)\n\n def test_purchaseitem_model_str_method(self):\n purchaseitem = PurchaseItemFactory()\n self.assertEqual(str(purchaseitem),\n \"{0}-{1}\".format(purchaseitem.purchase,\n purchaseitem.item))\n\n\nclass SaleMethodTests(TestCase):\n def test_sale_model_str_method(self):\n sale = SaleFactory()\n self.assertEqual(str(sale), sale.delivery_receipt_number)\n\n def test_sale_model_save_method_should_fail_client_field_is_not_a_client(\n self):\n supplier = AssociateFactory(type='S')\n with self.assertRaises(ValidationError):\n SaleFactory(client=supplier)\n\n def test_sale_model_total_revenue_should_return_correct_value(self):\n sale = SaleFactory()\n sale_item = SaleItemFactory(sale=sale)\n sale_items = SaleItem.objects.filter(sale=sale)\n result = 0\n for i in sale_items:\n result += i.total_revenue()\n self.assertEqual(sale.total_revenue(), result)\n\n def test_sale_model_total_profit_should_return_correct_value(self):\n sale = SaleFactory()\n sale_item = SaleItemFactory(sale=sale)\n sale_items = SaleItem.objects.filter(sale=sale)\n result = 0\n for i in sale_items:\n result += i.total_profit()\n self.assertEqual(sale.total_profit(), result)\n\n\nclass SaleItemMethodTests(TestCase):\n def setUp(self):\n self.item = ItemFactory()\n self.sale = SaleFactory()\n self.saleitem = SaleItem(sale=self.sale, item=self.item, quantity=1,\n sale_unit_cost=1, discount=0)\n self.saleitem.save()\n\n def test_saleitem_model_str_method(self):\n saleitem = SaleItemFactory()\n self.assertEqual(str(saleitem),\n \"{0}-{1}\".format(saleitem.sale, saleitem.item))\n\n def test_orig_unit_price_should_be_saved_on_sale_item_save(self):\n item = Item.objects.get(id=self.item.id)\n self.assertEqual(item.id, self.saleitem.item.id) # sanity check\n self.assertNotEqual(item.quantity, self.saleitem.item.quantity)\n self.assertEqual(item.unit_price, self.saleitem.sale_orig_cost)\n\n def test_add_item_should_be_triggered_on_sale_item_post_delete(self):\n item = Item.objects.get(id=self.item.id)\n self.saleitem.delete()\n self.assertEqual(item.id, self.saleitem.item.id) # sanity check\n self.assertNotEqual(item.quantity, self.saleitem.item.quantity)\n\n def test_sale_item_model_total_revenue_should_return_correct_value(self):\n sale_item = SaleItemFactory()\n self.assertEqual(sale_item.total_revenue(),\n sale_item.sale_unit_cost * sale_item.quantity)\n\n def test_sale_item_model_total_profit_should_return_correct_value(self):\n sale_item = SaleItemFactory()\n self.assertEqual(sale_item.total_profit(),\n (sale_item.sale_unit_cost - sale_item.sale_orig_cost) *\n sale_item.quantity)\n\n\nclass PaymentMethodTests(TestCase):\n def test_str_should_return_proper_value(self):\n sale = SaleFactory()\n saleitem = SaleItemFactory(sale=sale)\n payment = PaymentFactory(sale=sale)\n self.assertEqual(str(payment), str(payment.client) + \"-\" + str(\n payment.sale) + \"-\" + str(payment.date))\n\n def test_save_should_raise_validation_error_if_amount_exceeds_sale_amount(\n self):\n with self.assertRaises(ValidationError):\n PaymentFactory(amount=1000000)\n\n def test_save_should_work(self):\n sale = SaleFactory()\n saleitem = SaleItemFactory(sale=sale)\n payment = PaymentFactory(sale=sale, amount=0.001)\n self.assertIsNotNone(payment)\n\n\nclass ItemReturnMethodTests(TestCase):\n def test_str_should_return_proper_value(self):\n itemreturn = ItemReturnFactory()\n self.assertEqual(str(itemreturn),\n str(itemreturn.date) + \"-\" + str(itemreturn.saleitem))\n\n def test_save_should_raise_validation_error_if_return_quantity_is_more_than_sold_quantity(\n self):\n with self.assertRaises(ValidationError):\n ItemReturnFactory(returned_quantity=10)\n\n def test_if_replaced_quantity_is_more_than_zero_it_should_reduce_quantity_in_item(\n self):\n item = ItemFactory(quantity=10)\n saleitem = SaleItemFactory(item=item, quantity=5)\n itemreturn = ItemReturnFactory(saleitem=saleitem, replaced_quantity=3)\n result_item = Item.objects.get(pk=item.id)\n self.assertEqual(result_item.quantity, 2)\n\n def test_if_restock_is_True_then_add_item_quantity(self):\n item = ItemFactory(quantity=10)\n saleitem = SaleItemFactory(item=item, quantity=5)\n itemreturn = ItemReturnFactory(saleitem=saleitem, restock=True,\n returned_quantity=5)\n result_item = Item.objects.get(pk=item.id)\n self.assertEqual(result_item.quantity, item.quantity)\n", "sub_path": "server/accounting/tests/test_models.py", "file_name": "test_models.py", "file_ext": "py", "file_size_in_byte": 8269, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "django.test.TestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "accounting.models.Associate.objects.create", "line_number": 14, "usage_type": "call"}, {"api_name": "accounting.models.Associate.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "accounting.models.Associate", "line_number": 14, "usage_type": "name"}, {"api_name": "accounting.models.Associate", "line_number": 18, "usage_type": "argument"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 21, "usage_type": "argument"}, {"api_name": "accounting.models.Associate.objects.create", "line_number": 22, "usage_type": "call"}, {"api_name": "accounting.models.Associate.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "accounting.models.Associate", "line_number": 22, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 25, "usage_type": "argument"}, {"api_name": "accounting.models.Associate.objects.create", "line_number": 26, "usage_type": "call"}, {"api_name": "accounting.models.Associate.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "accounting.models.Associate", "line_number": 26, "usage_type": "name"}, {"api_name": "accounting_factory.AssociateFactory", "line_number": 32, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 36, "usage_type": "name"}, {"api_name": "accounting_factory.PurchaseFactory", "line_number": 38, "usage_type": "call"}, {"api_name": "accounting_factory.AssociateFactory", "line_number": 44, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 45, "usage_type": "argument"}, {"api_name": "accounting_factory.PurchaseFactory", "line_number": 46, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 49, "usage_type": "name"}, {"api_name": "inventory.tests.item_factory.ItemFactory", "line_number": 51, "usage_type": "call"}, {"api_name": "accounting_factory.PurchaseFactory", "line_number": 52, "usage_type": "call"}, {"api_name": "accounting.models.PurchaseItem", "line_number": 53, "usage_type": "call"}, {"api_name": "inventory.models.Item.objects.get", "line_number": 59, "usage_type": "call"}, {"api_name": "inventory.models.Item.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "inventory.models.Item", "line_number": 59, "usage_type": "name"}, {"api_name": "inventory.models.Item.objects.get", "line_number": 65, "usage_type": "call"}, {"api_name": "inventory.models.Item.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "inventory.models.Item", "line_number": 65, "usage_type": "name"}, {"api_name": "accounting_factory.PurchaseItemFactory", "line_number": 72, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 78, "usage_type": "name"}, {"api_name": "accounting_factory.SaleFactory", "line_number": 80, "usage_type": "call"}, {"api_name": "accounting_factory.AssociateFactory", "line_number": 85, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 86, "usage_type": "argument"}, {"api_name": "accounting_factory.SaleFactory", "line_number": 87, "usage_type": "call"}, {"api_name": "accounting_factory.SaleFactory", "line_number": 90, "usage_type": "call"}, {"api_name": "accounting_factory.SaleItemFactory", "line_number": 91, "usage_type": "call"}, {"api_name": "accounting.models.SaleItem.objects.filter", "line_number": 92, "usage_type": "call"}, {"api_name": "accounting.models.SaleItem.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "accounting.models.SaleItem", "line_number": 92, "usage_type": "name"}, {"api_name": "accounting_factory.SaleFactory", "line_number": 99, "usage_type": "call"}, {"api_name": "accounting_factory.SaleItemFactory", "line_number": 100, "usage_type": "call"}, {"api_name": "accounting.models.SaleItem.objects.filter", "line_number": 101, "usage_type": "call"}, {"api_name": "accounting.models.SaleItem.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "accounting.models.SaleItem", "line_number": 101, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 108, "usage_type": "name"}, {"api_name": "inventory.tests.item_factory.ItemFactory", "line_number": 110, "usage_type": "call"}, {"api_name": "accounting_factory.SaleFactory", "line_number": 111, "usage_type": "call"}, {"api_name": "accounting.models.SaleItem", "line_number": 112, "usage_type": "call"}, {"api_name": "accounting_factory.SaleItemFactory", "line_number": 117, "usage_type": "call"}, {"api_name": "inventory.models.Item.objects.get", "line_number": 122, "usage_type": "call"}, {"api_name": "inventory.models.Item.objects", "line_number": 122, "usage_type": "attribute"}, {"api_name": "inventory.models.Item", "line_number": 122, "usage_type": "name"}, {"api_name": "inventory.models.Item.objects.get", "line_number": 128, "usage_type": "call"}, {"api_name": "inventory.models.Item.objects", "line_number": 128, "usage_type": "attribute"}, {"api_name": "inventory.models.Item", "line_number": 128, "usage_type": "name"}, {"api_name": "accounting_factory.SaleItemFactory", "line_number": 134, "usage_type": "call"}, {"api_name": "accounting_factory.SaleItemFactory", "line_number": 139, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 145, "usage_type": "name"}, {"api_name": "accounting_factory.SaleFactory", "line_number": 147, "usage_type": "call"}, {"api_name": "accounting_factory.SaleItemFactory", "line_number": 148, "usage_type": "call"}, {"api_name": "accounting_factory.PaymentFactory", "line_number": 149, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 155, "usage_type": "argument"}, {"api_name": "accounting_factory.PaymentFactory", "line_number": 156, "usage_type": "call"}, {"api_name": "accounting_factory.SaleFactory", "line_number": 159, "usage_type": "call"}, {"api_name": "accounting_factory.SaleItemFactory", "line_number": 160, "usage_type": "call"}, {"api_name": "accounting_factory.PaymentFactory", "line_number": 161, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 165, "usage_type": "name"}, {"api_name": "accounting_factory.ItemReturnFactory", "line_number": 167, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 173, "usage_type": "argument"}, {"api_name": "accounting_factory.ItemReturnFactory", "line_number": 174, "usage_type": "call"}, {"api_name": "inventory.tests.item_factory.ItemFactory", "line_number": 178, "usage_type": "call"}, {"api_name": "accounting_factory.SaleItemFactory", "line_number": 179, "usage_type": "call"}, {"api_name": "accounting_factory.ItemReturnFactory", "line_number": 180, "usage_type": "call"}, {"api_name": "inventory.models.Item.objects.get", "line_number": 181, "usage_type": "call"}, {"api_name": "inventory.models.Item.objects", "line_number": 181, "usage_type": "attribute"}, {"api_name": "inventory.models.Item", "line_number": 181, "usage_type": "name"}, {"api_name": "inventory.tests.item_factory.ItemFactory", "line_number": 185, "usage_type": "call"}, {"api_name": "accounting_factory.SaleItemFactory", "line_number": 186, "usage_type": "call"}, {"api_name": "accounting_factory.ItemReturnFactory", "line_number": 187, "usage_type": "call"}, {"api_name": "inventory.models.Item.objects.get", "line_number": 189, "usage_type": "call"}, {"api_name": "inventory.models.Item.objects", "line_number": 189, "usage_type": "attribute"}, {"api_name": "inventory.models.Item", "line_number": 189, "usage_type": "name"}]} +{"seq_id": "37042557", "text": "from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\n\nhtml = urlopen(\"http://www.pythonscraping.com/pages/warandpeace.html\")\nbsObj = BeautifulSoup(html, \"html.parser\")\nnameList = bsObj.findAll(\"span\", {\"class\":\"green\"})\n\n\nnameList = bsObj.findAll(text=\"the prince\") \nprint (nameList) # 返回一个包含该n个text的list\n\n'''\nfor name in nameList:\n print(name) # 不用get_text（）会打印整个标签\n print(name.get_text())\n'''", "sub_path": "chapter2/1-selectByClass.py", "file_name": "1-selectByClass.py", "file_ext": "py", "file_size_in_byte": 467, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "urllib.request.urlopen", "line_number": 4, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "181138722", "text": "from django.urls import path\nfrom . import views\n\napp_name = \"job\"\n\nurlpatterns = [\n path('', views.jobs, name=\"jobs\"),\n path('job/', views.detail, name=\"detail\"),\n path('add_job/', views.add_job, name=\"add_job\"),\n path('update/', views.update_job, name=\"update\"),\n path('delete/', views.delete_job, name=\"delete\"),\n path('1', views.listing, name=\"listing\"),\n\n]\n\n", "sub_path": "job/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 414, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "341359932", "text": "from rockit import *\nimport matplotlib.pyplot as plt\nimport numpy as np \n\nocp = Ocp(T=20)\nx1 = ocp.state()\nx2 = ocp.state()\nu = ocp.control(order=1)\np = ocp.control(order=1)\nz = ocp.algebraic()\n\nocp.set_der(x1, x1 - z + p)\nocp.set_der(x2, x1 + p + np.exp(z))\nocp.add_alg(z-p+u+x2)\n\nocp.add_objective(ocp.integral((z-0.5)**2))\n\nocp.subject_to(-1 <= (u <= 1))\nocp.subject_to(-3 <= p)\nocp.subject_to(-0.25 <= (x1 <= 1))\nocp.subject_to(-0.3 <=(ocp.der(p)<=0.3))\nocp.subject_to(-ocp.der(p) + ocp.der(u) + x1 + p + np.exp(z)<0.1) # -dz/dt\nocp.subject_to(ocp.at_t0(x1) == 0)\nocp.subject_to(ocp.at_tf(x2) == 1)\n\nocp.solver('ipopt')\nmethod = DirectCollocation(N=40)\nocp.method(method)\nsol = ocp.solve()\n\ntsc, x1c = sol.sample(x1, grid='integrator')\ntsc, x2c = sol.sample(x2, grid='integrator')\ntsc, uc = sol.sample(u, grid='integrator')\ntsc, pc = sol.sample(p, grid='integrator')\ntsc, zc = sol.sample(z, grid='integrator')\nplt.plot(tsc, x1c, '-o', label='state 1')\nplt.plot(tsc, x2c, '-^', label='state 2')\nplt.plot(tsc, zc, '-v', label='algebraic')\nplt.plot(tsc, uc, '-x', label='control 1')\nplt.plot(tsc, pc, '-+', label='control 2')\nplt.xlabel('time, s')\nplt.legend()\nplt.show()\n\n", "sub_path": "P2-OCP(der_algebraic).py", "file_name": "P2-OCP(der_algebraic).py", "file_ext": "py", "file_size_in_byte": 1174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.exp", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "114725889", "text": "import pickle\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tools\n\n\npathcore = 'D:\\studia\\II stopień\\Praca Magisterska\\wyniki\\\\'\n# folders = ['raw_map', 'averaging_filter_3-3', 'averaging_filter_5-5', 'gaussian_filter_k3', 'gaussian_filter_k5', 'gaussian_filter_k7']\nfolders = ['raw_map', 'averaging_filter_3-3', 'averaging_filter_5-5', 'gaussian_filter_k3', 'gaussian_filter_k5', 'gaussian_filter_k7', 'median_filter_k3', 'median_filter_k5', 'median_filter_k7', 'wiener']\nfiles = ['alpha_map', 'alpha_map_err', 'beta_map', 'beta_map_err']\n\nmaps = []\nfor filter in folders:\n with open (tools.get_map(files=filter, directory=pathcore, extension=files[1]), 'rb') as fp:\n maps.append(pickle.load(fp))\nxmap = np.arange(120)\nymap = np.arange(64)\nfig = plt.figure()\n\nfor i, map in enumerate(maps):\n ax = fig.add_subplot(5, 2, i + 1)\n z = np.array(map)\n # narysowanie beta wymaga thresholdu, ponieważ są duże błędy\n mean = z.mean()\n std = np.std(z)\n #threshold do beta_eff\n # mean = z[np.where(np.logical_and(z > 2000, z < 3000))].mean()\n # std=np.std(z[np.where(np.logical_and(z > 2000, z < 3000))])\n # z_out = np.where(np.logical_or(z < 2000, z > 3000))\n # z[z_out] = mean\n #####################\n # threshold do beta_eff_err\n # mean = z[np.where(np.logical_and(z >= 150, z < 700))].mean()\n # std=np.std(z[np.where(np.logical_and(z >= 150, z < 700))])\n # z_out = np.where(np.logical_or(z <= 150, z > 700))\n # z[z_out] = 0\n #####################\n # threshold do alfa\n # mean = z[np.where(np.logical_and(z >= 0.00002, z < 0.00004))].mean()\n # std=np.std(z[np.where(np.logical_and(z >= 0.00002, z < 0.00004))])\n # z_out = np.where(np.logical_or(z <= 0.00002, z > 0.00004))\n # z[z_out] = mean\n #####################\n # threshold do alfa_err\n mean = z[np.where(np.logical_and(z >= 0, z < 0.00002))].mean()\n std=np.std(z[np.where(np.logical_and(z >= 0, z < 0.00002))])\n z_out = np.where(np.logical_or(z <= 0, z > 0.00002))\n z[z_out] = 0\n #####################\n print(folders[i], ': ', mean, '+/-', std)\n ############################################################\n ax.set_title(folders[i])\n cax = ax.pcolormesh(ymap, xmap, z, cmap='inferno')\n fig.colorbar(cax)\n ax.set_xlim([0,63])\n ax.set_ylim([0,119])\n#histogram błędów\n # ax.hist(z[np.where(np.logical_and(z >= 150, z < 700))], bins='scott') #beta\n # ax.hist(z[np.where(np.logical_and(z > 0, z < 0.00002))], bins='scott') #alfa\n # ax.set_xlim([0,0.00002])\n\nplt.show()", "sub_path": "LIT_map_comparison.py", "file_name": "LIT_map_comparison.py", "file_ext": "py", "file_size_in_byte": 2557, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "tools.get_map", "line_number": 14, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "29241680", "text": "import httplib2\nimport json\n\nget_http=httplib2.Http()\n\ndef get_geo_location(location_str):\n \n Google_api_key='AIzaSyBdmaN-TIMFejXVYa3zasOeg8-JV2_wSFQ'\n \n url='https://maps.googleapis.com/maps/api/geocode/json?address={0}&key={}'.format(location_str, Google_api_key)\n result=json.loads(get_http.request(url,'GET'))\n\n if result:\n \n latitude=result['results'][0]['geometry']['loctaion']['lat']\n langtitude=result['results'][0]['geometry']['loctaion']['lan']\n else:\n return \"No Result\"\n return latitude, langtitude\n \n \ndef get_restuarant(food,location_str):\n \n lat,lan=get_geo_location(location_str)\n foursq_client_id ='1LVBZOQ3UJQWZV2SX5FQU5BKUALE2J1EQOZPKX5OJWCSPTJC'\n foursq_client_secret ='43HXB5CD5LNO4KUFCLCKS3JSFOS10JT2SEEY5UVQXUQYVR3M'\n \n url='https://api.foursquare.com/v2/venues/search?client_id={0}&client_secret={1}&v=20160201&ll={2},{3}&query={4}'.format(foursq_client_id,\n foursq_client_secret, lat,lan,food)\n result=json.loads(get_http.request(url,'GET'))\n \n if result:\n response=result['response']['venues']\n \n for venue in response:\n venue_id=str(venue['id'])\n shop_name=str(venue['name'])\n contact=str(venue['contact']['phone'])\n address=str(venue['location']['formattedAddress'])\n restuarant_details={'rest_id':venue_id, 'rest_name':shop_name, 'rest_contact':contact, 'rest_address':address}\n \n else:\n return \"No Result\"\n\n return restuarant_details\n\nif __name__=='__main__':\n \n get_restuarant(\"Pizza\", \"Tokyo, Japan\")\n get_restuarant(\"Tacos\", \"Jakarta, Indonesia\")\n get_restuarant(\"Tapas\", \"Maputo, Mozambique\")\n get_restuarant(\"Falafel\", \"Cairo, Egypt\")\n get_restuarant(\"Spaghetti\", \"New Delhi, India\")\n get_restuarant(\"Cappuccino\", \"Geneva, Switzerland\") \n get_restuarant(\"Sushi\", \"Los Angeles, California\")\n get_restuarant(\"Steak\", \"La Paz, Bolivia\")\n get_restuarant(\"Gyros\", \"Sydney Austrailia\")\n ", "sub_path": "UDACITY_REST/Restuarant_ID/Fetch_resturanat.py", "file_name": "Fetch_resturanat.py", "file_ext": "py", "file_size_in_byte": 2049, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "httplib2.Http", "line_number": 4, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 11, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "609155014", "text": "# Purpose: Python 2/3 compatibility layer\n# Created: 12.05.13\n# Copyright (c) 2013-2018, Manfred Moitzi\n# License: MIT License\n\nimport sys\nimport functools\nimport array\n\nPY3 = sys.version_info.major > 2\nif sys.version_info[:2] > (3, 2):\n from collections.abc import Sequence\nelse:\n from collections import Sequence\n\n\nif PY3:\n import html\n escape = functools.partial(html.escape, quote=True)\n basestring = str\n ustr = str\n unicode2bytes = lambda s: bytes(s, encoding='utf-8')\n import reprlib\nelse: # Python 2.7\n import cgi\n import repr as reprlib\n escape = functools.partial(cgi.escape, quote=True)\n ustr = unicode\n unicode2bytes = lambda s: s.encode('utf-8')\n\n\ndef byte_to_hexstr(byte):\n if PY3:\n return \"%0.2X\" % byte\n else:\n return \"%0.2X\" % ord(byte)\n\n\ndef encode_hex_code_string_to_bytes(data):\n byte_array = array.array('B', (int(data[index:index+2], 16) for index in range(0, len(data), 2)))\n if PY3:\n return byte_array.tobytes()\n else:\n return byte_array.tostring()\n\n\ndef isstring(s):\n return isinstance(s, basestring)\n", "sub_path": "ezdxf/tools/c23.py", "file_name": "c23.py", "file_ext": "py", "file_size_in_byte": 1112, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "sys.version_info", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 11, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 19, "usage_type": "call"}, {"api_name": "html.escape", "line_number": 19, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 27, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 27, "usage_type": "attribute"}, {"api_name": "array.array", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "285772858", "text": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nimport requests\nimport base64\nimport codecs\nimport os\nimport re\nimport urllib3\nurllib3.disable_warnings()\n\ndef safe_base64_decode(s): # 解码\n try:\n if len(s) % 4 != 0:\n s = s + '=' * (4 - len(s) % 4)\n base64_str = base64.urlsafe_b64decode(s)\n return bytes.decode(base64_str)\n except Exception as e:\n print('解码错误:', e)\n\ndef Retry_request(url): #超时重传\n flag=True\n while flag:\n try:\n header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36'}\n res = requests.get(url, headers=header, timeout=5, verify=False) # verify =false 防止请求时因为代理导致证书不安全\n if res.headers['Connection']!='close':\n flag=False\n return res.text\n except Exception as e:\n print('注意检查网络，下载文件出错，对应的url地址为：'+url,e)\n\n\ndef writeRules(sublink): #写回配置\n try:\n other=[]\n data = Retry_request(sublink) #请求订阅\n ssrdata=safe_base64_decode(data).strip().split('\\n')\n rules = Retry_request('https://raw.githubusercontent.com/ConnersHua/Profiles/master/Clash/Pro.yaml') #请求规则_神机规则\n p_rule= Retry_request('https://raw.githubusercontent.com/lzdnico/ToClash/master/General.yml') #基础规则_默认不配置DNS\n #p_rule=rules.split('Proxy:')[0] #基础规则_默认配置DNS,与上面二选一\n l_rule = rules.split('Rule:\\n')[1]\n Peoxies = 'Proxy:\\n'\n \n\n name =''\n for i in range(len(ssrdata)): #节点组\n \n ssrlink=safe_base64_decode(ssrdata[i].replace('ssr://', ''))\n config=re.split(':|&|=|/\\?',ssrlink)\n remark1 =safe_base64_decode(config[11])\n\n\n # 匹配不同订阅格式\n if i < len(ssrdata)-1:\n ssrlink2=safe_base64_decode(ssrdata[i+1].replace('ssr://', ''))\n config2=re.split(':|&|=|/\\?',ssrlink2)\n remark2 =safe_base64_decode(config2[11])\n\n if remark1 == remark2:\n remark = safe_base64_decode(config[-1])\n else :\n remark = remark1\n remark2 = remark1\n # 匹配不同订阅格式结束\n\n #简单粗暴的解决一些机场节点名字重复的问题\n if remark in name: \n continue\n name += remark #占用空间大，不会出错\n #name = remark #占用空间小一点，可能会出错\n #简单粗暴的解决一些机场节点名字重复的问题结束\n \n #接下来是给节点加图标的，需要深度自定义，可以全部删除\n if \"30倍\" in remark:\n continue\n if \"香港\" in remark:\n remark = '🇭🇰' + remark\n if \"美国\" in remark or \"狮城\" in remark :\n remark = '🇺🇸' + remark\n if \"深港\" in remark or \"沪港\" in remark or \"京港\" in remark or \"杭港\" in remark:\n remark = '🇨🇳 👉👉 🇭🇰' + remark\n if \"深美\" in remark or \"沪美\" in remark or \"京美\" in remark or \"杭美\" in remark:\n remark = '🇨🇳 👉👉 🇺🇸' + remark\n if \"深日\" in remark or \"沪日\" in remark or \"京日\" in remark or \"杭日\" in remark:\n remark = '🇨🇳 👉👉 🇯🇵' + remark\n if \"深台\" in remark or \"沪台\" in remark or \"京台\" in remark or \"杭台\" in remark:\n remark = '🇨🇳 👉👉 🇨🇳' + remark\n #加图标到此结束\n\n name += remark\n print(remark)\n pwd = safe_base64_decode(config[5])\n obfsparam=safe_base64_decode(config[7])\n protoparam =safe_base64_decode(config[9]) \n Json={ 'name': remark, 'type': 'ssr', 'server': config[0], 'port': config[1], 'password':pwd , 'cipher': config[3], 'protocol': config[2], 'protocolparam': protoparam, 'obfs': config[4], 'obfsparam': obfsparam }\n #print(Json)\n Peoxies +='- '+str(Json)+'\\n'\n other.append(remark)\n\n\n #策略组\n ProxyGroup='\\n\\nProxy Group:\\n\\n'\\\n '- { name: \"😀故障切换\", type: \"fallback\", \"proxies\": ' + str(other) + ', url: \"http://www.gstatic.com/generate_204\", interval: 300'+ '}\\n\\n\\n'\\\n '- { name: \"🚀手动选择\", type: \"select\", \"proxies\": ' + str(other) + '}\\n\\n\\n'\\\n '- { name: \"PROXY\", type: select, proxies: [ \"😀故障切换\",\"🚀手动选择\",\"DIRECT\"] }\\n'\\\n '- { name: \"ForeignMedia\", type: select, proxies: [\"PROXY\",\"🚀手动选择\"] }\\n'\\\n '- { name: \"DomesticMedia\", type: select, proxies: [\"DIRECT\",\"PROXY\",\"🚀手动选择\"] }\\n'\\\n '- { name: \"Hijacking\", type: select, proxies: [\"REJECT\", \"DIRECT\"] }\\n'\\\n '- { name: \"Apple\", type: select, proxies: [\"DIRECT\", \"PROXY\"] }\\n'\\\n '- { name: \"Final\", type: select, proxies: [\"PROXY\", \"DIRECT\"] }\\n\\n\\n'\\\n 'Rule:\\n'\n return p_rule+Peoxies+ProxyGroup+l_rule #回传配置\n except Exception as e:\n print('返回规则错误:',e)\n\n\ndef getClash(nodes): #写文件\n \n try:\n\n\n with codecs.open('./config.yaml', \"w\",encoding = 'utf-8') as f:\n f.writelines(nodes)\n\n \n except Exception as e:\n print('main Error:', e)\n\nif __name__ == \"__main__\":\n try:\n url = \"\" \n data = writeRules(url)\n getClash(data)\n input('任意键退出')\n except Exception as e:\n print('main Error:', e)\n", "sub_path": "旧脚本不推荐使用/SSR_Clash_NoGroup.py", "file_name": "SSR_Clash_NoGroup.py", "file_ext": "py", "file_size_in_byte": 6030, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 9, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "re.split", "line_number": 49, "usage_type": "call"}, {"api_name": "re.split", "line_number": 56, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 122, "usage_type": "call"}]} +{"seq_id": "522582227", "text": "from .baseschema import ma\nfrom marshmallow import fields, post_dump, post_load, pre_load\nfrom src.models.learning_point import LearningPoint, LearningPointTags\nfrom src.database.utils.crud import read_rows\nfrom nltk.tokenize import TweetTokenizer\nfrom src.database.db import get_db_session\nimport re\nimport string \n#TODO:\n# finish LearningPoints Tags schema\n# overide the learningStreamLearningPoints and learningStreams fields\n# ensure that overridden fields do not have refrences to circular models\n# ensure that refrences to tags do not include fields that point to other models\nclass LearningPointSchema(ma.ModelSchema):\n episodes = fields.Nested('EpisodeSchema',\n many = True, \n exclude=('learningPoints', 'episodeLearningPoints', 'episodeTags', 'tags'))\n episodeLearningPoints = fields.Nested('EpisodeLearningPointsSchema', \n many = True, \n exclude = ('learningPoint','learningPointId'), dump_only = True)\n tags = fields.Nested('TagSchema', \n many = True, \n exclude = ('learningPoints', 'learningPointTags', \n 'episodes', 'episodeTags', \n 'learningPracticesTags', 'learningPractices',\n 'learningStreamTags', 'learningStreams'))\n learningPointTags = fields.Nested('LearningPointTagsSchema', \n many = True, \n exclude = ('learningPoint', 'learningPointId'), \n dump_only = True)\n learningPracticeLearningPoints = fields.Nested('LearningPracticeLearningPointsSchema', \n many = True, \n exclude = ('learningPoint', 'learningPointID'),\n dump_only = True)\n learningPractices = fields.Nested('LearningPracticeSchema', \n many = True, \n exclude= ('learningPoints', 'learningPracticesLearningPoints'))\n class Meta:\n model = LearningPoint\n init_session, _ = get_db_session()\n sqla_session = init_session\n href = ma.Hyperlinks({\n \"self\":[\n ma.URLFor('apiV1_0.learning_points_id', id = ''),\n ma.URLFor('apiV1_0.learning_points_slug', slug = '')\n ],\n \"collection\": ma.URLFor('apiV1_0.learning_points')\n })\n @pre_load\n def check_data(self, data):\n if data.get('id') is None:\n if data.get('name') is None:\n raise ValueError('Must Include name')\n punct = set(string.punctuation)\n #if both the id and the slug is none then this is a completely new blog\n #generate the slug from the title by tokenizing the lowered title and filtering for only alphanumeric characters\n #then use the join method on the filtered slug tokens to form a slug_like_this from ['slug','like','this']\n slug_array = TweetTokenizer().tokenize(data['name'].lower())\n if len(slug_array) == 1:\n data['slug'] = slug_array[0]\n else:\n slug_array = list(filter(lambda x: not re.match(\"(\\\\d|\\\\W)+\", x) and not x in punct, slug_array))\n data['slug'] = '_'.join(slug_array)\n query = read_rows(LearningPoint, filters= [\n {\n 'slug': {\n 'comparitor': '==',\n 'data': data['slug']\n }\n }\n ]).one_or_none()\n count = 1\n #loop over until you find a unique slug by appending an incrementing count to the end of the slug\n while query is not None:\n slug = data['slug'] + '_' + str(count)\n query = read_rows(LearningPoint, filters= [\n {\n 'slug': {\n 'comparitor': '==',\n 'data': slug\n }\n }\n ]).one_or_none()\n data['slug'] = slug\n count += 1\n else:\n print(\"learning point working 1\")\n for key in list(data.keys()):\n if key != 'id':\n del data[key]\n print(\"learning point done\")\n @post_dump\n def cleanup(self, data):\n if data.get('episodeLearnigPoints') is not None:\n data['episodes'] = data['episodeLearningPoints']\n del data['episodeLearningPoints']\n if data.get('learningPointTags') is not None:\n data['tags'] = data['learningPointTags']\n del data['learningPointTags']\n if data.get('learningPracticeLearningPoints') is not None:\n data['learningPractices'] = data['learningPracticeLearningPoints']\n del data['learningPracticeLearningPoints']\n return data\n @post_load\n def load_learning_point(self, data):\n pass\nclass LearningPointTagsSchema(ma.ModelSchema):\n tag = fields.Nested('TagSchema', \n exclude = ('learningPoints', 'learningPointTags', \n 'episodes', 'episodeTags', \n 'learningPracticesTags', 'learningPractices',\n 'learningStreamTags', 'learningStreams'))\n learningPoint = fields.Nested(LearningPointSchema,\n exclude = ('learningPointTags', 'tags', \n 'episodes', 'episodeLearningPoints', \n 'learningPracticeLearningPoint', 'learningPractice'))\n class Meta:\n model = LearningPointTags", "sub_path": "src/utils/marshmallow/learning_point_schema.py", "file_name": "learning_point_schema.py", "file_ext": "py", "file_size_in_byte": 5168, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "baseschema.ma.ModelSchema", "line_number": 14, "usage_type": "attribute"}, {"api_name": "baseschema.ma", "line_number": 14, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 15, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 15, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 18, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 21, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 21, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 27, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 27, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 31, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 31, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 35, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 35, "usage_type": "name"}, {"api_name": "src.models.learning_point.LearningPoint", "line_number": 39, "usage_type": "name"}, {"api_name": "src.database.db.get_db_session", "line_number": 40, "usage_type": "call"}, {"api_name": "baseschema.ma.Hyperlinks", "line_number": 42, "usage_type": "call"}, {"api_name": "baseschema.ma", "line_number": 42, "usage_type": "name"}, {"api_name": "baseschema.ma.URLFor", "line_number": 44, "usage_type": "call"}, {"api_name": "baseschema.ma", "line_number": 44, "usage_type": "name"}, {"api_name": "baseschema.ma.URLFor", "line_number": 45, "usage_type": "call"}, {"api_name": "baseschema.ma", "line_number": 45, "usage_type": "name"}, {"api_name": "baseschema.ma.URLFor", "line_number": 47, "usage_type": "call"}, {"api_name": "baseschema.ma", "line_number": 47, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 54, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.TweetTokenizer", "line_number": 58, "usage_type": "call"}, {"api_name": "re.match", "line_number": 62, "usage_type": "call"}, {"api_name": "src.database.utils.crud.read_rows", "line_number": 64, "usage_type": "call"}, {"api_name": "src.models.learning_point.LearningPoint", "line_number": 64, "usage_type": "argument"}, {"api_name": "src.database.utils.crud.read_rows", "line_number": 76, "usage_type": "call"}, {"api_name": "src.models.learning_point.LearningPoint", "line_number": 76, "usage_type": "argument"}, {"api_name": "marshmallow.pre_load", "line_number": 49, "usage_type": "name"}, {"api_name": "marshmallow.post_dump", "line_number": 92, "usage_type": "name"}, {"api_name": "marshmallow.post_load", "line_number": 104, "usage_type": "name"}, {"api_name": "baseschema.ma.ModelSchema", "line_number": 107, "usage_type": "attribute"}, {"api_name": "baseschema.ma", "line_number": 107, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 108, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 108, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 113, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 113, "usage_type": "name"}, {"api_name": "src.models.learning_point.LearningPointTags", "line_number": 118, "usage_type": "name"}]} +{"seq_id": "295285589", "text": "\nimport numpy as np\n\nimport argparse\nimport logging\n\n\nimport os, glob, pathlib\n\nfrom matplotlib import pyplot\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nfrom mpl_toolkits.mplot3d import Axes3D\n\nmpl.rcParams.update({'axes.labelsize': 45})\nmpl.rcParams.update({'xtick.labelsize': 50})\nmpl.rcParams.update({'ytick.labelsize': 50})\nmpl.rcParams.update({'axes.titlesize':40})\nmpl.rcParams.update({'legend.fontsize':14})\n\ndef gen_2d_pdist(logweights, data_n, data_sub_n, bins):\n\n logpdist = np.zeros((bins.size-1, bins.size-1))\n logpdist[:] = -np.inf\n\n # bin for N_v\n for i, lb_n in enumerate(bins[:-1]):\n ub_n = bins[i+1]\n\n ## All Ntwid vals that are in this bin\n mask_n = (data_n >= lb_n) & (data_n < ub_n)\n\n # bin for N_v1 (subvol)\n for j, lb_sub in enumerate(bins[:-1]):\n ub_sub = bins[j+1]\n\n ## All Ntwid_sub that are in this bin\n mask_sub = (data_sub_n >= lb_sub) & (data_sub_n < ub_sub)\n\n ## All values that fall in this bin\n mask = mask_n & mask_sub\n\n ## No values fall in this bin - continue\n if not mask.any():\n continue\n\n assert np.isinf(-logpdist[i,j])\n\n this_logweights = logweights[mask]\n max_val = this_logweights.max()\n this_logweights -= max_val\n\n logpdist[i,j] = np.log(np.exp(this_logweights).sum()) + max_val\n\n return -logpdist\n\n\nfnames = glob.glob(\"nstar_*/nstar_*/phiout.dat\")\n\nall_n_dat = np.array([])\nall_sub_n_dat = np.array([])\n\nstart = 300\n\nfor fname in fnames:\n print(\"doing fname: {}\".format(os.path.dirname(fname)))\n ds = dr.loadPhiSub(fname)\n\n all_n_dat = np.append(all_n_dat, ds.data[start:][\"N\"])\n all_sub_n_dat = np.append(all_sub_n_dat, ds.ds_sub.data[start:][\"N\"])\n\nbins_n = np.arange(0, all_n_dat.max()+2, 1)\n\nhist, xb, yb = np.histogram2d(all_n_dat, all_sub_n_dat, bins=bins_n)\nxx, yy = np.meshgrid(bins_n, bins_n, indexing='ij')\n\nplt.pcolormesh(xx, yy, np.log(hist))\n\n\nplt.close('all')\nwham_ds = np.load('all_data.dat.npz')\ndata = wham_ds['data_aux']\nlogweights = wham_ds['logweights']\n\nassert np.allclose(data, all_n_dat)\n\n\nneglogpdist = gen_2d_pdist(logweights, all_n_dat, all_sub_n_dat, bins_n)\nplt.pcolormesh(xx, yy, neglogpdist, cmap='jet')\nplt.colorbar()\n\n\ndef get_bias(kappa, nstar, neglogpdist, bins_n):\n bias = 0.5*kappa*((bins_n[:-1]-nstar)**2)\n biased_neglogpdist = neglogpdist + bias[:,None]\n biased_neglogpdist -= biased_neglogpdist.min()\n\n biased_Nv = -np.log(np.exp(-biased_neglogpdist).sum(axis=1))\n biased_Ns = -np.log(np.exp(-biased_neglogpdist).sum(axis=0))\n\n\n return biased_neglogpdist, biased_Nv, biased_Ns\n\n\nplt.close('all')\nkappa = beta * 0.54\n\nnstars = np.arange(-40, 110, 10)\n#plt.pcolormesh(xx, yy, biased_neglogpdist, cmap='jet')\n\nfor nstar in nstars:\n biased_neglogpdist, biased_Nv, biased_Ns = get_bias(kappa, nstar, neglogpdist, bins_n)\n\n print(\"nstar: {} : {:.1f} : {:.1f}\".format(nstar, bins_n[biased_Nv.argmin()], bins_n[biased_Ns.argmin()]))\n\n\n\n", "sub_path": "scratch/sam/pattern_prep/analyze/plot_2d_overlap.py", "file_name": "plot_2d_overlap.py", "file_ext": "py", "file_size_in_byte": 3072, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "matplotlib.rcParams.update", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 16, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams.update", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 17, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams.update", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 18, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams.update", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 19, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams.update", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.isinf", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 54, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.histogram2d", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "491268661", "text": "import os\nimport tarfile\nfrom typing import Tuple\n\nimport PIL\nimport pandas as pd\nimport torch\nfrom PIL.Image import Image\nfrom torch import Tensor\nfrom torch.nn.functional import one_hot\nfrom torch.utils.data import Dataset, DataLoader, BatchSampler, RandomSampler\nfrom torchvision import transforms\nfrom torchvision.datasets.folder import default_loader\nfrom torchvision.datasets.utils import download_url, check_integrity\n\nfrom utilities.path import data_path\n\n\ndef default_loader_rgb(path):\n return PIL.Image.open(path).convert('RGB')\n\n\ndef generate_transform_dict(origin_width: int = 64, width: int = 64, scale_ratio: float = 0.6) -> dict:\n \"\"\"\n Source: https://github.com/bnu-wangxun/Deep_Metric/blob/master/DataSet/CUB200.py\n \"\"\"\n normalize = transforms.Normalize(mean=[0.502, 0.459, 0.408], std=[0.5, 0.5, 0.5])\n return {\n 'rand-crop': transforms.Compose([\n transforms.Resize(origin_width),\n transforms.RandomResizedCrop(scale=(scale_ratio, 1), size=width),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n normalize,\n ]),\n 'random_crop': transforms.Compose([\n transforms.Resize(origin_width),\n transforms.RandomResizedCrop(scale=(scale_ratio, 1.0), size=width),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n normalize,\n ]),\n 'center-crop': transforms.Compose([\n transforms.Resize(origin_width),\n transforms.CenterCrop(width),\n transforms.ToTensor(),\n normalize,\n ]),\n 'center-crop-no-norm': transforms.Compose([\n transforms.Resize(origin_width),\n transforms.CenterCrop(width),\n transforms.ToTensor()\n ]),\n 'resize': transforms.Compose([\n transforms.Resize(width),\n transforms.ToTensor(),\n normalize,\n ]),\n 'auto_augment': transforms.Compose([\n transforms.Resize(origin_width),\n transforms.RandomResizedCrop(scale=(scale_ratio, 1.0), size=width),\n transforms.RandomHorizontalFlip(),\n transforms.RandAugment(num_ops=2, magnitude=9),\n transforms.ToTensor(),\n normalize,\n ]),\n }\n\n\nclass CubDataset(Dataset):\n ZIP_URL = 'https://data.caltech.edu/records/65de6-vp158/files/CUB_200_2011.tgz?download=1'\n ZIP_FNAME = 'CUB_200_2011.tgz'\n ZIP_MD5 = '97eceeb196236b17998738112f37df78'\n\n IMG_WIDTH = 64\n TRANSFORMS = generate_transform_dict(origin_width=IMG_WIDTH, width=IMG_WIDTH)\n\n def __init__(self, train: bool = True, transforms_key: str = 'center-crop', load_bboxes: bool = False,\n use_one_hot: bool = False):\n self.data_path = os.path.join(data_path, 'cub-200-2011')\n self.img_dir_path = os.path.join(self.data_path, 'CUB_200_2011/images')\n self.transforms = self.__class__.TRANSFORMS[transforms_key]\n self.train = train\n\n self._ensure_data_exist()\n # Data Columns: ['img_id', 'filepath', 'target', 'target_name', 'is_training_img', (optional) 'bbox.{x,y,w,h}']\n self.data: pd.DataFrame = self._load(load_bboxes=load_bboxes)\n self.load_bboxes = load_bboxes\n self.bbox_cols = [c for c in self.data if c.startswith('bbox.')]\n self.use_one_hot = use_one_hot\n\n def _ensure_data_exist(self):\n os.makedirs(self.data_path, exist_ok=True)\n if not os.path.exists(self.img_dir_path):\n # Download\n zip_fpath = os.path.join(self.data_path, self.__class__.ZIP_FNAME)\n if not os.path.exists(zip_fpath) or not check_integrity(zip_fpath):\n download_url(self.__class__.ZIP_URL, self.data_path, self.__class__.ZIP_FNAME, self.__class__.ZIP_MD5)\n # Unzip\n with tarfile.open(zip_fpath, \"r:gz\") as tar:\n print(f'\\t[{self.__class__.__name__}::_ensure_data_exist] Extracting \"{zip_fpath}\"')\n tar.extractall(path=self.data_path)\n\n def _load(self, load_bboxes: bool = False):\n images = pd.read_csv(os.path.join(self.data_path, 'CUB_200_2011', 'images.txt'), sep=' ',\n names=['img_id', 'filepath'])\n image_class_labels = pd.read_csv(os.path.join(self.data_path, 'CUB_200_2011', 'image_class_labels.txt'),\n sep=' ', names=['img_id', 'target'])\n train_test_split = pd.read_csv(os.path.join(self.data_path, 'CUB_200_2011', 'train_test_split.txt'),\n sep=' ', names=['img_id', 'is_training_img'])\n class_names = pd.read_csv(os.path.join(self.data_path, 'CUB_200_2011', 'classes.txt'),\n sep=' ', names=['target', 'target_name'])\n data = images.merge(image_class_labels.merge(class_names, on='target'), on='img_id')\n data = data.merge(train_test_split, on='img_id')\n if load_bboxes:\n data = data.merge(\n pd.read_csv(os.path.join(self.data_path, 'CUB_200_2011', 'bounding_boxes.txt'),\n sep=' ', names=['img_id', 'bbox.x', 'bbox.y', 'bbox.w', 'bbox.h']),\n on='img_id')\n return data[data.is_training_img == (1 if self.train else 0)]\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, idx: int) -> Tuple[Image or Tensor, int] or Tuple[Image or Tensor, int, Tensor]:\n sample = self.data.iloc[idx]\n path = os.path.join(self.img_dir_path, sample.filepath)\n target = sample.target - 1 # Targets start at 1 by default, so shift to 0\n target = torch.tensor(target) if not self.use_one_hot else one_hot(torch.tensor(target), 200)\n img = default_loader_rgb(path)\n if self.transforms is not None:\n img = self.transforms(img)\n if self.load_bboxes:\n return img, target.float(), torch.from_numpy(sample[self.bbox_cols].astype(float).to_numpy())\n return img, target.float()\n\n\nclass CubSegmentationDataset(CubDataset):\n SEGM_ZIP_URL = 'https://data.caltech.edu/records/w9d68-gec53/files/segmentations.tgz?download=1'\n SEGM_ZIP_FNAME = 'segmentations.tgz'\n SEGM_ZIP_MD5 = '4d47ba1228eae64f2fa547c47bc65255'\n\n def __init__(self, segm_transforms_key: str = 'center-crop-no-norm', *cub_args, **cub_kwargs):\n super(CubSegmentationDataset, self).__init__(*cub_args, **cub_kwargs)\n self.segm_dir_path = os.path.join(self.data_path, 'CUB_200_2011/segmentations')\n self._ensure_segmentation_data_exit()\n self.segm_transforms = self.__class__.TRANSFORMS[segm_transforms_key]\n\n def _ensure_segmentation_data_exit(self):\n if not os.path.exists(self.segm_dir_path):\n # Download\n zip_fpath = os.path.join(self.data_path, self.__class__.SEGM_ZIP_FNAME)\n if not os.path.exists(zip_fpath) or not check_integrity(zip_fpath):\n download_url(self.__class__.SEGM_ZIP_URL, self.data_path, self.__class__.SEGM_ZIP_FNAME,\n self.__class__.SEGM_ZIP_MD5)\n # Unzip\n with tarfile.open(zip_fpath, \"r:gz\") as tar:\n print(f'\\t[{self.__class__.__name__}::_ensure_data_exist] Extracting \"{zip_fpath}\"')\n tar.extractall(path=os.path.dirname(self.segm_dir_path))\n\n def __getitem__(self, idx: int) -> Tuple[Image or Tensor, int, Tensor] or \\\n Tuple[Image or Tensor, int, Tensor, Tensor]:\n img, target, bbox = super(CubSegmentationDataset, self).__getitem__(idx)\n segm_fpath = os.path.join(self.segm_dir_path, self.data.iloc[idx].filepath.replace(\".jpg\", \".png\"))\n seg = default_loader(segm_fpath)\n if self.segm_transforms is not None:\n seg = self.segm_transforms(seg)\n if self.load_bboxes:\n return img, target, bbox, seg\n return img, target, seg # seg is a 0-1 mask of the same shape as img\n\n\nclass CubDataLoader(DataLoader):\n \"\"\"\n CubDataLoader Class:\n This class is used to access CUB-200-2011 dataset via PyTorch's Dataloading API.\n \"\"\"\n\n def __init__(self, train=True, ds_transforms_key: str = 'center-crop', device: str = 'cpu', use_val: bool = False,\n val_size=None, use_one_hot: bool = True, **kwargs):\n self.use_one_hot = use_one_hot\n train_ds, val_ds = CubDataset(train=train, transforms_key=ds_transforms_key, use_one_hot=use_one_hot), None\n if train and use_val and val_size is not None:\n ts = len(train_ds)\n vs = int(ts * val_size) if type(val_size) == float and val_size < 1.0 else val_size\n ts -= vs\n train_ds, val_ds = torch.utils.data.random_split(train_ds, [ts, vs])\n ds = train_ds if not use_val or val_ds is None else val_ds\n if 'pin_memory' not in kwargs:\n kwargs['pin_memory'] = device != 'cpu'\n super(CubDataLoader, self).__init__(dataset=ds, shuffle=True, **kwargs)\n\n @property\n def vis_transforms(self) -> transforms.Compose:\n return transforms.Compose([\n transforms.Normalize(mean=[0., 0., 0.], std=[2.0, 2.0, 2.0]),\n transforms.Normalize(mean=[-0.502, -0.459, -0.408], std=[1.0, 1.0, 1.0])\n ])\n\n @property\n def n_classes(self) -> int:\n # noinspection PyUnresolvedReferences\n return 200\n\n\nif __name__ == '__main__':\n # ds_ = CubDataset(load_bboxes=True)\n # print(ds_.data.columns)\n # print(ds_.data.iloc[100])\n #\n # ds_ = CubSegmentationDataset(load_bboxes=True)\n # print(ds_.data.columns)\n # print(ds_.data.iloc[100])\n # ds_100_ = ds_[100]\n # print(ds_100_[0].shape, ds_100_[3].shape)\n # print(ds_100_[3].min(), ds_100_[3].max())\n\n # Dataloader\n dl_ = CubDataLoader(train=True, batch_size=1, device='cpu')\n batch_ = next(iter(dl_))\n print(batch_[0].shape)\n\n import torchvision.transforms.functional as F\n import matplotlib.pyplot as plt\n\n # plt.imshow(F.to_pil_image(batch_[0][0]))\n plt.imshow(F.to_pil_image(dl_.vis_transforms(batch_[0][0])))\n plt.show()\n", "sub_path": "src/dataset/cub.py", "file_name": "cub.py", "file_ext": "py", "file_size_in_byte": 10142, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "PIL.Image.open", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 27, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 29, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 29, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 30, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 30, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomResizedCrop", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 36, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 36, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 37, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 37, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomResizedCrop", "line_number": 38, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 38, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 39, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 40, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 40, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 43, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 43, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 44, "usage_type": "name"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 45, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 45, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 46, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 46, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 49, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 50, "usage_type": "name"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 51, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 51, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 52, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 52, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 54, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 54, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 55, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 55, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 56, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 56, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 59, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 59, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 60, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 60, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomResizedCrop", "line_number": 61, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 61, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 62, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 62, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandAugment", "line_number": 63, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 63, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 64, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 70, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "utilities.path.data_path", "line_number": 80, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.utils.check_integrity", "line_number": 97, "usage_type": "call"}, {"api_name": "torchvision.datasets.utils.download_url", "line_number": 98, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 134, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 125, "usage_type": "name"}, {"api_name": "PIL.Image.Image", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 125, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.utils.check_integrity", "line_number": 153, "usage_type": "call"}, {"api_name": "torchvision.datasets.utils.download_url", "line_number": 154, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.folder.default_loader", "line_number": 165, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 161, "usage_type": "name"}, {"api_name": "PIL.Image.Image", "line_number": 161, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 161, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 162, "usage_type": "name"}, {"api_name": "PIL.Image.Image", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.utils.data.random_split", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 187, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 195, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 195, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 196, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 196, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 197, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 197, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 194, "usage_type": "attribute"}, {"api_name": "torchvision.transforms", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.to_pil_image", "line_number": 227, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}]} +{"seq_id": "630396731", "text": "import logging\nfrom abc import abstractmethod\n\nfrom commands import AddKarmaCommand\nfrom config import Config\nfrom model.karma import KarmaType\nfrom model.member import Member\nfrom slack_channel.abstract_event_handler import AbstractEventHandler\nfrom slack_channel.slack_parser import SlackParser\n\n\nclass AbstractKarmaEventHandler(AbstractEventHandler):\n\n @property\n @abstractmethod\n def name(self) -> str:\n pass\n\n @abstractmethod\n def _get_command_symbol(self) -> str:\n pass\n\n @property\n def command(self) -> AddKarmaCommand:\n return AddKarmaCommand()\n\n def get_usage(self):\n return self._get_command_symbol() + \" recipient [[for ] reason]\"\n\n @property\n def _help_message(self):\n return self._get_command_symbol() + \" -?\"\n\n def can_handle(self, slack_event):\n text = slack_event[\"text\"]\n return text.startswith(self._get_command_symbol())\n\n def _invoke_handler_logic(self, slack_event):\n try:\n command_text = slack_event['text']\n args = self._parse_command_text(command_text)\n self.command.execute(awarded_to=args[\"recipient\"],\n awarded_by=slack_event[\"user\"],\n reason=args[\"reason\"],\n karma_type=args[\"karma_type\"])\n self._send_reaction_response(slack_event)\n except Exception as ex:\n logging.exception(ex)\n\n def _parse_command_text(self, command_text):\n command_text = SlackParser.replace_slack_id_tokens_with_usernames(command_text)\n karma_type_arg = command_text[:2]\n karma_type = KarmaType.POZZYPOZ if karma_type_arg == \"++\" else KarmaType.NEGGYNEG\n command_text = command_text[3:]\n\n if command_text.find(\" for \") != -1:\n command_split = command_text.split(\" for \")\n recipient = self._parse_recipient(command_split[0].split(\" \"))\n else:\n command_split = command_text.split(\" \")\n recipient = self._parse_recipient(command_split)\n\n reason = self._parse_reason(command_text, recipient)\n\n return {\"recipient\": recipient, \"reason\": reason, \"karma_type\": karma_type}\n\n def _parse_recipient(self, command_split):\n possible_username = command_split[0]\n decided_username = \" \".join(command_split)\n\n username_is_known = self._username_is_known(possible_username)\n if username_is_known:\n decided_username = possible_username\n\n return decided_username\n\n @staticmethod\n def _username_is_known(username):\n m = Member.get_member_by_username(username)\n if m is not None:\n return True\n else:\n return False\n\n @staticmethod\n def _parse_reason(command_text, recipient):\n recipient_length = len(recipient) + 1 # +1 to account for space\n if command_text.find(\" for \") != -1:\n recipient_length += 4\n reason = command_text[recipient_length:]\n return reason\n\n def __init__(self, debug=False):\n self.config = Config()\n self.config.connect_to_db()\n super(AbstractKarmaEventHandler, self).__init__(debug)\n\n\nclass IncrementKarmaEventHandler(AbstractKarmaEventHandler):\n\n @property\n def name(self):\n return \"Pozzy-poz\"\n\n def _get_command_symbol(self):\n return \"++\"\n\n def __init__(self, debug=False):\n super(IncrementKarmaEventHandler, self).__init__(debug)\n\n\nclass DecrementKarmaEventHandler(AbstractKarmaEventHandler):\n\n @property\n def name(self):\n return \"Neggy-neg\"\n\n def _get_command_symbol(self):\n return \"--\"\n\n def __init__(self, debug=False):\n self.debug = debug\n super(DecrementKarmaEventHandler, self).__init__(debug)\n", "sub_path": "slack_channel/add_karma_event_handler.py", "file_name": "add_karma_event_handler.py", "file_ext": "py", "file_size_in_byte": 3831, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "slack_channel.abstract_event_handler.AbstractEventHandler", "line_number": 12, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 15, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 19, "usage_type": "name"}, {"api_name": "commands.AddKarmaCommand", "line_number": 25, "usage_type": "call"}, {"api_name": "commands.AddKarmaCommand", "line_number": 24, "usage_type": "name"}, {"api_name": "logging.exception", "line_number": 48, "usage_type": "call"}, {"api_name": "slack_channel.slack_parser.SlackParser.replace_slack_id_tokens_with_usernames", "line_number": 51, "usage_type": "call"}, {"api_name": "slack_channel.slack_parser.SlackParser", "line_number": 51, "usage_type": "name"}, {"api_name": "model.karma.KarmaType.POZZYPOZ", "line_number": 53, "usage_type": "attribute"}, {"api_name": "model.karma.KarmaType", "line_number": 53, "usage_type": "name"}, {"api_name": "model.karma.KarmaType.NEGGYNEG", "line_number": 53, "usage_type": "attribute"}, {"api_name": "model.member.Member.get_member_by_username", "line_number": 79, "usage_type": "call"}, {"api_name": "model.member.Member", "line_number": 79, "usage_type": "name"}, {"api_name": "config.Config", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "425140648", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport traceback as tb\nimport yaml\nimport time\nimport export_to_telegraph\nfrom common import telegraph_token\nimport re\n\nclass DBClass(object):\n def __init__(self, name, default = {}):\n self.name = \"db/%s.yaml\" % name\n try:\n with open(self.name) as f:\n self.db = yaml.load(f, Loader=yaml.FullLoader)\n except Exception as e:\n print(e)\n tb.print_exc()\n self.db = default\n\n def save(self):\n with open(self.name, 'w') as f:\n f.write(yaml.dump(self.db, sort_keys=True, indent=2, allow_unicode=True))\n\n\nclass _Source(DBClass):\n def __init__(self):\n super().__init__(\"source\")\n\n def add(self, chatname):\n if chatname not in self.db:\n self.db[chatname] = 0\n self.save()\n return 'success'\n return 'source already added'\n\n def remove(self, chatname):\n if chatname in self.db:\n self.db.pop(chatname, None)\n return 'success'\n return 'no such source'\n\n def iterate(self, chatname, max):\n while self.db[chatname] < max:\n self.db[chatname] += 1\n self.save()\n yield self.db[chatname]\n\nclass _Subscription(DBClass):\n def __init__(self):\n super().__init__(\"subscription\")\n\n def add(self, x, mode):\n if x not in self.db or self.db[x]['mode'] != mode:\n self.db[x] = mode\n self.save()\n\n def remove(self, x):\n self.db.pop(x, None)\n\ndef getLan(title):\n if re.search(u'[\\u4e00-\\u9fff]', title):\n return 'zh'\n return 'en'\n\nclass _Pool(DBClass):\n def __init__(self):\n super().__init__(\"pool\")\n\n def add(self, x):\n export_to_telegraph.token = telegraph_token\n r = export_to_telegraph.get(x)\n self.db[x] = {\n \"view\": r['views'],\n \"language\": getLan(r['title']),\n } \n self.save()\n\nclass _Sent(DBClass):\n def __init__(self):\n super().__init__(\"sent\")\n\n def forget(self, x):\n self.db.pop(x, None)\n self.save()\n\n def add(self, gid, url):\n if not gid in self.db:\n self.db[gid] = set()\n self.db[gid].add(url)\n self.save()\n\nSubscription = _Subscription()\nSent = _Sent()\nPool = _Pool()\nSource = _Source()", "sub_path": "db/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2357, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "yaml.load", "line_number": 16, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 16, "usage_type": "attribute"}, {"api_name": "traceback.print_exc", "line_number": 19, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 24, "usage_type": "call"}, {"api_name": "re.search", "line_number": 63, "usage_type": "call"}, {"api_name": "export_to_telegraph.token", "line_number": 72, "usage_type": "attribute"}, {"api_name": "common.telegraph_token", "line_number": 72, "usage_type": "name"}, {"api_name": "export_to_telegraph.get", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "406848331", "text": "from matplotlib import pyplot as plt\n\ndef visualize_images(legend, *args):\n if(len(args) == 0):\n return\n \n nrows, ncols = len(args), len(args[0]) \n fig, axs = plt.subplots(nrows=nrows, ncols=ncols) \n if nrows == 1:\n for row, images in enumerate(args):\n for col in range(ncols):\n axs[col].imshow(images[col], cmap=plt.cm.gray)\n return\n \n for row, images in enumerate(args): # for each bunch of objects\n for col, ax in enumerate(axs[row]): # for each object show it on correct row and col\n if (row == 0):\n ax.set_title(legend[col])\n \n ax.imshow(images[col], cmap=plt.cm.gray)\n ax.set_xticks([])\n\ndef visualize_one(title, image):\n plt.title(title)\n plt.imshow(image)\n\ndef visualize_pairs(*pairs):\n visualize_images((\"lr\", \"hr\"), *pairs)\n \ndef visualize_triplets(*triplets):\n visualize_images((\"lr\", \"prediction\", \"hr\"), *triplets)\n", "sub_path": "utils/visualization.py", "file_name": "visualization.py", "file_ext": "py", "file_size_in_byte": 994, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 12, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 20, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "518254489", "text": "# -*- coding: utf-8 -*-\n\nfrom datetime import date\nfrom odoo import models, fields, api, _\nfrom odoo.exceptions import UserError\nimport requests\n\n\nclass EmployeeVerification(models.Model):\n _name = 'employee.verification'\n _rec_name = 'verification_id'\n\n verification_id = fields.Char('ID', readonly=True, copy=False)\n employee = fields.Many2one('hr.employee', string='Employee', required=True, help='You can choose the employee for background verification')\n address = fields.Many2one(related='employee.address_home_id', string='Address', readonly=False)\n assigned_by = fields.Many2one('res.users', string='Assigned By', readonly=1, default=lambda self: self.env.uid)\n agency = fields.Many2one('res.partner', string='Agency', domain=[('verification_agent', '=', True)], help='You can choose a Verification Agent')\n resume_uploaded = fields.Many2many('ir.attachment', string=\"Resume of Applicant\",\n help='You can attach the copy of your document', copy=False)\n description_by_agency = fields.Char(string='Description', readonly=True)\n agency_attachment_id = fields.Many2one('ir.attachment', string='Attachment',help='Attachment from Agency')\n field_check = fields.Boolean(string='Check', invisible=True)\n assigned_date = fields.Date(string=\"Assigned Date\", readonly=True, default=date.today())\n expected_date = fields.Date(state='Expected Date', help='Expected date of completion of background varification')\n state = fields.Selection([\n ('draft', 'Draft'),\n ('assign', 'Assigned'),\n ('submit', 'Varification Completed'),\n ], string='Status', default='draft')\n company_id = fields.Many2one('res.company', 'Company',\n default=lambda self: self.env['res.company'].browse(1))\n\n\n \n def download_attachment(self):\n if self.agency_attachment_id:\n return {\n 'type': 'ir.actions.act_url',\n 'url': '/web/binary/image?model=ir.attachment&field=datas&id=%s&filename=%s' % (self.agency_attachment_id.id,self.agency_attachment_id.name),\n 'target': 'new',\n }\n else:\n raise UserError(_(\"No attachments available.\"))\n\n \n def assign_statusbar(self):\n if self.agency:\n if self.address or self.resume_uploaded:\n self.state = 'assign'\n template = self.env.ref('employee_background.assign_agency_email_template')\n self.env['mail.template'].browse(template.id).send_mail(self.id, force_send=True)\n else:\n raise UserError(_(\"There should be at least address or resume of the employee.\"))\n else:\n raise UserError(_(\"Agency is not assigned. Please select one of the Agency.\"))\n\n # sequence generation for employee verification\n @api.model\n def create(self, vals):\n seq = self.env['ir.sequence'].next_by_code('res.users') or '/'\n vals['verification_id'] = seq\n return super(EmployeeVerification, self).create(vals)\n\n \n def unlink(self):\n if self.state not in 'draft':\n raise UserError(_('You cannot delete the verification created.'))\n super(EmployeeVerification, self).unlink()\n", "sub_path": "employee_background/models/employee_verification.py", "file_name": "employee_verification.py", "file_ext": "py", "file_size_in_byte": 3266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "odoo.models.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 9, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 13, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 13, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 14, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 14, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 15, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 15, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 16, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 16, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 17, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "odoo.fields.Many2many", "line_number": 18, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 20, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 20, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 21, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 21, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 22, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 22, "usage_type": "name"}, {"api_name": "odoo.fields.Date", "line_number": 23, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 23, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 23, "usage_type": "name"}, {"api_name": "odoo.fields.Date", "line_number": 24, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 24, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 25, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 25, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 30, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 43, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 43, "usage_type": "call"}, {"api_name": "odoo.exceptions.UserError", "line_number": 53, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 53, "usage_type": "call"}, {"api_name": "odoo.exceptions.UserError", "line_number": 55, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 55, "usage_type": "call"}, {"api_name": "odoo.api.model", "line_number": 58, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 58, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 67, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "162304786", "text": "from flask import Flask, request, render_template, jsonify, flash, redirect\nfrom handlers import handle_amount, handle_firstCurr, handle_secondCurr\nfrom flashers import flash_conversion, flash_errors\n\n\n# the main app for the currency converter\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'currency12$$'\n\n\n# render index.html when in the root route\n\n@app.route('/')\ndef home():\n return render_template('index.html')\n\n\n# make a post request with the convertFrom, convertTo & amount values from the form.\n\n@app.route('/conversion', methods=['POST'])\ndef convert():\n errors = []\n\n firstCurr = request.form.get('convertFrom')\n secondCurr = request.form.get('convertTo')\n amount = request.form.get('amount')\n\n # check to see if all values are valid - if not, redirect and flash appropriate error message(s).\n handle_amount(errors, amount)\n handle_firstCurr(errors, firstCurr)\n handle_secondCurr(errors, secondCurr)\n\n # if all values are valid, redirect and flash the new converted value.\n if len(errors) > 0:\n flash_errors(errors)\n else:\n flash_conversion(firstCurr, secondCurr, amount)\n return redirect('/')\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1162, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "handlers.handle_amount", "line_number": 30, "usage_type": "call"}, {"api_name": "handlers.handle_firstCurr", "line_number": 31, "usage_type": "call"}, {"api_name": "handlers.handle_secondCurr", "line_number": 32, "usage_type": "call"}, {"api_name": "flashers.flash_errors", "line_number": 36, "usage_type": "call"}, {"api_name": "flashers.flash_conversion", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "347134097", "text": "import json\nimport time\n\nfrom query_db import get_phase_time, query_data, query_data_job, query_sample_data, query_current_job\nfrom process_data import process_data, process_data_job\n\n\ndef demo(read_client: object, sys_measurements: list, job_measurements: list) -> None:\n \"\"\"\n Convertor demo, it only process 10 minutes of data\n \"\"\"\n start = 1571346551\n half_day = 12 * 60 * 60\n end = start + half_day\n\n error_count = 0\n\n st = time.strftime(\"%Y-%m-%dT%H:%M:%SZ\", time.localtime(start))\n et = time.strftime(\"%Y-%m-%dT%H:%M:%SZ\", time.localtime(end))\n\n process_dict = [\n \"CPU_Temperature\",\n \"CPU_Usage\",\n \"Fan_Speed\",\n \"Inlet_Temperature\",\n \"Job_Info\",\n \"Memory_Usage\",\n \"Node_Power_Usage\",\n \"cluster_unified_metrics\",\n \"node_job_info\",\n \"system_metrics\"\n ]\n\n # print(\"-------------------------------------------------------\")\n # print(f\"All measurements :{len(job_measurements) + len(sys_measurements)}\")\n # print(f\"Numerical measurements :{len(process_dict)}\")\n # print(f\"Jobs measurements :{len(job_measurements)}\")\n # print(f\"Other measurements :{len(sys_measurements) - len(process_dict)}\")\n # print(\"-------------------------------------------------------\")\n\n # for mea in sys_measurements:\n # if mea in process_dict:\n # json_data = query_data(mea, read_client, st, et)\n # if json_data:\n # print(f\"Converting {mea}...\")\n # print(\"---- Original data point ----\")\n # print(json.dumps(json_data[0], indent=4))\n\n # converted_data_point = process_data(json_data[0], mea, error_count)\n # print(\"---- Converted data point ----\")\n # print(json.dumps(converted_data_point, indent=4))\n # print(\"-------------------------------------------------------\")\n # print(error_count)\n # data_points.append(converted_data_point)\n # print(json.dumps(data_points, indent=4))\n\n # # Convert job metrics\n # job_measurements = [\"i764687\", \"j-775882\", \"qu_1082110A434\"]\n # # data_points = []\n # for mea in job_measurements:\n # print(f\"Converting {mea}...\")\n # json_data = query_data_job(mea, read_client)\n # if json_data:\n # print(\"---- Original data point ----\")\n # print(json.dumps(json_data, indent=4))\n\n # converted_data_point = process_data_job(json_data, mea)\n # print(\"---- Converted data point ----\")\n # print(json.dumps(converted_data_point, indent=4))\n # print(\"-------------------------------------------------------\")\n\n current_job_data = query_current_job(read_client)\n print(json.dumps(current_job_data, indent=4))\n return", "sub_path": "tools/MBConvertor/demo.py", "file_name": "demo.py", "file_ext": "py", "file_size_in_byte": 2868, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "time.strftime", "line_number": 18, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 18, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 19, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 19, "usage_type": "call"}, {"api_name": "query_db.query_current_job", "line_number": 72, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "547789365", "text": "import yaml\n\n\nclass Compute:\n \"\"\"\n Represents a Compute resource\n \"\"\"\n\n def __init__(self, c_name, cores):\n self.name = c_name\n self.cores = cores\n self.free_cores = self.cores\n\n def __repr__(self):\n \"\"\"\n Pretty print Compute\n :return:\n \"\"\"\n return \"\" % (\n self.name, self.cores, self.free_cores)\n\n\nclass ComputeCreator:\n \"\"\"\n Compute Creator\n \"\"\"\n\n def __init__(self):\n self.computes = []\n\n def read_computes(self, computes_file):\n \"\"\"\n Reads yaml compute file and generates Compute objects\n :param computes_file: yaml compute file\n :return: list of Compute objects\n \"\"\"\n with open(computes_file, 'r') as c_file:\n try:\n for name, cores in yaml.load(c_file).items():\n compute = Compute(name, cores)\n self.computes.append(compute)\n except yaml.YAMLError as exc:\n raise exc\n return self.computes\n", "sub_path": "src/compute.py", "file_name": "compute.py", "file_ext": "py", "file_size_in_byte": 1076, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "yaml.load", "line_number": 39, "usage_type": "call"}, {"api_name": "yaml.YAMLError", "line_number": 42, "usage_type": "attribute"}]} +{"seq_id": "572514511", "text": "#!/usr/bin/env python3\nfrom robot_ros import RobotRos\nfrom robot import Robot\nimport rospy\nimport numpy as np\n\nKICK_TIMEOUT = 3\nGETUPFRONT_TIMEOUT = 7\nGETUPBACK_TIMEOUT = 10\ngoal_list = [np.array([2, 2, 0]), np.array([-2, 2, 0]), np.array([-2, -2, 0]), np.array([2, -2, 0])]\ncurrent_stage = 0\nrospy.init_node(\"soccer_strategy\")\n\nrobot = RobotRos(team=Robot.Team.FRIENDLY, role=Robot.Role.GOALIE, status=Robot.Status.READY, robot_name=\"robot1\")\n\nrospy.sleep(1)\nr = rospy.Rate(10)\nwhile rospy.get_param(\"walking_engine_ready\") == \"false\":\n r.sleep()\n\nwhile not rospy.is_shutdown():\n rostime = rospy.get_rostime().secs + rospy.get_rostime().nsecs * 1e-9\n if robot.status == Robot.Status.WALKING:\n # publish a goal robot.goal_position geometry_msgs/Pose2D to /robot_name/goal\n pass\n\n elif robot.status == Robot.Status.FALLEN_BACK:\n robot.terminate_walking_publisher.publish()\n robot.trajectory_publisher.publish(\"getupback\")\n robot.trajectory_complete = False\n robot.status = Robot.Status.TRAJECTORY_IN_PROGRESS\n print(\"getupback\")\n\n elif robot.status == Robot.Status.FALLEN_FRONT:\n robot.terminate_walking_publisher.publish()\n robot.trajectory_publisher.publish(\"getupfront\")\n robot.trajectory_complete = False\n robot.status = Robot.Status.TRAJECTORY_IN_PROGRESS\n print(\"getupback\")\n\n elif robot.status == Robot.Status.READY:\n robot.set_navigation_position(goal_list[current_stage])\n robot.status = Robot.Status.WALKING\n current_stage = (current_stage + 1) % 4\n\n elif robot.status == Robot.Status.TRAJECTORY_IN_PROGRESS:\n if robot.trajectory_complete:\n robot.status = Robot.Status.READY\n else:\n pass\n\n if robot.status != robot.previous_status:\n print(robot.robot_name + \" status changes to \" + str(robot.status))\n robot.previous_status = robot.status\n\n", "sub_path": "soccer_strategy/src/walking_demo.py", "file_name": "walking_demo.py", "file_ext": "py", "file_size_in_byte": 1928, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 12, "usage_type": "call"}, {"api_name": "robot_ros.RobotRos", "line_number": 14, "usage_type": "call"}, {"api_name": "robot.Robot.Team", "line_number": 14, "usage_type": "attribute"}, {"api_name": "robot.Robot", "line_number": 14, "usage_type": "name"}, {"api_name": "robot.Robot.Role", "line_number": 14, "usage_type": "attribute"}, {"api_name": "robot.Robot.Status", "line_number": 14, "usage_type": "attribute"}, {"api_name": "rospy.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "rospy.Rate", "line_number": 17, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 18, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 21, "usage_type": "call"}, {"api_name": "rospy.get_rostime", "line_number": 22, "usage_type": "call"}, {"api_name": "robot.status", "line_number": 23, "usage_type": "attribute"}, {"api_name": "robot.Robot.Status", "line_number": 23, "usage_type": "attribute"}, {"api_name": "robot.Robot", "line_number": 23, "usage_type": "name"}, {"api_name": "robot.status", "line_number": 27, "usage_type": "attribute"}, {"api_name": "robot.Robot.Status", "line_number": 27, "usage_type": "attribute"}, {"api_name": "robot.Robot", "line_number": 27, "usage_type": "name"}, {"api_name": "robot.terminate_walking_publisher.publish", "line_number": 28, "usage_type": "call"}, {"api_name": "robot.terminate_walking_publisher", "line_number": 28, "usage_type": "attribute"}, {"api_name": "robot.trajectory_publisher.publish", "line_number": 29, "usage_type": "call"}, {"api_name": "robot.trajectory_publisher", "line_number": 29, "usage_type": "attribute"}, {"api_name": "robot.trajectory_complete", "line_number": 30, "usage_type": "attribute"}, {"api_name": "robot.status", "line_number": 31, "usage_type": "attribute"}, {"api_name": "robot.Robot.Status", "line_number": 31, "usage_type": "attribute"}, {"api_name": "robot.Robot", "line_number": 31, "usage_type": "name"}, {"api_name": "robot.status", "line_number": 34, "usage_type": "attribute"}, {"api_name": "robot.Robot.Status", "line_number": 34, "usage_type": "attribute"}, {"api_name": "robot.Robot", "line_number": 34, "usage_type": "name"}, {"api_name": "robot.terminate_walking_publisher.publish", "line_number": 35, "usage_type": "call"}, {"api_name": "robot.terminate_walking_publisher", "line_number": 35, "usage_type": "attribute"}, {"api_name": "robot.trajectory_publisher.publish", "line_number": 36, "usage_type": "call"}, {"api_name": "robot.trajectory_publisher", "line_number": 36, "usage_type": "attribute"}, {"api_name": "robot.trajectory_complete", "line_number": 37, "usage_type": "attribute"}, {"api_name": "robot.status", "line_number": 38, "usage_type": "attribute"}, {"api_name": "robot.Robot.Status", "line_number": 38, "usage_type": "attribute"}, {"api_name": "robot.Robot", "line_number": 38, "usage_type": "name"}, {"api_name": "robot.status", "line_number": 41, "usage_type": "attribute"}, {"api_name": "robot.Robot.Status", "line_number": 41, "usage_type": "attribute"}, {"api_name": "robot.Robot", "line_number": 41, "usage_type": "name"}, {"api_name": "robot.set_navigation_position", "line_number": 42, "usage_type": "call"}, {"api_name": "robot.status", "line_number": 43, "usage_type": "attribute"}, {"api_name": "robot.Robot.Status", "line_number": 43, "usage_type": "attribute"}, {"api_name": "robot.Robot", "line_number": 43, "usage_type": "name"}, {"api_name": "robot.status", "line_number": 46, "usage_type": "attribute"}, {"api_name": "robot.Robot.Status", "line_number": 46, "usage_type": "attribute"}, {"api_name": "robot.Robot", "line_number": 46, "usage_type": "name"}, {"api_name": "robot.trajectory_complete", "line_number": 47, "usage_type": "attribute"}, {"api_name": "robot.status", "line_number": 48, "usage_type": "attribute"}, {"api_name": "robot.Robot.Status", "line_number": 48, "usage_type": "attribute"}, {"api_name": "robot.Robot", "line_number": 48, "usage_type": "name"}, {"api_name": "robot.status", "line_number": 52, "usage_type": "attribute"}, {"api_name": "robot.previous_status", "line_number": 52, "usage_type": "attribute"}, {"api_name": "robot.robot_name", "line_number": 53, "usage_type": "attribute"}, {"api_name": "robot.status", "line_number": 53, "usage_type": "attribute"}, {"api_name": "robot.previous_status", "line_number": 54, "usage_type": "attribute"}, {"api_name": "robot.status", "line_number": 54, "usage_type": "attribute"}]} +{"seq_id": "300868827", "text": "import os\nimport numpy as np\nimport math\nimport re\nimport datetime\n\nDAY = 1 # days\n\n\nclass Parser:\n CONFIG = None\n \n # shrink data size by averaging over a time period\n # twoD_list: list of values (one commit value for each day)\n # x: number of days to average on\n # returns list with of x elements from commit_list averaged\n @staticmethod\n def avg_x_days(twoD_list, x):\n def avg(commit_list):\n result = []\n for count in xrange(1, 1 + int(math.ceil(len(commit_list)/float(x)))):\n arr = commit_list[(count-1) * x: count * x]\n avg = sum(arr)/float(len(arr))\n result.append(avg)\n return result\n twoD_list_avg_x_days = []\n for word in twoD_list:\n twoD_list_avg_x_days.append(avg(word))\n return twoD_list_avg_x_days\n\n @staticmethod\n def add_dict_entry(dict_, key, num):\n if key in dict_:\n dict_[key].append(num)\n else:\n dict_[key] = [num]\n \n # Create file with list of top words\n @classmethod\n def write_word_file(cls, words):\n f = open(cls.CONFIG['DATA_DIR'] + 'commit_words.txt', 'w')\n for key in words:\n f.write(key + '\\n')\n f.close()\n\n # Create file with list of commit dates\n @classmethod\n def write_dates_file(cls, dates):\n def to_pretty_date(date):\n year,month,day = date.split('-')\n return datetime.date(int(year), int(month), int(day)).strftime('%B %d %Y')\n \n f = open(cls.CONFIG['DATA_DIR'] + 'commit_dates.txt', 'w')\n for date in dates:\n f.write(to_pretty_date(date) + '\\n')\n f.close()\n\n # process the input commit data file\n @classmethod\n def process_file(cls, file_name):\n f = open(file_name, 'r')\n repo_name = None\n word_dict = {}\n dates = []\n \n for line in f:\n if not repo_name:\n repo_name = line\n continue\n \n lineSplit = re.split('\\t| ', line)\n \n if len(lineSplit) == 2 and lineSplit[0] is not '%':\n word = lineSplit[0]\n num = int(lineSplit[1])\n Parser.add_dict_entry(word_dict, word, num)\n elif lineSplit[0] is '%':\n dates.append(lineSplit[1])\n f.close()\n # iterate over dictionary, make into array of values\n twoD_list = [word_dict[key] for key in word_dict]\n\n # Create file with list of commit words\n cls.write_word_file(word_dict)\n\n # Create file with list of commit dates\n cls.write_dates_file(dates)\n\n # shrink data size by averaging over a time period\n if cls.CONFIG['granularity'] > DAY:\n twoD_list = cls.avg_x_days(twoD_list, cls.CONFIG['granularity'])\n \n if cls.CONFIG['overlay_words']:\n return twoD_list\n else:\n # order 2D array structure as a list of date entries\n # with each date entry containing a list of entries for each word\n return np.flipud(np.rot90(twoD_list)).tolist()\n \n @classmethod\n def parse(cls):\n data_dir = cls.CONFIG['BASE_DIR']\n if (cls.CONFIG['test']):\n in_file = 'test.txt'\n data_dir += '/test/'\n else:\n in_file = 'commits.txt'\n data_dir += '/Analyzer/'\n data = []\n for file_name in os.listdir(data_dir):\n if file_name.endswith(in_file):\n data += cls.process_file(data_dir + file_name)\n return data\n \n", "sub_path": "WavMaker/Parser/Parser.py", "file_name": "Parser.py", "file_ext": "py", "file_size_in_byte": 3639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "math.ceil", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 51, "usage_type": "call"}, {"api_name": "re.split", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 98, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "271522202", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 11 13:03:23 2017\n\n@author: claire\n\"\"\"\n\n# set of functions\nimport mne\nfrom mne import io\n\n\ndef import_bdf(data_path, subject):\n # function to import bdf file and do basic preprocessing :\n# - chanloc and chan info\n# - ref to mastoids\n\n # import data\n raw = mne.io.read_raw_edf(data_path + subject + '_task.bdf', stim_channel=-1, misc=['EXG6', 'EXG7', 'EXG8', 'GSR1', 'GSR2', 'Erg1', 'Erg2', 'Resp', 'Plet', 'Temp'], preload=True)\n raw.rename_channels(mapping={'E1H1\\t//EXG1 HE ': 'EOG L', 'E2H2\\t//EXG2 HE ': 'EOG R', 'E3LE\\t//EXG3 LE ': 'EOG V L', 'E5M2\\t//EXG5 M2 ': 'M2', 'E4M1\\t//EXG4 M1 ': 'M1' })\n raw.set_channel_types(mapping={'EOG L': 'eog', 'EOG R': 'eog', 'EOG V L': 'eog'})\n \n raw, _ =mne.io.set_eeg_reference(raw, ref_channels=['M1', 'M2'])\n raw.info['bads'] = ['M1', 'M2']\n \n # get eletrodes loc\n montage= mne.channels.read_montage('standard_1020', path = '/home/claire/Appli/mne-python/mne/channels/data/montages/')\n raw.set_montage(montage)\n raw.interpolate_bads(reset_bads=False) # \n events = mne.find_events(raw, verbose=True)\n\n raw.pick_types(raw.info, eeg=True, eog=True, exclude='bads') \n\n return raw, events\n \n\n\n", "sub_path": "Old/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "mne.io.read_raw_edf", "line_number": 19, "usage_type": "call"}, {"api_name": "mne.io", "line_number": 19, "usage_type": "attribute"}, {"api_name": "mne.io.set_eeg_reference", "line_number": 23, "usage_type": "call"}, {"api_name": "mne.io", "line_number": 23, "usage_type": "attribute"}, {"api_name": "mne.channels.read_montage", "line_number": 27, "usage_type": "call"}, {"api_name": "mne.channels", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mne.find_events", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "150137041", "text": "import mysql.connector\nfrom mysql.connector import Error\nfrom mysql.connector import errorcode\nfrom random import seed\n#from random import random\nfrom random import randint\nimport random\nimport string\nimport serial\n\nser=serial.Serial(\"/dev/ttyUSB0\",9600)\nser.baudrate=9600\n\n#readSerial = ser.readline()\n#print(readSerial)\n\ndef randomString(stringLenght=8):\n letters = string.ascii_lowercase\n return ''.join(random.choice(letters) for i in range(stringLenght))\n\ndef InserirValoresNaTabela(inteiro, macaco):\n\n try:\n conn = mysql.connector.connect( \n host=\"localhost\",\n user=\"rushadores\",\n password=\"rushadores@123\",\n database=\"rushadores\"\n )\n cursor = conn.cursor()\n mySql_query = \"INSERT INTO python_teste (inteiro, macaco) VALUES (%s, %s)\"\n record = (inteiro, macaco)\n #cursor = conn.cursor()\n cursor.execute(mySql_query, record)\n conn.commit() \n #print(cursor.rowcount, \"Inserido com sucesso\")\n #cursor.close()\n\n except mysql.connector.Error as error:\n print(\"Erro {}\".format(error))\n \n finally:\n if(conn.is_connected()):\n conn.close()\n print(\"Conexao finalizada\")\n\n#InserirValoresNaTabela(randomString(), randomString())\nwhile 1:\n readSerial = ser.readline()\n print(\"Inserindo valores:\",readSerial,readSerial)\n InserirValoresNaTabela(readSerial, readSerial)\n#mycursor = mydb.cursor()\n#sql = \"INSERT INTO python_teste (inteiro) VALUES (1)\"\n#mycursor.execute(sql)\n#mydb.commit\n\n#print(mycursor.rowcount, \"rushadores\")\n", "sub_path": "LinkaTech-Python/testemysql.py", "file_name": "testemysql.py", "file_ext": "py", "file_size_in_byte": 1599, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "serial.Serial", "line_number": 11, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 18, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 19, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 24, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 24, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 24, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 39, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "464111415", "text": "import collections\nimport json\nimport logging\nimport sys\nimport time\n\nfrom django import db\nfrom django.core import exceptions as django_exceptions\nfrom django.http import Http404\n\nfrom rest_framework import request as api_request\nfrom ws4redis import publisher, redis_store\n\nfrom . import exceptions\nfrom .connection import get_queryobserver_settings\n\n# Logger.\nlogger = logging.getLogger(__name__) # pylint: disable=invalid-name\n\n# Observable method options attribute name prefix.\nOBSERVABLE_OPTIONS_PREFIX = 'observable_'\n\n\nclass Options(object):\n \"\"\"\n Query observer options.\n \"\"\"\n\n # Valid change detection types.\n CHANGE_DETECTION_PUSH = 'push'\n CHANGE_DETECTION_POLL = 'poll'\n\n def __init__(self, viewset, viewset_method):\n self._viewset = viewset\n self._viewset_method = viewset_method\n\n # Determine the primary key.\n self.primary_key = self.get_option('primary_key')\n if self.primary_key is None:\n # Primary key attribute is not defined, attempt to autodiscover it from the queryset.\n try:\n self.primary_key = viewset.get_queryset().model._meta.pk.name\n except AssertionError:\n # No queryset is defined.\n raise exceptions.MissingPrimaryKey(\n \"Observable method does not define a primary key and the viewset \"\n \"does not provide a queryset. Define a queryset or use the primary_key \"\n \"decorator.\"\n )\n\n # Determine change detection type.\n self.change_detection = self.get_option('change_detection', Options.CHANGE_DETECTION_PUSH)\n self.poll_interval = self.get_option('poll_interval')\n\n def get_option(self, name, default=None):\n return getattr(self._viewset_method, '{}{}'.format(OBSERVABLE_OPTIONS_PREFIX, name), default)\n\n\nclass QueryObserver(object):\n \"\"\"\n A query observer observes a specific viewset for changes and propagates these\n changes to all interested subscribers.\n \"\"\"\n\n # Valid observer statuses.\n STATUS_NEW = 'new'\n STATUS_INITIALIZING = 'initializing'\n STATUS_OBSERVING = 'observing'\n STATUS_STOPPED = 'stopped'\n\n # Valid message types.\n MESSAGE_ADDED = 'added'\n MESSAGE_CHANGED = 'changed'\n MESSAGE_REMOVED = 'removed'\n\n def __init__(self, pool, request):\n \"\"\"\n Creates a new query observer.\n\n :param pool: QueryObserverPool instance\n :param request: A `queryobserver.request.Request` instance\n \"\"\"\n\n self.status = QueryObserver.STATUS_NEW\n self._pool = pool\n\n # Obtain a serializer by asking the viewset to provide one. We instantiate the\n # viewset with a fake request, so that the viewset methods work as expected.\n viewset = request.viewset_class()\n viewset.request = api_request.Request(request)\n viewset.request.method = request.method\n viewset.format_kwarg = None\n viewset.args = request.args\n viewset.kwargs = request.kwargs\n self._viewset = viewset\n self._request = request\n self._viewset_method = getattr(viewset, request.viewset_method)\n self._meta = Options(viewset, self._viewset_method)\n\n self._evaluating = 0\n self._last_evaluation = None\n self._last_results = collections.OrderedDict()\n self._subscribers = set()\n self._dependencies = set()\n self._initialization_future = None\n self.id = request.observe_id\n\n def add_dependency(self, table):\n \"\"\"\n Registers a new dependency for this query observer.\n\n :param table: Name of the dependent database table\n \"\"\"\n\n if table in self._dependencies:\n return\n\n self._dependencies.add(table)\n self._pool.register_dependency(self, table)\n\n @property\n def stopped(self):\n \"\"\"\n True if the query observer has been stopped.\n \"\"\"\n\n return self.status == QueryObserver.STATUS_STOPPED\n\n @property\n def last_evaluation(self):\n \"\"\"\n Timestamp of last evaluation. May be None if the observer was\n never evaluated.\n \"\"\"\n\n return self._last_evaluation\n\n def evaluate(self, return_full=True, return_emitted=False):\n \"\"\"\n Evaluates the query observer and checks if there have been any changes. This function\n may yield.\n\n :param return_full: True if the full set of rows should be returned\n :param return_emitted: True if the emitted diffs should be returned\n \"\"\"\n\n # Sanity check (should never happen).\n if self._evaluating < 0:\n logger.error(\"Corrupted internal observer state: _evaluating < 0\")\n logger.error(\"Stopping observer: {}\".format(repr(self)))\n self.stop()\n return []\n\n if self._evaluating and not return_full:\n # Ignore evaluate requests if the observer is already being evaluated. Do\n # not ignore requests when full results are requested as in that case we\n # need to wait for the results (the caller needs them).\n return\n\n self._evaluating += 1\n\n try:\n # Increment evaluation statistics counter.\n self._pool._evaluations += 1\n self._pool._running += 1\n settings = get_queryobserver_settings()\n\n # After an update is processed, all incoming requests are batched until\n # the update batch delay passes. Batching is not performed when full\n # results are requested as in that case we want them as fast as possible.\n if self._last_evaluation is not None and not return_full:\n delta = time.time() - self._last_evaluation\n remaining = settings['update_batch_delay'] - delta\n\n if remaining > 0:\n try:\n self._pool._sleeping += 1\n\n # We assume that time.sleep has been patched and will correctly yield.\n time.sleep(remaining)\n finally:\n self._pool._sleeping -= 1\n\n start = time.time()\n result = self._evaluate(return_full, return_emitted)\n duration = time.time() - start\n self._last_evaluation = time.time()\n\n # Log slow observers.\n if duration > settings['warnings']['max_processing_time']:\n # pylint: disable=logging-format-interpolation\n logger.warning(\"Slow observer took {} seconds to evaluate.\".format(duration))\n logger.warning(\"Potentially slow observer is: {}\".format(repr(self)))\n\n # Stop really slow observers.\n if duration > settings['errors']['max_processing_time']:\n # pylint: disable=logging-format-interpolation\n logger.error(\"Stopping slow observer that took {} seconds to evaluate.\".format(duration))\n self.stop()\n\n return result\n except exceptions.ObserverStopped:\n return []\n except: # pylint: disable=bare-except\n # Stop crashing observers.\n self.stop()\n\n # pylint: disable=logging-format-interpolation\n logger.exception(\"Error while evaluating observer: {}\".format(repr(self)))\n return []\n finally:\n self._evaluating -= 1\n self._pool._running -= 1\n\n # Cleanup any leftover connections. This is something that should not be executed\n # during tests as it would terminate the database connection.\n is_testing = sys.argv[1:2] == ['test']\n if not is_testing:\n db.close_old_connections()\n\n def _evaluate(self, return_full=True, return_emitted=False):\n \"\"\"\n Evaluates the query observer and checks if there have been any changes. This function\n may yield.\n\n :param return_full: True if the full set of rows should be returned\n :param return_emitted: True if the emitted diffs should be returned\n \"\"\"\n\n if self.status == QueryObserver.STATUS_STOPPED:\n raise exceptions.ObserverStopped\n\n # Be sure to handle status changes before any yields, so that the other greenlets\n # will see the changes and will be able to wait on the initialization future.\n if self.status == QueryObserver.STATUS_INITIALIZING:\n self._initialization_future.wait()\n elif self.status == QueryObserver.STATUS_NEW:\n self._initialization_future = self._pool.backend.create_future()\n self.status = QueryObserver.STATUS_INITIALIZING\n\n # Evaluate the query (this operation yields).\n tables = set()\n stop_observer = False\n with self._pool.query_interceptor.intercept(tables):\n try:\n response = self._viewset_method(\n self._viewset.request,\n *self._request.args,\n **self._request.kwargs\n )\n\n if response.status_code == 200:\n results = response.data\n\n if not isinstance(results, list):\n if isinstance(results, dict):\n if 'results' in results and isinstance(results['results'], list):\n # Support paginated results.\n results = results['results']\n else:\n results[self._meta.primary_key] = 1\n results = [collections.OrderedDict(results)]\n else:\n raise ValueError(\"Observable views must return a dictionary or a list of dictionaries!\")\n else:\n results = []\n except Http404:\n results = []\n except django_exceptions.ObjectDoesNotExist:\n # The evaluation may fail when certain dependent objects (like users) are removed\n # from the database. In this case, the observer is stopped.\n stop_observer = True\n\n if stop_observer:\n self.stop()\n return []\n\n if self._meta.change_detection == Options.CHANGE_DETECTION_PUSH:\n # Register table dependencies for push observables.\n for table in tables:\n self.add_dependency(table)\n elif self._meta.change_detection == Options.CHANGE_DETECTION_POLL:\n # Register poller.\n self._pool.register_poller(self)\n else:\n raise NotImplementedError(\"Change detection mechanism '{}' not implemented.\".format(\n self._meta.change_detection\n ))\n\n # TODO: Only compute difference between old and new, ideally on the SQL server using hashes.\n new_results = collections.OrderedDict()\n\n if self.status == QueryObserver.STATUS_STOPPED:\n return []\n\n # Log viewsets with too much output.\n if len(results) > get_queryobserver_settings()['warnings']['max_result_length']:\n # pylint: disable=logging-format-interpolation\n logger.warning(\"Observed viewset returned {} results.\".format(len(results)))\n logger.warning(\"Potentially slow observer is: {}\".format(repr(self)))\n\n for order, row in enumerate(results):\n if not isinstance(row, dict):\n raise ValueError(\"Observable views must return a dictionary or a list of dictionaries!\")\n\n row._order = order\n try:\n new_results[row[self._meta.primary_key]] = row\n except KeyError:\n raise KeyError(\"Observable view did not return primary key field '{}'!\".format(self._meta.primary_key))\n\n # Process difference between old results and new results.\n added = []\n changed = []\n removed = []\n for row_id, row in self._last_results.iteritems():\n if row_id not in new_results:\n removed.append(row)\n\n for row_id, row in new_results.iteritems():\n if row_id not in self._last_results:\n added.append(row)\n else:\n old_row = self._last_results[row_id]\n if row != old_row:\n changed.append(row)\n if row._order != old_row._order:\n changed.append(row)\n\n self._last_results = new_results\n\n if self.status == QueryObserver.STATUS_INITIALIZING:\n self.status = QueryObserver.STATUS_OBSERVING\n if self._initialization_future is not None:\n future = self._initialization_future\n self._initialization_future = None\n future.set()\n elif self.status == QueryObserver.STATUS_OBSERVING:\n self.emit(added, changed, removed)\n\n if return_emitted:\n return (added, changed, removed)\n\n if return_full:\n return self._last_results.values()\n\n def emit(self, added, changed, removed):\n \"\"\"\n Notifies all subscribers about query changes.\n\n :param added: A list of rows there were added\n :param changed: A list of rows that were changed\n :param removed: A list of rows that were removed\n \"\"\"\n\n # TODO: Instead of duplicating messages to all subscribers, handle subscriptions within redis.\n for message_type, rows in (\n (QueryObserver.MESSAGE_ADDED, added),\n (QueryObserver.MESSAGE_CHANGED, changed),\n (QueryObserver.MESSAGE_REMOVED, removed),\n ):\n # Make a copy of the subscribers set as the publish operation may yield and modify the set.\n for subscriber in self._subscribers.copy():\n session_publisher = publisher.RedisPublisher(facility=subscriber, broadcast=True)\n for row in rows:\n session_publisher.publish_message(redis_store.RedisMessage(json.dumps({\n 'msg': message_type,\n 'observer': self.id,\n 'primary_key': self._meta.primary_key,\n 'order': getattr(row, '_order', None),\n 'item': row,\n })))\n\n def subscribe(self, subscriber):\n \"\"\"\n Adds a new subscriber.\n \"\"\"\n\n self._subscribers.add(subscriber)\n\n def unsubscribe(self, subscriber):\n \"\"\"\n Unsubscribes a specific subscriber to this query observer. If no subscribers\n are left, this query observer is stopped.\n \"\"\"\n\n try:\n self._subscribers.remove(subscriber)\n except KeyError:\n pass\n\n if not self._subscribers:\n self.stop()\n\n def stop(self):\n \"\"\"\n Stops this query observer.\n \"\"\"\n\n if self.status == QueryObserver.STATUS_STOPPED:\n return\n\n self.status = QueryObserver.STATUS_STOPPED\n self._last_results.clear()\n\n # Unregister all dependencies.\n for dependency in self._dependencies:\n self._pool.unregister_dependency(self, dependency)\n\n # Unsubscribe all subscribers.\n for subscriber in self._subscribers:\n self._pool._remove_subscriber(self, subscriber)\n\n self._pool._remove_observer(self)\n\n def __eq__(self, other):\n return self.id == other.id\n\n def __hash__(self):\n return hash(self.id)\n\n def __repr__(self):\n return ''.format(\n id=self.id,\n request=repr(self._request)\n )\n", "sub_path": "rest_framework_reactive/observer.py", "file_name": "observer.py", "file_ext": "py", "file_size_in_byte": 15741, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "rest_framework.request.Request", "line_number": 90, "usage_type": "call"}, {"api_name": "rest_framework.request", "line_number": 90, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 102, "usage_type": "call"}, {"api_name": "connection.get_queryobserver_settings", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 172, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 180, "usage_type": "call"}, {"api_name": "time.time", "line_number": 184, "usage_type": "call"}, {"api_name": "time.time", "line_number": 186, "usage_type": "call"}, {"api_name": "time.time", "line_number": 187, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 217, "usage_type": "attribute"}, {"api_name": "django.db.close_old_connections", "line_number": 219, "usage_type": "call"}, {"api_name": "django.db", "line_number": 219, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 262, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 267, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 269, "usage_type": "attribute"}, {"api_name": "django.core.exceptions", "line_number": 269, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 291, "usage_type": "call"}, {"api_name": "connection.get_queryobserver_settings", "line_number": 297, "usage_type": "call"}, {"api_name": "ws4redis.publisher.RedisPublisher", "line_number": 364, "usage_type": "call"}, {"api_name": "ws4redis.publisher", "line_number": 364, "usage_type": "name"}, {"api_name": "ws4redis.redis_store.RedisMessage", "line_number": 366, "usage_type": "call"}, {"api_name": "ws4redis.redis_store", "line_number": 366, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 366, "usage_type": "call"}]} +{"seq_id": "42874981", "text": "import os\nfrom typing import List, Mapping, Optional, Tuple\n\ncur_dir = os.path.dirname(os.path.abspath(__file__))\n\n\nAMPHIPOD_COSTS = {\n \"A\": 1,\n \"B\": 10,\n \"C\": 100,\n \"D\": 1000,\n}\n\nHALLWAY_Y = 1\nHOLE_X = [3, 5, 7, 9]\n\n\nclass Point:\n def __init__(self, x: int, y: int) -> None:\n self.x = x\n self.y = y\n\n def as_tuple(self) -> Tuple[int, int]:\n return self.x, self.y\n\n def __hash__(self) -> int:\n return hash(self.as_tuple())\n\n def __eq__(self, __o: object) -> bool:\n if not isinstance(__o, Point):\n return NotImplemented\n return self.as_tuple() == __o.as_tuple()\n\n def path_to_horiz(self, end_pt: \"Point\") -> List[\"Point\"]:\n if self.y != end_pt.y:\n raise Exception()\n direction = 1\n if end_pt.x < self.x:\n direction = -1\n\n pts = [\n Point(x, self.y)\n for x in range(self.x + direction, end_pt.x + direction, direction)\n ]\n return pts\n\n def path_to_vert(self, end_pt: \"Point\") -> List[\"Point\"]:\n if self.x != end_pt.x:\n raise Exception()\n direction = 1\n if end_pt.y < self.y:\n direction = -1\n\n pts = [\n Point(self.x, y)\n for y in range(self.y + direction, end_pt.y + direction, direction)\n ]\n return pts\n\n def path_to(self, end_pt: \"Point\") -> List[\"Point\"]:\n # if end_pt.y < self.y:\n # interm = Point(self.x, end_pt.y)\n # return self.path_to_vert(interm) + interm.path_to_horiz(end_pt)\n # else:\n # interm = Point(end_pt.x, self.y)\n # return self.path_to_horiz(interm) + interm.path_to_vert(end_pt)\n if self.x == end_pt.x:\n return self.path_to_vert(end_pt)\n interm1 = Point(self.x, HALLWAY_Y)\n interm2 = Point(end_pt.x, HALLWAY_Y)\n\n return (\n self.path_to_vert(interm1)\n + interm1.path_to_horiz(interm2)\n + interm2.path_to_vert(end_pt)\n )\n\n def __repr__(self) -> str:\n return str(self.as_tuple())\n\n\ndef generate_open_points(positions: Mapping[Point, str]) -> List[Point]:\n return [x for x in positions if positions[x] == \".\"]\n\n\ndef generate_amphipod_positions(\n positions: Mapping[Point, str]\n) -> Mapping[str, Tuple[Point, Point]]:\n amphipod_positions = {}\n for x in AMPHIPOD_COSTS:\n amphipod_positions[x] = []\n\n for x in positions:\n if positions[x] in AMPHIPOD_COSTS:\n amphipod_positions[positions[x]].append(x)\n\n return {\n x: (amphipod_positions[x][0], amphipod_positions[x][1])\n for x in amphipod_positions\n }\n\n\ndef compute_heuristic_single_type_no_hole(\n amphipod_type: str, pt1: Point, pt2: Point\n) -> int:\n hole_dists = [abs(pt1.x - hx) + abs(pt2.x - hx) for hx in HOLE_X]\n min_dist = min(hole_dists)\n\n # Plus 3 to go down into hole\n return AMPHIPOD_COSTS[amphipod_type] * (min_dist + 3)\n\n\ndef compute_heuristic_single_type(amphipod_type: str, pt1: Point, pt2: Point) -> int:\n heuristic_dist = 0\n if pt1.y == HALLWAY_Y and pt2.y == HALLWAY_Y:\n return compute_heuristic_single_type_no_hole(amphipod_type, pt1, pt2)\n if pt1.x == pt2.x:\n # This is a simplification, but since amphipods cannot park outside a hole it is valid\n return 0\n if pt1.y == HALLWAY_Y + 2:\n end_pt = pt1\n start_pt = pt2\n elif pt2.y == HALLWAY_Y + 2:\n end_pt = pt2\n start_pt = pt1\n elif pt1.y == HALLWAY_Y + 1:\n end_pt = pt1\n start_pt = pt2\n heuristic_dist += 2\n elif pt2.y == HALLWAY_Y + 1:\n end_pt = pt2\n start_pt = pt1\n heuristic_dist += 2\n else:\n raise Exception(\"This should not happen.\")\n\n interm = Point(start_pt.x, HALLWAY_Y)\n\n path = start_pt.path_to(interm) + interm.path_to(end_pt)\n\n # -1 because we want to end up next to, not on\n heuristic_dist += len(path) - 1\n\n return AMPHIPOD_COSTS[amphipod_type] * heuristic_dist\n\n\ndef compute_heuristic(amphipod_positions: Mapping[str, Tuple[Point, Point]]) -> int:\n return sum(\n [\n compute_heuristic_single_type(\n x, amphipod_positions[x][0], amphipod_positions[x][1]\n )\n for x in amphipod_positions\n ]\n )\n\n\ndef generate_unique_id(positions: Mapping[Point, str]) -> str:\n hallway_pts = [x.x for x in positions if x.y == HALLWAY_Y]\n hallway_min_x = min(hallway_pts)\n hallway_max_x = max(hallway_pts)\n\n identifier = \"\"\n\n for x in range(hallway_min_x, hallway_max_x + 1):\n identifier += positions[Point(x, HALLWAY_Y)]\n\n for x in HOLE_X:\n for y in [HALLWAY_Y + 1, HALLWAY_Y + 2]:\n identifier += positions[Point(x, y)]\n\n return identifier\n\n\nclass BoardState:\n def __init__(\n self,\n positions: Mapping[Point, str],\n cost: int = 0,\n parent: Optional[\"BoardState\"] = None,\n ) -> None:\n self.parent = parent\n self.positions = positions\n self.cost = cost\n self.open_points = generate_open_points(self.positions)\n self.amphipod_positions = generate_amphipod_positions(self.positions)\n self.heuristic = compute_heuristic(self.amphipod_positions)\n self.uid = generate_unique_id(self.positions)\n\n def estimated_cost(self) -> int:\n return self.cost + self.heuristic\n\n def __hash__(self) -> int:\n return hash(self.uid)\n\n def __eq__(self, __o: object) -> bool:\n if not isinstance(__o, BoardState):\n return NotImplemented\n return self.uid == __o.uid\n\n def is_valid_destination(\n self, amphipod_type: str, start: Point, end: Point\n ) -> bool:\n if end.x in HOLE_X and end.y == HALLWAY_Y:\n return False\n if (\n end.y == HALLWAY_Y + 1\n and self.positions[Point(end.x, end.y + 1)] != amphipod_type\n ):\n return False\n if start.y == HALLWAY_Y and end.y == HALLWAY_Y:\n return False\n if (\n start.y == HALLWAY_Y + 1\n and self.positions[Point(start.x, start.y + 1)] == amphipod_type\n ):\n return False\n return True\n\n def is_path_open(self, start: Point, end: Point) -> bool:\n path = start.path_to(end)\n\n for x in path:\n if self.positions[x] != \".\":\n return False\n\n return True\n\n def generate_next_valid_boards(self) -> List[\"BoardState\"]:\n next_boards = []\n\n for x in self.amphipod_positions:\n for start_pt in self.amphipod_positions[x]:\n for end_pt in self.open_points:\n\n # print(f\"Move {start_pt} to {end_pt}\")\n\n if self.is_valid_destination(\n self.positions[start_pt], start_pt, end_pt\n ) and self.is_path_open(start_pt, end_pt):\n new_positions = {x: self.positions[x] for x in self.positions}\n new_positions[end_pt] = self.positions[start_pt]\n new_positions[start_pt] = \".\"\n path_len = len(start_pt.path_to(end_pt))\n cost = (\n self.cost\n + AMPHIPOD_COSTS[self.positions[start_pt]] * path_len\n )\n\n next_boards.append(BoardState(new_positions, cost, self))\n\n return next_boards\n\n def visualize(self) -> None:\n for y in range(HALLWAY_Y + 4):\n for x in range(14):\n if Point(x, y) in self.positions:\n print(self.positions[Point(x, y)], end=\"\")\n else:\n print(\"#\", end=\"\")\n print(\"\")\n\n\npts = {}\nwith open(f\"{cur_dir}/sample_input\") as f:\n for y, line in enumerate(f):\n for x, c in enumerate(line.rstrip()):\n if c in [\".\", \"A\", \"B\", \"C\", \"D\"]:\n pts[Point(x, y)] = c\n\nprint(pts)\nstate = BoardState(pts)\nstate.visualize()\n\nstates = [state]\nvisited_states = set()\nsolution_found = len([x for x in states if x.heuristic == 0]) != 0\nwhile not solution_found:\n state = states[0]\n print(state.estimated_cost())\n state.visualize()\n states = states[1:]\n\n visited_states.add(state)\n\n next_states = state.generate_next_valid_boards()\n next_states = [x for x in next_states if x not in visited_states]\n\n states += next_states\n\n states.sort(key=lambda x: x.estimated_cost())\n solution_found = len([x for x in states if x.heuristic == 0]) != 0\n if solution_found:\n solution = [x for x in states if x.heuristic == 0][0]\n\n states = [x for x in states if x not in visited_states]\n\n # for x in states:\n # print(x.estimated_cost())\n # x.visualize()\n\nprint(solution.cost)\n\npath = []\nwhile solution is not None:\n path.append(solution)\n solution = solution.parent\nprint(\"Full path\")\nfor x in reversed(path):\n print(x.cost)\n x.visualize()\n", "sub_path": "2021/23/part1.py", "file_name": "part1.py", "file_ext": "py", "file_size_in_byte": 9021, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 4, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 87, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 147, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 147, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 158, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 178, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 180, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 229, "usage_type": "name"}]} +{"seq_id": "159536571", "text": "import requests, hashlib, time, random, sys, OpenSSL\nfrom OpenSSL import crypto\nimport base64\nkey_file = open(\"private.pem\", \"r\")\nkey = key_file.read()\nkey_file.close()\n\npkey = crypto.load_privatekey(crypto.FILETYPE_PEM, key)\n\nchave = sys.argv[1]\nauth = hashlib.sha256(chave.encode('utf-8')).hexdigest()\n\nprint ('auth medidor: ' + auth)\n\nurl = 'https://sc-gir.rhcloud.com/modulo_entrada'\n\n\t\nwhile True:\n\tvalor = random.uniform(2,4)\n\tvalor_sign = OpenSSL.crypto.sign(pkey, valor, \"sha256\") \n\ttimestamp = time.time()\n\ttimestamp_sign = OpenSSL.crypto.sign(pkey, timestamp, \"sha256\") \n\t\n\t\n\tdata = {\n\t\t'valor': valor,\n\t\t'valor_sign': base64.b64encode(valor_sign),\n\t\t'timestamp': timestamp,\n\t\t'timestamp_sign': base64.b64encode(timestamp_sign),\n\t\t'auth': auth\n\t}\n\t\t\n\tr = requests.post(url, data)\n\ttime.sleep(1)\n\t\n\tprint (r.text)\n", "sub_path": "medidor-new.py", "file_name": "medidor-new.py", "file_ext": "py", "file_size_in_byte": 824, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "OpenSSL.crypto.load_privatekey", "line_number": 8, "usage_type": "call"}, {"api_name": "OpenSSL.crypto", "line_number": 8, "usage_type": "name"}, {"api_name": "OpenSSL.crypto.FILETYPE_PEM", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "hashlib.sha256", "line_number": 11, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 19, "usage_type": "call"}, {"api_name": "OpenSSL.crypto.sign", "line_number": 20, "usage_type": "call"}, {"api_name": "OpenSSL.crypto", "line_number": 20, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "OpenSSL.crypto.sign", "line_number": 22, "usage_type": "call"}, {"api_name": "OpenSSL.crypto", "line_number": 22, "usage_type": "attribute"}, {"api_name": "base64.b64encode", "line_number": 27, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 33, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "170877367", "text": "from django.shortcuts import render\nfrom user.models import UserSensorDetail\nfrom sensor_owner.models import SensorDetail\nfrom django.core.paginator import Paginator\nfrom django.core.paginator import EmptyPage\nfrom django.core.paginator import PageNotAnInteger\n\n\ndef is_sensor_present(request, sensor_data):\n if UserSensorDetail.objects.filter(user_name=request.user, sensor_id=sensor_data).exists():\n return True\n else:\n return False\n\n\ndef manage_sensors(request):\n user_sensor_data = UserSensorDetail.objects.filter(user_name=request.user)\n available_sensor_data = SensorDetail.objects.all()\n\n # Show 5 user sensors per page\n paginator = Paginator(user_sensor_data, 5)\n user_sensor_page = request.GET.get('user-sensor-page')\n try:\n user_sensors = paginator.page(user_sensor_page)\n except PageNotAnInteger:\n # If sensor_page is not an integer, deliver first sensor_page.\n user_sensors = paginator.page(1)\n except EmptyPage:\n # If sensor_page is out of range (e.g. 9999), deliver last sensor_page of results.\n user_sensors = paginator.page(paginator.num_pages)\n\n # Show 5 available_sensors per sensor_page\n paginator = Paginator(available_sensor_data, 5)\n sensor_page = request.GET.get('sensor-page')\n try:\n available_sensors = paginator.page(sensor_page)\n except PageNotAnInteger:\n available_sensors = paginator.page(1)\n except EmptyPage:\n available_sensors = paginator.page(paginator.num_pages)\n return render(request, 'manage_sensors.html', {'user_sensors': user_sensors,\n 'available_sensors': available_sensors,\n 'user_sensor_page': \"user-sensor-page\",\n 'sensor_page':\"sensor-page\"})\n\n\ndef add_sensor(request, pk):\n available_sensor_data = SensorDetail.objects.all()\n sensor_data = available_sensor_data.get(id=pk)\n current_user = request.user\n error_message = ''\n success_message = ''\n if is_sensor_present(request, sensor_data):\n error_message = 'Sensor already subscribed'\n else:\n new_sensor = UserSensorDetail()\n new_sensor.user_name = current_user\n new_sensor.sensor_id = sensor_data\n new_sensor.save()\n success_message = 'Sensor with ID '+ str(sensor_data.sensor_id) + ' subscribed'\n\n user_sensor_data = UserSensorDetail.objects.filter(user_name=request.user)\n return render(request, 'manage_sensors.html', {'user_sensors': user_sensor_data, 'available_sensors': available_sensor_data,\n 'error_message': 'Sensor already subscribed', 'error_message': error_message,\n 'success_message': success_message})\n\n\ndef delete_sensor(request, pk):\n sensor = UserSensorDetail.objects.get(id=pk)\n sensor.delete()\n\n user_sensor_data = UserSensorDetail.objects.filter(user_name=request.user)\n available_sensor_data = SensorDetail.objects.all()\n success_message = 'Sensor deleted'\n\n return render(request, 'manage_sensors.html', {'user_sensors': user_sensor_data, 'available_sensors': available_sensor_data,\n 'success_message': success_message})\n\n\ndef sensor_data_table(request):\n all_available_sensors = SensorDetail.objects.all()\n paginator = Paginator(all_available_sensors, 2) # Show 25 available_sensors per page\n\n page = request.GET.get('page-available')\n try:\n available_sensors = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer, deliver first page.\n available_sensors = paginator.page(1)\n except EmptyPage:\n # If page is out of range (e.g. 9999), deliver last page of results.\n available_sensors = paginator.page(paginator.num_pages)\n\n return render(request, 'manage_sensors.html', {'available_sensors': available_sensors})\n\n", "sub_path": "manage_sensors/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4019, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "user.models.UserSensorDetail.objects.filter", "line_number": 10, "usage_type": "call"}, {"api_name": "user.models.UserSensorDetail.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "user.models.UserSensorDetail", "line_number": 10, "usage_type": "name"}, {"api_name": "user.models.UserSensorDetail.objects.filter", "line_number": 17, "usage_type": "call"}, {"api_name": "user.models.UserSensorDetail.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "user.models.UserSensorDetail", "line_number": 17, "usage_type": "name"}, {"api_name": "sensor_owner.models.SensorDetail.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "sensor_owner.models.SensorDetail.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sensor_owner.models.SensorDetail", "line_number": 18, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 21, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 25, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 28, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 33, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 37, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 39, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "sensor_owner.models.SensorDetail.objects.all", "line_number": 48, "usage_type": "call"}, {"api_name": "sensor_owner.models.SensorDetail.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sensor_owner.models.SensorDetail", "line_number": 48, "usage_type": "name"}, {"api_name": "user.models.UserSensorDetail", "line_number": 56, "usage_type": "call"}, {"api_name": "user.models.UserSensorDetail.objects.filter", "line_number": 62, "usage_type": "call"}, {"api_name": "user.models.UserSensorDetail.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "user.models.UserSensorDetail", "line_number": 62, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}, {"api_name": "user.models.UserSensorDetail.objects.get", "line_number": 69, "usage_type": "call"}, {"api_name": "user.models.UserSensorDetail.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "user.models.UserSensorDetail", "line_number": 69, "usage_type": "name"}, {"api_name": "user.models.UserSensorDetail.objects.filter", "line_number": 72, "usage_type": "call"}, {"api_name": "user.models.UserSensorDetail.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "user.models.UserSensorDetail", "line_number": 72, "usage_type": "name"}, {"api_name": "sensor_owner.models.SensorDetail.objects.all", "line_number": 73, "usage_type": "call"}, {"api_name": "sensor_owner.models.SensorDetail.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "sensor_owner.models.SensorDetail", "line_number": 73, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}, {"api_name": "sensor_owner.models.SensorDetail.objects.all", "line_number": 81, "usage_type": "call"}, {"api_name": "sensor_owner.models.SensorDetail.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sensor_owner.models.SensorDetail", "line_number": 81, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 82, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 87, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 90, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "565111769", "text": "import base64\nimport string\n\nciphertext = base64.b64decode(\"2xv1pdJYc54wu4Q+YJeRAMc10dOc2Ragr0Fb3YpOv/4=\")\n\nplaintext1 = b'Texp8BQv'\nall_string = string.digits + string.ascii_letters\n\nfor i in all_string:\n for j in all_string:\n for k in all_string:\n iv = key = \"ZeroA\" + i + j + k\n des = DES.new(key, DES.MODE_CBC, iv)\n plaintext = des.decrypt(ciphertext)\n if plaintext.startswith(b'Texp8BQv'):\n print(plaintext)", "sub_path": "crypto2_script.py", "file_name": "crypto2_script.py", "file_ext": "py", "file_size_in_byte": 481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "base64.b64decode", "line_number": 4, "usage_type": "call"}, {"api_name": "string.digits", "line_number": 7, "usage_type": "attribute"}, {"api_name": "string.ascii_letters", "line_number": 7, "usage_type": "attribute"}]} +{"seq_id": "129540347", "text": "import time\n\nfrom django.core.cache import cache\nfrom django.http import HttpResponse\nfrom django.shortcuts import redirect, render\nfrom django.urls import reverse\nfrom django.utils.deprecation import MiddlewareMixin\n\n\nclass HelloMiddleware(MiddlewareMixin):\n def process_request(self,request):\n print('有个小崽子在访问你，他的IP是:'+request.META.get('REMOTE_ADDR'))\n # request.path能够返回对方访问的url\n # print(request.path)\n # 黑名单配置\n if request.path == '/hello/':\n ip = request.META.get('REMOTE_ADDR')\n # if cache.get(ip):\n # return HttpResponse(\"10S之后再来，一直访问个屁啊\")\n # cache.set(ip,ip,timeout=10)\n # if request.META.get('REMOTE_ADDR') == '10.0.122.202':\n # return HttpResponse('滚犊子')\n black_list = cache.get(ip,[])\n requests = cache.get(ip,[])\n if ip in black_list:\n return HttpResponse('滚')\n else:\n while requests and time.time() - requests[-1] > 60:\n requests.pop()\n requests.insert(0, time.time())\n cache.set(ip,requests, timeout=60)\n if len(requests) > 30:\n black_list.append(ip)\n cache.set(ip,black_list,timeout=60*60*24)\n return HttpResponse('屏蔽了谢谢')\n if len(requests) > 10:\n return HttpResponse('你这也太急了吧')\n\n def process_exception(self,request,exception):\n print(\"出现错误\")\n return render(request,'404.html')\n ", "sub_path": "middleware/middlewares.py", "file_name": "middlewares.py", "file_ext": "py", "file_size_in_byte": 1680, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "django.utils.deprecation.MiddlewareMixin", "line_number": 10, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 23, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 23, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 24, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 24, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 26, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "time.time", "line_number": 30, "usage_type": "call"}, {"api_name": "django.core.cache.cache.set", "line_number": 31, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 31, "usage_type": "name"}, {"api_name": "django.core.cache.cache.set", "line_number": 34, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 34, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 35, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 37, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "143115135", "text": "\"\"\"\n# ! /usr/bin/python3.6\n# -------------------------------------------------------------------\n# file_module.py\n# -------------------------------------------------------------------\n A class which enables you to automate some of file functions easily\n still in development\n tested only on unix\n Read from and write to .csv .txt .docx .pdf files\n Search files in a library\nTODO:the home path should be in your .bashrc file in ~/\n \"\"\"\n# home_path='' # define your home path here\nimport csv\nimport os, sys\nimport glob\nimport click\nimport shutil\nimport pandas as pd\nfrom datetime import datetime\nfrom PyPDF2 import PdfFileReader, PdfFileWriter\nimport json\n\n\nclass FilesError(Exception):\n \"\"\"Base Exception\"\"\"\n pass\n\n\nclass BasicWritingError(FilesError):\n \"\"\"Any error encountered during writing of the files using normal method\"\"\"\n pass\n\n\nclass ReadingError(FilesError):\n \"\"\"Reading errors using normal methods\"\"\"\n pass\n\n\nclass FileError(FilesError):\n \"\"\" basic error in any file operation\"\"\"\n pass\n\n\nclass FileNamingError(FilesError):\n \"\"\" file naming error \"\"\"\n pass\n\n\nclass NoFileFoundError(FileError):\n \"\"\"file not found error\"\"\"\n pass\n\n\nclass FileCopyingError(FileError):\n \"\"\"if any error occurs during copying of files or folders\"\"\"\n pass\n\n\nclass InvalidPathError(FileError):\n \"\"\"Invalid path raise an error\"\"\"\n pass\n\n\ndef path_validator(path):\n global debug\n if os.path.exists(path) and os.path.isfile(path) and os.path.getsize(path) > 0:\n debug = 0 # no error\n else:\n debug = 1\n return debug\n\n\nclass file_Module:\n\n def __init__(self, path_=os.getcwd(), data='', file_name='', mode='r', delimiter_=';'):\n modes = {\"r\": \"r\", \"a\": \"a\", \"w\": \"w\"}\n self.path = path_\n if mode not in modes:\n raise ValueError(\"mode must be either r,w or a\")\n self.mode = mode\n self.data = data\n self.file_name = file_name\n self.delimiter_ = delimiter_\n\n # -----------------------\n # Common File Functions\n # -----------------------\n @staticmethod\n def write_read_files(data='', path=os.getcwd(), file_type='.txt', mode='r'):\n \"\"\"\n write/append data to .txt files or .json files\n :param data: the data you want to write if mode = 'w' default_path=current_dir\n :param path: the path of the file you want to read/write\n :param file_type: .txt\n :param mode: read/write\n :return:\n \"\"\"\n global msg\n try:\n if mode == 'w':\n if os.path.isfile(path):\n \"\"\"such a file exists so just \"\"\"\n if file_type == '.txt':\n with open(path, mode='a') as data:\n data.write('\\n')\n data.write(data)\n msg = 'success'\n print(msg)\n elif file_type == '.json':\n with open(path, mode='a') as json_:\n\n json.dump(json_, data, separators=' ')\n msg = 'success'\n print(msg)\n\n else:\n \"\"\"file does not exist\"\"\"\n if file_type == '.txt':\n with open(path, mode='w') as data:\n data.write(data)\n msg = 'success'\n elif file_type == '.json':\n with open(path, mode='w') as json_:\n json.dump(json_, data, separators=' ')\n msg = 'success'\n print(msg)\n elif mode == 'r':\n \"\"\"read the file\"\"\"\n if os.path.exists(path):\n\n with open(path) as content:\n if content.readable():\n while True:\n content = content.read()\n if content == '':\n break\n print(content)\n print('')\n print('\\ndone reading')\n else:\n raise FilesError(\"the file is not readable\")\n\n else:\n raise FileNotFoundError(f\"The {path} file does not exist \")\n except IOError as e:\n msg = 'failed'\n print(msg)\n if e.errno != e.ENOENT:\n raise\n\n def file_info(self):\n \"\"\"\n give information about a file in a dir\n * The file name\n * The location of the file\n * The size of the file in bytes\n * The creation time: Day example.Mon Month Day in numerals example.11 Hour (24hr system) Minutes Seconds Year\n * Last Accessed Time : Day example.Mon Month Day in numerals example.11 Hour (24hr system) Minutes Seconds Year\n :return str info\"\"\"\n debug = path_validator(self.path)\n if debug is 0:\n\n last_time_accessed = datetime.fromtimestamp(os.path.getatime(self.path)).strftime(\"%c\")\n creation_time = datetime.fromtimestamp(os.path.getctime(self.path)).strftime(\"%c\")\n size_in_bytes = os.path.getsize(self.path)\n \"\"\"df=pd.DataFrame()\n df['file_name']=self.path\n df['creation_time']=creation_time\n df['last_access_time']=last_time_accessed\n df['size_in_bytes']=size_in_bytes\n print(df)\"\"\"\n info = f\"\"\"\n Information about: {os.path.basename(self.path)}\n Location: {self.path}\n Size in bytes: {size_in_bytes} bytes\n Creation time: {creation_time}\n Last Access Time: {last_time_accessed}\n \"\"\"\n print(info)\n return info\n else:\n raise FileError(\"The given path points to a dir not a file or the given path is invalid !!\")\n\n @staticmethod\n def create_folder(path):\n global msg\n try:\n os.makedirs(path)\n msg = 'success'\n print(msg)\n except OSError as e:\n msg = 'failed '\n print(e, file=sys.stderr)\n\n @staticmethod\n def file_sorter(path, file_type='', sort_method='alphabetical'):\n \"\"\"\n takes a path as its param & sorts all of the files in the given dir and print them in their alphabetical order\n file_type: optional param if the user wants to sort specific types of files\n nb:not yet finished\n :param sort_method:alphabetical\n :param path:\n :param file_type:\n :return: list of the sorted files\n \"\"\"\n list_ = []\n\n if os.path.exists(path) and os.path.isdir(path):\n # check whether the path points to an existing\n # dir else raise a FileError\n # scan through the dir\n for entry in os.scandir(path):\n if entry.is_file():\n list_.append(entry.name)\n item = [str(item).capitalize() for item in list_]\n item.sort()\n print(item)\n return item\n\n else:\n raise FileError(\"The given path points to a file not a dir or the given path is invalid !!\")\n\n @staticmethod\n def file_open_default(path: str):\n \"\"\"\n uses the default applications present to launch some files example pdfs ,images, audios & videos\n :param path: path of the file to be opened\n :return:\n \"\"\"\n if os.path.exists(path) and os.path.isfile(path):\n click.launch(path, locate=True)\n else:\n raise FileNotFoundError(\"The file does not exist!\")\n\n @staticmethod\n def file_search(pattern='.*', file_name='', path=os.getcwd()):\n \"\"\"\n search for file in the current dir path : current dir\n pattern: example *.py (any file ending with .py) or .*\n (any file starting with . eg .bashrc patterns *.txt / ?.gif/ .c*\n file_name: name of the file you are looking for\n recursive: False\n :param pattern:\n :param file_name:\n :param path:\n :return:\n \"\"\"\n\n global list_\n with os.scandir(os.getcwd()) as entries:\n for entry in entries:\n list_ = glob.glob(pattern)\n if not list_:\n for entry in os.scandir(os.getcwd()):\n if entry.name == file_name:\n print(entry.name)\n else:\n raise FileNotFoundError('The pattern/filename given does not match any file')\n for file in list_:\n print(file)\n return file\n\n @staticmethod\n def delete_files_folder(path: str):\n \"\"\"\n deletes files or dirs in the given path\n :param path:\n :return: msg success or failure\n \"\"\"\n msg = ''\n if os.path.exists(path) and os.path.isfile(path):\n os.remove(path)\n msg = f'success, {os.path.basename(path)} deleted successfully'\n elif os.path.exists(path) and os.path.isfile(path):\n os.rmdir(path)\n msg = f'{os.path.dirname(path)} deleted successfully !'\n else:\n msg = f\"\"\"an error occurred deletion failed .The {os.path.dirname(\n path)} path does not exist or the {os.path.basename(\n path)} file does not exist or it is already deleted!\"\"\"\n print(msg)\n return msg\n\n @staticmethod\n def copy_files(src: str, dst: str, stat_info=False):\n \"\"\"\n # src is the path of the file whose contents are to be copied\n # dst is the path of he file/ dir where the contents are copied to\n # assert that the path actually exists and the src is not a directory\n # stat_info: False the stat_info of the src file is not shown if True then it is shown\n :param src:\n :param dst:\n :param stat_info:\n :return: msg:success or failure\n \"\"\"\n global msg\n if os.path.isfile(src) and os.path.exists(src) and os.path.getsize(src) > 0 and os.path.exists(dst):\n try:\n if stat_info:\n path_ = shutil.copy(src, dst)\n creation_time = datetime.fromtimestamp(os.path.getctime(path_)).strftime(\"%c\")\n last_access_time = datetime.fromtimestamp(os.path.getatime(path_)).strftime(\"%c\")\n size_in_bytes = os.path.getsize(path_)\n msg = f'''\n Successfully copied to : {path_}\n Information about: {os.path.basename(path_)}\n Creation time: {creation_time}\n Last Acess Time: {last_access_time}\n File_size(bytes): {size_in_bytes}\n '''\n\n print(msg)\n else:\n path_ = shutil.copy2(src, dst)\n msg = f'successfully copied to: {path_}'\n print(msg)\n except shutil.Error as e:\n msg = 'failure'\n print(e, file=sys.stderr)\n else:\n msg = 'failure'\n raise FileCopyingError(f\"The {src} is a directory or the {src}/{dst} do not exist!! \")\n return msg\n\n @staticmethod\n def copy_directory(src: str, dst: str):\n \"\"\"\n # src is the path of the dir whose contents are to be copied\n # dst is the path of the dir where the contents are copied to,\n # note: the dst must not exist\n # assert that the path actually exists and the src is not a directory\n :param src: source path\n :param dst: destination path\n :return: msg:success or failure\n \"\"\"\n global msg\n if os.path.exists(src) and os.path.isdir(src):\n if not os.path.exists(dst):\n try:\n path_ = shutil.copytree(src, dst)\n creation_time = datetime.fromtimestamp(os.path.getctime(path_)).strftime(\"%c\")\n last_access_time = datetime.fromtimestamp(os.path.getatime(path_)).strftime(\"%c\")\n msg = f'''\n Successfully copied to : {path_}\n Information about: {os.path.basename(path_)}\n Creation time: {creation_time}\n Last Acess Time: {last_access_time}\n '''\n print(msg)\n except shutil.Error as e:\n msg = 'failure'\n print(e, file=sys.stderr)\n else:\n raise FileCopyingError(f\"{dst} exist! The destination folder should not exist\")\n else:\n raise FileCopyingError(f\"The {src} does not exist or it is not a dir!!!\")\n\n # -----------------------\n # CSV File Functions\n # -----------------------\n def writing_csv(self):\n \"\"\"create a csv in the path given\n if the given path does not point to an existing csv file create a new csv file\n else append the data given to the existing csv file\n assert that the path exists else raise an os_error\"\"\"\n try:\n if os.path.exists(self.path) and os.path.isfile(self.path):\n # there exists a file in the given path thus\n # just append the data\n with open(self.path, newline='', mode='a') as f:\n appender = csv.writer(f, delimiter=self.delimiter_)\n for line in self.data:\n appender.writerow(line)\n print(\"data written successfully\")\n # end of data\n elif os.path.exists(self.path) and os.path.isdir(self.path):\n # there is no file in the existing dir\n # so create a new csv file\n file_name = self.path + '/' + self.file_name\n if not file_name.endswith('.csv'):\n raise FileNamingError('The filename must end with a .csv')\n # using the wrong suffix raise a FileNamingError else write a new file in the given path\n with open(file_name, newline='', mode='w') as f:\n writer = csv.writer(f, delimiter=self.delimiter_)\n for line in self.data:\n writer.writerow(line)\n print(\"data written successfully\")\n else:\n print(\"failed to write data\", file=sys.stderr)\n raise BasicWritingError(\"The path given is not valid or the path does not point to an existing file\")\n\n except csv.Error as e:\n print(e)\n\n def write_csv_from_dict(self, dic: dict):\n \"\"\"\n create a csv file from a dictionary\n :param dic:\n :return: msg\n \"\"\"\n global msg\n if os.path.exists(self.path):\n try:\n df = pd.DataFrame.from_dict(dic, orient=\"index\")\n df.to_csv(self.path)\n msg = f\"\"\"\n Data :{dic} written successfully\n \"\"\"\n except BasicWritingError as e:\n msg = \"failed\"\n print(e)\n else:\n msg = \"failed\"\n raise InvalidPathError(f\"The path {self.path} given is invalid\")\n return msg\n\n def read_csv_data(self):\n try:\n with open(self.path, newline='') as csv_data:\n content = csv.reader(csv_data, delimiter=self.delimiter_)\n for row in content:\n if row == '':\n break\n print(row)\n print('')\n print(\"data written successfully\")\n except csv.Error as e:\n print(e, file=sys.stderr)\n\n def read_csv_from_dict(self):\n if os.path.exists(self.path) and os.path.isfile(self.path):\n try:\n with open(self.path, newline='') as csv_data:\n reader = csv.DictReader(csv_data)\n for row in reader:\n print(row)\n except csv.Error as e:\n print(e)\n else:\n raise ReadingError(f\"The path : {self.path} given does not point to an existing file or does not exist!\")\n\n def convert_csv_to_dict(self):\n if os.path.isfile(self.path) and os.path.exists(self.path):\n try:\n data = pd.read_csv(self.path)\n data = data.to_dict()\n print(data)\n except csv.Error as e:\n print(e, file=sys.stderr)\n else:\n raise BasicWritingError(f\"The path: {self.path} does not exist or does not point to an existing file\")\n\n # -----------------------\n # PDF File Functions\n # -----------------------\n\n def pdf_reader(self, page_number=0):\n \"\"\"\n nb: This function may not work well with all types of pdf thus is does not open call\n file_open_default\n :param page_number:\n :return:\n \"\"\"\n if os.path.exists(self.path) and os.path.isfile(self.path):\n content = PdfFileReader(self.path)\n content = content.getPage(page_number)\n page_content = content.extractText()\n print(page_content)\n else:\n raise ReadingError(f\"The path : {self.path} given does not point to an existing file or does not exist!\")\n\n def extract_information(self):\n\n if os.path.isfile(self.path) and os.path.exists(self.path):\n with open(self.path, 'rb') as f:\n pdf = PdfFileReader(f)\n information = pdf.getDocumentInfo()\n number_of_pages = pdf.getNumPages()\n txt = f'''\n\n Information about {self.path}: \n Author: {information.author}\n Creator: {information.creator}\n Subject: {information.subject}\n Title: {information.subject}\n Number of pages: {number_of_pages}\n\n '''\n print(txt)\n return information\n else:\n\n raise BasicWritingError(\n f\"The path : {self.path} given does not point to an existing file or does not exist!\")\n\n @staticmethod\n def create_watermark(input_pdf, output, watermark):\n \"\"\"\n\n :param input_pdf:\n :param output:\n :param watermark:\n :return:\n \"\"\"\n watermark_obj = PdfFileReader(watermark)\n watermark_page = watermark_obj.getPage(0)\n pdf_reader = PdfFileReader(input_pdf)\n pdf_writer = PdfFileWriter()\n for page in range(pdf_reader.getNumPages()):\n page = pdf_reader.getPage(page)\n page.mergePage(watermark_page)\n pdf_writer.addPage(page)\n try:\n with open(output, 'wb') as out:\n pdf_writer.write(out)\n except Exception as e:\n msg = \"failed to create a watermark the file \"\n print(msg)\n print(e)\n\n @staticmethod\n def add_encrypt(input_pdf, output_pdf, password):\n \"\"\"\n encrypts a pdf with the password of your choice\n :param input_pdf: the path of the pdf you want to encrypt\n :param output_pdf: the path of where you want to save the pdf,the input_pdf path can be the same as the output_pdf path\n :param password: your desired password\n :return:\n \"\"\"\n pdf_writer = PdfFileWriter()\n pdf_reader = PdfFileReader(input_pdf)\n for page in range(pdf_reader.getNumPages()):\n pdf_writer.addPage(pdf_reader.getPage(page))\n pdf_writer.encrypt(user_pwd=password, owner_pwd='', use_128bit=True)\n try:\n with open(output_pdf, 'wb') as f:\n pdf_writer.write(f)\n msg = \"successfully encrypted\"\n print(msg)\n except Exception as e:\n msg = \"failed to encrypt the file \"\n print(msg)\n print(e)\n", "sub_path": "file_manager.py", "file_name": "file_manager.py", "file_ext": "py", "file_size_in_byte": 20200, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 67, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 76, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 113, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 164, "usage_type": "name"}, {"api_name": "os.path.getatime", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 165, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 165, "usage_type": "name"}, {"api_name": "os.path.getctime", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 189, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 194, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 209, "usage_type": "call"}, {"api_name": "os.scandir", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 231, "usage_type": "call"}, {"api_name": "click.launch", "line_number": 232, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 237, "usage_type": "call"}, {"api_name": "os.scandir", "line_number": 251, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 251, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 253, "usage_type": "call"}, {"api_name": "os.scandir", "line_number": 255, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path", "line_number": 272, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 272, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 273, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 274, "usage_type": "call"}, {"api_name": "os.path", "line_number": 274, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 275, "usage_type": "call"}, {"api_name": "os.path", "line_number": 275, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 275, "usage_type": "call"}, {"api_name": "os.rmdir", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 277, "usage_type": "call"}, {"api_name": "os.path", "line_number": 277, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path", "line_number": 279, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 280, "usage_type": "call"}, {"api_name": "os.path", "line_number": 280, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path", "line_number": 298, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 298, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 301, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 302, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 302, "usage_type": "name"}, {"api_name": "os.path.getctime", "line_number": 302, "usage_type": "call"}, {"api_name": "os.path", "line_number": 302, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 303, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 303, "usage_type": "name"}, {"api_name": "os.path.getatime", "line_number": 303, "usage_type": "call"}, {"api_name": "os.path", "line_number": 303, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 304, "usage_type": "call"}, {"api_name": "os.path", "line_number": 304, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 307, "usage_type": "call"}, {"api_name": "os.path", "line_number": 307, "usage_type": "attribute"}, {"api_name": "shutil.copy2", "line_number": 315, "usage_type": "call"}, {"api_name": "shutil.Error", "line_number": 318, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 320, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 338, "usage_type": "call"}, {"api_name": "os.path", "line_number": 338, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 338, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 339, "usage_type": "call"}, {"api_name": "os.path", "line_number": 339, "usage_type": "attribute"}, {"api_name": "shutil.copytree", "line_number": 341, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 342, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 342, "usage_type": "name"}, {"api_name": "os.path.getctime", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path", "line_number": 342, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 343, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 343, "usage_type": "name"}, {"api_name": "os.path.getatime", "line_number": 343, "usage_type": "call"}, {"api_name": "os.path", "line_number": 343, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 346, "usage_type": "call"}, {"api_name": "os.path", "line_number": 346, "usage_type": "attribute"}, {"api_name": "shutil.Error", "line_number": 351, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 353, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path", "line_number": 368, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 368, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 372, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 377, "usage_type": "call"}, {"api_name": "os.path", "line_number": 377, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 377, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 385, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 390, "usage_type": "attribute"}, {"api_name": "csv.Error", "line_number": 393, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 403, "usage_type": "call"}, {"api_name": "os.path", "line_number": 403, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 405, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 405, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 421, "usage_type": "call"}, {"api_name": "csv.Error", "line_number": 428, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 429, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 432, "usage_type": "call"}, {"api_name": "os.path", "line_number": 432, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 432, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 435, "usage_type": "call"}, {"api_name": "csv.Error", "line_number": 438, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 444, "usage_type": "call"}, {"api_name": "os.path", "line_number": 444, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 444, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 446, "usage_type": "call"}, {"api_name": "csv.Error", "line_number": 449, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 450, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 465, "usage_type": "call"}, {"api_name": "os.path", "line_number": 465, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 465, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 466, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 475, "usage_type": "call"}, {"api_name": "os.path", "line_number": 475, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 475, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 477, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 506, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 508, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileWriter", "line_number": 509, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileWriter", "line_number": 531, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 532, "usage_type": "call"}]} +{"seq_id": "387768309", "text": "# Importing necessary libraries\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\nimport urllib\r\nimport re\r\n\r\n# Getting html response in \"response\" variable\r\nresponse = urllib.request.urlopen(\r\n \"http://cs230.stanford.edu/proj-spring-2018.html\")\r\n\r\n# Parsing html response via beautifulsoup\r\nsoup = BeautifulSoup(response, \"lxml\")\r\nsoup.prettify\r\n\r\n# Declaring empty lists to store scraped data\r\nnames, posters, reports, link_reports, link_posters, save_r, save_p = [\r\n], [], [], [], [], [], []\r\n\r\n# Finding all links in \"ul\" class\r\na = soup.findAll(\"ul\")\r\nfor i in a:\r\n b = i.findAll(\"li\")\r\n\r\n# Collecting project names, report urls and poster urls from links\r\nfor i in b:\r\n names.append(i.find(\"strong\").text)\r\n reports.append(i.find(\"a\")[\"href\"].lstrip(\".\"))\r\n try:\r\n posters.append(i.findAll(\"a\")[1][\"href\"].lstrip(\".\"))\r\n except:\r\n pass\r\n# Removing special characters from project names\r\nfor i, j in enumerate(names):\r\n names[i] = re.sub(r\"[^a-zA-Z0-9]\", \"\", j)\r\n\r\n# Joining url and downloading response of each report and poster(pdf)\r\nfor i in reports:\r\n link_reports.append(\"http://cs230.stanford.edu{}\".format(i))\r\nfor i in posters:\r\n link_posters.append(\"http://cs230.stanford.edu{}\".format(i))\r\nfor i in link_reports:\r\n save_r.append(requests.get(i))\r\nfor i in link_posters:\r\n save_p.append(requests.get(i))\r\n\r\n# Writing downloaded response in file format\r\nfor i, j in enumerate(save_r):\r\n with open(\"{}_report.pdf\".format(names[i]), \"wb\") as fp:\r\n fp.write(j.content)\r\nfor i, j in enumerate(save_p):\r\n with open(\"{}_posters.pdf\".format(names[i]), \"wb\") as fp:\r\n fp.write(j.content)\r\n\r\n\r\n \r\n", "sub_path": "scraping_stanford.py", "file_name": "scraping_stanford.py", "file_ext": "py", "file_size_in_byte": 1670, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "urllib.request.urlopen", "line_number": 8, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 8, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 12, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 42, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "447473396", "text": "from collections import namedtuple\nfrom typing import Optional, List, Tuple, Union, Type\n\nfrom bs4 import BeautifulSoup\nfrom django.conf import settings\nfrom django.core.cache import cache\nfrom django.db.models import signals\nfrom django.utils.timezone import now\nfrom requests import Response\nfrom requests.exceptions import ConnectionError\n\nfrom .utils import make_soup, get_from_url, run_threads\n\nif False: # pragma: nocover\n from ..generics.realms import RealmBase\n from ..generics.models import RealmBaseModel\n from ..models import PartnerLink, ModelWithPartnerLinks\n\n_PARTNERS_REGISTRY: Optional[dict] = None\n_CACHE_TIMEOUT: int = 28800 # 8 часов\n\n\nclass PartnerBase:\n \"\"\"Базовый класс для работы с партнёрскими сайтами.\"\"\"\n\n ident: str = None\n title: str = None\n link_mutator: str = None\n\n def __init__(self, partner_id: str):\n self.partner_id = partner_id\n\n def get_link_data(self, realm: 'RealmBase', link: 'PartnerLink') -> dict:\n \"\"\"Возвращает словарь с данными партнёрской ссылки.\n\n :param realm:\n :param link:\n\n \"\"\"\n link_url = link.url\n\n link_mutator = self.link_mutator.replace('{partner_id}', self.partner_id)\n\n if '?' in link_url and link_mutator.startswith('?'):\n link_mutator = link_mutator.replace('?', '&')\n\n url = f'{link_url}{link_mutator}'\n\n title = f'{realm.model.get_verbose_name()} на {self.title}'\n description = link.description\n\n if description:\n title = f'{title} — {description}'\n\n page_soup = self.get_page_soup(link_url)\n\n if not page_soup:\n return {}\n\n price = self.get_price(\n page_soup\n ).lower().strip(' .').replace('руб', 'руб.').replace('₽', 'руб.').strip()\n\n if price.isdigit():\n price += ' руб.'\n\n data = {\n 'icon_url': f'https://favicon.yandex.net/favicon/{self.title}',\n 'title': title,\n 'url': url,\n 'price': price,\n 'time': now()\n }\n return data\n\n @classmethod\n def get_page(cls, url: str) -> Response:\n return get_from_url(url, timeout=20)\n\n @classmethod\n def get_page_soup(cls, url: str) -> Optional[BeautifulSoup]:\n\n try:\n page = cls.get_page(url)\n\n except ConnectionError:\n return\n\n return make_soup(page.text)\n\n @classmethod\n def get_price(cls, page_soup: BeautifulSoup) -> str:\n return ''\n\n\nclass BooksRu(PartnerBase):\n \"\"\"Класс реализует работу по партнёрской программе сайта books.ru.\"\"\"\n\n ident: str = 'booksru'\n title: str = 'books.ru'\n link_mutator: str = '?partner={partner_id}'\n\n @classmethod\n def get_price(cls, page_soup: BeautifulSoup) -> str:\n\n price = ''\n\n if page_soup:\n matches = page_soup.select('h3.book-price')\n\n if matches:\n price = matches[0].text\n\n return price\n\n\nclass LitRes(PartnerBase):\n \"\"\"Класс реализует работу по партнёрской программе сайта litres.ru.\"\"\"\n\n ident: str = 'litres'\n title: str = 'litres.ru'\n link_mutator: str = '?lfrom={partner_id}'\n\n @classmethod\n def get_price(cls, page_soup: BeautifulSoup) -> str:\n\n price = ''\n\n if page_soup:\n matches = page_soup.select('.simple-price')\n\n if matches:\n price = matches[0].text\n\n return price\n\n\nclass Ozon(PartnerBase):\n \"\"\"Класс реализует работу по партнёрской программе сайта ozon.ru.\"\"\"\n\n ident: str = 'ozon'\n title: str = 'ozon.ru'\n link_mutator: str = '?partner={partner_id}'\n\n @classmethod\n def get_price(cls, page_soup: BeautifulSoup) -> str:\n\n price = ''\n\n if page_soup:\n matches = page_soup.findAll('span', attrs={'itemprop': 'price', 'class': 'hidden'})\n\n if matches:\n price = matches[0].text\n\n return price\n\n\nclass ReadRu(PartnerBase):\n \"\"\"Класс реализует работу по партнёрской программе сайта ozon.ru.\"\"\"\n\n ident: str = 'readru'\n title: str = 'read.ru'\n link_mutator: str = '?pp={partner_id}'\n\n @classmethod\n def get_price(cls, page_soup: BeautifulSoup) -> str:\n\n price = ''\n\n if page_soup:\n matches = page_soup.select('.read2__book_price__fullprice')\n\n if not matches:\n matches = page_soup.select('.book_price3__fullprice')\n\n if matches:\n price = matches[0].text\n if price:\n try:\n price = price.encode('latin1').decode('cp1251').strip().split(' ')[0]\n except UnicodeEncodeError:\n pass\n\n return price\n\n\nclass LabirintRu(PartnerBase):\n \"\"\"Класс реализует работу по партнёрской программе сайта labirint.ru.\"\"\"\n\n ident: str = 'labirint'\n title: str = 'labirint.ru'\n link_mutator: str = '?p={partner_id}'\n\n @classmethod\n def get_price(cls, page_soup: BeautifulSoup) -> str:\n\n price = ''\n\n if page_soup:\n matches = page_soup.select('.buying-price-val-number')\n\n if matches:\n price = matches[0].text\n\n return price\n\n\ndef get_cache_key(instance: 'RealmBaseModel') -> str:\n \"\"\"Возвращает ключ записи кэша для указанного экземпляра сущности.\n\n :param instance:\n\n \"\"\"\n return f'partner_links|{instance.__class__.__name__}|{instance.pk}'\n\n\ndef init_partners_module():\n \"\"\"Инициализирует объекты известных партнёров и заносит их в реестр.\"\"\"\n\n global _PARTNERS_REGISTRY\n\n if _PARTNERS_REGISTRY is not None:\n return\n\n _PARTNERS_REGISTRY = {}\n\n PARTNER_CLASSES = [BooksRu, LitRes, Ozon, ReadRu, LabirintRu]\n\n partners_settings = settings.PARTNER_IDS\n\n for partner_class in PARTNER_CLASSES:\n ident = partner_class.ident\n if ident in partners_settings:\n _PARTNERS_REGISTRY[ident] = partner_class(partners_settings[ident])\n\n from ..models import PartnerLink\n\n def partner_links_cache_invalidate(*args, **kwargs):\n \"\"\"Сбрасывает кеш партнёрских ссылок при изменении данных\n моделей ссылок или их удалении.\n\n \"\"\"\n cache_key = get_cache_key(kwargs.get('instance').linked_object)\n cache.delete(cache_key)\n\n signals.post_save.connect(partner_links_cache_invalidate, sender=PartnerLink, weak=False)\n signals.post_delete.connect(partner_links_cache_invalidate, sender=PartnerLink, weak=False)\n\n\ninit_partners_module()\n\n\ndef get_partners_choices() -> List[Tuple[str, str]]:\n \"\"\"Возвращает варианты выбора известных партнёров для раскрывающихся списков.\"\"\"\n\n choices = []\n\n for partner in _PARTNERS_REGISTRY.values():\n choices.append((partner.ident, partner.title))\n\n return choices\n\n\ndef get_partner_links(realm: Type['RealmBase'], item: Union['RealmBaseModel', 'ModelWithPartnerLinks']) -> dict:\n \"\"\"Возвращает словарь с данными по партнёрским ссылкам,\n готовый для передачи в шаблон.\n\n :param realm:\n :param item:\n\n \"\"\"\n cache_key = get_cache_key(item)\n links_data = cache.get(cache_key)\n\n Task = namedtuple('Task', ['link', 'realm', 'partner'])\n\n def contribute_info(task: Task):\n data = task.partner.get_link_data(task.realm, task.link)\n\n if data:\n links_data.append(data)\n\n if links_data is None:\n\n links_data = []\n tasks = []\n\n for link in item.partner_links.order_by('partner_alias', 'description').all():\n partner = _PARTNERS_REGISTRY.get(link.partner_alias)\n\n if partner:\n tasks.append(Task(\n link=link,\n realm=realm,\n partner=partner\n ))\n\n if tasks:\n run_threads(tasks, contribute_info)\n\n cache.set(cache_key, links_data, _CACHE_TIMEOUT)\n\n return {'links': links_data}\n", "sub_path": "pythonz/apps/integration/partners.py", "file_name": "partners.py", "file_ext": "py", "file_size_in_byte": 8565, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "typing.Optional", "line_number": 19, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.get_from_url", "line_number": 78, "usage_type": "call"}, {"api_name": "requests.Response", "line_number": 77, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 86, "usage_type": "name"}, {"api_name": "utils.make_soup", "line_number": 89, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 81, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 81, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 92, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 104, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 125, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 146, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 167, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 196, "usage_type": "name"}, {"api_name": "django.conf.settings.PARTNER_IDS", "line_number": 230, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 230, "usage_type": "name"}, {"api_name": "django.core.cache.cache.delete", "line_number": 245, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 245, "usage_type": "name"}, {"api_name": "django.db.models.signals.post_save.connect", "line_number": 247, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 247, "usage_type": "attribute"}, {"api_name": "django.db.models.signals", "line_number": 247, "usage_type": "name"}, {"api_name": "models.PartnerLink", "line_number": 247, "usage_type": "name"}, {"api_name": "django.db.models.signals.post_delete.connect", "line_number": 248, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_delete", "line_number": 248, "usage_type": "attribute"}, {"api_name": "django.db.models.signals", "line_number": 248, "usage_type": "name"}, {"api_name": "models.PartnerLink", "line_number": 248, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 254, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 254, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 265, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 265, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 274, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 274, "usage_type": "name"}, {"api_name": "collections.namedtuple", "line_number": 276, "usage_type": "call"}, {"api_name": "utils.run_threads", "line_number": 300, "usage_type": "call"}, {"api_name": "django.core.cache.cache.set", "line_number": 302, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 302, "usage_type": "name"}]} +{"seq_id": "199064850", "text": "import json\nimport logging\n\nimport venusian\nfrom pyramid.exceptions import ConfigurationError\nfrom pyramid.httpexceptions import HTTPForbidden\nfrom pyramid.httpexceptions import HTTPNotFound\nfrom pyramid.response import Response\nfrom pyramid.security import NO_PERMISSION_REQUIRED\n\nfrom pyramid_rpc.api import MapplyViewMapper\nfrom pyramid_rpc.api import ViewMapperArgsInvalid\n\n\nlog = logging.getLogger(__name__)\n\n\nclass JsonRpcError(Exception):\n code = -32603 # sane default\n message = 'internal error' # sane default\n data = None\n\n def __init__(self, code=None, message=None, data=None):\n if code is not None:\n self.code = code\n if message is not None:\n self.message = message\n if data is not None:\n self.data = data\n\n def as_dict(self):\n \"\"\"Return a dictionary representation of this object for\n serialization in a JSON-RPC response.\"\"\"\n error = dict(code=self.code,\n message=self.message)\n if self.data is not None:\n error['data'] = self.data\n return error\n\n\nclass JsonRpcParseError(JsonRpcError):\n code = -32700\n message = 'parse error'\n\n\nclass JsonRpcRequestInvalid(JsonRpcError):\n code = -32600\n message = 'invalid request'\n\n\nclass JsonRpcMethodNotFound(JsonRpcError):\n code = -32601\n message = 'method not found'\n\n\nclass JsonRpcParamsInvalid(JsonRpcError):\n code = -32602\n message = 'invalid params'\n\n\nclass JsonRpcInternalError(JsonRpcError):\n code = -32603\n message = 'internal error'\n\n\ndef jsonrpc_error_response(error, id=None):\n \"\"\" Marshal a Python Exception into a webob ``Response``\n object with a body that is a JSON string suitable for use as\n a JSON-RPC response with a content-type of ``application/json``\n and return the response.\"\"\"\n\n body = json.dumps({\n 'jsonrpc': '2.0',\n 'id': id,\n 'error': error.as_dict(),\n })\n\n response = Response(body)\n response.content_type = 'application/json'\n response.content_length = len(body)\n return response\n\n\ndef exception_view(exc, request):\n rpc_id = getattr(request, 'rpc_id', None)\n if isinstance(exc, JsonRpcError):\n fault = exc\n log.debug('json-rpc error rpc_id:%s \"%s\"',\n rpc_id, exc.message)\n elif isinstance(exc, HTTPNotFound):\n fault = JsonRpcMethodNotFound()\n log.debug('json-rpc method not found rpc_id:%s \"%s\"',\n rpc_id, request.rpc_method)\n elif isinstance(exc, HTTPForbidden):\n fault = JsonRpcRequestInvalid()\n log.debug('json-rpc method forbidden rpc_id:%s \"%s\"',\n rpc_id, request.rpc_method)\n elif isinstance(exc, ViewMapperArgsInvalid):\n fault = JsonRpcParamsInvalid()\n log.debug('json-rpc invalid method params')\n else:\n fault = JsonRpcInternalError()\n log.exception('json-rpc exception rpc_id:%s \"%s\"', rpc_id, exc)\n\n return jsonrpc_error_response(fault, rpc_id)\n\n\ndef jsonrpc_renderer(info):\n def _render(value, system):\n request = system.get('request')\n if request is not None:\n rpc_id = getattr(request, 'rpc_id', None)\n response = request.response\n\n if rpc_id is None:\n response.status = 204\n del response.content_type\n return ''\n\n ct = response.content_type\n if ct == response.default_content_type:\n response.content_type = 'application/json'\n\n out = {\n 'jsonrpc': '2.0',\n 'id': rpc_id,\n 'result': value,\n }\n return json.dumps(out)\n return _render\n\n\ndef setup_jsonrpc(request):\n try:\n body = request.json_body\n except ValueError:\n raise JsonRpcParseError\n\n request.rpc_id = body.get('id')\n request.rpc_args = body.get('params', ())\n request.rpc_method = body.get('method')\n request.rpc_version = body.get('jsonrpc')\n\n if request.rpc_version != '2.0':\n log.debug('id:%s invalid rpc version %s',\n request.rpc_id, request.rpc_version)\n raise JsonRpcRequestInvalid\n\n if request.rpc_method is None:\n log.debug('id:%s invalid rpc method %s',\n request.rpc_id, request.rpc_method)\n raise JsonRpcRequestInvalid\n\n log.debug('handling id:%s method:%s',\n request.rpc_id, request.rpc_method)\n\n\ndef add_jsonrpc_endpoint(self, name, *args, **kw):\n \"\"\"Add an endpoint for handling JSON-RPC.\n\n name\n\n The name of the endpoint.\n\n A JSON-RPC method also accepts all of the arguments supplied to\n Pyramid's ``add_route`` method.\n\n \"\"\"\n def jsonrpc_endpoint_predicate(info, request):\n # potentially setup either rpc v1 or v2 from the parsed body\n setup_jsonrpc(request)\n\n # Always return True so that even if it isn't a valid RPC it\n # will fall through to the notfound_view which will still\n # return a valid JSON-RPC response.\n return True\n predicates = kw.setdefault('custom_predicates', [])\n predicates.append(jsonrpc_endpoint_predicate)\n self.add_route(name, *args, **kw)\n self.add_view(exception_view, route_name=name, context=Exception,\n permission=NO_PERMISSION_REQUIRED)\n\n\ndef add_jsonrpc_method(self, view, **kw):\n \"\"\"Add a method to a JSON-RPC endpoint.\n\n endpoint\n\n The name of the endpoint.\n\n method\n\n The name of the method.\n\n A JSON-RPC method also accepts all of the arguments supplied to\n Pyramid's ``add_view`` method.\n\n A view mapper is registered by default which will match the\n ``request.rpc_args`` to parameters on the view. To override this\n behavior simply set the ``mapper`` argument to None or another\n view mapper.\n\n \"\"\"\n endpoint = kw.pop('endpoint', kw.pop('route_name', None))\n if endpoint is None:\n raise ConfigurationError(\n 'Cannot register a JSON-RPC endpoint without specifying the '\n 'name of the endpoint.')\n\n method = kw.pop('method', None)\n if method is None:\n raise ConfigurationError(\n 'Cannot register a JSON-RPC method without specifying the '\n '\"method\"')\n\n def jsonrpc_method_predicate(context, request):\n return getattr(request, 'rpc_method', None) == method\n predicates = kw.setdefault('custom_predicates', [])\n predicates.append(jsonrpc_method_predicate)\n kw.setdefault('mapper', MapplyViewMapper)\n kw.setdefault('renderer', 'pyramid_rpc:jsonrpc')\n self.add_view(view, route_name=endpoint, **kw)\n\n\nclass jsonrpc_method(object):\n \"\"\"This decorator may be used with pyramid view callables to enable\n them to respond to JSON-RPC method calls.\n\n If ``method`` is not supplied, then the callable name will be used\n for the method name.\n\n This is the lazy analog to the\n :func:`~pyramid_rpc.jsonrpc.add_jsonrpc_method`` and accepts all of\n the same arguments.\n\n \"\"\"\n def __init__(self, method=None, **kw):\n self.method = method\n self.kw = kw\n\n def __call__(self, wrapped):\n kw = self.kw.copy()\n kw['method'] = self.method or wrapped.__name__\n\n def callback(context, name, ob):\n config = context.config.with_package(info.module)\n config.add_jsonrpc_method(view=ob, **kw)\n\n info = venusian.attach(wrapped, callback, category='pyramid')\n if info.scope == 'class':\n # ensure that attr is set if decorating a class method\n kw.setdefault('attr', wrapped.__name__)\n\n kw['_info'] = info.codeinfo # fbo action_method\n return wrapped\n\n\ndef includeme(config):\n \"\"\" Set up standard configurator registrations. Use via:\n\n .. code-block:: python\n\n config = Configurator()\n config.include('pyramid_rpc.jsonrpc')\n\n Once this function has been invoked, two new directives will be\n available on the configurator:\n\n - ``add_jsonrpc_endpoint``: Add an endpoint for handling JSON-RPC.\n\n - ``add_jsonrpc_method``: Add a method to a JSON-RPC endpoint.\n\n \"\"\"\n config.add_directive('add_jsonrpc_endpoint', add_jsonrpc_endpoint)\n config.add_directive('add_jsonrpc_method', add_jsonrpc_method)\n config.add_renderer('pyramid_rpc:jsonrpc', jsonrpc_renderer)\n config.add_view(exception_view, context=JsonRpcError,\n permission=NO_PERMISSION_REQUIRED)\n", "sub_path": "pyramid_rpc/jsonrpc.py", "file_name": "jsonrpc.py", "file_ext": "py", "file_size_in_byte": 8449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 72, "usage_type": "call"}, {"api_name": "pyramid.response.Response", "line_number": 78, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPNotFound", "line_number": 90, "usage_type": "argument"}, {"api_name": "pyramid.httpexceptions.HTTPForbidden", "line_number": 94, "usage_type": "argument"}, {"api_name": "pyramid_rpc.api.ViewMapperArgsInvalid", "line_number": 98, "usage_type": "argument"}, {"api_name": "json.dumps", "line_number": 129, "usage_type": "call"}, {"api_name": "pyramid.security.NO_PERMISSION_REQUIRED", "line_number": 181, "usage_type": "name"}, {"api_name": "pyramid.exceptions.ConfigurationError", "line_number": 206, "usage_type": "call"}, {"api_name": "pyramid.exceptions.ConfigurationError", "line_number": 212, "usage_type": "call"}, {"api_name": "pyramid_rpc.api.MapplyViewMapper", "line_number": 220, "usage_type": "argument"}, {"api_name": "venusian.attach", "line_number": 249, "usage_type": "call"}, {"api_name": "pyramid.security.NO_PERMISSION_REQUIRED", "line_number": 278, "usage_type": "name"}]} +{"seq_id": "259048611", "text": "#!/usr/bin/env pypy3\r\n\"\"\"\r\nFlexAEADv1.py - this class is used to define the FlexAEADv1 cipher\r\nUsage:\r\n import FlexAEADv1\r\nOptions:\r\n no options\r\n\"\"\"\r\n__author__ = 'Eduardo Marsola do Nascimento'\r\n__copyright__ = 'Copyright 2018-11-25'\r\n__credits__ = ''\r\n__license__ = 'MIT'\r\n__version__ = '0.01'\r\n__maintainer__ = ''\r\n__email__ = ''\r\n__status__ = 'Development'\r\n\r\nfrom FlexAESBox import FlexAESBox\r\nimport math\r\n\r\ndef byteToHex(data):\r\n \"\"\"\r\n byteToHex - convert a byte array into a comma sepparated hex representation\r\n Args:\r\n data: the byte array to be converted\r\n Returns:\r\n returns comma separated hex representation.\r\n \"\"\"\r\n result = '0x{:02X}'.format(data[0])\r\n for ch in data[1:]:\r\n result += ',0x{:02X}'.format(ch)\r\n return result\r\n\r\nclass FlexAEADv1:\r\n dirSBox = [ FlexAESBox.dirSBox0 ]\r\n invSBox = [ FlexAESBox.invSBox0 ]\r\n nSBoxes = len( dirSBox )\r\n \r\n def __init__(self, key = bytes(16), nBytes = 8, nRounds = 0):\r\n self.nBytes = nBytes\r\n self.counter = bytes([0] * self.nBytes)\r\n self.checksum = bytes([0] * self.nBytes)\r\n\r\n t0 = bytes([0] * int(len(key)/2))\r\n t1 = b''\r\n ### init nRounds for the subkey generation\r\n self.nRounds = int(math.log(len(t0),2)+2)\r\n while(len(t1)<(nBytes*8)):\r\n t0 = self.dirPFK( t0, key)\r\n t0 = self.dirPFK( t0, key)\r\n t0 = self.dirPFK( t0, key)\r\n t1 += t0\r\n self.key0 = t1[nBytes*0:nBytes*2]\r\n self.key1 = t1[nBytes*2:nBytes*4]\r\n self.key2 = t1[nBytes*4:nBytes*6]\r\n self.key3 = t1[nBytes*6:nBytes*8]\r\n\r\n if(nRounds==0):\r\n self.nRounds = int(math.log(nBytes,2)+2)\r\n else:\r\n self.nRounds = nRounds\r\n \r\n ### Debug - comment next line\r\n \"\"\"\r\n print(' ### FlexAEADv1 - init ### - Debug Start')\r\n print(' self.nRounds : ',self.nRounds)\r\n print(' self.key0 : '+byteToHex(self.key0))\r\n print(' self.key1 : '+byteToHex(self.key1))\r\n print(' self.key2 : '+byteToHex(self.key2))\r\n print(' self.key3 : '+byteToHex(self.key3))\r\n print('self.checksum : '+byteToHex(self.checksum))\r\n print(' self.counter : '+byteToHex(self.counter))\r\n print(' ### FlexAEADv1 - init ### - Debug End')\r\n #\"\"\"\r\n \r\n def encryptMessage( self, nonce, AD, message ):\r\n self.counter = FlexAEADv1.inc32(self.dirPFK( nonce, self.key3))\r\n self.checksum = bytes([0]*self.nBytes)\r\n ### Debug - comment next line\r\n \"\"\"\r\n print(' ### FlexAEADv1 - encryptmessage ### - Debug Start')\r\n print('self.checksum : '+byteToHex(self.checksum))\r\n print(' self.counter : '+byteToHex(self.counter))\r\n print(' s0 : '+byteToHex(self.dirPFK( self.counter, self.key3)))\r\n print(' ### FlexAEADv1 - encryptmessage ### - Debug End')\r\n #\"\"\"\r\n ### Encrypt the Associate Data just to calculate the tag\r\n AD += bytes([0] * (len(AD)%self.nBytes))\r\n i = 0\r\n while( i < len(AD)):\r\n self.encryptBlock(AD[i:i+self.nBytes],isAD=True)\r\n i += self.nBytes\r\n ### Encrypt the PlainText\r\n state = b''\r\n while( len(state)+self.nBytes < len(message)):\r\n state += self.encryptBlock(message[len(state):len(state)+self.nBytes])\r\n lastblock, tag = self.encryptBlock(message[len(state):], final = True)\r\n return state+lastblock, tag\r\n\r\n def decryptMessage( self, nonce, AD, message, tag ):\r\n self.counter = FlexAEADv1.inc32(self.dirPFK( nonce, self.key3))\r\n self.checksum = bytes([0]*self.nBytes)\r\n ### Debug - comment next line\r\n \"\"\"\r\n print(' ### FlexAEADv1 - decryptmessage ### - Debug Start')\r\n print('self.checksum : '+byteToHex(self.checksum))\r\n print(' self.counter : '+byteToHex(self.counter))\r\n print(' s0 : '+byteToHex(self.dirPFK( self.counter, self.key2)))\r\n print(' ### FlexAEADv1 - decryptmessage ### - Debug End')\r\n #\"\"\"\r\n ### Encrypt the Associate Data just to calculate the tag\r\n AD += bytes([0] * (len(AD)%self.nBytes))\r\n i = 0\r\n while( i < len(AD)):\r\n self.encryptBlock(AD[i:i+self.nBytes],isAD=True)\r\n i += self.nBytes\r\n ### Encrypt the PlainText\r\n state = b''\r\n while( len(state)+self.nBytes < len(message)):\r\n state += self.decryptBlock(message[len(state):len(state)+self.nBytes],None)\r\n lastblock, validmessage = self.decryptBlock(message[len(state):], tag, final = True)\r\n if( validmessage ):\r\n return state+lastblock, validmessage\r\n else:\r\n return b'', validmessage\r\n\r\n def encryptBlock( self, block, final=False, isAD=False ):\r\n if( final ):\r\n paddingXOR = bytes([0xAA] * self.nBytes)\r\n if( len(block)0) ):\r\n i -= 1\r\n if( (int(state[i])==0x80) and (i>0)):\r\n return state[:i], True\r\n ### Debug - comment next line\r\n \"\"\"\r\n print(' i : ',i)\r\n print(' state[i] : ',state[i])\r\n print(' state : '+byteToHex(state))\r\n #\"\"\"\r\n return b'', False\r\n else:\r\n self.counter = FlexAEADv1.inc32(self.counter)\r\n return state\r\n\r\n def inc32( block, inc=1 ):\r\n state = b''\r\n for i in range(0,len(block),4):\r\n state += ((int.from_bytes(block[i:(i+4)],'big')+inc).to_bytes(4,'big'))\r\n return state\r\n\r\n def shuffleLayer( block ):\r\n zero = 0\r\n half = int(len(block)/2)\r\n state = [0]*len(block)\r\n for i in range(half):\r\n state[(2*i)+0] = (int(block[i+zero])&0xF0) + \\\r\n ((int(block[i+half])&0xF0)>>4)\r\n state[(2*i)+1] = ((int(block[i+zero])&0x0F)<<4)+\\\r\n (int(block[i+half])&0x0F)\r\n return bytes(state)\r\n\r\n def invshuffleLayer( block ):\r\n zero = 0\r\n half = int(len(block)/2)\r\n state = [0]*len(block)\r\n for i in range(half):\r\n state[i+zero] = (int(block[(2*i)+0])&0xF0) + \\\r\n ((int(block[(2*i)+1])&0xF0)>>4)\r\n state[i+half] = (int(block[(2*i)+1])&0x0F) + \\\r\n ((int(block[(2*i)+0])&0x0F)<<4)\r\n return bytes(state)\r\n\r\n def dirSBoxLayer( block ):\r\n state = [0]*len(block)\r\n for i in range(len(block)):\r\n state[i] = FlexAEADv1.dirSBox[i%FlexAEADv1.nSBoxes][int(block[i])]\r\n return bytes(state)\r\n\r\n def invSBoxLayer( block ):\r\n state = [0]*len(block)\r\n for i in range(len(block)):\r\n state[i] = FlexAEADv1.invSBox[i%FlexAEADv1.nSBoxes][int(block[i])]\r\n return bytes(state)\r\n\r\n def dirPFK( self, plaintext, key_pfk):\r\n if len(plaintext)*2 != len(key_pfk):\r\n print('wrong block({})/key({}) size on dirPFK'.format(len(plaintext),len(key_pfk)))\r\n return plaintext\r\n \r\n half = int(len(plaintext)/2)\r\n ciphertext = bytes([int(a)^int(b) for a,b in zip(plaintext,key_pfk)])\r\n \r\n for i in range(self.nRounds):\r\n ### Shuffle Layer\r\n ciphertext = FlexAEADv1.shuffleLayer(ciphertext)\r\n left = ciphertext[:half]\r\n right = ciphertext[half:]\r\n ### SBox Layer (right)\r\n right = FlexAEADv1.dirSBoxLayer(right)\r\n #### XOR L + R -> L\r\n left = bytes([int(a)^int(b) for a,b in zip(left,right)])\r\n ### SBox Layer (left)\r\n left = FlexAEADv1.dirSBoxLayer(left)\r\n #### XOR L + R -> R\r\n right = bytes([int(a)^int(b) for a,b in zip(left,right)])\r\n ### SBox Layer (right)\r\n right = FlexAEADv1.dirSBoxLayer(right)\r\n ### ciphertext = left+right\r\n ciphertext = left+right\r\n \r\n ciphertext = bytes([int(a)^int(b) for a,b in zip(ciphertext,key_pfk[len(ciphertext):])])\r\n\r\n return ciphertext\r\n\r\n def invPFK( self, ciphertext, key_pfk):\r\n if len(ciphertext)*2 != len(key_pfk):\r\n print('wrong block({})/key({}) size on dirPFK'.format(len(ciphertext),len(key_pfk)))\r\n return ciphertext\r\n \r\n half = int(len(ciphertext)/2)\r\n plaintext = bytes([int(a)^int(b) for a,b in zip(ciphertext,key_pfk[len(ciphertext):])])\r\n \r\n for i in range(self.nRounds):\r\n ### ciphertext = left+right\r\n left = plaintext[:half]\r\n right = plaintext[half:]\r\n ### SBox Layer (right)\r\n right = FlexAEADv1.invSBoxLayer(right)\r\n #### XOR L + R -> R\r\n right = bytes([int(a)^int(b) for a,b in zip(left,right)])\r\n ### SBox Layer (left)\r\n left = FlexAEADv1.invSBoxLayer(left)\r\n #### XOR L + R -> L\r\n left = bytes([int(a)^int(b) for a,b in zip(left,right)])\r\n ### SBox Layer (right)\r\n right = FlexAEADv1.invSBoxLayer(right)\r\n ### Shuffle Layer\r\n plaintext = left + right\r\n plaintext = FlexAEADv1.invshuffleLayer(plaintext)\r\n \r\n plaintext = bytes([int(a)^int(b) for a,b in zip(plaintext,key_pfk)])\r\n\r\n return plaintext\r\n\r\n \r\ndef __templatefunc( input1, input2, input3):\r\n \"\"\"\r\n __templatefunc - this function ....\r\n Args:\r\n input1: first arg ....\r\n input2: second arg ....\r\n input3: third arg ....\r\n Returns:\r\n the function result.\r\n \"\"\"\r\n pass\r\n return\r\n\r\nif __name__ == \"__main__\":\r\n import sys\r\n # track execution time\r\n from datetime import datetime\r\n startTime=datetime.now()\r\n #\r\n print(sys.version)\r\n \"\"\"\r\n your code\r\n \"\"\"\r\n # track execution time\r\n finishTime=datetime.now()\r\n print( '\\nStart: {}, Finish:{}, Running Time: {}'\r\n ''.format(startTime.replace(microsecond=0),\r\n finishTime.replace(microsecond=0),\r\n finishTime-startTime))\r\n ################### END #################\r\n", "sub_path": "flexaead/python/FlexAEADv1.py", "file_name": "FlexAEADv1.py", "file_ext": "py", "file_size_in_byte": 13531, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "FlexAESBox.FlexAESBox.dirSBox0", "line_number": 35, "usage_type": "attribute"}, {"api_name": "FlexAESBox.FlexAESBox", "line_number": 35, "usage_type": "name"}, {"api_name": "FlexAESBox.FlexAESBox.invSBox0", "line_number": 36, "usage_type": "attribute"}, {"api_name": "FlexAESBox.FlexAESBox", "line_number": 36, "usage_type": "name"}, {"api_name": "math.log", "line_number": 47, "usage_type": "call"}, {"api_name": "math.log", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 331, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 331, "usage_type": "name"}, {"api_name": "sys.version", "line_number": 333, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 338, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 338, "usage_type": "name"}]} +{"seq_id": "500326081", "text": "#!/usr/bin/python\n# encoding: utf-8\n\n\nimport urllib2\nimport requests\nimport re\nfrom bs4 import BeautifulSoup\n\n\nclass Result(object):\n def __init__(self, from_lang=None, to_lang=None, translation_tuples=None):\n self.from_lang = from_lang\n self.to_lang = to_lang\n self.translation_tuples = list(translation_tuples) \\\n if translation_tuples else []\n\n @property\n def n_results(self):\n return len(self.translation_tuples)\n\n @property\n def from_words(self):\n return map(lambda tuple: tuple[0], self.translation_tuples)\n\n @property\n def to_words(self):\n return map(lambda tuple: tuple[1], self.translation_tuples)\n\n @property\n def from_words_lowercase(self):\n return map(lambda tuple: tuple[0].lower(), self.translation_tuples)\n\n @property\n def to_words_lowercase(self):\n return map(lambda tuple: tuple[1].lower(), self.translation_tuples)\n\n\nclass Dict(object):\n\n def __init__(self, search_string, from_language, to_language):\n self.search_string = search_string\n self.from_language = from_language\n self.to_language = to_language\n\n @property\n def request_subdomain(self):\n subdomain = self.from_language.subdomain.lower() + self.to_language.subdomain.lower()\n\n if len(subdomain) > 4:\n return \"www\"\n else:\n return subdomain\n\n def translate(self):\n response = self.get_response()\n result = self.parse_response(response.content)\n return self.correct_translation_order(result)\n\n def get_response(self):\n subdomain = self.request_subdomain\n\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; WOW64; rv:30.0) Gecko/20100101 Firefox/30.0'\n }\n\n params = {\n \"s\": self.search_string\n }\n\n return requests.get(\"https://\" + subdomain + \".dict.cc\", params=params, headers=headers)\n\n def parse_response(self, response_body):\n\n in_list = []\n out_list = []\n\n def sanitize(word):\n return re.sub(\"[\\\\\\\\\\\"]\", \"\", word)\n\n javascript_list_pattern = \"\\\"[^,]+\\\"\"\n\n for line in response_body.split(\"\\n\"):\n if \"var c1Arr\" in line:\n in_list = map(sanitize, re.findall(javascript_list_pattern, line))\n elif \"var c2Arr\" in line:\n out_list = map(sanitize, re.findall(javascript_list_pattern, line))\n\n if not any([in_list, out_list]):\n return Result()\n\n soup = BeautifulSoup(response_body, \"html.parser\")\n\n left_lang = soup.find_all(\"td\", width=\"307\")[0].b.text\n right_lang = soup.find_all(\"td\", width=\"306\")[0].b.text\n\n in_list = map(lambda word: unicode(word, 'utf-8'), in_list)\n out_list = map(lambda word: unicode(word, 'utf-8'), out_list)\n\n return Result(\n from_lang=left_lang,\n to_lang=right_lang,\n translation_tuples=zip(in_list, out_list),\n )\n\n def correct_translation_order(self, result):\n\n if not result.translation_tuples:\n return result\n\n left_occurrences = len(filter(lambda word: word.count(self.search_string.lower()), result.from_words_lowercase))\n right_occurrences = len(filter(lambda word: word.count(self.search_string.lower()), result.to_words_lowercase))\n\n if left_occurrences >= right_occurrences:\n return result\n else:\n return Result(from_lang=result.to_lang,\n to_lang=result.from_lang,\n translation_tuples=zip(result.to_words, result.from_words)\n )\n", "sub_path": "dictcc_translator.py", "file_name": "dictcc_translator.py", "file_ext": "py", "file_size_in_byte": 3655, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "requests.get", "line_number": 71, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 79, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 85, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 87, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "345142464", "text": "import json\r\nimport os\r\nimport random\r\nimport pandas as pd\r\nfrom keras.preprocessing.sequence import pad_sequences\r\n\r\n# The original dataset is 50 videos of pushing, and 50 videos of other activity.\r\n# The reason to separate videos is to facilitate data preparation and avoid manual labeling.\r\n# openpose is used to get landmark features of each video frame as json file\r\n# To keep track of all frames of the videos including their landmark features and coresponding labels, a dictionary is defined\r\n# where keys are the label of each video (push_0, push_1, ..., other_0, other_1, ..) and values are vector of size (n,75)\r\n# where 'n' is the number of frames and 75 is fixed for all json files which represent the vector size of the land mark: 'pose_keypoints_2d'\r\n\r\n\r\n# input = push_0_000000000000_keypoints\r\n# output= push_0\r\ndef get_video_name(file_name):\r\n parts = file_name.split('_')\r\n return (parts[0] + '_' + parts[1])\r\n\r\n\r\n# the if statement is added since some json files did not have 'pose_keypoints_2d'\r\n\r\n# returning a dict where keys are video labels(push_0,push_1, ..)\r\n# and values are landmark vectors corresponding to frames of the video\r\ndef read_data_from_landmarks(json_dir):\r\n video_to_landmarks = {}\r\n for file in os.listdir(json_dir):\r\n file_path = json_dir + '\\\\' + file\r\n temp = json.load(open(file_path))\r\n if len(temp['people']) == 0:\r\n pass\r\n else:\r\n key = get_video_name(file)\r\n value = temp['people'][0]['pose_keypoints_2d']\r\n video_to_landmarks.setdefault(key, []).append(value)\r\n\r\n return video_to_landmarks\r\n\r\n# dictonary is a hashtable and shuffle not work.To shuffle :\r\n# get the list of keys, shuffle the keys and get the values of the corresponding keys\r\n# define a seed for random to be iterable --> random.Random(seed)\r\ndef shuffle_data (video_to_landmarks):\r\n shuffled_keys = list(video_to_landmarks.keys())\r\n random.Random(4).shuffle(shuffled_keys)\r\n labels = shuffled_keys\r\n train_data = []\r\n for key in labels:\r\n train_data.append(video_to_landmarks[key])\r\n\r\n return train_data, labels\r\n\r\n# After this step, No SHUFFLE should be applied in any other step\r\n\r\n# Driver\r\njson_dir = \"D:\\\\tamu\\\\courses\\\\DeepLearning\\\\ProjectPart5\\\\openpose_json\"\r\nvideo_to_landmarks = read_data_from_landmarks(json_dir)\r\ntrain_data, labels = shuffle_data(video_to_landmarks)\r\n\r\n# padding frames of different videos, the bellow function will automatically pad to max length\r\ninput_data = pad_sequences(train_data, dtype='float32', maxlen = 125, padding='post')\r\n\r\n# get dataframe so the data could be saved as CSV file as input: (dataframe input should be 2d)\r\n# reshaping the data so it could convertto dataframe\r\nr = input_data.shape[0]\r\nm = input_data.shape[1]\r\nn = input_data.shape[2]\r\n\r\nreshaped_inputdata = input_data.reshape(r, m*n)\r\ninput_df = pd.DataFrame(data=reshaped_inputdata, index=labels)\r\ncsv_filepath = \"D:\\\\tamu\\\\courses\\\\DeepLearning\\\\ProjectPart5\\\\input_data_2.csv\"\r\ninput_df.to_csv(csv_filepath)\r\n\r\n\r\nprint('input_data.shape: ',input_data.shape)\r\nprint('reshaped_inputdata.shape: ', reshaped_inputdata.shape)", "sub_path": "sub_3/source_code/reading_json_file.py.py", "file_name": "reading_json_file.py.py", "file_ext": "py", "file_size_in_byte": 3164, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.listdir", "line_number": 28, "usage_type": "call"}, {"api_name": "json.load", "line_number": 30, "usage_type": "call"}, {"api_name": "random.Random", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "34430158", "text": "import psutil\nimport argparse\nimport datetime\nimport csv\nimport time\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--file',\n help='input file name (required)',\n type=str)\nargs = parser.parse_args()\n\n# pull args out\nfilename = args.file\n\nwith open(filename) as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n mem_used = 0\n for row in reader:\n mem_row = int(row[4])/1024/1024/1024\n mem_used = max(mem_row,mem_used)\n\n mem_str = \"Max memory used: %.3f GB\" % mem_used\n print(mem_str)\n", "sub_path": "profiling/report_max_memory.py", "file_name": "report_max_memory.py", "file_ext": "py", "file_size_in_byte": 566, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "63811754", "text": "\r\n\"\"\"\r\n Copyright (c) 2005-2019 Colin Pearse.\r\n All scripts are free in the binscripts repository but please refer to the\r\n LICENSE file at the top-level directory for the conditions of distribution.\r\n\r\n Name: verbose.py\r\n Description: Implement verbosity. Full description and test at the bottom.\r\n\"\"\"\r\n\r\nfrom __future__ import print_function \r\nimport sys\r\nimport os\r\nimport time\r\nimport datetime\r\n\r\n__author__ = \"Colin Pearse \"\r\n__status__ = \"beta\"\r\n__version__ = \"0.0.1\"\r\n__date__ = \"20 February 2018\"\r\n\r\nvlevels = [1]\r\nvpathname = None\r\nvfh = sys.stderr\r\n\r\n\r\ndef splitLevelsList(levels):\r\n strlevels = [l for l in levels if type(l) is str]\r\n numlevels = [l for l in levels if type(l) is int]\r\n if numlevels == []:\r\n numlevel = None\r\n else:\r\n numlevel = max(numlevels)\r\n return numlevel,strlevels\r\n\r\ndef splitLevelsStr(levels,separator=','):\r\n strlevels = levels.split(separator)\r\n numlevels = [n for n in strlevels if n.isdigit() is True]\r\n strlevels = [n for n in strlevels if n.isdigit() is False]\r\n if numlevels == []:\r\n numlevel = None\r\n else:\r\n numlevels = map(int,numlevels)\r\n numlevel = max(numlevels)\r\n return numlevel,strlevels\r\n\r\ndef splitLevelsInt(level):\r\n return level,[]\r\n\r\n# levels can be a list [2,\"blah\",\"pod\"] or str \"2,blah,pod\" or int 2\r\ndef splitLevels(levels,separator=','):\r\n ''' split verbosity levels into int and strs\r\n\r\n >>> splitLevels([1,\"blah\",\"pod\"])\r\n (1, ['blah', 'pod'])\r\n\r\n >>> splitLevels(\"1,blah,pod\")\r\n (1, ['blah', 'pod'])\r\n\r\n >>> splitLevels(\"1:blah:pod\",separator=':')\r\n (1, ['blah', 'pod'])\r\n\r\n >>> splitLevels(99)\r\n (99, [])\r\n\r\n >>> splitLevels(\"blah,pod,6,9,pie\")\r\n (9, ['blah', 'pod', 'pie'])\r\n\r\n >>> splitLevels(\"info\")\r\n (None, ['info'])\r\n '''\r\n if type(levels) is list:\r\n return splitLevelsList(levels)\r\n elif type(levels) is str:\r\n return splitLevelsStr(levels,separator)\r\n elif type(levels) is int:\r\n return splitLevelsInt(levels)\r\n else:\r\n return 1,[]\r\n\r\n# levels can be a list or str - see splitLevels\r\ndef setLevels(levels,separator=','):\r\n global vlevels\r\n ivlevels,svlevels = splitLevels(levels)\r\n vlevels = [ivlevels] + svlevels\r\n\r\n# if I've already opened a file, close it before setting a new fh\r\ndef setStream(fh):\r\n global vpathname\r\n global vfh\r\n if vpathname is not None:\r\n closeFile()\r\n vfh = fh\r\n\r\n# if I've already opened a file, close it before opening the new one\r\ndef openFile(filename,mode=\"a\"):\r\n global vpathname\r\n global vfh\r\n if vpathname is not None:\r\n closeFile()\r\n vpathname = os.path.abspath(filename)\r\n vfh = open(vpathname,mode)\r\n\r\n# don't close if vpathname is None, implying vfh was not opened by me\r\ndef closeFile():\r\n global vpathname\r\n global vfh\r\n if vpathname is not None:\r\n try:\r\n vfh.close()\r\n vpathname = None\r\n except:\r\n sys.exit(\"cannot close %s\" % (vpathname))\r\n\r\ndef showLabel(labels,dt,mod,func):\r\n label = \"\"\r\n if \"dt\" in labels:\r\n label = label + \"%s: \"%(dt)\r\n if \"mod\" in labels:\r\n label = label + \"%s: \"%(mod)\r\n if \"func\" in labels:\r\n label = label + \"%s: \"%(func)\r\n return label\r\n\r\ndef isLevel(levels):\r\n ilevel,slevels = splitLevels(levels)\r\n ivlevel,svlevels = splitLevels(vlevels)\r\n if (ivlevel is not None and ilevel is not None and ivlevel >= ilevel) or set(slevels) & set(svlevels):\r\n return True\r\n else:\r\n return False\r\n\r\n# NOTE: don't think this label is very useful, but just in case...\r\n# ilevel,slevels = splitLevels(levels)\r\n# showlevels = \"%s\" % (','.join([str(ilevel)] + slevels))\r\ndef verbose(levels,message,tee=None,labels=[\"dt\",\"mod\",\"func\"],teelabels=[\"dt\",\"mod\",\"func\"]):\r\n ts = time.time()\r\n dt = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\r\n mod = os.path.basename(sys._getframe(1).f_code.co_filename)\r\n func = sys._getframe(1).f_code.co_name\r\n if isLevel(levels):\r\n print (\"%s%s\" % (showLabel(labels,dt,mod,func),message), file=vfh)\r\n if tee is not None:\r\n print (\"%s%s\" % (showLabel(teelabels,dt,mod,func),message), file=tee)\r\n\r\n\r\n\"\"\"\r\nDescription:\r\nAllows granular verbose messaging. verbose.verbose() commands can be used everywhere\r\nin the code, but only activated with a specific level or multiple strings.\r\nFor example: myprog.py -v 5,read,boot ... would display all messages level 5 and under\r\nplus those labelled \"read\" and \"boot\" which might be a very specified area of the code\r\nyou wish to debug.\r\n\r\nCode:\r\nimport verbose\r\nverbose.setLevels(vlist) # vlist can be a str: \"2,readcmds,blah\" or list [2,\"readcmds\",\"blah\"]\r\nverbose.openFile(\"log/verbose_test.log\",\"w\") # output to a file (open with truncate); default: sys.stderr output\r\nverbose.verbose([2,\"loop\"],\"message\") # output if verbose level >= 2 or one verbose string is \"loop\"\r\nverbose.verbose([\"info\"],\"message\") # output if one verbose string is \"info\"\r\nverbose.verbose([\"info\"],\"message\",tee=sys.stderr) # as above, but write to stderr too\r\nverbose.verbose([99],\"message\") # output if verbose level >= 99\r\nverbose.verbose(\"99\",\"message\") # output if verbose level >= 99\r\nverbose.verbose(99,\"message\") # output if verbose level >= 99\r\nverbose.isLevel(\"99,blah\") # True if verbose level >= 99 or verbose str is \"blah\"\r\nverbose.setStream(sys.stderr) # will call closeFile() if necessary before redirecting\r\nverbose.closeFile()\r\n\r\nTesting (doctest):\r\npython -m doctest verbose.py -v\r\n\r\nTesting (manual):\r\npython bin/verbose.py 1 # 2 tests below should be displayed\r\npython bin/verbose.py 2 # 3 tests below should be displayed\r\npython bin/verbose.py 3 # 4 tests below should be displayed\r\npython bin/verbose.py 3 show # 5 tests below should be displayed\r\npython bin/verbose.py nothing # no tests below should be displayed\r\n\r\nEg output for \"3 show\":\r\n2018-02-28 19:33:06: myTestFunc: True for test: [1, 'show']\r\n2018-02-28 19:33:06: myTestFunc: True for test: [2, 'func']\r\n2018-02-28 19:33:06: myTestFunc: True for test: [3, 'func']\r\n2018-02-28 19:33:06: myTestFunc: True for test: [1]\r\n2018-02-28 19:33:06: myTestFunc: True for test: ['show']\r\n\r\n\"\"\"\r\n\r\nif __name__ == \"__main__\":\r\n def myTestFunc():\r\n tests = [[1,\"show\"],\r\n [2,\"func\"],\r\n [3,\"func\"],\r\n 1,\r\n \"show\"]\r\n for test in tests:\r\n print (\"test:\",test)\r\n for test in tests:\r\n verbose(test,\"%s for test: %s\" % (isLevel(test),test))\r\n #verbose(test,\"%s for test: %s\" % (isLevel(test),test), tee=sys.stderr)\r\n\r\n if len(sys.argv) >= 2:\r\n setLevels(sys.argv[1])\r\n #openFile(\"log/verbose_test.log\",\"w\")\r\n #setStream(sys.stdout)\r\n #setStream(sys.stderr)\r\n print (\"vlevels:\",vlevels)\r\n print (\"vpathname:\",vpathname)\r\n myTestFunc()\r\n #closeFile()\r\n\r\n", "sub_path": "binpy/verbose.py", "file_name": "verbose.py", "file_ext": "py", "file_size_in_byte": 7205, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "sys.stderr", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 113, "usage_type": "call"}, {"api_name": "time.time", "line_number": 137, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "sys._getframe", "line_number": 139, "usage_type": "call"}, {"api_name": "sys._getframe", "line_number": 140, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 201, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 202, "usage_type": "attribute"}]} +{"seq_id": "225165581", "text": "from pathlib import Path\nfrom windcomponents import *\nfrom settings import *\nimport os\nimport shutil\nimport json\nimport explorer\nimport widget\nimport DefaultWidget\nimport re\n\ndef getSTR(strd):\n if(strd == None):\n return \"\"\n else:\n return str(strd)\n\nclass WidgetEmeeter:\n name: str\n desc: str\n version: str\n update: float\n uid: str\n path: str\n\n def getSettings(self):\n try:\n if(os.path.exists(self.dir + \"/properties.json\")):\n with open(self.dir + \"/properties.json\", \"r\") as f:\n decode_s = json.JSONDecoder().decode(f.read())\n return decode_s.get(self.uid) if decode_s.get(self.uid) != None else {}\n else:\n return {}\n except:\n return {}\n\n def saveSettings(self, settings):\n js = None\n try:\n if(os.path.exists(self.dir + \"/properties.json\")):\n with open(self.dir + \"/properties.json\", \"r\") as f:\n js = json.JSONDecoder().decode(f.read())\n else:\n js = {}\n except:\n js = {}\n\n with open(self.dir + \"/properties.json\", \"w\") as f:\n js[self.uid] = settings\n f.write(json.dumps(js))\n\n def __init__(self, name: str, desc: str, version: str, update: float, uid: str, dir: str, path: str):\n self.name = name\n self.desc = desc\n self.version = version\n self.update = update\n self.uid = uid\n self.dir = dir\n self.path = path\n\nclass Emeeter:\n dir: str\n name: str\n desc: str\n author: str\n uid: str\n widgets: list\n\n def __init__(self, dir: str, name: str, desc: str, author: str, uid: str):\n self.dir = dir\n self.name = name\n self.desc = desc\n self.author = author\n self.localisation = None\n self.uid = uid\n self.widgets = []\n\n \n\ndef listdir_fullpath(a_dir):\n return [name for name in os.listdir(a_dir)\n if os.path.isdir(os.path.join(a_dir, name))]\n\nasync def save(a, b):\n with open(str(Path(__file__).resolve().parent) + \"/data/\" + a, \"w\") as f:\n f.write(b)\n\ndef LoadLocale():\n try:\n global WINDOWLOCALe\n WINDOWLOCALe = json.loads(open(str(Path(__file__).resolve().parent)+\"/data/localisation.json\").read())[LOCALe]\n print(\"Localization set \" + LOCALe)\n except:\n print(\"An error occurred while loading the localization...\")\n\nclass WorkWindow(QMainWindow):\n items = {}\n\n def __init__(self, config):\n super().__init__()\n self.active_w = []\n self.config = config\n self.setMinimumSize(800, 390)\n self.setWindowFlags(Qt.Dialog)\n self.initUI()\n \n \"\"\"PRIVATE\"\"\"\n def initUI(self):\n self.theme = [\n 0x000000, #Default_color\n 0xffffff, #back_color\n 0x008cff, #Sellected_color\n 0x333333, #Active_color\n 0x333333, #BACTIVE\n 0xffffff, #BACTIVE\n [\n 0x363636,\n 0x0066ff\n ],\n 0x000000, #scrollbar\n [\n 0x333333, #checkbox active\n 0xfcfcfc #checkbox n active\n ]\n ]\n\n w = QDesktopWidget().screenGeometry()\n\n self.statusBar().setStyleSheet(\"background-color: #ffffff\")\n\n self.setWindowTitle('Wrain')\n\n self.setStatus(\"done_01\")\n\n self.setGeometry((w.width() - w.width() * 0.6)*0.5, (w.height() - w.height() * 0.5)*0.5, w.width() * 0.6, w.height() * 0.5)\n\n self.items[\"itemlist\"] = QSpeciaList(self)\n\n if(\"empty_01\" in WINDOWLOCALe):\n self.items[\"itemlist\"].empty_text = WINDOWLOCALe[\"empty_01\"]\n\n self.items[\"add-item\"] = QButtonE(self)\n self.items[\"add-item\"].logger = self.statusBar().showMessage\n\n self.items[\"d_tools\"] = layer()\n self.items[\"d_tools\"].setVisable(False)\n\n if(\"st_button_01\" in WINDOWLOCALe and \"st_button_03\" in WINDOWLOCALe and \"st_button_05\" in WINDOWLOCALe):\n self.items[\"header\"] = QHeader(self, [WINDOWLOCALe[\"st_button_01\"], WINDOWLOCALe[\"st_button_03\"], WINDOWLOCALe[\"st_button_05\"]])\n self.items[\"header\"].handler = self.headerTracker\n \n if(\"st_version_01\" in WINDOWLOCALe and \"st_desc_01\" in WINDOWLOCALe):\n self.items[\"body-left\"] = infoBlock(self, WINDOWLOCALe[\"st_version_01\"], WINDOWLOCALe[\"st_desc_01\"])\n \n self.items[\"body-right\"] = widgetManager(self)\n self.items[\"body-settings\"] = systemSettings(self)\n\n self.items[\"body-settings\"].listener = self.settingsChangeListener\n\n self.items[\"d_tools\"].add(self.items[\"header\"])\n self.items[\"d_tools\"].add(self.items[\"body-left\"])\n self.items[\"d_tools\"].add(self.items[\"body-right\"])\n self.items[\"d_tools\"].add(self.items[\"body-settings\"])\n\n self.updateProjectList()\n self.initStartup()\n\n def buildDescription(self, desc, localisation):\n def replacer(math):\n print(localisation.keys())\n for local in localisation.keys():\n if(local == math.group(1)):\n return localisation[local]\n else:\n return math.group(0)\n\n return re.sub(r\"\\{\\$([aA-zZ0-9_]+)\\}\", replacer, desc)\n\n def settingsChangeListener(self, index, pd, bl):\n item = self.getSellection()\n s = item.odata.widgets[item.sellected].getSettings()\n if(index == 0):\n s[\"on-the-top\"] = bl\n item.odata.widgets[item.sellected].saveSettings(s)\n\n \"\"\"PUBLIC\"\"\"\n def removeSellection(self):\n index = 0\n for item in self.items[\"itemlist\"].items:\n if(item.sellected != -2):\n del self.items[\"itemlist\"].items[index]\n shutil.rmtree(item.odata.dir)\n self.items[\"d_tools\"].setVisable(False)\n self.items[\"itemlist\"].update()\n index+=1\n\n \"\"\"PRIVATE\"\"\"\n def headerTracker(self, index):\n item = self.getSellection()\n\n if(index == 0):\n itemid = str(item.odata.uid + \"#\" + str(item.sellected))\n\n if(item.sellected != -1):\n if(self.getWindowByUid(itemid) == None):\n self.startup(item)\n if(\"st_button_02\" in WINDOWLOCALe):\n self.items[\"header\"].setText(0, WINDOWLOCALe[\"st_button_02\"])\n self.items[\"itemlist\"].update()\n else:\n index = 0\n for active in self.active_w:\n if(str(active.id) == itemid):\n self.setStatus(\"st_rm_complete_01\")\n self.active_w[index].close()\n item.active.remove(item.sellected)\n del self.active_w[index]\n if(\"st_button_01\" in WINDOWLOCALe):\n self.items[\"header\"].setText(0, WINDOWLOCALe[\"st_button_01\"])\n self.items[\"itemlist\"].update()\n return\n index += 1\n return\n \n if(index == 1):\n #AUTOSTART\n if(not item.odata.widgets[item.sellected].uid in self.config.get(\"active_emiters\")):\n self.config.get(\"active_emiters\").append(item.odata.widgets[item.sellected].uid)\n if(\"st_button_04\" in WINDOWLOCALe):\n self.items[\"header\"].setText(1, WINDOWLOCALe[\"st_button_04\"])\n aysync.run_await(save(\"config.json\", json.dumps(self.config, sort_keys=True, indent=4)))\n else:\n self.config.get(\"active_emiters\").remove(item.odata.widgets[item.sellected].uid)\n if(\"st_button_03\" in WINDOWLOCALe):\n self.items[\"header\"].setText(1, WINDOWLOCALe[\"st_button_03\"])\n aysync.run_await(save(\"config.json\", json.dumps(self.config, sort_keys=True, indent=4)))\n return\n \n if(index == 2):\n explorer.openFolder(item.odata.dir)\n\n \"\"\"PUBLIC\"\"\"\n def getSellection(self):\n for item in self.items[\"itemlist\"].items:\n if(item.sellected != -2):\n return item\n return -2\n \"\"\"PRIVATE\"\"\"\n def updateProjectList(self):\n rt_list = []\n self.setStatus(\"loading_01\")\n decoder = json.JSONDecoder()\n \n if(os.path.exists(installer.WORKPATH)):\n\n for dir in listdir_fullpath(installer.WORKPATH):\n dir = installer.WORKPATH + dir\n\n if(os.path.exists(dir + \"/project.json\")):\n with open(dir + \"/project.json\") as f:\n decoded = decoder.decode(f.read())\n\n section = QTSection(decoded.get(\"package\").get(\"name\"))\n\n section.odata = Emeeter(\n dir,\n decoded.get(\"package\").get(\"name\").replace(\"\\n\", \"|\"),\n decoded.get(\"package\").get(\"desc\"),\n decoded.get(\"package\").get(\"author\"),\n decoded.get(\"uid\")\n )\n if(os.path.exists(dir + \"/localisation.json\")):\n try:\n with open(dir + \"/localisation.json\") as F:\n local = decoder.decode(F.read())\n section.odata.localisation = local.get(LOCALe)\n except:\n self.setStatus(\"st_no_localisation\")\n else:\n self.setStatus(\"st_no_localisation\")\n\n section.addHandler(self.loadWidgetMenu)\n\n for wg in decoded.get(\"widgets\"):\n with open(dir + \"/\" + str(wg) + \".json\") as w:\n wdecode = decoder.decode(w.read())\n section.addChildren(wdecode.get(\"widget\").get(\"name\"))\n section.odata.widgets.append(WidgetEmeeter(\n wdecode.get(\"widget\").get(\"name\").replace(\"\\n\", \"|\"),\n wdecode.get(\"widget\").get(\"description\"),\n str(wdecode.get(\"widget\").get(\"version\") if wdecode.get(\"widget\").get(\"version\") != None else decoded.get(\"package\").get(\"version\")),\n wdecode.get(\"widget\").get(\"update\"),\n wdecode.get(\"uid\"),\n dir,\n dir + \"/\" + str(wg) + \".json\"\n ))\n \n rt_list.append(section)\n else:\n shutil.rmtree(dir)\n self.items[\"itemlist\"].items = rt_list\n self.items[\"itemlist\"].update()\n self.setStatus(\"done_01\")\n \n \"\"\"PRIVATE\"\"\"\n def loadWidgetMenu(self, data, type):\n self.items[\"d_tools\"].setVisable(type != None and data.sellected != -1)\n sets = data.odata.widgets[data.sellected].getSettings()\n itemid = str(data.odata.uid + \"#\" + str(data.sellected))\n sellected = data.odata.widgets[data.sellected]\n self.items[\"header\"].setText(-1, sellected.name)\n\n if(type != None):\n self.items[\"body-settings\"].clear()\n\n description = sellected.desc if sellected.desc != None else WINDOWLOCALe[\"st_no_description\"]\n description = self.buildDescription(description, data.odata.localisation if data.odata.localisation != None else {})\n\n if(sets != None):\n self.items[\"body-settings\"].addParameter(WINDOWLOCALe[\"dr_dx11_mp\"], sets.get(\"on-the-top\") if sets.get(\"on-the-top\") != None else False)\n\n if(not sellected.uid in self.config.get(\"active_emiters\")):\n if(\"st_button_03\" in WINDOWLOCALe):\n self.items[\"header\"].setText(1, WINDOWLOCALe[\"st_button_03\"])\n else:\n if(\"st_button_04\" in WINDOWLOCALe):\n self.items[\"header\"].setText(1, WINDOWLOCALe[\"st_button_04\"])\n \n if(self.getWindowByUid(itemid) == None):\n if(\"st_button_01\" in WINDOWLOCALe):\n self.items[\"header\"].setText(0, WINDOWLOCALe[\"st_button_01\"])\n else:\n if(\"st_button_02\" in WINDOWLOCALe):\n self.items[\"header\"].setText(0, WINDOWLOCALe[\"st_button_02\"])\n\n self.items[\"body-left\"].restore()\n self.items[\"body-left\"].setText(getSTR(sellected.version), description)\n \n self.update()\n \n return\n \"\"\"PRIVATE\"\"\"\n\n def getWindowByUid(self, uid):\n for act in self.active_w:\n if(act.id == uid):\n return act\n else:\n return None\n\n \"\"\"PRIVATE\"\"\"\n def startup(self, item, index = None):\n index = index if index != None else item.sellected\n\n DECODER = json.JSONDecoder()\n\n with open(item.odata.widgets[index].path) as f:\n decode_s = DECODER.decode(f.read())\n\n if(decode_s.get(\"widget\") != None):\n drawer = os.path.join(item.odata.dir, str(index)+\".py\")\n drawername = \".py\"\n\n if(decode_s.get(\"widget\").get(\"drawer\") != None):\n drawer = os.path.join(item.odata.dir, decode_s.get(\"widget\").get(\"drawer\"))\n drawername = decode_s.get(\"widget\").get(\"drawer\")\n\n settings = Settings(None, None, item.odata.name, None)\n if(decode_s.get(\"widget\").get(\"default\") != None):\n settings = Settings(\n decode_s.get(\"widget\").get(\"default\").get(\"width\"),\n decode_s.get(\"widget\").get(\"default\").get(\"height\"),\n item.odata.name,\n decode_s.get(\"widget\").get(\"default\").get(\"origin\")\n )\n\n item.active.append(index)\n item.wid = item.odata.uid\n\n self.items[\"d_tools\"].update()\n\n try:\n w = widget.Widget(decode_s.get(\"widget\"), drawer, drawername, settings, self, item.odata.uid + \"#\" + str(index))\n \n s = item.odata.widgets[index].getSettings()\n if(s.get(\"x\") != None and s.get(\"y\") != None):\n w.move(s[\"x\"], s[\"y\"])\n \n self.active_w.append(w)\n except:\n self.setStatus(\"st_initialisation_error\")\n else:\n self.setStatus(\"st_initialisation_error\")\n\n def initStartup(self):\n emiters = []\n for item in self.items[\"itemlist\"].items:\n index = 0\n for sl in item.odata.widgets:\n if(sl.uid in self.config[\"active_emiters\"]):\n print(\"Run \"+sl.uid)\n self.startup(item, index)\n emiters.append(sl.uid)\n index += 1\n for emiter in self.config[\"active_emiters\"]:\n if(not emiter in emiters):\n self.config[\"active_emiters\"].remove(emiter)\n aysync.run_await(save(\"config.json\", json.dumps(self.config, indent=4)))\n\n def closeEvent(self, event):\n self.hide()\n if(len(self.active_w) == 0):\n def show():\n self.show()\n del self.active_w[0]\n if(\"st_open_01\" in WINDOWLOCALe):\n dw = DefaultWidget.Widget(WINDOWLOCALe[\"st_open_01\"])\n dw.show()\n dw.mousePressEvent = lambda event: show()\n self.active_w.append(dw)\n event.ignore()\n\n def resizeEvent(self, event):\n l_bar = self.width() * 0.4 if self.width() * 0.4 < 300 else 300\n \n self.items[\"itemlist\"].resize(l_bar, self.height() - 52)\n\n self.items[\"add-item\"].resize(l_bar, 30)\n self.items[\"add-item\"].move(0, self.height() - 52)\n\n self.items[\"header\"].move(l_bar, 0)\n self.items[\"header\"].resize(self.width() - l_bar, 30)\n\n self.items[\"body-right\"].resize(self.width() - l_bar*2, self.height() - 30 - 150)\n self.items[\"body-right\"].move(l_bar*2, 30)\n\n self.items[\"body-left\"].resize(l_bar, self.height() - 30 - 150)\n self.items[\"body-left\"].move(l_bar, 30)\n\n self.items[\"body-settings\"].resize(l_bar, 150)\n self.items[\"body-settings\"].move(l_bar, self.height() - 150)\n\n #common_autostart\n def setStatus(self, status):\n if(status in WINDOWLOCALe):\n self.statusBar().showMessage(WINDOWLOCALe[status])", "sub_path": "main_window.py", "file_name": "main_window.py", "file_ext": "py", "file_size_in_byte": 16968, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "json.JSONDecoder", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "json.JSONDecoder", "line_number": 42, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 50, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 85, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 91, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 91, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 177, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 192, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 231, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 236, "usage_type": "call"}, {"api_name": "explorer.openFolder", "line_number": 240, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 252, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path", "line_number": 254, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path", "line_number": 259, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path", "line_number": 272, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 300, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 355, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 361, "usage_type": "call"}, {"api_name": "os.path", "line_number": 361, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 365, "usage_type": "call"}, {"api_name": "os.path", "line_number": 365, "usage_type": "attribute"}, {"api_name": "widget.Widget", "line_number": 383, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 408, "usage_type": "call"}, {"api_name": "DefaultWidget.Widget", "line_number": 417, "usage_type": "call"}]} +{"seq_id": "98094622", "text": "from __future__ import print_function\nimport operator\nimport codecs\nimport glob\nimport backendDefs as bk\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.cluster import KMeans, MiniBatchKMeans\nfrom time import time\n\nimport numpy as np\nimport sys,os\n\nimport pLSABet\nimport pickle\ndef getRelTweets(newsID,dtpure,tweetPre,tweetIDset,tweetSet):\n t_path = glob.glob(tweetPre+dtpure+\"/\"+str(newsID)+\"_*\")\n if len(t_path) != 1:\n print('no tweets for news ',newsID,'len(t_path)',len(t_path))\n return ([],[])\n if os.path.exists(t_path[0]):\n t = codecs.open(t_path[0], encoding = 'utf-8') \n tweets = []\n tweetsObj = []\n# stupid redundancy\n for line in t:\n fields = line.strip().split(\"\\t\")\n if len(fields) < 24:\n # tweets_log.write(\"not 27:\"+line.strip()+\"\\n\")\n continue\n ID, raw_text, created_at, contained_url, hash_tags, retw_id_str, retw_favorited, retw_favorite_count, is_retweet, retweet_count, \\\n tw_favorited, tw_favorite_count, tw_retweeted, tw_retweet_count, user_id_str, verified, follower_count, statuses_count, friends_count, \\\n favorites_count, user_created_at= fields[:21]\n try:\n ID = int(ID)\n except:\n continue\n try:\n is_retweet=bool(is_retweet)\n except:\n is_retweet=False\n try:\n retweet_count = int(retweet_count)\n except:\n retweet_count = -1\n if \"http\" not in raw_text and \"RT @\" not in raw_text \\\n and ID not in tweetIDset and raw_text not in tweetSet:\n tweet = bk.Tweet(ID,raw_text,created_at,is_retweet,retweet_count,hash_tags)\n tweetsObj.append(tweet)\n tweets.append(raw_text)\n tweetIDset.add(ID)\n tweetSet.add(raw_text)\n t.close()\n return (tweets,tweetsObj)\ndef rankTweets(tweets, tweetsObj, newsVec, vocab, t_topK):\n tweetVectorizer = TfidfVectorizer(max_df=0.5,\n min_df=2, stop_words='english',\n vocabulary=vocab)\n X = tweetVectorizer.fit_transform(tweets)\n scores = X.dot(newsVec)\n tweetsInd = scores.argsort()[::-1][:t_topK]\n topTweetsObj = [tweetsObj[i] for i in tweetsInd]\n topTweetsScore = {}\n for i in tweetsInd:\n topTweetsScore[tweetsObj[i].ID] = scores[i]\n return topTweetsObj,topTweetsScore\n\n\ndef printCluster(X,i,terms,outfile,ind2obj,t_topK,tweetPre,Pw_z,wordInd,docInd):\n print(\"Cluster %d:\" % i, end='')\n outfile.write(\"Cluster %d:\" % i)\n print()\n outfile.write('\\n')\n tweets = []\n tweetsObj = []\n M = 50\n N = 10\n for j in range(M):\n sys.stdout.write('\\t'+terms[wordInd[j,i]])\n sys.stdout.write('\\n')\n for k in range(N):\n news = ind2obj[docInd[i,k]]\n print(news.title)\n\n\n# newsList = [ind2obj[ind] for ind in clus2doc[i]]\n# for news in sorted(newsList, key=operator.attrgetter('created_at')):\n# for ind in clus2doc[i]:\n# news = ind2obj[ind]\n\n# print(str(news.created_at)+\"\\t\"+news.title) #+\"\\t\"+news.raw_text+\"\\t\"+news.source)\n outfile.write(str(news.created_at)+\"\\t\"+news.title+\"\\n\")\n print(\"-------\")\n outfile.write(\"-------\\n\")\n newsID = news.ID\n dtpure = news.dtpure\n tweetIDset = set()\n tweetSet = set()\n #if getRelTweets(newsID,dtpure,tweetPre, tweetIDset,tweetSet):\n addtweets,addtweetsObj = getRelTweets(newsID,dtpure,tweetPre,tweetIDset,tweetSet) \n tweets = tweets + addtweets\n tweetsObj = tweetsObj + addtweetsObj\n # tweets = tweets | getRelTweets(newsID)\n # tweets = list(tweets)\n if tweets:\n newsCenter = Pw_z[:,i]\n #newsCenter = np.squeeze(np.asarray(getNewsCenter(X,clus2doc[i])))\n for term in newsCenter.argsort()[::-1][:20]:\n print(' %s' % terms[term], end='')\n outfile.write(' %s' % terms[term])\n #topTweets = rankTweets(tweets, clusModel.cluster_centers_[i,:], vectorizer.vocabulary_,t_topK)\n topTweetsObj,topTweetsScore = rankTweets(tweets,tweetsObj, newsCenter, terms,t_topK)\n print(\"*******total tweets: \"+str(len(tweets)))\n outfile.write(\"\\n*******top tweets:********total tweets: \" + str(len(tweets))+\"\\n\")\n print(\"top tweets:\")\n for t in sorted(topTweetsObj, key=operator.attrgetter('created_at')):\n print(str(topTweetsScore[t.ID])+\"\\t\"+str(t.created_at)+\"\\t\" + t.raw_text )\n outfile.write(str(topTweetsScore[t.ID])+\"\\t\"+str(t.created_at)+\"\\t\" + t.raw_text+\"\\n\")\n outfile.write('\\n-------\\n')\n print(\"-------\")\n else:\n print(\"no tweets retrieved\")\n outfile.write(\"no tweets retrieved\\n\")\n print(\"=========\")\n outfile.write(\"=========\\n\\n\")\n print()\ndef inittime(DT,K,labels):\n mu = np.zeros(K)\n sigma = np.zeros(K)\n for i in range(K):\n ts = np.array(DT)[labels==i]\n mu[i] = np.mean(ts)\n sigma[i] = np.std(ts)\n return mu,sigma\n \n\n# input args: K display\nwith open('test30.pickle') as f:\n [X,Xp,Xl,Xo,X_all,K,Learn,Pz_d_km,Pw_z_km,Pw_z,Pz_d,Pd,Li,\\\n labels,terms,termsp,termsl,termso,terms_all,DT,ind2obj,clusModel]=pickle.load(f)\nif K!=int(sys.argv[1]):\n km = MiniBatchKMeans(n_clusters=k, init='k-means++', n_init=100,init_size=1000,\n batch_size=1000,verbose=True)\n km.fit(X)\n labels = km.labels_\n centers = km.cluster_centers_\n clus2doc = {}\n for i in range(len(labels)):\n clus2doc[labels[i]] = clus2doc.get(labels[i],set())\n clus2doc[labels[i]].add(i) \n## print number of docs in each cluster \n for i in clus2doc:\n print (str(i+1)+\"\\t\"+str(len(clus2doc[i])))\n\nt0 = time()\nLearn=(1,10)\nselectTime = 1\nnumX = 1\n#K=30\ndata = [X, DT]\nmu_km, sigma_km= inittime(DT,K,labels)\ninits = [Pz_d_km,Pw_z_km,mu_km,sigma_km]\nwt = 0.5\nlambdaB = 0.5\n# data = [Xs,DT]\n# inits = [Pz_d,Pw_z, Pp_z,Pl_z,Po_z,mu,sigma] \nPw_zs,Pz_d,Pd,mu,sigma,Li = pLSABet.pLSABet(selectTime,numX,Learn,data,inits,wt,lambdaB,1)\nprint( \"pLSA done in \"+str(time() - t0))\ntweetPre=\"/home/wtong8/NewsTwitter/tweets/\"\noutfile = codecs.open(sys.argv[3], 'w', encoding = 'utf-8')\nM = 50\nN = 10\nwordInd = Pw_zs[0].argsort(axis=0)[::-1,:]\ndocInd = Pz_d.argsort()[:,::-1]\nt_topK=100\nfor i in range(K): \n printCluster(X,i,terms,outfile,ind2obj,t_topK,tweetPre,Pw_zs[0],wordInd,docInd)\nexit(0)\n###################### split event###########################\ndef weightX(X,Pw_z,Pz_d):\n K = Pz_d.shape[0]\n X = X.tocoo()\n docind,wordind,value = (X.row,X.col,X.data)\n # Pz_do_f = Pz_do.*(Pz_do>(1-lambdaB)/double(K-1))\n Pz_d_f = Pz_d*(Pz_d>0.01)\n Pz_dw_ = Pw_z[wordind,:].T*Pz_d_f[:,docind]\n Pw_d = Pz_dw_.sum(axis=0) # 1 x nnz\n Pz_wd = Pz_dw_[:-1,:]/np.tile(Pw_d,(K-1,1))\n n_wdxPz_wd = np.tile(value,(K-1,1))*Pz_wd\n n_wdxPz_wd = n_wdxPz_wd *(n_wdxPz_wd>0.0001) ####\n return n_wdxPz_wd\n\nn_wdxPz_wds = []\nfor i in range(numX):\n n_wdxPz_wds.append( weightX(data[i],Pw_zs[i],Pz_d) )\nfrom scipy.sparse import coo_matrix\n# get event matrices\ndef selectTopic(Xs,n_wdxPz_wds,event):\n Xevents = [] \n for i in range(len(Xs)):\n X = Xs[i]\n n_wdxPz_wd = n_wdxPz_wds[i]\n nDocs,nWords=X.shape\n docind,wordind,value = (X.row,X.col,X.data) \n value = n_wdxPz_wd[event,:]\n select = (value!=0)\n value_f = value[select]\n row_f = docind[select] # 1 3 3 5 5 6 6 6 \n col_f = wordind[select] \n if i==0:\n dID = np.unique(row_f) # 1 3 5 6\n dID2ind = -np.ones(nDocs) # -1 -1 -1 -1 -1 -1 -1 assume nDocs = 7\n dID2ind[dID] = np.arange(len(dID)) # 0 0 1 0 2 3 0\n row_f_new = dID2ind[row_f] # 0 1 1 2 2 3 3 3\n if i>0:\n select = (row_f_new!=-1)\n Xevent = coo_matrix((value_f[select],(row_f_new[select],col_f[select])),shape=(len(dID),nWords))\n else:\n Xevent = coo_matrix((value_f,(row_f_new,col_f)),shape=(len(dID),nWords))\n Xevents.append(Xevent) \n return Xevents,dID\n###################### step 1 ###############\nevent = 0 # event number\nXevents,dID = selectTopic(data[:numX],n_wdxPz_wds,event)\nDTevent = np.array(DT)[dID]\n#data = Xevents+[DTevent] \n########################## event example ###############\nKevent=5\nkm = KMeans(n_clusters=Kevent, init='k-means++', max_iter=100, n_init=5)\nkm.fit(Xevents[0])\nlabels = km.labels_\ncenters = km.cluster_centers_\n\nnDocs,nWords = Xevents[0].shape\nPz_d_km = np.zeros((Kevent,nDocs))\nfor i in range(nDocs):\n Pz_d_km[labels[i],i] = 1\nPz_d_km = Pz_d_km +0.01;\nPz_d_km = Pz_d_km / np.tile(sum(Pz_d_km),(Kevent,1))\nC = centers.T+1/nWords/nWords\nPw_z_km = C/np.tile(sum(C),(nWords,1))\n\nt0 = time()\nLearn=(1,10)\nselectTime = 1\nnumX = 1\n#K=30\ndata = [Xevents[0], DTevent]\nmu_km, sigma_km= inittime(DTevent,Kevent,labels)\ninits = [Pz_d_km,Pw_z_km,mu_km,sigma_km]\nwt = 0.1\nlambdaB = 0.5\n# data = [Xs,DT]\n# inits = [Pz_d,Pw_z, Pp_z,Pl_z,Po_z,mu,sigma] \nPw_zs,Pz_d,Pd,mu,sigma,Li = pLSABet.pLSABet(selectTime,numX,Learn,data,inits,wt,lambdaB,1)\nprint (\"pLSA done in \"+str(time() - t0))\n#################################################################################\n# print topics\ndisplay = int(sys.argv[2])\nif display == 1:\n M = 50\n N = 10\n wordInd = Pw_zs[0].argsort(axis=0)[::-1,:]\n docInd = Pz_d.argsort()[:,::-1]\n for i in range(Kevent): #\n sys.stdout.write(\"topic \"+str(i))\n for j in range(M):\n sys.stdout.write('\\t'+terms[wordInd[j,i]])\n sys.stdout.write('\\n')\n for k in range(N):\n print(ind2obj[dID[docInd[i,k]]].title) #\n", "sub_path": "scripts/dataJesse.py", "file_name": "dataJesse.py", "file_ext": "py", "file_size_in_byte": 9904, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "glob.glob", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 21, "usage_type": "call"}, {"api_name": "backendDefs.Tweet", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 78, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 78, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 79, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 79, "usage_type": "attribute"}, {"api_name": "operator.attrgetter", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 132, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 139, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 140, "usage_type": "attribute"}, {"api_name": "sklearn.cluster.MiniBatchKMeans", "line_number": 141, "usage_type": "call"}, {"api_name": "time.time", "line_number": 154, "usage_type": "call"}, {"api_name": "pLSABet.pLSABet", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 167, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 169, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.tile", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 212, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 216, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 224, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 240, "usage_type": "call"}, {"api_name": "time.time", "line_number": 242, "usage_type": "call"}, {"api_name": "pLSABet.pLSABet", "line_number": 254, "usage_type": "call"}, {"api_name": "time.time", "line_number": 255, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 258, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 265, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 265, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 267, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 267, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 268, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 268, "usage_type": "attribute"}]} +{"seq_id": "453323200", "text": "from configparser import ConfigParser\nfrom pattern.counter import Counter\n\nimport pdb\nimport logging\nimport datetime\nimport math\n\n# create logger\ntl_logger = logging.getLogger(__name__)\ntl_logger.setLevel(logging.DEBUG)\n\nclass TradeList(object):\n '''\n Class that represents a list of Trade objects\n\n Class variables\n ---------------\n tlist : list, Required\n List of Trade objects\n settingf : str, Optional\n Path to *.ini file with settings\n settings : ConfigParser object generated using 'settingf'\n Optional\n ser_data_obj : ser_data_obj, Optional\n ser_data_obj with serialized data\n '''\n\n def __init__(self, tlist, settingf=None, settings=None, ser_data_obj=None):\n self.settingf = settingf\n self.tlist = tlist\n self.ser_data_obj = ser_data_obj\n\n if self.settingf is not None:\n # parse settings file (in .ini file)\n parser = ConfigParser()\n parser.read(settingf)\n self.settings = parser\n else:\n self.settings = settings\n\n def analyze(self):\n '''\n Analyze each of the Trade objects in TradeList depending\n on value of settings.get('counter', 'strats') and add the\n calculated attributes to Trade\n\n :returns\n TradeList\n '''\n\n #these are the strategies that will be analysed using the Counter pattern\n tl_logger.info(\"Strategies that will be analysed: {0}\".format(self.settings.get('counter', 'strats')))\n strats = self.settings.get('counter', 'strats').split(\",\")\n trade_list = []\n for t in self.tlist:\n tl_logger.info(\"Processing trade: {0}-{1}\".format(t.pair, t.start))\n if t.strat in strats:\n if t.entered is False and (not hasattr(t, 'outcome') or math.isnan(t.outcome) is True):\n t.run_trade()\n c = Counter(trade=t,\n settingf=self.settingf,\n settings=self.settings,\n ser_data_obj=self.ser_data_obj,\n init_feats=True)\n tl_logger.debug(\"Counter attributes analysed:{0}\".format(self.settings.get('counter', 'attrbs').split(\",\")))\n attrb_ls = self.settings.get('counter', 'attrbs').split(\",\")\n for a in attrb_ls:\n if hasattr(c, a) is True:\n # add 'a' attribute to Trade object\n setattr(t, a, getattr(c, a))\n else:\n tl_logger.warn(\"Attribute {0} is not defined in Counter object. Skipping...\".format(a))\n setattr(t, a, \"n.a.\")\n else:\n tl_logger.debug(\"Trade.strat ({0}) is not within list of trades to analyse. Skipping...\".format(t.strat))\n trade_list.append(t)\n tl_logger.info(\"Done\")\n tl = TradeList(settingf=self.settingf,\n settings=self.settings,\n tlist=trade_list)\n\n return tl\n\n def win_rate(self, strats):\n '''\n Calculate win rate and pips balance\n for this TradeList. If outcome attrb is not\n defined then it will invoke the run_trade method\n on each particular trade\n\n Parameters\n ----------\n strats : str\n Comma-separated list of strategies to analyse: i.e. counter,counter_b1\n\n :return:\n int : number of successes\n int : number of failures\n pips : pips balance in this TradeList\n '''\n\n strat_l = strats.split(\",\")\n number_s = 0\n number_f = 0\n tot_pips = 0\n for t in self.tlist:\n if t.strat not in strat_l:\n continue\n if not hasattr(t, 'outcome'):\n t.run_trade()\n if t.outcome == 'success':\n number_s += 1\n elif t.outcome == 'failure':\n number_f += 1\n tot_pips += t.pips\n tot_pips = round(tot_pips, 2)\n tot_trades = number_s+number_f\n perc_wins = round(number_s*100/tot_trades, 2)\n perc_losses = round(number_f*100/tot_trades, 2)\n print(\"Tot number of trades: {0}\\n-------------\".format(tot_trades))\n print(\"Win trades: {0}; Loss trades: {1}\".format(number_s, number_f))\n print(\"% win trades: {0}; % loss trades: {1}\".format(perc_wins, perc_losses))\n print(\"Pips balance: {0}\".format(tot_pips))\n\n return number_s, number_f, tot_pips\n\n\n\n", "sub_path": "trade_journal/trade_list.py", "file_name": "trade_list.py", "file_ext": "py", "file_size_in_byte": 4592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 11, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 36, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 59, "usage_type": "call"}, {"api_name": "pattern.counter.Counter", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "631579600", "text": "import cv2\nimport numpy as np\nimport os\nimport sys\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\n\n#Total number of categories in your data\nNUM_CATEGORIES=6\n#Resize of your image\nIMG_WIDTH = 400\nIMG_HEIGHT = 400\n#Identifier for your data i.e: if you want to give a more descriptive name \nclass_names = ['Candado', 'Tostadora', 'Tornillo', 'Micro', 'Tanque', 'Hazard']\n#Test percentage to divide into train and validation\nTEST_SIZE=0.5\n\n\ndef main():\n model = tf.keras.models.load_model('modelo.h5')\n images, labels = load_data(\"database\\\\validation\")\n # Split data into training and testing sets\n labels = tf.keras.utils.to_categorical(labels)\n images_test, images_train, labels_test, labels_train = train_test_split(np.array(images), np.array(labels), test_size=TEST_SIZE)\n #Predict all images in images_test\n predictions = model.predict(images_test)\n\n #print('La imagen predecida es: ',np.argmax(predictions[0]))\n #print('La imagen real es: ',np.argmax(labels_test[0]))\n\n #Get just one image from the predicions and plot its results\n #i = 4\n #plt.figure(figsize=(6,3))\n #plt.subplot(1,2,1)\n #plot_image(i, predictions[i], labels_test, images_test)\n #plt.subplot(1,2,2)\n #plot_value_array(i, predictions[i], labels_test)\n #plt.show()\n\n # Plot X test images, their predicted labels, and the true labels.\n # Color correct predictions in Blue and incorrect predictions in Red, Low predictions Gray.\n #Quantity of images to show\n num_rows = 7\n num_cols = 7\n num_images = num_rows*num_cols\n plt.figure(figsize=(4*num_cols, 2*num_rows))\n for i in range(num_images):\n plt.subplot(num_rows, 2*num_cols, 2*i+1)\n plot_image(i, predictions[i], labels_test, images_test)\n plt.subplot(num_rows, 2*num_cols, 2*i+2)\n plot_value_array(i, predictions[i], labels_test)\n plt.tight_layout()\n plt.show()\n\n#Functions Load Data\ndef load_data(data_dir):\n #Initialize list to store images and labels\n images = []\n labels = []\n #Get the path of the data\n filepath = os.path.abspath(data_dir)\n #Iterate through all folder categories\n for i in range(NUM_CATEGORIES):\n #Join the path of the data with the exact category folder to iterate\n #Change to that NEW path\n os.chdir(os.path.join(filepath, str(i)))\n #Iterate through all the images inside that category\n for image in os.listdir(os.getcwd()):\n #Read that image as an array\n img = cv2.imread(image, cv2.IMREAD_COLOR)\n #If it has data\n if img.size != 0:\n #Resize it accordingly\n img = cv2.resize(img, (IMG_WIDTH, IMG_HEIGHT))\n #Append information of image and category\n images.append(img)\n labels.append(i)\n #Change path to folder to sotre the model on root\n os.chdir(filepath)\n return (images, labels)\n\n#Plot images predicted\ndef plot_value_array(i, predictions_array, true_label):\n predictions_array, true_label = predictions_array, true_label[i]\n plt.grid(False) #Turn off grid\n plt.xticks(range(NUM_CATEGORIES)) #X label with 43 ticks as we have 43 possible categories\n plt.yticks([]) #Without y ticks\n thisplot = plt.bar(range(NUM_CATEGORIES), predictions_array, color=\"#777777\") #Plot the bar\n plt.ylim([0, 1]) #Y label limit from 0 to 1 i.e: 0 no match, 1 fully identified\n predicted_label = np.argmax(predictions_array) #Get the index of the max value\n label = np.argmax(true_label) #Get the index of the max value\n #MAtch the values accordingly if it is a value inside predicted RED, label BLUE\n thisplot[predicted_label].set_color('red')\n thisplot[label].set_color('blue')\n\ndef plot_image(i, predictions_array, true_label, img):\n predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n plt.grid(False) #Trun off grids\n plt.xticks([]) #Without axis\n plt.yticks([])\n\n plt.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))\n #Store de index of the max value i.e: the probable image\n predicted_label = np.argmax(predictions_array) \n #Store de index of the max label i.e: the correct image\n label = np.argmax(true_label) \n #If the image was identified correctly\n if predicted_label == label:\n #Plot it blue\n color = 'blue'\n #If not plot it red\n else:\n color = 'red'\n #The label for the image: The predicted image, the percentage of accuracy, the ID.\n plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n 100*np.max(predictions_array),\n class_names[label]),\n color=color)\n\nif __name__ == \"__main__\":\n main()", "sub_path": "predictor.py", "file_name": "predictor.py", "file_ext": "py", "file_size_in_byte": 4770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "tensorflow.keras.models.load_model", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 69, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 71, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 75, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "52306678", "text": "import sqlite3\nimport datetime\n\ntoday = datetime.date.today()\n\n\ndef create_connection(db_file):\n \"\"\" create a database connection to the SQLite database\n specified by the db_file\n :param db_file: database file\n :return: Connection object or None\n \"\"\"\n try:\n conn = sqlite3.connect(db_file)\n return conn\n except Error as e:\n print(e)\n\n return None\n\n\ndef delete_task(conn, check_out):\n \"\"\"\n Delete a task by task id\n :param conn: Connection to the SQLite database\n :param id: id of the task\n :return:\n \"\"\"\n sql = 'DELETE FROM user WHERE check_out=?'\n cur = conn.cursor()\n cur.execute(sql, (str(check_out),))\n\n\ndef delete_all_tasks(conn):\n \"\"\"\n Delete all rows in the tasks table\n :param conn: Connection to the SQLite database\n :return:\n \"\"\"\n sql = 'DELETE FROM user'\n cur = conn.cursor()\n cur.execute(sql)\n\n\ndef main():\n database = \"site.db\"\n\n # create a database connection\n conn = create_connection(database)\n with conn:\n delete_task(conn, today)\n # delete_all_tasks(conn);\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "ef_palace/filterDB.py", "file_name": "filterDB.py", "file_ext": "py", "file_size_in_byte": 1142, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "datetime.date.today", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "598448705", "text": "from keras.layers import Dense, LSTM, Activation, Embedding, Dropout\nfrom keras.models import Sequential\n\nhparams = {\n 'optimizer': 'adma',\n 'loss': 'sparse_categorical_crossentropy',\n 'activation': 'softmax'\n}\n\ndef build_lstm_model(vocab_size, embedding_size, pretrained_weights):\n ''' \n Neural Network Architecture\n '''\n\n model = Sequential()\n model.add(Embedding(input_dim=vocab_size, output_dim=embedding_size, weights=[pretrained_weights]))\n model.add(LSTM(units=embedding_size, return_sequences=True))\n model.add(Dropout(0.4))\n model.add(LSTM(100))\n model.add(Dropout(0.2))\n model.add(Dense(units=vocab_size))\n model.add(Activation(hparams['activation']))\n model.compile(optimizer=hparams['optimizer'], loss=hparams['loss'])\n\n model.summary()\n return model\n\n\n'''\nmodel = Sequential()\nmodel.add(Bidirectional(LSTM(128), input_shape=(SEQUENCE_LEN, len(words))))\nif dropout > 0:\n model.add(Dropout(dropout))\nmodel.add(Dense(len(words)))\nmodel.add(Activation('softmax'))\n'''", "sub_path": "Iteration-1/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 1030, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "keras.models.Sequential", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 17, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "185908320", "text": "\"\"\"\nauthor : zhancc\nfilename : md5sum.py\ndate : 2020/06/09 16:24:56\ndescription : 计算文件的MD5值，在当前目录生成md5.txt;\n 查找某个目录下的所有特定格式文件，在同目录下生成md5.txt。\n\"\"\"\n\nimport os\nimport codecs\nimport hashlib\nimport argparse\n\n\ndef md5sum(file_path: str):\n \"\"\"\n 计算文件的MD5\n :param file_path: str\n :return: str\n \"\"\"\n if not os.path.exists(file_path):\n raise FileExistsError(\"{0} cannot be found\".format(file_path))\n if not os.path.isfile(file_path):\n raise TypeError(\"{0} is not a file\".format(file_path))\n sh = hashlib.md5()\n with open(file_path, \"rb\") as fp:\n buff = b\"\"\n while True:\n buff = fp.read(4096)\n if buff:\n sh.update(buff)\n else:\n break\n return sh.hexdigest()\n\n\ndef output_to_file(file_path: str, md5_dict: dict):\n \"\"\"\n 将MD5值和文件名输出到同级目录的md5.txt中\n :param file_path:\n :param md5_dict:\n :return:\n \"\"\"\n with codecs.open(file_path, \"w\", \"utf-8\") as fp:\n for key in md5_dict:\n md5_string = \"{0} {1}\".format(key, md5_dict.get(key))\n fp.write(md5_string + \"\\n\")\n print(md5_string)\n\n\ndef find_files(directory: str, formats: tuple = (\"sql\",)):\n \"\"\"\n 递归查找特定格式的文件\n :param directory: str\n :param formats: list\n :return: str\n \"\"\"\n for item in os.listdir(directory):\n path = os.path.join(directory, item)\n if os.path.isfile(path):\n if item.split(\".\")[-1] in formats:\n base_dir = \"\\\\\".join(path.split(\"\\\\\")[:-1])\n md5_path = os.path.join(base_dir, \"md5.txt\")\n output_to_file(md5_path, {item: md5sum(path)})\n else:\n pass\n elif os.path.isdir(path):\n find_files(path, formats)\n\n\ndef get_options():\n parser = argparse.ArgumentParser(\n prog=\"md5sum\", description=\"\", prefix_chars=\"-\",\n add_help=True, allow_abbrev=True, usage=\"\"\"\n python md5sum.py -d directory1 directory2 -t sql\n python md5sum.py -f game1.sql game2.sql\"\"\"\n )\n parser.add_argument(\"-f\", \"--file\", dest=\"files\", nargs=\"+\", help=\"file to md5sum\")\n parser.add_argument(\"-d\", \"--dir\", dest=\"dir\", nargs=\"+\", help=\"dir to md5sum\")\n parser.add_argument(\"-t\", \"--type\", dest=\"type\", nargs=\"+\", default=\"sql\", help=\"default=sql\")\n args = parser.parse_args()\n return args\n\n\nif __name__ == \"__main__\":\n args = get_options()\n formats = args.type if args.type else []\n if args.dir:\n for directory in args.dir:\n find_files(directory, formats)\n if args.files:\n md5_dict = dict()\n for file in args.files:\n if not os.path.exists(file):\n raise FileExistsError(\"{0} cannot be found\".format(file))\n md5_value = md5sum(file)\n md5_dict.update({md5_value: file})\n output_to_file(\"md5.txt\", md5_dict)\n\n", "sub_path": "app/md5sum.py", "file_name": "md5sum.py", "file_ext": "py", "file_size_in_byte": 3048, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "hashlib.md5", "line_number": 25, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 44, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}]} +{"seq_id": "174720389", "text": "# -*- coding: utf-8 -*-\nfrom flask import Flask, current_app\nfrom keras.models import load_model\nfrom service import utils\n\n\ndef predict(image):\n image_size = current_app.config['IMAGE_SIZE']\n model_path = current_app.config['MODEL_PATH']\n\n model = load_model(model_path)\n\n # useful when testing with full size image\n if image.shape != (image_size, image_size, 3):\n image = utils.resize_with_pad(image, image_size, image_size)\n\n image = image.reshape((1, image_size, image_size, 3))\n image = image.astype('float32')\n image /= 255\n\n result = model.predict_proba(image) # numpy.ndarray\n print(result)\n\n # predictions = result[0].tolist()\n # if predictions[0] > 0.8:\n # return 0 # boss\n # else:\n # return 1 # non boss\n\n result = model.predict_classes(image)\n\n return result[0]\n", "sub_path": "service/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 841, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.current_app.config", "line_number": 8, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 8, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 9, "usage_type": "name"}, {"api_name": "keras.models.load_model", "line_number": 11, "usage_type": "call"}, {"api_name": "service.utils.resize_with_pad", "line_number": 15, "usage_type": "call"}, {"api_name": "service.utils", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "583605673", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Sep 22 13:02:51 2019\r\n\r\n@author: sidhant\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport tensorflow\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn import preprocessing\r\nfrom keras.utils import plot_model\r\nfrom keras.datasets import fashion_mnist\r\nfrom keras.models import Model\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Input,Flatten,Dropout,Activation\r\nfrom sklearn.metrics import r2_score,confusion_matrix\r\nfrom tensorflow.keras import regularizers\r\nfrom keras.utils import np_utils\r\nfrom keras.optimizers import SGD,Adam,Adamax\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.naive_bayes import GaussianNB\r\nfrom sklearn.svm import SVC\r\nfrom sklearn import metrics\r\nfrom sklearn.multiclass import OneVsRestClassifier\r\n#\r\ndef metrices(data):\r\n data1=pd.DataFrame(columns=['Car','Still','Train','Walking','Bus','Precision','Recall','F1'])\r\n data1['Car']=data[0]\r\n data1['Still']=data[1]\r\n data1['Train']=data[2]\r\n data1['Walking']=data[3]\r\n data1['Bus']=data[4]\r\n precision=[]\r\n recall=[]\r\n sums=data.sum(axis=0)\r\n\r\n# print(\"preci\",sums)\r\n for i in range(len(sums)):\r\n recall.append(data[i][i]/sums[i])\r\n \r\n\r\n \r\n# print(\"recall\",recall)\r\n sum2=data.sum(axis=1)\r\n for i in range(len(sums)):\r\n precision.append(data[i][i]/sum2[i])\r\n \r\n\r\n# print(precision)\r\n data1['Precision']=precision\r\n data1['Recall']=recall\r\n# print(data1)\r\n data1['F1']=data1.F1.fillna(data1['Precision']*data1['Recall']/(data1['Precision']+data1['Recall']))\r\n \r\n return data1\r\n \r\n\r\n\r\ndef AE_method2(x_train,Ytrain):\r\n model = Sequential()\r\n input_size=x_train.shape[1]\r\n \r\n model.add(Dense(240,input_dim=input_size,activation='relu' ,kernel_regularizer=regularizers.l2(0.01)))\r\n model.add(Dense(240,input_dim=input_size,activation='relu' ,kernel_regularizer=regularizers.l2(0.01)))\r\n model.add(Dense(200,input_dim=input_size,activation='relu' ,kernel_regularizer=regularizers.l2(0.01)))\r\n \r\n model.add(Dropout(0.1))\r\n model.add(Dense(200,input_dim=input_size,activation='relu' ,kernel_regularizer=regularizers.l2(0.01)))\r\n \r\n model.add(Dropout(0.1))\r\n# model.add(Dense(32, activation='relu' ,kernel_regularizer=regularizers.l2(0.01)))\r\n# model.add(Dropout(0.3))\r\n model.add(Dense(200, activation='relu' ,kernel_regularizer=regularizers.l2(0.01)))\r\n model.add(Dropout(0.1))\r\n# sgd=SGD(lr=0.03,decay=1e-6,momentum=0.5,nesterov=True)\r\n adam=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False) \r\n \r\n model.add(Dense(5))\r\n model.add(Activation('softmax'))\r\n \r\n model.compile(loss='categorical_crossentropy',\r\n optimizer=adam,\r\n metrics=['accuracy'])\r\n return model\r\ndef model_randomForrest(X_train,y_train):\r\n rfclassifier = RandomForestClassifier(n_estimators=90, oob_score=True)\r\n rfclassifier.fit(X_train, y_train)\r\n return rfclassifier\r\n\r\ndata =pd.read_csv('C:\\sidhant\\CA3\\Dataset\\Data.csv')\r\n#print(data.info())\r\ndata=data.drop(['android.sensor.accelerometer#min','android.sensor.accelerometer#max',\r\n 'android.sensor.game_rotation_vector#min','android.sensor.game_rotation_vector#max',\r\n 'android.sensor.gyroscope#min','android.sensor.gyroscope#max',\r\n 'android.sensor.gyroscope_uncalibrated#min','android.sensor.gyroscope_uncalibrated#max',\r\n 'android.sensor.linear_acceleration#min','android.sensor.linear_acceleration#max',\r\n 'android.sensor.orientation#min','android.sensor.orientation#max',\r\n 'android.sensor.rotation_vector#min','android.sensor.rotation_vector#max',\r\n 'sound#min','sound#max','speed#min','speed#max'],axis=1)\r\n\r\ndata['target']=data['target'].astype('category')\r\ndata['target']=data['target'].cat.codes\r\n\r\ndata=data.drop('time',axis=1)\r\nX=data.drop('target',axis=1)\r\ny=data['target']\r\n#dummy_y = np_utils.to_categorical(y)\r\n\r\nXtrain,Xtest,Ytrain,Ytest=train_test_split(X,y,test_size=0.30,random_state=4)\r\n#Xtrain=Xtrain.values\r\n#Xtest=Xtest.values\r\nXtrain=preprocessing.StandardScaler().fit_transform(Xtrain)\r\nXtest=preprocessing.StandardScaler().fit_transform(Xtest)\r\n\r\n\r\n#\r\n#model=AE_method2(Xtrain,Ytrain)\r\n#model.fit(Xtrain, Ytrain, batch_size=40, epochs=70, validation_split=0.3)\r\n#\r\n#\r\n#prediction=model.predict(Xtest)\r\n#result=model.summary()\r\n#result=tensorflow.keras.metrics.Accuracy(Ytest,prediction)\r\n#result=r2_score(Ytest,prediction)\r\n#print(result)\r\n\r\nmodel=model_randomForrest(Xtrain,Ytrain)\r\nprediction=model.predict(Xtest)\r\nresult=metrics.accuracy_score(Ytest,prediction)\r\n#result=r2_score(Ytest,prediction)\r\nconf_matrix=confusion_matrix(Ytest,prediction)\r\nprint(result)\r\ndata=pd.DataFrame(conf_matrix)\r\noutput= metrices(data)\r\nprint(\"confusion matrix with metrices for random forest:\\n\",output)\r\n\r\n\r\n\r\nmodel= DecisionTreeClassifier(max_depth=19)\r\nmodel.fit(Xtrain, Ytrain)\r\nprediction=model.predict(Xtest)\r\nresult=metrics.accuracy_score(Ytest,prediction)\r\n#result=r2_score(Ytest,prediction)\r\nconf_matrix=confusion_matrix(Ytest,prediction)\r\nprint(result)\r\ndata=pd.DataFrame(conf_matrix)\r\noutput= metrices(data)\r\nprint(\"confusion matrix with metrices for decision tree:\\n\",output)\r\n\r\n#model = GaussianNB()\r\n#model.fit(Xtrain,Ytrain)\r\n#prediction=model.predict(Xtest)\r\n#result=metrics.accuracy_score(Ytest,prediction)\r\n#print(result)\r\n#conf_matrix=confusion_matrix(Ytest,prediction)\r\n#data=pd.DataFrame(conf_matrix)\r\n#output= metrices(data)\r\n#print(\"confusion matrix with metrices for naive gaussian:\\n\",output)\r\n\r\n\r\n\r\nsvclassifier = SVC(kernel='rbf', gamma=0.09,C=22,tol=0.9) \r\nsvclassifier.fit(Xtrain,Ytrain)\r\nprediction=svclassifier.predict(Xtest)\r\nprint(svclassifier)\r\nresult1=metrics.accuracy_score(Ytest,prediction)\r\n\r\n#result=r2_score(Ytest,prediction)\r\nprint(result1)\r\nconf_matrix=confusion_matrix(Ytest,prediction)\r\ndata=pd.DataFrame(conf_matrix)\r\noutput= metrices(data)\r\nprint(\"confusion matrix with metrices for SVC:\\n\",output)\r\n\r\n", "sub_path": "CA3.py", "file_name": "CA3.py", "file_ext": "py", "file_size_in_byte": 6143, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers.l2", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers", "line_number": 66, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers.l2", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers", "line_number": 67, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers.l2", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers", "line_number": 68, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers.l2", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers", "line_number": 71, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers.l2", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers", "line_number": 76, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 82, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 93, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 112, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 115, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 115, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 116, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 116, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 132, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 134, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 136, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 142, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 145, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 145, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 147, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 149, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 165, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 169, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 169, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 173, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "52212923", "text": "import os\nimport os.path\nimport shutil\n\nimport psutil\nimport nose.tools\n\n# perform an acpidump as root\ndef test_acpidump(dir_str='./acpi_dump'):\n\n cur_dir = os.path.abspath(os.curdir)\n\n if os.path.exists(dir_str):\n # if file, remove it\n if os.path.isfile(dir_str):\n os.remove(test_dir)\n if os.path.isdir(dir_str):\n shutil.rmtree(dir_str)\n\n os.makedirs(dir_str)\n os.chdir(dir_str)\n proc = psutil.Popen('sudo acpidump -o acpi.bin', shell=True)\n proc.wait()\n\n msg = 'Was not able to dump ACPI tables'\n nose.tools.eq_(os.path.isfile('acpi.bin'), True, msg)\n\n stat = os.stat('acpi.bin')\n msg = 'ACPI Tables File is 0 length'\n nose.tools.eq_(stat.st_size == 0, False, msg)\n\n os.chdir(cur_dir)\n\n\n# perform an acpixtract , extracts files\ndef test_acpixtract(dir_str='./acpi_dump'):\n\n cur_dir = os.path.abspath(os.curdir)\n if os.path.exists(dir_str) is False:\n msg = '{0} is not created'.format(dir_str)\n nose.tools.eq_(True, True, msg)\n\n os.chdir(dir_str)\n proc = psutil.Popen('acpixtract acpi.bin', shell=True)\n proc.wait()\n\n msg = 'Was not able to extract DSDT'\n nose.tools.eq_(os.path.isfile('dsdt.dat'), True, msg)\n\n stat = os.stat('dsdt.dat')\n msg = 'ACPI DSDT is 0 length'\n nose.tools.eq_(stat.st_size == 0, False, msg)\n os.chdir(cur_dir)\n\n\n# perform an iasl decompile, -d dsdt.dat ssdt*.dat\ndef test_iasl_decompile(dir_str='./acpi_dump'):\n\n cur_dir = os.path.abspath(os.curdir)\n if os.path.exists(dir_str) is False:\n msg = '{0} is not created'.format(dir_str)\n nose.tools.eq_(True, True, msg)\n\n os.chdir(dir_str)\n proc = psutil.Popen('iasl -d dsdt.dat ssdt*.dat', shell=True)\n proc.wait()\n\n msg = 'Was not able to Disassmble DSDT'\n nose.tools.eq_(os.path.isfile('dsdt.dsl'), True, msg)\n\n stat = os.stat('dsdt.dsl')\n msg = 'ACPI DSDT Disassembly is 0 length'\n nose.tools.eq_(stat.st_size == 0, False, msg)\n os.chdir(cur_dir)\n\n", "sub_path": "testing/tests/test_acpi.py", "file_name": "test_acpi.py", "file_ext": "py", "file_size_in_byte": 2003, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.curdir", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 18, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 20, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 21, "usage_type": "call"}, {"api_name": "psutil.Popen", "line_number": 22, "usage_type": "call"}, {"api_name": "nose.tools.tools.eq_", "line_number": 26, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 26, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 28, "usage_type": "call"}, {"api_name": "nose.tools.tools.eq_", "line_number": 30, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 30, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 30, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.curdir", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "nose.tools.tools.eq_", "line_number": 41, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 41, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 41, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 43, "usage_type": "call"}, {"api_name": "psutil.Popen", "line_number": 44, "usage_type": "call"}, {"api_name": "nose.tools.tools.eq_", "line_number": 48, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 48, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 50, "usage_type": "call"}, {"api_name": "nose.tools.tools.eq_", "line_number": 52, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 52, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 52, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.curdir", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "nose.tools.tools.eq_", "line_number": 62, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 62, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 62, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 64, "usage_type": "call"}, {"api_name": "psutil.Popen", "line_number": 65, "usage_type": "call"}, {"api_name": "nose.tools.tools.eq_", "line_number": 69, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 69, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 69, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 71, "usage_type": "call"}, {"api_name": "nose.tools.tools.eq_", "line_number": 73, "usage_type": "call"}, {"api_name": "nose.tools.tools", "line_number": 73, "usage_type": "attribute"}, {"api_name": "nose.tools", "line_number": 73, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "91565795", "text": "# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom neutron_lib.callbacks import events\nfrom neutron_lib.callbacks import registry\nfrom neutron_lib.callbacks import resources\nfrom neutron_lib.db import model_base\nfrom oslo_log import log as logging\nfrom oslo_utils import uuidutils\nimport sqlalchemy as sa\nfrom sqlalchemy import and_\n\nfrom neutron.db import api as db_api\nfrom neutron.db.models import segment as segments_model\nfrom neutron.objects import base as base_obj\nfrom neutron.objects import network as network_obj\n\nLOG = logging.getLogger(__name__)\n\nNETWORK_TYPE = segments_model.NetworkSegment.network_type.name\nPHYSICAL_NETWORK = segments_model.NetworkSegment.physical_network.name\nSEGMENTATION_ID = segments_model.NetworkSegment.segmentation_id.name\nNETWORK_ID = segments_model.NetworkSegment.network_id.name\n\n\ndef _make_segment_dict(obj):\n \"\"\"Make a segment dictionary out of an object.\"\"\"\n #NOTE(jrichard) drop change in next rebase.\n return {'id': obj.id,\n NETWORK_TYPE: obj.network_type,\n PHYSICAL_NETWORK: obj.physical_network,\n SEGMENTATION_ID: obj.segmentation_id,\n NETWORK_ID: getattr(obj, 'network_id', None)}\n\n\nclass SubnetSegment(model_base.BASEV2, model_base.HasId):\n \"\"\"Represent persistent state of a subnet segment.\n\n A subnet segment is a portion of a neutron subnet with a\n specific physical realization. A neutron subnet can consist of\n one or more segments.\n \"\"\"\n\n # TODO(alegacy): rename this similar to how the NetworkSegments table was\n # renamed?\n __tablename__ = 'ml2_subnet_segments'\n\n subnet_id = sa.Column(sa.String(36),\n sa.ForeignKey('subnets.id', ondelete=\"CASCADE\"),\n nullable=False)\n network_type = sa.Column(sa.String(32), nullable=False)\n physical_network = sa.Column(sa.String(64))\n segmentation_id = sa.Column(sa.Integer)\n is_dynamic = sa.Column(sa.Boolean, default=False, nullable=False,\n server_default=sa.sql.false())\n segment_index = sa.Column(sa.Integer, nullable=False, server_default='0')\n\n\ndef add_network_segment(context, network_id, segment, segment_index=0,\n is_dynamic=False):\n with db_api.context_manager.writer.using(context):\n netseg_obj = network_obj.NetworkSegment(\n context, id=uuidutils.generate_uuid(), network_id=network_id,\n network_type=segment.get(NETWORK_TYPE),\n physical_network=segment.get(PHYSICAL_NETWORK),\n segmentation_id=segment.get(SEGMENTATION_ID),\n segment_index=segment_index, is_dynamic=is_dynamic)\n netseg_obj.create()\n registry.notify(resources.SEGMENT,\n events.PRECOMMIT_CREATE,\n trigger=add_network_segment,\n context=context,\n segment=netseg_obj)\n segment['id'] = netseg_obj.id\n LOG.info(\"Added segment %(id)s of type %(network_type)s for network \"\n \"%(network_id)s\",\n {'id': netseg_obj.id,\n 'network_type': netseg_obj.network_type,\n 'network_id': netseg_obj.network_id})\n\n\ndef get_network_segments(context, network_id, filter_dynamic=False):\n return get_networks_segments(\n context, [network_id], filter_dynamic)[network_id]\n\n\ndef get_networks_segments(context, network_ids, filter_dynamic=False):\n if not network_ids:\n return {}\n\n with db_api.context_manager.reader.using(context):\n filters = {\n 'network_id': network_ids,\n }\n if filter_dynamic is not None:\n filters['is_dynamic'] = filter_dynamic\n objs = network_obj.NetworkSegment.get_objects(context, **filters)\n result = {net_id: [] for net_id in network_ids}\n for record in objs:\n result[record.network_id].append(_make_segment_dict(record))\n return result\n\n\ndef get_segment_by_id(context, segment_id):\n with db_api.context_manager.reader.using(context):\n net_obj = network_obj.NetworkSegment.get_object(context, id=segment_id)\n if net_obj:\n return _make_segment_dict(net_obj)\n\n\ndef get_dynamic_segment(context, network_id, physical_network=None,\n segmentation_id=None):\n \"\"\"Return a dynamic segment for the filters provided if one exists.\"\"\"\n with db_api.context_manager.reader.using(context):\n filters = {\n 'network_id': network_id,\n 'is_dynamic': True,\n }\n if physical_network:\n filters['physical_network'] = physical_network\n if segmentation_id:\n filters['segmentation_id'] = segmentation_id\n pager = base_obj.Pager(limit=1)\n objs = network_obj.NetworkSegment.get_objects(\n context, _pager=pager, **filters)\n\n if objs:\n return _make_segment_dict(objs[0])\n else:\n LOG.debug(\"No dynamic segment found for \"\n \"Network:%(network_id)s, \"\n \"Physical network:%(physnet)s, \"\n \"segmentation_id:%(segmentation_id)s\",\n {'network_id': network_id,\n 'physnet': physical_network,\n 'segmentation_id': segmentation_id})\n\n\ndef delete_network_segment(context, segment_id):\n \"\"\"Release a dynamic segment for the params provided if one exists.\"\"\"\n with db_api.context_manager.writer.using(context):\n network_obj.NetworkSegment.delete_objects(context, id=segment_id)\n\n\ndef network_segments_exist(session, network_type, physical_network,\n segment_range=None):\n with session.begin(subtransactions=True):\n query = (session.query(segments_model.NetworkSegment).\n filter_by(network_type=network_type,\n physical_network=physical_network))\n if segment_range:\n minimum_id = segment_range['minimum']\n maximum_id = segment_range['maximum']\n query = (query.filter(and_(\n segments_model.NetworkSegment.segmentation_id >= minimum_id,\n segments_model.NetworkSegment.segmentation_id <= maximum_id\n )))\n return bool(query.count() > 0)\n\n\ndef add_subnet_segment(session, subnet_id, segment, segment_index=0,\n is_dynamic=False):\n with session.begin(subtransactions=True):\n record = SubnetSegment(\n id=uuidutils.generate_uuid(),\n subnet_id=subnet_id,\n network_type=segment.get(NETWORK_TYPE),\n physical_network=segment.get(PHYSICAL_NETWORK),\n segmentation_id=segment.get(SEGMENTATION_ID),\n segment_index=segment_index,\n is_dynamic=is_dynamic\n )\n session.add(record)\n LOG.info(\"Added segment %(id)s of type %(network_type)s for subnet\"\n \" %(subnet_id)s\",\n {'id': record.id,\n 'network_type': record.network_type,\n 'subnet_id': record.subnet_id})\n\n\ndef get_subnet_segments(session, subnet_id, filter_dynamic=False):\n return get_subnets_segments(\n session, [subnet_id], filter_dynamic)[subnet_id]\n\n\ndef get_subnets_segments(session, subnet_ids, filter_dynamic=False):\n if not subnet_ids:\n return {}\n with session.begin(subtransactions=True):\n query = (session.query(SubnetSegment).\n filter(SubnetSegment.subnet_id.in_(subnet_ids)).\n order_by(SubnetSegment.segment_index))\n if filter_dynamic is not None:\n query = query.filter_by(is_dynamic=filter_dynamic)\n records = query.all()\n result = {subnet_id: [] for subnet_id in subnet_ids}\n for record in records:\n result[record.subnet_id].append(_make_segment_dict(record))\n return result\n\n\ndef subnet_segments_exist(session, network_type, physical_network,\n segment_range=None):\n with session.begin(subtransactions=True):\n query = (session.query(SubnetSegment).\n filter_by(network_type=network_type,\n physical_network=physical_network))\n if segment_range:\n minimum_id = segment_range['minimum']\n maximum_id = segment_range['maximum']\n query = (query.filter(\n and_(SubnetSegment.segmentation_id >= minimum_id,\n SubnetSegment.segmentation_id <= maximum_id)))\n return bool(query.count() > 0)\n", "sub_path": "neutron/db/segments_db.py", "file_name": "segments_db.py", "file_ext": "py", "file_size_in_byte": 9045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "oslo_log.log.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 27, "usage_type": "name"}, {"api_name": "neutron.db.models.segment.NetworkSegment", "line_number": 29, "usage_type": "attribute"}, {"api_name": "neutron.db.models.segment", "line_number": 29, "usage_type": "name"}, {"api_name": "neutron.db.models.segment.NetworkSegment", "line_number": 30, "usage_type": "attribute"}, {"api_name": "neutron.db.models.segment", "line_number": 30, "usage_type": "name"}, {"api_name": "neutron.db.models.segment.NetworkSegment", "line_number": 31, "usage_type": "attribute"}, {"api_name": "neutron.db.models.segment", "line_number": 31, "usage_type": "name"}, {"api_name": "neutron.db.models.segment.NetworkSegment", "line_number": 32, "usage_type": "attribute"}, {"api_name": "neutron.db.models.segment", "line_number": 32, "usage_type": "name"}, {"api_name": "neutron_lib.db.model_base.BASEV2", "line_number": 45, "usage_type": "attribute"}, {"api_name": "neutron_lib.db.model_base", "line_number": 45, "usage_type": "name"}, {"api_name": "neutron_lib.db.model_base.HasId", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 58, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 60, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 60, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 61, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 61, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 62, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 63, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sqlalchemy.sql.false", "line_number": 64, "usage_type": "call"}, {"api_name": "sqlalchemy.sql", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 65, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 65, "usage_type": "attribute"}, {"api_name": "neutron.db.api.context_manager.writer.using", "line_number": 70, "usage_type": "call"}, {"api_name": "neutron.db.api.context_manager", "line_number": 70, "usage_type": "attribute"}, {"api_name": "neutron.db.api", "line_number": 70, "usage_type": "name"}, {"api_name": "neutron.objects.network.NetworkSegment", "line_number": 71, "usage_type": "call"}, {"api_name": "neutron.objects.network", "line_number": 71, "usage_type": "name"}, {"api_name": "oslo_utils.uuidutils.generate_uuid", "line_number": 72, "usage_type": "call"}, {"api_name": "oslo_utils.uuidutils", "line_number": 72, "usage_type": "name"}, {"api_name": "neutron_lib.callbacks.registry.notify", "line_number": 78, "usage_type": "call"}, {"api_name": "neutron_lib.callbacks.registry", "line_number": 78, "usage_type": "name"}, {"api_name": "neutron_lib.callbacks.resources.SEGMENT", "line_number": 78, "usage_type": "attribute"}, {"api_name": "neutron_lib.callbacks.resources", "line_number": 78, "usage_type": "name"}, {"api_name": "neutron_lib.callbacks.events.PRECOMMIT_CREATE", "line_number": 79, "usage_type": "attribute"}, {"api_name": "neutron_lib.callbacks.events", "line_number": 79, "usage_type": "name"}, {"api_name": "neutron.db.api.context_manager.reader.using", "line_number": 100, "usage_type": "call"}, {"api_name": "neutron.db.api.context_manager", "line_number": 100, "usage_type": "attribute"}, {"api_name": "neutron.db.api", "line_number": 100, "usage_type": "name"}, {"api_name": "neutron.objects.network.NetworkSegment.get_objects", "line_number": 106, "usage_type": "call"}, {"api_name": "neutron.objects.network.NetworkSegment", "line_number": 106, "usage_type": "attribute"}, {"api_name": "neutron.objects.network", "line_number": 106, "usage_type": "name"}, {"api_name": "neutron.db.api.context_manager.reader.using", "line_number": 114, "usage_type": "call"}, {"api_name": "neutron.db.api.context_manager", "line_number": 114, "usage_type": "attribute"}, {"api_name": "neutron.db.api", "line_number": 114, "usage_type": "name"}, {"api_name": "neutron.objects.network.NetworkSegment.get_object", "line_number": 115, "usage_type": "call"}, {"api_name": "neutron.objects.network.NetworkSegment", "line_number": 115, "usage_type": "attribute"}, {"api_name": "neutron.objects.network", "line_number": 115, "usage_type": "name"}, {"api_name": "neutron.db.api.context_manager.reader.using", "line_number": 123, "usage_type": "call"}, {"api_name": "neutron.db.api.context_manager", "line_number": 123, "usage_type": "attribute"}, {"api_name": "neutron.db.api", "line_number": 123, "usage_type": "name"}, {"api_name": "neutron.objects.base.Pager", "line_number": 132, "usage_type": "call"}, {"api_name": "neutron.objects.base", "line_number": 132, "usage_type": "name"}, {"api_name": "neutron.objects.network.NetworkSegment.get_objects", "line_number": 133, "usage_type": "call"}, {"api_name": "neutron.objects.network.NetworkSegment", "line_number": 133, "usage_type": "attribute"}, {"api_name": "neutron.objects.network", "line_number": 133, "usage_type": "name"}, {"api_name": "neutron.db.api.context_manager.writer.using", "line_number": 150, "usage_type": "call"}, {"api_name": "neutron.db.api.context_manager", "line_number": 150, "usage_type": "attribute"}, {"api_name": "neutron.db.api", "line_number": 150, "usage_type": "name"}, {"api_name": "neutron.objects.network.NetworkSegment.delete_objects", "line_number": 151, "usage_type": "call"}, {"api_name": "neutron.objects.network.NetworkSegment", "line_number": 151, "usage_type": "attribute"}, {"api_name": "neutron.objects.network", "line_number": 151, "usage_type": "name"}, {"api_name": "neutron.db.models.segment.NetworkSegment", "line_number": 157, "usage_type": "attribute"}, {"api_name": "neutron.db.models.segment", "line_number": 157, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 163, "usage_type": "call"}, {"api_name": "neutron.db.models.segment.NetworkSegment", "line_number": 164, "usage_type": "attribute"}, {"api_name": "neutron.db.models.segment", "line_number": 164, "usage_type": "name"}, {"api_name": "neutron.db.models.segment.NetworkSegment", "line_number": 165, "usage_type": "attribute"}, {"api_name": "neutron.db.models.segment", "line_number": 165, "usage_type": "name"}, {"api_name": "oslo_utils.uuidutils.generate_uuid", "line_number": 174, "usage_type": "call"}, {"api_name": "oslo_utils.uuidutils", "line_number": 174, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 221, "usage_type": "call"}]} +{"seq_id": "257127614", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nx,y = np.loadtxt('ANUscan.dat',delimiter=',',unpack=True)\nLx,Ly = np.loadtxt('Lykke.csv',unpack=True)\ndx,dy = np.loadtxt('../Digitizer/DIB-8037.csv',delimiter=',',unpack=True)\nsx,sy = np.loadtxt('../Digitizer/sar07b.csv',delimiter=',',unpack=True)\nsx2,sy2 = np.loadtxt('../Digitizer/sar07c2.csv',delimiter=',',unpack=True)\nmx,my = np.loadtxt('../Digitizer/mabs17a.csv',delimiter=',',unpack=True)\nspec = np.loadtxt('0wavenumber10K.txt', usecols=(0, 1, 2), unpack=True)\n\n#Mabbs17 Model\nmx = spec[0]+12468\nmy = spec[2]\n\n#Mabbs17 Data\nv = (max(my)-min(my))/(max(y)-min(y))\nmy /=v\nmy -= 600\n\n\n#Sarre07 Rotational Model\nsx *=0.1\nsx = 1e7/sx\nv = (max(sy)-min(sy))/(max(y)-min(y))\nsy /=v\nv = np.mean(sy)\nsy +=1800-v\n#Sarre07 at 2.7K\nsx2 *=0.1\nsx2 = 1e7/sx2\nv = (max(sy2)-min(sy2))/(max(y)-min(y))\nsy2 /=v\nv = np.mean(sy2)\nsy2 +=1900-v\n\n#DIB Astro\ndx *=0.1\ndx = 1e7/dx\nv = (max(dy)-min(dy))/(max(y)-min(y))\ndy /=v\nv = np.mean(dy)\ndy +=2400-v\n#ANU\nx = 1e7/x\n#Lineberger\nsubr = np.logical_and(Lx>x[-1],Lx12449,x<12465)\nay = y[subr]\nax = x[subr]\nb = max(ay)\nsubi = np.logical_and(ay>b-0.001,ay<1000)\nc = ax[subi]\nprint(b)\nprint(c)\n\nsubr = np.logical_and(Lx>12449,Lx<12465)\nay = Ly[subr]\nax = Lx[subr]\nLb = max(ay)\nsubi = np.logical_and(ay>Lb-0.001,ay<1000)\nLc = ax[subi]\nprint(Lb)\nprint(Lc)\n\nratio = b/Lb\nLy *= ratio\nshift = Lc-c\n#Lx -= shift\nLz = Lx-shift\nprint(shift)\n\n#Plotting Shifts\nLy +=600\n\nplt.plot(x,y,label='ANU')\nplt.plot(Lx,Ly,label='Lineberger')\nplt.plot(dx,dy,label='DIB')\nplt.plot(sx,sy,label='Sarre07 Model')\nplt.plot(sx2,sy2,label='Sarre07 2.7K')\nplt.plot(mx,my,label='Mabbs17 Model')\nplt.plot((12459.033,12459.033),(2500,-600),'C7--')\nplt.plot((12440.77,12440.77),(2500,-600),'C7--')\nplt.xlim(12380,12510)\nplt.legend()\nplt.show()\n", "sub_path": "Scripts/compplot.py", "file_name": "compplot.py", "file_ext": "py", "file_size_in_byte": 1901, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.loadtxt", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}]} +{"seq_id": "140332141", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 27 22:40:12 2021\n\n@author: junyanee\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# 코인데스크 사이트에서 다운로드한 1년치 비트코인 가격 데이터 읽기\nf = open('BTC_USD_2020-05-28_2021-05-27-CoinDesk.csv', 'r')\ncoindesk_data = pd.read_csv(f, header = 0)\nseq = coindesk_data[['Closing Price (USD)']].to_numpy() #종가만 취함\nprint('데이터 길이:', len(seq),'\\n앞쪽 5개 값:', seq[0:5])\n\n# 그래프로 데이터 확인\nplt.plot(seq, color = 'red')\nplt.title('Bitcoin Prices (1 year from 2019-02-28)')\nplt.xlabel('Days'); plt.ylabel('Price in USD')\nplt.show()\n\n# 시계열 데이터를 위도우 단위로 자르는 함수\ndef seq2dataset(seq, window, horizon):\n X = []; Y = []\n for i in range(len(seq) - (window + horizon) + 1):\n x = seq[i : (i + window)]\n y = (seq[i + window + horizon - 1])\n X.append(x); Y.append(y);\n return np.array(X), np.array(Y)\n \nw = 7 #윈도우 크기\nh = 1 #수평선 계수\n\nX, Y = seq2dataset(seq, w, h)\nprint(X.shape, Y.shape)\nprint(X[0], Y[0]); print(X[-1], Y[-1])", "sub_path": "1_year_price_from_coindesk.py", "file_name": "1_year_price_from_coindesk.py", "file_ext": "py", "file_size_in_byte": 1151, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "332488372", "text": "import struct\nfrom typing import Optional\nfrom binascii import hexlify, unhexlify\n\nfrom torba.client.baseheader import BaseHeaders\nfrom torba.client.util import ArithUint256\nfrom torba.client.hash import sha512, double_sha256, ripemd160\n\n\nclass Headers(BaseHeaders):\n\n header_size = 112\n chunk_size = 10**16\n\n max_target = 0x0000ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff\n genesis_hash = b'9c89283ba0f3227f6c03b70216b9f665f0118d5e0fa729cedf4fb34d6a34f463'\n target_timespan = 150\n\n @property\n def claim_trie_root(self):\n return self[self.height]['claim_trie_root']\n\n @staticmethod\n def serialize(header):\n return b''.join([\n struct.pack(' ArithUint256:\n # https://github.com/lbryio/lbrycrd/blob/master/src/lbry.cpp\n if previous is None and current is None:\n return max_target\n if previous is None:\n previous = current\n actual_timespan = current['timestamp'] - previous['timestamp']\n modulated_timespan = self.target_timespan + int((actual_timespan - self.target_timespan) / 8)\n minimum_timespan = self.target_timespan - int(self.target_timespan / 8) # 150 - 18 = 132\n maximum_timespan = self.target_timespan + int(self.target_timespan / 2) # 150 + 75 = 225\n clamped_timespan = max(minimum_timespan, min(modulated_timespan, maximum_timespan))\n target = ArithUint256.from_compact(current['bits'])\n new_target = min(max_target, (target * clamped_timespan) / self.target_timespan)\n return new_target\n\n @classmethod\n def get_proof_of_work(cls, header_hash: bytes):\n return super().get_proof_of_work(\n cls.header_hash_to_pow_hash(header_hash)\n )\n\n @staticmethod\n def header_hash_to_pow_hash(header_hash: bytes):\n header_hash_bytes = unhexlify(header_hash)[::-1]\n h = sha512(header_hash_bytes)\n pow_hash = double_sha256(\n ripemd160(h[:len(h) // 2]) +\n ripemd160(h[len(h) // 2:])\n )\n return hexlify(pow_hash[::-1])\n\n\nclass UnvalidatedHeaders(Headers):\n validate_difficulty = False\n max_target = 0x7fffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff\n genesis_hash = b'6e3fcf1299d4ec5d79c3a4c91d624a4acf9e2e173d95a1a0504f677669687556'\n", "sub_path": "lbry/lbry/wallet/header.py", "file_name": "header.py", "file_ext": "py", "file_size_in_byte": 3286, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torba.client.baseheader.BaseHeaders", "line_number": 10, "usage_type": "name"}, {"api_name": "struct.pack", "line_number": 26, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 27, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 28, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 29, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 30, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 35, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 36, "usage_type": "call"}, {"api_name": "binascii.hexlify", "line_number": 39, "usage_type": "call"}, {"api_name": "binascii.hexlify", "line_number": 40, "usage_type": "call"}, {"api_name": "binascii.hexlify", "line_number": 41, "usage_type": "call"}, {"api_name": "torba.client.util.ArithUint256", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 49, "usage_type": "name"}, {"api_name": "torba.client.util.ArithUint256.from_compact", "line_number": 60, "usage_type": "call"}, {"api_name": "torba.client.util.ArithUint256", "line_number": 60, "usage_type": "name"}, {"api_name": "torba.client.util.ArithUint256", "line_number": 49, "usage_type": "name"}, {"api_name": "binascii.unhexlify", "line_number": 72, "usage_type": "call"}, {"api_name": "torba.client.hash.sha512", "line_number": 73, "usage_type": "call"}, {"api_name": "torba.client.hash.double_sha256", "line_number": 74, "usage_type": "call"}, {"api_name": "torba.client.hash.ripemd160", "line_number": 75, "usage_type": "call"}, {"api_name": "torba.client.hash.ripemd160", "line_number": 76, "usage_type": "call"}, {"api_name": "binascii.hexlify", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "289749886", "text": "import multiprocessing as mp\r\nimport numpy as np\r\nimport math\r\nimport matplotlib.pyplot as plt\r\nfrom time import time\r\n\r\ndef simulate_geometric_brownian_motion(p):\r\n M, I = p\r\n # time steps, paths\r\n S0 = 100; r = 0.05; sigma = 0.2; T = 1.0\r\n # model parameters\r\n dt = T / M\r\n paths = np.zeros((M + 1, I))\r\n paths[0] = S0\r\n for t in range(1, M + 1):\r\n paths[t] = paths[t - 1] * np.exp((r - 0.5 * sigma ** 2) * dt +\r\n sigma * math.sqrt(dt) * np.random.standard_normal(I))\r\n return paths\r\n\r\nif __name__ == '__main__':\r\n I = 10000 # number of paths\r\n M = 50 # number of time steps\r\n t = 20 # number of tasks/simulations\r\n \r\n times = []\r\n process_ = range(1,10)\r\n for w in process_:\r\n t0 = time()\r\n pool = mp.Pool(processes=w)\r\n # the pool of workers\r\n result = pool.map(simulate_geometric_brownian_motion, t * [(M, I), ])\r\n # the mapping of the function to the list of parameter tuples\r\n t_=time()-t0\r\n times.append(t_)\r\n print(t_)\r\n \r\n \r\n plt.plot(process_, times)\r\n plt.plot(process_, times, 'ro')\r\n plt.grid(True)\r\n plt.xlabel('number of processes')\r\n plt.ylabel('time in seconds')\r\n plt.title('%d Monte Carlo simulations' % t)\r\n plt.show() \r\n \r\n \r\n \r\n \r\n ", "sub_path": "Ejercicios/Brownian_Paths_mp.py", "file_name": "Brownian_Paths_mp.py", "file_ext": "py", "file_size_in_byte": 1369, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 16, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random.standard_normal", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 29, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "459201263", "text": "import os\nimport json\nimport luigi\n\nfrom .cluster_tasks import WorkflowBase\nfrom .watershed import WatershedWorkflow\nfrom .graph import GraphWorkflow\n# TODO more features and options to choose which features to choose\nfrom .features import EdgeFeaturesWorkflow\nfrom .costs import EdgeCostsWorkflow\nfrom .multicut import MulticutWorkflow\nfrom .decomposition_multicut import DecompositionWorkflow\nfrom .debugging import CheckSubGraphsWorkflow\nfrom . import write as write_tasks\n\n\nclass MulticutSegmentationWorkflow(WorkflowBase):\n input_path = luigi.Parameter()\n input_key = luigi.Parameter()\n # where to save the watersheds\n ws_path = luigi.Parameter()\n ws_key = luigi.Parameter()\n # where to save the multicut problems\n problem_path = luigi.Parameter()\n # where to save the node labels\n node_labels_path = luigi.Parameter()\n node_labels_key = luigi.Parameter()\n # where to save the resulting segmentation\n output_path = luigi.Parameter()\n output_key = luigi.Parameter()\n # number of scales\n n_scales = luigi.IntParameter()\n # optional path to mask\n mask_path = luigi.Parameter(default='')\n mask_key = luigi.Parameter(default='')\n # number of jobs used in feature merging\n max_jobs_merge_features = luigi.IntParameter(default=1)\n # number of jobs used for sub multicuts\n max_jobs_multicut = luigi.IntParameter(default=1)\n # use decomposer workflow\n skip_ws = luigi.BoolParameter(default=False)\n # path to random forest (if available)\n rf_path = luigi.Parameter(default='')\n # run some sanity checks for sub-results\n sanity_checks = luigi.BoolParameter(default=False)\n # TODO list to skip jobs\n\n def _get_mc_wf(self, dep):\n # hard-coded keys\n mc_wf = MulticutWorkflow(tmp_folder=self.tmp_folder,\n max_jobs=self.max_jobs_multicut,\n config_dir=self.config_dir,\n target=self.target,\n dependency=dep,\n problem_path=self.problem_path,\n n_scales=self.n_scales,\n assignment_path=self.node_labels_path,\n assignment_key=self.node_labels_key)\n return mc_wf\n\n # TODO implement mechanism to skip existing dependencies\n def requires(self):\n # hard-coded keys\n graph_key = 's0/graph'\n features_key = 'features'\n costs_key = 's0/costs'\n if self.skip_ws:\n assert os.path.exists(os.path.join(self.ws_path, self.ws_key)), \"%s:%s\" % (self.ws_path,\n self.ws_key)\n dep = self.dependency\n else:\n dep = WatershedWorkflow(tmp_folder=self.tmp_folder,\n max_jobs=self.max_jobs,\n config_dir=self.config_dir,\n target=self.target,\n dependency=self.dependency,\n input_path=self.input_path,\n input_key=self.input_key,\n output_path=self.ws_path,\n output_key=self.ws_key,\n mask_path=self.mask_path,\n mask_key=self.mask_key)\n # TODO in the current implementation, we can only compute the\n # graph with n_scales=1, otherwise we will clash with the\n # multicut merged graphs\n dep = GraphWorkflow(tmp_folder=self.tmp_folder,\n max_jobs=self.max_jobs,\n config_dir=self.config_dir,\n target=self.target,\n dependency=dep,\n input_path=self.ws_path,\n input_key=self.ws_key,\n graph_path=self.problem_path,\n output_key=graph_key,\n n_scales=1)\n if self.sanity_checks:\n graph_block_prefix = os.path.join(self.problem_path,\n 's0', 'sub_graphs', 'block_')\n dep = CheckSubGraphsWorkflow(tmp_folder=self.tmp_folder,\n max_jobs=self.max_jobs,\n config_dir=self.config_dir,\n target=self.target,\n ws_path=self.ws_path,\n ws_key=self.ws_key,\n graph_block_prefix=graph_block_prefix,\n dependency=dep)\n # TODO add options to choose which features to use\n dep = EdgeFeaturesWorkflow(tmp_folder=self.tmp_folder,\n max_jobs=self.max_jobs,\n config_dir=self.config_dir,\n target=self.target,\n dependency=dep,\n input_path=self.input_path,\n input_key=self.input_key,\n labels_path=self.ws_path,\n labels_key=self.ws_key,\n graph_path=self.problem_path,\n graph_key=graph_key,\n output_path=self.problem_path,\n output_key=features_key,\n max_jobs_merge=self.max_jobs_merge_features)\n dep = EdgeCostsWorkflow(tmp_folder=self.tmp_folder,\n max_jobs=self.max_jobs,\n config_dir=self.config_dir,\n target=self.target,\n dependency=dep,\n features_path=self.problem_path,\n features_key=features_key,\n output_path=self.problem_path,\n output_key=costs_key,\n rf_path=self.rf_path)\n dep = self._get_mc_wf(dep)\n write_task = getattr(write_tasks, self._get_task_name('Write'))\n dep = write_task(tmp_folder=self.tmp_folder,\n max_jobs=self.max_jobs,\n config_dir=self.config_dir,\n dependency=dep,\n input_path=self.ws_path,\n input_key=self.ws_key,\n output_path=self.output_path,\n output_key=self.output_key,\n assignment_path=self.node_labels_path,\n assignment_key=self.node_labels_key,\n identifier='multicut')\n return dep\n\n @staticmethod\n def get_config():\n config = {**WatershedWorkflow.get_config(),\n **GraphWorkflow.get_config(),\n **EdgeFeaturesWorkflow.get_config(),\n **EdgeCostsWorkflow.get_config(),\n **MulticutWorkflow.get_config()}\n return config\n", "sub_path": "cluster_tools/workflows.py", "file_name": "workflows.py", "file_ext": "py", "file_size_in_byte": 7361, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "cluster_tasks.WorkflowBase", "line_number": 17, "usage_type": "name"}, {"api_name": "luigi.Parameter", "line_number": 18, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 19, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 21, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 22, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 24, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 26, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 27, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 29, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 30, "usage_type": "call"}, {"api_name": "luigi.IntParameter", "line_number": 32, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 34, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 35, "usage_type": "call"}, {"api_name": "luigi.IntParameter", "line_number": 37, "usage_type": "call"}, {"api_name": "luigi.IntParameter", "line_number": 39, "usage_type": "call"}, {"api_name": "luigi.BoolParameter", "line_number": 41, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 43, "usage_type": "call"}, {"api_name": "luigi.BoolParameter", "line_number": 45, "usage_type": "call"}, {"api_name": "multicut.MulticutWorkflow", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "watershed.WatershedWorkflow", "line_number": 72, "usage_type": "call"}, {"api_name": "graph.GraphWorkflow", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "debugging.CheckSubGraphsWorkflow", "line_number": 99, "usage_type": "call"}, {"api_name": "features.EdgeFeaturesWorkflow", "line_number": 108, "usage_type": "call"}, {"api_name": "costs.EdgeCostsWorkflow", "line_number": 122, "usage_type": "call"}, {"api_name": "watershed.WatershedWorkflow.get_config", "line_number": 149, "usage_type": "call"}, {"api_name": "watershed.WatershedWorkflow", "line_number": 149, "usage_type": "name"}, {"api_name": "graph.GraphWorkflow.get_config", "line_number": 150, "usage_type": "call"}, {"api_name": "graph.GraphWorkflow", "line_number": 150, "usage_type": "name"}, {"api_name": "features.EdgeFeaturesWorkflow.get_config", "line_number": 151, "usage_type": "call"}, {"api_name": "features.EdgeFeaturesWorkflow", "line_number": 151, "usage_type": "name"}, {"api_name": "costs.EdgeCostsWorkflow.get_config", "line_number": 152, "usage_type": "call"}, {"api_name": "costs.EdgeCostsWorkflow", "line_number": 152, "usage_type": "name"}, {"api_name": "multicut.MulticutWorkflow.get_config", "line_number": 153, "usage_type": "call"}, {"api_name": "multicut.MulticutWorkflow", "line_number": 153, "usage_type": "name"}]} +{"seq_id": "466397498", "text": "import time\nfrom seleniumbase import BaseCase\n\n\nclass MyTestClass(BaseCase):\n\n def test_proxy(self):\n self.open('https://ipinfo.io/')\n ip_address = self.get_text(\"div.home-ip-details span.value\")[1:-1]\n print(\"\\n\\nMy IP Address = %s\\n\" % ip_address)\n print(\"Displaying Host Info:\")\n print(self.get_text('div.home-ip-details').split('asn: ')[0])\n print(\"\\nThe browser will close automatically in 7 seconds...\")\n time.sleep(7)\n", "sub_path": "examples/proxy_test.py", "file_name": "proxy_test.py", "file_ext": "py", "file_size_in_byte": 477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "seleniumbase.BaseCase", "line_number": 5, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "621481716", "text": "import sys,time,os\nfrom CSM37F58_APP_ui import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import *\nfrom serial.tools.list_ports import *\nfrom picture_qrc import *\nimport datetime\nfrom IIC_CH341 import *\nfrom PyQt5.QtCore import QTimer\nimport numpy as np\n\n\nclass MyApp(QtWidgets.QMainWindow, Ui_MainWindow):\n CMD_CSM37F58_IAP_CHECKSUM_ADDRESS = 0xEC00\n CMD_CSM37F58_IAP_GET_VERSION_ADDRESS = 0xFFFF\n\n def __init__(self):\n super(MyApp, self).__init__()\n QtWidgets.QMainWindow.__init__(self)\n self.setupUi(self)\n Ui_MainWindow.__init__(self)\n # logo\n self.setWindowIcon(QIcon(\":picture/img/110.png\"))\n # 默认时间戳\n self.time_stamp = datetime.datetime.now().strftime('%Y-%m-%d')\n\n # #初始化显示大小\n self.init_default_display()\n self.init_watch_table_all()\n self.init_read_timer()\n\n ##\n # #初始化信号槽\n self.btn_update_all.clicked.connect(self.update_watch_table_display)\n self.btn_cmd_1.clicked.connect(self.on_click_btn_cmd_1)\n self.btn_cmd_2.clicked.connect(self.on_click_btn_cmd_2)\n self.btn_cmd_3.clicked.connect(self.on_click_btn_cmd_3)\n self.btn_cmd_4.clicked.connect(self.on_click_btn_cmd_4)\n self.btn_cmd_5.clicked.connect(self.on_click_btn_cmd_5)\n self.btn_cmd_6.clicked.connect(self.on_click_btn_cmd_6)\n self.btn_cmd_7.clicked.connect(self.on_click_btn_cmd_7)\n self.btn_cmd_8.clicked.connect(self.on_click_btn_cmd_8)\n self.btn_cmd_9.clicked.connect(self.on_click_btn_cmd_9)\n self.btn_cmd_query.clicked.connect(self.on_click_btn_cmd_query)\n self.btn_cmd_write.clicked.connect(self.on_click_btn_cmd_write)\n self.btn_cmd_read.clicked.connect(self.on_click_btn_cmd_read)\n self.btn_cmd_reset.clicked.connect(self.on_clicked_btn_cmd_reset)\n self.btn_query_state.clicked.connect(self.on_clicked_btn_query_state)\n # IAP\n self.btn_iap_loadfile.clicked.connect(self.on_clicked_btn_iap_loadfile)\n self.btn_iap_start.clicked.connect(self.on_clicked_btn_iap_start)\n self.btn_iap_erase.clicked.connect(self.on_clicked_btn_iap_erase)\n self.btn_iap_get_version.clicked.connect(self.on_clicked_btn_iap_get_version)\n self.btn_iap_read_flash.clicked.connect(self.on_clicked_btn_iap_read_flash)\n #line\n self.line_body_height.textChanged.connect(self.on_changed_line_body_data)\n self.line_body_weight.textChanged.connect(self.on_changed_line_body_data)\n self.line_body_years_old.textChanged.connect(self.on_changed_line_body_data)\n self.line_body_gender.textChanged.connect(self.on_changed_line_body_data)\n self.line_body_mode.textChanged.connect(self.on_changed_line_body_data)\n\n self.btn_read_timer.clicked.connect(self.on_clicked_btn_read_timer)\n #\n self.read_timer = QTimer()\n self.read_timer.timeout.connect(self.read_timer_event)\n # self.read_timer.start(int(self.line_read_time.text()))\n\n def on_clicked_btn_read_timer(self):\n print(\"click read_timer\")\n if(self.btn_read_timer.text()==\"结束\"):\n self.btn_read_timer.setText(\"开始\")\n self.timer_event_enable = False\n # print(\"stop\")\n self.read_timer.stop()\n else:\n #print(\"start\")\n self.btn_read_timer.setText(\"结束\")\n self.timer_event_enable = True\n # self.read_timer.timeout.connect(self.read_timer_event)\n self.read_timer.start(int(self.line_read_time.text()))\n\n def read_timer_event(self):\n print(\"timer_event:\",datetime.datetime.now().strftime('%Y-%m-%d:%H:%M:%S'))\n if self.timer_event_enable == True:\n self.i2c_read_bytes()\n\n def read_save_file(self,s):\n with open(\"./record.csv\",\"a+\") as f:\n f.write(s)\n\n def i2c_read_bytes(self):\n try:\n protocol = CH341AIIC()\n save_mode ='big'\n if self.comboBox_read_timer.currentText() == \"小端模式\":\n save_mode = 'little'\n\n address_read = int(self.line_timer_read_addr.text(),16)\n length = int(self.line_timer_read_byte_len.text())\n print(\"read：\", hex(address_read))\n result = False\n read = bytearray()\n if length == 1:\n result, read = protocol.read_byte(address_read)\n else:\n result,read = protocol.read_bytes(address_read,length)\n # print(\"type:\",type(read)) #bytes\n\n print(str(result),read.hex())\n\n if result:\n # QMessageBox.information(self, \"提示\", \"读取成功\")\n value = int.from_bytes(read,byteorder= save_mode)\n self.plainTextEdit_read_timer.appendPlainText(\"[\" + datetime.datetime.now().strftime('%H:%M:%S') + \"]: 0x\" + read.hex()+\",\"+str(value))\n self.read_save_file(str(value)+'\\n')\n print(str(value))\n else:\n QMessageBox.information(self, \"错误\", \"读取失败,请检查硬件\")\n\n except Exception as e:\n print(str(e))\n self.timer_event_enable =False\n QMessageBox.information(self, \"错误\", \"读取失败,请检查硬件\" + str(e))\n\n def init_read_timer(self):\n self.line_timer_read_addr.setText(\"0x11AC\")\n self.line_timer_read_byte_len.setText(\"1\")\n self.comboBox_read_timer.addItems([\"大端模式\",\"小端模式\"])\n f = open(\"./record.csv\", \"a+\")\n f.close()\n\n def on_clicked_btn_iap_read_flash(self):\n try:\n protocol = CH341AIIC()\n self.progress_bar = QProgressDialog()\n self.progress_bar.setWindowTitle(\"读取Flash中....\")\n self.progress_bar.setWindowIcon(QIcon(\":picture/img/110.png\"))\n self.progress_bar.show()\n self.textBrowser_iap_read.clear()\n self.refresh_app()\n print(\"read_flash:\"+str(self.frame_cnt+2))\n self.textBrowser_iap_read.append(\"共加载%s包,加1包校验\"%(self.frame_cnt+2))\n for i in range(self.frame_cnt+1):\n result,ret = protocol.read_bytes(i*512,512)\n print(\"正在读取第%s包\"%(i+1))\n if not result:\n self.progress_bar.close()\n QMessageBox.information(self, \"提示\", \"没有ACK信号,请检查模块\")\n break\n self.textBrowser_iap_read.append(\"第%s包(512):\"%(i+1))\n self.progress_bar.setValue((i / (self.frame_cnt + 2) * 100))\n self.textBrowser_iap_read.append(ret.hex())\n self.refresh_app()\n # 读校验区\n time.sleep(0.1)\n result, ret = protocol.read_bytes(self.CMD_CSM37F58_IAP_CHECKSUM_ADDRESS, 512)\n self.textBrowser_iap_read.append(\"第%s包(校验码):\" % (self.frame_cnt+2))\n self.textBrowser_iap_read.append(ret.hex())\n self.progress_bar.setValue(100)\n except Exception as e:\n print(\"on_clicked_btn_iap_read_flash\",str(e))\n QMessageBox.information(self, \"提示\", \"初始化CS341失败,请检查硬件\")\n\n def on_clicked_btn_iap_get_version(self):\n print(\"获取版本\")\n try:\n protocol = CH341AIIC()\n time.sleep(0.01)\n result,version = protocol.read_bytes(self.CMD_CSM37F58_IAP_GET_VERSION_ADDRESS,4)\n if result:\n print(bytes(version).hex())\n QMessageBox.information(self, \"提示\",\"得到版本号: %s\"%((bytes(version)).hex()))\n else:\n QMessageBox.information(self, \"提示\", \"发送命令失败\")\n except Exception as e:\n QMessageBox.information(self, \"提示\", \"初始化CS341失败,请检查硬件\")\n\n def on_clicked_btn_iap_erase(self):\n\n self.CSM37F58_IAP_CMD = [0xA0, 0x00, 0x00, 0xAA, 0x55, 0xA5, 0x5A]\n try:\n protocol = CH341AIIC()\n protocol.reset_io_D0(0.005)\n time.sleep(0.01)\n result = protocol.write_bytes(self.CSM37F58_IAP_CMD)\n if result:\n self.btn_iap_start.setEnabled(True)\n QMessageBox.information(self, \"提示\",\"命令发送成功\")\n else:\n QMessageBox.information(self, \"提示\", \"发送命令失败\")\n except Exception as e:\n QMessageBox.information(self, \"提示\", \"初始化CS341失败,请检查硬件\")\n def on_clicked_btn_iap_start(self):\n\n print(\"clicked iap start\")\n try:\n file = open(self.bin_path,\"rb\")\n bin_data = file.read()\n file.close()\n print(len(bin_data),int(len(bin_data)/512))\n self.iic_send_bin_file(bin_data)\n self.btn_iap_start.setEnabled(False)\n except Exception as e:\n print(str(e))\n QMessageBox.information(self, \"提示\", \"请先选择Bin文件！\")\n\n def iic_send_bin_file(self,bin_data):\n self.progress_bar = QProgressDialog()\n print(\"正在准备发送...\")\n try:\n protocol = CH341AIIC()\n self.frame_cnt = int(len(bin_data) / 512)\n self.progress_bar.setWindowTitle(\"在线升级中（IAP）....\")\n self.progress_bar.setWindowIcon(QIcon(\":picture/img/110.png\"))\n self.progress_bar.show()\n self.refresh_app()\n for i in range(self.frame_cnt):\n data_512bytes = bin_data[i*512:i*512+512]\n result = protocol.write_iap_bytes(i*512,bytearray(data_512bytes))\n self.progress_bar.setValue((i/(self.frame_cnt+1)*100))\n if not result:\n self.progress_bar.close()\n QMessageBox.information(self, \"提示\", \"发送失败,请检查硬件\")\n return\n time.sleep(0.01)\n # print(\"升级中...\"+str(i))\n # 发送最后一帧，可能不满512bytes,补足0xff\n last_frame = bin_data[(self.frame_cnt)*512:]\n print(\"last_frame:\"+str(len(last_frame))+\":\"+(last_frame.hex()))\n last = bytearray(512)\n for i in range(512):\n last[i] = 0xff\n for i in range(len(last_frame)):\n last[i] = last_frame[i]\n protocol.write_iap_bytes(self.frame_cnt*512,last)\n print((\"发送last: \"+bytes(last).hex()))\n # send checksum\n protocol.write_iap_bytes(self.CMD_CSM37F58_IAP_CHECKSUM_ADDRESS,self.iap_checksum)\n self.progress_bar.setValue(100)\n print(\"升级完成\")\n except Exception as e:\n print(str(e))\n QMessageBox.information(self, \"提示\", \"发送失败,请检查硬件\")\n\n def on_clicked_btn_iap_loadfile(self):\n print(\"load file clicked\")\n try:\n self.bin_path, describe = QFileDialog.getOpenFileName(self, 'Open file', '.', \"txt files (*.bin)\")\n print(self.bin_path)\n\n file = open(self.bin_path, \"rb\");bin_data = file.read();file.close()\n # get checksum\n self.frame_cnt = int(len(bin_data)/512)\n # load\n self.textBrowser_iap.append(\"共加载到%sBytes,将发送%s包(加1包校验)\"%(len(bin_data),int(len(bin_data)/512)+2))\n for i in range(self.frame_cnt):\n data_512bytes = bin_data[512*i:512*i + 512]\n self.textBrowser_iap.append(\"第%s包(512):\"%(i+1))\n self.textBrowser_iap.append(data_512bytes.hex())\n self.refresh_app()\n # load_end\n self.iap_checksum = bytearray(512)\n for i in range(512):\n self.iap_checksum[i] = 0xff\n for i in range(self.frame_cnt):\n data_512bytes = bin_data[512*i:i*512+512]\n checksum = 0x00\n for j in range(512):\n checksum += data_512bytes[j]\n self.iap_checksum[i+5] = checksum&0xff\n print(hex(checksum&0xff))\n # 最后一帧处理\n last_frame = bin_data[(self.frame_cnt)*512:]\n last = bytearray(512)\n for i in range(512):\n last[i] = 0xff\n for i in range(len(last_frame)):\n last[i] = last_frame[i]\n checksum = 0x00\n print(\"last_len:\",len(last))\n for i in range(512):\n checksum += last[i]\n # last\n #\n self.iap_checksum[5+self.frame_cnt] = checksum&0xff\n self.iap_checksum[4] = self.frame_cnt+1\n self.textBrowser_iap.append(\"第%s包(512):\" % (self.frame_cnt + 1))\n self.textBrowser_iap.append(bytes(last).hex())\n self.refresh_app()\n #main_checksum\n version = 0x00\n for i in range(self.frame_cnt+1):\n version +=self.iap_checksum[5+i]\n print(\"main_CHECKSUM:\",version)\n version = version&0xffff\n main_version = (version&0xff00)>>8\n other_version = (version&0xff)\n self.iap_checksum[0] = main_version\n self.iap_checksum[1] = other_version\n self.iap_checksum[2] = (~main_version)&0xff\n self.iap_checksum[3] = (~other_version)&0xff\n checksum =0x00\n self.iap_checksum[511]=0x00\n for i in range(512):\n checksum +=self.iap_checksum[i]\n self.iap_checksum[511] = checksum&0xff\n\n self.textBrowser_iap.append(\"第%s包(校验码):\" % (self.frame_cnt + 2))\n self.textBrowser_iap.append(bytes(self.iap_checksum).hex())\n print(\"checksum:\"+bytes(self.iap_checksum).hex())\n self.btn_iap_loadfile.setText(\"已选择: \"+self.bin_path)\n self.btn_iap_read_flash.setEnabled(True)\n except Exception as e:\n print(str(e))\n\n def on_clicked_btn_query_state(self):\n print(\"查询状态...\")\n try:\n protocol = CH341AIIC()\n if not protocol.get_input_D7():\n QMessageBox.information(self,\"提示\",\"检测到低电平\")\n else:\n QMessageBox.information(self, \"提示\", \"检测到高电平\")\n except Exception as e:\n QMessageBox.information(self,\"提示\",\"失败,请检查硬件\")\n def on_clicked_btn_cmd_reset(self):\n try:\n protocol = CH341AIIC()\n protocol.reset_io_D0(0.005)\n print(\"event:按键复位\")\n QMessageBox.information(self,\"提示\",\"发送成功\")\n except Exception as e:\n QMessageBox.information(self,\"提示\",\"失败,请检查硬件\")\n def on_changed_line_body_data(self):\n try:\n body_list = [0xA0,0x10,0x58,0x02,0xC1,0xAA,0x9E,0x00]\n body_height = int(self.line_body_height.text())\n body_weight = int(float(self.line_body_weight.text())*10)\n body_years_old = int(self.line_body_years_old.text())\n body_gender = self.line_body_gender.text() == \"男\"\n body_mode = 0\n body_list[3] = body_weight>>8\n body_list[4] = body_weight&0xff\n body_list[5] = body_height\n body_list[6] = body_gender*0x80 + body_years_old\n str_dis = ('0x'+' 0x'.join('{:02x}'.format(x) for x in body_list))\n print(str_dis)\n self.line_cmd_2.setText(str_dis)\n print(str(body_height),str(body_weight),str(body_years_old),str(body_gender),str(body_mode))\n except Exception as e:\n print(str(e))\n print(\"editing...\")\n\n def on_click_btn_cmd_1(self):\n\n self.iic_send_bytes(self.line_cmd_1.text(),True)\n\n def on_click_btn_cmd_2(self):\n hex_str = self.line_cmd_2.text()\n cmd_hex = hex_str.replace(\"0x\",\"\")\n cmd_bytes = bytes.fromhex(cmd_hex)\n cmd = [cmd_bytes[0],cmd_bytes[1],cmd_bytes[2],cmd_bytes[3]]\n self.iic_send_bytes(bytes(cmd).hex())\n cmd[2] = cmd_bytes[2]+1\n cmd[3] = cmd_bytes[4]\n self.iic_send_bytes(bytes(cmd).hex())\n cmd[2] = cmd_bytes[2]+2\n cmd[3] = cmd_bytes[5]\n self.iic_send_bytes(bytes(cmd).hex())\n cmd[2] = cmd_bytes[2]+3\n cmd[3] = cmd_bytes[6]\n self.iic_send_bytes(bytes(cmd).hex())\n cmd[2] = cmd_bytes[2]+4\n cmd[3] = cmd_bytes[7]\n self.iic_send_bytes(bytes(cmd).hex(),True)\n\n def on_click_btn_cmd_3(self):\n self.btn_cmd_3.setEnabled(False)\n self.iic_send_bytes(self.line_cmd_3.text(),True)\n self.btn_cmd_3.setEnabled(True)\n\n def on_click_btn_cmd_4(self):\n self.btn_cmd_4.setEnabled(False)\n self.iic_send_bytes(self.line_cmd_4.text(),True)\n self.btn_cmd_4.setEnabled(True)\n\n def on_click_btn_cmd_5(self):\n self.btn_cmd_5.setEnabled(False)\n self.iic_send_bytes(self.line_cmd_5.text(),True)\n self.btn_cmd_5.setEnabled(True)\n\n def on_click_btn_cmd_6(self):\n self.btn_cmd_6.setEnabled(False)\n self.iic_send_bytes(self.line_cmd_6.text(),True)\n self.btn_cmd_6.setEnabled(True)\n\n def on_click_btn_cmd_7(self):\n self.btn_cmd_7.setEnabled(False)\n self.iic_send_bytes(self.line_cmd_7.text(),True)\n self.btn_cmd_7.setEnabled(True)\n def on_click_btn_cmd_8(self):\n self.btn_cmd_8.setEnabled(False)\n self.iic_send_bytes(self.line_cmd_8.text(),True)\n self.btn_cmd_8.setEnabled(True)\n def on_click_btn_cmd_9(self):\n self.btn_cmd_9.setEnabled(False)\n self.iic_send_bytes(self.line_cmd_9.text(),True)\n self.btn_cmd_9.setEnabled(True)\n def on_click_btn_cmd_query(self):\n self.btn_cmd_query.setEnabled(False)\n self.iic_read_byte(self.line_cmd_query.text(), True, self.line_cmd_query_dis)\n self.btn_cmd_query.setEnabled(True)\n\n def on_click_btn_cmd_write(self):\n self.btn_cmd_write.setEnabled(False)\n self.iic_send_bytes(self.line_cmd_write.text(),True)\n self.btn_cmd_write.setEnabled(True)\n\n def on_click_btn_cmd_read(self):\n self.btn_cmd_read.setEnabled(False)\n self.iic_read_byte(self.line_cmd_read.text(),True,self.line_cmd_read_dis)\n self.btn_cmd_read.setEnabled(True)\n\n def iic_read_byte(self,hex_str,dis_success,dis_line):\n try:\n cmd_hex = hex_str.replace(\"0x\", \"\")\n cmd_bytes = bytes.fromhex(cmd_hex)\n print(hex_str)\n protocol = CH341AIIC()\n print(hex(cmd_bytes[1]),hex(cmd_bytes[2]))\n address_read = (cmd_bytes[1]*256+cmd_bytes[2])\n print(\"read：\", hex(address_read))\n result,read = protocol.read_byte(address_read)\n dis_line.setText(\"读取到数据：\"+hex(read[0]))\n if dis_success & result:\n QMessageBox.information(self, \"提示\", \"读取成功\")\n elif dis_success:\n QMessageBox.information(self, \"错误\", \"读取失败,请检查硬件\")\n except Exception as e:\n print(str(e))\n QMessageBox.information(self, \"错误\", \"读取失败,请检查硬件\" + str(e))\n\n def refresh_app(self):\n\n qApp.processEvents()\n\n def iic_send_bytes(self,hex_str, dis_success = False):\n\n try:\n cmd_hex = hex_str.replace(\"0x\",\"\")\n cmd_bytes = bytes.fromhex(cmd_hex)\n\n protocol = CH341AIIC()\n protocol.set_clk(protocol.IIC_CLK_100kHz)\n result = protocol.write_bytes(cmd_bytes)\n print(str(cmd_bytes.hex()))\n if dis_success&result:\n QMessageBox.information(self,\"提示\",\"发送成功\")\n elif dis_success:\n QMessageBox.information(self, \"错误\", \"发送失败,请检查硬件\" )\n except Exception as e:\n print(str(e))\n QMessageBox.information(self,\"错误\",\"发送失败,请检查硬件\"+str(e))\n\n\n\n def init_default_display(self):\n # size\n self.__desktop = QApplication.desktop()\n qRect = self.__desktop.screenGeometry() # 设备屏幕尺寸\n self.resize(qRect.width() * 45/ 100, qRect.height() * 90 / 100)\n self.move(qRect.width() / 3, qRect.height() / 30)\n\n\n def init_watch_table_all(self):\n\n\n self.watch_modle_dev_set = QStandardItemModel(64, 3)\n self.watch_table_dev_set.setModel(self.watch_modle_dev_set)\n\n\n self.watch_modle_dev_info = QStandardItemModel(24, 3)\n self.watch_table_dev_info.setModel(self.watch_modle_dev_info)\n\n self.watch_modle_usr_info = QStandardItemModel(128, 3)\n self.watch_table_usr_info.setModel(self.watch_modle_usr_info)\n\n self.watch_modle_usr_bia = QStandardItemModel(128, 3)\n self.watch_table_usr_bia.setModel(self.watch_modle_usr_bia)\n\n # page2\n self.watch_modle_analy_result = QStandardItemModel(128, 3)\n self.watch_table_analy_result.setModel(self.watch_modle_analy_result)\n\n self.watch_modle_tst_middle = QStandardItemModel(128, 3)\n self.watch_table_tst_middle.setModel(self.watch_modle_tst_middle)\n\n self.watch_modle_com_log = QStandardItemModel(24, 3)\n self.watch_table_com_log.setModel(self.watch_modle_com_log)\n\n self.watch_modle_res_real = QStandardItemModel(128, 3)\n self.watch_table_res_real.setModel(self.watch_modle_res_real)\n\n\n\n def update_watch_table_display(self):\n\n # 查询数据库数据\n # self.watch_modle.setItem(0,0,QStandardItem(\"示例\"))\n self.progress_bar = QProgressDialog()\n\n try:\n protocol = CH341AIIC()\n protocol.set_clk(protocol.IIC_CLK_100kHz)\n # print(\"逐个读地址：\", hex(address_read))\n self.progress_bar.setWindowTitle(\"更新RAM中....\")\n self.progress_bar_current = 0\n self.progress_bar_total = (64 + 24 + 128 + 128 + 128 + 128 + 128 + 24)\n self.progress_bar.setWindowIcon(QIcon(\":picture/img/110.png\"))\n self.progress_bar.show()\n\n self.update_table(protocol, start_addr=0x1000, read_length=64, watch_modle=self.watch_modle_dev_set)\n\n self.update_table(protocol, start_addr=0x1040, read_length=24, watch_modle=self.watch_modle_dev_info)\n\n self.update_table(protocol, start_addr=0x1058, read_length=128, watch_modle=self.watch_modle_usr_info)\n\n self.update_table(protocol, start_addr=0x10d8, read_length=128, watch_modle=self.watch_modle_usr_bia)\n\n # page 2\n self.update_table(protocol, start_addr=0x1158, read_length=128, watch_modle=self.watch_modle_analy_result)\n\n self.update_table(protocol, start_addr=0x11d8, read_length=128, watch_modle=self.watch_modle_tst_middle)\n\n self.update_table(protocol, start_addr=0x1258, read_length=24, watch_modle=self.watch_modle_com_log)\n\n self.update_table(protocol, start_addr=0x1270, read_length=128, watch_modle=self.watch_modle_res_real)\n except Exception as e:\n QMessageBox.information(self,\"错误\",str(e))\n\n\n\n\n\n def update_table(self,protocol, start_addr,read_length,watch_modle):\n for i in range(read_length):\n ret = protocol.read_byte(start_addr + i)\n if ret[0] == True:\n for x in ret[1]:\n watch_modle.setItem(i, 0, QStandardItem(\"%s + \"%(hex(start_addr))+str(hex(i)+\"=\"+hex(start_addr+i))))\n watch_modle.setItem(i, 1, QStandardItem(str(hex(x))))\n self.progress_bar_current = self.progress_bar_current+1\n self.progress_bar.setValue(self.progress_bar_current*100/self.progress_bar_total)\n QtCore.QCoreApplication.processEvents()\n else:\n print(\"读取失败...\")\n self.progress_bar.close()\n raise Exception(\"读取失败，请检查硬件。\")\n\n\n\n\n def on_click_watch_table_view(self, model_index):\n pass\n print(\"add:\",model_index.row(),model_index.column())\n # QMessageBox.information(self,\"提示\",\"隐藏当前列\",QMessageBox.Yes|QMessageBox.No)\n\nclass Custum_complains(QThread):\n # const\n def __init__(self):\n super(Custum_complains, self).__init__()\n def run(self):\n pass\n try:\n # 串口工作主流程\n \"\"\"主循环\"\"\"\n while True:\n pass\n time.sleep(0.1)\n except Exception as e:\n print(str(e))\n\n def mainloop_app(self):\n try:\n pass\n app = QtWidgets.QApplication(sys.argv)\n window = MyApp()\n window.show()\n pass\n except Exception as e:\n print(str(e))\n finally:\n sys.exit(app.exec_())\n\nif __name__ == \"__main__\":\n try:\n custum = Custum_complains()\n custum.start()\n custum.mainloop_app()\n except Exception as e:\n print(str(e))\n finally:\n pass\n\n\n\n\n", "sub_path": "CSM37F58_APP.py", "file_name": "CSM37F58_APP.py", "file_ext": "py", "file_size_in_byte": 25156, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 84, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 156, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 169, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 185, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 226, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 580, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 587, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 594, "usage_type": "call"}]} +{"seq_id": "258463708", "text": "import numpy as np\nfrom scipy.spatial.distance import pdist, squareform, cosine\nfrom tqdm import tqdm\nimport pickle\n\n\ndef save_obj(obj, name):\n with open(name, 'wb') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n\n\ndef load_obj(name):\n with open(name, 'rb') as f:\n return pickle.load(f)\n\n\nclass NeGraph(object):\n def __init__(self, graph, value, similarity_matrix):\n self.graph = graph\n self.value = value\n self.similarity_matrix = similarity_matrix\n self.pr = []\n for item in range(len(self.graph)):\n self.pr.append(1/len(self.graph))\n\n def calculate_pr(self, d):\n modify_sum = []\n for modify_idx in range(len(self.similarity_matrix)):\n modify_sum.append(np.sum(self.similarity_matrix[modify_idx]))\n temp_pr = self.pr.copy()\n for item in self.graph:\n # 确定节点序号\n item_idx = self.graph[item]\n # 首先计算一个偏置值\n item_bias = (1 - d) * self.value[item_idx]\n modify_value = 0.0\n for temp_idx in range(len(self.similarity_matrix[item_idx])):\n if item_idx == temp_idx or modify_sum[temp_idx] == 0.0:\n # 不计算自己\n continue\n modify_value = modify_value + self.similarity_matrix[item_idx][temp_idx]/modify_sum[temp_idx] * \\\n temp_pr[temp_idx]\n if modify_value == 0.0:\n self.pr[item_idx] = item_bias\n continue\n self.pr[item_idx] = item_bias + d * self.value[item_idx] * modify_value\n\n\n def calculate_pr_times(self, d, times):\n for i in range(times):\n self.calculate_pr(d)\n\n def calculate_pr_converge(self, d, threshold):\n if len(self.graph) >= 2:\n count_number = 0\n changes = np.array([100.0] * len(self.graph))\n while (changes > threshold).any() and count_number <= 100:\n old_pr = self.pr.copy()\n self.calculate_pr(d)\n for i in range(len(changes)):\n changes[i] = abs(self.pr[i] - old_pr[i])\n count_number = count_number + 1\n\n def get_final_pr_score_dic(self):\n temp_pr_score = {}\n final_pr_score = {}\n for item in self.graph:\n item_idx = self.graph[item]\n temp_pr_score[item] = self.pr[item_idx]\n final_pr_score_list = sorted(temp_pr_score.items(), key=lambda kv: (kv[1], kv[0]), reverse=True)\n for i in final_pr_score_list:\n final_pr_score[i[0]] = i[1]\n return final_pr_score\n\n\ndef test(graph_list_file, graph_embedding_list_file, graph_value_list_file, output_final_pr_file):\n graph_list = load_obj(graph_list_file)\n graph_embedding_list = load_obj(graph_embedding_list_file)\n graph_value_list = load_obj(graph_value_list_file)\n final_phrase = []\n for idx in tqdm(range(len(graph_list))):\n if len(graph_list[idx]) == 0:\n final_phrase.append({})\n continue\n temp_similarity = pdist(graph_embedding_list[idx], metric='cosine')\n temp_similarity_matrix = squareform(temp_similarity)\n # 算相似度矩阵\n temp_ones = np.ones((len(graph_list[idx]), len(graph_list[idx])), dtype='float64')\n used_similarity_matrix = temp_ones - temp_similarity_matrix\n graph_score = NeGraph(graph_list[idx], graph_value_list[idx], used_similarity_matrix)\n graph_score.calculate_pr_converge(0.85, 0.0001)\n final_phrase.append(graph_score.get_final_pr_score_dic())\n save_obj(final_phrase, output_final_pr_file)\n\n\nif __name__ == '__main__':\n graph_list_file = 'patent/title/title_graph/title_graph_list.pkl'\n graph_embedding_list_file = 'patent/title/title_graph/title_graph_embedding_list.pkl'\n graph_value_list_file = 'patent/title/title_score/title_influence_phrase_list_normalized_score.pkl'\n test_file = 'patent/title/title_rank/ranked_title_influence_phrase_score.pkl'\n test(graph_list_file, graph_embedding_list_file, graph_value_list_file, test_file)\n", "sub_path": "patent/title/title_rank/title_rank.py", "file_name": "title_rank.py", "file_ext": "py", "file_size_in_byte": 4127, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pickle.dump", "line_number": 9, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 81, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 85, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "225289969", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nlabel=['week days', 'weekend']\ntweet_count=[148657, 57078]\n\ncolors = ['#ff9999', '#66b3ff']\n\nfig1, ax1 = plt.subplots()\nax1.pie(tweet_count, colors=colors, labels=label, autopct='%1.1f%%', startangle=90)\n# draw circle\ncentre_circle = plt.Circle((0, 0), 0.70, fc='white')\nfig = plt.gcf()\nfig.gca().add_artist(centre_circle)\n# Equal aspect ratio ensures that pie is drawn as a circle\nax1.axis('equal')\nplt.tight_layout()\nplt.show()", "sub_path": "phase2/visualization/query4.py", "file_name": "query4.py", "file_ext": "py", "file_size_in_byte": 480, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Circle", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "340228128", "text": "from glob import glob\nimport cv2\nimport os\nimport numpy as np\nimport re\n#import pyson.vision as pv\nimport numpy as np\n#import pyson.utils as pu\nfrom concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor\nfrom tqdm import tqdm\nimport json\nREGEX = re.compile('.*(\\d{4})-(.+)-(\\d{1,4}).png')\n\n\n \n \ndef read_image(path, output_channels=3, resize_factor=1):\n if output_channels == 3:\n img = cv2.imread(path)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n else:\n img = cv2.imread(path, 0)\n \n if resize_factor !=1 :\n img = cv2.resize(img, (0, 0), fx=resize_factor, fy=resize_factor)\n return img\n\ndef get_text_boundingboxes(label_text, ex_border, resize_factor=1):\n ex_border = cv2.morphologyEx(ex_border, cv2.MORPH_CLOSE, np.ones([1, int(50*resize_factor)]))\n ex_border = cv2.morphologyEx(ex_border, cv2.MORPH_CLOSE, np.ones([int(10*resize_factor), 1]))\n\n dilating = cv2.morphologyEx(label_text, cv2.MORPH_CLOSE, np.ones([int(5*resize_factor), int(300*resize_factor)]))\n \n idxs = np.where(ex_border> 0) \n dilating[idxs] = 0\n\n #cnts, hiers = pv.findContours(dilating)\n ret, thresh = cv2.threshold(dilating, 127, 255, 0)\n im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n bb = []\n mask = np.zeros_like(dilating)\n # Split by excell\n mask = np.zeros_like(dilating)\n #for cnt in cnts:\n for cnt in contours:\n x,y,w,h = cv2.boundingRect(cnt)\n pad = label_text[y:y+h, x:x+w]\n if pad.mean() > 0:\n idxs = np.vstack(np.where(pad==255))\n min_y = idxs[0].min()\n max_y = idxs[0].max()\n min_x = idxs[1].min()\n max_x = idxs[1].max()\n\n ay = y+min_y\n ax = x + min_x\n by = y+max_y\n bx = x + max_x\n a, b = (ax,ay), (bx, by)\n bb.append((a, b))\n cv2.rectangle(mask, a, b, 255, -1)\n # merge over cell\n mask_merge = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, np.ones([1, int(10*resize_factor)]))\n #cnts, _ = pv.findContours(mask_merge)\n ret, thresh1 = cv2.threshold(mask_merge, 127, 255, 0)\n im3, cnts, hierarchy1 = cv2.findContours(thresh1,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n# pv.show(mask_merge, dpi=300, size=10)\n return [cv2.boundingRect(cnt) for cnt in cnts]\n\ndef get_bb_from_dict_path(dict_path, resize_factor):\n# input = read_image(dict_path['input'], 3, resize_factor)\n label_exborder = read_image(dict_path['label-ex_border'], 1, resize_factor)\n label_text = read_image(dict_path['label-text'], 1, resize_factor)\n \n boxes = get_text_boundingboxes(label_text, label_exborder, resize_factor)\n \n for (x, y, w, h) in boxes:\n a = (x, y)\n b = (x+w, y+h)\n return boxes\n\ndef perform(paths, multithread=4):\n input_paths = [path for path in paths if '-input-' in path]\n trainval_set = []\n \n for input_path in input_paths:\n mo = REGEX.search(input_path)\n m1 = mo.group(1)\n m3 = mo.group(3)\n d = {\n 'input': input_path,\n #'label-image': input_path.replace('-input-{}'.format(m3), '-image'),\n 'label-ex_border': input_path.replace('-input-{}'.format(m3), '-ex_border'),\n 'label-text': input_path.replace('-input-', '-text-'),\n }\n \n trainval_set.append(d)\n \n for path in d.values(): assert(os.path.exists(path)), path\n \n# for dict_path in tqdm(trainval_set):\n def fn(dict_path):\n input_path = dict_path['input']\n input_image = read_image(input_path, 1, 1)\n mask = np.zeros_like(input_image)\n boxes = get_bb_from_dict_path(dict_path, 1)\n for (x,y,w,h) in boxes:\n cv2.rectangle(mask, (x, y), (x+w, y+h), 255, 2)\n\n\n #### Huynh modify ######\n i = 0\n boxes_mod = {}\n for (x,y,w,h) in boxes:\n #boxes_mod.append((x,y,x+w, y+h)) \n boxes_mod[str(i)] = {'x': x, 'width': w, 'y':y, 'height':h}\n i = i+1\n\n file_name_ext = input_path.split('/')[-1]\n file_name = file_name_ext.split('.')[0] + '.json'\n file_path = os.path.dirname(input_path)\n json_fullpath = os.path.join(file_path,file_name)\n with open(json_fullpath,'w') as outfile:\n json.dump(boxes_mod,outfile)\n ########################\n #out_path_text_box = input_path.replace('-input-', '-textbox-')\n #cv2.imwrite(out_path_text_box, mask)\n #np.save(out_path_text_box.replace('.png', '.npy'), np.array(boxes))\n #return out_path_text_box\n \n if multithread>1:\n with tqdm(total=len(trainval_set), desc=\"Executing Pipeline\", unit=\" Samples\") as progress_bar:\n with ThreadPoolExecutor(max_workers=multithread) as executor:\n for result in executor.map(fn, trainval_set):\n progress_bar.set_description(\"Processing %s\" % result)\n progress_bar.update(1)\n else:\n with tqdm(total=len(trainval_set), desc=\"Executing Pipeline\", unit=\" Samples\") as progress_bar:\n for dict_path in trainval_set:\n result =fn(dict_path)\n progress_bar.set_description(\"Processing %s\" % result)\n progress_bar.update(1)", "sub_path": "build_text_bb.py", "file_name": "build_text_bb.py", "file_ext": "py", "file_size_in_byte": 5339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "re.compile", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 105, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 124, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 132, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 133, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "81011409", "text": "#lda_2(关于均值有点问题还没有弄清楚)\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfilename = \"c:\\\\tfmodels\\\\dataset2_data_mining_course.csv\"\n\ndef make_matrix(filename):\n matrix = np.loadtxt(open(filename,\"rb\"), delimiter=\",\", skiprows=0)\n return matrix\n\ndef lda(x_raw, x_raw_1, x_raw_2, x_raw_3 ,d):#x_in为输入矩阵（n*p），d为维数\n with tf.name_scope(\"lda\"):\n #得到矩阵的大小\n x_in = tf.convert_to_tensor(x_raw)\n x_in = tf.cast(x_in, tf.float32)\n x_in_1 = tf.convert_to_tensor(x_raw_1)\n x_in_1 = tf.cast(x_in_1, tf.float32)#A类\n x_in_2 = tf.convert_to_tensor(x_raw_2)\n x_in_2 = tf.cast(x_in_2, tf.float32)#B类\n x_in_3 = tf.convert_to_tensor(x_raw_3)\n x_in_3 = tf.cast(x_in_3, tf.float32)#C类\n\n n = tf.to_float(x_in.get_shape()[0]),tf.to_int32(x_in.get_shape()[1])\n #总样本均值和各类均值\n mean = tf.reduce_mean(x_in, axis=1)\n mean_1 = tf.reduce_mean(x_in_1, axis=1)\n mean_2 = tf.reduce_mean(x_in_2, axis=1)\n mean_3 = tf.reduce_mean(x_in_3, axis=1)\n mean_total = x_in - tf.reshape(mean,(-1,1))\n mean_1_tmp = x_in_1 - tf.reshape(mean_1, (-1,1))\n mean_2_tmp = x_in_2 - tf.reshape(mean_2, (-1,1))\n mean_3_tmp = x_in_3 - tf.reshape(mean_3, (-1,1))\n mean_diff = tf.concat([mean_1_tmp, mean_2_tmp], 0)\n mean_diff = tf.concat([mean_diff, mean_3_tmp], 0)\n #计算类内散度矩阵Sw\n Sw_mean = x_in - mean_diff\n Sw = tf.matmul(Sw_mean, Sw_mean, transpose_a=True)\n #计算类间散度矩阵Sb\n St_mean = x_in - mean_total\n St = tf.matmul(St_mean, St_mean, transpose_a=True)#全局散度\n Sb = St - Sw\n cov = tf.matmul(tf.matrix_inverse(Sw), Sb)\n #特征值分解\n e, v = tf.linalg.eigh(cov)\n #对得到的特征值中取前d个最大的\n e_index = tf.math.top_k(e, sorted=True, k =d)[1]\n #取前d个最大特征向量\n v_lda = tf.gather(v, indices=e_index)\n #得到lda结果矩阵\n x_lda = tf.matmul(x_in, v_lda, transpose_b=True) \n sess = tf.Session()\n #转为numpy矩阵\n x_lda_np = x_lda.eval(session=sess)\n return x_lda_np\n\nx_raw = make_matrix(filename)\nd = int(input(\"输入维度\"))\nx_raw1 = x_raw[0:100,:]\nx_raw2 = x_raw[100:300,:]\nx_raw3 = x_raw[300:500,:]\nresult = lda(x_raw, x_raw1, x_raw2, x_raw3, d)\n\ndef showmodel():\n if (d == 2):#二维分布 \n result_1 = result[0:100,:]\n result_2 = result[100:300,:]\n result_3 = result[300:500,:]\n plt.scatter(result_1[:,0], result_1[:,1], c='blue')\n plt.scatter(result_2[:,0], result_2[:,1], c='orange')\n plt.scatter(result_3[:,0], result_3[:,1], c='red')\n plt.show()\n elif (d == 1):#一维分布\n result_1 = result[0:100,:]\n result_2 = result[100:300,:]\n result_3 = result[300:500,:]\n plt.scatter(result_1[:,0], np.zeros(100), c='blue')\n plt.scatter(result_2[:,0], np.zeros(200), c='orange')\n plt.scatter(result_3[:,0], np.zeros(200), c='red')\n plt.show()\n else:#原始3d分布\n result_1 = x_raw[0:100,0:3]\n result_2 = x_raw[100:300,0:3]\n result_3 = x_raw[300:500,0:3]\n model = plt.subplot(111, projection='3d')\n plt.scatter(result_1[:,0], result_1[:,1], result_1[:,2], c='blue')\n plt.scatter(result_2[:,0], result_2[:,1], result_2[:,2], c='orange')\n plt.scatter(result_3[:,0], result_3[:,1], result_3[:,2], c='red')\n plt.show()\n\nshowmodel()\n\n", "sub_path": "Tensorflow/LDA_2.py", "file_name": "LDA_2.py", "file_ext": "py", "file_size_in_byte": 3662, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.loadtxt", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.to_float", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.to_int32", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.matrix_inverse", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.linalg.eigh", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.linalg", "line_number": 45, "usage_type": "attribute"}, {"api_name": "tensorflow.math.top_k", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tensorflow.gather", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}]} +{"seq_id": "337812794", "text": "import os\nfrom unittest import mock\n\nfrom django.test import TestCase\n\nfrom kino.kinoparser.afisha_parser import (\n CinemaListParser,\n CinemaPageParser,\n MovieListParser,\n MovieShowtimeParser,\n CinemaIdNameMetro)\n\n\nclass KinoAfishaParseCase(TestCase):\n\n def setUp(self):\n pass\n\n def read_data_file(self, file_path_in_data):\n path = os.path.dirname(os.path.abspath(__file__))\n path = os.path.join(path, 'data', file_path_in_data)\n with open(path) as f:\n return f.read()\n\n def test_cinema_list_parsing(self):\n parser = CinemaListParser()\n\n content = self.read_data_file('afisha_cinema_list.html')\n\n with mock.patch.object(parser, 'get_movies_page', return_value=content) as patched_method:\n cinema_list = parser.parse()\n\n movie_1 = CinemaIdNameMetro(8168846, 'Балтика', 'Сходненская')\n movie_2 = CinemaIdNameMetro(8325961, 'Тула', None)\n movie_3 = CinemaIdNameMetro(1714544, 'Юность', 'Октябрьское поле')\n\n self.assertIn(movie_1, cinema_list)\n self.assertIn(movie_2, cinema_list)\n self.assertIn(movie_3, cinema_list)\n\n def test_cinema_full_info_parsing(self):\n parser = CinemaPageParser()\n\n content = self.read_data_file('afisha_cinema_info.html')\n\n with mock.patch.object(parser, 'get_cinema_page', return_value=content) as p_method:\n cinema_info = parser.parse(1234567)\n\n self.assertAlmostEqual(7.8, cinema_info.rating)\n self.assertEqual(550, cinema_info.votes)\n\n def test_movie_list_parsing(self):\n parser = MovieListParser()\n\n content = self.read_data_file('afisha_films_list.html')\n\n with mock.patch.object(parser, 'get_movie_list', return_value=content) as patched_method:\n movie_list = parser.parse('some_date')\n\n movie_first = movie_list[0]\n movie_last = movie_list[-1]\n\n self.assertEqual(movie_first.name, 'Гоголь. Страшная месть')\n self.assertEqual(movie_first.movie_id, '8330075')\n\n self.assertEqual(movie_last.name, 'Болот��')\n self.assertEqual(movie_last.movie_id, '8355150')\n\n def test_movie_info_parsing(self):\n parser = MovieShowtimeParser()\n\n content = self.read_data_file('afisha_film_info.html')\n\n with mock.patch.object(parser, 'get_movie_page', return_value=content) as p_method:\n movie_info = parser.parse(1234567)\n\n for raw_cinema in movie_info:\n self.assertEqual(raw_cinema.name, 'Loft Cinema')\n self.assertEqual(len(movie_info[raw_cinema]), 1)\n self.assertEqual(movie_info[raw_cinema][0].time, '03:10')\n break\n", "sub_path": "kino/kinoparser/tests/tests_afisha_parsing.py", "file_name": "tests_afisha_parsing.py", "file_ext": "py", "file_size_in_byte": 2749, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.test.TestCase", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "kino.kinoparser.afisha_parser.CinemaListParser", "line_number": 26, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 30, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 30, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 30, "usage_type": "name"}, {"api_name": "kino.kinoparser.afisha_parser.CinemaIdNameMetro", "line_number": 33, "usage_type": "call"}, {"api_name": "kino.kinoparser.afisha_parser.CinemaIdNameMetro", "line_number": 34, "usage_type": "call"}, {"api_name": "kino.kinoparser.afisha_parser.CinemaIdNameMetro", "line_number": 35, "usage_type": "call"}, {"api_name": "kino.kinoparser.afisha_parser.CinemaPageParser", "line_number": 42, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 46, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 46, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 46, "usage_type": "name"}, {"api_name": "kino.kinoparser.afisha_parser.MovieListParser", "line_number": 53, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 57, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 57, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 57, "usage_type": "name"}, {"api_name": "kino.kinoparser.afisha_parser.MovieShowtimeParser", "line_number": 70, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 74, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 74, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 74, "usage_type": "name"}]} +{"seq_id": "263409996", "text": "import data, analysis\n\n# Handle data\ndata_obj = data.Data()\ndata_obj.parse_csv('ongo.csv')\ndata_obj.format_dataset()\n\nlifelines_dataset = data_obj.dataset\n\n# Handle reporting\nconfiguration = {}\nreport = analysis.Analysis(lifelines_dataset, configuration)\n\n# reports\ndata = report.dataset\n\nreport.fit_data()\nmedian = report.return_median()\nsurvival_function = report.return_survival_function()\nhazard_function = report.return_hazard_function()\n\n# handle display\nprint(data)\nprint('MEDIAN ', median)\nprint('SURVIVAL ', survival_function)\nprint('HAZARD ', hazard_function)\n\n# operations\n# subtract\n\n# graphs\nlifetime_plot = report.return_lifetimes()\nkmf_plot = report.plot_kmf()\nnaf_plot = report.plot_naf()\nreport.show_plots()\n\n# groups\nkmf_plot_2 = report.plot_kmf(config = ['it'])\nreport.show_plots()\n\nkmf_plot_4 = report.plot_kmf(config = ['manager'])\nreport.show_plots()\n\nkmf_plot_4 = report.plot_kmf(config = ['hardware'])\nreport.show_plots()\n\nkmf_plot_3 = report.plot_kmf(config = ['it', 'manager'])\nreport.show_plots()\n\nkmf_plot_3 = report.plot_kmf(config = ['it', 'manager', 'hardware'])\nreport.show_plots()\n\n# groups\nnaf_plot_2 = report.plot_naf(config = ['it'])\nreport.show_plots()\n\nnaf_plot_4 = report.plot_naf(config = ['manager'])\nreport.show_plots()\n\nnaf_plot_4 = report.plot_naf(config = ['hardware'])\nreport.show_plots()\n\nnaf_plot_3 = report.plot_naf(config = ['it', 'manager'])\nreport.show_plots()\n\nnaf_plot_3 = report.plot_naf(config = ['it', 'manager', 'hardware'])\nreport.show_plots()\n\nreport.show_plots()", "sub_path": "Survival/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1536, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "data.Data", "line_number": 4, "usage_type": "call"}, {"api_name": "analysis.Analysis", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "631866988", "text": "# ANT - Cadence, Speed Sensor AND Heart Rate Monitor - Example\n#\n# Copyright (c) 2012, Gustav Tiger \n#\n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation files (the \"Software\"),\n# to deal in the Software without restriction, including without limitation\n# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n# and/or sell copies of the Software, and to permit persons to whom the\n# Software is furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n# DEALINGS IN THE SOFTWARE.\n\nfrom __future__ import absolute_import, print_function\n\nfrom ant.easy.node import Node\nfrom ant.easy.channel import Channel\nfrom ant.base.message import Message\n\nimport logging\nimport struct\nimport threading\nimport sys\nimport time\nimport math\nfrom pymongo import MongoClient\n\nNETWORK_KEY= [0xb9, 0xa5, 0x21, 0xfb, 0xbd, 0x72, 0xc3, 0x45]\n\nclass Monitor():\n def __init__(self):\n self.crank_revs = 0\n self.speed = \"n/a\"\n self.cadence = 0\n self.session_length = 0\n self.last_data_time = 0\n #time reported by sensor (in 1/1024ths of a second)\n self.device_time = 0\n self.session_start_time = 0\n\n #when we last got an update from the device\n self.last_device_time = 0\n\n def on_data_speed(self, data):\n self.speed = str(data[7]*256 + data[6])\n self.display()\n\n def on_data_cadence(self, data):\n old_crank_revs = self.crank_revs\n old_device_time = self.device_time\n current_time = time.time()\n \n\n #parse data from device\n self.crank_revs = data[7]*256 + data[6]\n self.device_time = data[5]*256 + data[4]\n\n revdiff = self.crank_revs - old_crank_revs\n timediff = self.device_time - old_device_time\n\n #try to calculate the cadence\n if (timediff == 0):\n pass\n else:\n self.cadence = (60 * 1024 / timediff) * revdiff\n\n \n\n #if it's been more than 2 seconds since we heard from the device, new session\n if self.last_data_time + 2 < current_time:\n self.new_session()\n else:\n #has the device time changed?\n if self.device_time != old_device_time:\n self.update_session()\n self.last_device_time = current_time\n else:\n #how long has it been?\n if self.last_device_time + 2 > current_time:\n self.update_session()\n else:\n self.new_session()\n \n #update our saved times\n self.last_data_time = current_time\n \n \n self.display()\n\n def new_session(self):\n self.session_length = 0\n self.session_start_time = time.time()\n\n #it's a new session so start a new record \n result = self.db.sessions.insert_one(\n {\n \"start_time\" : self.session_start_time,\n \"merits_earned\" : self.session_length,\n \"last_updated\" : self.session_start_time,\n \"cadence\" : self.cadence\n }\n )\n\n #save the session Id for now\n self.session_id = result\n\n def update_session(self):\n current_time = time.time()\n\n self.session_length = current_time - self.session_start_time\n \n #update our mongo record\n self.db.sessions.update_one(\n {\n '_id' : self.session_id.inserted_id\n },\n {\n \"$set\" : {\n \"merits_earned\" : self.session_length,\n \"last_updated\" : current_time,\n \"cadence\" : self.cadence\n }\n }\n\n\n )\n\n def display(self):\n merits = math.floor(self.session_length)\n string = \" Pedal revolutions: \" + str(self.crank_revs) + \" Cadence: \" + str(self.cadence) + \" Merits this session: \" + str(merits)\n\n sys.stdout.write(string)\n sys.stdout.flush()\n sys.stdout.write(\"\\b\" * len(string))\n\n\ndef main():\n # logging.basicConfig()\n\n mongo = MongoClient()\n\n monitor = Monitor()\n monitor.db = mongo.test2\n\n\n node = Node()\n node.set_network_key(0x00, NETWORK_KEY)\n\n channel = node.new_channel(Channel.Type.BIDIRECTIONAL_RECEIVE)\n\n channel.on_broadcast_data = monitor.on_data_speed\n channel.on_burst_data = monitor.on_data_speed\n\n channel.set_period(8188)\n channel.set_search_timeout(255)\n channel.set_rf_freq(57)\n channel.set_id(0, 123, 0)\n\n channel_cadence_speed = node.new_channel(Channel.Type.BIDIRECTIONAL_RECEIVE)\n\n channel_cadence_speed.on_broadcast_data = monitor.on_data_cadence\n channel_cadence_speed.on_burst_data = monitor.on_data_cadence\n\n channel_cadence_speed.set_period(8102)\n channel_cadence_speed.set_search_timeout(255)\n channel_cadence_speed.set_rf_freq(57)\n channel_cadence_speed.set_id(0, 122, 0)\n\n try:\n channel.open()\n channel_cadence_speed.open()\n node.start()\n print(\"after start\")\n finally:\n node.stop()\n \nif __name__ == \"__main__\":\n main()\n\n", "sub_path": "testing.py", "file_name": "testing.py", "file_ext": "py", "file_size_in_byte": 5776, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "time.time", "line_number": 60, "usage_type": "call"}, {"api_name": "time.time", "line_number": 101, "usage_type": "call"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 138, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 141, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 141, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 142, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 142, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 143, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 149, "usage_type": "call"}, {"api_name": "ant.easy.node.Node", "line_number": 155, "usage_type": "call"}, {"api_name": "ant.easy.channel.Channel.Type", "line_number": 158, "usage_type": "attribute"}, {"api_name": "ant.easy.channel.Channel", "line_number": 158, "usage_type": "name"}, {"api_name": "ant.easy.channel.Channel.Type", "line_number": 168, "usage_type": "attribute"}, {"api_name": "ant.easy.channel.Channel", "line_number": 168, "usage_type": "name"}]} +{"seq_id": "164499204", "text": "import tensorflow as tf\nimport tensorflow_addons as tfa\nimport tensorflow.keras as keras\nfrom keras import backend, Model, Input, Sequential\nfrom keras.optimizers import Adam\n\n# ==============================================================================\n# = networks =\n# ==============================================================================\nfrom keras.constraints import max_norm\nfrom keras.initializers import RandomNormal\nfrom keras.layers import UpSampling2D, LeakyReLU, Add, Dense, Reshape, AveragePooling2D, Flatten\n\n\ndef _get_norm_layer(norm):\n if norm == 'none':\n return lambda: lambda x: x\n elif norm == 'batch_norm':\n return keras.layers.BatchNormalization\n elif norm == 'instance_norm':\n return tfa.layers.InstanceNormalization\n elif norm == 'layer_norm':\n return keras.layers.LayerNormalization\n\n\n# pixel-wise feature vector normalization layer\nclass PixelNormalization(keras.layers.Layer):\n # initialize the layer\n def __init__(self, **kwargs):\n super(PixelNormalization, self).__init__(**kwargs)\n\n # perform the operation\n def call(self, inputs):\n # calculate square pixel values\n values = inputs ** 2.0\n # calculate the mean pixel values\n mean_values = backend.mean(values, axis=-1, keepdims=True)\n # ensure the mean is not zero\n mean_values += 1.0e-8\n # calculate the sqrt of the mean squared value (L2 norm)\n l2 = backend.sqrt(mean_values)\n # normalize values by the l2 norm\n normalized = inputs / l2\n return normalized\n\n # define the output shape of the layer\n def compute_output_shape(self, input_shape):\n return input_shape\n\n\n# weighted sum output\nclass WeightedSum(Add):\n # init with default value\n def __init__(self, alpha=0.0, **kwargs):\n super(WeightedSum, self).__init__(**kwargs)\n self.alpha = backend.variable(alpha, name='ws_alpha')\n\n # output a weighted sum of inputs\n def _merge_function(self, inputs):\n # only supports a weighted sum of two inputs\n assert (len(inputs) == 2)\n # ((1-a) * input1) + (a * input2)\n output = ((1.0 - self.alpha) * inputs[0]) + (self.alpha * inputs[1])\n return output\n\n\nclass MinibatchStdev(keras.layers.Layer):\n # initialize the layer\n def __init__(self, **kwargs):\n super(MinibatchStdev, self).__init__(**kwargs)\n\n # perform the operation\n def call(self, inputs):\n # calculate the mean value for each pixel across channels\n mean = backend.mean(inputs, axis=0, keepdims=True)\n # calculate the squared differences between pixel values and mean\n squ_diffs = backend.square(inputs - mean)\n # calculate the average of the squared differences (variance)\n mean_sq_diff = backend.mean(squ_diffs, axis=0, keepdims=True)\n # add a small value to avoid a blow-up when we calculate stdev\n mean_sq_diff += 1e-8\n # square root of the variance (stdev)\n stdev = backend.sqrt(mean_sq_diff)\n # calculate the mean standard deviation across each pixel coord\n mean_pix = backend.mean(stdev, keepdims=True)\n # scale this up to be the size of one input feature map for each sample\n shape = backend.shape(inputs)\n output = backend.tile(mean_pix, (shape[0], shape[1], shape[2], 1))\n # concatenate with the output\n combined = backend.concatenate([inputs, output], axis=-1)\n return combined\n\n # define the output shape of the layer\n def compute_output_shape(self, input_shape):\n # create a copy of the input shape as a list\n input_shape = list(input_shape)\n # add one to the channel dimension (assume channels-last)\n input_shape[-1] += 1\n # convert list to a tuple\n return tuple(input_shape)\n\n\n# add a generator block\ndef add_generator_block(old_model):\n # weight initialization\n init = RandomNormal(stddev=0.02)\n # weight constraint\n const = max_norm(1.0)\n # get the end of the last block\n block_end = old_model.layers[-2].output\n # upsample, and define new block\n upsampling = UpSampling2D()(block_end)\n g = keras.layers(128, (3, 3), padding='same', kernel_initializer=init, kernel_constraint=const)(upsampling)\n g = PixelNormalization()(g)\n g = LeakyReLU(alpha=0.2)(g)\n g = keras.layers.Conv2D(128, (3, 3), padding='same', kernel_initializer=init, kernel_constraint=const)(g)\n g = PixelNormalization()(g)\n g = LeakyReLU(alpha=0.2)(g)\n # add new output layer\n out_image = keras.layers.Conv2D(3, (1, 1), padding='same', kernel_initializer=init, kernel_constraint=const)(g)\n # define model\n model1 = Model(old_model.input, out_image)\n # get the output layer from old model\n out_old = old_model.layers[-1]\n # connect the upsampling to the old output layer\n out_image2 = out_old(upsampling)\n # define new output image as the weighted sum of the old and new models\n merged = WeightedSum()([out_image2, out_image])\n # define model\n model2 = Model(old_model.input, merged)\n return [model1, model2]\n\n\n# calculate wasserstein loss\ndef wasserstein_loss(y_true, y_pred):\n return backend.mean(y_true * y_pred)\n\n\n# define generator models\ndef define_generator(latent_dim, n_blocks, in_dim=4):\n # weight initialization\n init = RandomNormal(stddev=0.02)\n # weight constraint\n const = max_norm(1.0)\n model_list = list()\n # base model latent input\n in_latent = Input(shape=(latent_dim,))\n # linear scale up to activation maps\n g = Dense(128 * in_dim * in_dim, kernel_initializer=init, kernel_constraint=const)(in_latent)\n g = Reshape((in_dim, in_dim, 128))(g)\n # conv 4x4, input block\n g = keras.layers.Conv2D(128, (3, 3), padding='same', kernel_initializer=init, kernel_constraint=const)(g)\n g = PixelNormalization()(g)\n g = LeakyReLU(alpha=0.2)(g)\n # conv 3x3\n g = keras.layers.Conv2D(128, (3, 3), padding='same', kernel_initializer=init, kernel_constraint=const)(g)\n g = PixelNormalization()(g)\n g = LeakyReLU(alpha=0.2)(g)\n # conv 1x1, output block\n out_image = keras.layers.Conv2D(3, (1, 1), padding='same', kernel_initializer=init, kernel_constraint=const)(g)\n # define model\n model = Model(in_latent, out_image)\n # store model\n model_list.append([model, model])\n # create submodels\n for i in range(1, n_blocks):\n # get prior model without the fade-on\n old_model = model_list[i - 1][0]\n # create new model for next resolution\n models = add_generator_block(old_model)\n # store model\n model_list.append(models)\n return model_list\n\n\ndef add_discriminator_block(old_model, n_input_layers=3):\n # weight initialization\n init = RandomNormal(stddev=0.02)\n # weight constraint\n const = max_norm(1.0)\n # get shape of existing model\n in_shape = list(old_model.input.shape)\n # define new input shape as double the size\n input_shape = (in_shape[-2].value * 2, in_shape[-2].value * 2, in_shape[-1].value)\n in_image = Input(shape=input_shape)\n # define new input processing layer\n d = keras.layers.Conv2D(128, (1, 1), padding='same', kernel_initializer=init, kernel_constraint=const)(in_image)\n d = LeakyReLU(alpha=0.2)(d)\n # define new block\n d = keras.layers.Conv2D(128, (3, 3), padding='same', kernel_initializer=init, kernel_constraint=const)(d)\n d = LeakyReLU(alpha=0.2)(d)\n d = keras.layers.Conv2D(128, (3, 3), padding='same', kernel_initializer=init, kernel_constraint=const)(d)\n d = LeakyReLU(alpha=0.2)(d)\n d = AveragePooling2D()(d)\n block_new = d\n # skip the input, 1x1 and activation for the old model\n for i in range(n_input_layers, len(old_model.layers)):\n d = old_model.layers[i](d)\n # define straight-through model\n model1 = Model(in_image, d)\n # compile model\n model1.compile(loss=wasserstein_loss, optimizer=Adam(lr=0.001, beta_1=0, beta_2=0.99, epsilon=10e-8))\n # downsample the new larger image\n downsample = AveragePooling2D()(in_image)\n # connect old input processing to downsampled new input\n block_old = old_model.layers[1](downsample)\n block_old = old_model.layers[2](block_old)\n # fade in output of old model input layer with new input\n d = WeightedSum()([block_old, block_new])\n # skip the input, 1x1 and activation for the old model\n for i in range(n_input_layers, len(old_model.layers)):\n d = old_model.layers[i](d)\n # define straight-through model\n model2 = Model(in_image, d)\n # compile model\n model2.compile(loss=wasserstein_loss, optimizer=Adam(lr=0.001, beta_1=0, beta_2=0.99, epsilon=10e-8))\n return [model1, model2]\n\n\n# define the discriminator models for each image resolution\ndef define_discriminator(n_blocks, input_shape=(4, 4, 3)):\n # weight initialization\n init = RandomNormal(stddev=0.02)\n # weight constraint\n const = max_norm(1.0)\n model_list = list()\n # base model input\n in_image = Input(shape=input_shape)\n # conv 1x1\n d = keras.layers.Conv2D(128, (1, 1), padding='same', kernel_initializer=init, kernel_constraint=const)(in_image)\n d = LeakyReLU(alpha=0.2)(d)\n # conv 3x3 (output block)\n d = MinibatchStdev()(d)\n d = keras.layers.Conv2D(128, (3, 3), padding='same', kernel_initializer=init, kernel_constraint=const)(d)\n d = LeakyReLU(alpha=0.2)(d)\n # conv 4x4\n d = keras.layers.Conv2D(128, (4, 4), padding='same', kernel_initializer=init, kernel_constraint=const)(d)\n d = LeakyReLU(alpha=0.2)(d)\n # dense output layer\n d = Flatten()(d)\n out_class = Dense(1)(d)\n # define model\n model = Model(in_image, out_class)\n # compile model\n model.compile(loss=wasserstein_loss, optimizer=Adam(lr=0.001, beta_1=0, beta_2=0.99, epsilon=10e-8))\n # store model\n model_list.append([model, model])\n # create submodels\n for i in range(1, n_blocks):\n # get prior model without the fade-on\n old_model = model_list[i - 1][0]\n # create new model for next resolution\n models = add_discriminator_block(old_model)\n # store model\n model_list.append(models)\n return model_list\n\n\n# define composite models for training generators via discriminators\ndef define_composite(discriminators, generators):\n model_list = list()\n # create composite models\n for i in range(len(discriminators)):\n g_models, d_models = generators[i], discriminators[i]\n # straight-through model\n d_models[0].trainable = False\n model1 = Sequential()\n model1.add(g_models[0])\n model1.add(d_models[0])\n model1.compile(loss=wasserstein_loss, optimizer=Adam(lr=0.001, beta_1=0, beta_2=0.99, epsilon=10e-8))\n # fade-in model\n d_models[1].trainable = False\n model2 = Sequential()\n model2.add(g_models[1])\n model2.add(d_models[1])\n model2.compile(loss=wasserstein_loss, optimizer=Adam(lr=0.001, beta_1=0, beta_2=0.99, epsilon=10e-8))\n # store\n model_list.append([model1, model2])\n return model_list\n\n\ndef ResnetGenerator(input_shape=(256, 256, 3),\n output_channels=3,\n dim=64,\n n_downsamplings=2,\n n_blocks=9,\n norm='instance_norm'):\n Norm = _get_norm_layer(norm)\n\n def _residual_block(x):\n dim = x.shape[-1]\n h = x\n\n h = tf.pad(h, [[0, 0], [1, 1], [1, 1], [0, 0]], mode='REFLECT')\n h = keras.layers.Conv2D(dim, 3, padding='valid', use_bias=False)(h)\n h = Norm()(h)\n h = tf.nn.relu(h)\n\n h = tf.pad(h, [[0, 0], [1, 1], [1, 1], [0, 0]], mode='REFLECT')\n h = keras.layers.Conv2D(dim, 3, padding='valid', use_bias=False)(h)\n h = Norm()(h)\n\n return keras.layers.add([x, h])\n\n # 0\n h = inputs = keras.Input(shape=input_shape)\n\n # 1\n h = tf.pad(h, [[0, 0], [3, 3], [3, 3], [0, 0]], mode='REFLECT')\n h = keras.layers.Conv2D(dim, 7, padding='valid', use_bias=False)(h)\n h = Norm()(h)\n h = tf.nn.relu(h)\n\n # 2\n for _ in range(n_downsamplings):\n dim *= 2\n h = keras.layers.Conv2D(dim, 3, strides=2, padding='same', use_bias=False)(h)\n h = Norm()(h)\n h = tf.nn.relu(h)\n\n # 3\n for _ in range(n_blocks):\n h = _residual_block(h)\n\n # 4\n for _ in range(n_downsamplings):\n dim //= 2\n h = keras.layers.Conv2DTranspose(dim, 3, strides=2, padding='same', use_bias=False)(h)\n h = Norm()(h)\n h = tf.nn.relu(h)\n\n # 5\n h = tf.pad(h, [[0, 0], [3, 3], [3, 3], [0, 0]], mode='REFLECT')\n h = keras.layers.Conv2D(output_channels, 7, padding='valid')(h)\n h = tf.tanh(h)\n\n return keras.Model(inputs=inputs, outputs=h)\n\n\ndef ConvDiscriminator(input_shape=(256, 256, 3),\n dim=64,\n n_downsamplings=3,\n norm='instance_norm'):\n dim_ = dim\n Norm = _get_norm_layer(norm)\n\n # 0\n h = inputs = keras.Input(shape=input_shape)\n\n # 1\n h = keras.layers.Conv2D(dim, 4, strides=2, padding='same')(h)\n h = tf.nn.leaky_relu(h, alpha=0.2)\n\n for _ in range(n_downsamplings - 1):\n dim = min(dim * 2, dim_ * 8)\n h = keras.layers.Conv2D(dim, 4, strides=2, padding='same', use_bias=False)(h)\n h = Norm()(h)\n h = tf.nn.leaky_relu(h, alpha=0.2)\n\n # 2\n dim = min(dim * 2, dim_ * 8)\n h = keras.layers.Conv2D(dim, 4, strides=1, padding='same', use_bias=False)(h)\n h = Norm()(h)\n h = tf.nn.leaky_relu(h, alpha=0.2)\n\n # 3\n h = keras.layers.Conv2D(1, 4, strides=1, padding='same')(h)\n\n return keras.Model(inputs=inputs, outputs=h)\n\n\n# ==============================================================================\n# = learning rate scheduler =\n# ==============================================================================\n\nclass LinearDecay(keras.optimizers.schedules.LearningRateSchedule):\n # if `step` < `step_decay`: use fixed learning rate\n # else: linearly decay the learning rate to zero\n\n def __init__(self, initial_learning_rate, total_steps, step_decay):\n super(LinearDecay, self).__init__()\n self._initial_learning_rate = initial_learning_rate\n self._steps = total_steps\n self._step_decay = step_decay\n self.current_learning_rate = tf.Variable(initial_value=initial_learning_rate, trainable=False, dtype=tf.float32)\n\n def __call__(self, step):\n self.current_learning_rate.assign(tf.cond(\n step >= self._step_decay,\n true_fn=lambda: self._initial_learning_rate * (\n 1 - 1 / (self._steps - self._step_decay) * (step - self._step_decay)),\n false_fn=lambda: self._initial_learning_rate\n ))\n return self.current_learning_rate\n", "sub_path": "module.py", "file_name": "module.py", "file_ext": "py", "file_size_in_byte": 14924, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "tensorflow.keras.layers", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 19, "usage_type": "name"}, {"api_name": "tensorflow_addons.layers", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 23, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 27, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 37, "usage_type": "name"}, {"api_name": "keras.backend.sqrt", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 41, "usage_type": "name"}, {"api_name": "keras.layers.Add", "line_number": 52, "usage_type": "name"}, {"api_name": "keras.backend.variable", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 56, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 67, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 75, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 77, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 79, "usage_type": "name"}, {"api_name": "keras.backend.sqrt", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 83, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 85, "usage_type": "name"}, {"api_name": "keras.backend.shape", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 87, "usage_type": "name"}, {"api_name": "keras.backend.tile", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 88, "usage_type": "name"}, {"api_name": "keras.backend.concatenate", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 90, "usage_type": "name"}, {"api_name": "keras.initializers.RandomNormal", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.constraints.max_norm", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.layers.UpSampling2D", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 113, "usage_type": "name"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 116, "usage_type": "name"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 120, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 120, "usage_type": "name"}, {"api_name": "keras.Model", "line_number": 122, "usage_type": "call"}, {"api_name": "keras.Model", "line_number": 130, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 136, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 136, "usage_type": "name"}, {"api_name": "keras.initializers.RandomNormal", "line_number": 142, "usage_type": "call"}, {"api_name": "keras.constraints.max_norm", "line_number": 144, "usage_type": "call"}, {"api_name": "keras.Input", "line_number": 147, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 149, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 152, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 152, "usage_type": "name"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 156, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 156, "usage_type": "name"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 160, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 160, "usage_type": "name"}, {"api_name": "keras.Model", "line_number": 162, "usage_type": "call"}, {"api_name": "keras.initializers.RandomNormal", "line_number": 178, "usage_type": "call"}, {"api_name": "keras.constraints.max_norm", "line_number": 180, "usage_type": "call"}, {"api_name": "keras.Input", "line_number": 185, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 187, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 187, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 187, "usage_type": "name"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 190, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 190, "usage_type": "name"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 191, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 192, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 192, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 192, "usage_type": "name"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 193, "usage_type": "call"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 194, "usage_type": "call"}, {"api_name": "keras.Model", "line_number": 200, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 202, "usage_type": "call"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 204, "usage_type": "call"}, {"api_name": "keras.Model", "line_number": 214, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 216, "usage_type": "call"}, {"api_name": "keras.initializers.RandomNormal", "line_number": 223, "usage_type": "call"}, {"api_name": "keras.constraints.max_norm", "line_number": 225, "usage_type": "call"}, {"api_name": "keras.Input", "line_number": 228, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 230, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 230, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 230, "usage_type": "name"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 231, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 234, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 234, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 234, "usage_type": "name"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 235, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 237, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 237, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 237, "usage_type": "name"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 238, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 240, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 241, "usage_type": "call"}, {"api_name": "keras.Model", "line_number": 243, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 245, "usage_type": "call"}, {"api_name": "keras.Sequential", "line_number": 267, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 270, "usage_type": "call"}, {"api_name": "keras.Sequential", "line_number": 273, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 276, "usage_type": "call"}, {"api_name": "tensorflow.pad", "line_number": 294, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 295, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 295, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 295, "usage_type": "name"}, {"api_name": "tensorflow.nn.relu", "line_number": 297, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 297, "usage_type": "attribute"}, {"api_name": "tensorflow.pad", "line_number": 299, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 300, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 300, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 300, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.add", "line_number": 303, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 303, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 303, "usage_type": "name"}, {"api_name": "tensorflow.keras.Input", "line_number": 306, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 306, "usage_type": "name"}, {"api_name": "tensorflow.pad", "line_number": 309, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 310, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 310, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 310, "usage_type": "name"}, {"api_name": "tensorflow.nn.relu", "line_number": 312, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 312, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 317, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 317, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 317, "usage_type": "name"}, {"api_name": "tensorflow.nn.relu", "line_number": 319, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 319, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2DTranspose", "line_number": 328, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 328, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 328, "usage_type": "name"}, {"api_name": "tensorflow.nn.relu", "line_number": 330, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 330, "usage_type": "attribute"}, {"api_name": "tensorflow.pad", "line_number": 333, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 334, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 334, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 334, "usage_type": "name"}, {"api_name": "tensorflow.tanh", "line_number": 335, "usage_type": "call"}, {"api_name": "tensorflow.keras.Model", "line_number": 337, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 337, "usage_type": "name"}, {"api_name": "tensorflow.keras.Input", "line_number": 348, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 348, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 351, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 351, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 351, "usage_type": "name"}, {"api_name": "tensorflow.nn.leaky_relu", "line_number": 352, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 352, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 356, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 356, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 356, "usage_type": "name"}, {"api_name": "tensorflow.nn.leaky_relu", "line_number": 358, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 358, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 362, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 362, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 362, "usage_type": "name"}, {"api_name": "tensorflow.nn.leaky_relu", "line_number": 364, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 364, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 367, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 367, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 367, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 369, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 369, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers", "line_number": 376, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 376, "usage_type": "name"}, {"api_name": "tensorflow.Variable", "line_number": 385, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 385, "usage_type": "attribute"}, {"api_name": "tensorflow.cond", "line_number": 388, "usage_type": "call"}]} +{"seq_id": "158606994", "text": "\"\"\"trivial3\n\nRevision ID: 990db0844728\nRevises: 191b892bb787\nCreate Date: 2019-06-18 05:25:39.256337\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '990db0844728'\ndown_revision = '191b892bb787'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('answer', sa.Column('answerer_name', sa.String(), nullable=False))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('answer', 'answerer_name')\n # ### end Alembic commands ###\n", "sub_path": "allaamus/migrations/versions/990db0844728_trivial3.py", "file_name": "990db0844728_trivial3.py", "file_ext": "py", "file_size_in_byte": 661, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "171331167", "text": "from datetime import datetime\nclass Employee:\n def __init__(self, name, age, salary, employment_year):\n self.name = name\n self.age = age\n self.salary = salary\n self.employment_year = employment_year\n\n def get_working_years(self):\n return datetime.now().year - self.employment_year\n\n def __str__(self):\n return \"name: %s age: %s salary: %s employment year %s \" % (self.name, self.age, self.salary, self.employment_year)\n\n\nclass Manager(Employee):\n def __init__(self, name, age, salary, employment_year, bonus_percentage):\n Employee.__init__(self, name, age, salary, employment_year)\n self.bonus_percentage = bonus_percentage\n\n def get_working_years(self):\n return datetime.now().year - self.employment_year\n\n def get_bonus(self):\n return self.bonus_percentage * self.salary\n\n def __str__(self):\n return \"name: %s age: %s salary: %s employment year %s bonus percentage %s \" % (self.name, self.age, self.salary, self.employment_year, self.bonus_percentage)\n\n\n\ndef main():\n elist = []\n mlist = []\n while 0 == 0:\n print(\"----------------------\\n\\n----------------------\\nWelcome to HR Pro 2020\\n\\n\")\n print(\"Options: \\n\\n 1. Show Employees \\n 2. Show Managers \\n 3. Add An Employee \\n 4. Add A Manager \\n 5. Exit\")\n option = input(\"\\n\\nWhat would you like to do?\")\n if option == \"1\":\n print(\"Employees\")\n for emp in elist:\n print(emp)\n elif option == \"2\":\n print(\"Managers\")\n for mngr in mlist:\n print (mngr)\n elif option == \"3\":\n name = input(\"name: \")\n age = input(\"age: \")\n salary = input(\"salary: \")\n employment_year = input(\"Employement year: \")\n e = Employee(name, age, salary, employment_year)\n elist.append(e)\n print(\"Employee added succesfully\")\n elif option == \"4\":\n name = input(\"name: \")\n age = input(\"age: \")\n salary = input(\"salary: \")\n employment_year = input(\"Employement year: \")\n bonus_percentage = input(\"bonus percentage: \")\n m = Manager(name, age, salary, employment_year, bonus_percentage)\n mlist.append(m)\n print(\"Manager added succesfully\")\n elif option == \"5\":\n exit()\n\n\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "hr_pro.py", "file_name": "hr_pro.py", "file_ext": "py", "file_size_in_byte": 2441, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "datetime.datetime.now", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "501444700", "text": "'''\n\nAni Ambroladze\n\nNumber Maze\n\n'''\n\nimport numpy as np\nfrom collections import defaultdict\nimport math\n\nclass PuzzleBoard(object):\n def __init__(self, size, myboard = None):\n\n self.boardSize = size\n \n if myboard is None: \n myboard = np.random.randint(1,size-1, size = (size, size))\n self.myboard = myboard\n \n else:\n self.myboard = myboard\n \n self.myMatrix = np.zeros(((self.boardSize**2),(self.boardSize**2)))\n self.V = self.boardSize**2\n \n self.graph = defaultdict(list)\n self.Po = np.zeros(((self.boardSize**2),(4)))\n \n for a in range(self.boardSize):\n for b in range(self.boardSize):\n cValue = myboard[a][b]\n cPos = a*self.boardSize+b\n\n if a >= cValue:\n posRow = a - cValue\n posCol = b\n \n self.Po[cPos][0] = 1\n posPos = posRow*self.boardSize+posCol\n \n self.myMatrix[cPos][posPos] = 1\n self.addEdge(cPos,posPos)\n\n if self.boardSize-1-a >= cValue:\n posRow = a + cValue\n posCol = b\n \n self.Po[cPos][2] = 1\n posPos = posRow*self.boardSize + posCol\n \n self.myMatrix[cPos][posPos] = 1\n self.addEdge(cPos,posPos)\n \n if b >= cValue:\n posRow=a\n posCol=b-cValue\n \n self.Po[cPos][3] = 1\n posPos = posRow*self.boardSize + posCol\n \n self.myMatrix[cPos][posPos] = 1\n self.addEdge(cPos,posPos)\n \n if self.boardSize-1-b>=cValue:\n posRow = a\n posCol = b + cValue\n \n self.Po[cPos][1] = 1 \n posPos = posRow*self.boardSize + posCol\n \n self.myMatrix[cPos][posPos] = 1\n self.addEdge(cPos,posPos)\n \n self.currA = 0\n self.currB = 0\n self.current = 0\n self.paths = []\n\n def makeMove(self,direction):\n \n self.current = self.currA*self.boardSize + self.currB\n\n if direction == 0 and self.Po[self.current][0]:\n \n self.currA = self.currA-myboard[self.currA][self.currB]\n return True\n \n elif direction == 1 and self.Po[self.current][1]:\n \n self.currB = self.currB+myboard[self.currA][self.currB]\n return True\n \n elif direction == 2 and self.Po[self.current][2]:\n \n self.currA = self.currA+myboard[self.currA][self.currB]\n return True\n\n elif direction == 3 and self.Po[self.current][3]:\n \n self.currB = self.currB-myboard[self.currA][self.currB]\n return True\n \n else:\n return False\n \n \n \n def __str__(self):\n return str(self.myboard)\n \n\n def printMatrix(self):\n print(\"My Matrix: \")\n print(self.myMatrix)\n \n\n def printPosition(self):\n print('Position: ')\n print(self.currA,' ',self.currB)\n \n \n def getResult(self):\n if self.currA == self.boardSize-1 and self.currB == self.boardSize-1:\n return True\n else: \n return False\n \n \n def addEdge(self,u,v):\n self.graph[u].append(v)\n \n\n def getAll(self, u, d, visited, path, result):\n visited[u]= True\n path.append(u)\n\n if u==d:\n result.append(list(path))\n\n else:\n for a in self.graph[u]:\n if visited[a] == False:\n self.getAll(a, d, visited, path, result)\n path.pop()\n visited[u]= False\n return result\n \n \n def getAllPaths(self,s, d):\n visited =[False]*(self.V)\n path = []\n result = []\n result = self.getAll(s, d,visited, path, result)\n\n return result\n \n\n def solve(self):\n paths = self.getAllPaths(0,self.V-1)\n \n if not paths:\n return -1\n else:\n mIndex = 0\n mini =len(paths[0])\n for a in range(len(paths)):\n if len(paths[a]) <= mini:\n mIndex=a\n mini = len(paths[a])\n \n print('The shortest path to solve the problem: ')\n print(paths[mIndex])\n \n return mini\n \n \nmyboard=[[1,2,1,3], [2,3,3,2], [3,1,2,2],[2,1,1,1]]\n\npuzz = PuzzleBoard(len(myboard),myboard)\nprint(\"My Puzzleboard:\")\nprint(puzz)\nprint('')\n\npuzz.printMatrix()\nprint('')\n\npuzz.printPosition()\n\npuzz.makeMove(2)\npuzz.printPosition()\n\npuzz.makeMove(2)\npuzz.printPosition()\n\npuzz.makeMove(1)\npuzz.printPosition()\n\npuzz.makeMove(1)\npuzz.printPosition()\n\nprint('')\nprint(puzz.getResult())\n\nprint('')\npaths = puzz.solve()\n\nprint('')\nprint('The minimum number of moves to solve the problem: ')\nprint(paths)\n\n\n#Source code: getAllPaths implementation -> www.geeksforgeeks.org\n", "sub_path": "maze.py", "file_name": "maze.py", "file_ext": "py", "file_size_in_byte": 5421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "559669982", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom urllib import request\nfrom bs4 import BeautifulSoup\n\nimport requests\nimport xlwt\nimport bs4\n\nurl = \"https://www.zhipin.com/\"\n\ndef get_job_info(index):\n params = {\n \"query\":\"index\",\n \"city\":100010000\n }\n request_url = url + \"/job_detail/?\" + urlencode(params)\n print(\"请求的URL:{}\".format(request_url))\n post_infos = []\n flip_flag = True\n while flip_flag:\n print(\"[DEBUG INFO]: 请求网址:{}\".format(request_url))\n try:\n soup = bs4.BeautifulSoup(request.get(request_url).text,\"lxml\")\n job_list = soup.find(\"div\",{\"class\":\"job-box\"}).find(\"div\",{\"class\":\"job-list\"})\n except:\n print(\"没有查询到相关记录\")\n break\n for job in job_list:\n job_primary = job.find(\"div\",{\"class\":\"job-primary\"})\n info_primary = job_primary.find(\"div\",{\"class\":\"info-primary\"})\n info_company = job_primary.find(\"div\",{\"class\":\"info-company\"})\n info_publis = job_primary.find(\"div\", {\"class\": \"info_publis\"})\n\n\n", "sub_path": "pacong/boss直聘.py", "file_name": "boss直聘.py", "file_ext": "py", "file_size_in_byte": 1102, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request.get", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "36254961", "text": "from django.urls import path\n\nfrom . import views\n\napp_name = 'tasks'\n\nurlpatterns = [\n path('get_tasks//', views.get_tasks_for_language, name='get_tasks'),\n path('get_task_details///', views.get_task_details, name='get_task_details'),\n path('send_result_audio///', views.send_result_audio, name='send_result_audio'),\n path('send_result_text///', views.send_result_text, name='send_result_text'),\n path('get_task_results//', views.get_task_results, name='get_task_results'),\n path('login/', views.login, name='login'),\n path('logout/', views.logout, name='logout'),\n path('register/', views.register, name='register'),\n path('user_info/', views.user_info, name=\"user_info\"),\n path('languages/', views.languages, name='languages'),\n path('profile_image_upload/', views.profile_image_upload, name=\"profile_image_upload\")\n]", "sub_path": "tasks/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 947, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "215213714", "text": "#############################################################################\n##\n## Copyright (C) 2019 The Qt Company Ltd.\n## Contact: http://www.qt.io/licensing/\n##\n## This file is part of the Qt for Python examples of the Qt Toolkit.\n##\n## $QT_BEGIN_LICENSE:BSD$\n## You may use this file under the terms of the BSD license as follows:\n##\n## \"Redistribution and use in source and binary forms, with or without\n## modification, are permitted provided that the following conditions are\n## met:\n## * Redistributions of source code must retain the above copyright\n## notice, this list of conditions and the following disclaimer.\n## * Redistributions in binary form must reproduce the above copyright\n## notice, this list of conditions and the following disclaimer in\n## the documentation and/or other materials provided with the\n## distribution.\n## * Neither the name of The Qt Company Ltd nor the names of its\n## contributors may be used to endorse or promote products derived\n## from this software without specific prior written permission.\n##\n##\n## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n## \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n## LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n## A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n## OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n## SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n## LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n## DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\n## THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n## (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n## OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\"\n##\n## $QT_END_LICENSE$\n##\n#############################################################################\n\nfrom PySide2.QtSql import QSqlDatabase, QSqlQuery\nfrom datetime import date\n\ndef add_book(q, title, year, authorId, genreId, rating):\n q.addBindValue(title)\n q.addBindValue(year)\n q.addBindValue(authorId)\n q.addBindValue(genreId)\n q.addBindValue(rating)\n q.exec_()\n\n\ndef add_genre(q, name):\n q.addBindValue(name)\n q.exec_()\n return q.lastInsertId()\n\n\ndef add_author(q, name, birthdate):\n q.addBindValue(name)\n q.addBindValue(str(birthdate))\n q.exec_()\n return q.lastInsertId()\n\nBOOKS_SQL = \"\"\"\n create table books(id integer primary key, title varchar, author integer,\n genre integer, year integer, rating integer)\n \"\"\"\nAUTHORS_SQL = \"\"\"\n create table authors(id integer primary key, name varchar, birthdate text)\n \"\"\"\nGENRES_SQL = \"\"\"\n create table genres(id integer primary key, name varchar)\n \"\"\"\nINSERT_AUTHOR_SQL = \"\"\"\n insert into authors(name, birthdate) values(?, ?)\n \"\"\"\nINSERT_GENRE_SQL = \"\"\"\n insert into genres(name) values(?)\n \"\"\"\nINSERT_BOOK_SQL = \"\"\"\n insert into books(title, year, author, genre, rating)\n values(?, ?, ?, ?, ?)\n \"\"\"\n\ndef init_db():\n \"\"\"\n init_db()\n Initializes the database.\n If tables \"books\" and \"authors\" are already in the database, do nothing.\n Return value: None or raises ValueError\n The error value is the QtSql error instance.\n \"\"\"\n def check(func, *args):\n if not func(*args):\n raise ValueError(func.__self__.lastError())\n db = QSqlDatabase.addDatabase(\"QSQLITE\")\n db.setDatabaseName(\":memory:\")\n\n check(db.open)\n\n q = QSqlQuery()\n check(q.exec_, BOOKS_SQL)\n check(q.exec_, AUTHORS_SQL)\n check(q.exec_, GENRES_SQL)\n check(q.prepare, INSERT_AUTHOR_SQL)\n\n asimovId = add_author(q, \"Isaac Asimov\", date(1920, 2, 1))\n greeneId = add_author(q, \"Graham Greene\", date(1904, 10, 2))\n pratchettId = add_author(q, \"Terry Pratchett\", date(1948, 4, 28))\n\n check(q.prepare,INSERT_GENRE_SQL)\n sfiction = add_genre(q, \"Science Fiction\")\n fiction = add_genre(q, \"Fiction\")\n fantasy = add_genre(q, \"Fantasy\")\n\n check(q.prepare,INSERT_BOOK_SQL)\n add_book(q, \"Foundation\", 1951, asimovId, sfiction, 3)\n add_book(q, \"Foundation and Empire\", 1952, asimovId, sfiction, 4)\n add_book(q, \"Second Foundation\", 1953, asimovId, sfiction, 3)\n add_book(q, \"Foundation's Edge\", 1982, asimovId, sfiction, 3)\n add_book(q, \"Foundation and Earth\", 1986, asimovId, sfiction, 4)\n add_book(q, \"Prelude to Foundation\", 1988, asimovId, sfiction, 3)\n add_book(q, \"Forward the Foundation\", 1993, asimovId, sfiction, 3)\n add_book(q, \"The Power and the Glory\", 1940, greeneId, fiction, 4)\n add_book(q, \"The Third Man\", 1950, greeneId, fiction, 5)\n add_book(q, \"Our Man in Havana\", 1958, greeneId, fiction, 4)\n add_book(q, \"Guards! Guards!\", 1989, pratchettId, fantasy, 3)\n add_book(q, \"Night Watch\", 2002, pratchettId, fantasy, 3)\n add_book(q, \"Going Postal\", 2004, pratchettId, fantasy, 3)\n", "sub_path": "venv/Lib/site-packages/PySide2/examples/sql/books/createdb.py", "file_name": "createdb.py", "file_ext": "py", "file_size_in_byte": 5001, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "PySide2.QtSql.QSqlDatabase.addDatabase", "line_number": 97, "usage_type": "call"}, {"api_name": "PySide2.QtSql.QSqlDatabase", "line_number": 97, "usage_type": "name"}, {"api_name": "PySide2.QtSql.QSqlQuery", "line_number": 102, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 108, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "78823043", "text": "#Ordering tools\nimport json\nimport numpy as np, treelog\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom matplotlib import collections\n# import matplotlib.pyplot as plt\nimport seaborn as sns;\n\nfrom files.myModellib import get_welldata\n\nsns.set()\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport math\nimport matplotlib.style as style\nstyle.use('seaborn-paper')\nsns.set_context(\"paper\")\nsns.set_style(\"whitegrid\")\n\n## Parameters txt file reader\ndef read_from_txt ( filename ):\n\n # Open the parameter file to read\n with open(filename, 'r') as json_file:\n data = json.load(json_file)\n\n return data['aquifer'][0], data['well'][0]\n\n## Generates a Parameters txt file\ndef generate_txt( filename):\n import json\n\n data = {}\n data['aquifer'] = []\n data['aquifer'].append({\n 'H': 70,\n 'dtop': 2387,\n 'dbasis': 2528,\n 'dpump': 710,\n 'dsensor': 2196,\n 'labda': 0.032,\n 'Tsurface': 20+273,\n 'porosity': 0.2, #'porosity': 0.046,\n 'permeability': 9e-10,\n 'rhof': 996.9,\n 'rhos': 2400,\n 'viscosity': 0.0003142,\n 'K': 4e-13,\n 'cpf' : 4183,\n 'cps' : 2650, #870\n 'labdas' : 4.2, # thermal conductivity solid [W/mK]\n 'labdaf': 0.663, # thermal conductivity fluid [W/mK]\n 'saltcontent': 0.155, # [kg/l]\n 'pref': 222.5e5,\n 'Tref': 90 + 273,\n 'g' : 9.81,\n 'rw': 0.1, #0.126\n 'rmax': 1000,\n 'Q': 250 / 3600,\n 'L': 1000,\n 'Tinj': 30 + 273,\n 'patm' : 1e5,\n 'ε' : 1.2, # tubing roughness [m]\n 'ct' : 1e-10, # total compressibility\n\n })\n data['well'] = []\n data['well'].append({\n 'Q': 250/3600,\n })\n\n with open(filename, 'w') as outfile:\n json.dump(data, outfile)\n\n## Using map() and lambda\ndef listOfTuples(l1, l2):\n return list(map(lambda x, y: (x, y), l1, l2))\n\n## Plot the solution on a finite element mesh\ndef plot_solution(sol, outfile, title=None ):\n import plotly.figure_factory as ff\n import plotly.express as px\n\n p_inlet = sol[0]\n T_prod = sol[1]\n\n df = pd.DataFrame(listOfTuples(permeability, porosity), columns=[\"Permeability\", \"Porosity\"])\n\n # fig = px.histogram(df, x=\"Permeability\", y=\"Porosity\",\n # marginal=\"box\", # or violin, rug\n # hover_data=df.columns)\n # fig.show()\n\n # sns.jointplot(x=\"Permeability\", y=\"Porosity\", data=df, kind=\"kde\", n_levels=10);\n #\n # f, ax = plt.subplots(figsize=(6, 6))\n #\n # sns.kdeplot(df.Permeability, df.Porosity, n_levels=10, ax=ax)\n # sns.rugplot(df.Permeability, color=\"g\", ax=ax)\n # sns.rugplot(df.Porosity, vertical=True, ax=ax)\n #\n # df2 = pd.DataFrame(listOfTuples(T_prod, p_inlet), columns=[\"T_prod\", \"p_inlet\"])\n # print(\"df2\", df2)\n #\n # sns.jointplot(x=\"T_prod\", y=\"p_inlet\", data=df2, kind=\"kde\", n_levels=10);\n #\n # f, ax = plt.subplots(figsize=(6, 6))\n #\n # sns.kdeplot(df2.T_prod, df2.p_inlet, n_levels=10, ax=ax)\n # sns.rugplot(df2.T_prod, color=\"g\", ax=ax)\n # sns.rugplot(df2.p_inlet, vertical=True, ax=ax)\n\n\n\n #Plotting\n #plt....\n #plt....\n if title:\n plt.title(title)\n\n #Plot configuration\n # sns.set(color_codes=True)\n\n #Save the figure to the output file\n plt.savefig( outfile )\n plt.show()\n print( 'Output written to {}'.format( outfile ) )\n\ndef show_seaborn_plot( filename, label):\n print('filename', filename)\n with open(filename, 'rb') as file:\n data = np.transpose(np.load(file))\n\n df = pd.DataFrame(data[0:, 0:], columns=['f' + str(i) for i in range(data.shape[1])])\n\n draw_df = df.reset_index().melt(id_vars=['index'], var_name='col')\n\n # pass custom palette:\n sns.set_palette(\"Spectral\")\n ax = sns.lineplot(x='index',\n y='value',\n ci=95,\n #style=True,\n dashes=[(2,2)],\n legend=\"brief\",\n palette=(\"Blues_d\"), #sns.color_palette('Greys')\n hue_norm=mpl.colors.LogNorm(),\n data=draw_df)\n plt.legend([label])\n\ndef show_uniform_plot():\n Qpdf = Q = np.random.uniform(low=0.1, high=1.0, size=50)\n\n fig, ax = plt.subplots(1, 1,\n figsize=(10, 7),\n tight_layout=True)\n\n ax.set(xlabel=r'$Q [m^3/s]$', ylabel='Probability')\n ax.hist(Q, density=True, histtype='stepfilled', alpha=0.2, bins=20)\n plt.show()\n\n# show_seaborn_plot('pnode8.npy', \"node8\")\n# plt.show()\n\ndef show_realdata_plot():\n print('Posterior distributions real data')\n plt.figure(figsize=(8, 2))\n for param in ['TBH', 'TESP']:\n samples = get_welldata(param)\n x = get_welldata('CORRECTED_TIME')/60\n y = samples\n plt.plot(x, y)\n plt.xlabel(\"t [min]\", size=14)\n plt.ylabel(\"T(t) [K]\", size=14)\n plt.axvline(x=10320/60, c='k')\n plt.figure(figsize=(8, 2))\n for param in ['PBH', 'PESP']:\n samples = get_welldata(param)/1e6\n x = get_welldata('CORRECTED_TIME') / 60\n y = samples\n plt.plot(x, y)\n plt.xlabel(\"t [min]\", size=14)\n plt.ylabel(\"p(t) [MPa]\", size=14)\n plt.axvline(x=10320/60, c='k')\n # secay = ax.secondary_yaxis('top', functions=(deg2rad, rad2deg))\n # secax.set_ylabel('angle [rad]')\n plt.show()\n plt.tight_layout();\n\n plt.show()\n\n# for node in range(len(pnodelist)):\n# with open('pnode' + str(node+2) + '.npy', 'wb') as f:\n# np.save(f, pnodelist[node])\n# show_seaborn_plot('pnode' + str(node+2) + '.npy', str(node+2))\n# # plt.legend(str(node+2))\n\n", "sub_path": "files/myIOlib.py", "file_name": "myIOlib.py", "file_ext": "py", "file_size_in_byte": 5744, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "seaborn.set", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.style.use", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.style", "line_number": 18, "usage_type": "name"}, {"api_name": "seaborn.set_context", "line_number": 19, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 20, "usage_type": "call"}, {"api_name": "json.load", "line_number": 27, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 134, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 136, "usage_type": "call"}, {"api_name": "seaborn.set_palette", "line_number": 141, "usage_type": "call"}, {"api_name": "seaborn.lineplot", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 149, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "numpy.random.uniform", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 154, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "files.myModellib.get_welldata", "line_number": 171, "usage_type": "call"}, {"api_name": "files.myModellib.get_welldata", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "files.myModellib.get_welldata", "line_number": 180, "usage_type": "call"}, {"api_name": "files.myModellib.get_welldata", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}]} +{"seq_id": "185162406", "text": "from django import forms\nfrom .models import Tickets\n\nclass ContactForm(forms.Form):\n name = forms.CharField()\n course = forms.ChoiceField(choices=[('121', '121'), ('other', 'Other')])\n\n\nclass TicketsForm(forms.ModelForm):\n\n class Meta:\n model = Tickets\n fields = ('name','course','question')\n", "sub_path": "ptcticket/tickets/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 316, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "django.forms.Form", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 4, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 5, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 6, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "models.Tickets", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "585484554", "text": "from datetime import datetime\nfrom typing import Union, Generator, List\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.engine.base import Engine\nfrom sqlalchemy.sql import text\n\nfrom . import queries\nfrom .config import DBConfig as config\n\n\ndef create_alchemy_engine() -> Engine:\n \"\"\"\n Creates an sqlalchemy engine.\n :return: The sqlalchemy engine.\n \"\"\"\n return create_engine(config.DATABASE_URL)\n\n\ndef fetch_records(engine: Engine, query: Union[text, str], values: dict = {}) -> Generator:\n \"\"\"\n Executes sql query and yields rows.\n :param engine: The sqlalchemy engine.\n :param query: The sql query to execute.\n :param values: The values to inject.\n :return: The rows of the results.\n \"\"\"\n for row in engine.execute(query, values):\n yield dict(row)\n\n\ndef calculate_ocr(engine: Engine, start_date: datetime, end_date: datetime, interval: str) -> List[dict]:\n \"\"\"\n Executes OCR query for a given date range and interval.\n :param engine: The sqlalchemy engine.\n :param start_date: Specifies the start date of the report.\n :param end_date: Specifies the end date of the report.\n :param interval: Specifies the breakdown time interval e.g. hour, day, month, year.\n :return: Report results.\n \"\"\"\n\n start_unix_timestamp = int(datetime.timestamp(start_date))\n end_unix_timestamp = int(datetime.timestamp(end_date))\n\n records = fetch_records(\n engine=engine,\n query=text(queries.OCR_QUERY),\n values={\n 'since': start_unix_timestamp,\n 'until': end_unix_timestamp,\n 'interval': config.INTERVAL_MAPPING[interval]['format']\n }\n )\n return list(records)\n\n\ndef calculate_ocr_breakdown(engine: Engine, start_date: datetime, end_date: datetime, interval: str) -> List[dict]:\n \"\"\"\n Executes OCR Breakdown query for a given date range and interval.\n :param engine: The sqlalchemy engine.\n :param start_date: Specifies the start date of the report.\n :param end_date: Specifies the end date of the report.\n :param interval: Specifies the breakdown time interval e.g. hour, day, month, year.\n :return: Report results.\n \"\"\"\n start_unix_timestamp = int(datetime.timestamp(start_date))\n end_unix_timestamp = int(datetime.timestamp(end_date))\n\n records = fetch_records(\n engine=engine,\n query=text(queries.OCR_BREAKDOWN_QUERY),\n values={\n 'since': start_unix_timestamp,\n 'until': end_unix_timestamp,\n 'interval': config.INTERVAL_MAPPING[interval]['format']\n }\n )\n return list(records)\n\n\ndef fetch_visitor_sessions(engine: Engine, visitor_id: str) -> List[dict]:\n \"\"\"\n Fetches the list of session for a given visitor.\n :param engine: The sqlalchemy engine.\n :param visitor_id: The ID for a specific visitor.\n :return: the visitor sessions\n \"\"\"\n records = fetch_records(\n engine=engine,\n query=text(queries.FETCH_VISITOR_SESSIONS),\n values={\n 'visitor_id': visitor_id\n }\n )\n return list(records)\n", "sub_path": "efood/db/utilities.py", "file_name": "utilities.py", "file_ext": "py", "file_size_in_byte": 3088, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 17, "usage_type": "call"}, {"api_name": "config.DBConfig.DATABASE_URL", "line_number": 17, "usage_type": "attribute"}, {"api_name": "config.DBConfig", "line_number": 17, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.base.Engine", "line_number": 12, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.base.Engine", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Generator", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.base.Engine", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 47, "usage_type": "call"}, {"api_name": "config.DBConfig.INTERVAL_MAPPING", "line_number": 51, "usage_type": "attribute"}, {"api_name": "config.DBConfig", "line_number": 51, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 32, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.base.Engine", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 71, "usage_type": "call"}, {"api_name": "config.DBConfig.INTERVAL_MAPPING", "line_number": 75, "usage_type": "attribute"}, {"api_name": "config.DBConfig", "line_number": 75, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 57, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.base.Engine", "line_number": 81, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 90, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "129781522", "text": "# pylint:disable=missing-docstring,line-too-long\n\n__author__ = 'Chintan Shah'\n\nfrom scrapy import Spider\nfrom spiders.others.epa.items import EPAItem\nfrom helpers.string_processor import process_string\nfrom urlparse import urljoin\n\n\nclass OARTechPublications(Spider):\n\n name = \"epa-oar-techpublications\"\n allowed_domains = [\"epa.gov\"]\n start_urls = [\"http://www.epa.gov/ttn/atw/publicat.html\",]\n\n def parse(self, response):\n elements = response.xpath(\"//table/tr[not(position()=1)]\")\n for each_elem in elements:\n item = EPAItem()\n item['url'] = response.url\n title = each_elem.css(\"td:nth-child(2) ::text\").extract()\n item['title'] = process_string(\"\".join(title))\n url = each_elem.css(\"a::attr(href)\").extract()\n bad_url = each_elem.css(\"a[href*='disclaim']::attr(href)\").extract()\n if url and not bad_url:\n item['url'] = urljoin(response.url, url[0])\n item['type'] = \"Technical Publications\"\n yield item\n", "sub_path": "spiders/others/epa/OARTechPublications.py", "file_name": "OARTechPublications.py", "file_ext": "py", "file_size_in_byte": 1046, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "scrapy.Spider", "line_number": 11, "usage_type": "name"}, {"api_name": "spiders.others.epa.items.EPAItem", "line_number": 20, "usage_type": "call"}, {"api_name": "helpers.string_processor.process_string", "line_number": 23, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "296528563", "text": "from comet_ml import Optimizer\nimport yaml\nimport models\nfrom tensorflow.keras.optimizers import Adam, Nadam\nfrom tensorflow.keras.layers import Input\nfrom callbacks import all_callbacks\nfrom tensorflow.keras import callbacks\nfrom train import get_features\nfrom sklearn.metrics import roc_auc_score\nfrom tensorflow_model_optimization.python.core.sparsity.keras import prune, pruning_callbacks, pruning_schedule\nfrom tensorflow_model_optimization.sparsity.keras import strip_pruning\n\nparameters = open(\"NNparameters.yml\")\nyamlparameters = yaml.load(parameters,Loader=yaml.FullLoader)\n\nconfig = {\n # We pick the Bayes algorithm:\n \"algorithm\": \"bayes\",\n\n # Declare your hyperparameters in the Vizier-inspired format:\n \"parameters\": {\n\n\n \"learning_rate\": { \"type\": \"float\", \"mu\": yamlparameters[\"Training_learning_rate\"], \"sigma\": 0.0001, \"scalingType\": \"normal\"},\n \"Regularization\": { \"type\": \"float\", \"min\": 0.0001, \"max\": 0.01, \"scalingType\": \"uniform\"},\n \"pruning_begin_epoch\":{\"type\": \"int\", \"min\": 50, \"max\": 200, \"scalingType\": \"uniform\"},\n \"pruning_end_epoch\": {\"type\": \"int\", \"min\": 100, \"max\": 800, \"scalingType\": \"uniform\"},\n \"pruning_lr_factor_1\":{\"type\": \"float\", \"min\": 0.0, \"max\": 1.0, \"scalingType\": \"uniform\"},\n \"pruning_lr_factor_2\":{\"type\": \"float\", \"min\": 0.0, \"max\": 1.0, \"scalingType\": \"uniform\"},\n \"pruning_lr_factor_3\":{\"type\": \"float\", \"min\":-10.0, \"max\": 10.0, \"scalingType\": \"uniform\"},\n\n\n },\n\n # Declare what we will be optimizing, and how:\n \"spec\": {\n \"metric\": \"ROC\",\n \"objective\": \"maximize\",\n },\n}\n\n\n\n\nopt = Optimizer(config, api_key=yamlparameters[\"comet_api_key\"], project_name=\"NNqhmv6\",auto_metric_logging=True)\n\nX_train, X_test, y_train, y_test = get_features(yamlparameters[\"DataDir\"])\n \n\nfor experiment in opt.get_experiments():\n steps_per_epoch = int(len(X_train)/yamlparameters[\"Training_batch_size\"])\n\n pruning_params = {\"pruning_schedule\" : pruning_schedule.PolynomialDecay(initial_sparsity=0.0,\n final_sparsity=yamlparameters[\"Sparsity\"],\n begin_step=experiment.get_parameter(\"pruning_begin_epoch\")*steps_per_epoch, \n end_step=experiment.get_parameter(\"pruning_end_epoch\")*steps_per_epoch)}\n keras_model = models.qdense_model(Input(shape=X_train.shape[1:]), \n l1Reg=experiment.get_parameter(\"Regularization\"),\n bits=yamlparameters[\"Layer_bits\"],\n ints=yamlparameters[\"Layer_ints\"])\n keras_model = prune.prune_low_magnitude(keras_model, **pruning_params)\n\n startlearningrate=experiment.get_parameter(\"learning_rate\")\n\n adam = Adam(lr=startlearningrate,\n beta_1=yamlparameters[\"Training_learning_beta1\"],\n beta_2=yamlparameters[\"Training_learning_beta2\"],\n amsgrad=True)\n\n keras_model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['binary_accuracy'])\n\n callbacks=all_callbacks(stop_patience=yamlparameters[\"Training_early_stopping\"], \n initial_lr=experiment.get_parameter(\"learning_rate\"),\n lr_factor=yamlparameters[\"Training_lr_factor\"],\n lr_patience=yamlparameters[\"Training_lr_patience\"],\n lr_epsilon=yamlparameters[\"Training_lr_min_delta\"], \n lr_cooldown=yamlparameters[\"Training_lr_cooldown\"], \n lr_minimum=yamlparameters[\"Training_lr_minimum\"],\n Prune_begin=experiment.get_parameter(\"pruning_begin_epoch\"),\n Prune_end=experiment.get_parameter(\"pruning_end_epoch\"),\n prune_lrs=[experiment.get_parameter(\"pruning_lr_factor_1\"),\n experiment.get_parameter(\"pruning_lr_factor_2\"),\n experiment.get_parameter(\"pruning_lr_factor_3\")],\n outputDir=yamlparameters[\"TrainDir\"])\n\n callbacks.callbacks.append(pruning_callbacks.UpdatePruningStep())\n\n keras_model.fit(X_train, y_train, \n batch_size = experiment.get_parameter(\"batch_size\"), \n epochs = experiment.get_parameter(\"epochs\"),\n validation_split = yamlparameters[\"Training_validation_split\"], \n shuffle = True, \n callbacks =callbacks.callbacks,\n verbose=0)\n \n model = strip_pruning(keras_model)\n model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['binary_accuracy'])\n\n y_predict = model.predict(X_test,verbose=0)\n loss,binary_accuracy = keras_model.evaluate(X_test, y_test,verbose=0)\n auc = roc_auc_score(y_test,y_predict)\n print(\"AUC:\",auc)\n print(\"ACC:\",binary_accuracy)\n\n experiment.log_metric(\"ROC\",auc)\n experiment.log_metric(\"Loss\",loss)\n experiment.log_metric(\"Binary_Accuracy\",binary_accuracy)\n\n", "sub_path": "pruningoptimiser.py", "file_name": "pruningoptimiser.py", "file_ext": "py", "file_size_in_byte": 5255, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "yaml.load", "line_number": 14, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 14, "usage_type": "attribute"}, {"api_name": "comet_ml.Optimizer", "line_number": 45, "usage_type": "call"}, {"api_name": "train.get_features", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow_model_optimization.python.core.sparsity.keras.pruning_schedule.PolynomialDecay", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow_model_optimization.python.core.sparsity.keras.pruning_schedule", "line_number": 53, "usage_type": "name"}, {"api_name": "models.qdense_model", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow_model_optimization.python.core.sparsity.keras.prune.prune_low_magnitude", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow_model_optimization.python.core.sparsity.keras.prune", "line_number": 61, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks", "line_number": 72, "usage_type": "name"}, {"api_name": "callbacks.all_callbacks", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.callbacks.append", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.callbacks", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks", "line_number": 86, "usage_type": "name"}, {"api_name": "tensorflow_model_optimization.python.core.sparsity.keras.pruning_callbacks.UpdatePruningStep", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow_model_optimization.python.core.sparsity.keras.pruning_callbacks", "line_number": 86, "usage_type": "name"}, {"api_name": "tensorflow.keras.callbacks.callbacks", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks", "line_number": 93, "usage_type": "name"}, {"api_name": "tensorflow_model_optimization.sparsity.keras.strip_pruning", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "299755358", "text": "from django.shortcuts import render_to_response\nfrom django.http import HttpResponse\n\ndef holaMundo(request):\n\thtml = \"Muerte a los Murlocks!\"\n\treturn HttpResponse(html)\n\t\ndef suma(request,num1,num2):\n\top1 = int(num1)\n\top2 = int(num2)\n\tresponse=render_to_response('suma.html',{'result': op1+op2})\n\treturn response\n", "sub_path": "prueba/vistas/vista.py", "file_name": "vista.py", "file_ext": "py", "file_size_in_byte": 340, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.http.HttpResponse", "line_number": 6, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "362576466", "text": "import json\r\nimport tornado.web\r\n\r\nfrom nbconvert.preprocessors.execute import executenb\r\nfrom nbconvert import HTMLExporter\r\nfrom notebook.base.handlers import IPythonHandler\r\n\r\nclass NoInputHandler(IPythonHandler):\r\n\r\n def initialize(self, config=None, nbconvert_template_path=None):\r\n self.template_path = nbconvert_template_path\r\n self.exporter_config = config\r\n\r\n @tornado.web.authenticated\r\n @tornado.gen.coroutine\r\n def get(self, path=None):\r\n\r\n if path is None:\r\n raise tornado.web.HTTPError(404, 'notebook not found')\r\n\r\n model = self.contents_manager.get(path)\r\n\r\n if 'content' not in model:\r\n raise tornado.web.HTTPError(404, 'notebook not found')\r\n\r\n # notebook = model['content']\r\n\r\n # if 'metadata' not in notebook:\r\n # raise tornado.web.HTTPError(404, 'notebook not found')\r\n\r\n # kernel_name = notebook.metadata.get('kernelspec', {}).get('name', self.kernel_manager.default_kernel_name)\r\n # kernel_id = yield tornado.gen.maybe_future(self.kernel_manager.start_kernel(kernel_name=kernel_name))\r\n # km = self.kernel_manager.get_kernel(kernel_id)\r\n # result = executenb(notebook, km=km)\r\n\r\n # exporter = HTMLExporter(\r\n # template_file='noinput.tpl',\r\n # template_path=self.template_path,\r\n # config=self.exporter_config\r\n # )\r\n\r\n # exporter.exclude_input = True\r\n # exporter.exclude_output_prompt = True\r\n # exporter.exclude_input_prompt = True\r\n\r\n # html, resources = exporter.from_notebook_node(result, resources={\r\n # 'kernel_id': kernel_id,\r\n # 'base_url': self.base_url,\r\n # 'nbextensions': []\r\n # })\r\n\r\n # self.set_header('Content-Type', 'text/html')\r\n # self.set_header('Content-Security-Policy', 'frame-ancestors *')\r\n # #self.set_header('Content-Security-Policy', 'frame-ancestors * http://localhost:8080')\r\n # self.write(html)\r\n\r\n # data = json.dumps(model)\r\n\r\n html = \"\"\"\r\n \r\n \r\n \r\n Notebook\r\n \r\n \r\n \r\n \r\n

\r\n \r\n \r\n \r\n \"\"\"\r\n\r\n self.write(html)", "sub_path": "nb_noinput/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 2905, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "notebook.base.handlers.IPythonHandler", "line_number": 8, "usage_type": "name"}, {"api_name": "tornado.web.web.HTTPError", "line_number": 19, "usage_type": "call"}, {"api_name": "tornado.web.web", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 19, "usage_type": "name"}, {"api_name": "tornado.web.web.HTTPError", "line_number": 24, "usage_type": "call"}, {"api_name": "tornado.web.web", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 24, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 14, "usage_type": "name"}, {"api_name": "tornado.web.gen", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "259018885", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jan 20 23:51:06 2021\n\n@author: anahi\n\"\"\"\n\nimport numpy as np\nimport math \nimport matplotlib.pyplot as plt\nimport tkinter as tk \nimport pandas as pd\n\nventana= tk.Tk()\nventana.title(\"Calculo de voltaje y potencial\")\nventana.geometry('700x400')\nventana.configure(background='blue')\nvar=tk.StringVar()\n\n\ngs =tk.Label(ventana,text=\"GRADIENTE SALINO\",\n bg=\"red\",fg=\"yellow\")\ngs.place(x=10,y=10, width=690, height=50)\n\nNm =tk.Label(ventana,text=\"Ingrese el número de membranas pares:\",bg=\"black\",fg=\"white\")\nNm.place(x=25,y=70, width=300, height=30)\nentrada1=tk.Entry(ventana)\nentrada1.place(x=80,y=105, width=200, height=30)\n\nAri =tk.Label(ventana,text=\"Ingrese el area de las membranas (m^2):\",bg=\"black\",fg=\"white\")\nAri.place(x=380,y=70, width=300, height=30)\nentrada2=tk.Entry(ventana)\nentrada2.place(x=430,y=105, width=200, height=30)\n\nConc =tk.Label(ventana,text=\"Ingrese la concentración concentrada:\",bg=\"black\",fg=\"white\")\nConc.place(x=25,y=145, width=300, height=30)\nentrada3=tk.Entry(ventana)\nentrada3.place(x=80,y=180, width=200, height=30)\n\nCond =tk.Label(ventana,text=\"Ingrese la concentración diluida:\",bg=\"black\",fg=\"white\")\nCond.place(x=380,y=145, width=300, height=30)\nentrada4=tk.Entry(ventana)\nentrada4.place(x=430,y=180, width=200, height=30)\n\n#DATOS DE LAS MEMBRANAS\nPcem = float(0.99)\nPaem = float(0.95)\nRaem = float(0.017)\nRcem = float(0.02)\nT = float(25+273.15) #temperatura en grados Kelvin\nR = float(8.314) #Constante universal de los gases J/molK\nCF = float(96485.3365) #Constante de Faraday C/mol\nz = float(1**2) #valencia\nEs = float(0.0003) # Espaciamiento de membranas \nRe =np.array([92,47,22,10,6.8,5.6,4.7,3.3,2.2,1.8,1.2,0.56,0.39,0.22,0.1,0])\n\n\n# coeficiente de actividad \nANa = 450 #radio de efectividad del ion hidratado \nACl = 300\n\ndef Ecell():\n \n Ai = float(entrada2.get())\n Cc = float(entrada3.get())\n Cd = float(entrada4.get())\n N = int(entrada1.get())\n \n Ccm=Cc/58.44 #g/mol\n Cdm=Cd/58.44\n gnac = math.exp((-0.5*z*math.sqrt(Ccm))/(1+(ANa/305)*math.sqrt(Ccm)))\n gnad = math.exp((-0.5*z*math.sqrt(Cdm))/(1+(ANa/305)*math.sqrt(Cdm)))\n gclc = math.exp((-0.5*z*math.sqrt(Ccm))/(1+(ACl/305)*math.sqrt(Ccm)))\n gcld = math.exp((-0.5*z*math.sqrt(Cdm))/(1+(ACl/305)*math.sqrt(Cdm)))\n\n acem = math.log((gnac*Ccm)/(gnad*Cdm))\n aaem = math.log((gclc*Ccm)/(gcld*Cdm))\n \n#Calculo de la resistencia \n\n fo = 1.8\n \n Rl = fo*(1/0.7)*(Es/Ai)\n Rh = fo*(1/5)*(Es/Ai)\n r = Raem+Rcem+Rh+Rl\n Rel = 0.54\n Ri = N*r+Rel\n \n #calculo de voltaje \n Ecem = Pcem*((R*T)/(z*CF))*acem\n Eaem = Paem*((R*T)/(z*CF))*aaem\n Ecell = N*(Ecem+Eaem)\n\n# intensidad \n i = Ri+Re\n I = Ecell/i #corriente electrica \n\n#Potencia \n Pgross = (I**2)*Re\n Pd = Pgross/(N*Ai)\n#Voltaje \n Ec = Pgross/I\n \n df = pd.DataFrame({'Ri (ohms)': Ri,'Re (ohms)':Re,'Potencia (W)':Pgross,\n 'DPotencia (W/m^2)':Pd,'CE (A)':I,'Voltaje (V)':Ec})\n\n#Graficas\n fig = plt.figure(figsize=(15,15))\n fig.tight_layout()\n ax1 = fig.add_subplot(1,3,1)\n ax2 = fig.add_subplot(1,3,2)\n ax3 = fig.add_subplot(1,3,3)\n ax1.plot(I,Pgross,'ro')\n ax2.plot(I,Ec,marker='*')\n ax3.plot(I,Pd,'g+')\n ax1.set_xlabel('Intensidad')\n ax1.set_ylabel('Potencia')\n ax2.set_xlabel('Intensidad')\n ax2.set_ylabel('Voltaje')\n ax3.set_xlabel('Intensidad')\n ax3.set_ylabel('potencia/Área')\n ax1.set_title('Potencial maximo')\n ax2.set_title('Intensidad vs Voltaje')\n ax1.grid()\n ax2.grid()\n ax3.grid()\n\n plt.show()\n \n return var.set(Ecell)\n\ndef cerrar ():\n ventana.destroy() \n \nbotonaceptar=tk.Button(ventana, text=\"Aceptar\",fg=\"red\",bg=\"yellow\",command=Ecell)\nbotonaceptar.place(x=310,y=220, width=80, height=30)\n\nEcellf=tk.Label(ventana,text=\"Voltaje máxima:\",bg=\"dark turquoise\",fg=\"black\")\nEcellf.place(x=100,y=260, width=500, height=30)\nEf=tk.Label(ventana,bg=\"white\",textvariable=var)\nEf.place(x=200,y=300, width=300, height=30)\n\nbotoncierra=tk.Button(ventana,text=\"Cerrar\",fg=\"red\",bg=\"yellow\",command=cerrar)\nbotoncierra.place(x=310,y=340, width=80, height=30)\n\nventana.mainloop() ", "sub_path": "Codigo_GS_C.py", "file_name": "Codigo_GS_C.py", "file_ext": "py", "file_size_in_byte": 4173, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "tkinter.Tk", "line_number": 14, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 18, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 21, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 25, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 27, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 30, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 32, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 35, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 37, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 40, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 71, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 71, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 72, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 72, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 73, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 73, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 74, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 74, "usage_type": "call"}, {"api_name": "math.log", "line_number": 76, "usage_type": "call"}, {"api_name": "math.log", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 135, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 138, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 140, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "374248552", "text": "from django.db.models.signals import post_save\nfrom django.dispatch import receiver\nfrom django.utils.datetime_safe import datetime\n\nfrom api.enums import IssueImportance, IssueStatus\nfrom api.models import Crawl, CrawlIssue, PageIssue, WebsiteIssue, Page\n\n\ndef recalculate_website_issues(page_issue):\n website_issues = WebsiteIssue.objects.filter(issue=page_issue.issue)\n if website_issues.count() == 0:\n WebsiteIssue.objects.create(issue=page_issue.issue,\n website=page_issue.page.website,\n pages=1,\n status=IssueStatus.NEW.value,\n importance=IssueImportance.M.value,\n visits=page_issue.page.hits\n )\n else:\n for website_issue in website_issues:\n website_issue.recalculate()\n\n\n@receiver(post_save, sender=PageIssue)\ndef update_website_issues_after_page_issues_save(sender, instance=None, created=False, **kwargs):\n if created:\n page = Page.objects.get(id=instance.page.id)\n page.healthy = False\n page.save()\n recalculate_website_issues(instance)\n\n\ndef recalculate_page_issues(crawl_issue):\n page_issues = PageIssue.objects.filter(issue=crawl_issue.issue, page=crawl_issue.crawl.page)\n if page_issues.count() == 0:\n PageIssue.objects.create(issue=crawl_issue.issue,\n page=crawl_issue.crawl.page,\n status=IssueStatus.NEW.value,\n importance=IssueImportance.M.value,\n )\n\n\n@receiver(post_save, sender=CrawlIssue)\ndef update_page_issues_after_crawl_issue_save(sender, instance=None, created=False, **kwargs):\n if created:\n recalculate_page_issues(instance)\n\n\n@receiver(post_save, sender=Crawl)\ndef update_page_issues_after_crawl_save(sender, instance=None, created=False, **kwargs):\n if created:\n instance.page.last_crawled = datetime.now()\n instance.save()\n\n\n", "sub_path": "api/signals.py", "file_name": "signals.py", "file_ext": "py", "file_size_in_byte": 2098, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "api.models.WebsiteIssue.objects.filter", "line_number": 10, "usage_type": "call"}, {"api_name": "api.models.WebsiteIssue.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "api.models.WebsiteIssue", "line_number": 10, "usage_type": "name"}, {"api_name": "api.models.WebsiteIssue.objects.create", "line_number": 12, "usage_type": "call"}, {"api_name": "api.models.WebsiteIssue.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "api.models.WebsiteIssue", "line_number": 12, "usage_type": "name"}, {"api_name": "api.enums.IssueStatus.NEW", "line_number": 15, "usage_type": "attribute"}, {"api_name": "api.enums.IssueStatus", "line_number": 15, "usage_type": "name"}, {"api_name": "api.enums.IssueImportance.M", "line_number": 16, "usage_type": "attribute"}, {"api_name": "api.enums.IssueImportance", "line_number": 16, "usage_type": "name"}, {"api_name": "api.models.Page.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "api.models.Page.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "api.models.Page", "line_number": 27, "usage_type": "name"}, {"api_name": "django.dispatch.receiver", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 24, "usage_type": "argument"}, {"api_name": "api.models.PageIssue", "line_number": 24, "usage_type": "name"}, {"api_name": "api.models.PageIssue.objects.filter", "line_number": 34, "usage_type": "call"}, {"api_name": "api.models.PageIssue.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "api.models.PageIssue", "line_number": 34, "usage_type": "name"}, {"api_name": "api.models.PageIssue.objects.create", "line_number": 36, "usage_type": "call"}, {"api_name": "api.models.PageIssue.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "api.models.PageIssue", "line_number": 36, "usage_type": "name"}, {"api_name": "api.enums.IssueStatus.NEW", "line_number": 38, "usage_type": "attribute"}, {"api_name": "api.enums.IssueStatus", "line_number": 38, "usage_type": "name"}, {"api_name": "api.enums.IssueImportance.M", "line_number": 39, "usage_type": "attribute"}, {"api_name": "api.enums.IssueImportance", "line_number": 39, "usage_type": "name"}, {"api_name": "django.dispatch.receiver", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 43, "usage_type": "argument"}, {"api_name": "api.models.CrawlIssue", "line_number": 43, "usage_type": "name"}, {"api_name": "django.utils.datetime_safe.datetime.now", "line_number": 52, "usage_type": "call"}, {"api_name": "django.utils.datetime_safe.datetime", "line_number": 52, "usage_type": "name"}, {"api_name": "django.dispatch.receiver", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 49, "usage_type": "argument"}, {"api_name": "api.models.Crawl", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "560137313", "text": "# -*- coding: utf-8 -*-\n\n# prod/dev/test\nENV = 'prod'\n\nimport importlib\nenv = importlib.import_module('config.env_{}'.format(ENV)).env\n\n# MYSQL_CONFIG\n# MYSQL_CONFIG = {\n# 'user': env['mysql']['user'],\n# 'password': env['mysql']['password'],\n# 'host': env['mysql']['host'],\n# 'database': env['mysql']['database']\n# }\n\n# REDIS_CONFIG\nREDIS_CONFIG = {\n 'host' : env['redis']['host'],\n 'port' : env['redis']['port'],\n 'db' : env['redis']['db']\n}\n\nEXPIRE_TIME = 3600\n\n# MONGO_CONFIG\nMONGO_CONFIG = {\n 'host': env['mongo']['host'],\n 'port': env['mongo']['port'],\n 'db': env['mongo']['db'],\n 'collection': env['mongo']['collection']\n}\n", "sub_path": "config/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 666, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "importlib.import_module", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "72942353", "text": "import requests\nimport json\nimport re\nimport time\nimport random\n\n\nclass People:\n id = \"\",\n name = \"\"\n level = \"\"\n\n def __init__(self, id, name, level):\n self.id = id\n self.name = name\n self.level = str(level)\n\n def getJson(self):\n return {\"id\": self.id, \"name\": self.name, \"symbolSize\": \"10\", \"level\": self.level}\n\n\nclass Link:\n sourceId = \"\",\n targetId = \"\"\n\n def __init__(self, sourceId, targetId):\n self.sourceId = sourceId\n self.targetId = targetId\n\n def getJson(self):\n return {\"source\": self.sourceId, \"target\": self.targetId}\n\n\nclass PreLink:\n sourceName = \"\",\n targetId = \"\"\n\n def __init__(self, sourceName, targetId):\n self.sourceName = sourceName\n self.targetId = targetId\n\n def isSource(self, name):\n return self.sourceName == name\n\n\ndef getMonth(month):\n if month == \"Apr\":\n return \"4\"\n else:\n if month == \"Mar\":\n return \"3\"\n else:\n print(month)\n return \"2\"\n\n\ndef getIdByName(name, headers):\n if name == \"\":\n print(\"name error\")\n return \"-1\"\n else:\n try:\n url = 'https://m.weibo.cn/n/{}'.format(name)\n response = requests.head(url).headers\n return response['location'].replace('/u/', '')\n except:\n url = 'https://m.weibo.cn/api/container/getIndex'\n params = {\n 'containerid': '100103type=3&q={}'.format(name),\n 'page_type': 'searchall'\n }\n response = requests.get(url, headers=random.choice(headers), params=params)\n print(response.text)\n while response.status_code != 200:\n time.sleep(3*60)\n response = requests.get(url, headers=random.choice(headers), params=params)\n response_json = json.loads(response.text)\n times = 0\n while response_json['ok'] != 1:\n times += 1\n if times > 1:\n break\n time.sleep(3 * 60)\n response = requests.get(url, headers=random.choice(headers), params=params)\n print(response.text)\n response_json = json.loads(response.text)\n if times > 1:\n return name\n id = response_json['data']['cards'][1]['card_group'][0]['user']['id']\n return str(id)\n\n\ndef getOnePage(response_json, peopleList, links, newsid, preLinks, levelList, peopleIdList, repostTime_dict):\n datas = response_json['data']['data']\n repeat = 0\n for data in datas:\n id = str(data['user']['id'])\n if id not in peopleIdList:\n repostTime_date = data['created_at']\n repostTime_dates = repostTime_date.split(\" \")\n repostYear = repostTime_dates[5]\n repostMonth = getMonth(repostTime_dates[1])\n repostDay = repostTime_dates[2]\n repostDate = \"{}年{}月{}日\".format(repostYear, repostMonth, repostDay)\n print(repostDate)\n peopleIdList.append(id)\n name = data['user']['screen_name']\n content = data['raw_text']\n print(data['raw_text'])\n for preLink in preLinks:\n if preLink.isSource(name):\n link = Link(id, preLink.targetId)\n links.append(link)\n preLinks.remove(preLink)\n content = content.replace(\"：\", \":\")\n items = re.findall('//@(.*?):', content)\n if items:\n if len(items) > 5:\n continue\n if repostDate in repostTime_dict.keys():\n repostTime_dict[repostDate] += 1\n print(repostTime_dict[repostDate])\n else:\n repostTime_dict[repostDate] = 1\n print(\"新添加主键:{}\".format(repostDate))\n levelList[len(items)] = str(int(levelList[len(items)]) + 1)\n print(\"该用户等级为\" + str(len(items) + 1))\n item = items[0]\n print(items)\n preLinks.append(PreLink(item, id))\n people = People(id, name, len(items)+1)\n else:\n links.append(Link(str(newsid), id))\n levelList[0] = str(int(levelList[0]) + 1)\n print(\"该用户等级为1\")\n if repostDate in repostTime_dict.keys():\n repostTime_dict[repostDate] += 1\n else:\n repostTime_dict[repostDate] = 1\n people = People(id, name, 1)\n peopleList.append(people)\n else:\n repeat += 1\n if repeat == len(datas):\n return False\n else:\n return True\n\n\ndef getOneNew(weibo_id, name, peopleList, links, newsid, news, levelList, peopleIdList, repostTime_dict):\n url = 'https://m.weibo.cn/api/statuses/repostTimeline?id={}&page=1'.format(weibo_id)\n headers = [{\n 'Cookie': '_ALF=1619262243; SCF=AvS6HupuL_1KkWxlafVcVyhPIZaKxVangJBjtREdcgAY98g1joNY2swIl9kLiAo5-zI2ASFjCWqgIRLQSsv6G6g.; SUB=_2A25NWB5zDeRhGeBI6loT8i3JzjqIHXVuoqI7rDV6PUJbktAfLWbzkW1NRot20TO5flZgAj7zP-QE9XwazxZCo31m; SUBP=0033WrSXqPxfM725Ws9jqgMF55529P9D9WheurEb4cKqvylaRF0NMCmz5JpX5K-hUgL.FoqceKnEeoefSKq2dJLoI7RLxKnLB.qLBoM4ShM4e5tt; _T_WM=19725237635; XSRF-TOKEN=b4c3bf; WEIBOCN_FROM=1110006030; MLOGIN=1; M_WEIBOCN_PARAMS=luicode%3D10000011%26lfid%3D231522type%253D1%2526t%253D10%2526q%253D%2523%25E6%2596%25B0%25E5%2586%25A0%25E7%2597%2585%25E6%25AF%2592%25E7%2596%25AB%25E8%258B%2597%25E5%2585%25A8%25E6%25B0%2591%25E5%2585%258D%25E8%25B4%25B9%2523%26uicode%3D20000061%26fid%3D4588175568407059%26oid%3D4588175568407059',\n 'User-Agent': 'Mozilla / 5.0(Windows NT 10.0;Win64;x64;rv: 86.0) Gecko / 20100101Firefox / 86.0'\n },\n {\n 'Cookie': 'ALF=1619892355; SCF=AvS6HupuL_1KkWxlafVcVyhPIZaKxVangJBjtREdcgAYeJh7yvZ_pH_j9WuN4KYNdG87Bruc4OM8Y-BvbQO6foY.; _T_WM=80943054270; SUB=_2A25NYnvTDeRhGeBI6loT8i3JzjqIHXVurQWbrDV6PUJbktAKLVrZkW1NRot20RT5Hy1aMc_Uvg5hVAlPDOfknaTl; SUBP=0033WrSXqPxfM725Ws9jqgMF55529P9D9WheurEb4cKqvylaRF0NMCmz5JpX5K-hUgL.FoqceKnEeoefSKq2dJLoI7RLxKnLB.qLBoM4ShM4e5tt; XSRF-TOKEN=0867fa; WEIBOCN_FROM=1110006030; MLOGIN=1; M_WEIBOCN_PARAMS=luicode%3D10000011%26lfid%3D100103type%253D1%2526q%253D%25E6%2596%25B0%25E5%2586%25A0%25E7%2597%2585%25E6%25AF%2592',\n 'User-Agent': 'Mozilla / 5.0(Windows NT 10.0;Win64;x64;rv: 86.0) Gecko / 20100101Firefox / 86.0'\n },\n {\n 'Cookie': '_T_WM=51445041497; WEIBOCN_FROM=1110006030; XSRF-TOKEN=91e8c6; loginScene=102003; SUB=_2A25NYgq1DeRhGeFL41YX-SjFyjmIHXVurJb9rDV6PUJbktAfLWvmkW1NfY0erHDa4_j2145zQOi2WiFiivFmFXHw; SUBP=0033WrSXqPxfM725Ws9jqgMF55529P9D9W5LgfZD7w4Azw1IZam6GTM15JpX5KzhUgL.FoMf1hBc1Kq4eK-2dJLoIp7LxKML1KBLBKnLxKqL1hnLBoMNSKnXSo.c1K2f; SSOLoginState=1617328869; MLOGIN=1; M_WEIBOCN_PARAMS=oid%3D4620459935269257%26luicode%3D10000011%26lfid%3D100103type%253D1%2526q%253D%25E6%2596%25B0%25E5%2586%25A0%25E7%2597%2585%25E6%25AF%2592; BAIDU_SSP_lcr=https://login.sina.com.cn/',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.114 Safari/537.36'\n }]\n preLinks = []\n response = requests.get(url, headers=random.choice(headers))\n response_json = json.loads(response.text)\n while response_json['ok'] != 1:\n print(\"出错了\")\n print(response_json)\n time.sleep(3 * 60)\n response = requests.get(url, headers=random.choice(headers))\n response_json = json.loads(response.text)\n max = response_json['data']['max']\n print(max)\n getOnePage(response_json, peopleList, links, newsid, preLinks, levelList, peopleIdList, repostTime_dict)\n i = 2\n while i < max + 1:\n try:\n url = 'https://m.weibo.cn/api/statuses/repostTimeline?id={}&page={}'.format(weibo_id, i)\n response = requests.get(url, headers=random.choice(headers))\n if response.status_code != 200:\n time.sleep(3 * 60)\n i -= 1\n continue\n response_json = json.loads(response.text)\n # print(response_json)\n max = response_json['data']['max']\n if getOnePage(response_json, peopleList, links, newsid, preLinks, levelList, peopleIdList, repostTime_dict):\n print(\"共{}页\".format(max))\n print(\"第{}页爬取完成\".format(i))\n else:\n print(\"后面均已爬取\")\n break\n except:\n print(response_json)\n print(\"有一个页面出现错误：第{}页\".format(i))\n i += 1\n time.sleep(3)\n nonPeople = []\n nonPeopleId = []\n print(len(preLinks))\n for preLink in preLinks:\n print(preLink.sourceName)\n print(preLink.targetId)\n if preLink.sourceName not in nonPeople:\n print(preLink.sourceName)\n print(nonPeople)\n j = getIdByName(preLink.sourceName, headers)\n print(j)\n nonPeopleId.append(j)\n nonPeople.append(preLink.sourceName)\n if j not in peopleIdList:\n print(\"新出现的人\")\n peopleIdList.append(j)\n people = People(j, preLink.sourceName, 1)\n peopleList.append(people)\n levelList[0] = str(int(levelList[0]) + 1)\n links.append(Link(str(newsid), j))\n else:\n j = nonPeopleId[nonPeople.index(preLink.sourceName)]\n print(j)\n links.append(Link(j, preLink.targetId))\n print(len(preLinks))\n news.append({\"name\": name, \"url\": \"https://m.weibo.cn/detail/{}\".format(weibo_id)})\n return True\n\n\ndef getPreData(repostTime_dict, levelList):\n level_f = open(\"level.json\", \"r+\", encoding=\"utf-8\")\n level_content = level_f.read()\n if not level_content == \"\":\n level_json = json.loads(level_content)\n levelList[0] = level_json['1']\n levelList[1] = level_json['2']\n levelList[2] = level_json['3']\n levelList[3] = level_json['4']\n levelList[4] = level_json['5']\n levelList[5] = level_json['6']\n level_f.close()\n startDate_f = open(\"repostSum.json\", \"r+\", encoding=\"utf-8\")\n startDate = startDate_f.read()\n if not startDate == \"\":\n startDate_json = json.loads(startDate)\n startDate_time = startDate_json['Date']\n startDate_sum = startDate_json['data']\n for i in range(0, len(startDate_time)):\n repostTime_dict[startDate_time[i]] = startDate_sum[i]\n startDate_f.close()\n\n\ndef getOnePreDate(peopleList, links, peopleIdList, newsId):\n peopleList.clear()\n links.clear()\n peopleIdList.clear()\n id_f = open(\"newsSpread{}.json\".format(newsId), \"r+\", encoding=\"utf-8\")\n newsSpread = id_f.read()\n if not newsSpread == \"\":\n newsSpread_json = json.loads(newsSpread)\n nodes = newsSpread_json['nodes']\n for node in nodes:\n one_id = node['id']\n one_name = node['name']\n one_level = node['level']\n peopleIdList.append(one_id)\n peopleList.append(People(one_id, one_name, one_level))\n startLinks = newsSpread_json['links']\n for startLink in startLinks:\n links.append(Link(startLink['source'], startLink['target']))\n id_f.close()\n\n\ndef writeToFile(levels, repost_dict):\n level_Json = {\n \"1\": str(levels[0]),\n \"2\": str(levels[1]),\n \"3\": str(levels[2]),\n \"4\": str(levels[3]),\n \"5\": str(levels[4]),\n \"6\": str(levels[5])\n }\n level_f = open('level.json', 'w+', encoding='utf-8')\n level_f.write(json.dumps(level_Json, ensure_ascii=False))\n level_f.close()\n repostDate_f = open('repostSum.json', 'w+', encoding='utf-8')\n keys = []\n values = []\n for key in repost_dict:\n keys.append(str(key))\n values.append(repost_dict[key])\n repostDate = {\n \"Date\": keys,\n \"data\": values\n }\n repostDate_f.write(json.dumps(repostDate, ensure_ascii=False))\n repostDate_f.close()\n\n\ndef writeOneToFile(peopleList, links, newsId):\n peopleJsonList = []\n linkJsonList = []\n for people in peopleList:\n peopleJsonList.append(people.getJson())\n for link in links:\n linkJsonList.append(link.getJson())\n total_json = {\n \"nodes\": peopleJsonList,\n \"links\": linkJsonList\n }\n f = open('newsSpread{}.json'.format(newsId), 'w+', encoding='utf-8')\n f.write(json.dumps(total_json, ensure_ascii=False))\n f.close()\n\n\nif __name__ == '__main__':\n weiboList = [\n {\n \"id\": 4620578218577363,\n \"name\": \"华南海鲜市场不是新冠疫情最初来源\"\n },\n {\n \"id\": 4620219815037861,\n \"name\": \"李梓萌不由自主把打疫苗标语唱了出来\"\n },\n {\n \"id\": 4621235869452542,\n \"name\": \"建议60岁及以上人群接种新冠疫苗\"\n },\n {\n \"id\": 4621074911988625,\n \"name\": \"为什么应尽快接种新冠疫苗\"\n },\n {\n \"id\": 4620749769540859,\n \"name\": \"哪些人不能打新冠疫苗\"\n }\n ]\n repostTime_dict = {}\n peopleList = []\n links = []\n news = []\n peopleIdList = []\n levelList = [0, 0, 0, 0, 0, 0]\n getPreData(repostTime_dict, levelList)\n newsid = 0\n for weibo in weiboList:\n print(weiboList.index(weibo))\n getOnePreDate(peopleList, links, peopleIdList, newsid)\n getOneNew(weibo['id'], weibo['name'], peopleList, links, newsid, news, levelList, peopleIdList, repostTime_dict)\n writeOneToFile(peopleList, links, newsid)\n newsid += 1\n print(repostTime_dict)\n writeToFile(levelList, repostTime_dict)\n", "sub_path": "微博/repost.py", "file_name": "repost.py", "file_ext": "py", "file_size_in_byte": 13890, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "requests.head", "line_number": 64, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 72, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 72, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 75, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 76, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 76, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 77, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 84, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 84, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 86, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 116, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 165, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 165, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 166, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 170, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 171, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 171, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 172, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 180, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 180, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 182, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 185, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 198, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 232, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 243, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 258, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 282, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 294, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 310, "usage_type": "call"}]} +{"seq_id": "479443766", "text": "import pytest\n\nfrom rest_framework import status\n\nfrom api.searches.serializers import SearchSerializer\nfrom constants import content_types\nfrom constants.urls import API_V1\nfrom db.models.searches import Search\nfrom factories.factory_projects import ProjectFactory\nfrom factories.factory_searches import SearchFactory\nfrom tests.utils import BaseViewTest\n\n\n@pytest.mark.search_mark\nclass BaseTestSearchListView(BaseViewTest):\n DISABLE_RUNNER = True\n HAS_AUTH = True\n model_class = Search\n factory_class = SearchFactory\n serializer_class = SearchSerializer\n entity = ''\n content_type = ''\n num_objects = 3\n\n def setUp(self):\n super().setUp()\n self.project = ProjectFactory(user=self.auth_client.user)\n self.other_project = ProjectFactory()\n self.url = '/{}/searches/{}/{}/{}'.format(API_V1,\n self.project.user.username,\n self.project.name,\n self.entity)\n self.other_url = '/{}/searches/{}/{}/{}'.format(API_V1,\n self.other_project.user.username,\n self.other_project.name,\n self.entity)\n self.objects = [\n self.factory_class(user=self.auth_client.user,\n project=self.project,\n content_type=self.content_type) for _ in range(self.num_objects)]\n\n # Other objects that do not belong to the filter\n self.factory_class(project=self.other_project, content_type=self.content_type)\n self.queryset = self.model_class.objects.filter(project=self.project)\n self.queryset = self.queryset.order_by('-updated_at')\n\n def test_get(self):\n resp = self.auth_client.get(self.url)\n assert resp.status_code == status.HTTP_200_OK\n\n assert resp.data['next'] is None\n assert resp.data['count'] == len(self.objects)\n\n data = resp.data['results']\n assert len(data) == self.queryset.count()\n assert data == self.serializer_class(self.queryset, many=True).data\n\n resp = self.auth_client.get(self.other_url)\n assert resp.status_code == status.HTTP_200_OK\n assert resp.data['count'] == 0\n\n def test_create(self):\n data = {}\n resp = self.auth_client.post(self.url, data)\n assert resp.status_code == status.HTTP_400_BAD_REQUEST\n\n data = {'query': {'query': 'project.id: 1|2', 'sort': '-created_at'}}\n resp = self.auth_client.post(self.url, data)\n\n assert resp.status_code == status.HTTP_201_CREATED\n assert self.queryset.count() == self.num_objects + 1\n\n # Test other\n resp = self.auth_client.post(self.other_url, data)\n assert resp.status_code in (status.HTTP_401_UNAUTHORIZED, status.HTTP_403_FORBIDDEN)\n\n\n@pytest.mark.search_mark\nclass BaseTestSearchDeleteView(BaseViewTest):\n DISABLE_RUNNER = True\n HAS_AUTH = True\n model_class = Search\n factory_class = SearchFactory\n serializer_class = SearchSerializer\n entity = ''\n content_type = ''\n\n def get_url(self, obj):\n return '/{}/searches/{}/{}/{}/{}'.format(API_V1,\n self.project.user.username,\n self.project.name,\n self.entity,\n obj.id)\n\n def setUp(self):\n super().setUp()\n self.project = ProjectFactory(user=self.auth_client.user)\n some_object = self.factory_class(project=self.project,\n content_type=self.content_type)\n self.url = self.get_url(some_object)\n\n def test_delete(self):\n assert Search.objects.count() == 1\n resp = self.auth_client.delete(self.url)\n assert resp.status_code == status.HTTP_404_NOT_FOUND\n obj = self.factory_class(user=self.auth_client.user,\n project=self.project,\n content_type=self.content_type)\n assert Search.objects.count() == 2\n resp = self.auth_client.delete(self.get_url(obj))\n assert resp.status_code == status.HTTP_204_NO_CONTENT\n assert Search.objects.count() == 1\n\n\n@pytest.mark.search_mark\nclass TestExperimentSearchCreateView(BaseTestSearchListView):\n entity = 'experiments'\n content_type = content_types.EXPERIMENT\n\n\n@pytest.mark.search_mark\nclass TestExperimentSearchDeleteView(BaseTestSearchDeleteView):\n entity = 'experiments'\n content_type = content_types.EXPERIMENT\n\n\n@pytest.mark.search_mark\nclass TestExperimentGroupSearchCreateView(BaseTestSearchListView):\n entity = 'groups'\n content_type = content_types.EXPERIMENT_GROUP\n\n\n@pytest.mark.search_mark\nclass TestExperimentGroupSearchDeleteView(BaseTestSearchDeleteView):\n entity = 'groups'\n content_type = content_types.EXPERIMENT_GROUP\n\n\n@pytest.mark.search_mark\nclass TestJobSearchCreateView(BaseTestSearchListView):\n entity = 'jobs'\n content_type = content_types.JOB\n\n\n@pytest.mark.search_mark\nclass TestJobSearchDeleteView(BaseTestSearchDeleteView):\n entity = 'jobs'\n content_type = content_types.JOB\n\n\n@pytest.mark.search_mark\nclass TestBuildSearchCreateView(BaseTestSearchListView):\n entity = 'builds'\n content_type = content_types.BUILD_JOB\n\n\n@pytest.mark.search_mark\nclass TestBuildSearchDeleteView(BaseTestSearchDeleteView):\n entity = 'builds'\n content_type = content_types.BUILD_JOB\n\n\ndel BaseTestSearchListView\ndel BaseTestSearchDeleteView\n", "sub_path": "tests/test_searches/test_views.py", "file_name": "test_views.py", "file_ext": "py", "file_size_in_byte": 5760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "tests.utils.BaseViewTest", "line_number": 15, "usage_type": "name"}, {"api_name": "db.models.searches.Search", "line_number": 18, "usage_type": "name"}, {"api_name": "factories.factory_searches.SearchFactory", "line_number": 19, "usage_type": "name"}, {"api_name": "api.searches.serializers.SearchSerializer", "line_number": 20, "usage_type": "name"}, {"api_name": "factories.factory_projects.ProjectFactory", "line_number": 27, "usage_type": "call"}, {"api_name": "factories.factory_projects.ProjectFactory", "line_number": 28, "usage_type": "call"}, {"api_name": "constants.urls.API_V1", "line_number": 29, "usage_type": "argument"}, {"api_name": "constants.urls.API_V1", "line_number": 33, "usage_type": "argument"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 49, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 59, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 65, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 65, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 70, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 75, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 75, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tests.utils.BaseViewTest", "line_number": 79, "usage_type": "name"}, {"api_name": "db.models.searches.Search", "line_number": 82, "usage_type": "name"}, {"api_name": "factories.factory_searches.SearchFactory", "line_number": 83, "usage_type": "name"}, {"api_name": "api.searches.serializers.SearchSerializer", "line_number": 84, "usage_type": "name"}, {"api_name": "constants.urls.API_V1", "line_number": 89, "usage_type": "argument"}, {"api_name": "factories.factory_projects.ProjectFactory", "line_number": 97, "usage_type": "call"}, {"api_name": "db.models.searches.Search.objects.count", "line_number": 103, "usage_type": "call"}, {"api_name": "db.models.searches.Search.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "db.models.searches.Search", "line_number": 103, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 105, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 105, "usage_type": "name"}, {"api_name": "db.models.searches.Search.objects.count", "line_number": 109, "usage_type": "call"}, {"api_name": "db.models.searches.Search.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "db.models.searches.Search", "line_number": 109, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 111, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 111, "usage_type": "name"}, {"api_name": "db.models.searches.Search.objects.count", "line_number": 112, "usage_type": "call"}, {"api_name": "db.models.searches.Search.objects", "line_number": 112, "usage_type": "attribute"}, {"api_name": "db.models.searches.Search", "line_number": 112, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 78, "usage_type": "attribute"}, {"api_name": "constants.content_types.EXPERIMENT", "line_number": 118, "usage_type": "attribute"}, {"api_name": "constants.content_types", "line_number": 118, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 115, "usage_type": "attribute"}, {"api_name": "constants.content_types.EXPERIMENT", "line_number": 124, "usage_type": "attribute"}, {"api_name": "constants.content_types", "line_number": 124, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 121, "usage_type": "attribute"}, {"api_name": "constants.content_types.EXPERIMENT_GROUP", "line_number": 130, "usage_type": "attribute"}, {"api_name": "constants.content_types", "line_number": 130, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 127, "usage_type": "attribute"}, {"api_name": "constants.content_types.EXPERIMENT_GROUP", "line_number": 136, "usage_type": "attribute"}, {"api_name": "constants.content_types", "line_number": 136, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 133, "usage_type": "attribute"}, {"api_name": "constants.content_types.JOB", "line_number": 142, "usage_type": "attribute"}, {"api_name": "constants.content_types", "line_number": 142, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 139, "usage_type": "attribute"}, {"api_name": "constants.content_types.JOB", "line_number": 148, "usage_type": "attribute"}, {"api_name": "constants.content_types", "line_number": 148, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 145, "usage_type": "attribute"}, {"api_name": "constants.content_types.BUILD_JOB", "line_number": 154, "usage_type": "attribute"}, {"api_name": "constants.content_types", "line_number": 154, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 151, "usage_type": "attribute"}, {"api_name": "constants.content_types.BUILD_JOB", "line_number": 160, "usage_type": "attribute"}, {"api_name": "constants.content_types", "line_number": 160, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 157, "usage_type": "attribute"}]} +{"seq_id": "323266397", "text": "#-*-coding:utf-8-*-\nimport os\nimport os.path as osp\nimport torch\nfrom torch.autograd import Variable\nfrom utils.MyBCEWithLogitsLoss import BCEWithLogitsLoss as MyBCEWithLogitsLoss\nfrom tqdm import tqdm\n\ndef GetTrueLabel(label):\n index = []\n for s_label in label:\n temp = []\n temp = [m for m,j in enumerate(s_label) if j==1]\n index.append(temp)\n true_label = []\n for i in index:\n temp = []\n for j in i:\n if j in range(0,14):\n temp.append([0, j])\n elif j in range(14,23,1):\n temp.append([1, j-14])\n elif j in range(23,100,1):\n temp.append([2, j-23])\n elif j in range(100,107,1):\n temp.append([3, j-100])\n elif j in range(107,115,1):\n temp.append([4, j-107])\n elif j in range(115,141,1):\n temp.append([5, j-115])\n elif j in range(141,162,1):\n temp.append([6, j-141])\n elif j in range(162,169,1):\n temp.append([7, j-162])\n elif j in range(169,184,1):\n temp.append([8, j-169])\n elif j in range(184,223,1):\n temp.append([9, j-184])\n elif j in range(223,235,1):\n temp.append([10, j-223])\n elif j in range(235,245,1):\n temp.append([11, j-235])\n elif j in range(245,254,1):\n temp.append([12, j-245])\n elif j in range(254,262,1):\n temp.append([13, j-254])\n elif j in range(262,273,1):\n temp.append([14, j-262])\n elif j in range(273,285,1):\n temp.append([15, j-273])\n elif j in range(285,292,1):\n temp.append([16, j-285])\n elif j in range(292,303,1):\n temp.append([17, j-292])\n true_label.append(temp)\n return true_label\n\n########### for sigmoid_cross_entropy_with_logits ##########\n\n# change +1 to torch.FloatTensor(1) and -1 into torch.FloatTensor()\ndef change_label(label):\n true_label = []\n for s_label in label:\n temp = [1 if i==1 else 0 for i in s_label]\n true_label.append(temp)\n \n return Variable(torch.FloatTensor(true_label).cuda()) # N X C\n\n#### single branch\ndef sigmoid_train_batch(model, criterion, optimizer, batch_label):\n optimizer.zero_grad() #\n C = batch_label[0][0].shape[0]\n H = batch_label[0][0].shape[1]\n W = batch_label[0][0].shape[2]\n batch = torch.cat((batch_label[i][0] for i in range(len(batch_label))),0).view(-1,C,H,W) # N C H W\n input = Variable(batch.cuda())\n label = [batch_label[i][1] for i in range(len(batch_label))]\n target = change_label(label)\n output = model(input)\n loss = criterion(output, target)\n loss.backward()\n optimizer.step()\n\n return loss.data\n\ndef sigmoid_train_epoch(model, num_batches, train_loader, print_freq, optimizer=None):\n criterion = torch.nn.BCEWithLogitsLoss()\n for batch_label in train_loader:\n loss = sigmoid_train_batch(model, criterion, optimizer, batch_label)\n if num_batches%print_freq == 0:\n print('%23s%-9s%-13s'%('the '+str(num_batches)+'th batch, ','loss is: ',str(round(loss[0],8))))\n num_batches +=1\n return num_batches\n\n#### multi branch\n# no category branch\nAttr_Num_dict = {'category':23, 'length_of_upper_body_clothes':5, 'length_of_trousers':5, 'length_of_dresses':5, 'length_of_sleeves':8,\\\n'fitness_of_clothes':5, 'design_of_dresses':10, 'type_of_sleeves':10, 'type_of_trousers':7, 'type_of_dresses':12,\\\n'type_of_collars':10, 'type_of_waistlines':7, 'type_of_clothes_buttons':7, 'thickness_of_clothes':4, 'fabric_of_clothes':20,\\\n'style_of_clothes':23, 'part_details_of_clothes':72, 'graphic_elements_texture':47}\n\nAttr = ['length_of_upper_body_clothes', 'length_of_trousers', 'length_of_dresses', 'length_of_sleeves', 'fitness_of_clothes',\\\n'design_of_dresses', 'type_of_sleeves', 'type_of_trousers', 'type_of_dresses', 'type_of_collars', 'type_of_waistlines', \\\n'type_of_clothes_buttons', 'thickness_of_clothes', 'fabric_of_clothes', 'style_of_clothes', \\\n'part_details_of_clothes', 'graphic_elements_texture']\n\n# input: labels, Each_Attr_id\n# labels = [1,+1,-1,-1,+1,+1,...]*batch_size\n# out branch_ids: [[0,1,2],[1,3],...] branch_label: [[1,0,1,0,0,0,1..],[1,0,0,1,1,0,...]...]\ndef get_branch(Each_Attr_id, labels):\n branch_label = []\n branch_ids = []\n index1_index0 = {}\n Attr_ids = []\n for key in Attr:\n Attr_ids.append(Each_Attr_id[key])\n index1 = 0\n for index0, label in enumerate(labels):\n temp = []\n temp1 = []\n cur_ids = []\n for i,j in enumerate(label):\n if j==1:\n for m, ids in enumerate(Attr_ids):\n if i in ids and m not in temp:\n temp.append(m)\n cur_ids += ids \n if len(cur_ids)!=0:\n index1_index0[index1] = index0 # index0 is smaple id in batch, index1 is the out sample id(has at least one attr)\n branch_ids.append(temp)\n branch_label.append(Variable(torch.unsqueeze(torch.FloatTensor([label[k] for k in cur_ids]), dim=0).cuda()))\n index1+=1\n\n return index1_index0, branch_ids, branch_label\n\n## only multi label\ndef multi_task_train_batch(model, Each_Attr_id, criterion, optimizer, batch_label):\n optimizer.zero_grad() #\n C = batch_label[0][0].shape[0]\n H = batch_label[0][0].shape[1]\n W = batch_label[0][0].shape[2]\n batch = torch.cat((batch_label[i][0] for i in range(len(batch_label))),0).view(-1,C,H,W) # N C H W\n input = Variable(batch.cuda())\n\n labels = [batch_label[i][1] for i in range(len(batch_label))] # [[1,-1..],[1,1,-1..]]\n for i, label in enumerate(labels):\n labels[i] = [1 if j==1 else 0 for j in label]\n index1_index0, branch_ids, branch_label = get_branch(Each_Attr_id, labels)\n\n output = model(input) # [NxC1, NxC2, NxC3, NxC4, NxC5, ..., NxC17]\n\n loss = []\n for i,(branch_id,label) in enumerate(zip(branch_ids, branch_label)):\n index0 = index1_index0[i]\n if len(branch_id)>=1:\n cur_output = torch.unsqueeze(torch.cat(tuple((output[j][index0] for j in branch_id)),0), dim=0)\n else:\n cur_output = output[branch_id[0]][index0]\n loss.append(criterion(cur_output, label))\n\n total_loss = sum(loss)/len(loss)\n total_loss.backward() \n optimizer.step()\n\n return total_loss.data\n\ndef multi_task_train_epoch(model, Each_Attr_id, num_batches, train_loader, print_freq, optimizer=None):\n criterion = torch.nn.BCEWithLogitsLoss()\n for batch_label in train_loader:\n loss = multi_task_train_batch(model, Each_Attr_id, criterion, optimizer, batch_label)\n if num_batches%print_freq == 0:\n print('%23s%-9s%-13s'%('the '+str(num_batches)+'th batch, ','loss is: ',str(round(loss[0],8))))\n num_batches +=1\n return num_batches\n\n\n## multi_class + multi_label training\n# ignore category\n# 'length_of_upper_body_clothes':0,\n# 'length_of_trousers':0,\n# 'length_of_dresses':0,\n# 'length_of_sleeves':1,\n# 'fitness_of_clothes':1,\n# 'design_of_dresses':1,\n# 'type_of_sleeves':0,\n# 'type_of_trousers':1,\n# 'type_of_dresses':0,\n# 'type_of_collars':0,\n# 'type_of_waistlines':1,\n# 'type_of_clothes_buttons':1,\n# 'thickness_of_clothes':0,\n# 'fabric_of_clothes':1,\n# 'style_of_clothes':1,\n# 'part_details_of_clothes':1,\n# 'graphic_elements_texture':1\ndef mix_multi_task_train_batch(model, Each_Attr_id, multi_label, multi_class, optimizer, batch_label):\n multi_class_indexs = [0,1,2,6,8,9,12]\n multi_label_indexs = [3,4,5,7,10,11,13,14,15,16]\n num_each_attr = [5, 5, 5, 8, 5, 10, 10, 7, 12, 10, 7, 7, 4, 20, 23, 72, 47]\n if optimizer is None:\n pass\n else:\n optimizer.zero_grad() #\n C = batch_label[0][0].shape[0]\n H = batch_label[0][0].shape[1]\n W = batch_label[0][0].shape[2]\n batch = torch.cat((batch_label[i][0] for i in range(len(batch_label))),0).view(-1,C,H,W) # N C H W\n input = Variable(batch.cuda())\n\n labels = [batch_label[i][1] for i in range(len(batch_label))] # [[1,-1..],[1,1,-1..]]\n for i, label in enumerate(labels):\n labels[i] = [1 if j==1 else 0 for j in label]\n index1_index0, branch_ids, _ = get_branch(Each_Attr_id, labels)\n\n output = model(input) # [NxC1, NxC2, NxC3, NxC4, NxC5, ..., NxC17]\n\n loss = []\n for i,(branch_id,label) in enumerate(zip(branch_ids, labels)):\n index0 = index1_index0[i]\n for index in branch_id:\n if index in multi_class_indexs:\n cur_output = torch.unsqueeze(output[index][index0], dim=0)\n if index==0:\n start = 23\n else:\n start = sum(num_each_attr[0:index])+23\n end = num_each_attr[index]+start\n for tt,qq in enumerate(label[start:end]):\n if qq==1:\n cur_label = Variable(torch.LongTensor([tt])).cuda()\n loss.append(multi_class(cur_output, cur_label))\n break\n elif index in multi_label_indexs:\n if index==0:\n start = 23\n else:\n start = sum(num_each_attr[0:index])+23\n end = num_each_attr[index]+start\n cur_output = torch.unsqueeze(output[index][index0], dim=0)\n cur_label = Variable(torch.unsqueeze(torch.FloatTensor(label[start:end]), dim=0).cuda())\n loss.append(multi_label(cur_output, cur_label))\n \n\n total_loss = sum(loss)/len(loss)\n total_loss.backward()\n if optimizer is None:\n pass\n else:\n optimizer.step()\n\n return total_loss.data\n\ndef mix_multi_task_train_epoch(csvfile, writer, model, Each_Attr_id, num_batches, train_loader, print_freq, optimizer=None):\n multi_label = MyBCEWithLogitsLoss()\n multi_class = torch.nn.CrossEntropyLoss()\n for batch_label in train_loader:\n loss = mix_multi_task_train_batch(model, Each_Attr_id, multi_label, multi_class, optimizer, batch_label)\n if num_batches%print_freq == 0:\n print('%23s%-9s%-13s'%('the '+str(num_batches)+'th batch, ','loss is: ',str(round(loss[0],8))))\n num_batches +=1\n return num_batches\n\n\n########### get sample loss ###########\n\ndef mix_multi_task_test_loss_batch(csvfile, writer, jpg_folders, model, Each_Attr_id, multi_label, multi_class, batch_label):\n multi_class_indexs = [0,1,2,6,8,9,12]\n multi_label_indexs = [3,4,5,7,10,11,13,14,15,16]\n num_each_attr = [5, 5, 5, 8, 5, 10, 10, 7, 12, 10, 7, 7, 4, 20, 23, 72, 47]\n\n C = batch_label[0][0].shape[0]\n H = batch_label[0][0].shape[1]\n W = batch_label[0][0].shape[2]\n batch = torch.cat((batch_label[i][0] for i in range(len(batch_label))),0).view(-1,C,H,W) # N C H W\n input = Variable(batch.cuda())\n\n labels = [batch_label[i][1] for i in range(len(batch_label))] # [[1,-1..],[1,1,-1..]]\n for i, label in enumerate(labels):\n labels[i] = [1 if j==1 else 0 for j in label]\n index1_index0, branch_ids, _ = get_branch(Each_Attr_id, labels)\n\n output = model(input) # [NxC1, NxC2, NxC3, NxC4, NxC5, ..., NxC17]\n\n loss = []\n sample_loss = [0.0]*len(batch_label)\n for i,(branch_id,label) in enumerate(zip(branch_ids, labels)):\n index0 = index1_index0[i]\n for index in branch_id:\n if index in multi_class_indexs:\n cur_output = torch.unsqueeze(output[index][index0], dim=0)\n if index==0:\n start = 23\n else:\n start = sum(num_each_attr[0:index])+23\n end = num_each_attr[index]+start\n for tt,qq in enumerate(label[start:end]):\n if qq==1:\n cur_label = Variable(torch.LongTensor([tt])).cuda()\n sample_loss[i]+=float(multi_class(cur_output, cur_label).cpu().data)\n loss.append(multi_class(cur_output, cur_label))\n break\n elif index in multi_label_indexs:\n if index==0:\n start = 23\n else:\n start = sum(num_each_attr[0:index])+23\n end = num_each_attr[index]+start\n cur_output = torch.unsqueeze(output[index][index0], dim=0)\n cur_label = Variable(torch.unsqueeze(torch.FloatTensor(label[start:end]), dim=0).cuda())\n sample_loss[i]+=float(multi_label(cur_output, cur_label).cpu().data)\n loss.append(multi_label(cur_output, cur_label))\n\n for test_loss, folder in zip(sample_loss, jpg_folders):\n writer.writerow({'img path': folder, \\\n 'total loss': round(test_loss,8)})\n\n\n\ndef mix_multi_task_test_loss_epoch(csvfile, writer, model, Each_Attr_id, data_loader):\n multi_label = torch.nn.BCEWithLogitsLoss()\n multi_class = torch.nn.CrossEntropyLoss()\n for (batch_label,indexs) in tqdm(data_loader,desc='Test loss',ncols=100):\n jpg_folders = [data_loader.dataset.samples[index][0] for index in indexs]\n loss = mix_multi_task_test_loss_batch(csvfile, writer, jpg_folders, model, Each_Attr_id, multi_label, multi_class, batch_label)\n \n\n########### for sigmoid_cross_entropy_with_logits ##########\n\n\n########### for softmax_cross_entropy_with_logits ##########\ndef train_batch(model, optimizer, batch_label):\n optimizer.zero_grad() #\n C = batch_label[0][0].shape[0]\n H = batch_label[0][0].shape[1]\n W = batch_label[0][0].shape[2]\n batch = torch.cat((batch_label[i][0] for i in range(len(batch_label))),0).view(-1,C,H,W)\n label = [batch_label[i][1] for i in range(len(batch_label))]\n input = Variable(batch.cuda())\n output = [i for i in model(input)]\n criterion = torch.nn.CrossEntropyLoss()\n loss = []\n true_label = GetTrueLabel(label)\n for i,j in enumerate(true_label):\n for Attr, Attr_Value in j:\n cur_output = torch.unsqueeze(output[Attr][i], dim=0)\n target = Variable(torch.LongTensor([Attr_Value]).cuda())\n loss.append(criterion(cur_output, target))\n Avg_loss = sum(loss)/input.shape[0] # you should calcu each sample loss, and get a mean value\n Avg_loss.backward()\n\n optimizer.step()\n return Avg_loss.data\n\ndef train_epoch(model, num_batches, train_loader, print_freq, optimizer=None):\n for batch_label in train_loader:\n loss = train_batch(model, optimizer, batch_label)\n if num_batches%print_freq == 0:\n print('%23s%-9s%-13s'%('the '+str(num_batches)+'th batch, ','loss is: ',str(round(loss[0],8))))\n num_batches +=1\n return num_batches\n\n\n########### for softmax_cross_entropy_with_logits ##########", "sub_path": "FashionBeta/classify/consumer/utils/train_Components.py", "file_name": "train_Components.py", "file_ext": "py", "file_size_in_byte": 14875, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torch.autograd.Variable", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 168, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 240, "usage_type": "call"}, {"api_name": "utils.MyBCEWithLogitsLoss.BCEWithLogitsLoss", "line_number": 254, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 255, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 274, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 290, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 298, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 298, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 308, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 320, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 320, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 321, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 322, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 336, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 338, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 340, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 340, "usage_type": "attribute"}, {"api_name": "torch.unsqueeze", "line_number": 345, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 346, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 346, "usage_type": "call"}]} +{"seq_id": "112639609", "text": "#!/usr/bin/env python3.7\n\n# module(s)\nimport sys\nfrom itertools import chain, combinations, product\nfrom parseJFLAP import *\n\n# algorithm defined in more-itertools\ndef powerSet(mainSet):\n s = list(mainSet)\n return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))\n\ndef buildState(mainTuple):\n sortedTuple = sorted(mainTuple)\n state = str()\n\n for member in sortedTuple:\n state = state + member\n\n return state\n\ndef computeStates(nfa):\n states = list(powerSet(nfa.states))\n return states\n\n result = set()\n\n for member in states:\n tempState = tuple()\n\n for tupleMember in member:\n tempState = tempState + (tupleMember, )\n\n result.add(buildState(tempState))\n\n print(result)\n return result\n\ndef computeStateTransition(stateTuple, dfaTransitions, alphabet):\n newTrans = dict()\n\n for symbol in alphabet:\n destination = tuple()\n\n for member in stateTuple:\n # iterate through states in state set\n for state, stateTrans in dfaTransitions.items():\n if member == state:\n if symbol in stateTrans:\n for element in stateTrans[symbol]:\n if element not in destination:\n destination = destination + (element, )\n\n newTrans[symbol] = destination\n\n return newTrans\n\ndef computeTransitions(nfa, states):\n dfaTransitions = dict()\n transitions = nfa.transitions\n\n # initialize transition\n for state in states:\n dfaTransitions[state] = dict()\n\n # build from previous states\n for state, stateTrans in transitions.items():\n newTrans = dict()\n\n if stateTrans:\n # dictionary not empty\n for symbol, symbolTrans in stateTrans.items():\n toState = tuple()\n\n for symbolState in symbolTrans:\n toState = toState + (symbolState, )\n\n # symbol built\n newTrans[symbol] = toState\n\n dfaTransitions[state] = newTrans\n\n # build from new states\n for stateTuple, stateTrans in dfaTransitions.items():\n if isinstance(stateTuple, tuple):\n newTrans = computeStateTransition(stateTuple, dfaTransitions, nfa.input_symbols)\n\n # add new transitions to state\n if dfaTransitions[stateTuple]:\n # dictionary is not empty\n dfaTransitions[stateTuple].update(newTrans)\n else:\n dfaTransitions[stateTuple] = newTrans\n\n return dfaTransitions\n\ndef computeFinalStates(nfa, dfaStates):\n finals = set()\n\n # add base final states\n for state in nfa.final_states:\n finals.add(state)\n\n for state in nfa.final_states:\n for dfaState in dfaStates:\n if state in dfaState:\n finals.add(buildState(dfaState))\n\n return finals\n\ndef NFAtoDFA(nfa):\n dfaStates = list()\n dfaSymbols = set()\n dfaTransitions = dict()\n\n # build the set of states\n dfaStates = computeStates(nfa)\n\n # set the symbols\n dfaSymbols = nfa.input_symbols\n\n # set transitions\n dfaTransitions = computeTransitions(nfa, dfaStates)\n\n # set initial state\n dfaInitialState = nfa.initial_state\n\n # set final state\n dfaFinalStates = computeFinalStates(nfa, dfaStates)\n\n # format states\n formatStates = set()\n for member in dfaStates:\n formatStates.add(buildState(member))\n\n formatTransitions = dict()\n\n # initialize keys\n for state, stateTrans in dfaTransitions.items():\n if isinstance(state, tuple):\n tempTrans = dict()\n for symbol, symbolTrans in stateTrans.items():\n tempTrans[symbol] = buildState(symbolTrans)\n\n if isinstance(state, tuple):\n formatTransitions[buildState(state)] = tempTrans\n\n # build DFA\n dfa = DFA(\n states = formatStates,\n input_symbols = dfaSymbols,\n transitions = formatTransitions,\n initial_state = dfaInitialState,\n final_states = dfaFinalStates\n )\n\n return dfa\n\ndef to_complement(dfa):\n accepted = set()\n rejected = set()\n\n # build accepted states set\n for state in dfa.final_states:\n accepted.add(state)\n\n # build rejected state set\n for state in dfa.states:\n if state not in dfa.final_states:\n rejected.add(state)\n\n # build complement DFA\n comp_dfa = DFA(\n states = dfa.states,\n input_symbols = dfa.input_symbols,\n transitions = dfa.transitions,\n initial_state = dfa.initial_state,\n final_states = rejected\n )\n\n return comp_dfa\n\ndef remove_braces(string):\n result = str()\n\n for character in string:\n if character != '{' and character != '}':\n result = result + character\n\n return \"{\" + result + \"}\"\n\ndef cross_states(spc_states, sys_states):\n states = list()\n\n for state in product(spc_states, sys_states):\n if state not in states:\n states.append(state)\n\n # remove repeating sets\n size = len(states)\n for i in range(0, size):\n for j in range(i, size):\n if i != j and set(states[i]).issubset(states[j]):\n states.remove(states[i])\n size = size - 1\n break # break second for loop (nested)\n\n # add deadlock state\n states.append((\"{}\", \"{}\"))\n\n # build set\n result = set()\n for state in states:\n # build string\n temp = str()\n\n for member in state:\n temp = temp + member\n\n # add string to set\n result.add(temp)\n\n return result\n\ndef cross_transitions(spc_trans, sys_trans, states):\n transitions = dict()\n\n # build base dictionary\n for state in states:\n transitions.update({state: None})\n\n # build transitions\n for spc_key, spc_val in spc_trans.items():\n for spc_val_key, spc_val_val in spc_val.items():\n for sys_key, sys_val in sys_trans.items():\n temp_trans = dict()\n for sys_val_key, sys_val_val in sys_val.items():\n if spc_val_key == sys_val_key:\n temp_trans[spc_val_key] = spc_val_val + sys_val_val\n\n # add transition functions to state\n if transitions[spc_key + sys_key] != None:\n transitions[spc_key + sys_key] = {**(transitions[spc_key + sys_key]), **temp_trans}\n else:\n transitions[spc_key + sys_key] = temp_trans\n\n return transitions\n\ndef cross_finals(spc_finals, sys_finals):\n finals = set()\n\n for spc_state in spc_finals:\n for sys_state in sys_finals:\n finals.add(spc_state + sys_state)\n\n return finals\n\ndef to_intersection(spc_dfa, sys_dfa):\n # build cartesian product of states\n inter_states = cross_states(spc_dfa.states, sys_dfa.states)\n\n # build transitions\n inter_transitions = cross_transitions(spc_dfa.transitions, sys_dfa.transitions, inter_states)\n\n # set initial state\n inter_initial = spc_dfa.initial_state + sys_dfa.initial_state\n\n # set final states\n inter_finals = cross_finals(spc_dfa.final_states, sys_dfa.final_states)\n\n # build intersection DFA\n inter_dfa = DFA(\n states = inter_states,\n input_symbols = spc_dfa.input_symbols,\n transitions = inter_transitions,\n initial_state = inter_initial,\n final_states = inter_finals\n )\n\n return inter_dfa\n\ndef convert_graph(comp):\n graph = dict()\n\n # build base state(s)\n for state in comp.transitions:\n graph.update({state: list()})\n\n # add transition state(s)\n for state in graph:\n for key, value in comp.transitions.items():\n if(state == key):\n for curr_key, curr_val in value.items():\n graph[state].append({curr_key: curr_val})\n\n return graph\n\n\n# find_path method implementation used from\n# https://www.python.org/doc/essays/graphs/\n# with some variation\n\ndef find_path(graph, start, end, path=[]):\n path = path + [start]\n if start == end:\n return path\n if not start in graph:\n return None\n for trans, node in graph[start].items():\n if node not in path:\n newpath = find_path(graph, node, end, path)\n if newpath:\n return newpath\n\n return None\n\ndef path_to_string(path, dfa):\n string = \"\"\n\n if len(path) > 1:\n for i in range(len(path) - 1):\n state = path[i]\n transitions = dfa.transitions[state]\n\n for symbol in transitions:\n if transitions[symbol] == path[i + 1]:\n string = string + symbol\n else:\n string = \"EPS\"\n\n return string\n\ndef write_DFA(dfa, filename):\n with open(filename, \"w\") as f:\n f.write(\"% Input alphabet\\n\")\n\n for symbol in dfa.input_symbols:\n f.write(symbol + '\\n')\n\n f.write(\"% Intersectional Language\\n\")\n\n f.write(\"% Transition function\\n\")\n for state, transitions in dfa.transitions.items():\n for symbol, toState in transitions.items():\n f.write(state + \" \" + symbol + \" \" + toState + \"\\n\")\n\n f.write(\"% Initial state\\n\")\n f.write(dfa.initial_state + '\\n')\n\n f.write(\"% Final states\\n\")\n for state in dfa.final_states:\n f.write(state + '\\n')\n\ndef rename_deadlocks(dfa):\n replacement = \"{}\"\n\n if \"\" in dfa.states:\n dfa.states.add(replacement)\n dfa.states.remove(\"\")\n\n for state, transitions in dfa.transitions.items():\n for symbol in transitions:\n if transitions[symbol] == \"\":\n transitions[symbol] = replacement\n\n if \"\" in dfa.transitions.keys():\n dfa.transitions[replacement] = dfa.transitions.pop(\"\")\n\ndef is_subset(spec, sys):\n # find the complement of the first automaton\n comp_automaton = to_complement(spec)\n\n # find the intersection of the complement and system automaton\n inter_automaton = to_intersection(comp_automaton, sys)\n\n # convert transitions to graph\n inter_graph = convert_graph(inter_automaton)\n\n # find shortest path to accepting state\n for final_state in inter_automaton.final_states:\n path = find_path(inter_automaton.transitions, inter_automaton.initial_state, final_state)\n\n if(path != None):\n break;\n\n if path != None:\n result = path_to_string(path, inter_automaton)\n else:\n result = None\n\n return result\n\ndef main(args):\n # read the arguments\n if(len(args) > 2):\n specAutomata = args[1]\n systemAutomata = args[2]\n\n spc_nfa = parseFA(specAutomata, 'q')\n sys_nfa = parseFA(systemAutomata, 's')\n\n # convert specification automaton to DFA\n spc_dfa = NFAtoDFA(spc_nfa)\n sys_dfa = NFAtoDFA(sys_nfa)\n\n # rename deadlock state\n rename_deadlocks(spc_dfa)\n rename_deadlocks(sys_dfa)\n\n sys_result = is_subset(spc_dfa, sys_dfa)\n spc_result = is_subset(sys_dfa, spc_dfa)\n\n if sys_result == None and spc_result == None:\n print(\"PASS\" + \";\" + \"None\" + \";\" \"None\", end='')\n elif sys_result == None and spc_result != None:\n print(\"FAIL\" + \";\" + spc_result + \";\" + \"None\", end='')\n elif sys_result != None and spc_result == None:\n print(\"FAIL\" + \";\" + \"None\" + \";\" + sys_result, end='')\n elif sys_result != None and spc_result != None:\n print(\"FAIL\" + \";\" + spc_result + \";\" + sys_result, end='');\n else:\n print(\"FAIL\" + \";\" + \"None\" + \";\" + \"None\", end='')\n\nmain(sys.argv)\n", "sub_path": "scripts/compareFA.py", "file_name": "compareFA.py", "file_ext": "py", "file_size_in_byte": 11664, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "itertools.chain.from_iterable", "line_number": 11, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 11, "usage_type": "name"}, {"api_name": "itertools.combinations", "line_number": 11, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 195, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 424, "usage_type": "attribute"}]} +{"seq_id": "483582637", "text": "from webargs import fields\n\nfrom project.api.models import db, WebsiteDuplicated\nfrom project.api.schemas import OneOf, length_validator\nfrom project.api.exceptions.customs import RecordNotFound\n\n\ndef id_in_db(id):\n if not db.session.query(WebsiteDuplicated).filter_by(id=id).first():\n raise RecordNotFound('无此数据')\n\n\ndelete_args = {\n 'ids': fields.List(fields.Int(validate=id_in_db), required=True)\n}\n\nrestore_args = {\n 'ids': fields.List(fields.Int(validate=id_in_db), required=True)\n}\n\nquery_args = {\n 'page': fields.Int(missing=0),\n 'size': fields.Int(missing=25, validate=length_validator),\n 'order': fields.Nested({\n 'field': fields.Str(missing='create_time'),\n 'direction': fields.Str(missing='desc', validate=OneOf(['asc', 'desc']))\n }, missing={}),\n 'filter': fields.Nested({\n 'url': fields.Str(),\n 'title': fields.Str(),\n 'effective_url': fields.Str()\n }, missing={})\n}\n", "sub_path": "project/api/schemas/website_duplicated.py", "file_name": "website_duplicated.py", "file_ext": "py", "file_size_in_byte": 956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "project.api.models.db.session.query", "line_number": 9, "usage_type": "call"}, {"api_name": "project.api.models.WebsiteDuplicated", "line_number": 9, "usage_type": "argument"}, {"api_name": "project.api.models.db.session", "line_number": 9, "usage_type": "attribute"}, {"api_name": "project.api.models.db", "line_number": 9, "usage_type": "name"}, {"api_name": "project.api.exceptions.customs.RecordNotFound", "line_number": 10, "usage_type": "call"}, {"api_name": "webargs.fields.List", "line_number": 14, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 14, "usage_type": "name"}, {"api_name": "webargs.fields.Int", "line_number": 14, "usage_type": "call"}, {"api_name": "webargs.fields.List", "line_number": 18, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "webargs.fields.Int", "line_number": 18, "usage_type": "call"}, {"api_name": "webargs.fields.Int", "line_number": 22, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 22, "usage_type": "name"}, {"api_name": "webargs.fields.Int", "line_number": 23, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 23, "usage_type": "name"}, {"api_name": "project.api.schemas.length_validator", "line_number": 23, "usage_type": "name"}, {"api_name": "webargs.fields.Nested", "line_number": 24, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 24, "usage_type": "name"}, {"api_name": "webargs.fields.Str", "line_number": 25, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 25, "usage_type": "name"}, {"api_name": "webargs.fields.Str", "line_number": 26, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 26, "usage_type": "name"}, {"api_name": "project.api.schemas.OneOf", "line_number": 26, "usage_type": "call"}, {"api_name": "webargs.fields.Nested", "line_number": 28, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "webargs.fields.Str", "line_number": 29, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 29, "usage_type": "name"}, {"api_name": "webargs.fields.Str", "line_number": 30, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "webargs.fields.Str", "line_number": 31, "usage_type": "call"}, {"api_name": "webargs.fields", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "72882093", "text": "import json\n\n\ndef parse_win_chassis_specs(the_dir: str):\n with open(f\"{the_dir}/chassis.win\", \"r\") as file:\n data = json.load(file)\n object = [\n {\n \"features\": {\n \"brand\": data[\"Manufacturer\"],\n \"sn\": data[\"SerialNumber\"],\n },\n \"type\": \"case\",\n }\n ]\n return object\n\n\ndef parse_win_cpu_specs(the_dir: str):\n architectures = {\n 0: \"x86-32\",\n 1: \"mips\",\n 2: \"alpha\",\n 3: \"powerpc\",\n 6: \"ia64\",\n 9: \"x86-64\",\n }\n object = []\n with open(f\"{the_dir}/lscpu.win\", \"r\") as file:\n data = json.load(file)\n object.append(\n {\n \"brand\": data[\"Manufacturer\"],\n \"model\": data[\"Name\"],\n \"features\": {\n \"type\": \"cpu\",\n \"isa\": architectures[data[\"Architecture\"]],\n \"core-n\": data[\"NumberOfCores\"],\n \"thread-n\": data[\"ThreadCount\"],\n \"frequency-hertz\": int(data[\"MaxClockSpeed\"]) * 1000000,\n },\n \"type\": \"cpu\",\n }\n )\n with open(f\"{the_dir}/graphics.win\", \"r\") as file:\n data = json.load(file)\n for entry in data:\n if \"Service\" in entry and entry[\"Service\"] == \"igfx\":\n object[0][\"features\"][\"integrated-graphics-brand\"] = entry[\"Manufacturer\"]\n object[0][\"features\"][\"integrated-graphics-model\"] = entry[\"Name\"]\n break\n return object\n\n\ndef parse_win_ram_specs(the_dir: str):\n with open(f\"{the_dir}/dimms.win\", \"r\") as file:\n data = json.load(file)\n object = []\n for entry in data:\n object.append(\n {\n \"brand\": entry[\"Manufacturer\"],\n \"model\": entry[\"PartNumber\"],\n \"features\": {\n \"frequency-hertz\": entry[\"Speed\"] * 1000000,\n \"capacity-byte\": entry[\"Capacity\"],\n \"ram-type\": \"\",\n \"ram-ecc\": \"\",\n \"ram-timings\": \"\",\n \"sn\": entry[\"SerialNumber\"],\n },\n \"type\": \"ram\",\n }\n )\n return object\n\n\ndef parse_win_motherboard_specs(the_dir: str):\n with open(f\"{the_dir}/baseboard.win\", \"r\") as file:\n data = json.load(file)\n object = [\n {\n \"brand\": data[\"Manufacturer\"],\n \"model\": data[\"Product\"],\n \"features\": {\n \"parallel-ports-n\": 0,\n \"usb-ports-n\": 0,\n \"mini-jack-ports-n\": 0,\n \"vga-ports-n\": 0,\n \"serial-ports-n\": 0,\n \"sata-ports-n\": 0,\n \"ide-ports-n\": 0,\n \"ps2-ports-n\": 0,\n \"ethernet-ports-1000m-n\": 0,\n },\n \"type\": \"motherboard\",\n }\n ]\n with open(f\"{the_dir}/lspci.win\", \"r\") as file:\n data = json.load(file)\n for entry in data:\n pnp_class = entry[\"PNPClass\"]\n if pnp_class == \"USB\":\n object[0][\"features\"][\"usb-ports-n\"] += 1\n continue\n elif pnp_class == \"USB\":\n object[0][\"features\"][\"usb-ports-n\"] += 1\n continue\n elif pnp_class == \"AudioEndpoint\":\n object[0][\"features\"][\"mini-jack-ports-n\"] += 1\n continue\n elif pnp_class == \"DiskDrive\":\n object[0][\"features\"][\"sata-ports-n\"] += 1\n continue\n return object\n", "sub_path": "parsers/windows_parser.py", "file_name": "windows_parser.py", "file_ext": "py", "file_size_in_byte": 3766, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "json.load", "line_number": 6, "usage_type": "call"}, {"api_name": "json.load", "line_number": 30, "usage_type": "call"}, {"api_name": "json.load", "line_number": 46, "usage_type": "call"}, {"api_name": "json.load", "line_number": 57, "usage_type": "call"}, {"api_name": "json.load", "line_number": 80, "usage_type": "call"}, {"api_name": "json.load", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "79924369", "text": "# Documentation is like sex.\n# When it's good, it's very good.\n# When it's bad, it's better than nothing.\n# When it lies to you, it may be a while before you realize something's wrong.\n# https://github.com/quentinsf/qhue\n\nfrom typing import Callable, Optional\n\nfrom qhue import Bridge, QhueException, create_new_username\n\n# the IP address of your bridge\nBRIDGE_IP = \"192.168.1.51\"\n\n# the path for the username credentials file\nCRED_FILE_PATH = r\"D:\\Lesko\\workspace\\householdHub\\scrambledeggs\\qhue_username.txt\"\n\n\nclass Hue:\n\n def __init__(self, bridge_ip: str, credentials: Optional[str] = None):\n self.bridge_ip = bridge_ip\n self.credentials = credentials\n\n self.bridge = None\n self.__lights = {}\n\n def register_hue(self, save_credentials_cb: Optional[Callable] = None):\n if self.credentials is None:\n while True:\n try:\n self.credentials = create_new_username(BRIDGE_IP)\n if save_credentials_cb is not None:\n save_credentials_cb(self.credentials)\n break\n except QhueException as err:\n print(\"Error occurred while creating a new username: {}\".format(err))\n\n def __create_bridge(self):\n if self.bridge is None:\n self.bridge = Bridge(self.bridge_ip, self.credentials)\n\n def __find_all_lights(self):\n self.__create_bridge()\n lights = self.bridge.lights\n for key, val in lights().items():\n self.__lights[int(key)] = lights[key]\n\n @property\n def lights(self) -> dict:\n self.__find_all_lights()\n return self.__lights\n\n def list_devices(self):\n for hue_num, light in self.lights.items():\n print(f\"{hue_num} - {light}\")\n for key, val in light().items():\n print(f\"\\t{key} - {val}\")\n\n\nif __name__ == \"__main__\":\n with open(CRED_FILE_PATH, \"r\") as cred_file:\n username = cred_file.read()\n\n\n def p(tes):\n print(tes)\n\n\n h = Hue(BRIDGE_IP, credentials=username)\n\n h.list_devices()\n", "sub_path": "app/common/hue/hue.py", "file_name": "hue.py", "file_ext": "py", "file_size_in_byte": 2101, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "typing.Optional", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 27, "usage_type": "name"}, {"api_name": "qhue.create_new_username", "line_number": 31, "usage_type": "call"}, {"api_name": "qhue.QhueException", "line_number": 35, "usage_type": "name"}, {"api_name": "qhue.Bridge", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "235289069", "text": "'''\n\tThis is the NHL crawler. \n\nScattered throughout are TODO tips on what to look for.\n\nAssume this job isn't expanding in scope, but pretend it will be pushed into production to run \nautomomously. So feel free to add anywhere (not hinted, this is where we see your though process..)\n * error handling where you see things going wrong. \n * messaging for monitoring or troubleshooting\n * anything else you think is necessary to have for restful nights\n'''\nimport logging\nfrom pathlib import Path\nfrom datetime import datetime\nfrom dataclasses import dataclass\nimport boto3\nimport requests\nimport pandas as pd\nfrom botocore.config import Config\nfrom dateutil.parser import parse as dateparse\nimport sys\n\n\nlogging.basicConfig(level=logging.INFO)\nLOG = logging.getLogger(__name__)\n\nclass NHLApi:\n SCHEMA_HOST = \"https://statsapi.web.nhl.com/\"\n VERSION_PREFIX = \"api/v1\"\n\n def __init__(self, base=None):\n self.base = base if base else f'{self.SCHEMA_HOST}/{self.VERSION_PREFIX}'\n\n\n def schedule(self, start_date: datetime, end_date: datetime) -> dict:\n ''' \n returns a dict tree structure that is like\n \"dates\": [ \n {\n \" #.. meta info, one for each requested date \",\n \"games\": [\n { #.. game info },\n ...\n ]\n },\n ...\n ]\n '''\n return self._get(self._url('schedule'), {'startDate': start_date.strftime('%Y-%m-%d'), 'endDate': end_date.strftime('%Y-%m-%d')})\n\n def boxscore(self, game_id):\n '''\n returns a dict tree structure that is like\n \"teams\": {\n \"home\": {\n \" #.. other meta \",\n \"players\": {\n $player_id: {\n \"person\": {\n \"id\": $int,\n \"fullName\": $string,\n #-- other info\n \"currentTeam\": {\n \"name\": $string,\n #-- other info\n },\n \"stats\": {\n \"skaterStats\": {\n \"assists\": $int,\n \"goals\": $int,\n #-- other status\n }\n #-- ignore \"goalieStats\"\n }\n }\n },\n #...\n }\n },\n \"away\": {\n #... same as \"home\" \n }\n }\n\n See tests/resources/boxscore.json for a real example response\n '''\n url = self._url(f'game/{game_id}/boxscore')\n return self._get(url)\n\n def _get(self, url, params=None):\n try:\n response = requests.get(url, params=params)\n response.raise_for_status()\n except requests.exceptions.HTTPError as ehtp:\n print (\"Http Error: \",ehtp)\n raise SystemExit(ehtp)\n except requests.exceptions.ConnectionError as econ:\n print (\"Connection Error: \",econ)\n raise SystemExit(econ)\n except requests.exceptions.Timeout as etim:\n print (\"Timeout Error: \",etim)\n raise SystemExit(etim)\n except requests.exceptions.RequestException as ex:\n print (\"Error in API Request: \",ex)\n raise SystemExit(ex)\n return response.json()\n\n def _url(self, path):\n return f'{self.base}/{path}'\n\n@dataclass\nclass StorageKey():\n def __init__(self, gameid, gamedate):\n self._gameid = gameid\n self._gamedate = gamedate.strftime('%Y%m%d')\n\n def key(self):\n ''' renders the s3 key for the given set of properties '''\n return f'{self._gamedate}_{self._gameid}.csv'\n\nclass Storage():\n def __init__(self, dest_bucket, s3_client):\n self._s3_client = s3_client\n self.bucket = dest_bucket\n\n def store_game(self, key: StorageKey, game_data) -> bool:\n self._s3_client.put_object(Bucket=self.bucket, Key=key.key(), Body=game_data)\n return True\n\nclass Crawler():\n def __init__(self, api: NHLApi, storage: Storage):\n self.api = api\n self.storage = storage\n\n def crawl(self, startdate: datetime, enddate: datetime) -> None:\n '''\n Crawl for player scoring stats.\n Writes CSV files to S3 Bucket for NHL Games in date range specified (inclusive).\n Files partitioned by Date and Game ID.\n '''\n schedule = self.api.schedule(startdate, enddate)\n \n ##iterate over game dates to properly partition\n if schedule is not None:\n for day in schedule.get(\"dates\"):\n gamedate = datetime.strptime(day.get(\"date\"),'%Y-%m-%d')\n\n logging.info(f'Processing games for {gamedate}')\n games_df = pd.DataFrame()\n games_df = games_df.append(pd.json_normalize(day.get(\"games\")), ignore_index = True)\n \n column_names = [\"player_person_id\", \"player_person_currentTeam_name\", \"player_person_fullName\", \"player_stats_skaterStats_assists\", \"player_stats_skaterStats_goals\", \"side\"]\n\n for index, row in games_df.iterrows():\n gameid = row[\"gamePk\"]\n\n stats = self.api.boxscore(gameid)\n stats_df = pd.DataFrame()\n\n for side in ('home','away'):\n teamname = stats.get(\"teams\").get(side).get(\"team\")[\"name\"]\n players = stats.get(\"teams\").get(side).get(\"players\").keys()\n\n for p in players:\n if stats.get(\"teams\").get(side).get(\"players\").get(f'{p}').get(\"stats\").get(\"skaterStats\") is not None:\n playername = stats.get(\"teams\").get(side).get(\"players\").get(f'{p}').get(\"person\").get(\"fullName\")\n goals,assists = [stats.get(\"teams\").get(side).get(\"players\").get(f'{p}').get(\"stats\").get(\"skaterStats\").get(k) for k in [\"goals\",\"assists\"]]\n \n playerstats = pd.Series([p.replace('ID',''),teamname,playername,assists,goals,side], index=column_names)\n stats_df = stats_df.append(playerstats, ignore_index=True)\n \n s3Key = StorageKey(gameid,gamedate)\n \n logging.info(f'Writing file: {s3Key.key()}')\n\n self.storage.store_game(s3Key, stats_df[column_names].to_csv(index=False))\n else:\n logging.info(f'No games found for date range {startdate} - {enddate}')\n\n \ndef main():\n import os\n import argparse\n\n parser = argparse.ArgumentParser(description='NHL Stats crawler')\n parser.add_argument('--start_date',\n required=True,\n type=str,\n help='Set start date to begin to retrieve data (inclusive). Format: yyyymmdd')\n parser.add_argument('--end_date',\n required=True,\n type=str,\n help='Set end date to stop retrieving data (inclusive). Format: yyyymmdd')\n args = vars(parser.parse_args())\n\n dest_bucket = os.environ.get('DEST_BUCKET', 'output')\n start_date = dateparse(args['start_date'])\n end_date = dateparse(args['end_date'])\n\n api = NHLApi()\n s3client = boto3.client('s3', config=Config(signature_version='s3v4'), endpoint_url=os.environ.get('S3_ENDPOINT_URL'))\n storage = Storage(dest_bucket, s3client)\n crawler = Crawler(api, storage)\n crawler.crawl(start_date, end_date)\n\nif __name__ == '__main__':\n try:\n main()\n except Exception as ex:\n print(f'exception: {ex}')\n sys.exit(1)\n\n", "sub_path": "nhldata/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 8157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.basicConfig", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 24, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 92, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 94, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 97, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 100, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 103, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 111, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 135, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 146, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 146, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 148, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 149, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 150, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 158, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 169, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 174, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 178, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 185, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 196, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 196, "usage_type": "attribute"}, {"api_name": "dateutil.parser.parse", "line_number": 197, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 198, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 201, "usage_type": "call"}, {"api_name": "botocore.config.Config", "line_number": 201, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 201, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 201, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 211, "usage_type": "call"}]} +{"seq_id": "154093239", "text": "import pandas as pd\n\nimport pdb\n\n#Functions from NSA Are called from shell because they run in Python3\nimport subprocess\nimport os\nimport pickle\nimport csv\n\n\nimport umap\nimport json\n\nfrom topic_extractor import topic_extractor\nfrom classes.document_class import Document\nfrom classes.encoder_class import Encoder\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n#TO load UMAP SESS MOdel\nimport joblib\n\n#Session\nclass Session:\n \"\"\"Session Class\"\"\"\n def __init__(self, session_id):\n self.id = session_id\n self.sess_folder = './sessData/' + session_id\n self.documents = False\n self.topics = False\n self.topic_params = False\n self.authorList = False\n self.words = False\n self.UMAP = False\n self.topic_UMAP = False\n self.text_min_lenght = 0\n self.topic_min_length = 0\n self.abstract_conclusion_min_length = 0\n self.vectors2D = False\n self.vectors2D_topics = False\n self.encoder = False\n self.text_max_length = 0;\n #Initialize Values, now I always initialize session from scratch but store things in global so I load global\n if session_id == 'globalSess':\n try:\n # self.documents = pd.read_csv(self.sess_folder + '/documents.csv',encoding='utf-8',index_col='index')\n self.documents = pd.read_json(self.sess_folder + '/documents.json')\n # self.documents = self.read_csv()\n except ValueError:\n self.documents = pd.DataFrame()\n else:\n self.documents = pd.DataFrame()\n ##LOAD THE REST OF THE DATA MAYBE I DONT NEED THIS NEED TO THINK ABOUT IT I STORE EVERYTHING IN DOCS EXCEPT THE SESS TOPICS THAT I CALCULATE EVERY TIME\n try:\n self.topics = pd.read_csv(self.sess_folder + '/topics.csv',encoding='utf-8')\n except IOError:\n self.topics = pd.DataFrame()\n try:\n self.authorList = pd.read_csv(self.sess_folder + '/authors.csv',encoding='utf-8',index_col='index')\n except IOError:\n self.authorList = pd.DataFrame()\n try:\n self.words = pd.read_csv(self.sess_folder + '/words.csv',encoding='utf-8')\n except IOError:\n self.words = pd.DataFrame()\n\n def addDoc(self, doc):\n \"\"\" Function to add a new document to the session \"\"\"\n doc_to_add = doc.create_document_msg()\n document = pd.DataFrame([doc_to_add],columns=doc_to_add.keys(),index=[doc_to_add['globalID']])\n self.documents = self.documents.append(document)\n\n def storeSessData(self):\n '''Store Session'''\n try:\n self.documents.to_json(self.sess_folder + '/documents.json')\n except ValueError:\n print('')\n pdb.set_trace()\n def returnDoc(self,doc):\n \"\"\"Returns a specific document from a session\"\"\"\n return self.documents.loc[doc]\n \n def returnDocsBy(self, type):\n \"\"\"Returns the documents of session ordered by type passed (authors and years) \"\"\"\n docs_by_array = []\n if type == 'author':\n if isinstance(self.authorList.index,list):\n for each_author in self.authorList.index:\n element = {\n 'author':each_author,\n 'Paper_Ids': self.authorList.loc[each_author]['Paper_Ids'] \n }\n docs_by_array.append(element)\n else: #Only one paper\n element = {\n 'author':self.authorList.index,\n 'Paper_Ids': self.authorList.loc[self.authorList.index]['Paper_Ids'] \n }\n docs_by_array.append(element)\n return docs_by_array\n\n \n def docInSess(self,doc):\n \"\"\"Aux function to show if a document is already in a session\"\"\"\n try:\n is_doc_in_sess = self.documents['globalID'].isin([doc]).any()\n except KeyError:\n is_doc_in_sess = False\n return is_doc_in_sess\n\n def addDocTopics(self, doc):\n for topic in doc.topics:\n self.topics.append(topic)\n \n def addAuthor(self, author,doc_id):\n \"\"\"Function to add author to session authorList\"\"\"\n try:\n papers_in_collection = author['Papers_in_collection']\n self.authorList.loc[author.Author, 'Papers_in_collection'] = papers_in_collection + 1\n paper_id_array = []\n paper_id_array.append(self.authorList.loc[author.Author, 'Paper_Ids'])\n paper_id_array.append(doc_id)\n self.authorList.loc[author.Author, 'Paper_Ids'] = paper_id_array.append(doc_id)\n\n except KeyError:\n author['Papers_in_collection'] = 1\n author['Paper_Ids'] = [doc_id]\n self.authorList = self.authorList.append(author)\n \n def searchAuthor(self, author):\n \"\"\" Aux Function to search for an author in the session returns True if in Session and False if not\"\"\"\n # pdb.set_trace()\n try:\n self.authorList.loc[author]\n is_author = True\n except KeyError:\n is_author = False\n return is_author\n\n def returnAuthor(self, author):\n author_name = author['firstName'] + ' ' + author['lastName']\n return self.authorList.loc[author_name]\n \n \n def get_topics_by(self,data,organized_by):\n \"\"\"Calculates the topics of the session organized by authors or years\"\"\"\n if organized_by == 'author':\n df = pd.DataFrame()\n #Get papers for each author\n # pdb.set_trace()\n for each_author in data:\n for each_paper in each_author['Paper_Ids']:\n # pdb.set_trace()\n df = df.append(self.returnDoc(each_paper))\n return self.get_topics(df)\n\n\n def get_topics(self,doc_dictionary): #Good One\n \"\"\"Returns Topics object and Words Object from documents df passed\"\"\"\n topics_data = topic_extractor(doc_dictionary,'session')\n self.topics = topics_data['topics']\n self.topic_params = topics_data['topic_params']\n self.words = topics_data['lvls_df']\n return {'topics':self.topics,'words':self.words}\n \n \"\"\"Function to calculate 2D projections of Paper Vectors in session\"\"\"\n def train_fit_UMAP_2D(self,doc_dictionary):\n if self.UMAP == False:\n self.UMAP = umap.UMAP(n_neighbors=3, n_components=2, metric='euclidean')\n self.topic_UMAP = umap.UMAP(n_neighbors=3, n_components=2, metric='euclidean')\n #Filter False Values\n clean_bert_vectors = doc_dictionary['abstract_vector'][doc_dictionary['abstract_vector']!= False]\n # clean_bert_abstracts= doc_dictionary['abstract_vector'][doc_dictionary['abstract_vector']!= False]\n # clean_bert_conclusions = doc_dictionary['conclusion_vector'][doc_dictionary['conclusion_vector']!= False]\n clean_topic_vectors = doc_dictionary['topics_vector'][doc_dictionary['topics_vector']!=False]\n #Since text are different sizes we need to set them to the same size. We extend smaller vectors to the size of the longest by duplicating its content.\n if self.text_max_length == 0:\n text_lenght = clean_bert_vectors.apply(lambda x: len(x))\n self.text_max_length = text_lenght.max()\n for index, doc_vector in clean_bert_vectors.iteritems():\n if len(doc_vector) > self.text_max_length:\n print (\"document\" + index + \"has a longer size than any in previous\")\n doc_vector = doc_vector[:self.text_max_length]\n if len(doc_vector) != self.text_max_length:\n while True:\n \n size_to_extend = self.text_max_length - len(doc_vector)\n doc_vector.extend(doc_vector[:size_to_extend])\n if len(doc_vector) == self.text_max_length:\n break\n #topics have different sizes too\n if self.topic_min_length == 0:\n topic_length = clean_topic_vectors.apply(lambda x: len(x))\n self.topic_min_length = topic_length.min()\n clean_topic_vectors = clean_topic_vectors.apply(lambda x: x[:self.topic_min_length])\n #since abstract and conclusions are different sizes I need to set them all to same size I find smallest and cut the rest\n # if self.abstract_conclusion_min_length == 0:\n # abstract_lenght = clean_bert_abstracts.apply(lambda x: len(x))\n # conclusion_length = clean_bert_conclusions.apply(lambda x: len(x))\n # abstract_min_length = abstract_lenght.min()\n # conclusion_min_length = conclusion_length.min()\n # self.abstract_conclusion_text_min_length = min([abstract_min_length,conclusion_min_length])\n # clean_bert_abstracts = clean_bert_abstracts.apply(lambda x: x[:self.abstract_conclusion_text_min_length])\n # clean_bert_conclusions = clean_bert_conclusions.apply(lambda x: x[:self.abstract_conclusion_text_min_length])\n #Calculate Vectors\n # pdb.set_trace()\n if self.encoder == False :\n print('Encoder has not been trained yet so Using UMAP for fitting')\n vectors_list = clean_bert_vectors.values.tolist()\n topics_list = clean_topic_vectors.values.tolist()\n self.UMAP = self.UMAP.fit(vectors_list)\n self.topic_UMAP = self.topic_UMAP.fit(topics_list)\n vec_2d = self.UMAP.transform(vectors_list)\n vec_topic_2d = self.topic_UMAP.transform(topics_list)\n else:\n print('Using Enconder to Fit')\n papers_to_fit_indexes = doc_dictionary[doc_dictionary.isna().any(axis=1)].index.values.tolist()\n papers_to_fit = clean_bert_vectors.loc[papers_to_fit_indexes]\n # pdb.set_trace()\n try:\n new_projections = self.encoder.transform(np.array(papers_to_fit.values.tolist()))\n # pdb.set_trace()\n except ValueError:\n print('error')\n pdb.set_trace()\n vec_2d = np.append(self.vectors2D,new_projections,axis=0)\n papers_to_fit_topics = clean_topic_vectors.loc[papers_to_fit_indexes]\n new_projections = self.topic_UMAP.transform(papers_to_fit_topics.values.tolist())\n vec_topic_2d = np.append(self.vectors2D_topics,new_projections,axis=0)\n # vec_2d = self.UMAP.transform(clean_bert_vectors.values.tolist())\n # vec_topic_2d = self.topic_UMAP.transform(clean_topic_vectors.values.tolist())\n #Store values for subsequent runs\n self.vectors2D = vec_2d\n self.vectors2D_topics = vec_topic_2d\n # SECTIONS PART COMMENTED RIGHT NOW I HAVE TO SEE IF I WILL USE THIS OR NOT\n # #put abstract and conclusion together to project \n # abstract_and_conclusion_to_project = clean_bert_abstracts.append(clean_bert_conclusions)\n # #convert to DataFrame to keep indexes when I merge below\n # pdb.set_trace()\n # abstract_and_conclusion_to_project_df = pd.DataFrame(abstract_and_conclusion_to_project, columns=['original_vector'])\n # abstract_and_conclusion_2D = fit.fit_transform(abstract_and_conclusion_to_project.values.tolist()) \n # #add to Dataframe \n # abstract_and_conclusion_to_project_df['2D'] = abstract_and_conclusion_2D.tolist()\n # #separate them to put back into pandas\n # abstract_vec_2d = abstract_and_conclusion_to_project_df['2D'].iloc[ :len(clean_bert_abstracts)]\n # conclusion_vec_2d = abstract_and_conclusion_to_project_df['2D'].iloc[len(clean_bert_abstracts): ]\n #add to pandas columns\n doc_dictionary['vec_2d'] = vec_2d.tolist()\n doc_dictionary['vec_topic_2d']= vec_topic_2d.tolist()\n # doc_dictionary['abstract_2d'] = abstract_vec_2d\n # doc_dictionary['conclusion_2d'] = conclusion_vec_2d\n #change NA for false\n doc_dictionary['vec_2d'].fillna(False)\n doc_dictionary['vec_topic_2d'].fillna(False)\n # doc_dictionary['abstract_2d'].fillna(False)\n # doc_dictionary['conclusion_2d'].fillna(False)\n return doc_dictionary\n\n # conclusion_vec_2d.tolist()\n \n def get_years(self):\n return self.documents.groupby('year')['year'].count()\n\n def assign_topics_to_documents(self):\n \"\"\"Function to assign session topics to doc topics\"\"\"\n def calculate_cosine_similarity(vect1,vect2,size):\n vect1 = np.array(vect1).reshape(1,size)\n vect2 = np.array(vect2).reshape(1,size)\n return cosine_similarity(vect1,vect2)\n \n sess_topic_params = self.topic_params\n # pdb.set_trace()\n for each_document in self.documents.iterrows():\n doc_topic_params_df = pd.DataFrame(each_document[1]['topic_params'])\n similarity_vector = []\n for each_topic in doc_topic_params_df.iterrows():\n #We compare each topic in paper with session_topics if its above a threshold we assign the topic to paper\n # pdb.set_trace()\n similarity = sess_topic_params['vector300'].apply(calculate_cosine_similarity,vect2=each_topic[1]['vector300'],size=300)\n #apply threshold\n similarity = similarity.apply(lambda x: x[0][0] > 0.7) #returns true false vectors\n similarity_vector.append(similarity)\n # pdb.set_trace()\n #sum(similarity_vector) collapses Trues and Falses but several trues are summed. I changed those to be 1.\n collapsed_similarity = sum(similarity_vector)\n collapsed_similarity = collapsed_similarity.where(collapsed_similarity==0,1) #Returns vector of ceros and ones\n sess_topic_params[each_document[1]['globalID']] = collapsed_similarity * sess_topic_params['weight']\n # pdb.set_trace()\n\n def update_model(self,new_data):\n self.already_encoded_papers = new_data\n # pdb.set_trace()\n #loop list of papers\n for index, row in new_data.iterrows():\n #Get from paper in session\n this_paper = self.documents.loc[row['key']]\n #Set new x,y coordinates from paper in session\n this_paper.vec_2d[0] = new_data['x'][index]\n this_paper.vec_2d[1] = new_data['y'][index]\n #Get 300Vectors and 2D Vectors\n clean_bert_vectors = self.documents['abstract_vector']\n #Since text are different sizes we need to set them to the same size. We extend smaller vectors to the size of the longest by duplicating its content.\n # text_lenght = clean_bert_vectors.apply(lambda x: len(x))\n # self.text_max_length = text_lenght.max()\n # for index, doc_vector in clean_bert_vectors.iteritems():\n # if len(doc_vector) > self.text_max_length:\n # print (\"document\" + index + \"has a longer size than any in previous\")\n # doc_vector = doc_vector[:self.text_max_length]\n # # pdb.set_trace()\n # if len(doc_vector) != self.text_max_length:\n # while True:\n # # pdb.set_trace()\n # size_to_extend = self.text_max_length - len(doc_vector)\n # doc_vector.extend(doc_vector[:size_to_extend])\n # if len(doc_vector) == self.text_max_length:\n # break\n vec_2d = np.array(self.documents['vec_2d'].values.tolist())\n self.train_encoder(clean_bert_vectors,vec_2d)\n # pdb.set_trace()\n \n def train_encoder(self,vectors,vec_2d):\n vectors_list = np.array(vectors.values.tolist())\n #Train Encoder\n vector_size = self.text_max_length\n self.encoder = Encoder(vector_size,2)\n self.encoder.fit(vectors_list,vec_2d)\n new_vec_2d = self.encoder.transform(vectors_list)\n # pdb.set_trace()\n #add to pandas columns\n # self.documents.loc[self.already_encoded_papers['key']]['vec_2d'] = new_vec_2d.tolist()\n self.documents['vec_2d'] = new_vec_2d.tolist()\n self.vectors2D = new_vec_2d\n\n ", "sub_path": "classes/session_class.py", "file_name": "session_class.py", "file_ext": "py", "file_size_in_byte": 16190, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pandas.read_json", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 66, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 71, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 80, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 150, "usage_type": "call"}, {"api_name": "topic_extractor.topic_extractor", "line_number": 162, "usage_type": "call"}, {"api_name": "umap.UMAP", "line_number": 171, "usage_type": "call"}, {"api_name": "umap.UMAP", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 223, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 270, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 271, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 324, "usage_type": "call"}, {"api_name": "classes.encoder_class.Encoder", "line_number": 327, "usage_type": "call"}]} +{"seq_id": "563489342", "text": "import sys\n#import threading\n#import random\nimport time\n#import logging\nimport os\n\n#sys.path.append('.')\nimport rem_server\n\n#import rem.connmanager\n#import rem.scheduler\n#import rem.job\n#import rem.osspec\n#from rem.packet import PacketState\n\n#sys.path.append(\n #os.path.abspath(os.path.dirname(rem_server.__file__)) + '/client')\n\n#rem.osspec.reg_signal_handler = lambda *args: None\n\nimport tempfile\nimport shutil\n\nclass NamedTemporaryDir(object):\n def __init__(self, *args, **kwargs):\n self._args = args\n self._kwargs = kwargs\n\n def __enter__(self):\n self.name = tempfile.mkdtemp(*self._args, **self._kwargs)\n print >>sys.stderr, self.name\n return self.name\n\n def __exit__(self, e, t, bt):\n shutil.rmtree(self.name)\n self.name = None\n\ndef produce_config(out, work_dir, hostname):\n print >>out, \"\"\"\n[DEFAULT]\nproject_dir = {project_dir}\n\n[store]\npck_dir = %(project_dir)s/packets\nrecent_tags_file = %(project_dir)s/backups/recent_tags.db\ntags_db_file = %(project_dir)s/backups/tags.db\nremote_tags_db_file = %(project_dir)s/backups/tags-remote.db\nbackup_dir = %(project_dir)s/backups\nbackup_period = 300\nbackup_count = 10\nbackup_child_max_working_time = 900\njournal_lifetime = 3600\nbinary_dir = %(project_dir)s/bin\nbinary_lifetime = 86400\nerror_packet_lifetime = 604800\nsuccess_packet_lifetime = 259200\ncloud_tags_server = localhost:17773\ncloud_tags_masks = file://%(project_dir)s/cloud_tags.masks\n\n[log]\ndir = %(project_dir)s/log\nwarnlevel = debug\nfilename = rem.log\nrollcount = 8\n\n[run]\nsetup_script = %(project_dir)s/setup_env.sh\npoolsize = 100\nxmlrpc_poolsize = 20\nreadonly_xmlrpc_poolsize = 10\n\n[server]\nport = 8104\nreadonly_port = 8103\nsystem_port = 8105\nnetwork_topology = local://%(project_dir)s/network_topology.cfg\nnetwork_hostname = {network_hostname}\nsend_emails = yes\nsend_emergency_emails = no\nuse_memory_profiler = no\n\"\"\".format(\n project_dir=work_dir,\n network_hostname=hostname,\n )\n\ndef create_scheduler(work_dir):\n config_filename = work_dir + \"/rem.cfg\"\n\n with open(config_filename, \"w\") as conf:\n produce_config(conf, work_dir, hostname='foobar')\n\n import rem.context\n ctx = rem.context.Context(config_filename, \"start\")\n\n rem_server._init_fork_locking(ctx)\n\n #import json\n #print json.dumps(ctx.__dict__, indent=3)\n\n with open(work_dir + '/cloud_tags.masks', 'w') as out:\n print >>out, '_cloud_.*'\n\n with open(work_dir + '/network_topology.cfg', 'w') as out:\n print >>out, '[servers]'\n print >>out, 'foobar = http://localhost:8884, http://localhost:8885'\n\n return rem_server.CreateScheduler(ctx)\n\nclass Scheduler(object):\n def __init__(self, *args, **kwargs):\n self._args = args\n self._kwargs = kwargs\n self._sched = None\n\n def __enter__(self):\n self._sched = create_scheduler(*self._args, **self._kwargs)\n self._sched.Start()\n return self._sched\n\n def __exit__(self, e, t, bt):\n sched = self._sched\n self._sched = None\n sched.Stop()\n\nif __name__ == '__main__':\n #print >>sys.stderr, 'helpers.py pid =', os.getpid()\n #sched, join_daemon = start(with_daemon=True)\n #join_daemon()\n\n def remove_if(dir, cond):\n for item in os.listdir(dir):\n if cond(item):\n os.unlink(dir + '/' + item)\n\n def remove_backups(work_dir):\n remove_if(work_dir + '/backups', lambda file : file.startswith('sched-'))\n\n def remove_journal(work_dir):\n remove_if(work_dir + '/backups', lambda file : file.startswith('recent_tags.db'))\n\n print >>sys.stderr, os.getpid()\n\n def test_01(do_intermediate_backup=False,\n do_final_backup=False,\n do_remove_journal=False,\n do_remove_backups=False):\n\n #print do_intermediate_backup, do_final_backup, do_remove_journal, do_remove_backups\n\n def get_updates():\n return sched.tagRef._safe_cloud.get_state_updates()\n\n def backup():\n sched.RollBackup()\n time.sleep(1.5) # hack for same-timestamp-in-journal-filename problem\n\n with NamedTemporaryDir(prefix='remd-') as work_dir:\n with Scheduler(work_dir) as sched:\n tags = sched.tagRef\n\n assert get_updates() == []\n\n tags.AcquireTag('_cloud_tag_01').Reset('message01')\n assert len(get_updates()) == 1\n\n if do_intermediate_backup:\n backup()\n #print os.listdir(work_dir + '/backups')\n\n tags.AcquireTag('_cloud_tag_02').Reset('message02')\n\n all_updates = get_updates()\n assert len(all_updates) == 2\n\n if do_final_backup:\n backup()\n #print os.listdir(work_dir + '/backups')\n\n if do_remove_journal:\n remove_journal(work_dir)\n\n if do_remove_backups:\n remove_backups(work_dir)\n\n #print \"-----------------------------------------------\"\n with Scheduler(work_dir) as sched:\n #print os.listdir(work_dir + '/backups')\n updates = get_updates()\n\n if do_remove_journal and do_remove_backups:\n assert updates == []\n\n elif do_remove_backups:\n assert updates == all_updates\n\n elif do_remove_journal:\n if do_final_backup:\n assert updates == all_updates\n elif do_intermediate_backup:\n assert updates == all_updates[0:1]\n else:\n assert updates == []\n\n for do_intermediate_backup in [True, False]:\n for do_final_backup in [True, False]:\n for do_remove_journal in [True, False]:\n for do_remove_backups in [True, False]:\n test_01(\n do_intermediate_backup=do_intermediate_backup,\n do_final_backup=do_final_backup,\n do_remove_journal=do_remove_journal,\n do_remove_backups=do_remove_backups,\n )\n", "sub_path": "server/rem/test_safe_cloud.py", "file_name": "test_safe_cloud.py", "file_ext": "py", "file_size_in_byte": 6229, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "tempfile.mkdtemp", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 32, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 36, "usage_type": "call"}, {"api_name": "rem.context.context.Context", "line_number": 94, "usage_type": "call"}, {"api_name": "rem.context.context", "line_number": 94, "usage_type": "attribute"}, {"api_name": "rem.context", "line_number": 94, "usage_type": "name"}, {"api_name": "rem_server._init_fork_locking", "line_number": 96, "usage_type": "call"}, {"api_name": "rem_server.CreateScheduler", "line_number": 108, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 132, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 134, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.getpid", "line_number": 142, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "299172779", "text": "import unittest\nfrom unittest import mock\nfrom uuid import uuid4\n\nfrom jose import JWTError\n\nfrom frontstage import app\nfrom frontstage.common.authorisation import jwt_authorization\nfrom frontstage.common.session import Session\nfrom frontstage.exceptions.exceptions import JWTValidationError\n\nvalid_jwt = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJwYXJ0eV9pZCI6ImY5NTZlOGFlLTZ\" \\\n \"lMGYtNDQxNC1iMGNmLWEwN2MxYWEzZTM3YiIsImV4cGlyZXNfYXQiOiIxMDAxMjM0NTY\" \\\n \"3ODkiLCJyb2xlIjoicmVzcG9uZGVudCIsInVucmVhZF9tZXNzYWdlX2NvdW50Ijp7InZh\" \\\n \"bHVlIjowLCJyZWZyZXNoX2luIjozMjUyNzY3NDAwMC4wfSwiZXhwaXJlc19pbiI6MzI1M\" \\\n \"jc2NzQwMDAuMH0.m94R50EPIKTJmE6gf6PvCmCq8ZpYwwV8PHSqsJh5fnI\"\nexpired_jwt = \"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJyZWZyZXNoX3Rva2VuIjoiNGYzMmI0YjQtNGUwYS00NTUyLThiOTYtODIzNjRjO\" \\\n \"Dk2ZjFiIiwiYWNjZXNzX3Rva2VuIjoiMWMxNGJhOGMtOTlhMS00NjBjLTllYmUtMTFlY2U4NGY1ZTAzIiwic2NvcGUiOlsiIl0sImV\" \\\n \"4cGlyZXNfYXQiOjk0NjY4ODQ2MS4wLCJ1c2VybmFtZSI6InRlc3R1c2VyQGVtYWlsLmNvbSIsInJvbGUiOiJyZXNwb25kZW50Iiwic\" \\\n \"GFydHlfaWQiOiJkYjAzNmZkNy1jZTE3LTQwYzItYThmYy05MzJlN2MyMjgzOTcifQ.ro95XUJ2gqgz7ecF2r3guSi-kh4wI_XYTgUF\" \\\n \"8IZFHDA\"\nno_expiry_jwt = \"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJyZWZyZXNoX3Rva2VuIjoiMGE0NGQ4YzYtZWEzYy00ZTMzLTg4MDctNjJkYmV\" \\\n \"iOTNlMzZhIiwiYWNjZXNzX3Rva2VuIjoiYWVmZTkyYjAtNTYxYi00ZWM0LTljNTYtMTYwZGZhNGIzNzY0Iiwicm9sZSI6InJlc3B\" \\\n \"vbmRlbnQiLCJwYXJ0eV9pZCI6IjU2NWJjMDc5LWVkMDItNDk0MS04ODgyLWRhZTZmYzE4NWEzZCJ9.unskbEm5dWQfCTvE25cxrO\" \\\n \"hAf1_Ii8ZXiLhBioQq8OE\"\n\n\nclass TestJWTAuthorization(unittest.TestCase):\n\n def setUp(self):\n self.app = app.test_client()\n self.app.testing = True\n self.session = Session.from_party_id(\"test\")\n\n def tearDown(self):\n self.session.delete_session()\n\n @staticmethod\n def decorator_test(request):\n @jwt_authorization(request)\n def test_function(session):\n pass\n test_function()\n\n def test_jwt_authorization_success(self):\n self.session.encoded_jwt_token = valid_jwt\n self.session.session_key = str(uuid4())\n self.session.save()\n request = mock.MagicMock(cookies={\"authorization\": self.session.session_key})\n\n # If this function runs without exceptions the test is considered passed\n self.decorator_test(request)\n\n def test_jwt_authorization_expired_jwt(self):\n self.session.encoded_jwt_token = expired_jwt\n self.session.session_key = str(uuid4())\n self.session.save()\n request = mock.MagicMock(cookies={\"authorization\": self.session.session_key})\n\n with self.assertRaises(JWTValidationError):\n self.decorator_test(request)\n\n def test_jwt_authorization_no_expiry(self):\n self.session.encoded_jwt_token = no_expiry_jwt\n self.session.session_key = str(uuid4())\n self.session.save()\n request = mock.MagicMock(cookies={\"authorization\": self.session.session_key})\n\n with self.assertRaises(JWTValidationError):\n self.decorator_test(request)\n\n @mock.patch('frontstage.common.authorisation.decode')\n def test_jwt_authorization_decode_failure(self, mock_decode):\n self.session.encoded_jwt_token = valid_jwt\n self.session.session_key = str(uuid4())\n self.session.save()\n request = mock.MagicMock(cookies={\"authorization\": self.session.session_key})\n mock_decode.side_effect = JWTError\n\n with self.assertRaises(JWTValidationError):\n self.decorator_test(request)\n", "sub_path": "tests/integration/test_jwt_authorization.py", "file_name": "test_jwt_authorization.py", "file_ext": "py", "file_size_in_byte": 3614, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "unittest.TestCase", "line_number": 28, "usage_type": "attribute"}, {"api_name": "frontstage.app.test_client", "line_number": 31, "usage_type": "call"}, {"api_name": "frontstage.app", "line_number": 31, "usage_type": "name"}, {"api_name": "frontstage.common.session.Session.from_party_id", "line_number": 33, "usage_type": "call"}, {"api_name": "frontstage.common.session.Session", "line_number": 33, "usage_type": "name"}, {"api_name": "frontstage.common.authorisation.jwt_authorization", "line_number": 40, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 47, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 49, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 49, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 56, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 58, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 58, "usage_type": "name"}, {"api_name": "frontstage.exceptions.exceptions.JWTValidationError", "line_number": 60, "usage_type": "argument"}, {"api_name": "uuid.uuid4", "line_number": 65, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 67, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 67, "usage_type": "name"}, {"api_name": "frontstage.exceptions.exceptions.JWTValidationError", "line_number": 69, "usage_type": "argument"}, {"api_name": "uuid.uuid4", "line_number": 75, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 77, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 77, "usage_type": "name"}, {"api_name": "jose.JWTError", "line_number": 78, "usage_type": "name"}, {"api_name": "frontstage.exceptions.exceptions.JWTValidationError", "line_number": 80, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 72, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 72, "usage_type": "name"}]} +{"seq_id": "608623435", "text": "from __future__ import absolute_import, unicode_literals\nfrom __future__ import print_function\n\nfrom builtins import str\nfrom builtins import map\nimport random\nimport time\nimport base64\nimport mimetypes\nimport signal\n\nimport os\nimport time\nfrom celery.decorators import task\nfrom config.settings.config_file_name_to_run import CONFIG_FILE_NAME\nfrom django.conf import settings\nimport datetime\nimport simplejson as json\nimport copy\nfrom api.helper import get_random_model_id, get_mails_from_outlook\nfrom django.contrib.auth.models import User\n\n\n@task(name=\"sum_two_numbers\")\ndef add(x, y):\n print(\"crazy bird {0}{1}\".format(x, y))\n return x + y\n\n\n@task(name=\"multiply_two_numbers\")\ndef mul(x, y):\n total = x * (y * random.randint(3, 100))\n return total\n\n\n@task(name=\"sum_list_numbers\")\ndef xsum(numbers):\n return sum(numbers)\n\n\nimport subprocess\nimport re\nimport requests\nfrom api.models import Job, Dataset, Score, Insight, Trainer, StockDataset, Robo, DatasetScoreDeployment, CustomApps, \\\n OutlookToken\n\n\n@task(name='hum_se_hai_zamana_sara', queue=CONFIG_FILE_NAME)\ndef submit_job_separate_task(command_array, slug):\n import subprocess, os\n my_env = os.environ.copy()\n if settings.HADOOP_CONF_DIR:\n my_env[\"HADOOP_CONF_DIR\"] = settings.HADOOP_CONF_DIR\n my_env[\"HADOOP_USER_NAME\"] = settings.HADOOP_USER_NAME\n\n try:\n cur_process = subprocess.Popen(command_array, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, bufsize=-1,\n universal_newlines=True, env=my_env)\n print(cur_process)\n except Exception as e:\n from api.helper import get_db_object\n model_instance = get_db_object(model_name=Job.__name__,\n model_slug=slug\n )\n model_instance.status = \"KILLED\"\n model_instance.message = json.dumps({'message': 'Killed while submitting job',\n 'error': e})\n model_instance.save()\n return \"Failed\"\n\n for line in iter(lambda: cur_process.stdout.readline(), ''):\n print(line.strip())\n line = line.strip()\n match = re.search('Submitted application (.*)$', line)\n if match:\n application_id = match.groups()[0]\n print((\"<------------------------ YARN APPLICATION ID ---------------------->\", application_id))\n print((\"<------------------------ YARN APPLICATION ID ---------------------->\", application_id))\n from api.helper import get_db_object\n\n model_instance = get_db_object(model_name=Job.__name__,\n model_slug=slug\n )\n model_instance.url = application_id\n model_instance.save()\n # Break statement is commented in order to get the complete log of the subprocess\n # break\n\n\n'''\n time.sleep(10)\n exists = os.path.isfile('/tmp/SparkDriver.log')\n while( exists != True):\n exists = os.path.isfile('/tmp/SparkDriver.log')\n time.sleep(1)\n with open(\"/tmp/SparkDriver.log\") as file:\n data = file.readlines()\n for line in data:\n match = re.search('Submitted application (.*)$', line)\n if match:\n application_id = match.groups()[0]\n print (\"############################## Application ID ################################# \", application_id)\n from api.helper import get_db_object\n model_instance = get_db_object(model_name=Job.__name__,\n model_slug=slug\n )\n model_instance.url = application_id\n model_instance.save()\n dist_file_name = \"/tmp/\" + str(application_id) + \".driver.log\"\n os.rename(\"/tmp/SparkDriver.log\",dist_file_name)\n break\n'''\n\n\ndef submit_job_separate_task1(command_array, slug):\n import subprocess, os\n my_env = os.environ.copy()\n if settings.HADOOP_CONF_DIR:\n my_env[\"HADOOP_CONF_DIR\"] = settings.HADOOP_CONF_DIR\n my_env[\"HADOOP_USER_NAME\"] = settings.HADOOP_USER_NAME\n cur_process = subprocess.Popen(command_array, stderr=subprocess.PIPE, env=my_env)\n print(cur_process)\n # TODO: @Ankush need to write the error to error log and standard out to normal log\n for line in iter(lambda: cur_process.stderr.readline(), ''):\n # print(line.strip())\n match = re.search('Submitted application (.*)$', line.decode(\"utf-8\"))\n if match:\n application_id = match.groups()[0]\n from api.helper import get_db_object\n\n model_instance = get_db_object(model_name=Job.__name__,\n model_slug=slug\n )\n model_instance.url = application_id\n model_instance.save()\n break\n\n\n@task(name='write_into_databases', queue=CONFIG_FILE_NAME)\ndef write_into_databases(job_type, object_slug, results):\n from api import helper\n # import json\n from api.helper import get_db_object\n from api.views import chart_changes_in_metadata_chart, add_slugs\n\n if job_type in [\"metadata\", \"subSetting\"]:\n dataset_object = get_db_object(model_name=Dataset.__name__,\n model_slug=object_slug\n )\n\n if \"error_message\" in results:\n dataset_object.status = \"FAILED\"\n dataset_object.save()\n return results\n columnData = results['columnData']\n for data in columnData:\n # data[\"chartData\"] = helper.find_chart_data_and_replace_with_chart_data(data[\"chartData\"])\n card_data = data[\"chartData\"]\n if 'dataType' in card_data and card_data['dataType'] == 'c3Chart':\n chart_data = card_data['data']\n final_chart_data = helper.decode_and_convert_chart_raw_data(chart_data, object_slug=object_slug)\n data[\"chartData\"] = chart_changes_in_metadata_chart(final_chart_data)\n data[\"chartData\"][\"table_c3\"] = []\n\n results['columnData'] = columnData\n # results['possibleAnalysis'] = settings.ANALYSIS_FOR_TARGET_VARIABLE\n da = []\n for d in results.get('sampleData'):\n da.append(list(map(str, d)))\n results['sampleData'] = da\n # results[\"modified\"] = False\n\n dataset_object.meta_data = json.dumps(results)\n dataset_object.analysis_done = True\n dataset_object.status = 'SUCCESS'\n dataset_object.save()\n print(\"Every thing went well. Lets see if more can be done\")\n check_if_dataset_is_part_of_datascore_table_and_do_we_need_to_trigger_score(dataset_object.id)\n #### Check if model job needs to be triggered for email AutoML ###\n check_if_autoML_model_job_needs_to_be_triggered(dataset_object.id)\n return \"Done Succesfully.\"\n elif job_type == \"master\":\n insight_object = get_db_object(model_name=Insight.__name__,\n model_slug=object_slug\n )\n\n if \"error_message\" in results:\n insight_object.status = \"FAILED\"\n insight_object.save()\n return results\n\n results = add_slugs(results, object_slug=object_slug)\n insight_object.data = json.dumps(results)\n insight_object.analysis_done = True\n insight_object.status = 'SUCCESS'\n insight_object.save()\n return \"Done Succesfully.\"\n elif job_type == \"model\":\n trainer_object = get_db_object(model_name=Trainer.__name__,\n model_slug=object_slug\n )\n\n if \"error_message\" in results or \"model_summary\" not in results:\n trainer_object.status = \"FAILED\"\n trainer_object.save()\n #------------------------------------------------------------------#\n #Sending failure mail on autoML model failure\n if trainer_object.mode == 'autoML':\n outlook_autoML_failure_mail(\n trainer_object_id=trainer_object.id,\n error='ML-failure',\n mail_id=trainer_object.email\n )\n #------------------------------------------------------------------#\n return results\n\n results['model_summary'] = add_slugs(results['model_summary'], object_slug=object_slug)\n trainer_object.data = json.dumps(results)\n trainer_object.analysis_done = True\n trainer_object.status = 'SUCCESS'\n trainer_object.save()\n\n if 'model_management_summary' in results:\n train_algo_details = results['model_management_summary']\n for algo_detail in train_algo_details:\n if len(algo_detail['listOfNodes']) > 1:\n from api.utils import TrainAlgorithmMappingSerializer\n temp_data = dict()\n temp_data['name'] = get_random_model_id(algo_detail['name'])\n temp_data['data'] = json.dumps(add_slugs(algo_detail, object_slug=object_slug))\n temp_data['trainer'] = trainer_object.id\n temp_data['app_id'] = trainer_object.app_id\n temp_data['created_by'] = trainer_object.created_by.id\n temp_config = {}\n for i in results['model_dropdown']:\n if algo_detail['name'] == i['name']:\n temp_config['selectedModel'] = i\n temp_config['variablesSelection'] = {}\n temp_config['app_id'] = trainer_object.app_id\n temp_data['config'] = json.dumps(temp_config)\n\n serializer = TrainAlgorithmMappingSerializer(data=temp_data)\n if serializer.is_valid():\n train_algo_object = serializer.save()\n else:\n print(serializer.errors)\n if \"one_click\" in results:\n trainer_object.fe_config = json.dumps(results[\"one_click\"])\n results[\"one_click\"]={}\n trainer_object.data=json.dumps(results)\n trainer_object.save()\n\n outlook_autoML_success_mail(trainer_object.id)\n return \"Done Succesfully.\"\n elif job_type == 'score':\n score_object = get_db_object(model_name=Score.__name__,\n model_slug=object_slug\n )\n\n if \"error_message\" in results:\n score_object.status = \"FAILED\"\n score_object.save()\n return results\n\n results = add_slugs(results, object_slug=object_slug)\n score_object.data = json.dumps(results)\n score_object.analysis_done = True\n score_object.status = 'SUCCESS'\n score_object.save()\n return \"Done Succesfully.\"\n elif job_type == 'robo':\n robo_object = get_db_object(model_name=Robo.__name__,\n model_slug=object_slug\n )\n\n if \"error_message\" in results:\n robo_object.status = \"FAILED\"\n robo_object.save()\n return results\n\n results = add_slugs(results, object_slug=object_slug)\n robo_object.data = json.dumps(results)\n robo_object.robo_analysis_done = True\n robo_object.status = 'SUCCESS'\n robo_object.save()\n return results\n elif job_type == 'stockAdvisor':\n stock_objects = get_db_object(model_name=StockDataset.__name__,\n model_slug=object_slug\n )\n results['name'] = stock_objects.name\n results = add_slugs(results, object_slug=object_slug)\n stock_objects.data = json.dumps(results)\n stock_objects.analysis_done = True\n stock_objects.status = 'SUCCESS'\n stock_objects.save()\n return results\n else:\n print(\"No where to write\")\n\n\ndef write_into_databases1(job_type, object_slug, results):\n from api import helper\n # import json\n from api.helper import get_db_object\n from api.views import chart_changes_in_metadata_chart, add_slugs\n\n if job_type in [\"metadata\", \"subSetting\"]:\n dataset_object = get_db_object(model_name=Dataset.__name__,\n model_slug=object_slug\n )\n\n if \"error_message\" in results:\n dataset_object.status = \"FAILED\"\n dataset_object.save()\n return results\n columnData = results['columnData']\n for data in columnData:\n # data[\"chartData\"] = helper.find_chart_data_and_replace_with_chart_data(data[\"chartData\"])\n card_data = data[\"chartData\"]\n if 'dataType' in card_data and card_data['dataType'] == 'c3Chart':\n chart_data = card_data['data']\n final_chart_data = helper.decode_and_convert_chart_raw_data(chart_data, object_slug=object_slug)\n data[\"chartData\"] = chart_changes_in_metadata_chart(final_chart_data)\n data[\"chartData\"][\"table_c3\"] = []\n\n results['columnData'] = columnData\n # results['possibleAnalysis'] = settings.ANALYSIS_FOR_TARGET_VARIABLE\n da = []\n for d in results.get('sampleData'):\n da.append(list(map(str, d)))\n results['sampleData'] = da\n # results[\"modified\"] = False\n\n dataset_object.meta_data = json.dumps(results)\n dataset_object.analysis_done = True\n dataset_object.status = 'SUCCESS'\n dataset_object.save()\n return results\n elif job_type == \"master\":\n insight_object = get_db_object(model_name=Insight.__name__,\n model_slug=object_slug\n )\n\n if \"error_message\" in results:\n insight_object.status = \"FAILED\"\n insight_object.save()\n return results\n\n results = add_slugs(results, object_slug=object_slug)\n insight_object.data = json.dumps(results)\n insight_object.analysis_done = True\n insight_object.status = 'SUCCESS'\n insight_object.save()\n return results\n elif job_type == \"model\":\n trainer_object = get_db_object(model_name=Trainer.__name__,\n model_slug=object_slug\n )\n\n if \"error_message\" in results or \"model_summary\" not in results:\n trainer_object.status = \"FAILED\"\n trainer_object.save()\n return results\n\n results['model_summary'] = add_slugs(results['model_summary'], object_slug=object_slug)\n trainer_object.data = json.dumps(results)\n trainer_object.analysis_done = True\n trainer_object.status = 'SUCCESS'\n trainer_object.save()\n\n if 'model_management_summary' in results:\n train_algo_details = results['model_management_summary']\n for algo_detail in train_algo_details:\n if len(algo_detail['listOfNodes']) > 1:\n from api.utils import TrainAlgorithmMappingSerializer\n temp_data = dict()\n temp_data['name'] = algo_detail['name']\n temp_data['data'] = json.dumps(add_slugs(algo_detail, object_slug=object_slug))\n temp_data['trainer'] = trainer_object\n temp_data['created_by'] = trainer_object.created_by.id\n temp_config = {}\n for i in results['model_dropdown']:\n if algo_detail['name'] == i['name']:\n temp_config['selectedModel'] = i\n temp_config['variablesSelection'] = {}\n temp_config['app_id'] = trainer_object.app_id\n temp_data['config'] = json.dumps(temp_config)\n\n serializer = TrainAlgorithmMappingSerializer(data=temp_data)\n if serializer.is_valid():\n train_algo_object = serializer.save()\n else:\n print(serializer.errors)\n return results\n elif job_type == 'score':\n score_object = get_db_object(model_name=Score.__name__,\n model_slug=object_slug\n )\n\n if \"error_message\" in results:\n score_object.status = \"FAILED\"\n score_object.save()\n return results\n\n results = add_slugs(results, object_slug=object_slug)\n score_object.data = json.dumps(results)\n score_object.analysis_done = True\n score_object.status = 'SUCCESS'\n score_object.save()\n return results\n elif job_type == 'robo':\n robo_object = get_db_object(model_name=Robo.__name__,\n model_slug=object_slug\n )\n\n if \"error_message\" in results:\n robo_object.status = \"FAILED\"\n robo_object.save()\n return results\n\n results = add_slugs(results, object_slug=object_slug)\n robo_object.data = json.dumps(results)\n robo_object.robo_analysis_done = True\n robo_object.status = 'SUCCESS'\n robo_object.save()\n return results\n elif job_type == 'stockAdvisor':\n stock_objects = get_db_object(model_name=StockDataset.__name__,\n model_slug=object_slug\n )\n results['name'] = stock_objects.name\n results = add_slugs(results, object_slug=object_slug)\n stock_objects.data = json.dumps(results)\n stock_objects.analysis_done = True\n stock_objects.status = 'SUCCESS'\n stock_objects.save()\n return results\n else:\n print(\"No where to write\")\n\n\n@task(name='save_results_to_job', queue=CONFIG_FILE_NAME)\ndef save_results_to_job(slug, results):\n from api.helper import get_db_object\n # import json\n\n job = get_db_object(model_name=Job.__name__,\n model_slug=slug\n )\n\n if isinstance(results, str):\n job.results = results\n elif isinstance(results, dict):\n results = json.dumps(results)\n job.results = results\n job.save()\n\n\n@task(name='save_job_messages', queue=CONFIG_FILE_NAME)\ndef save_job_messages(slug, messages):\n from api.helper import get_db_object\n # import json\n try:\n job = get_db_object(model_name=Job.__name__,\n model_slug=slug\n )\n\n if isinstance(messages, str):\n job.messages = messages\n elif isinstance(messages, dict):\n results = json.dumps(messages)\n job.messages = messages\n job.save()\n except Exception as err:\n print(err)\n\n\n@task(name='cleanup_logentry', queue=CONFIG_FILE_NAME)\ndef save_results_to_job1(slug, results):\n from api.helper import get_db_object\n # import json\n\n job = get_db_object(model_name=Job.__name__,\n model_slug=slug\n )\n\n if isinstance(results, str):\n job.results = results\n elif isinstance(results, dict):\n results = json.dumps(results)\n job.results = results\n job.save()\n print(\"save hogaya \" * 100)\n\n\n@task(name='cleanup_logentry')\ndef clean_up_logentry():\n from auditlog.models import LogEntry\n from django.contrib.auth.models import User\n\n all_users = User.objects.all()\n\n for user in all_users:\n log_entries = LogEntry.objects.filter(actor=user.id).count()\n LogEntry.objects.all().delete()\n print(\"delete object(s) :- %{0}\".format(log_entries))\n\n\n@task(name='cleanup_on_delete', queue=CONFIG_FILE_NAME)\ndef clean_up_on_delete(slug, model_name):\n from api.models import SaveAnyData, Job, SaveData\n\n job_instance = Job.objects.filter(object_id__contains=slug).first()\n if job_instance:\n job_instance.data = '{}'\n job_instance.save()\n\n sad_instance = SaveAnyData.objects.filter(slug__contains=slug)\n print(len(sad_instance))\n sad_instance.delete()\n\n sd_instance = SaveData.objects.filter(object_slug__contains=slug)\n print(len(sd_instance))\n sd_instance.delete()\n\n\n@task(name='kill_job_using_application_id', queue=CONFIG_FILE_NAME)\ndef kill_application_using_fabric(app_id=None):\n if None == app_id:\n return -1\n from fabric.api import env, run\n from django.conf import settings\n import subprocess\n MODE = settings.MODE\n print((\"MODE\", MODE))\n if MODE == 'docker':\n # HDFS = settings.KILL_JOB\n # BASEDIR = settings.BASE_DIR\n # env.key_filename = settings.PEM_KEY\n # env.host_string = \"{0}@{1}\".format(HDFS[\"user.name\"], HDFS[\"host\"])\n\n try:\n capture = subprocess.Popen(\n \"docker exec -t hadoop_spark_compose_hadoop_1 sh -c '/opt/hadoop/bin/yarn application --kill {0}'\".format(\n app_id), shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n stdout, stderr = capture.communicate()\n if 'finished' in stdout:\n return False\n else:\n return True\n except:\n return True\n else:\n HDFS = settings.HDFS\n BASEDIR = settings.BASE_DIR\n emr_file = BASEDIR + settings.PEM_KEY\n env.key_filename = [emr_file]\n\n if CONFIG_FILE_NAME == 'cwpoc':\n env.host_string = \"{0}@{1}\".format(\"ankush\", HDFS[\"host\"])\n else:\n env.host_string = \"{0}@{1}\".format(HDFS[\"user.name\"], HDFS[\"host\"])\n try:\n capture = run(\"yarn application --kill {0}\".format(app_id))\n if 'finished' in capture:\n return False\n else:\n return True\n except:\n return True\n\n\n@task(name='stock_sense_crawling', queue=CONFIG_FILE_NAME)\ndef stock_sense_crawl(object_slug):\n from api.helper import get_db_object\n print(\"stock_sense_crawl\" * 2)\n stock_dataset_object = get_db_object(model_name=StockDataset.__name__,\n model_slug=object_slug\n )\n stock_dataset_object.generate_meta_data()\n stock_dataset_object.save()\n\n\n@task(name='print_this_every_minute', queue=CONFIG_FILE_NAME)\ndef print_this_every_minute(data):\n print(data)\n\n\n@task(name='call_dataset_then_score', queue=CONFIG_FILE_NAME)\ndef call_dataset_then_score(*args, **kwrgs):\n print(args)\n print(kwrgs)\n # collect all configs\n config = kwrgs\n dataset_details = config['dataset_details']\n # score_details = config['score_details']\n # trainer_details = config['trainer_details']\n modeldeployment_details = config['modeldeployment_details']\n user_details = config['user_details']\n\n # fetch modeldeployment instance\n from api.models import ModelDeployment\n model_deployment_object = ModelDeployment.objects.get(slug=modeldeployment_details['modeldeployment_slug'])\n\n # fetch user instance\n from django.contrib.auth.models import User\n user_object = User.objects.get_by_natural_key(username=user_details['username'])\n\n # fetch trainer model\n # trainer_object = model_deployment_object.deploytrainer.trainer\n dataset_score_deployment_details = {\n 'name': model_deployment_object.name + ' - ' + str(datetime.datetime.now().time()),\n 'deployment': model_deployment_object.id,\n 'created_by': user_object.id,\n 'config': '{}',\n 'data': '{}'\n }\n from api.utils import DatasetScoreDeploymentSerializer\n dataset_score_deployment_serializer = DatasetScoreDeploymentSerializer(data=dataset_score_deployment_details)\n if dataset_score_deployment_serializer.is_valid():\n dataset_score_deployment_object = dataset_score_deployment_serializer.save()\n print(dataset_score_deployment_object)\n else:\n return\n # create dataset\n dataset_details['input_file'] = None\n if 'datasetname' in dataset_details['datasource_details']:\n dataset_details['name'] = dataset_details['datasource_details']['datasetname']\n dataset_details['created_by'] = user_object.id\n from api.datasets.helper import convert_to_string\n from api.datasets.serializers import DatasetSerializer\n\n dataset_details = convert_to_string(dataset_details)\n serializer = DatasetSerializer(data=dataset_details)\n if serializer.is_valid():\n dataset_object = serializer.save()\n dataset_score_deployment_object.dataset = dataset_object\n dataset_score_deployment_object.save()\n print(dataset_object)\n dataset_object.create()\n else:\n print(serializer.errors)\n\n\n'''\nThings to do\n- call dataset object create function (uncomment in above code)\n- Once dataset is completed from ML side they will\n -> call set_result API\n -> which will call write_into_database\n -> where we need to check if database is part of DatasetScore Table\n -> if yes, trigger score object create function\n'''\n\n\ndef check_if_dataset_is_part_of_datascore_table_and_do_we_need_to_trigger_score(dataset_object_id):\n print(('received this dataset_object_id : ', dataset_object_id))\n\n if dataset_object_id is None:\n print(\"No dataset id given found\")\n\n return\n\n from api.models import DatasetScoreDeployment\n\n try:\n dataset_object = Dataset.objects.get(id=dataset_object_id)\n datasetscore_deployment_object = DatasetScoreDeployment.objects.filter(dataset=dataset_object_id).first()\n\n if datasetscore_deployment_object is not None:\n # fetch modeldeployment instance\n print(\"Found the dataset in datasetscoredeployment table.\")\n from api.models import ModelDeployment\n model_deployment_object = datasetscore_deployment_object.deployment\n print(\"Found deployment.\")\n\n # fetch trainer insctance\n trainer_object = model_deployment_object.deploytrainer.trainer\n print(\"Found trainer_object.\")\n\n # fetch user instance\n from django.contrib.auth.models import User\n user_object = dataset_object.created_by\n print(\"Found User\")\n\n # create score\n # import json\n original_meta_data_from_scripts = json.loads(dataset_object.meta_data)\n print(\"Got metedata from dataset\")\n\n if original_meta_data_from_scripts is None:\n uiMetaData = dict()\n if original_meta_data_from_scripts == {}:\n uiMetaData = dict()\n else:\n permissions_dict = {\n 'create_signal': user_object.has_perm('api.create_signal'),\n 'subsetting_dataset': user_object.has_perm('api.subsetting_dataset')\n }\n from api.datasets.helper import add_ui_metadata_to_metadata\n uiMetaData = add_ui_metadata_to_metadata(original_meta_data_from_scripts,\n permissions_dict=permissions_dict)\n print(\"Got uiMetaData from dataset\")\n\n from api.utils import convert_to_string\n # import json\n # config = json.loads(model_deployment_object.config)\n print(\"Got model_deployment_object config\")\n\n # dataset_metadata = json.loads(dataset_object.meta_data)\n score_details = model_deployment_object.get_trainer_details_for_score(\n datasetscore_deployment_object.name + \"_score\")\n score_details['config']['variablesSelection'] = uiMetaData['varibaleSelectionArray']\n score_details['trainer'] = trainer_object.id\n score_details['dataset'] = dataset_object.id\n score_details['created_by'] = user_object.id\n score_details['app_id'] = int(score_details['config']['app_id'])\n score_details = convert_to_string(score_details)\n print(\"Constructed score_details\")\n from api.utils import ScoreSerlializer\n score_serializer = ScoreSerlializer(data=score_details, context={})\n if score_serializer.is_valid():\n score_object = score_serializer.save()\n # we will not call score_object.create() here it will be called in write_into_databases\n datasetscore_deployment_object.score = score_object\n datasetscore_deployment_object.save()\n print(score_object)\n score_object.create()\n else:\n print(score_serializer.errors)\n else:\n print('datasetscore_deployment_object si None.')\n return\n except Exception as err:\n print(err)\n\n\ndef check_if_autoML_model_job_needs_to_be_triggered(dataset_object_id):\n print(('received this dataset_object_id : ', dataset_object_id))\n\n if dataset_object_id is None:\n print(\"No dataset id given found\")\n\n return\n\n from api.models import Trainer\n try:\n dataset_object = Dataset.objects.get(id=dataset_object_id)\n trainer_object = Trainer.objects.filter(dataset=dataset_object_id).first()\n\n if trainer_object is not None:\n # fetch modeldeployment instance\n print(\"Found the dataset in trainer table.\")\n ########### Trigger autoML model job ##############\n create_model_autoML.delay(dataset_object.id)\n else:\n print('Its not a email autoML job.')\n return\n\n except Exception as err:\n outlook_autoML_failure_mail(trainer_object_id=None, error=err)\n print(err)\n\n\nfrom celery.task.schedules import crontab\nfrom celery.decorators import periodic_task\n\n\n@periodic_task(run_every=(crontab(minute='*/10')), name=\"trigger_outlook_periodic_job\", ignore_result=False,\n queue=CONFIG_FILE_NAME)\ndef trigger_outlook_periodic_job():\n\n mails = get_mails_from_outlook()\n if mails is not None:\n mail_id = ''\n if 'status' and 'err' not in list(mails.keys()):\n print(\"All set to proceed to upload dataset.\")\n\n for configkey, configvalue in mails.items():\n data = {}\n for key, value in configvalue.items():\n try:\n print(\"inside try ... \")\n # print key,value\n # value is a dict\n ############# Create config and trigger metadata job for train and test Dataset #################\n if key == 'sub_target':\n data['sub_target'] = value\n if key == 'target':\n data['target'] = value\n if 'train_dataset' in key:\n input_file = value\n data['Traindataset'] = input_file\n data['name'] = input_file\n if 'test_dataset' in key:\n input_file = value\n data['Testdataset'] = input_file\n data['score_name'] = configkey\n if 'emailAddress' in key:\n data['email'] = value['emailAddress']['address']\n mail_id = data['email']\n except Exception as error:\n outlook_autoML_failure_mail(trainer_object_id=None, error=error, mail_id=mail_id)\n print('failure mail sent')\n break\n if len(data) > 0:\n print(\"Here is the collected data\")\n print(data)\n trigger_metaData_autoML.delay(data)\n else:\n print(\"No mails found\")\n break\n ##########################################################################################\n else:\n outlook_autoML_failure_mail(trainer_object_id=None, error=mails['err'], mail_id=mail_id)\n print('failure mail sent')\n else:\n print(\"No mails.\")\n\n '''\n Task1: Look for auth Code, Access Token and Refresh Token : DONE\n Task2: Get mails from outlook\n Task3: Extract Text Data and attachments from mail\n Task4: Put Attachments in HDFS\n Task5: Prepare config for Data Upload.\n Task6: Trigger model Once Task5 is done.\n '''\n\n\n@task(name='trigger_metaData_autoML', queue=CONFIG_FILE_NAME)\ndef trigger_metaData_autoML(data):\n print(\"metaData job triggered for autoML\")\n ###################### User id for Email AutoML ################\n print(data)\n '''\n Create one user with Username \"email\" in order to use email for AutoML model creation.\n\n '''\n from django.contrib.auth.models import User\n user_id = User.objects.get(username=\"email\")\n ###################################################################\n #################### Upload file from local ####################\n from django.core.files import File\n from api.datasets.helper import convert_to_string\n from api.datasets.serializers import DatasetSerializer\n\n fail_log = dict()\n test_dataset_serializer = None\n train_dataset_serializer = None\n\n try:\n ######### Trainer dataset config #########\n train_file = open(settings.BASE_DIR + '/media/datasets/' + data['Traindataset'])\n train_f = File(train_file)\n train_dataset_config = dict()\n train_dataset_config['name'] = data['name']\n train_dataset_config['input_file'] = train_f\n train_dataset_config['datasource_type'] = 'fileUpload'\n train_dataset_config['created_by'] = user_id.id\n train_dataset_details = convert_to_string(train_dataset_config)\n train_dataset_serializer = DatasetSerializer(data=train_dataset_details)\n ###################################################################\n except Exception as err:\n fail_log['train_config_error'] = str(err)\n pass\n try:\n ######### Test dataset config #########\n if 'Testdataset' in data:\n test_file = open(settings.BASE_DIR + '/media/datasets/' + data['Testdataset'])\n test_f = File(test_file)\n test_dataset_config = dict()\n test_dataset_config['name'] = data['score_name'] + '_Test'\n test_dataset_config['input_file'] = test_f\n test_dataset_config['datasource_type'] = 'fileUpload'\n test_dataset_config['created_by'] = user_id.id\n test_dataset_details = convert_to_string(test_dataset_config)\n test_dataset_serializer = DatasetSerializer(data=test_dataset_details)\n ###################################################################\n except Exception as err:\n # fail_log['test_config_error'] = str(err)\n pass\n\n if train_dataset_serializer.is_valid():\n print(\"Saving train dataset Serializer\")\n train_dataset_object = train_dataset_serializer.save()\n print(train_dataset_object)\n ################################ Create config for model object that to be triggered after metadata job ##################\n try:\n model_config = dict()\n model_config['name'] = data['name'].split(\".\")[0] + '_Trainer'\n model_config['app_id'] = 2\n model_config['mode'] = \"autoML\"\n model_config['email'] = data['email']\n model_config['dataset'] = train_dataset_object.id\n model_config['config'] = dict()\n model_config['config']['targetColumn'] = data['target']\n model_config['config']['targetLevel'] = data['sub_target']\n model_config['created_by'] = user_id.id\n\n from api.utils import convert_to_string\n model_config = convert_to_string(model_config)\n print(\"Constructed model_config\")\n print(model_config)\n\n from api.utils import TrainerSerlializer\n trainer_serializer = TrainerSerlializer(data=model_config, context={})\n if trainer_serializer.is_valid():\n print(\"Saving trainer Serializer\")\n trainer_object = trainer_serializer.save()\n try:\n if 'Testdataset' in data:\n if test_dataset_serializer.is_valid():\n print(\"Saving test dataset Serializer\")\n test_dataset_object = test_dataset_serializer.save()\n print(test_dataset_object)\n ################ Create config for Score object that to be triggered after model job ##############\n score_config = dict()\n score_config['name'] = data['name'] + '_Score'\n score_config['app_id'] = 2\n score_config['trainer'] = trainer_object.id\n score_config['dataset'] = test_dataset_object.id\n score_config['created_by'] = user_id.id\n\n from api.utils import ScoreSerlializer\n score_serializer = ScoreSerlializer(data=score_config, context={})\n if score_serializer.is_valid():\n print(\"Saving score Serializer\")\n score_object = score_serializer.save()\n print(score_object)\n test_dataset_object.create()\n else:\n fail_log['score_serializer_error'] = str(score_serializer.errors)\n # print(score_serializer.errors)\n else:\n print(\"There's no test data!\")\n except Exception as err:\n fail_log['score_generation_error'] = str(err)\n pass\n # outlook_autoML_failure_mail(trainer_object_id=None, error=err)\n # print e\n else:\n fail_log['trainer_serializer_error'] = str(trainer_serializer.errors)\n # print(trainer_serializer.errors)\n ######### MODEL OBJECT SAVED ----> GO FOR METADATA CREATE ###########\n print(\"Going for metadata creation!!!\")\n train_dataset_object.create()\n except Exception as err:\n fail_log['model_config_error'] = str(err)\n # outlook_autoML_failure_mail(trainer_object_id=None, error=err)\n pass\n else:\n fail_log['train_dataset_serializer_error'] = str(train_dataset_serializer.errors)\n print(train_dataset_serializer.errors)\n\n if fail_log:\n error = ''\n for i in fail_log:\n error = error + '\\n' + i\n print('fail log >> ', fail_log)\n outlook_autoML_failure_mail(trainer_object_id=None, error=error, mail_id=data['email'])\n print('failure mail sent')\n\n\n@task(name='create_model_autoML', queue=CONFIG_FILE_NAME)\ndef create_model_autoML(dataset_object_id=None, config=None):\n print('##################### Configs for Trainer ##################')\n validationTechnique = {\n \"displayName\": \"K Fold Validation\",\n \"name\": \"kFold\",\n \"value\": 2,\n }\n if config is not None:\n try:\n print(config)\n data = json.loads(config)\n dataset_object = Dataset.objects.get(slug=data['slug'])\n print(dataset_object)\n\n model_config = {\n \"name\": data['model_name'],\n \"app_id\": 2,\n \"mode\": \"autoML\",\n \"email\": data['email'],\n \"config\": {}\n }\n\n original_meta_data_from_scripts = json.loads(dataset_object.meta_data)\n print(\"Got metedata from dataset\")\n\n from django.contrib.auth.models import User\n user_object = dataset_object.created_by\n\n if original_meta_data_from_scripts is None:\n uiMetaData = dict()\n if original_meta_data_from_scripts == {}:\n uiMetaData = dict()\n else:\n permissions_dict = {\n 'create_signal': user_object.has_perm('api.create_signal'),\n 'subsetting_dataset': user_object.has_perm('api.subsetting_dataset')\n }\n from api.datasets.helper import add_ui_metadata_to_metadata\n uiMetaData = add_ui_metadata_to_metadata(original_meta_data_from_scripts,\n permissions_dict=permissions_dict)\n print(\"Got uiMetaData from dataset\")\n\n model_config['dataset'] = dataset_object.id\n model_config['config']['ALGORITHM_SETTING'] = copy.deepcopy(\n settings.AUTOML_ALGORITHM_LIST_CLASSIFICATION['ALGORITHM_SETTING'])\n model_config['config']['targetColumn'] = data['target']\n model_config['config']['targetLevel'] = data['subtarget']\n model_config['config']['variablesSelection'] = uiMetaData['varibaleSelectionArray']\n model_config['config']['validationTechnique'] = validationTechnique\n model_config['created_by'] = user_object.id\n\n from api.utils import convert_to_string\n model_config = convert_to_string(model_config)\n print(\"Constructed model_config\")\n\n from api.utils import TrainerSerlializer\n trainer_serializer = TrainerSerlializer(data=model_config, context={})\n if trainer_serializer.is_valid():\n trainer_object = trainer_serializer.save()\n trainer_object.create()\n else:\n print(trainer_serializer.errors)\n\n except Exception as err:\n print(err)\n else:\n try:\n dataset_object = Dataset.objects.get(id=dataset_object_id)\n original_meta_data_from_scripts = json.loads(dataset_object.meta_data)\n print(\"Got metedata from dataset\")\n\n from django.contrib.auth.models import User\n user_object = dataset_object.created_by\n\n if original_meta_data_from_scripts is None:\n uiMetaData = dict()\n if original_meta_data_from_scripts == {}:\n uiMetaData = dict()\n else:\n permissions_dict = {\n 'create_signal': user_object.has_perm('api.create_signal'),\n 'subsetting_dataset': user_object.has_perm('api.subsetting_dataset')\n }\n from api.datasets.helper import add_ui_metadata_to_metadata\n uiMetaData = add_ui_metadata_to_metadata(original_meta_data_from_scripts,\n permissions_dict=permissions_dict)\n print(\"Got uiMetaData from dataset\")\n try:\n trainer_obj = Trainer.objects.filter(dataset=dataset_object_id).first()\n model_config = {\n \"name\": trainer_obj.name,\n \"app_id\": 2,\n \"mode\": \"autoML\",\n \"email\": trainer_obj.email,\n \"config\": {}\n }\n config = json.loads(trainer_obj.config)\n model_config['dataset'] = dataset_object.id\n model_config['config']['ALGORITHM_SETTING'] = copy.deepcopy(\n settings.AUTOML_ALGORITHM_LIST_CLASSIFICATION['ALGORITHM_SETTING'])\n model_config['config']['validationTechnique'] = validationTechnique\n model_config['config']['targetLevel'] = config['targetLevel']\n model_config['config']['targetColumn'] = config['targetColumn']\n model_config['created_by'] = user_object.id\n model_config['config']['variablesSelection'] = uiMetaData['varibaleSelectionArray']\n except Exception as e:\n print(e)\n\n from api.utils import convert_to_string\n model_config = convert_to_string(model_config)\n print((\"Constructed model_config\", model_config))\n\n from api.utils import TrainerSerlializer\n trainer_serializer = TrainerSerlializer(data=model_config, context={})\n if trainer_serializer.is_valid():\n trainer_object = trainer_serializer.save()\n trainer_object.create()\n else:\n print(trainer_serializer.errors)\n\n except Exception as err:\n print(err)\n\n\n@task(name='outlook_autoML_success_mail', queue=CONFIG_FILE_NAME)\ndef outlook_autoML_success_mail(trainer_object_id=None):\n if trainer_object_id is None:\n return\n else:\n trainer_object = Trainer.objects.get(id=trainer_object_id)\n if trainer_object.mode == 'autoML':\n\n from api.helper import get_outlook_auth\n r = get_outlook_auth(settings.OUTLOOK_AUTH_CODE, settings.OUTLOOK_REFRESH_TOKEN,\n settings.OUTLOOK_DETAILS)\n result = r.json()\n access_token = result['access_token']\n protocol = 'http'\n if settings.USE_HTTPS:\n protocol = 'https'\n\n Result_URL = '{}://{}/api/view_model_summary_autoML/?slug={}'.format(protocol,\n settings.THIS_SERVER_DETAILS['host'],\n trainer_object.slug)\n # content = \"AutoML Dataupload successful. Model is created.\"\n content = Result_URL\n # app_slug = CustomApps.objects.get(app_id=trainer_object.app_id)\n # attachment_path = get_model_summary_pdf(app_slug.slug, trainer_object.slug)\n mail_data = dict()\n mail_data['modelName'] = trainer_object.name\n mail_data['datasetName'] = trainer_object.dataset.name\n mail_data['createdAt'] = trainer_object.created_at\n conf = json.loads(trainer_object.config)\n for i in conf['config']['COLUMN_SETTINGS']['variableSelection']:\n if i['targetColumn']:\n mail_data['variable'] = i['name']\n try:\n if trainer_object.email is not None:\n return_mail_id = trainer_object.email\n # mail('send', access_token=access_token, return_mail_id=return_mail_id,\n # subject='Marlabs-AutoML Success', content=content, attachments=attachment_path)\n mail('send', access_token=access_token, return_mail_id=return_mail_id,\n subject='Marlabs-AutoML Success', content=content, mail_options=mail_data)\n else:\n user_id = trainer_object.created_by_id\n user_object = User.objects.get(id=user_id)\n return_mail_id = user_object.email\n if return_mail_id is not None:\n # mail('send', access_token=access_token, return_mail_id=return_mail_id,\n # subject='Marlabs-AutoML Success', content=content, attachments=attachment_path)\n mail('send', access_token=access_token, return_mail_id=return_mail_id,\n subject='Marlabs-AutoML Success', content=content, mail_options=mail_data)\n\n except Exception as err:\n print(err)\n pass\n else:\n pass\n\n\n@task(name='outlook_autoML_failure_mail', queue=CONFIG_FILE_NAME)\ndef outlook_autoML_failure_mail(trainer_object_id=None, error=None, mail_id=None):\n print(\"Trying to send failure mail\")\n mail_data = dict()\n from api.helper import get_outlook_auth\n r = get_outlook_auth(settings.OUTLOOK_AUTH_CODE, settings.OUTLOOK_REFRESH_TOKEN,\n settings.OUTLOOK_DETAILS)\n result = r.json()\n print(result)\n access_token = result['access_token']\n print(\"got access token\")\n if trainer_object_id is None:\n mail_data['modelName'] = 'UNDEFINED'\n mail_data['datasetName'] = 'UNDEFINED'\n mail_data['createdAt'] = 'UNDEFINED'\n mail_data['variable'] = 'UNDEFINED'\n print(\"mail id : \", mail_id)\n err_mail('send', access_token=access_token, return_mail_id=mail_id,\n subject='Marlabs-AutoML Failure', error=error, mail_options=mail_data)\n else:\n trainer_object = Trainer.objects.get(id=trainer_object_id)\n if trainer_object.mode == 'autoML':\n\n mail_data['modelName'] = trainer_object.name\n mail_data['datasetName'] = trainer_object.dataset.name\n mail_data['createdAt'] = trainer_object.created_at\n conf = json.loads(trainer_object.config)\n for i in conf['config']['COLUMN_SETTINGS']['variableSelection']:\n if i['targetColumn']:\n mail_data['variable'] = i['name']\n try:\n if trainer_object.email is not None:\n return_mail_id = trainer_object.email\n err_mail('send', access_token=access_token, return_mail_id=return_mail_id,\n subject='Marlabs-AutoML Failure', error=error, mail_options=mail_data)\n else:\n user_id = trainer_object.created_by_id\n user_object = User.objects.get(id=user_id)\n return_mail_id = user_object.email\n if return_mail_id is not None:\n err_mail('send', access_token=access_token, return_mail_id=return_mail_id,\n subject='Marlabs-AutoML Failure', error=error, mail_options=mail_data)\n\n except Exception as err:\n print(err)\n pass\n else:\n pass\n\n\ndef mail(action_type=None, access_token=None, return_mail_id=None, subject=None, content=None, mail_options=None):\n # access_token = access_token\n # If there is no token in the session, redirect to home\n if not access_token:\n return HttpResponseRedirect(reverse('tutorial:home'))\n else:\n try:\n messages = send_my_messages(access_token, return_mail_id, subject, content, mail_options)\n if messages[:3] == '202':\n print(\"Mail Sent\")\n except Exception as e:\n print(e)\n print(\"Some issue with mail sending module...\")\n\n\ndef err_mail(action_type=None, access_token=None, return_mail_id=None, subject=None, error=None, mail_options=None):\n # access_token = access_token\n # If there is no token in the session, redirect to home\n if not access_token:\n return HttpResponseRedirect(reverse('tutorial:home'))\n else:\n try:\n messages = send_failure_messages(access_token, return_mail_id, subject, error, mail_options)\n if messages[:3] == '202':\n print(\"Mail Sent\")\n except Exception as e:\n print(e)\n print(\"Some issue with mail sending module...\")\n\n\ndef send_failure_messages(access_token, return_mail_id, subject, error, mail_options):\n get_messages_url = 'https://graph.microsoft.com/v1.0/me/' + '/sendmail'\n\n htmlData = \"\"\"Dear {},

Model creation has failed for some reason! \\\n

Please contact the Admin team for further assistance.

\\\n Sorry for the inconvenience.

Regards,
mAdvisor\"\"\" \\\n .format(return_mail_id.split('@')[0])\n '''\n htmlData = \"\"\"Dear {},

Model creation has failed! \\\n

Details:
Model Name : {}
Created on : {}
Dataset : {}
Target Variable : {}
Reason for failure : {} \\\n



Sorry for the inconvenience.

Regards,
mAdvisor\"\"\" \\\n .format(return_mail_id.split('@')[0],\n mail_options['modelName'],\n mail_options['createdAt'],\n mail_options['datasetName'],\n mail_options['variable'],\n str(error))'''\n\n payload = {\n\n \"Message\": {\n\n \"Subject\": subject,\n \"Body\": {\n\n \"ContentType\": \"HTML\",\n \"Content\": htmlData,\n\n },\n \"ToRecipients\": [\n {\n \"EmailAddress\": {\n \"Address\": return_mail_id\n }\n }\n ],\n # 'Attachments': [attached_files]\n },\n \"SaveToSentItems\": \"true\",\n\n }\n from api.helper import make_api_call\n import requests\n\n r = make_api_call('POST', get_messages_url, access_token, payload=payload)\n if r.status_code == requests.codes.ok:\n print(\"Mail Sent\")\n return r.json()\n else:\n return \"{0}: {1}\".format(r.status_code, r.text)\n\n\ndef send_my_messages(access_token, return_mail_id, subject, content, mail_options):\n '''\n Replies to the mail with attachments\n '''\n # get_messages_url = graph_endpoint.format('/me/messages?$select=sender,subject')\n # get_messages_url = 'https://graph.microsoft.com/v1.0' + '/users/' + settings.OUTLOOK_ID + '/sendmail'\n get_messages_url = 'https://graph.microsoft.com/v1.0/me/' + '/sendmail'\n # Use OData query parameters to control the results\n # - Only first 10 results returned\n # - Only return the ReceivedDateTime, Subject, and From fields\n # - Sort the results by the ReceivedDateTime field in descending order\n '''\n b64_content = base64.b64encode(open(file_to_attach, 'rb').read())\n mime_type = mimetypes.guess_type(file_to_attach)[0]\n mime_type = mime_type if mime_type else ''\n attached_files = {'@odata.type': '#microsoft.graph.fileAttachment',\n 'ContentBytes': b64_content.decode('utf-8'),\n 'ContentType': mime_type,\n 'Name': file_to_attach.split('/')[-1]}\n '''\n htmlData = \"\"\"Dear {},

Model has been successfully created through AutoML.\n

Details:
Model Name : {}
Created on : {}
Dataset : {}
Target Variable :\n {}

Please go through the attached link in order to view your Model Summary.\n


Model Summary

Have a great day ahead.

Regards,
mAdvisor\"\"\" \\\n .format(return_mail_id.split('@')[0],\n mail_options['modelName'],\n mail_options['createdAt'],\n mail_options['datasetName'],\n mail_options['variable'],\n content)\n\n payload = {\n\n \"Message\": {\n\n \"Subject\": subject,\n \"Body\": {\n\n \"ContentType\": \"HTML\",\n \"Content\": htmlData,\n\n },\n \"ToRecipients\": [\n {\n \"EmailAddress\": {\n \"Address\": return_mail_id\n }\n }\n ],\n # 'Attachments': [attached_files]\n },\n \"SaveToSentItems\": \"true\",\n\n }\n from api.helper import make_api_call\n import requests\n\n r = make_api_call('POST', get_messages_url, access_token, payload=payload)\n if r.status_code == requests.codes.ok:\n print(\"Mail Sent\")\n return r.json()\n else:\n return \"{0}: {1}\".format(r.status_code, r.text)\n\n\n'''\ndef send_devtools(driver, cmd, params={}):\n resource = \"/session/%s/chromium/send_command_and_get_result\" % driver.session_id\n url = driver.command_executor._url + resource\n body = json.dumps({'cmd': cmd, 'params': params})\n response = driver.command_executor._request('POST', url, body)\n return response.get('value')\n\n\ndef save_as_pdf(driver, path, options=None):\n if options is None:\n options = {}\n result = send_devtools(driver, \"Page.printToPDF\", options)\n with open(path, 'wb') as f:\n f.write(base64.b64decode(result['data']))\n\n\ndef get_model_summary_pdf(app_slug, model_slug):\n protocol = 'http'\n if settings.USE_HTTPS:\n protocol = 'https'\n\n login_url = '{}://{}'.format(protocol, settings.THIS_SERVER_DETAILS['host'])\n url = '{}/apps/{}/autoML/models/{}'.format(login_url, app_slug, model_slug)\n\n options = webdriver.ChromeOptions()\n options.add_argument(\"--headless\")\n options.add_argument(\"--disable-gpu\")\n\n driver = webdriver.Chrome(chrome_options=options)\n driver.get(login_url)\n driver.find_element_by_id('username').send_keys('email')\n driver.find_element_by_id('password').send_keys('emailuser')\n driver.find_element_by_xpath('//button').click()\n driver.get(url)\n path = '{}/{}.pdf'.format(settings.MODEL_SUMMARY_DOWNLOAD_PATH, model_slug)\n save_as_pdf(driver, path, {'landscape': False})\n return path\n'''\n\n\n@periodic_task(run_every=(crontab(0, 0, day_of_month='1')), name=\"trigger_outlook_token\", ignore_result=False,\n queue=CONFIG_FILE_NAME)\ndef trigger_outlook_token():\n\n outlook_data = settings.OUTLOOK_DETAILS\n token = OutlookToken.objects.first()\n\n post_data_auth_code = {\n 'grant_type': 'authorization_code',\n 'code': token.access_token,\n 'redirect_uri': outlook_data['redirect_uri'],\n 'scope': settings.OUTLOOK_SCOPES,\n 'client_id': outlook_data['client_id'],\n 'client_secret': outlook_data['client_secret']\n }\n post_data_refresh_token = {'grant_type': 'refresh_token',\n 'redirect_uri': outlook_data['redirect_uri'],\n 'scope': 'https://graph.microsoft.com/.default',\n 'refresh_token': token.refresh_token,\n 'client_id': outlook_data['client_id'],\n 'client_secret': outlook_data['client_secret']\n }\n\n if token.refresh_token is not None:\n r = requests.post(settings.OUTLOOK_TOKEN_URL, data=post_data_refresh_token)\n result = r.json()\n outlook_token = OutlookToken(refresh_token=result['refresh_token'], access_token=result['access_token'])\n outlook_token.save()\n else:\n r = requests.post(settings.OUTLOOK_TOKEN_URL, data=post_data_auth_code)\n result = r.json()\n outlook_token = OutlookToken(refresh_token=result['refresh_token'], access_token=result['access_token'])\n outlook_token.save()\n return result\n\n\n\n", "sub_path": "api/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 59036, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "celery.decorators.task", "line_number": 24, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 30, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 36, "usage_type": "call"}, {"api_name": "os.environ.copy", "line_number": 51, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 51, "usage_type": "attribute"}, {"api_name": "django.conf.settings.HADOOP_CONF_DIR", "line_number": 52, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 52, "usage_type": "name"}, {"api_name": "django.conf.settings.HADOOP_CONF_DIR", "line_number": 53, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 53, "usage_type": "name"}, {"api_name": "django.conf.settings.HADOOP_USER_NAME", "line_number": 54, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 54, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 57, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 57, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 57, "usage_type": "attribute"}, {"api_name": "api.helper.get_db_object", "line_number": 62, "usage_type": "call"}, {"api_name": "api.models.Job.__name__", "line_number": 62, "usage_type": "attribute"}, {"api_name": "api.models.Job", "line_number": 62, "usage_type": "name"}, {"api_name": "simplejson.dumps", "line_number": 66, "usage_type": "call"}, {"api_name": "re.search", "line_number": 74, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 81, "usage_type": "call"}, {"api_name": "api.models.Job.__name__", "line_number": 81, "usage_type": "attribute"}, {"api_name": "api.models.Job", "line_number": 81, "usage_type": "name"}, {"api_name": "celery.decorators.task", "line_number": 48, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 48, "usage_type": "name"}, {"api_name": "os.environ.copy", "line_number": 117, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 117, "usage_type": "attribute"}, {"api_name": "django.conf.settings.HADOOP_CONF_DIR", "line_number": 118, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 118, "usage_type": "name"}, {"api_name": "django.conf.settings.HADOOP_CONF_DIR", "line_number": 119, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 119, "usage_type": "name"}, {"api_name": "django.conf.settings.HADOOP_USER_NAME", "line_number": 120, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 120, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 121, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 121, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 126, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 131, "usage_type": "call"}, {"api_name": "api.models.Job.__name__", "line_number": 131, "usage_type": "attribute"}, {"api_name": "api.models.Job", "line_number": 131, "usage_type": "name"}, {"api_name": "api.helper.get_db_object", "line_number": 147, "usage_type": "call"}, {"api_name": "api.models.Dataset.__name__", "line_number": 147, "usage_type": "attribute"}, {"api_name": "api.models.Dataset", "line_number": 147, "usage_type": "name"}, {"api_name": "api.helper.decode_and_convert_chart_raw_data", "line_number": 161, "usage_type": "call"}, {"api_name": "api.helper", "line_number": 161, "usage_type": "name"}, {"api_name": "api.views.chart_changes_in_metadata_chart", "line_number": 162, "usage_type": "call"}, {"api_name": "builtins.map", "line_number": 169, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 169, "usage_type": "argument"}, {"api_name": "simplejson.dumps", "line_number": 173, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 183, "usage_type": "call"}, {"api_name": "api.models.Insight.__name__", "line_number": 183, "usage_type": "attribute"}, {"api_name": "api.models.Insight", "line_number": 183, "usage_type": "name"}, {"api_name": "api.views.add_slugs", "line_number": 192, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 193, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 199, "usage_type": "call"}, {"api_name": "api.models.Trainer.__name__", "line_number": 199, "usage_type": "attribute"}, {"api_name": "api.models.Trainer", "line_number": 199, "usage_type": "name"}, {"api_name": "api.views.add_slugs", "line_number": 217, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 218, "usage_type": "call"}, {"api_name": "api.helper.get_random_model_id", "line_number": 229, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 230, "usage_type": "call"}, {"api_name": "api.views.add_slugs", "line_number": 230, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 240, "usage_type": "call"}, {"api_name": "api.utils.TrainAlgorithmMappingSerializer", "line_number": 242, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 248, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 250, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 256, "usage_type": "call"}, {"api_name": "api.models.Score.__name__", "line_number": 256, "usage_type": "attribute"}, {"api_name": "api.models.Score", "line_number": 256, "usage_type": "name"}, {"api_name": "api.views.add_slugs", "line_number": 265, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 266, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 272, "usage_type": "call"}, {"api_name": "api.models.Robo.__name__", "line_number": 272, "usage_type": "attribute"}, {"api_name": "api.models.Robo", "line_number": 272, "usage_type": "name"}, {"api_name": "api.views.add_slugs", "line_number": 281, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 282, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 288, "usage_type": "call"}, {"api_name": "api.models.StockDataset.__name__", "line_number": 288, "usage_type": "attribute"}, {"api_name": "api.models.StockDataset", "line_number": 288, "usage_type": "name"}, {"api_name": "api.views.add_slugs", "line_number": 292, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 293, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 139, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 139, "usage_type": "name"}, {"api_name": "api.helper.get_db_object", "line_number": 309, "usage_type": "call"}, {"api_name": "api.models.Dataset.__name__", "line_number": 309, "usage_type": "attribute"}, {"api_name": "api.models.Dataset", "line_number": 309, "usage_type": "name"}, {"api_name": "api.helper.decode_and_convert_chart_raw_data", "line_number": 323, "usage_type": "call"}, {"api_name": "api.helper", "line_number": 323, "usage_type": "name"}, {"api_name": "api.views.chart_changes_in_metadata_chart", "line_number": 324, "usage_type": "call"}, {"api_name": "builtins.map", "line_number": 331, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 331, "usage_type": "argument"}, {"api_name": "simplejson.dumps", "line_number": 335, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 341, "usage_type": "call"}, {"api_name": "api.models.Insight.__name__", "line_number": 341, "usage_type": "attribute"}, {"api_name": "api.models.Insight", "line_number": 341, "usage_type": "name"}, {"api_name": "api.views.add_slugs", "line_number": 350, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 351, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 357, "usage_type": "call"}, {"api_name": "api.models.Trainer.__name__", "line_number": 357, "usage_type": "attribute"}, {"api_name": "api.models.Trainer", "line_number": 357, "usage_type": "name"}, {"api_name": "api.views.add_slugs", "line_number": 366, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 367, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 379, "usage_type": "call"}, {"api_name": "api.views.add_slugs", "line_number": 379, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 388, "usage_type": "call"}, {"api_name": "api.utils.TrainAlgorithmMappingSerializer", "line_number": 390, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 397, "usage_type": "call"}, {"api_name": "api.models.Score.__name__", "line_number": 397, "usage_type": "attribute"}, {"api_name": "api.models.Score", "line_number": 397, "usage_type": "name"}, {"api_name": "api.views.add_slugs", "line_number": 406, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 407, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 413, "usage_type": "call"}, {"api_name": "api.models.Robo.__name__", "line_number": 413, "usage_type": "attribute"}, {"api_name": "api.models.Robo", "line_number": 413, "usage_type": "name"}, {"api_name": "api.views.add_slugs", "line_number": 422, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 423, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 429, "usage_type": "call"}, {"api_name": "api.models.StockDataset.__name__", "line_number": 429, "usage_type": "attribute"}, {"api_name": "api.models.StockDataset", "line_number": 429, "usage_type": "name"}, {"api_name": "api.views.add_slugs", "line_number": 433, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 434, "usage_type": "call"}, {"api_name": "api.helper.get_db_object", "line_number": 448, "usage_type": "call"}, {"api_name": "api.models.Job.__name__", "line_number": 448, "usage_type": "attribute"}, {"api_name": "api.models.Job", "line_number": 448, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 452, "usage_type": "argument"}, {"api_name": "simplejson.dumps", "line_number": 455, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 443, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 443, "usage_type": "name"}, {"api_name": "api.helper.get_db_object", "line_number": 465, "usage_type": "call"}, {"api_name": "api.models.Job.__name__", "line_number": 465, "usage_type": "attribute"}, {"api_name": "api.models.Job", "line_number": 465, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 469, "usage_type": "argument"}, {"api_name": "simplejson.dumps", "line_number": 472, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 460, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 460, "usage_type": "name"}, {"api_name": "api.helper.get_db_object", "line_number": 484, "usage_type": "call"}, {"api_name": "api.models.Job.__name__", "line_number": 484, "usage_type": "attribute"}, {"api_name": "api.models.Job", "line_number": 484, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 488, "usage_type": "argument"}, {"api_name": "simplejson.dumps", "line_number": 491, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 479, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 479, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 502, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 502, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 502, "usage_type": "name"}, {"api_name": "auditlog.models.LogEntry.objects.filter", "line_number": 505, "usage_type": "call"}, {"api_name": "auditlog.models.LogEntry.objects", "line_number": 505, "usage_type": "attribute"}, {"api_name": "auditlog.models.LogEntry", "line_number": 505, "usage_type": "name"}, {"api_name": "auditlog.models.LogEntry.objects.all", "line_number": 506, "usage_type": "call"}, {"api_name": "auditlog.models.LogEntry.objects", "line_number": 506, "usage_type": "attribute"}, {"api_name": "auditlog.models.LogEntry", "line_number": 506, "usage_type": "name"}, {"api_name": "celery.decorators.task", "line_number": 497, "usage_type": "call"}, {"api_name": "api.models.Job.objects.filter", "line_number": 514, "usage_type": "call"}, {"api_name": "api.models.Job.objects", "line_number": 514, "usage_type": "attribute"}, {"api_name": "api.models.Job", "line_number": 514, "usage_type": "name"}, {"api_name": "api.models.SaveAnyData.objects.filter", "line_number": 519, "usage_type": "call"}, {"api_name": "api.models.SaveAnyData.objects", "line_number": 519, "usage_type": "attribute"}, {"api_name": "api.models.SaveAnyData", "line_number": 519, "usage_type": "name"}, {"api_name": "api.models.SaveData.objects.filter", "line_number": 523, "usage_type": "call"}, {"api_name": "api.models.SaveData.objects", "line_number": 523, "usage_type": "attribute"}, {"api_name": "api.models.SaveData", "line_number": 523, "usage_type": "name"}, {"api_name": "celery.decorators.task", "line_number": 510, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 510, "usage_type": "name"}, {"api_name": "django.conf.settings.MODE", "line_number": 535, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 535, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 544, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 546, "usage_type": "attribute"}, {"api_name": "django.conf.settings.HDFS", "line_number": 555, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 555, "usage_type": "name"}, {"api_name": "django.conf.settings.BASE_DIR", "line_number": 556, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 556, "usage_type": "name"}, {"api_name": "django.conf.settings.PEM_KEY", "line_number": 557, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 557, "usage_type": "name"}, {"api_name": "fabric.api.env.key_filename", "line_number": 558, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 558, "usage_type": "name"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 560, "usage_type": "name"}, {"api_name": "fabric.api.env.host_string", "line_number": 561, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 561, "usage_type": "name"}, {"api_name": "fabric.api.env.host_string", "line_number": 563, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 563, "usage_type": "name"}, {"api_name": "fabric.api.run", "line_number": 565, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 528, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 528, "usage_type": "name"}, {"api_name": "api.helper.get_db_object", "line_number": 578, "usage_type": "call"}, {"api_name": "api.models.StockDataset.__name__", "line_number": 578, "usage_type": "attribute"}, {"api_name": "api.models.StockDataset", "line_number": 578, "usage_type": "name"}, {"api_name": "celery.decorators.task", "line_number": 574, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 574, "usage_type": "name"}, {"api_name": "celery.decorators.task", "line_number": 585, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 585, "usage_type": "name"}, {"api_name": "config.settings.config_file_name_to_run", "line_number": 595, "usage_type": "name"}, {"api_name": "config.settings.config_file_name_to_run", "line_number": 596, "usage_type": "name"}, {"api_name": "config.settings.config_file_name_to_run", "line_number": 599, "usage_type": "name"}, {"api_name": "config.settings.config_file_name_to_run", "line_number": 600, "usage_type": "name"}, {"api_name": "api.models.ModelDeployment.objects.get", "line_number": 604, "usage_type": "call"}, {"api_name": "api.models.ModelDeployment.objects", "line_number": 604, "usage_type": "attribute"}, {"api_name": "api.models.ModelDeployment", "line_number": 604, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get_by_natural_key", "line_number": 608, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 608, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 608, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 613, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 613, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 613, "usage_type": "attribute"}, {"api_name": "api.utils.DatasetScoreDeploymentSerializer", "line_number": 620, "usage_type": "call"}, {"api_name": "api.datasets.helper.convert_to_string", "line_number": 634, "usage_type": "call"}, {"api_name": "api.datasets.serializers.DatasetSerializer", "line_number": 635, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 590, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 590, "usage_type": "name"}, {"api_name": "api.models.Dataset.objects.get", "line_number": 668, "usage_type": "call"}, {"api_name": "api.models.Dataset.objects", "line_number": 668, "usage_type": "attribute"}, {"api_name": "api.models.Dataset", "line_number": 668, "usage_type": "name"}, {"api_name": "api.models.DatasetScoreDeployment.objects.filter", "line_number": 669, "usage_type": "call"}, {"api_name": "api.models.DatasetScoreDeployment.objects", "line_number": 669, "usage_type": "attribute"}, {"api_name": "api.models.DatasetScoreDeployment", "line_number": 669, "usage_type": "name"}, {"api_name": "simplejson.loads", "line_number": 689, "usage_type": "call"}, {"api_name": "api.datasets.helper.add_ui_metadata_to_metadata", "line_number": 702, "usage_type": "call"}, {"api_name": "api.utils.convert_to_string", "line_number": 719, "usage_type": "call"}, {"api_name": "api.utils.ScoreSerlializer", "line_number": 722, "usage_type": "call"}, {"api_name": "api.models.Dataset.objects.get", "line_number": 749, "usage_type": "call"}, {"api_name": "api.models.Dataset.objects", "line_number": 749, "usage_type": "attribute"}, {"api_name": "api.models.Dataset", "line_number": 749, "usage_type": "name"}, {"api_name": "api.models.Trainer.objects.filter", "line_number": 750, "usage_type": "call"}, {"api_name": "api.models.Trainer.objects", "line_number": 750, "usage_type": "attribute"}, {"api_name": "api.models.Trainer", "line_number": 750, "usage_type": "name"}, {"api_name": "api.helper.get_mails_from_outlook", "line_number": 774, "usage_type": "call"}, {"api_name": "celery.decorators.periodic_task", "line_number": 770, "usage_type": "call"}, {"api_name": "celery.task.schedules.crontab", "line_number": 770, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 771, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 841, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 841, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 841, "usage_type": "name"}, {"api_name": "django.conf.settings.BASE_DIR", "line_number": 854, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 854, "usage_type": "name"}, {"api_name": "django.core.files.File", "line_number": 855, "usage_type": "call"}, {"api_name": "api.datasets.helper.convert_to_string", "line_number": 861, "usage_type": "call"}, {"api_name": "api.datasets.serializers.DatasetSerializer", "line_number": 862, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 865, "usage_type": "call"}, {"api_name": "django.conf.settings.BASE_DIR", "line_number": 870, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 870, "usage_type": "name"}, {"api_name": "django.core.files.File", "line_number": 871, "usage_type": "call"}, {"api_name": "api.datasets.helper.convert_to_string", "line_number": 877, "usage_type": "call"}, {"api_name": "api.datasets.serializers.DatasetSerializer", "line_number": 878, "usage_type": "call"}, {"api_name": "api.utils.convert_to_string", "line_number": 902, "usage_type": "call"}, {"api_name": "api.utils.TrainerSerlializer", "line_number": 907, "usage_type": "call"}, {"api_name": "api.utils.ScoreSerlializer", "line_number": 926, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 933, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 938, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 943, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 949, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 953, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 831, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 831, "usage_type": "name"}, {"api_name": "config.settings.config_file_name_to_run", "line_number": 973, "usage_type": "name"}, {"api_name": "config.settings.config_file_name_to_run", "line_number": 975, "usage_type": "argument"}, {"api_name": "simplejson.loads", "line_number": 976, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run", "line_number": 976, "usage_type": "argument"}, {"api_name": "api.models.Dataset.objects.get", "line_number": 977, "usage_type": "call"}, {"api_name": "api.models.Dataset.objects", "line_number": 977, "usage_type": "attribute"}, {"api_name": "api.models.Dataset", "line_number": 977, "usage_type": "name"}, {"api_name": "simplejson.loads", "line_number": 988, "usage_type": "call"}, {"api_name": "api.datasets.helper.add_ui_metadata_to_metadata", "line_number": 1004, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 1009, "usage_type": "call"}, {"api_name": "django.conf.settings.AUTOML_ALGORITHM_LIST_CLASSIFICATION", "line_number": 1010, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1010, "usage_type": "name"}, {"api_name": "api.utils.convert_to_string", "line_number": 1018, "usage_type": "call"}, {"api_name": "api.utils.TrainerSerlializer", "line_number": 1022, "usage_type": "call"}, {"api_name": "api.models.Dataset.objects.get", "line_number": 1033, "usage_type": "call"}, {"api_name": "api.models.Dataset.objects", "line_number": 1033, "usage_type": "attribute"}, {"api_name": "api.models.Dataset", "line_number": 1033, "usage_type": "name"}, {"api_name": "simplejson.loads", "line_number": 1034, "usage_type": "call"}, {"api_name": "api.datasets.helper.add_ui_metadata_to_metadata", "line_number": 1050, "usage_type": "call"}, {"api_name": "api.models.Trainer.objects.filter", "line_number": 1054, "usage_type": "call"}, {"api_name": "api.models.Trainer.objects", "line_number": 1054, "usage_type": "attribute"}, {"api_name": "api.models.Trainer", "line_number": 1054, "usage_type": "name"}, {"api_name": "config.settings.config_file_name_to_run", "line_number": 1062, "usage_type": "name"}, {"api_name": "simplejson.loads", "line_number": 1062, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 1064, "usage_type": "call"}, {"api_name": "django.conf.settings.AUTOML_ALGORITHM_LIST_CLASSIFICATION", "line_number": 1065, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1065, "usage_type": "name"}, {"api_name": "config.settings.config_file_name_to_run", "line_number": 1067, "usage_type": "name"}, {"api_name": "config.settings.config_file_name_to_run", "line_number": 1068, "usage_type": "name"}, {"api_name": "api.utils.convert_to_string", "line_number": 1075, "usage_type": "call"}, {"api_name": "api.utils.TrainerSerlializer", "line_number": 1079, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 965, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 965, "usage_type": "name"}, {"api_name": "api.models.Trainer.objects.get", "line_number": 1095, "usage_type": "call"}, {"api_name": "api.models.Trainer.objects", "line_number": 1095, "usage_type": "attribute"}, {"api_name": "api.models.Trainer", "line_number": 1095, "usage_type": "name"}, {"api_name": "api.helper.get_outlook_auth", "line_number": 1099, "usage_type": "call"}, {"api_name": "django.conf.settings.OUTLOOK_AUTH_CODE", "line_number": 1099, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1099, "usage_type": "name"}, {"api_name": "django.conf.settings.OUTLOOK_REFRESH_TOKEN", "line_number": 1099, "usage_type": "attribute"}, {"api_name": "django.conf.settings.OUTLOOK_DETAILS", "line_number": 1100, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1100, "usage_type": "name"}, {"api_name": "django.conf.settings.USE_HTTPS", "line_number": 1104, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1104, "usage_type": "name"}, {"api_name": "django.conf.settings.THIS_SERVER_DETAILS", "line_number": 1108, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1108, "usage_type": "name"}, {"api_name": "simplejson.loads", "line_number": 1118, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 1131, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 1131, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 1131, "usage_type": "name"}, {"api_name": "celery.decorators.task", "line_number": 1090, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 1090, "usage_type": "name"}, {"api_name": "api.helper.get_outlook_auth", "line_number": 1151, "usage_type": "call"}, {"api_name": "django.conf.settings.OUTLOOK_AUTH_CODE", "line_number": 1151, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1151, "usage_type": "name"}, {"api_name": "django.conf.settings.OUTLOOK_REFRESH_TOKEN", "line_number": 1151, "usage_type": "attribute"}, {"api_name": "django.conf.settings.OUTLOOK_DETAILS", "line_number": 1152, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1152, "usage_type": "name"}, {"api_name": "api.models.Trainer.objects.get", "line_number": 1166, "usage_type": "call"}, {"api_name": "api.models.Trainer.objects", "line_number": 1166, "usage_type": "attribute"}, {"api_name": "api.models.Trainer", "line_number": 1166, "usage_type": "name"}, {"api_name": "simplejson.loads", "line_number": 1172, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 1183, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 1183, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 1183, "usage_type": "name"}, {"api_name": "celery.decorators.task", "line_number": 1146, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 1146, "usage_type": "name"}, {"api_name": "api.helper.make_api_call", "line_number": 1270, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 1271, "usage_type": "attribute"}, {"api_name": "api.helper.make_api_call", "line_number": 1335, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 1336, "usage_type": "attribute"}, {"api_name": "django.conf.settings.OUTLOOK_DETAILS", "line_number": 1388, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1388, "usage_type": "name"}, {"api_name": "api.models.OutlookToken.objects.first", "line_number": 1389, "usage_type": "call"}, {"api_name": "api.models.OutlookToken.objects", "line_number": 1389, "usage_type": "attribute"}, {"api_name": "api.models.OutlookToken", "line_number": 1389, "usage_type": "name"}, {"api_name": "django.conf.settings.OUTLOOK_SCOPES", "line_number": 1395, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1395, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 1408, "usage_type": "call"}, {"api_name": "django.conf.settings.OUTLOOK_TOKEN_URL", "line_number": 1408, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1408, "usage_type": "name"}, {"api_name": "api.models.OutlookToken", "line_number": 1410, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 1413, "usage_type": "call"}, {"api_name": "django.conf.settings.OUTLOOK_TOKEN_URL", "line_number": 1413, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 1413, "usage_type": "name"}, {"api_name": "api.models.OutlookToken", "line_number": 1415, "usage_type": "call"}, {"api_name": "celery.decorators.periodic_task", "line_number": 1384, "usage_type": "call"}, {"api_name": "celery.task.schedules.crontab", "line_number": 1384, "usage_type": "call"}, {"api_name": "config.settings.config_file_name_to_run.CONFIG_FILE_NAME", "line_number": 1385, "usage_type": "name"}]} +{"seq_id": "233105112", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Go through a dump finding links to nonexistent pages.\n\nimport pywikibot, re, sys, argparse, codecs\n\nimport blib\nfrom blib import getparam, rmparam, msg, site\nfrom collections import defaultdict\n\nblib.getLanguageData()\n\ntemplates_to_check = {}\nfor template in [\n \"l\", \"link\",\n \"m\", \"mention\",\n \"m+\",\n \"cog\", \"cognate\",\n \"nc\", \"ncog\", \"noncog\", \"noncognate\",\n]:\n templates_to_check[template] = [\"1\", \"2\"]\nfor template in [\"inh\", \"inherited\", \"bor\", \"borrowed\", \"der\", \"derived\"]:\n templates_to_check[template] = [\"2\", \"3\"]\n\ndef zh_l(t, note):\n for page in getparam(t, \"1\").split(\"/\"):\n note(\"zh\", page)\n\ndef ko_l(t, note):\n for param in [\"1\", \"2\", \"3\", \"4\"]:\n # Yuck yuck yuck! The following is from [[Module:ko]], which allows\n # Hanja, Hangul, translit and gloss params in any order and has a complex\n # algorithm to disambiguate them.\n # FIXME: This needs changing for Python 3.\n val = getparam(t, param)\n if re.search(\"[가-힣㐀-䶵一-鿌\\uF900-\\uFADF𠀀-𯨟]\", val):\n note(\"ko\", val)\n\ndef ja_l(t, note):\n note(\"ja\", getparam(t, \"1\").replace(\"%\", \"\"))\n\ndef vi_l(t, note):\n note(\"vi\", getparam(t, \"1\"))\n note(\"vi\", getparam(t, \"2\"))\n\ndef vi_link(t, note):\n note(\"vi\", getparam(t, \"1\"))\n\ntemplates_to_check.update({\n \"zh-l\": zh_l,\n \"zh-m\": zh_l,\n \"ko-l\": ko_l,\n \"ja-l\": ja_l,\n \"ja-r\": ja_l,\n \"vi-l\": vi_l,\n \"vi-link\": vi_link,\n})\n\ndef read_existing_pages(filename):\n pages_with_langs = {}\n for line in codecs.getreader(\"utf-8\")(gzip.open(filename, \"rb\"), errors=\"replace\"):\n line = line.rstrip(\"\\n\")\n if re.search(\"^Page [0-9]+ .*: WARNING: .*\", line):\n msg(\"Skipping warning: %s\" % line)\n else:\n m = re.search(\"^Page [0-9-]+ (.*): Langs=(.*?)$\", line)\n if not m:\n msg(\"WARNING: Unrecognized line: %s\" % line)\n else:\n pages_with_langs[m.group(1)] = set(m.group(2).split(\",\"))\n return pages_with_langs\n\nunrecognized_pages_by_lang_with_count = {}\nunrecognized_pages_by_lang_with_source_pages = {}\n\ndef process_text_on_page(index, pagetitle, pagetext):\n def pagemsg(txt):\n msg(\"Page %s %s: %s\" % (index, pagetitle, txt))\n\n def note_unrecognized_link(lang, page):\n if lang not in unrecognized_pages_by_lang_with_count:\n unrecognized_pages_by_lang_with_count[lang] = defaultdict(int)\n unrecognized_pages_by_lang_with_count[lang][page] += 1\n if lang not in unrecognized_pages_by_lang_with_source_pages:\n unrecognized_pages_by_lang_with_source_pages[lang] = {}\n if page not in unrecognized_pages_by_lang_with_source_pages[lang]:\n unrecognized_pages_by_lang_with_source_pages[lang][page] = set()\n else:\n unrecognized_pages_by_lang_with_source_pages[lang][page].add(pagetitle)\n\n def note_link(lang, page):\n if not page:\n return\n if not lang and page not in pages_with_langs:\n note_unrecognized_link(lang, page)\n elif lang and (page not in pages_with_langs or lang not in pages_with_langs[page]):\n note_unrecognized_link(lang, page)\n\n # Look for raw links.\n for m in re.finditer(r\"\\[\\[(.*?)\\]\\]\", pagetext):\n linkparts = m.group(1).split(\"|\")\n if linkparts > 2:\n pagemsg(\"WARNING: Link has more than two parts: %s\" % m.group(0))\n else:\n page = linkparts[0]\n if \"#\" in page:\n page, anchor = page.split(\"#\", 1)\n if anchor in blib.languages_byCanonicalName:\n note_link(blib.languages_byCanonicalName[anchor][\"code\"], page)\n else:\n note_link(None, page)\n else:\n note_link(None, page)\n\n # Look for templated links.\n for t in blib.parse_text(pagetext).filter_templates():\n pass\n\nparser = blib.create_argparser(\"Find red links\", include_pagefile=True, include_stdin=True)\nparser.add_argument(\"--existing-pages\", help=\"Gzipped file containing existing pages by language\",\n required=True)\nargs = parser.parse_args()\nstart, end = blib.parse_start_end(args.start, args.end)\n\npages_with_langs = read_existing_pages(args.existing_pages)\n\nblib.do_pagefile_cats_refs(args, start, end, process_text_on_page, stdin=True)\n", "sub_path": "find_wanted_pages.py", "file_name": "find_wanted_pages.py", "file_ext": "py", "file_size_in_byte": 4089, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "blib.getLanguageData", "line_number": 12, "usage_type": "call"}, {"api_name": "blib.getparam", "line_number": 27, "usage_type": "call"}, {"api_name": "blib.getparam", "line_number": 36, "usage_type": "call"}, {"api_name": "re.search", "line_number": 37, "usage_type": "call"}, {"api_name": "blib.getparam", "line_number": 41, "usage_type": "call"}, {"api_name": "blib.getparam", "line_number": 44, "usage_type": "call"}, {"api_name": "blib.getparam", "line_number": 45, "usage_type": "call"}, {"api_name": "blib.getparam", "line_number": 48, "usage_type": "call"}, {"api_name": "codecs.getreader", "line_number": 62, "usage_type": "call"}, {"api_name": "re.search", "line_number": 64, "usage_type": "call"}, {"api_name": "blib.msg", "line_number": 65, "usage_type": "call"}, {"api_name": "re.search", "line_number": 67, "usage_type": "call"}, {"api_name": "blib.msg", "line_number": 69, "usage_type": "call"}, {"api_name": "blib.msg", "line_number": 79, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 83, "usage_type": "call"}, {"api_name": "re.finditer", "line_number": 101, "usage_type": "call"}, {"api_name": "blib.languages_byCanonicalName", "line_number": 109, "usage_type": "attribute"}, {"api_name": "blib.languages_byCanonicalName", "line_number": 110, "usage_type": "attribute"}, {"api_name": "blib.parse_text", "line_number": 117, "usage_type": "call"}, {"api_name": "blib.create_argparser", "line_number": 120, "usage_type": "call"}, {"api_name": "blib.parse_start_end", "line_number": 124, "usage_type": "call"}, {"api_name": "blib.do_pagefile_cats_refs", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "400603995", "text": "import os\nfrom flask import Flask, app, flash, session\nfrom flask_pymongo import PyMongo\nfrom datetime import date, datetime\n\napp = Flask(__name__)\napp.config[\"MONGO_DBNAME\"] = os.getenv('MONGO_DBNAME')\napp.config[\"MONGO_URI\"] = os.getenv('MONGO_URI')\napp.config[\"SECRET_KEY\"] = os.getenv('SECRET_KEY')\n\nmongo = PyMongo(app)\n\n\n\"\"\"Collections\"\"\"\nstories_collection = mongo.db.stories\nusers_collection = mongo.db.users\nfake_collection = None\n\n\n\"\"\"Helper functions\"\"\"\n\n\ndef list_by_type():\n list_by_type = {}\n ratings = []\n genres = []\n fandoms = []\n authors = []\n if session.get('is_adult') == True:\n selection = stories_collection.find()\n else:\n selection = stories_collection.find( {\"rating\": {\"$nin\": [\"R/Adult/NSFW\", \"Adult/NSFW\"]}})\n for story in selection:\n rating = story['rating']\n genres_in_story = story.get('genres')\n if genres_in_story != []:\n for genre in genres_in_story:\n genre\n fandoms_in_story = story.get('fandoms')\n if fandoms_in_story != []:\n for fandom in fandoms_in_story:\n fandom\n else:\n fandom = \"Fandom not added\"\n author = story['author']\n if rating not in ratings:\n ratings.append(rating)\n if genre not in genres:\n genres.append(genre)\n if fandom not in fandoms:\n fandoms.append(fandom)\n if author not in authors:\n authors.append(author)\n list_by_type.update({\"ratings\": ratings, \"genres\": genres,\n \"fandoms\": fandoms, \"authors\": authors})\n return list_by_type\n\n\ndef story_count():\n story_count = []\n ratings_list = list_by_type()[\"ratings\"]\n genres_list = list_by_type()[\"genres\"]\n fandoms_list = list_by_type()[\"fandoms\"]\n authors_list = list_by_type()[\"authors\"]\n for rating in ratings_list:\n count = stories_collection.count_documents({\"rating\": rating})\n count_rating = {\"rating\": rating, \"total\": count}\n story_count.append(count_rating)\n for genre in genres_list:\n count = stories_collection.count_documents({\"genres\": genre})\n count_genre = {\"genre\": genre, \"total\": count}\n story_count.append(count_genre)\n for fandom in fandoms_list:\n count = stories_collection.count_documents({\"fandoms\": fandom})\n count_fandom = {\"fandom\": fandom, \"total\": count}\n story_count.append(count_fandom)\n for author in authors_list:\n count = stories_collection.count_documents({\"author\": author})\n count_author = {\"author\": author, \"total\": count}\n story_count.append(count_author)\n return story_count\n\n\ndef report(item, reason_given, this_story, reported_by):\n stories_collection.find_one_and_update({\"url\": this_story}, {'$push': {\"reports\": {\"item_reported\": item, \"reported_by\": reported_by, \"reason_given\": reason_given}}}, upsert=True)\n return flash(\"Report sent to admins.\")\n\n\ndef calculate_age(born):\n today = date.today()\n bday = datetime.strptime(born, '%Y-%m-%d')\n age = today.year - bday.year - ((today.month, today.day) < (bday.month, bday.day))\n return age", "sub_path": "helper.py", "file_name": "helper.py", "file_ext": "py", "file_size_in_byte": 3169, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.app", "line_number": 6, "usage_type": "name"}, {"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.app.config", "line_number": 7, "usage_type": "attribute"}, {"api_name": "flask.app", "line_number": 7, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.app.config", "line_number": 8, "usage_type": "attribute"}, {"api_name": "flask.app", "line_number": 8, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.app.config", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.app", "line_number": 9, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_pymongo.PyMongo", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.app", "line_number": 11, "usage_type": "argument"}, {"api_name": "flask.session.get", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 90, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 91, "usage_type": "name"}]} +{"seq_id": "606219976", "text": "import serial\n\n# Set the port and baudrate to the ones you're using, Rafael/João Лорем.\n# Likely COM[something] in your case.\nser = serial.Serial(port='/dev/ttyUSB0', baudrate=9600)\n\n# In this next few lines, the files are created.\n# Do not forget to create the files you think you'll need.\ndata = open('/files/data.csv', 'w')\ntemp = open('/files/temp.txt', 'w')\npress = open('/files/press.txt', 'w')\n\n# You should go read about slicing notation. Ok?\ndef parser(string, start=None, end = None):\n \"\"\"\n Returns the parts of the string you want.\n \"\"\"\n try:\n return string[start - 1:end + 0]\n except:\n try:\n return string[start - 1:end]\n except:\n return string[start:end]\n\n# Again, please do add the necessary files below. They don't create themselves.\n# Or, they could. I could make them create themselves. But, it would be unecessary labour.\n# Nobody likes unecessary labour, am I correct, João? I'm not.\nwhile True:\n \"\"\"\n This writes things to the files. Having non-empty files is fun.\n \"\"\"\n\n ## Change values to sooth your needs.\n dataString = str(ser.readline())[:-5][2:]\n\n# Start by declaring the strings you want to use.\n# Later on, you should use the paser() function defined above.\n temperature = parser(dataString, end=4) # Change the arguments according to your needs.\n pressure = parser(dataString, 4) # Change the arguments according to your needs.\n\n print(dataString)\n\n# Everything should be written to this file,\n# i.e, you should add a data.write([the things you want to write] + \",\")\n# for every data point.\n# Please illucidate yourself on how csv files work. It isn't hard.\n data = open('/files/data.csv', 'a')\n data.write(temperature + \",\")\n data.write(pressure + \"\\n\")\n\n# This should remain as is.\n# Unless, of course, you'd like to include the time the data was read.\n# I don't think that'd be a bad idea.\n# But, since you're the ones with the hardware, that's your job to do.\n# You're smart. You can figure it out.\n temp = open('/files/temp.txt', 'a')\n temp.write(temperature + \"\\n\")\n temp.close()\n\n press = open('/files/press.txt', 'a')\n press.write(pressure + \"\\n\")\n press.close()\n\n# Please do create more files, as we're going to need more information than this.\n", "sub_path": "Data Processing/Ground Station/data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 2300, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "serial.Serial", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "236786920", "text": "########################################\n## Created on 2018/07/01 ##\n## author: Chia-Ching Lin ##\n## ##\n## 專案CHUNK名字必須與155上資料夾一致 ##\n########################################\nimport os, sys, zipfile, pathlib, datetime, shutil, subprocess\nimport Metashape\nfrom tkinter.filedialog import *\nfrom bs4 import BeautifulSoup\nfrom subprocess import PIPE\nfrom ftplib import FTP\nimport xml.etree.ElementTree as ET\nfrom xml.dom import minidom\nfrom gdalconst import *\nfrom osgeo import gdal\nimport osr, os, affine, time\n\n\n# 前置設定參數\ntw97 = Metashape.CoordinateSystem(\"EPSG::3826\")\nws84 = Metashape.CoordinateSystem(\"EPSG::3857\")\nds84 = Metashape.CoordinateSystem(\"EPSG::4326\") \ncrs = Metashape.CoordinateSystem('LOCAL_CS[\"Local CS\",LOCAL_DATUM[\"Local Datum\",0],UNIT[\"metre\",1]]')\n\n\npath = r'\\\\140.116.228.155\\geodac_uav\\2019'\ndir = r'C:\\Users\\RSLAB\\Desktop\\dir\\\\'\n\ndir_1 = '1.測繪產品'\ndir_1_1 = '1.1.Ortho_正射影像(包含附加檔)'\ndir_1_2 = '1.2.OrigPhoto_原始照片'\ndir_1_3 = '1.3.PrecEval_精度評估報告'\ndir_1_4 = '1.4.ContCoor_控制點座標)'\ndir_1_5 = '1.5.ContPhoto_控制點照片'\ndir_1_6 = '1.6.FlyRec_飛行記錄'\ndir_1_7 = '1.7.DSM_數值地表模型'\ndir_1_8 = '1.8.3DModel_3D模型'\n\n# info = ['140.116.249.139','geodac','rsej0hk45j/vup','/TCGEO/2019']\n# ftp = FTP(info[0], info[1], info[2])\n# ftp.encoding = 'big5'\n# ftp.cwd(info[3])\n# ftp_list = ftp.nlst(info[3])\n\n\n\n## - - - 從這裡開始!! - - - \nprint(\"\\n- - - - - - - - Script started - - - - - - - - \\n\")\n\ndef workflow():\n pass\n\ndef create_dir(name):\n #pathlib.Path(path+ name).mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/1.測繪產品').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/2.環景照片').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/3.一般產品').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/4.影片').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/Photoscan').mkdir(parents=True, exist_ok=0)\n #open('.\\\\'+ name + '\\\\Photoscan' + '\\\\' + name + '.psx','w')\n pathlib.Path(path+'\\\\'+ name + '/1.測繪產品' + './1.1.Ortho_正射影像(包含附加檔)').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/1.測繪產品' + './1.2.OrigPhoto_原始照片').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/1.測繪產品' + './1.3.PrecEval_精度評估報告').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/1.測繪產品' + './1.4.ContCoor_控制點座標)').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/1.測繪產品' + './1.5.ContPhoto_控制點照片').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/1.��繪產品' + './1.6.FlyRec_飛行記錄').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/1.測繪產品' + './1.7.DSM_數值地表模型').mkdir(parents=True, exist_ok=0)\n pathlib.Path(path+'\\\\'+ name + '/1.測繪產品' + './1.8.3DModel_3D模型').mkdir(parents=True, exist_ok=0)\n\ndef add_3D(name, date, wgs84, othro_location, cad_location, model_location, output):\n # check(dic['othro']+'index.html')\n # check(dic['cad'])\n # check(dic['model']+'tileset.json')\n root = ET.Element('tree',id=\"0\")\n one = ET.SubElement(root, \"item\", text = name[9:], id = name, nocheckbox=\"1\", im0=\"hd.gif\", im1=\"folderOpen.gif\", im2=\"folderClosed.gif\")\n ET.SubElement(one , \"item\", text = '正射影像_' + date + '(雙擊定位)' , id = wgs84 + ';;18@TileImage_ps@' + othro_location , im0='hd.gif', im1='folderOpen.gif', im2='folderClosed.gif').text = ' '\n ET.SubElement(one , \"item\", text = '工程圖說' , id = wgs84 + ';;18@Kml@' + cad_location , im0='hd.gif', im1='folderOpen.gif', im2='folderClosed.gif').text = ' '\n ET.SubElement(one , \"item\", text = '3D_模型' , id = wgs84 + ';;18@3DModel@' + model_location , im0='hd.gif', im1='folderOpen.gif', im2='folderClosed.gif').text = ' '\n xmlstr = minidom.parseString(ET.tostring(root)).toprettyxml(indent=\" \")\n \n with open(output, 'w',encoding = 'utf8') as myXML:\n myXML.write(xmlstr) \n\ndef create_xml(i, wgs84):\n url_front = 'https://geodac.ncku.edu.tw/TCGEO/2019/'\n # create each location in ftp\n othro_location = os.path.join(url_front, i, 'othro/').replace('\\\\', '/')\n cad_location = os.path.join(url_front, i, 'cad/' ).replace('\\\\', '/')\n model_location = os.path.join(url_front, i, 'model/').replace('\\\\', '/')\n \n # output file path\n output = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_8 + '\\\\' + r'xml.txt' \n \n # chunkname date wgs84 othro_location cad_location model_location outputFile\n add_3D(i, i[0:8], wgs84, othro_location, cad_location, model_location, output)\n\ndef retrieve_pixel_value(geo_coord, data_source):\n \"\"\"Return floating-point value that corresponds to given point.\"\"\"\n x, y = geo_coord[0], geo_coord[1]\n forward_transform = affine.Affine.from_gdal(*data_source.GetGeoTransform())\n reverse_transform = ~forward_transform\n px, py = reverse_transform * (x, y)\n px, py = int(px + 0.5), int(py + 0.5)\n pixel_coord = px, py\n data_array = np.array(data_source.GetRasterBand(1).ReadAsArray())\n # i = data_array[0]\n # j = data_array[1]\n return(data_array[pixel_coord[1]][pixel_coord[0]])\n\ndef get_lon_lat(file_in, file_out, data): \n with open(file_in, 'r', encoding='utf8') as origin:\n with open(file_out, 'w', encoding='utf8') as out:\n num = 0\n for line in origin.readlines(): \n if num == 1 :\n out.write('\\t\\t\\t\\t\\t\\t\\t')\n for j in line.split():\n if j != '' and j != '':\n xlon = float(j.split(',')[0])\n xlat = float(j.split(',')[1])\n print(xlon, xlat)\n \n h = str(retrieve_pixel_value((xlon, xlat), data)+0.7) \n out.write(j.split(',')[0] + ',' + j.split(',')[1] + ',' + h + ' ') \n # out.write('')\n num = 0\n else: \n if line.find('0') > 0:\n pass\n else:\n out.write(line) \n \n if line.find('') > 0:\n num = 1\n print('[OK] write kml.\\n')\n\ndef add_kml(i, dir_1_8, dsm84):\n start_time = time.time()\n dsm_path = dsm84\n origin_kml_name = \"RAW_{}_{}\".format(i.split('_')[1], i.split('_')[2])\n output_kml_name = \"{}_{}_{}\".format( i.split('_')[0], i.split('_')[1], i.split('_')[2])\n origin_kml_path = os.path.join(dir_1_8, origin_kml_name)\n output_kml_path = os.path.join(dir_1_8, output_kml_name)\n data = gdal.Open(dsm_path, GA_ReadOnly)\n array = get_lon_lat(origin_kml_path, output_kml_path, data)\n \n print(\"--- %s seconds ---\" % (time.time() - start_time))\n\n# 自動產生 TIFF、KMZ、TILE、MODEL(含解壓縮及中心座標檔案)\ndef export():\n \n for chunk in Metashape.app.document.chunks:\n for i in os.listdir(path):\n # print('chunk name: ' + chunk.label)\n # i為資料夾名稱 == chunk名字\n if i == chunk.label:\n \n #create_dir(i)\n \n # 1.1.Ortho_正射影像\n #------- 路徑 ---- 資料夾檔名 - 1.測繪產品 ------ 1.1 ----- 檔案名稱+副檔名 \n othro = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_1 + '\\\\' + i + '.tif'\n tile = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_1 + '\\\\' + i + '.zip'\n report = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_3 + '\\\\' + i + '.pdf'\n kmz = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_1 + '\\\\' + i + '.kmz'\n \n # 1.4.ContCoor_控制點座標)\n path_marker = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_4 + '\\\\' + i + '.txt'\n \n # 1.7.DSM_數值地表模型\n dsm97 = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_7 + '\\\\' + i + '_tw97.tif'\n dsm84 = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_7 + '\\\\' + i + '_wgs84.tif'\n \n # 1.8.3DModel_3D模型\n obj = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_8 + '\\\\' + 'model' + '.obj'\n kmz_3d = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_8 + '\\\\' + i + '.kmz'\n \n\n # 無法自動輸出控制點\n # T = chunk.transform.matrix\n # f = open(path_marker, 'wt')\n # for marker in chunk.markers:\n # if not marker.position:\n # continue\n # v_t = T.mulp(marker.position)\n # chunk.crs = Metashape.CoordinateSystem(\"EPSG::4326\")\n # v_out = chunk.crs.project(v_t)\n # f.write(marker.label + ',' + str(v_out[0]) + ',' + str(v_out[1]) + ',' + str(v_out[2]) + '\\n')\n # print(marker.label + ',' + str(v_out[0]) + ',' + str(v_out[1]) + ',' + str(v_out[2]) + '\\n')\n # f.close()\n \n \n # ## 正射影像 TIFF\n # chunk.exportOrthomosaic(othro,image_format=Metashape.ImageFormatTIFF,projection=tw97,raster_transform=Metashape.RasterTransformNone,write_kml=True,write_world=True,white_background=False)\n # print('[OK] export othro.')\n \n # ## 正射影像 KMZ\n # chunk.exportOrthomosaic(kmz ,format=Metashape.RasterFormatKMZ,raster_transform=Metashape.RasterTransformNone,write_kml=True,write_world=True)\n # print('[OK] export kmz.')\n\n # ## 報告\n # chunk.exportReport(report, title = i, description = 'Made by GEODAC')\n # print('[OK] export report.')\n\n # ## 圖專\n # chunk.exportOrthomosaic(tile,format=Metashape.RasterFormatXYZ,image_format=Metashape.ImageFormatPNG,raster_transform=Metashape.RasterTransformNone,projection=ws84,write_kml=True)\n # print('[OK] export tile.')\n \n # ## DSM\n # chunk.exportDem(path=dsm97,format=Metashape.RasterFormatTiles,image_format=Metashape.ImageFormatTIFF,projection= tw97, nodata=-32767)\n # chunk.exportDem(path=dsm84,format=Metashape.RasterFormatTiles,image_format=Metashape.ImageFormatTIFF,projection= ds84, nodata=-32767)\n # print('[OK] export dsm.') \n \n # ##三維模型 OBJ\n # chunk.exportModel(obj , binary=False, precision=6, texture_format=Metashape.ImageFormatJPEG, texture=True, normals=False, colors=False, cameras=False, udim=False, strip_extensions=False, format=Metashape.ModelFormatOBJ, projection=crs)\n # print('[OK] export obj.')\n\n # ##三維模型 KMZ\n # chunk.exportModel(kmz_3d , binary=False, precision=6, texture_format=Metashape.ImageFormatJPEG, texture=True, normals=False, colors=False, cameras=False, udim=False, strip_extensions=False, format=Metashape.ModelFormatKMZ, projection=crs)\n # print('[OK] export kmz_3d.')\n\n # ## 解壓縮KMZ_3D\n # with zipfile.ZipFile(kmz_3d, 'r') as kmz:\n # pathlib.Path(path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_8 + '\\\\' + i).mkdir(parents=True, exist_ok=1)\n # #os.mkdir(path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_8 + '\\\\' + i)\n # kmz.extractall(path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_8 + '\\\\' + i + '\\\\')\n # print('[OK] unzip kmz_3d')\n \n # ## - - - 批次輸出分隔線結束 - - - \n \n ## KML新增高程\n add_kml(i, dir_1_8, dsm84) \n \n # ## 輸出至D槽，創建資料夾\n # out_case = os.path.join('D:\\Backup139\\\\', i)\n # out_tile = os.path.join(out_case, 'othro')\n # out_cad = os.path.join(out_case, 'cad')\n # out_model = os.path.join(out_case, 'model')\n \n # pathlib.Path(out_case).mkdir(parents=True, exist_ok=1)\n # pathlib.Path(out_tile).mkdir(parents=True, exist_ok=1)\n # pathlib.Path(out_cad).mkdir(parents=True, exist_ok=1)\n # # pathlib.Path(out_model).mkdir(parents=True, exist_ok=1)\n\n # # 解壓縮圖專\n # print('Start unzip tile')\n # with zipfile.ZipFile(tile, 'r') as zf:\n # # pathlib.Path(path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_1 + '\\\\' + i).mkdir(parents=True, exist_ok=1)\n # # os.mkdir(path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_1 + '\\\\' + i)\n # zf.extractall(out_tile)\n # print('[OK] unzip tile') \n \n \n # ## 讀取中心座標 \n # kml = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_8 + '\\\\' + i + '\\\\' + 'doc.kml'\n # with open(kml, 'r', encoding = 'utf8')as f:\n\n # soup = BeautifulSoup(f.read(), 'html.parser')\n # lon = soup.select('longitude')[0].text\n # lat = soup.select('latitude')[0].text\n # center = lat+ ',' + lon \n # wgs84 = lat+ ';' + lon\n # output = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_8 + '\\\\' + center + '.txt'\n # open(output, 'a',encoding = 'utf8')\n # print('[OK] create center file')\n \n # ## 寫入處理名單\n # with open(r'\\\\140.116.228.155\\geodac_uav\\uav.txt', 'a', encoding = 'utf8') as txt:\n # txt.write(i + ',' + center +'\\n')\n # print('[OK] add to uav.txt')\n \n # ## 自動執行Tran3D\n # path_174 = r'\\\\140.116.228.174\\geodac_data_test\\RAW\\RSImage\\UAV\\3DModel'\n # path_174 = 'T:\\\\'\n # dst_path = ''\n # folder = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\' + dir_1_8\n # nowtime = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n # tran = ''\n # # print(folder, '\\n\\n', nowtime)\n # for item in os.listdir(folder):\n # # dir_174 .b3d所在的地方\n # dir_174 = os.path.join(path_174, nowtime)\n # dst_path = dir_174 \n # pathlib.Path(dir_174).mkdir(parents=True, exist_ok=1)\n # if item[-3:] == 'txt':\n # if item != 'xml.txt':\n # # print(nowtime, item)\n # lat = item[0:13]\n # lon = item[14:28]\n # tran = \"ssh user1@140.116.228.180 -p 2202 './trans3d \" + nowtime + ' ' + lon + ' ' + lat + \"'\"\n # print(tran)\n\n # if item[-3:] == 'obj':\n # shutil.copyfile(os.path.join(folder,item),os.path.join(dir_174,item))\n # elif item[-3:] == 'mtl':\n # shutil.copyfile(os.path.join(folder, item),os.path.join(dir_174,item))\n # elif item[-3:] == 'jpg':\n # shutil.copyfile(os.path.join(folder, item),os.path.join(dir_174,item))\n \n \n # process = subprocess.Popen('powershell.exe ' + tran, stdout=PIPE, stderr=PIPE, stdin=PIPE)\n # out, err = process.communicate()\n # print('[stdout]: ', out)\n # print('[stderr]: ', err)\n # print('\\n')\n # ## 自動執行Tran3D結束\n \n # ## move tran3d from 174 to 155\n # after_tran = path_174 + '\\\\' + nowtime + '\\\\' + 'Batchedmodel'\n # dst_155 = path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_8 + '\\\\' + 'tran3d'\n \n # print(os.path.isdir(after_tran), after_tran)\n \n # if os.path.isdir(dst_155):\n # shutil.rmtree(dst_155)\n # shutil.copytree(after_tran, dst_155)\n # else:\n # shutil.copytree(after_tran, dst_155)\n \n # ## D槽也一份\n # if os.path.isdir(out_model):\n # shutil.rmtree(out_model) \n # shutil.copytree(after_tran, out_model)\n # else:\n # shutil.copytree(after_tran, out_model)\n \n # prjno = i.split('_')[2]\n # raw_kml_dir = r'F:\\WorkFolder\\TCGE_工程圖說'\n \n\n \n # ## 自動產生xml\n # create_xml(i, wgs84)\n # shutil.copyfile(path + '\\\\' + i + '\\\\' + dir_1 + '\\\\'+ dir_1_8 + '\\\\' + r'xml.txt', out_case + '\\\\xml.txt')\n \ndef main():\n export()\n\nmain()\n\nprint(\"\\n- - - - - - - - Script End - - - - - - - - \\n\")", "sub_path": "batch_output_Metashape_2019.py", "file_name": "batch_output_Metashape_2019.py", "file_ext": "py", "file_size_in_byte": 18828, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "Metashape.CoordinateSystem", "line_number": 21, "usage_type": "call"}, {"api_name": "Metashape.CoordinateSystem", "line_number": 22, "usage_type": "call"}, {"api_name": "Metashape.CoordinateSystem", "line_number": 23, "usage_type": "call"}, {"api_name": "Metashape.CoordinateSystem", "line_number": 24, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 56, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 57, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 58, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 59, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 60, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 62, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 63, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 64, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 65, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 66, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 67, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 68, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 69, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 75, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 75, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 76, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 76, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 77, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 77, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 78, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 78, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 79, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 79, "usage_type": "name"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 80, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 80, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.tostring", "line_number": 80, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 80, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "affine.Affine.from_gdal", "line_number": 101, "usage_type": "call"}, {"api_name": "affine.Affine", "line_number": 101, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "osgeo.gdal.Open", "line_number": 145, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 145, "usage_type": "name"}, {"api_name": "time.time", "line_number": 148, "usage_type": "call"}, {"api_name": "Metashape.app", "line_number": 153, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 154, "usage_type": "call"}]} +{"seq_id": "441606083", "text": "# Importing functions which allows data to be extracted\nfrom lxml import html\nimport requests\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport os\n\ndef graph(weather, location):\n temprange = plt.axes()\n # Alpha gives the colour density\n # Decimals control spacing between bars\n temprange.bar([1.5, 2.5, 3.5, 4.5, 5.5], weather['Min'], width=0.5, alpha=0.4)\n # if statement to control the issue of any missing data\n if len(weather['Max']) == 4:\n temprange.bar([1, 2, 3, 4], weather['Max'], width=0.5, alpha=0.4)\n elif len(weather['Max']) == 5:\n temprange.bar([1, 2, 3, 4, 5], weather['Max'], width=0.5, alpha=0.4)\n # x axis (days)\n temprange.set_xticklabels(weather['Day'])\n # y axis, auto sets from min value to max value temp\n weather=np.ma.masked_array(weather, mask=(weather==-999), fill_value=0)\n # prints the graph\n plt.show()\n\ndef weather_report(URL):\n # Pulls the desired website\n page = requests.get(URL)\n # Extracting HTML\n data = html.fromstring(page.content)\n # Extacting data from BBC weather for days, min & max temp\n day = data.xpath('//*[@id=\"blq-content\"]/div[7]/div[2]/ul/li/a/div/h3/span/text()')\n max_temp = data.xpath('//*[@id=\"blq-content\"]/div[7]/div[2]/ul/li/a/span[2]/span/span[1]/text()')\n min_temp = data.xpath('//*[@id=\"blq-content\"]/div[7]/div[2]/ul/li/a/span[3]/span/span[1]/text()')\n location = data.xpath('//*[@id=\"blq-content\"]/div[1]/h1/span/text()')\n print (location[0] + \" five day forecast\")\n# When max temp for nightime is missing...\n if len(max_temp) == 4:\n weather=-999*np.ones((5,3), dtype='object') # Deals with the missing data issue, sets any missing temp as -999\n weather[:,0] = day\n # Note that this is different from the elif statement below: [1:,1] instead of [:,1]. Handling the missing data issue\n weather[1:,1] = [int(i) for i in max_temp]\n weather[:,2] = [int(i) for i in min_temp]\n # If the length of max list is 5 (i.e. it's daytime) do this:\n elif len(max_temp) == 5:\n weather=np.zeros((5,3), dtype='object')\n weather[:,0] = day\n weather[:,1] = [int(i) for i in max_temp]\n weather[:,2] = [int(i) for i in min_temp]\n # Masks any -999 in weather, fill value means to blank them out\n weather=np.ma.masked_array(weather, mask=(weather==-999), fill_value=0)\n weather = pd.DataFrame(weather, columns=['Day', 'Max', 'Min']) # Labels the columns\n\n #root = \"/home/grace/Desktop/Web Scraper Project/\"\n #weather = pd.DataFrame(weather, columns=['Day', 'Max', 'Min']) # Labels the columns\n #filename = city[0] + \".csv\"\n #with open(root + filename, \"a\") as f:\n # weather.to_csv(f, header=False) # Appending so that we can collect data over time\n #weather.to_csv(city[0] + \".csv\") Magic one line code for making a csv file\n #make_graph(weather, city)\n\n root = \"/home/sylke/Desktop/webscraper/\"\n filename = location[0] + \".csv\"\n with open(root + filename, \"a\") as d:\n weather.to_csv(d, header=False)\n # weather.to_csv = location[0] + \".csv\")\n print (weather)\n # graph(weather, location)\n\nLas_vegas_temp = 'http://www.bbc.co.uk/weather/5506956'\nPurley_temp = 'http://www.bbc.co.uk/weather/2639842'\nAmsterdam_temp = 'http://www.bbc.co.uk/weather/2759794'\n\nweather_report(Las_vegas_temp)\nweather_report(Purley_temp)\nweather_report(Amsterdam_temp)\n", "sub_path": "webscraper/webscrape.py", "file_name": "webscrape.py", "file_ext": "py", "file_size_in_byte": 3419, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "matplotlib.pyplot.axes", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.ma.masked_array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 23, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "lxml.html.fromstring", "line_number": 31, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.ma.masked_array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "260999452", "text": "from http.cookies import SimpleCookie\n\nfrom scrapy.exceptions import DropItem\nfrom scrapy import Request\nfrom scrapy.spiders import CrawlSpider\nfrom scrapy.utils.request import request_fingerprint\n\nfrom mpn_scraper.spiders.spider_helpers import get_spider_data, filter_links\n\n\nclass MpnSpider(CrawlSpider):\n name = \"mpn_spider\"\n cookies = SimpleCookie(\"\")\n source = \"\"\n allowed_domains = []\n\n # https://stackoverflow.com/a/15618520/5441099\n def __init__(self, **kwargs):\n spider_data = get_spider_data(self.__class__, **kwargs,)\n for k, v in spider_data.items():\n self.__setattr__(k, v)\n if not self.start_urls:\n raise ValueError(\"Missing start_urls in spider or constructor arguments.\")\n if not self.allowed_domains:\n raise ValueError(\"Missing allowed_domains in spider.\")\n if not self.source:\n raise ValueError(\"Missing source in spider.\")\n\n super().__init__(**{k: v for k, v in kwargs.items() if not hasattr(self, k)})\n\n def start_requests(self):\n for url in self.start_urls:\n yield Request(url, dont_filter=True, cookies=self.cookies)\n\n def filter_links(self, links):\n return filter_links(links)\n\n def add_cookies(self, request, response):\n for k, v in self.cookies.items():\n request.cookies[k] = v.value\n return request\n\n def finish_item(self, response, loader=None, item=None):\n extra_values = {\n \"provenance\": self.source,\n \"url\": response.url,\n \"url_fingerprint\": request_fingerprint(response.request),\n }\n if loader:\n for k, v in extra_values.items():\n loader.add_value(k, v)\n loader.add_css(\n \"canonical_url\",\n \"link[rel=canonical]::attr(href), meta[property='og:url']::attr(content)\",\n )\n return loader.load_item()\n elif item:\n for k, v in extra_values.items():\n if not item.get(k):\n item[k] = v\n if not item.get(\"canonical_url\"):\n canonical_url = response.css(\n \"link[rel=canonical]::attr(href), meta[property='og:url']::attr(content)\"\n ).extract_first()\n item[\"canonical_url\"] = canonical_url\n return item\n else:\n raise DropItem(\"Finish item has neither loader not item\")\n", "sub_path": "mpn_scraper/spiders/mpn_spider.py", "file_name": "mpn_spider.py", "file_ext": "py", "file_size_in_byte": 2450, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "scrapy.spiders.CrawlSpider", "line_number": 11, "usage_type": "name"}, {"api_name": "http.cookies.SimpleCookie", "line_number": 13, "usage_type": "call"}, {"api_name": "mpn_scraper.spiders.spider_helpers.get_spider_data", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 33, "usage_type": "call"}, {"api_name": "mpn_scraper.spiders.spider_helpers.filter_links", "line_number": 36, "usage_type": "call"}, {"api_name": "scrapy.utils.request.request_fingerprint", "line_number": 47, "usage_type": "call"}, {"api_name": "scrapy.exceptions.DropItem", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "442659050", "text": "#! /usr/bin/env python\n# coding:utf8\n\nfrom django.http import Http404\nfrom django.core.exceptions import PermissionDenied\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView, set_rollback\nfrom rest_framework.exceptions import APIException, NotAuthenticated, AuthenticationFailed, Throttled\n\nfrom .resp_code import SUCCESS\nfrom .exception import UnAuthorizationExc\n\n\nclass TriDrfApiView(APIView):\n\tdef initial(self, request, *args, **kwargs):\n\t\tif request.path.startswith(\"/api\"):\n\t\t\trequest._SET_COOKIE = False\n\t\t\trequest._DEL_COOKIE = False\n\t\t\t\"\"\"\n\t\t\tMake decrypt for each request view here.\n\t\t\t\"\"\"\n\t\treturn super(TriDrfApiView, self).initial(request, *args, **kwargs)\n\n\tdef finalize_response(self, request, response, *args, **kwargs):\n\t\tif not response:\n\t\t\tresponse = \"\"\n\n\t\tif isinstance(response, (dict, list, str)):\n\t\t\tresponse = Response({\n\t\t\t\t\"code\": SUCCESS, \"message\": \"\", \"data\": response\n\t\t\t})\n\n\t\tif request._SET_COOKIE:\n\t\t\tresponse.set_cookie(\"AUTHORIZATION\", \"Token {}\".format(request._SET_COOKIE), max_age=86400 * 7)\n\t\telif request._DEL_COOKIE:\n\t\t\tresponse.delete_cookie(\"AUTHORIZATION\")\n\t\treturn super(TriDrfApiView, self).finalize_response(request, response, *args, **kwargs)\n\n\tdef handle_exception(self, exc):\n\t\tset_rollback()\n\n\t\tresponse = None\n\t\tif isinstance(exc, UnAuthorizationExc):\n\t\t\tresponse = Response({\n\t\t\t\t\"code\": exc.code, \"message\": exc.message,\n\t\t\t})\n\t\t\tresponse.delete_cookie(\"AUTHORIZATION\")\n\t\telif isinstance(exc, NotAuthenticated):\n\t\t\tresponse = Response({\"code\": exc.default_code, \"message\": exc.detail}, exception=True)\n\t\telif isinstance(exc, AuthenticationFailed):\n\t\t\tresponse = Response({\"code\": exc.default_code, \"message\": exc.detail}, exception=True)\n\t\telif isinstance(exc, Throttled):\n\t\t\tresponse = Response({\"code\": exc.default_code, \"message\": exc.detail}, exception=True)\n\t\telif isinstance(exc, APIException):\n\t\t\tresponse = Response({\"code\": exc.default_code, \"message\": exc.detail}, exception=True)\n\t\telif isinstance(exc, Http404):\n\t\t\tresponse = Response({\"code\": 404, \"message\": \"404 Not Found\"}, status=404, exception=True)\n\t\telif isinstance(exc, PermissionDenied):\n\t\t\tresponse = Response({\"code\": 403, \"message\": \"403 Forbidden\"}, status=403, exception=True)\n\n\t\tif not response:\n\t\t\tself.raise_uncaught_exception(exc)\n\t\telse:\n\t\t\texc.__traceback__ = None\n\t\t\treturn response\n", "sub_path": "utils/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2355, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 14, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 29, "usage_type": "call"}, {"api_name": "resp_code.SUCCESS", "line_number": 30, "usage_type": "name"}, {"api_name": "rest_framework.views.set_rollback", "line_number": 40, "usage_type": "call"}, {"api_name": "exception.UnAuthorizationExc", "line_number": 43, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.NotAuthenticated", "line_number": 48, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 49, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.AuthenticationFailed", "line_number": 50, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 51, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.Throttled", "line_number": 52, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 53, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.APIException", "line_number": 54, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 55, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 56, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 57, "usage_type": "call"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 58, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "487903283", "text": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nimport calendar\nimport configparser\nimport csv\nimport datetime\nimport json\nimport sys\nimport pyinstaller\n\nimport time\nfrom time import strftime, localtime\nfrom urllib.request import urlopen\n\nimport openpyxl\n\n\n# 打印当前时间\ndef getNowTime():\n nowTime = strftime(\"%Y-%m-%d %H:%M:%S\", localtime())\n return nowTime\n\n\n'''\n获取临时的结果,结果从大到小排序\ntype：In 获取in流量，Out获取out流量\n'''\n\n\ndef getListByInOrOut(type=\"In\", device=\"\", interface=\"\", ):\n tempList = []\n try:\n if (type == \"In\"):\n url = param.InOctets2bps\n else:\n url = param.OutOctets2bps\n startTime = int(time.mktime(datetime.datetime.strptime(param.startTime, '%Y%m%d').timetuple()))\n endTime = int(time.mktime(datetime.datetime.strptime(param.endTime, '%Y%m%d').timetuple()))\n stepFrequency = int((endTime - startTime) / 10009)\n url = url.replace(\"device\", device)\n url = url.replace(\"interface\", interface)\n url = url.replace(\"startTime\", str(startTime))\n url = url.replace(\"endTime\", str(endTime))\n url = url.replace(\"stepFrequency\", str(stepFrequency))\n tempResult = get_result_page(url)\n values = json.loads(tempResult)['data']['result'][0]['values']\n for i in range(0, list(values).__len__()):\n temparr = [values[i][0], int(float(values[i][1]))]\n tempList.append(temparr)\n tempList = sorted(tempList, key=(lambda x: x[1]), reverse=True)\n except Exception as e:\n print(\"url:\" + url)\n raise\n return tempList\n\n\n'''\n根据list结果获取95%最大值最小值 平均数\n'''\n\n\ndef getAvgMinMaxByList(list):\n byList = {}\n nsum = 0\n for i in range(0, list.__len__()):\n nsum += list[i][1]\n byList[\"avg\"] = round(nsum / list.__len__() / 1000 / 1024, 2)\n byList[\"min\"] = round(list[round(list.__len__() * 0.95)][1] / 1000 / 1024, 2)\n byList[\"max\"] = round(list[list.__len__() - round(list.__len__() * 0.95)][1] / 1000 / 1024, 2)\n return byList\n\n\n'''\n根据 接口和设备获取参数\n'''\n\n\ndef getResult(bandwidth=\"\", device=\"\", interface=\"\"):\n result = []\n inList = getListByInOrOut(type=\"In\", device=device, interface=interface)\n outList = getListByInOrOut(type=\"Out\", device=device, interface=interface)\n inListR = getAvgMinMaxByList(inList)\n outListR = getAvgMinMaxByList(outList)\n result.append(inListR[\"avg\"])\n result.append(outListR[\"avg\"])\n result.append(inListR[\"max\"])\n result.append(outListR[\"max\"])\n imax95 = round(float(inListR[\"max\"]) * 100 / int(bandwidth), 2)\n omax95 = round(float(outListR[\"max\"]) * 100 / int(bandwidth), 2)\n result.append(imax95)\n result.append(omax95)\n if (imax95 >= omax95):\n result.append(imax95)\n else:\n result.append(omax95)\n return result\n\n\n# 读取execl 改变excel内容\ndef readExcel(filename):\n wb = openpyxl.load_workbook(filename)\n ws = wb.active\n for i in range(1, ws.max_row + 1):\n bandwidth = ws.cell(row=i, column=3).value\n device = ws.cell(row=i, column=4).value\n interface = ws.cell(row=i, column=5).value\n if (bandwidth != None and device != None and interface != None and str(bandwidth).isdigit()):\n try:\n print(\"device:\" + device + \"\\tinterface:\" + interface)\n result = getResult(bandwidth=bandwidth, device=device, interface=interface)\n except Exception as e:\n # print(e)\n continue\n ws.cell(row=i, column=6, value=str(result[0]))\n ws.cell(row=i, column=7, value=str(result[1]))\n ws.cell(row=i, column=8, value=str(result[2]))\n ws.cell(row=i, column=9, value=str(result[3]))\n ws.cell(row=i, column=10, value=str(result[4]))\n ws.cell(row=i, column=11, value=str(result[5]))\n ws.cell(row=i, column=12, value=str(result[6]))\n wb.save(filename)\n wb.close()\n\n\n# 访问\ndef get_result_page(url):\n res = urlopen(url).read()\n return str(res, 'utf-8')\n\n\n## 加载conf里面的配置\ndef getConfig(configName='config.conf'):\n config = configparser.ConfigParser()\n config.read(configName, encoding='UTF-8-sig')\n param.startTime = str(config.get('time_config', 'startTime'))\n param.endTime = str(config.get('time_config', 'endTime'))\n param.excelFileName = str(config.get('excel_config', 'excelFileName'))\n param.InOctets2bps = str(config.get('default_config', 'InOctets2bps')[1:-1])\n param.OutOctets2bps = str(config.get('default_config', 'OutOctets2bps')[1:-1])\n\n\nclass confParam:\n startTime = \"\"\n endTime = \"\"\n excelFileName = \"\"\n InOctets2bps = \"\"\n OutOctets2bps = \"\"\n\n\n'''\n获取上一个周五的日期\n'''\n\n\ndef getLastFriday(nowDate=\"\"):\n oneday = datetime.timedelta(days=1)\n if (nowDate == \"\"):\n lastFriday = datetime.date.today()\n else:\n lastFriday = datetime.datetime.strptime(nowDate, '%Y%m%d')\n if (lastFriday.weekday() == calendar.FRIDAY):\n lastFriday -= oneday\n while lastFriday.weekday() != calendar.FRIDAY:\n lastFriday -= oneday\n return lastFriday.strftime('%Y%m%d')\n\n\nclass Logger(object):\n def __init__(self, filename='log.txt', stream=sys.stdout):\n self.terminal = stream\n self.log = open(filename, 'a', encoding='utf8')\n\n def write(self, message):\n self.terminal.write(message)\n self.log.write(message)\n\n def flush(self):\n pass\n\n\nsys.stdout = Logger('log.txt', sys.stdout)\nsys.stderr = Logger('log.txt', sys.stderr)\n\nif __name__ == '__main__':\n param = confParam()\n getConfig()\n # print(\"excelFileName:\"+param.excelFileName)\n # print(\"InOctets2bps:\"+param.InOctets2bps)\n # print(\"OutOctets2bps:\"+param.OutOctets2bps)\n if (param.endTime == \"\"):\n param.endTime = getLastFriday()\n param.startTime = getLastFriday(param.endTime)\n readExcel(param.excelFileName)\n", "sub_path": "IP_transit_report/report/report.py", "file_name": "report.py", "file_ext": "py", "file_size_in_byte": 6000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "time.strftime", "line_number": 21, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 21, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "time.mktime", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 102, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 128, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 134, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 157, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 159, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 159, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 161, "usage_type": "attribute"}, {"api_name": "calendar.FRIDAY", "line_number": 162, "usage_type": "attribute"}, {"api_name": "calendar.FRIDAY", "line_number": 164, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 170, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 182, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 183, "usage_type": "attribute"}]} +{"seq_id": "5971103", "text": "import re\nimport math\nfrom collections import defaultdict\nfrom datetime import datetime\n\n#-------------- REGEX ---------------\nPID = r'Id:\\s*(\\d+)'\nASIN = r'ASIN:\\s*\\d+'\nCID = r'cutomer:\\s*(\\w+)'\nDISC = r'discontinued product'\nCSIZE = r'total:\\s*(\\d+)'\n#-------------- ---------------------\nMAX_CLIENTS = 5486\ntotal_capacity = 0\n#pid_temp\namazon_data = defaultdict(list) #client has many products\n\n\n\nf = open('../amazon-meta.txt', 'r')\nflog = open('log.txt', 'w')\nfgraph = open('graph.in', 'w')\n\n\nstime = datetime.now()\nsearchProduct = True\nreviews = 0\nproduct_reviews = {}\ntotal_reviews = 0\nfor line in f:\n\t#Esta procurando ou por produto ou por cliente\n\tif searchProduct:\n\t\tpid_temp = re.search(PID, line)\n\t\tif pid_temp:\n\t\t\tsearchProduct = False #Assim que encontra produto, eh hora de encontrar cliente\n\t\t\tindex = int(pid_temp.group(1))\n\telse:\n\t\tpid_temp2 = re.search(PID, line)\n\t\tif pid_temp2:\n\t\t\t pid_temp = pid_temp2 #discontinued product case. o produto nao tinha review portando pulou para o proximo produto\n\t\t\t index = int(pid_temp.group(1))\n\n\t\treview = re.search(CSIZE,line)\n\t\tif review:\n\t\t\treviews = int(review.group(1))\n\t\t\tif reviews > 0:\n\t\t\t\tproduct_reviews[pid_temp.group(1)] = reviews\n\t\t\t\ttotal_reviews += reviews\n\n\t\tcid = re.search(CID, line)\n\t\tif cid:\n\t\t\t#print pid_temp.group(1),cid.group(1)\n\t\t\tif not pid_temp.group(1) in amazon_data[cid.group(1)]: #do not repeat items on list\n\t\t\t\tamazon_data[cid.group(1)].append(pid_temp.group(1))\n\t\t\tif len(amazon_data) > MAX_CLIENTS:\n\t\t\t\tindex = int(pid_temp.group(1))\n\t\t\t\tbreak\n\n\n#index = 548551 #last product_id is 548551\n#1% is about 5486 clients\n#index = 200\n\n#source and sink\nfgraph.write('%s %d\\n' % (0,index+len(amazon_data) +1))\n\nfor k,v in amazon_data.iteritems():\n\tindex += 1\n\tfor p in v:\n\t\tfgraph.write('%s %s %d\\n' % (index,p,1))\n\t#40% of client reviews\n\tcapacity = math.ceil(0.4 * len(v))\n\tfgraph.write('%s %s %d\\n' % (0,index,capacity))\n\ttotal_capacity += capacity\n\n\n#60% product reviews\nfor k,v in product_reviews.iteritems():\n\tfgraph.write('%s %s %d\\n' % (k,index+1,v))\n\n\nflog.write(\"start time: %s\\n\" % stime)\nflog.write(\"end time: %s\\n\" % datetime.now())\nflog.write(\"number of users: %d\\n\" % index)\nflog.write(\"number of products: %d\\n\" % len(product_reviews))\nflog.write(\"total of reviews: %d\\n\" % total_reviews)\nflog.write(\"total capacity: %d\\n\" % total_capacity)\n\nflog.close()\nf.close()\nfgraph.close()\n\n", "sub_path": "parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 2381, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "collections.defaultdict", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "re.search", "line_number": 33, "usage_type": "call"}, {"api_name": "re.search", "line_number": 38, "usage_type": "call"}, {"api_name": "re.search", "line_number": 43, "usage_type": "call"}, {"api_name": "re.search", "line_number": 50, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 83, "usage_type": "name"}]} +{"seq_id": "248885124", "text": "# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.3'\n# jupytext_version: 0.8.6\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\nimport tidy_data\nimport numpy as np\nimport bebi103\nimport scipy\n\n# +\ndef conf_int_means(df):\n \"\"\"\n get 95% confidence intervals for labeled and unlabeled \n mean catastrophe times\n \n Inputs:\n df : dataframe of gardner_time_to_catastrophe_dic_tidy.csv dataset\n Outputs:\n (\n bs_unlabeled_mean_conf, \n bs_labeled_mean_conf\n )\n \"\"\"\n #Labeled data\n labeled_data = df.loc[df['labeled'] == True, 'time to catastrophe (s)'].values\n\n #Unlabeled data\n unlabeled_data = df.loc[df['labeled'] == False, 'time to catastrophe (s)'].values\n \n # get bootstrap replicates\n bs_labeled_means = bebi103.bootstrap.draw_bs_reps(labeled_data, np.mean, size=10000)\n bs_unlabeled_means = bebi103.bootstrap.draw_bs_reps(unlabeled_data, np.mean, size=10000)\n\n # calculate 95% confidence interval for labeled\n bs_labeled_mean_conf = np.percentile(bs_labeled_means, [2.5, 97.5])\n\n # calculate 95% confidence interval for unlabeled\n bs_unlabeled_mean_conf = np.percentile(bs_unlabeled_means, [2.5, 97.5])\n \n return (bs_unlabeled_mean_conf, bs_labeled_mean_conf)\n\n#output\n# Mean unlabeled conf int: [353.105, 477.475]\n# Mean labeled conf int: [402.275, 481.303]\n\n# +\ndef diff_means(df):\n \"\"\"\n get test statistic and and p-value for \n difference of mean catastrophe times.\n Null hypothesis that the two distributions are the same\n and thus the means are also the same\n \n Inputs:\n df : dataframe of gardner_time_to_catastrophe_dic_tidy.csv dataset\n Outputs:\n (diff_mean, p_val)\n \"\"\"\n \n #Labeled data\n labeled_data = df.loc[df['labeled'] == True, 'time to catastrophe (s)'].values\n\n #Unlabeled data\n unlabeled_data = df.loc[df['labeled'] == False, 'time to catastrophe (s)'].values\n \n # Compute test statistic for original data set\n diff_mean = np.mean(labeled_data) - np.mean(unlabeled_data)\n\n # Draw permutation replicates\n perm_reps = bebi103.bootstrap.draw_perm_reps(\n labeled_data, unlabeled_data, bebi103.bootstrap.diff_of_means, size = 10000\n )\n\n # Compute p-value\n p_val = np.sum(perm_reps >= diff_mean) / len(perm_reps)\n return (diff_mean, p_val)\n\n#Output\n# Experimental difference of means: 28.185\n# p-value = 0.229\n\n# +\ndef diff_means_student(df):\n \"\"\"\n get test statistic and and p-value for \n difference of mean catastrophe times, assuming\n student t distribution. Takes into account std\n \n Null hypothesis that the two distributions are the same\n and thus the means are also the same\n \n Inputs:\n df : dataframe of gardner_time_to_catastrophe_dic_tidy.csv dataset\n Outputs:\n (diff_mean_studentized, p_val_studentized)\n Notes: \n Uses bebi103.bootstrap.studentized_diff_of_means\n \"\"\"\n \n #Labeled data\n labeled_data = df.loc[df['labeled'] == True, 'time to catastrophe (s)'].values\n\n #Unlabeled data\n unlabeled_data = df.loc[df['labeled'] == False, 'time to catastrophe (s)'].values\n \n diff_mean_studentized = bebi103.bootstrap.studentized_diff_of_means(\n labeled_data, unlabeled_data\n )\n\n # Draw permutation replicates\n perm_reps_studentized = bebi103.bootstrap.draw_perm_reps(\n labeled_data, unlabeled_data, \n bebi103.bootstrap.studentized_diff_of_means, size = 10000\n )\n\n # Compute p-value\n p_val_studentized = np.sum(\n perm_reps_studentized >= diff_mean_studentized\n ) / len(perm_reps_studentized)\n \n return (diff_mean_studentized, p_val_studentized)\n\n#Output\n# Experimental studentized difference of means: 0.752\n# p-value = 0.239\n\n\n# +\ndef conf_int_means_normal(df):\n \"\"\"\n get 95% confidence intervals for labeled and unlabeled \n mean catastrophe times assuming normal distribution;\n interval over which 95% of the probability mass of the normal distribution lies \n \n Inputs:\n df : dataframe of gardner_time_to_catastrophe_dic_tidy.csv dataset\n Outputs:\n (\n conf_int_unlabeled,\n conf_int_labeled\n )\n \"\"\"\n #Labeled data\n labeled_data = df.loc[df['labeled'] == True, 'time to catastrophe (s)'].values\n\n #Unlabeled data\n unlabeled_data = df.loc[df['labeled'] == False, 'time to catastrophe (s)'].values\n\n # mean of labeled\n labeled_mean = np.mean(labeled_data)\n\n # mean of unlabled\n unlabeled_mean = np.mean(unlabeled_data)\n\n # CI of labeled\n conf_int_labeled = scipy.stats.norm.interval(0.95, loc=labeled_mean, scale=np.std(labeled_data)/np.sqrt(len(labeled_data)))\n \n # CI of unlabeled\n conf_int_unlabeled = scipy.stats.norm.interval(0.95, loc=unlabeled_mean, scale=np.std(unlabeled_data)/np.sqrt(len(unlabeled_data)))\n \n return (conf_int_unlabeled, conf_int_labeled)\n\n# #output\n# Mean unlabeled conf int: [351.165, 473.888]\n# Mean labeled conf int: [400.836, 480.586]\n\n# +\ndef main():\n df = tidy_data.tidy_dic()\n bs_unlabeled_mean_conf, bs_labeled_mean_conf = conf_int_means(df)\n\n print(\n \"\"\"\n Mean unlabeled conf int: [{0:.3f}, {1:.3f}]\n \"\"\".format(\n *tuple(bs_unlabeled_mean_conf)\n )\n )\n \n print(\n \"\"\"\n Mean labeled conf int: [{0:.3f}, {1:.3f}]\n \"\"\".format(\n *tuple(bs_labeled_mean_conf)\n )\n )\n \n print('\\n\\n')\n \n diff_mean, p_val = diff_means(df)\n \n print(f'Experimental difference of means: {diff_mean:.3f}')\n print(f'p-value = {p_val:.3f}')\n \n print('\\n\\n')\n \n diff_mean_studentized, p_val_studentized = diff_means_student(df)\n \n print(f'Experimental studentized difference of means: {diff_mean_studentized:.3f}')\n print(f'p-value = {p_val_studentized:.3f}')\n \n print('\\n\\n')\n \n normal_conf_int_unlabeled, normal_conf_int_labeled = conf_int_means_normal(df)\n \n print(\n \"\"\"\n Mean unlabeled conf int: [{0:.3f}, {1:.3f}]\n \"\"\".format(\n *tuple(normal_conf_int_unlabeled)\n )\n )\n \n print(\n \"\"\"\n Mean labeled conf int: [{0:.3f}, {1:.3f}]\n \"\"\".format(\n *tuple(normal_conf_int_labeled)\n )\n )\n return True\n \nif __name__ == '__main__': main()\n\n# +\n#!jupytext --to python controls.ipynb\n", "sub_path": "sandbox_code/controls.py", "file_name": "controls.py", "file_ext": "py", "file_size_in_byte": 6464, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "bebi103.bootstrap.draw_bs_reps", "line_number": 41, "usage_type": "call"}, {"api_name": "bebi103.bootstrap", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 41, "usage_type": "attribute"}, {"api_name": "bebi103.bootstrap.draw_bs_reps", "line_number": 42, "usage_type": "call"}, {"api_name": "bebi103.bootstrap", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.percentile", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 77, "usage_type": "call"}, {"api_name": "bebi103.bootstrap.draw_perm_reps", "line_number": 80, "usage_type": "call"}, {"api_name": "bebi103.bootstrap", "line_number": 80, "usage_type": "attribute"}, {"api_name": "bebi103.bootstrap", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 85, "usage_type": "call"}, {"api_name": "bebi103.bootstrap.studentized_diff_of_means", "line_number": 116, "usage_type": "call"}, {"api_name": "bebi103.bootstrap", "line_number": 116, "usage_type": "attribute"}, {"api_name": "bebi103.bootstrap.draw_perm_reps", "line_number": 121, "usage_type": "call"}, {"api_name": "bebi103.bootstrap", "line_number": 121, "usage_type": "attribute"}, {"api_name": "bebi103.bootstrap", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 163, "usage_type": "call"}, {"api_name": "scipy.stats.norm.interval", "line_number": 166, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 166, "usage_type": "attribute"}, {"api_name": "numpy.std", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 166, "usage_type": "call"}, {"api_name": "scipy.stats.norm.interval", "line_number": 169, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.std", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 169, "usage_type": "call"}, {"api_name": "tidy_data.tidy_dic", "line_number": 179, "usage_type": "call"}]} +{"seq_id": "368038289", "text": "from pymongo import Connection\n\nConnection=Connection('mongo.stuycs.org')\ndb = Connection.admin\nres=db.authenticate('ml7','ml7')\ndb = Connection['BathroomRating']\n\nAccounts = db.Accounts\nToiletRatings = db.ToiletRatings\nToiletSeatRatings = db.ToiletSeatRatings\nToiletPaperRatings = db.ToiletPaperRatings\nLegRoomRatings = db.LegRoomRatings\nFlusherRatings = db.FlusherRatings\nSmellRatings = db.SmellRatings\nAestheticsRatings = db.AestheticsRatings\nComments = db.Comments\n\ndef register(username,password):\n if Accounts.find({'usernames':username}).count() == 0:\n Accounts.insert({'usernames':username,'passwords':password})\n return True\n else:\n return False\n\ndef verify(username,password):\n if Accounts.find({'usernames':username,'passwords':password}).count() != 0:\n return True\n else:\n return False\n\ndef returnAllAccounts():\n accounts = []\n for account in Accounts.find():\n accounts.append('Username: '+str(account['usernames'])+'Password: '+str(account['passwords']))\n return accounts\n\ndef dropAccounts():\n Accounts.remove({})\n\ndef addToiletRatingByUser(username,lat,lng,rating):\n temp = lat,lng,rating\n account = Accounts.find_one({'usernames':username})\n if \"toiletList\" not in account:\n toiletList = [temp]\n Accounts.update({'usernames':username},{'$set':{'toiletList':toiletList}})\n else:\n toiletList = account['toiletList']\n if toiletList == None:\n toiletList = [temp]\n Accounts.update({'usernames':username},{'$set':{'toiletList':toiletList}})\n else:\n toiletList.append(temp)\n Accounts.update({'usernames':username},{'$set':{'toiletList':toiletList}})\n\n\ndef addToiletRating(username,lat,lng,rating):\n addToiletRatingByUser(username,lat,lng,rating)\n ratingList = ToiletRatings.find_one({'latitude':lat,'longitude':lng})\n if ratingList == None:\n ToiletRatings.insert({'latitude':lat,'longitude':lng,'rating':[rating]})\n else:\n ratingList = ratingList['rating']\n ratingList.append(rating)\n ToiletRatings.update({'latitude':lat,'longitude':lng},{'$set':{'rating':ratingList}})\n\ndef getToiletRating(lat,lng):\n tmp= ToiletRatings.find_one({'latitude':lat,'longitude':lng})\n if tmp:\n return tmp[\"rating\"]\n else:\n return []\n\ndef addToiletSeatRatingByUser(username,lat,lng,rating):\n temp = lat,lng,rating\n account = Accounts.find_one({'usernames':username})\n if \"toiletSeatList\" not in account:\n toiletSeatList = [temp]\n Accounts.update({'usernames':username},{'$set':{'toiletSeatList':toiletSeatList}})\n else:\n toiletSeatList = account['toiletSeatList']\n if toiletSeatList == None:\n toiletSeatList = [temp]\n Accounts.update({'usernames':username},{'$set':{'toiletSeatList':toiletSeatList}})\n else:\n toiletSeatList.append(temp)\n Accounts.update({'usernames':username},{'$set':{'toiletSeatList':toiletSeatList}})\n\n\ndef addToiletSeatRating(username,lat,lng,rating):\n addToiletSeatRatingByUser(username,lat,lng,rating)\n ratingList = ToiletSeatRatings.find_one({'latitude':lat,'longitude':lng})\n if ratingList == None:\n ToiletSeatRatings.insert({'latitude':lat,'longitude':lng,'rating':[rating]})\n else:\n ratingList = ratingList['rating']\n ratingList.append(rating)\n ToiletSeatRatings.update({'latitude':lat,'longitude':lng},{'$set':{'rating':ratingList}})\n\ndef getToiletSeatRating(lat,lng):\n tmp= ToiletSeatRatings.find_one({'latitude':lat,'longitude':lng})\n if tmp:\n return tmp[\"rating\"]\n else:\n return []\n\n\n\ndef addToiletPaperRatingByUser(username,lat,lng,rating):\n temp = lat,lng,rating\n account = Accounts.find_one({'usernames':username})\n if \"toiletPaperList\" not in account:\n toiletPaperList = [temp]\n Accounts.update({'usernames':username},{'$set':{'toiletPaperList':toiletPaperList}})\n else:\n toiletPaperList = account['toiletPaperList']\n if toiletPaperList == None:\n toiletPaperList = [temp]\n Accounts.update({'usernames':username},{'$set':{'toiletPaperList':toiletPaperList}})\n else:\n toiletPaperList.append(temp)\n Accounts.update({'usernames':username},{'$set':{'toiletPaperList':toiletPaperList}})\n\n\ndef addToiletPaperRating(username,lat,lng,rating):\n addToiletPaperRatingByUser(username,lat,lng,rating)\n ratingList = ToiletPaperRatings.find_one({'latitude':lat,'longitude':lng})\n if ratingList == None:\n ToiletPaperRatings.insert({'latitude':lat,'longitude':lng,'rating':[rating]})\n else:\n ratingList = ratingList['rating']\n ratingList.append(rating)\n ToiletPaperRatings.update({'latitude':lat,'longitude':lng},{'$set':{'rating':ratingList}})\n\ndef getToiletPaperRating(lat,lng):\n tmp= ToiletPaperRatings.find_one({'latitude':lat,'longitude':lng})\n if tmp:\n return tmp[\"rating\"]\n else:\n return []\n\n\ndef addLegRoomRatingByUser(username,lat,lng,rating):\n temp = lat,lng,rating\n account = Accounts.find_one({'usernames':username})\n if \"LegRoomList\" not in account:\n LegRoomList = [temp]\n Accounts.update({'usernames':username},{'$set':{'LegRoomList':LegRoomList}})\n else:\n LegRoomList = account['LegRoomList']\n if LegRoomList == None:\n LegRoomList = [temp]\n Accounts.update({'usernames':username},{'$set':{'LegRoomList':LegRoomList}})\n else:\n LegRoomList.append(temp)\n Accounts.update({'usernames':username},{'$set':{'LegRoomList':LegRoomList}})\n\n\ndef addLegRoomRating(username,lat,lng,rating):\n addLegRoomRatingByUser(username,lat,lng,rating)\n ratingList = LegRoomRatings.find_one({'latitude':lat,'longitude':lng})\n if ratingList == None:\n LegRoomRatings.insert({'latitude':lat,'longitude':lng,'rating':[rating]})\n else:\n ratingList = ratingList['rating']\n ratingList.append(rating)\n LegRoomRatings.update({'latitude':lat,'longitude':lng},{'$set':{'rating':ratingList}})\n\ndef getLegRoomRating(lat,lng):\n tmp= LegRoomRatings.find_one({'latitude':lat,'longitude':lng})\n if tmp:\n return tmp[\"rating\"]\n else:\n return []\n\n\ndef addFlusherRatingByUser(username,lat,lng,rating):\n temp = lat,lng,rating\n account = Accounts.find_one({'usernames':username})\n if \"FlusherList\" not in account:\n FlusherList = [temp]\n Accounts.update({'usernames':username},{'$set':{'FlusherList':FlusherList}})\n else:\n FlusherList = account['FlusherList']\n if FlusherList == None:\n FlusherList = [temp]\n Accounts.update({'usernames':username},{'$set':{'FlusherList':FlusherList}})\n else:\n FlusherList.append(temp)\n Accounts.update({'usernames':username},{'$set':{'FlusherList':FlusherList}})\n\n\ndef addFlusherRating(username,lat,lng,rating):\n addFlusherRatingByUser(username,lat,lng,rating)\n ratingList = FlusherRatings.find_one({'latitude':lat,'longitude':lng})\n if ratingList == None:\n FlusherRatings.insert({'latitude':lat,'longitude':lng,'rating':[rating]})\n else:\n ratingList = ratingList['rating']\n ratingList.append(rating)\n FlusherRatings.update({'latitude':lat,'longitude':lng},{'$set':{'rating':ratingList}})\n\ndef getFlusherRating(lat,lng):\n tmp= FlusherRatings.find_one({'latitude':lat,'longitude':lng})\n if tmp:\n return tmp[\"rating\"]\n else:\n return []\n\n\ndef addSmellRatingByUser(username,lat,lng,rating):\n temp = lat,lng,rating\n account = Accounts.find_one({'usernames':username})\n if \"SmellList\" not in account:\n SmellList = [temp]\n Accounts.update({'usernames':username},{'$set':{'SmellList':SmellList}})\n else:\n SmellList = account['SmellList']\n if SmellList == None:\n SmellList = [temp]\n Accounts.update({'usernames':username},{'$set':{'SmellList':SmellList}})\n else:\n SmellList.append(temp)\n Accounts.update({'usernames':username},{'$set':{'SmellList':SmellList}})\n\n\ndef addSmellRating(username,lat,lng,rating):\n addSmellRatingByUser(username,lat,lng,rating)\n ratingList = SmellRatings.find_one({'latitude':lat,'longitude':lng})\n if ratingList == None:\n SmellRatings.insert({'latitude':lat,'longitude':lng,'rating':[rating]})\n else:\n ratingList = ratingList['rating']\n ratingList.append(rating)\n SmellRatings.update({'latitude':lat,'longitude':lng},{'$set':{'rating':ratingList}})\n\ndef getSmellRating(lat,lng):\n tmp= SmellRatings.find_one({'latitude':lat,'longitude':lng})\n if tmp:\n return tmp[\"rating\"]\n else:\n return []\n\n\ndef addAestheticsRatingByUser(username,lat,lng,rating):\n temp = lat,lng,rating\n account = Accounts.find_one({'usernames':username})\n if \"AestheticsList\" not in account:\n AestheticsList = [temp]\n Accounts.update({'usernames':username},{'$set':{'AestheticsList':AestheticsList}})\n else:\n AestheticsList = account['SmellList']\n if AestheticsList == None:\n AestheticsList = [temp]\n Accounts.update({'usernames':username},{'$set':{'AestheticsList':AestheticsList}})\n else:\n AestheticsList.append(temp)\n Accounts.update({'usernames':username},{'$set':{'AestheticsList':AestheticsList}})\n\n\ndef addAestheticsRating(username,lat,lng,rating):\n addAestheticsRatingByUser(username,lat,lng,rating)\n ratingList = AestheticsRatings.find_one({'latitude':lat,'longitude':lng})\n if ratingList == None:\n AestheticsRatings.insert({'latitude':lat,'longitude':lng,'rating':[rating]})\n else:\n ratingList = ratingList['rating']\n ratingList.append(rating)\n AestheticsRatings.update({'latitude':lat,'longitude':lng},{'$set':{'rating':ratingList}})\n\ndef getAestheticsRating(lat,lng):\n tmp= AestheticsRatings.find_one({'latitude':lat,'longitude':lng})\n if tmp:\n return tmp[\"rating\"]\n else:\n return []\n\ndef addCommentByUser(username,lat,lng,rating):\n temp = lat,lng,rating\n account = Accounts.find_one({'usernames':username})\n if \"CommentList\" not in account:\n CommentList = [temp]\n Accounts.update({'usernames':username},{'$set':{'CommentList':CommentList}})\n else:\n CommentList = account['CommentList']\n if CommentList == None:\n CommentList = [temp]\n Accounts.update({'usernames':username},{'$set':{'CommentList':CommentList}})\n else:\n CommentList.append(temp)\n Accounts.update({'usernames':username},{'$set':{'CommentList':CommentList}})\n\n\ndef addComment(username,lat,lng,rating):\n addCommentByUser(username,lat,lng,rating)\n CommentList = Comments.find_one({'latitude':lat,'longitude':lng})\n if CommentList == None:\n Comments.insert({'latitude':lat,'longitude':lng,'rating':[rating]})\n else:\n CommentList = CommentList['rating']\n CommentList.append(rating)\n Comments.update({'latitude':lat,'longitude':lng},{'$set':{'rating':CommentList}})\n\ndef getComments(lat,lng):\n tmp = Comments.find_one({'latitude':lat,'longitude':lng})\n if tmp:\n return tmp[\"rating\"]\n else:\n return []\n", "sub_path": "database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 11361, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "pymongo.Connection", "line_number": 3, "usage_type": "name"}, {"api_name": "pymongo.Connection.admin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pymongo.Connection", "line_number": 4, "usage_type": "name"}, {"api_name": "pymongo.Connection", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "237615447", "text": "#!/usr/bin/env /data/mta/Script/Python3.8/envs/ska3-shiny/bin/python\n\n#################################################################################################\n# #\n# plot_grating_focus.py: update grating focus plots #\n# #\n# author: t. isobe (tisobe@cfa.harvard.edu) #\n# #\n# last update: Mar 12, 2021 #\n# #\n#################################################################################################\n\nimport os\nimport sys\nimport re\nimport random\nimport numpy\nimport time\nimport Chandra.Time\n\nimport matplotlib as mpl\nif __name__ == '__main__':\n mpl.use('Agg')\n\nfrom pylab import *\nimport matplotlib.pyplot as plt\nimport matplotlib.font_manager as font_manager\nimport matplotlib.lines as lines\n#\n#--- reading directory list\n#\npath = '/data/mta/Script/Grating/Focus/Scripts/house_keeping/dir_list'\n\nwith open(path, 'r') as f:\n data = [line.strip() for line in f.readlines()]\n\nfor ent in data:\n atemp = re.split(':', ent)\n var = atemp[1].strip()\n line = atemp[0].strip()\n exec(\"%s = %s\" %(var, line))\n#\n#--- append pathes to private folders to a python directory\n#\nsys.path.append(bin_dir)\nsys.path.append(mta_dir)\n\nimport mta_common_functions as mcf\nimport find_moving_average as mavg #---- contains moving average routine\n\n#----------------------------------------------------------------------------------------------\n#-- update_focus_data_plot: update grating focus plots ---\n#----------------------------------------------------------------------------------------------\n\ndef update_focus_data_plot():\n \"\"\"\n update grating focus plots\n input: none, but /acis_hetg, acis_letg, hrc_letg\n output: /Plots/__focus.phg\n \"\"\"\n for d_file in ['acis_hetg', 'acis_letg', 'hrc_letg']:\n out = read_focus_data(d_file)\n time = out[0]\n try:\n a_set = [out[1], out[2], out[5], out[7]]\n except:\n continue\n\n [ctime, c_set] = remove_non_data(time, a_set)\n titles = ['AX LRF at 10% Peak', 'AX LRF at 50% Peak', 'Gaussian FWHM']\n outname = web_dir + 'Plots/' + d_file + '_ax_lrf_focus.png'\n y_label = 'Width (microns)'\n y_limits = [[50,110], [20, 60], [20,60]]\n plot_data(ctime, c_set, titles, outname, y_label, y_limits)\n\n try:\n s_set = [out[3], out[4], out[6], out[8]]\n except:\n continue\n\n [ctime, c_set] = remove_non_data(time, a_set)\n titles = ['Streak LRF at 10% Peak', 'Streak LRF at 50% Peak', 'Gaussian FWHM']\n outname = web_dir + 'Plots/' + d_file + '_streak_lrf_focus.png'\n y_label = 'Width (microns)'\n y_limits = [[50, 110], [20, 60], [20, 60]]\n plot_data(ctime, c_set, titles, outname, y_label, y_limits)\n\n#----------------------------------------------------------------------------------------------\n#-- remove_non_data: removing non (-999) data and data outside of the useable valeus --\n#----------------------------------------------------------------------------------------------\n\ndef remove_non_data(x, t_set):\n \"\"\"\n removing non (-999) data and data outside of the useable valeus\n input: x --- time\n t_set --- a list of 4 lists; first three are value list and last one error list \n output: x --- cleaned time entry\n o_set --- a list of cleaned three value lists. no error list are retured\n \"\"\"\n x = numpy.array(x)\n yarray = []\n for k in range(0, 4):\n yarray.append(numpy.array(t_set[k]))\n\n for k in range(0, 2):\n index = (yarray[k] > 0) & (yarray[k] < 100)\n x = x[index]\n for m in range(0, 4):\n yarray[m] = yarray[m][index]\n \n index = yarray[3] < 10\n x = x[index]\n for m in range(0, 4):\n yarray[m] = yarray[m][index]\n\n return [x, [list(yarray[0]), list(yarray[1]), list(yarray[2])]]\n \n#----------------------------------------------------------------------------------------------\n#-- plot_data: plot data --\n#----------------------------------------------------------------------------------------------\n\ndef plot_data(xdata, y_set, titles, outname, y_label, y_limits):\n \"\"\"\n plot data\n input: xdata --- x data\n ydata --- y data\n grating --- tile of the data\n outname --- output plot file; assume it is png\n output: hetg_all_focus.png, metg_all_focus.png, letg_all_focus.png\n \"\"\"\n# \n#--- set sizes\n#\n fsize = 18\n color = 'blue'\n color2 = 'red'\n marker = '.'\n psize = 8\n lw = 3\n alpha = 0.3\n width = 10.0\n height = 10.0\n resolution = 200\n\n xmin = 1999\n xmax = max(xdata) \n diff = xmax - int(xmax)\n if diff > 0.7:\n xmax = int(xmax) + 2\n else:\n xmax = int(xmax) + 1\n diff = xmax - xmin\n xpos = xmin + 0.02 * diff\n#\n#--- close everything opened before\n#\n plt.close('all')\n#\n#--- set font size\n#\n mpl.rcParams['font.size'] = fsize\n props = font_manager.FontProperties(size=fsize)\n plt.subplots_adjust(hspace=0.08)\n#\n#--- set plotting range\n#\n for k in range(0, 3):\n plt.subplots_adjust(hspace=0.08)\n ymin = y_limits[k][0]\n ymax = y_limits[k][1]\n diff = ymax - ymin\n ypos = ymax - 0.1 * diff\n\n panel = '31' + str(k+1)\n ax = plt.subplot(panel)\n ax.set_autoscale_on(False)\n ax.set_xbound(xmin,xmax)\n ax.set_xlim(left=xmin, right=xmax, auto=False)\n ax.set_ylim(bottom=ymin, top=ymax, auto=False)\n \n plt.plot(xdata, y_set[k], color=color, marker=marker, markersize=psize, lw=0)\n plt.tight_layout()\n\n [x, y] = remove_extreme(xdata, y_set[k])\n [xv, movavg, sigma, min_sv, max_sv, ym, yb, yt, y_sig] \\\n = mavg.find_moving_average(x, y, 1.0, 3, nodrop=0)\n#\n#--- plot envelopes\n#\n plt.plot(xv, yb, color=color2, marker=marker, markersize=0, lw=lw, alpha=alpha)\n plt.plot(xv, ym, color=color2, marker=marker, markersize=0, lw=lw, alpha=alpha)\n plt.plot(xv, yt, color=color2, marker=marker, markersize=0, lw=lw, alpha=alpha)\n#\n#--- add label\n#\n plt.text(xpos, ypos, titles[k], color=color)\n if k == 2:\n plt.xlabel('Time (year)')\n else:\n plt.setp(ax.get_xticklabels(), visible=False)\n if k == 1:\n plt.ylabel(y_label)\n\n fig = matplotlib.pyplot.gcf()\n fig.set_size_inches(width, height)\n plt.tight_layout()\n plt.savefig(outname, format='png', dpi=resolution)\n\n plt.close('all')\n\n#----------------------------------------------------------------------------------------------\n#-- remove_extreme: remove extreme data points --\n#----------------------------------------------------------------------------------------------\n\ndef remove_extreme(x, y):\n \"\"\"\n remove extreme data points\n input: x --- a list of x data\n y --- a list of y data\n output: [x, y]\n \"\"\"\n x = numpy.array(x)\n y = numpy.array(y)\n avg = numpy.mean(y)\n sig = numpy.std(y)\n bot = avg - 3.0 * sig\n top = avg + 3.0 * sig\n\n index = (y >0) & (y < 300)\n x = x[index]\n y = y[index]\n\n return [x, y]\n\n#----------------------------------------------------------------------------------------------\n#-- read_focus_data: read data file and extract data needed --\n#----------------------------------------------------------------------------------------------\n\ndef read_focus_data(infile):\n \"\"\"\n read data file and return lists of times and values\n input: infile --- data file name\n output: t_list --- a list of time data\n c1_list --- a list of data (ax slf 10%)\n c2_list --- a list of data (ax slf 50%)\n c3_list --- a list of data (streak slf 10%)\n c4_list --- a list of data (streak slf 50%)\n c5_list --- a list of data (ax slf fwhm)\n c6_list --- a list of data (streak slf fwhm)\n c7_list --- a list of data (ax slf fwhm error)\n c8_list --- a list of data (streak slf fwhm error)\n \"\"\"\n infile = data_dir + infile\n print(\"Data: \" + str(infile))\n data = mcf.read_data_file(infile)\n\n t_list = []\n c1_list = []\n c2_list = []\n c3_list = []\n c4_list = []\n c5_list = []\n c6_list = []\n c7_list = []\n c8_list = []\n for ent in data:\n atemp = re.split('\\s+', ent)\n try:\n t = mcf.chandratime_to_fraq_year(float(atemp[0]))\n v1 = float(atemp[1])\n v2 = float(atemp[2])\n v3 = float(atemp[3])\n v4 = float(atemp[4])\n v5 = float(atemp[5])\n v6 = float(atemp[6])\n v7 = float(atemp[6])\n v8 = float(atemp[6])\n except:\n continue\n\n t_list.append(t)\n c1_list.append(v1)\n c2_list.append(v2)\n c3_list.append(v3)\n c4_list.append(v4)\n c5_list.append(v5)\n c6_list.append(v6)\n c7_list.append(v7)\n c8_list.append(v8)\n\n return [t_list, c1_list, c2_list, c3_list, c4_list, c5_list, c6_list, c7_list, c8_list]\n\n#---------------------------------------------------------------------------------------------\n\nif __name__ == \"__main__\":\n\n update_focus_data_plot()\n", "sub_path": "Grating/Focus/Scripts/plot_grating_fucus.py", "file_name": "plot_grating_fucus.py", "file_ext": "py", "file_size_in_byte": 10082, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "matplotlib.use", "line_number": 23, "usage_type": "call"}, {"api_name": "re.split", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.rcParams", "line_number": 161, "usage_type": "attribute"}, {"api_name": "matplotlib.font_manager.FontProperties", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.font_manager", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "find_moving_average.find_moving_average", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 225, "usage_type": "call"}, {"api_name": "mta_common_functions.read_data_file", "line_number": 255, "usage_type": "call"}, {"api_name": "re.split", "line_number": 267, "usage_type": "call"}, {"api_name": "mta_common_functions.chandratime_to_fraq_year", "line_number": 269, "usage_type": "call"}]} +{"seq_id": "185346184", "text": "\"\"\"キューを使って左端から貪欲法\n\"\"\"\nfrom math import ceil\nfrom collections import deque\n\n\nN, D, A = map(int, input().split())\nmonsters = []\nfor i in range(N):\n x, h = map(int, input().split())\n monsters.append((x, h))\nmonsters.sort()\n\n\ndamage_sum = 0\nans = 0\nq = deque([])\nfor i in range(N):\n x, h = monsters[i]\n while q and q[0][0] < x:\n # 有効期限が切れたら、キューから取り出し、累積ダメージから減算する\n damage_sum -= q[0][1]\n q.popleft()\n # 累積ダメージをモンスターのHPから減算する\n h -= damage_sum\n if h > 0:\n # HPが残っていたら追加攻撃する\n hit = ceil(h / A)\n ans += hit\n damage = hit * A\n damage_sum += damage\n # (有効期限、そのモンスターに与えたダメージ)\n q.append((x + 2 * D, damage))\n\nprint(ans)\n", "sub_path": "abc/153/f.py", "file_name": "f.py", "file_ext": "py", "file_size_in_byte": 895, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "collections.deque", "line_number": 17, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "166867363", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n\nfrom channel.models import Channel, Category\nfrom channel.serializers import ChannelSerializer, CategorySerializer\nfrom .models import ApiImplementation\n\n\ndef docs(request):\n '''\n @docs: List all API Calls\n '''\n docs = ApiImplementation.objects.all()\n return render(request, 'api_doc.html', {'docs': docs})\n\n\ndef doc_view(request, doc_id):\n '''\n @doc_view: View all Infos\n '''\n api = ApiImplementation.objects.get(id=doc_id)\n template_data = {'api': api}\n return render(request, 'api_view_modal.html', template_data)\n\n\n@api_view(['GET', 'POST'])\ndef channels(request):\n \"\"\"\"\n @channels: Method to List or Create a Channel according the\n HTTP Request, GET and POST\n \"\"\"\n if request.method == 'GET':\n # Pega uma listagem de Produtos\n channels = Channel.objects.all()\n serializer = ChannelSerializer(channels, many=True)\n return Response(serializer.data)\n\n elif request.method == 'POST':\n serializer = ChannelSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n else:\n return Response(\n serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n else:\n return HttpResponse('Unauthorized', status=401)\n\n\n@api_view(['GET', 'PUT', 'DELETE'])\ndef channel(request, channel_id):\n \"\"\"\n @channel: Method to GET, UPDATE or DELETE a Channel according\n the HTTP Request\n \"\"\"\n try:\n channel = Channel.objects.get(id=channel_id)\n except Channel.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n if request.method == 'GET':\n serializer = ChannelSerializer(channel)\n return Response(serializer.data)\n\n elif request.method == 'PUT':\n serializer = ChannelSerializer(channel, data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n else:\n return Response(\n serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n elif request.method == 'DELETE':\n channel.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n@api_view(['GET', 'POST'])\ndef categories(request):\n \"\"\"\"\n @categories: Method to List or Create a Channel according the\n HTTP Request, GET and POST\n \"\"\"\n if request.method == 'GET':\n categories = Category.objects.all()\n serializer = CategorySerializer(categories, many=True)\n return Response(serializer.data)\n\n elif request.method == 'POST':\n serializer = CategorySerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n else:\n return Response(\n serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n else:\n return HttpResponse('Unauthorized', status=401)\n\n\n@api_view(['GET', 'PUT', 'DELETE'])\ndef category(request, category_id):\n \"\"\"\n @category: Method to GET, UPDATE or DELETE a Channel according\n the HTTP Request\n \"\"\"\n try:\n category = Category.objects.get(id=category_id)\n except Category.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n if request.method == 'GET':\n serializer = CategorySerializer(category)\n return Response(serializer.data)\n\n elif request.method == 'PUT':\n serializer = CategorySerializer(category, data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n else:\n return Response(\n serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n elif request.method == 'DELETE':\n category.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n", "sub_path": "work-at-olist/api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4232, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "models.ApiImplementation.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "models.ApiImplementation.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.ApiImplementation", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "models.ApiImplementation.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "models.ApiImplementation.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.ApiImplementation", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}, {"api_name": "channel.models.Channel.objects.all", "line_number": 39, "usage_type": "call"}, {"api_name": "channel.models.Channel.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "channel.models.Channel", "line_number": 39, "usage_type": "name"}, {"api_name": "channel.serializers.ChannelSerializer", "line_number": 40, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 41, "usage_type": "call"}, {"api_name": "channel.serializers.ChannelSerializer", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 47, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 47, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 49, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 50, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 50, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 52, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 31, "usage_type": "call"}, {"api_name": "channel.models", "line_number": 62, "usage_type": "name"}, {"api_name": "channel.models.Channel.objects.get", "line_number": 62, "usage_type": "call"}, {"api_name": "channel.models.Channel.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "channel.models.Channel", "line_number": 62, "usage_type": "name"}, {"api_name": "channel.models.Channel.DoesNotExist", "line_number": 63, "usage_type": "attribute"}, {"api_name": "channel.models.Channel", "line_number": 63, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 64, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 64, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 64, "usage_type": "name"}, {"api_name": "channel.serializers.ChannelSerializer", "line_number": 67, "usage_type": "call"}, {"api_name": "channel.models", "line_number": 67, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 68, "usage_type": "call"}, {"api_name": "channel.serializers.ChannelSerializer", "line_number": 71, "usage_type": "call"}, {"api_name": "channel.models", "line_number": 71, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 74, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 76, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 77, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 77, "usage_type": "name"}, {"api_name": "channel.models.delete", "line_number": 80, "usage_type": "call"}, {"api_name": "channel.models", "line_number": 80, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 81, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 81, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 81, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 55, "usage_type": "call"}, {"api_name": "channel.models.Category.objects.all", "line_number": 91, "usage_type": "call"}, {"api_name": "channel.models.Category.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "channel.models.Category", "line_number": 91, "usage_type": "name"}, {"api_name": "channel.serializers.CategorySerializer", "line_number": 92, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 93, "usage_type": "call"}, {"api_name": "channel.serializers.CategorySerializer", "line_number": 96, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 99, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 99, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 99, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 101, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 102, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 102, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 104, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 84, "usage_type": "call"}, {"api_name": "channel.models.Category.objects.get", "line_number": 114, "usage_type": "call"}, {"api_name": "channel.models.Category.objects", "line_number": 114, "usage_type": "attribute"}, {"api_name": "channel.models.Category", "line_number": 114, "usage_type": "name"}, {"api_name": "channel.models.Category.DoesNotExist", "line_number": 115, "usage_type": "attribute"}, {"api_name": "channel.models.Category", "line_number": 115, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 116, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 116, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 116, "usage_type": "name"}, {"api_name": "channel.serializers.CategorySerializer", "line_number": 119, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 120, "usage_type": "call"}, {"api_name": "channel.serializers.CategorySerializer", "line_number": 123, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 126, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 128, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 129, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 129, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 133, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 133, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 133, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "651391596", "text": "# -*- coding: utf-8 -*-\n\nimport xml.etree.cElementTree as ET\nimport sys\nimport codecs\n\nif len(sys.argv) == 7:\n\n input = sys.argv[1]\n output = sys.argv[2]\n attribute = sys.argv[3]\n ancestor = sys.argv[4]\n elem = sys.argv[5]\n attr = sys.argv[6]\nelse:\n print(\"python pyaddatt.py input output attributeToAdd ancestor element attr\")\n print(\"The value - can be used as 'don't care' value for ancestor, element and attr\")\n exit\n\ntree = ET.ElementTree(file=input)\nroot = tree.getroot()\nif ancestor == '-':\n ancestorNode = root\nelse:\n ancestorNode = root.find(ancestor)\n if ancestorNode == None:\n ancestorNode = root\n\nfor i,elm in enumerate(ancestorNode):\n if elem == '-' or elm.tag == elem:\n if attr == '-':\n elm.set(attribute,\"\")\n else:\n att = elm.get('{http://www.w3.org/XML/1998/namespace}'+attr)\n if att == None:\n att = elm.get(attr)\n if att == None:\n att = elm.get('{http://www.tei-c.org/ns/1.0}'+attr)\n if att != None:\n elm.set(attribute,\"\")\n\ntree.write(output, encoding=\"utf-8\")\n\n", "sub_path": "CST-lemma/pyaddatt.py", "file_name": "pyaddatt.py", "file_ext": "py", "file_size_in_byte": 1139, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "xml.etree.cElementTree.ElementTree", "line_number": 20, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "542981891", "text": "from django.test import TestCase\n\nfrom binder import models, helpers\n\nclass HelperTests(TestCase):\n def test_ipinfo_ResolutionFail(self):\n response = helpers.ip_info(\"foobar.doesnotexist.local\")\n self.assertEqual([['Error', u'Unable to resolve foobar.doesnotexist.local: [Errno -2] Name or service not known']],\n response)\n response = helpers.ip_info(\"localhost\")\n self.assertEqual([['IPv4 (1)', u'127.0.0.1']],\n sorted(response))\n", "sub_path": "binder/tests/testHelpers.py", "file_name": "testHelpers.py", "file_ext": "py", "file_size_in_byte": 510, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.test.TestCase", "line_number": 5, "usage_type": "name"}, {"api_name": "binder.helpers.ip_info", "line_number": 7, "usage_type": "call"}, {"api_name": "binder.helpers", "line_number": 7, "usage_type": "name"}, {"api_name": "binder.helpers.ip_info", "line_number": 10, "usage_type": "call"}, {"api_name": "binder.helpers", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "649139547", "text": "# Calculate Plot the GCV function and find its minimum.\n#\n# [reg_min,G,reg_param] = gcv(U,s,b,method)\n#\n# Calculates the GCV-function\n# || A*x - b ||^2\n# G = -------------------\n# (trace(I - A*A_I)^2\n# as a function of the regularization parameter reg_param.\n# Here, A_I is a matrix which produces the regularized solution.\n# and x is the solution calculated with Tikhonov regularization\n# using the regularization parameter reg_param.\n#\n# If any output arguments are specified, then the minimum of G is\n# identified and the corresponding reg. parameter reg_min is returned.\n\n# Per Christian Hansen, IMM, Dec. 16, 2003.\n\n# Reference: G. Wahba, \"Spline Models for Observational Data\",\n# SIAM, 1990.\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import optimize\n\nfrom typing import Tuple\n\nfrom numba import njit\n\ndef gcv_blockdiag(\n U: np.ndarray, s: np.ndarray, b: np.ndarray, lambdarange: np.ndarray, plot: bool = False\n) -> Tuple[float, float, np.ndarray, np.ndarray]:\n npoints = lambdarange.size\n\n p = s.size\n beta = blkmul_adj(U, b)\n\n # Vector of regularization parameters.\n reg_param = np.zeros(npoints)\n G = np.copy(reg_param) # very important to copy here!!!\n s2 = s ** 2\n reg_param = np.copy(lambdarange)\n\n # Vector of GCV-function values.\n for i in range(npoints):\n G[i] = gcvfun(reg_param[i], s2, beta[:p], 0., 0) # , delta0, m - n)\n\n minGi = G.argmin(0) # Initial guess.\n reg_min = optimize.fmin(\n gcvfun,\n x0=reg_param[np.max([minGi, 0])],\n args=(s2, beta[:p], 0., 0), # delta0, m - n),\n disp=0,\n )[0]\n minG = gcvfun(reg_min, s2, beta[:p], 0., 0) # delta0, m - n) # Minimum of GCV function.\n\n if plot:\n # Plot GCV function.\n plt.plot(reg_param, G, \"-\")\n plt.xscale(\"log\")\n plt.yscale(\"log\")\n plt.xlabel(r\"$\\lambda$\")\n plt.ylabel(r\"$G(\\lambda)$\")\n plt.plot([reg_min], [minG], \"*r\")\n plt.plot([reg_min, reg_min], [minG / 1000, minG], \":r\")\n plt.title(r\"GCV function, minimum at $\\lambda = %.2e$\" % reg_min)\n plt.show()\n return float(reg_min), float(minG), G, reg_param\n\n\ndef gcvfun(lmbda, s2, beta, delta0, mn):\n # Auxiliary routine for gcv. PCH, IMM, Feb. 24, 2008.\n # Note: f = 1 - filter-factors.\n\n f = (lmbda ** 2) / (s2 + lmbda ** 2)\n G = (np.linalg.norm(f * beta) ** 2 + delta0) / (mn + np.sum(f)) ** 2\n return G\n\n\ndef blkmul_adj(mat: np.ndarray, v: np.ndarray) -> np.ndarray:\n \"\"\" Calculate (mat.H) @ v \"\"\"\n a, b, c = mat.shape\n assert ((a * b,) == v.shape)\n assert (a >= 1)\n MT0 = mat[0].T.conjugate()\n out0 = MT0 @ v[:c]\n out = np.empty(a * c, dtype=out0.dtype)\n out[:c] = out0\n for i in range(1, a):\n MT = mat[i].T.conjugate()\n out[i * c:i * c + c] = MT @ v[i * b:i * b + b]\n return out\n", "sub_path": "src/TFM/FTTC3d/gcv_block.py", "file_name": "gcv_block.py", "file_ext": "py", "file_size_in_byte": 2864, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.ndarray", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 42, "usage_type": "call"}, {"api_name": "scipy.optimize.fmin", "line_number": 49, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 76, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 87, "usage_type": "call"}]} +{"seq_id": "588252235", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models, transaction, connection\n\n\n@transaction.atomic\ndef drop_duplicates(apps, schema_editor):\n # We should leave only unique dirty objects\n DirtyInstance = apps.get_model('denorm', 'DirtyInstance')\n if DirtyInstance.objects.count() > 100000:\n raise ValueError(\"You should clear DirtyInstances table first\")\n if connection.vendor == 'postgresql':\n distinct = DirtyInstance.objects.distinct('object_id', 'content_type')\n DirtyInstance.objects.exclude(id__in=distinct).delete()\n else:\n pks_to_delete = []\n distinct_tuples = []\n for di in DirtyInstance.objects.all().iterator():\n if (di.object_id, di.content_type) in distinct_tuples:\n pks_to_delete.append(di.pk)\n else:\n distinct_tuples.append((di.object_id, di.content_type))\n DirtyInstance.objects.filter(id__in=pks_to_delete).delete()\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('denorm', '0002_auto_20160525_2242'),\n ]\n\n operations = [\n migrations.RunPython(drop_duplicates, reverse_code=migrations.RunPython.noop),\n migrations.AlterUniqueTogether(\n name='dirtyinstance',\n unique_together=set([('content_type', 'object_id')]),\n ),\n ]\n", "sub_path": "denorm/migrations/0003_auto_20160526_1335.py", "file_name": "0003_auto_20160526_1335.py", "file_ext": "py", "file_size_in_byte": 1382, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.db.connection.vendor", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.db.connection", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.Migration", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.migrations.RunPython", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterUniqueTogether", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "447880487", "text": "import PIL.Image as I\nimport numpy as np\nimport glob,os,sys\nimport random\ndef transform(max_,min_,im_ar):\n #print(im_ar)\n k=255/(max_-min_)\n b=255*min_/(min_-max_)\n ret=k*im_ar+b\n #print(ret)\n #exit()\n return ret.astype('uint8')\n\ndef norm(im_ar):\n #print(im_ar)\n mean=np.mean(im_ar)\n std=np.std(im_ar)\n ret=(im_ar-mean)/std\n ret=(ret-np.min(ret))*100\n ret=ret.astype('uint8')#transform(np.max(ret),np.min(ret),ret)\n #print(im_ar-ret)\n #exit()\n return ret\n\ndef add_noise(im_ar,pro,val):\n shape=im_ar.shape\n noise=np.zeros(shape)\n for i in range(shape[0]):\n for j in range(shape[1]):\n if random.random()/')\ndef articleTypes(id):\n page = request.args.get('page',1,type=int)\n menus = Menu.query.all()\n pagination = ArticleType.query.get_or_404(id).articles.order_by(Article.create_time.desc()).paginate(\n page,10,error_out=False\n )\n articles = pagination.items\n return render_template('articleLists.html',articles=articles,\n pagination=pagination,\n menus=menus,\n endpoint='.articleTypes',id=id)\n\n@main.route('/article-details/',methods=['GET','POST'])\n# @cache.cached(timeout=60)\ndef articleDetails(id):\n article = Article.query.get_or_404(id)\n # article.add_view(article,db)\n return render_template('article_details.html',article=article,id=article.id,endpoint='.articleDetails')\n return 'test'\n\n@main.route('/article-sources/')\ndef articleSources(id):\n page = request.args.get('page',1,type=int)\n pagination = Source.query.get_or_404(id).articles.order_by(\n Article.create_time.desc()).paginate(page,10,error_out=False)\n articles = pagination.items\n return render_template(\n 'articleLists.html',\n articles=articles,\n pagination=pagination,\n endpoint='.articleSources',\n id=id)\n\n\ndef gen_rnd_filename():\n filename_prefix = datetime.datetime.now().strftime('%Y%m%d%H%M%S')\n return '%s%s' % (filename_prefix, str(random.randrange(1000, 10000)))\n\n\n@main.route('/ckupload/', methods=['POST', 'OPTIONS'])\n@login_required\ndef ckupload():\n print(current_app.static_folder)\n \"\"\"CKEditor file upload\"\"\"\n error = ''\n url = ''\n callback = request.args.get(\"CKEditorFuncNum\")\n if request.method == 'POST' and 'upload' in request.files:\n fileobj = request.files['upload']\n fname, fext = os.path.splitext(fileobj.filename)\n rnd_name = '%s%s' % (gen_rnd_filename(), fext)\n filepath = os.path.join(current_app.static_folder, 'upload', rnd_name)\n # 检查路径是否存在，不存在则创建\n dirname = os.path.dirname(filepath)\n if not os.path.exists(dirname):\n try:\n os.makedirs(dirname)\n except:\n error = 'ERROR_CREATE_DIR'\n elif not os.access(dirname, os.W_OK):\n error = 'ERROR_DIR_NOT_WRITEABLE'\n if not error:\n fileobj.save(filepath)\n url = url_for('static', filename='%s/%s' % ('upload', rnd_name))\n else:\n error = 'post error'\n res = \"\"\"\"\"\" % (callback, url, error)\n response = make_response(res)\n response.headers[\"Content-Type\"] = \"text/html\"\n return response", "sub_path": "app/main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3404, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Menu", "line_number": 20, "usage_type": "name"}, {"api_name": "models.ArticleType", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "models.Menu.query.all", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Menu.query", "line_number": 25, "usage_type": "attribute"}, {"api_name": "models.Menu", "line_number": 25, "usage_type": "name"}, {"api_name": "models.ArticleType.query.get_or_404", "line_number": 26, "usage_type": "call"}, {"api_name": "models.ArticleType.query", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.ArticleType", "line_number": 26, "usage_type": "name"}, {"api_name": "models.Article.create_time.desc", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Article.create_time", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Article.query.get_or_404", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Article.query", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "models.Source.query.get_or_404", "line_number": 46, "usage_type": "call"}, {"api_name": "models.Source.query", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.Source", "line_number": 46, "usage_type": "name"}, {"api_name": "models.Article.create_time.desc", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Article.create_time", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.Article", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.current_app.static_folder", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request.files", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.current_app.static_folder", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 74, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 79, "usage_type": "call"}, {"api_name": "os.access", "line_number": 82, "usage_type": "call"}, {"api_name": "os.W_OK", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.url_for", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 92, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "639969406", "text": "import numpy as np\nimport open3d\nimport copy\n\nfrom sklearn.neighbors import KDTree\nfrom track_point import load_p2p_matching_series, track_one_point\n\n\ndef downsample_pcd(pcd, down_sample_rate=0.003):\n # voxel_size = np.mean(pcd.compute_nearest_neighbor_distance())\n return np.asarray(pcd.points)[::int(1/down_sample_rate)]\n\n\ndef get_sample_points_track(xyz, pcd_collector, mapping_collector):\n \"\"\"\n\n :param xyz: numpy array of shape N*3, the xyz coordinates of the points\n :return: the track of the input points in the time series\n \"\"\"\n\n track_collection = []\n for xyz_ in xyz:\n track = track_one_point(xyz_, pcd_collector, mapping_collector)\n complete = True\n for t in track:\n if t.shape[0] == 0:\n complete = False\n if complete:\n track_collection.append(track)\n return track_collection\n\n\ndef predict_position_from_track(track, dt=1):\n v = track[0] - track[1]\n a = (track[0] + track[2] - 2 * track[1]) * 2\n return track[0] + dt * v\n\n\ndef extrapolate(pcd, track_collection, predict_position_collection):\n current_position = np.vstack([t[0] for t in track_collection])\n next_position = np.vstack(predict_position_collection)\n\n translation = next_position - current_position\n\n xyz = np.asarray(pcd.points)\n tree = KDTree(current_position)\n\n distance, indices = tree.query(xyz, return_distance=True, k=3)\n\n xyz_extrapolated = copy.deepcopy(xyz)\n for i, xyz_ in enumerate(xyz_extrapolated):\n weights = 1 / (0.002 + distance[i])\n weights = weights / np.sum(weights)\n d_xyz = (weights * translation[indices[i]].T).sum(axis=1)\n xyz_ += d_xyz\n pcd_extrapolate = open3d.geometry.PointCloud()\n pcd_extrapolate.points = open3d.utility.Vector3dVector(xyz_extrapolated)\n return pcd_extrapolate\n\n\nif __name__ == \"__main__\":\n dataset = \"lyon2\"\n match_path_format = \"../data/{}/registration_result/{}_to_{}/\"\n days = [\"03-21_PM\", \"03-22_AM\", \"03-22_PM\"]\n\n pcd_collector, mapping_collector = load_p2p_matching_series(days, dataset, match_path_format)\n last_pcd = pcd_collector[days[-1]]\n xyz_sampled = downsample_pcd(\n last_pcd, down_sample_rate=0.02\n )\n track_collection = get_sample_points_track(xyz_sampled, pcd_collector, mapping_collector)\n\n pos_predict = []\n for track in track_collection:\n pos_predict.append(predict_position_from_track(track))\n pcd_extrapolate = extrapolate(last_pcd, track_collection, predict_position_collection=pos_predict)\n\n last_pcd = last_pcd.translate([0, 200, 0])\n last_2_pcd = pcd_collector[days[-2]].translate([0, 400, 0])\n open3d.visualization.draw_geometries([pcd_extrapolate, last_pcd, last_2_pcd])\n # xyz_ori = xyz_sampled\n # xyz_predict = np.vstack(pos_predict)\n # pcd_predict = open3d.geometry.PointCloud()\n #\n # pcd_predict.points = open3d.utility.Vector3dVector(xyz_ori)\n # open3d.visualization.draw_geometries([pcd_predict])\n #\n # pcd_predict.points = open3d.utility.Vector3dVector(xyz_predict)\n # open3d.visualization.draw_geometries([pcd_predict])\n print(\"\")\n", "sub_path": "extrapolation/extrapolation_pcd.py", "file_name": "extrapolation_pcd.py", "file_ext": "py", "file_size_in_byte": 3136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.asarray", "line_number": 11, "usage_type": "call"}, {"api_name": "track_point.track_one_point", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KDTree", "line_number": 46, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 53, "usage_type": "call"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 56, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 56, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 57, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 57, "usage_type": "attribute"}, {"api_name": "track_point.load_p2p_matching_series", "line_number": 66, "usage_type": "call"}, {"api_name": "open3d.visualization.draw_geometries", "line_number": 80, "usage_type": "call"}, {"api_name": "open3d.visualization", "line_number": 80, "usage_type": "attribute"}]} +{"seq_id": "101338551", "text": "import matplotlib.pyplot as plt\nfrom matplotlib.pyplot import *\nimport numpy as np\n\nclass Atom():\n def __init__(self, th=None, t=None):\n self.th = th\n self.t = t\n\ndef readData(file):\n f = open(file, 'r')\n res, lines = [], [ line[:-1] for line in f ] # store lines in this list without newline\n for line in lines:\n num = line.split(':')[1]\n if 'THREADS' in line:\n atom = Atom()\n atom.th = int(num)\n elif 'TIME' in line:\n atom.t = float(num)\n elif 'SUM' in line:\n res.append(atom)\n return res\n\ndef getdata(file):\n data = readData(file)\n x, y = [None for i in range(100)], [None for j in range(100)]\n for i in range(0, 100*10, 10):\n s, th = 0, data[i].th\n for j in range(10):\n s += data[i+j].t\n x[i//10] = th\n y[i//10] = s / 10\n return x, y\n\nd1 = getdata('s_c_1_10000.out')\nd2 = getdata('s_c_256_10000.out')\nd3 = getdata('s_c_512_10000.out')\nd4 = getdata('s_c_1024_10000.out')\nd5 = getdata('s_c_1_100000.out')\nd6 = getdata('s_c_256_100000.out')\nd7 = getdata('s_c_512_100000.out')\nd8 = getdata('s_c_1024_100000.out')\nd9 = getdata('s_c_1_1000000.out')\nd10 = getdata('s_c_256_1000000.out')\nd11 = getdata('s_c_512_1000000.out')\nd12 = getdata('s_c_1024_1000000.out')\n\np1, = plt.plot(d1[0], d1[1], 'g:')\np2, = plt.plot(d2[0], d2[1], 'g-.')\np3, = plt.plot(d3[0], d3[1], 'g--')\np4, = plt.plot(d4[0], d4[1], 'g')\np5, = plt.plot(d5[0], d5[1], 'b:')\np6, = plt.plot(d6[0], d6[1], 'b-.')\np7, = plt.plot(d7[0], d7[1], 'b--')\np8, = plt.plot(d8[0], d8[1], 'b')\np9, = plt.plot(d9[0], d9[1], 'r:')\np10, = plt.plot(d10[0], d10[1], 'r-.')\np11, = plt.plot(d11[0], d11[1], 'r--')\np12, = plt.plot(d12[0], d12[1], 'r')\n\nP = [p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12]\nL = [\"scount_1_10000\", \"scount_256_10000\", \"scount_512_10000\", \"scount_1024_10000\",\n \"scount_1_100000\", \"scount_256_100000\", \"scount_512_100000\", \"scount_1024_100000\",\n \"scount_1_1000000\", \"scount_256_1000000\", \"scount_512_1000000\", \"scount_1024_1000000\"]\nlegend(P, L)\n\n\nplt.xlabel('Number of Threads')\nplt.ylabel('Average Execution Time of 10 runs (second)')\n\nplt.xticks(np.arange(0, 101, 5.0), fontsize=8)\nplt.yticks(np.arange(0, 5.2, 0.2), fontsize=8)\n\nplt.title('Performance of the Correct Sloppy Counter')\nplt.show()\n\n\n", "sub_path": "sc/plotsc.py", "file_name": "plotsc.py", "file_ext": "py", "file_size_in_byte": 2340, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "49309185", "text": "# -*- coding: utf-8 -*-\r\n\r\n\r\n\r\nimport time\r\nimport datetime\r\n\r\nclass datechange(object):\r\n #localtime='2017-01-12 11:33:00'\r\n def formattime(self,localtime):\r\n self.localtime= localtime\r\n try: # 有的精确到分\r\n stamp1 = datetime.datetime.strptime(self.localtime, \"%Y-%m-%d %H:%M:%S\")\r\n time_local = stamp1.timetuple()\r\n except Exception as e: # 有的精确到秒\r\n stamp1 = datetime.datetime.strptime(self.localtime, \"%Y-%m-%d %H:%M\")\r\n time_local = stamp1.timetuple()\r\n timestamp = time.mktime(time_local)#是时间戳了\r\n date1= stamp1.strftime('%y.%m.%d')\r\n #return stamp1.year,stamp1.month,date1,stamp1.hour,stamp1.weekday()\r\n return timestamp\r\n\r\n\r\n def warps(No):\r\n def deco(func):\r\n def _deco(*args,**kwargs):\r\n print(No)\r\n start = time.clock()\r\n func(*args, **kwargs)\r\n end = time.clock()\r\n print(end-start)\r\n print(str(time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))+'---'\\\r\n + datetime.datetime.now().strftime('%A'))\r\n #print(str(datetime.datetime.now().strftime('%c')))\r\n return _deco\r\n return deco\r\n\r\n\r\n\r\n # def warps(t):\r\n # def deco(func):\r\n # def _deco(*args,**kwargs):\r\n # print(1)\r\n # start = time.clock()\r\n # func(*args, **kwargs)\r\n # print(4)\r\n # end = time.clock()\r\n # if end - start > t:\r\n # print('bad')\r\n # else:\r\n # print ('goods')\r\n # return _deco\r\n # return deco\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# localtime='2017-04-24 11:33:00'\r\n# stamp1=datetime.datetime.strptime(localtime, \"%Y-%m-%d %H:%M:%S\")\r\n# print(stamp1.date())\r\n# print(stamp1.weekday())\r\n#\r\n#\r\n# #今天星期几\r\n# today=int(time.strftime(\"%w\"))\r\n# print (today)\r\n# #某个日期星期几\r\n# anyday=datetime.datetime(2012,4,23).strftime(\"%w\")\r\n# print (anyday)\r\n#\r\n# dt = datetime.datetime.strptime(\"2012-09-12 21:08:12\", \"%Y-%m-%d %H:%M:%S\")\r\n# print (dt.year)\r\n# print(dt.month)\r\n# print(dt.day)\r\n# print(dt.hour)\r\n# print(dt.minute)\r\n# print(dt.second)\r\n# print(dt.microsecond)\r\n# print(dt.tzinfo)\r\n# print('3333333333')\r\n# print (dt.date())\r\n# print (dt.time())\r\n# print (dt.replace(year = 2013))\r\n# print (dt.timetuple())\r\n# print('-----')\r\n# print (time.mktime(dt.timetuple()))\r\n# print (dt.utctimetuple())\r\n# print (dt.toordinal())\r\n# print (dt.weekday())\r\n# print (dt.isocalendar())\r\n# print (dt.strftime('%y-%m-%d %I:%M:%S %p'))\r\n# print (dt.strftime('(%y,%m,%d)'))\r\n#\r\n# # datetime. strftime (format)\r\n# # %a 星期的简写。如 星期三为Web\r\n# # %A 星期的全写。如 星期三为Wednesday\r\n# # %b 月份的简写。如4月份为Apr\r\n# # %B月份的全写。如4月份为April\r\n# # %c: 日期时间的字符串表示。（如： 04/07/10 10:43:39）\r\n# # %d: 日在这个月中的天数（是这个月的第几天）\r\n# # %f: 微秒（范围[0,999999]）\r\n# # %H: 小时（24小时制，[0, 23]）\r\n# # %I: 小时（12小时制，[0, 11]）\r\n# # %j: 日在年中的天数 [001,366]（是当年的第几天）\r\n# # %m: 月份（[01,12]）\r\n# # %M: 分钟（[00,59]）\r\n# # %p: AM或者PM\r\n# # %S: 秒（范围为[00,61]，为什么不是[00, 59]，参考python手册~_~）\r\n# # %U: 周在当年的周数当年的第几周），星期天作为周的第一天\r\n# # %w: 今天在这周的天数，范围为[0, 6]，6表示星期天\r\n# # %W: 周在当年的周数（是当年的第几周），星期一作为周的第一天\r\n# # %x: 日期字符串（如：04/07/10）\r\n# # %X: 时间字符串（如：10:43:39）\r\n# # %y: 2个数字表示的年份\r\n# # %Y: 4个数字表示的年份\r\n# # %z: 与utc时间的间隔 （如果是本地时间，返回空字符串）\r\n# # %Z: 时区名称（如果是本地时间，返回空字符串）\r\n# # %%: %% => %", "sub_path": "Dateformat.py", "file_name": "Dateformat.py", "file_ext": "py", "file_size_in_byte": 4034, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "attribute"}, {"api_name": "time.mktime", "line_number": 18, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 28, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 30, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 32, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 32, "usage_type": "call"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "attribute"}]} +{"seq_id": "654435544", "text": "from PyQt5.QtCore import QSettings, QTranslator, qVersion, QCoreApplication, QFile, QFileInfo\nfrom PyQt5.QtGui import QIcon, QColor\nfrom PyQt5.QtWidgets import QAction\nfrom qgis.core import *\nfrom qgis.utils import iface\n\nimport os, shutil, glob, subprocess, fnmatch\nimport numpy as np\n\nfrom collections import defaultdict\nfrom osgeo import gdal, ogr, osr\nfrom pyproj import Proj, transform\nfrom skimage.io import imsave, imread\nfrom skimage.transform import resize\n\nDRY_RUN = 0\nnodata_threshold = 0.25\ncloud_threshold = 0.15\n#Clouds are 5th bit in 16 bit BQA image\nmaskClouds = 0b0000000000010000\n\ndef domainInRaster(rasterLayer: QgsRasterLayer, domainLayer: QgsVectorLayer) -> bool:\n\t\"\"\"Returns bool if domain is within bounds of geotiff in rasterLayer\n\t:param rasterLayer: QgsRasterLayer\n\t:param domainLayer: QgsVectorLayer\n\t\"\"\"\n\t# Get basic file name information on geotiff, raster image, masked raster subset image, and masked vector subset shp file\n\tfileSource = rasterLayer.source()\n\tfileInfo = QFileInfo(fileSource)\n\tfileName = fileInfo.baseName()\n\trowPath = fileName.split('_')[3]\n\t# Load geotiff and get domain layer/bounding box of area to mask\n\tgeotiff = gdal.Open(fileSource)\n\tfeature = domainLayer.getFeature(0)\n\tdomain = feature.geometry().boundingBox()\n\tprj = geotiff.GetProjection()\n\tsrs = osr.SpatialReference(wkt=prj)\n\tif srs.GetAttrValue(\"PROJCS|AUTHORITY\", 1) is not None:\n\t\tepsgCode = srs.GetAttrValue(\"PROJCS|AUTHORITY\", 1)\n\telif srs.GetAttrValue(\"AUTHORITY\", 1) is not None:\n\t\tepsgCode = srs.GetAttrValue(\"AUTHORITY\", 1)\n\telse:\n\t\tepsgCode = str(32621)\n\trasterCRS = \"EPSG:\" + epsgCode\n\t\n\tcrs = rasterLayer.crs()\n\tcrs.createFromId(int(epsgCode))\n\t\n\tdomainCRS = domainLayer.crs().authid()\n\tbounds = geotiffWorldToPixelCoords(geotiff, domain, rasterCRS, domainCRS)\n\t\n\t#Gather BQA info\n\tfileSourceBQA = fileSource[:-7] + '_BQA.TIF'\n\t#Save BQA subset\n\tgeotiffBQA = gdal.Open(fileSourceBQA)\n\t\n\tminX = int(round(bounds.yMinimum()))\n\tmaxX = int(round(bounds.yMaximum()))\n\tminY = int(round(bounds.xMinimum()))\n\tmaxY = int(round(bounds.xMaximum())) \n\t\n\tif minX < 0 or maxX > geotiff.RasterXSize or maxX > geotiffBQA.RasterXSize or minY < 0 or maxY > geotiff.RasterYSize or maxY > geotiffBQA.RasterYSize:\n\t\treturn False\n\telse:\n\t\t#Check image is above Nodata percentage threshold\n\t\tband = geotiff.GetRasterBand(1)\n\t\tnoDataValue = 0.0\n\t\timg = band.ReadAsArray(minX, minY, maxX - minX, maxY - minY).astype(np.uint16)\n\t\tnoDataCount = np.sum(img == noDataValue)\n\t\tpercentNoData = noDataCount / img.size\n\t\t\n\t\tif percentNoData > nodata_threshold:\n\t\t\tgeotiff = None\n\t\t\tgeotiffBQA = None\n\t\t\tprint('Skipping: Nodata percentage above threshold:', percentNoData, ' > ', nodata_threshold)\n\t\t\treturn False\n\t\t\n\t\t#Check image is above cloud percentage threshold\n\t\tbandBQA = geotiffBQA.GetRasterBand(1)\n\t\timgBQA = bandBQA.ReadAsArray(minX, minY, maxX - minX, maxY - minY).astype(np.uint16)\n\t\tmasked = imgBQA & maskClouds\n\t\tcloudCount = np.sum(masked)\n\t\tpercentCloud = cloudCount / 16.0 / imgBQA.size\n\t\tif percentCloud > cloud_threshold:\n\t\t\tgeotiff = None\n\t\t\tgeotiffBQA = None\n\t\t\tprint('Skipping: Cloud percentage above threshold:', percentCloud, ' > ', cloud_threshold)\n\t\t\treturn False\n\tgeotiff = None\n\tgeotiffBQA = None\n\treturn True\n\ndef geotiffBounds(geotiff) -> QgsRectangle:\n\t\"\"\"Returns QgsRectangle representing bounds of geotiff in projection coordinates\n\t\n\t:geotiff: geotiff\n\t:bounds: QgsRectangle\n\t\"\"\"\n\tgeoTransform = geotiff.GetGeoTransform()\n\t\n\txMin = geoTransform[0]\n\tyMax = geoTransform[3]\n\txMax = xMin + geoTransform[1] * geotiff.RasterXSize\n\tyMin = yMax + geoTransform[5] * geotiff.RasterYSize\n\t\n\treturn QgsRectangle(float(xMin), float(yMin), float(xMax), float(yMax))\n\ndef geotiffWorldToPixelCoords(geotiff, rectDomain:QgsRectangle, rasterCRS:str, domainCRS:str) -> QgsRectangle:\n\t\"\"\"Transforms QgsRectangle coordinates into geotiff image pixel coordinates\n\n\t:geotiff: geotiff\n\t:rect: QgsRectangle\n\t\"\"\"\n\t\n\t# Transform and scale rect by width/height to obtain normalized image coordiantes\n\trectRef = geotiffBounds(geotiff)\n\trectRefCenter = rectRef.center()\n\t\n\trectRefWidth = rectRef.width()\n\trectRefHeight = rectRef.height()\n\t\n\tdomainX = [rectDomain.xMinimum(), rectDomain.xMaximum()]\n\tdomainY = [rectDomain.yMinimum(), rectDomain.yMaximum()]\n\tinProj = Proj(init=domainCRS)\n\toutProj = Proj(init=rasterCRS)\n\t#print(inProj, outProj, domainCRS, rasterCRS)\n\trasterCRSDomainX, rasterCRSDomainY = transform(inProj, outProj, domainX, domainY)\n\t#print(rasterCRSDomainX, rasterCRSDomainY)\n\t\n\txMin = (rasterCRSDomainX[0] - rectRef.xMinimum()) / rectRefWidth\n\txMax = (rasterCRSDomainX[1] - rectRef.xMinimum()) / rectRefWidth\n\tyMin = (rasterCRSDomainY[0] - rectRef.yMinimum()) / rectRefHeight\n\tyMax = (rasterCRSDomainY[1] - rectRef.yMinimum()) / rectRefHeight\n\t\n\t# Scale by image dimensions to obtain pixel coordinates\n\txMin = xMin * geotiff.RasterXSize\n\txMax = xMax * geotiff.RasterXSize\n\tyMin = (1.0 - yMin) * geotiff.RasterYSize\n\tyMax = (1.0 - yMax) * geotiff.RasterYSize\n\t\n\t#print(rasterCRS, domainCRS)\n\t\n\t#Return pixel coordinates\n\trectOut = QgsRectangle(xMin, yMin, xMax, yMax)\n\treturn rectOut\n\ndef arrayToRaster(array:np.ndarray, geotiff, subset:QgsRectangle, destinationPath:str) -> ('Driver', 'Dataset'):\n\t\"\"\"Array > Raster\n\tSave a raster from a C order array.\n\t\n\t:param array: ndarray\n\t\"\"\"\n\tgeoBounds = geotiffBounds(geotiff)\n\tgeoTransform = geotiff.GetGeoTransform()\n\t\n\t# TODO: Fix X/Y coordinate mismatch and use ns/ew labels to reduce confusion. Also, general cleanup and refactoring.\n\th, w = array.shape[:2]\n\tx_pixels = w # number of pixels in x\n\ty_pixels = h # number of pixels in y\n\tx_pixel_size = geoTransform[1] # size of the pixel...\t\t\n\ty_pixel_size = geoTransform[5] # size of the pixel...\t\t\n\tx_min = geoTransform[0] \n\ty_max = geoTransform[3] # x_min & y_max are like the \"top left\" corner.\n\t\n\tx_subset_percentage = 1.0 - (float(subset.yMinimum()) / float(geotiff.RasterYSize))\n\ty_subset_percentage = (float(subset.xMinimum()) / float(geotiff.RasterXSize))\n\t\n\ty_coordinate_range = geoBounds.width()\n\tx_coordinate_range = geoBounds.height()\n\t\n\tx_offset = x_subset_percentage * x_coordinate_range\n\ty_offset = y_subset_percentage * y_coordinate_range\n\t\n\tx_min = geoBounds.xMinimum() + int(y_offset)\n\ty_max = geoBounds.yMinimum() + int(x_offset)\n\t\n\tdriver = gdal.GetDriverByName('GTiff')\n\t\n\tdataset = driver.Create(\n\t\tdestinationPath,\n\t\tx_pixels,\n\t\ty_pixels,\n\t\t1,\n\t\tgdal.GDT_Float32, )\n\t\n\tdataset.SetGeoTransform((\n\t\tx_min,\t# 0\n\t\tx_pixel_size, # 1\n\t\tgeoTransform[2],\t\t\t\t\t # 2\n\t\ty_max,\t# 3\n\t\tgeoTransform[4],\t\t\t\t\t # 4\n\t\ty_pixel_size)) #6\n\t\n\tdataset.SetProjection(geotiff.GetProjection())\n\tdataset.GetRasterBand(1).WriteArray(array)\n\tdataset.FlushCache() # Write to disk.\n\treturn dataset, dataset.GetRasterBand(1) #If you need to return, remenber to return also the dataset because the band don`t live without dataset.\n\ndef vectorizeRaster(rasterPath:str, outLineShp:str, lineName:str, outPolygonShp:str, polygonName:str) -> (QgsVectorLayer, QgsVectorLayer):\n\t\"\"\"Description: Creates a vector layer from a raster using processing:polygonize.\n\t\tMake sure to save the shapefile, as it will be deleted otherwise! \n\t\tInput: string rasterPath - path to raster image to polygonize\n\t\t\t\tstring outLineShp - file name to give new line shapefile\n\t\t\t\tstring lineName - layer name to give new line vector layer\n\t\t\t\tstring outLineShp - file name to give new closed polygon shapefile\n\t\t\t\tstring polygonName - layer name to give new closed polygon vector layer\n\t\tOutput: QgsVectorLayer, QgsVectorLayer - object referencing the new line and polygon vector layers\n\t\"\"\"\n\t# this allows GDAL to throw Python Exceptions\n\tgdal.UseExceptions()\n\t\n\t# Get raster datasource\n\tsrc_ds = gdal.Open(rasterPath)\n\tsrcband = src_ds.GetRasterBand(1)\n\tprj = src_ds.GetProjection()\n\traster_srs = osr.SpatialReference(wkt = prj)\n\t\n\t# Create output datasource\n\tdrv = ogr.GetDriverByName(\"ESRI Shapefile\")\n\t# Remove output shapefile if it already exists)\n\tif os.path.exists(outLineShp):\n\t\toutShapefileBase = outLineShp[0:-4]\n\t\tfor filename in glob.glob(outShapefileBase + \"*\"):\n\t\t\tos.remove(filename)\n\t\n\tdrv = None\n\tdrv = ogr.GetDriverByName(\"ESRI Shapefile\")\n\t\n\tprocessing.run(\"gdal:contour\",\n\t\t{\"INPUT\":rasterPath,\n\t\t\"BAND\":1,\n\t\t\"INTERVAL\":255,\n\t\t\"FIELD_NAME\":\"ELEV\",\n\t\t\"CREATE_3D\":False, #Bilinear\n\t\t\"IGNORE_NODATA\":False,\n\t\t\"NODATA\":None,\n\t\t\"OFFSET\":0,\n\t\t\"OUTPUT\":outLineShp[0:-4] + '_tmp.shp'\n\t\t})\n\t\n\tprocessing.run(\"native:simplifygeometries\",\n\t\t{\"INPUT\":outLineShp[0:-4] + '_tmp.shp',\n\t\t\"METHOD\": 0, #Distance (Douglas-Peucker)\n\t\t\"TOLERANCE\": 20, #20 meter tolerance (Landsat B5 resolution: 30m\n\t\t\"OUTPUT\":outLineShp\n\t\t})\n\t\n\tprocessing.run(\"qgis:linestopolygons\",\n\t\t{\"INPUT\":outLineShp,\n\t\t\"OUTPUT\":outPolygonShp\n\t\t})\n\t\n\tfor filename in glob.glob(outLineShp[0:-4] + '_tmp*'):\n\t\tos.remove(filename)\n\t#src_ds = None\n\t#srcband = None\n\t#dst_ds = None\n\t#dst_layer = None\n\t#drv = None\n\t#\n\t## If area is less than inMinSize or if it isn't forest, remove polygon \n\t#ioShpFile = ogr.Open(outShapefile, update = 1)\n\t#layer = ioShpFile.GetLayerByIndex(0)\n\t#\t\t\n\t#layer.ResetReading()\n\t#for feature in layer:\n\t#\tprint('feature', feature.GetFID(), feature.GetField('Class'))\n\t#\tlayer.SetFeature(feature)\n\t#\tif feature.GetField('Class')==0:\n\t#\t\tlayer.DeleteFeature(feature.GetFID())\t\t\n\t#ioShpFile.Destroy()\n\t#ioShpFile = None\n\t\n\treturn QgsVectorLayer(outLineShp, lineName, 'ogr'), QgsVectorLayer(outPolygonShp, polygonName, 'ogr')\n\ndef layerResize(rasterLayer:QgsRasterLayer, domainLayer:QgsVectorLayer, name:str, resolution:(int, int)) -> None:\n\t\"\"\"Description: Processes a raster image into a vector polygon ocean/land mask.\n\t\tMake sure to save the shapefile, as it will be deleted otherwise! \n\t\tInput: QgsRasterLayer rasterLayer - layer that contains the raster image to process\n\t\t\t\tQgsVectorLayer domainLayer - layer that contains a polygon specifying the bounds of the raster image to process\n\t\t\t\tQgsVectorLayer outputLayer - layer to save vector layer in. Warning: not supported yet. \n\t\tOutput: QgsRasterLayer, QgsVectorLayer - objects referencing the new mask layers\n\t\"\"\"\n\t\n\tpath = resolve('landsat_raw/' + domainLayer.name() + '/' + name + '.png')\n\timg = imread(path)\n\timg = resize(img, resolution)\n\tprint(img.shape, path)\n\tif not DRY_RUN:\n\t\timsave(path, img)\n\ndef layerWarp(rasterNode:QgsLayerTreeGroup, domainLayer:QgsVectorLayer) -> None:\n\t\"\"\"Description: Reprojects a raster if not already in the desired CRS.\n\t\tMake sure to save the shapefile, as it will be deleted otherwise! \n\t\tInput: QgsLayerTreeGroup rasterNode - node that contains the raster image to process\n\t\t\t\tQgsVectorLayer domainLayer - layer that contains a polygon specifying the bounds of the raster image to process\n\t\"\"\"\n\t\n\trasterLayer = rasterNode.layer()\n\tfileSource = rasterLayer.source()\n\tgeotiff = gdal.Open(fileSource)\n\tprj = geotiff.GetProjection()\n\tsrs = osr.SpatialReference(wkt=prj)\n\tdomainCRS = domainLayer.crs().authid()\n\tif srs.GetAttrValue(\"PROJCS|AUTHORITY\", 1) is not None:\n\t\tepsgCode = srs.GetAttrValue(\"PROJCS|AUTHORITY\", 1)\n\telif srs.GetAttrValue(\"AUTHORITY\", 1) is not None:\n\t\tepsgCode = srs.GetAttrValue(\"AUTHORITY\", 1)\n\telse:\n\t\tepsgCode = str(32621)\n\trasterCRS = \"EPSG:\" + epsgCode\n\tif (rasterCRS != domainCRS):\n\t\tprint('warping...', rasterCRS, domainCRS)\n\t\tif not DRY_RUN:\n\t\t\tparent = rasterNode.parent()\n\t\t\trasterName = rasterLayer.name()\n\t\t\t\n\t\t\toutSource = fileSource[0:-4] + \"_\" + domainCRS[5:] + \".tif\"\n\t\t\t#processing.algorithmHelp(\"gdal:warpreproject\")\n\t\t\tprint(fileSource, outSource)\n\t\t\tprocessing.run(\"gdal:warpreproject\",\n\t\t\t\t# {\"INPUT\":fileSource,\n\t\t\t\t# \"SOURCE_SRS\":rasterCRS,\n\t\t\t\t# \"TARGET_CRS\":domainCRS,\n\t\t\t\t# \"RESAMPLING\":1, #Bilinear\n\t\t\t\t# \"NO_DATA\":0,\n\t\t\t\t# \"DATA_TYPE\":5, #Float32\n\t\t\t\t# \"MULTITHREADING\":True,\n\t\t\t\t# \"OUTPUT\":outSource})\n\t\t\t\t{'DATA_TYPE': 5,#Float32\n\t\t\t\t'INPUT': rasterName,\n\t\t\t\t'MULTITHREADING': True,\n\t\t\t\t'NODATA': 0.0,\n\t\t\t\t'OPTIONS': '',\n\t\t\t\t'OUTPUT': outSource,\n\t\t\t\t'RESAMPLING': 1, #Bilinear\n\t\t\t\t'SOURCE_CRS': rasterCRS,\n\t\t\t\t'TARGET_CRS': domainCRS,\n\t\t\t\t'TARGET_EXTENT': None,\n\t\t\t\t'TARGET_EXTENT_CRS': None,\n\t\t\t\t'TARGET_RESOLUTION': None})\n\t\t\tprint('hello')\n\t\t\tQgsProject.instance().removeMapLayer(rasterLayer.id())\n\t\t\tgeotiff = None\n\t\t\tshutil.copy2(outSource, fileSource)\n\t\t\tos.remove(outSource)\n\t\t\trasterLayer = QgsRasterLayer(fileSource, rasterName)\n\t\t\tQgsProject.instance().addMapLayer(rasterLayer, False)\n\t\t\trasterNode = parent.insertLayer(0, rasterLayer)\n\t\t\treturn rasterNode\n\treturn rasterNode\n\ndef getVectorNames(fileName:str, domainName:str):\n\t\"\"\"Description: Processes a raster image into a vector polygon ocean/land mask.\n\t\tGet a standardized vector name from a Landsat raster file name.\n\t\tInput: str fileName - Landsat raster file name.\n\t\t\t\tstr domainName - Domain vector file name.\n\t\tOutput: str lineName, str polygonName - vector file names.\n\t\"\"\"\n\t\n\tdate = fileName.split('_')[2]\n\tlineName = '_'.join(['cf', domainName, date, 'closed'])\n\tpolygonName = '_'.join([lineName, 'polygon'])\n\treturn lineName, polygonName\n\ndef getSavePaths(fileName:str, domainLayer:QgsVectorLayer, typeDir:str):\n\t\"\"\"Description: Processes a raster image into a vector polygon ocean/land mask.\n\t\tGet a standardized vector name from a Landsat raster file name.\n\t\tInput: str fileName - Landsat raster file name.\n\t\t\t\tstr glacierName - Root vector file name.\n\t\t\t\tstr typeDir - name of the type subdirectory.\n\t\tOutput: str path - file save paths.\n\t\"\"\"\n\t\n\tdate = fileName.split('_')[2]\n\tyear = date.split('-')[0]\n\t\n\t# if (rootGroup.parent().name().startswith('2') or rootGroup.parent().name().startswith('1')):\n\t\t# rootGroup = rootGroup.parent()\n\t\n\tpath = resolve('CalvingFronts')\n\tif (not os.path.exists(path)):\n\t\tos.mkdir(path)\n\tpath = os.path.join(path, typeDir)\n\tif (not os.path.exists(path)):\n\t\tos.mkdir(path)\n\tpath = os.path.join(path, domainLayer.name())\n\tif (not os.path.exists(path)):\n\t\tos.mkdir(path)\n\tpath = os.path.join(path, year)\n\tif (not os.path.exists(path)):\n\t\tos.mkdir(path)\n\t\n\treturn path\n\ndef postprocess(image:np.ndarray) -> np.ndarray:\n\t\"\"\"Description: Postprocesses mask layer to remove small features.\n\t\"\"\"\n\timage = np.where(image > 127, 255, 0)\n\t# Close edges to join them and dilate them before removing small components\n\tkernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))\n\tclosing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)\n\tdilated = cv2.dilate(closing, kernel, iterations = 1)\n\tlargeComponents = removeSmallComponents(dilated, 0, 255)\n\t# largeComponents = dilated\n\t\n\t# Remove small components inside floodfill area\n\tlargeComponentsInverted = 255 - largeComponents\n\tlargeComponentsInverted = removeSmallComponents(largeComponentsInverted, 0, 255)\n\tlargeComponentsInverted2 = 255 - largeComponentsInverted\n\t\n\t# Reverse initial morphological operators to retrieve original edge mask\n\teroded = cv2.erode(largeComponentsInverted2, kernel, iterations = 1)\n\topening = cv2.morphologyEx(eroded, cv2.MORPH_OPEN, kernel)\n\t\n\tresult = removeSmallComponents(opening, 0, 255)\n\t\n\treturn result\n\ndef layerSubsetLoad(rasterLayer:QgsRasterLayer, domainLayer:QgsVectorLayer, rootGroup:QgsLayerTreeGroup, name:str) -> (QgsRasterLayer, QgsVectorLayer):\n\t\"\"\"Description: Processes a raster image into a vector polygon ocean/land mask.\n\t\tMake sure to save the shapefile, as it will be deleted otherwise! \n\t\tInput: QgsRasterLayer rasterLayer - layer that contains the raster image to process\n\t\t\t\tQgsVectorLayer domainLayer - layer that contains a polygon specifying the bounds of the raster image to process\n\t\t\t\tQgsLayerTreeGroup rootGroup - layer that contains the root name of the glacier\n\t\t\t\tstr name - name of file.\n\t\tOutput: QgsRasterLayer, QgsVectorLayer - objects referencing the new mask layers\n\t\"\"\"\n\t\n\t# Get basic file name information on geotiff, raster image, masked raster subset image, and masked vector subset shp file\n\t#print('Get basic file name information on geotiff, raster image, masked raster subset image, and masked vector subset shp file')\n\tfileSource = rasterLayer.source()\n\tfileInfo = QFileInfo(fileSource)\n\tfilePath = fileInfo.absolutePath()\n\tfileName = fileInfo.baseName()\n\tfileQASource = filePath + '/' + fileName[:fileName.rfind('_B')] + '_BQA.TIFF'\n\tmaskName = fileName + '_masked'\n\tmaskPath = filePath + '/' + maskName + '.tif'\n\tlineMaskName, polyMaskName = getVectorNames(fileName, domainLayer.name())\n\tvectorPath = getSavePaths(fileSource, domainLayer, 'shp')\n\ttifPath = getSavePaths(fileSource, domainLayer, 'tif')\n\tlineMaskPath = vectorPath + '/' + lineMaskName + '.shp'\n\tpolyMaskPath = vectorPath + '/' + polyMaskName + '.shp'\n\trawTifPath = tifPath + '/' + name + '.tif'\n\t\n\tif os.path.exists(lineMaskPath):\n\t\tlayers = QgsProject.instance().mapLayersByName(lineMaskName)\n\t\tif (len(layers) > 0):\n\t\t\tfor layer in layers:\n\t\t\t\tQgsProject.instance().removeMapLayer(layer.id())\n\tif os.path.exists(polyMaskPath):\n\t\tlayers = QgsProject.instance().mapLayersByName(polyMaskName)\n\t\tif (len(layers) > 0):\n\t\t\tfor layer in layers:\n\t\t\t\tQgsProject.instance().removeMapLayer(layer.id())\n\t\n\t# Load geotiff and get domain layer/bounding box of area to mask\n\t#print('Load geotiff and get domain layer/bounding box of area to mask')\n\tgeotiff = gdal.Open(fileSource)\n\tfeature = domainLayer.getFeature(0)\n\tdomain = feature.geometry().boundingBox()\n\trasterCRS = rasterLayer.crs().authid()\n\tdomainCRS = domainLayer.crs().authid()\n\tbounds = geotiffWorldToPixelCoords(geotiff, domain, rasterCRS, domainCRS)\n\t\n\t#print(\"mask = imread(resolve('landsat_preds/' + name + '_pred.png'))\")\n\ttry:\n\t\t# raw = imread(resolve('landsat_preds/' + domainLayer.name() + '/' + name + '_raw.png'))\n\t\t# arrayToRaster(raw, geotiff, bounds, rawTifPath)\n\t\t\n\t\tmask = imread(resolve('landsat_preds/' + domainLayer.name() + '/' + name + '_mask.png'))\n\t\t# mask = postprocess(mask)\n\t\t# Save results to files and layers\n\t\t#print('Save results to files and layers')\n\t\tarrayToRaster(mask, geotiff, bounds, maskPath)\n\t\trasterLayer = QgsRasterLayer(maskPath, maskName)\n\t\tlineLayer, polygonLayer = vectorizeRaster(maskPath, lineMaskPath, lineMaskName, polyMaskPath, polyMaskName)\n\t\t\n\t\tgeotiff = None\n\t\treturn lineLayer, polygonLayer\n\texcept:\n\t\tprint(resolve('landsat_preds/' + domainLayer.name() + '/' + name + '_mask.png'), 'not found - skipping.')\n\t\treturn None, None\n\ndef layerSubsetSave(rasterLayer:QgsRasterLayer, domainLayer:QgsVectorLayer, subsetName:str) -> None:\n\t\"\"\"Description: Processes a raster image into a vector polygon ocean/land mask.\n\t\tMake sure to save the shapefile, as it will be deleted otherwise! \n\t\tInput: QgsRasterLayer rasterLayer - layer that contains the raster image to process\n\t\t\t\tQgsVectorLayer domainLayer - layer that contains a polygon specifying the bounds of the raster image to process\n\t\t\t\tstring name - output file name.\n\t\tOutput: QgsRasterLayer, QgsVectorLayer - objects referencing the new mask layers\n\t\"\"\"\n\t\n\t# Get basic file name information on geotiff, raster image, masked raster subset image, and masked vector subset shp file\n\tfileSource = rasterLayer.source()\n\tfileInfo = QFileInfo(fileSource)\n\tfileName = fileInfo.baseName()\t\n\tsavePaths = getSavePaths(fileSource, domainLayer, 'tif') \n\tsubsetPath = savePaths + '/' + subsetName + '.tif'\n\t\n\t# Load geotiff and get domain layer/bounding box of area to mask\n\tgeotiff = gdal.Open(fileSource)\n\tfeature = domainLayer.getFeature(0)\n\tdomain = feature.geometry().boundingBox()\n\tprj = geotiff.GetProjection()\n\tsrs = osr.SpatialReference(wkt=prj)\n\tif srs.GetAttrValue(\"PROJCS|AUTHORITY\", 1) is not None:\n\t\tepsgCode = srs.GetAttrValue(\"PROJCS|AUTHORITY\", 1)\n\telif srs.GetAttrValue(\"AUTHORITY\", 1) is not None:\n\t\tepsgCode = srs.GetAttrValue(\"AUTHORITY\", 1)\n\telse:\n\t\tepsgCode = str(32621)\n\trasterCRS = \"EPSG:\" + epsgCode\n\t\n\tcrs = rasterLayer.crs()\n\tcrs.createFromId(int(epsgCode))\n\trasterLayer.setCrs(crs)\n\trasterLayer.triggerRepaint()\n\t\n\t#rasterCRS = rasterLayer.crs().authid()\n\tdomainCRS = domainLayer.crs().authid()\n\tbounds = geotiffWorldToPixelCoords(geotiff, domain, rasterCRS, domainCRS)\n\t\n\timg = geotiff.GetRasterBand(1)\n\timg = img.ReadAsArray(0,0,geotiff.RasterXSize,geotiff.RasterYSize)\n\timg = img[int(round(bounds.yMinimum())):int(round(bounds.yMaximum())), int(round(bounds.xMinimum())):int(round(bounds.xMaximum()))]\n\timg = (img.astype(np.float32) / img.max() * 65535).astype(np.uint16)\n\t\n\t# print('Save subset:', subsetPath, resolve('landsat_raw/' + domainLayer.name() + '/' + subsetName + '.png'))\n\tif not DRY_RUN:\n\t\tarrayToRaster(img, geotiff, bounds, subsetPath)\n\t\timsave(resolve('landsat_raw/' + domainLayer.name() + '/' + subsetName + '.png'), img)\n\t\t# imsave(resolve('small/' + domainLayer.name() + '/' + subsetName + '.png'), img)\n\t\t# imsave(os.path.join(r'../reprocessing\\images_1024', domainLayer.name(), subsetName + '.png'), img)\n\ttry:\n\t\t#Gather BQA info\n\t\tfileSourceBQA = fileSource[:-7] + '_BQA.TIF'\n\t\tfileInfoBQA = QFileInfo(fileSourceBQA)\n\t\tfileNameBQA = fileInfoBQA.baseName()\n\t\tsubsetNameBQA = fileNameBQA + '_' + domainLayer.name()\n\t\tsubsetPathBQA = savePaths + '/' + subsetNameBQA + '.tif'\n\t\t#Save BQA subset\n\t\tgeotiffBQA = gdal.Open(fileSourceBQA)\n\t\timgBQA = geotiffBQA.GetRasterBand(1)\n\t\timgBQA = imgBQA.ReadAsArray(0,0,geotiffBQA.RasterXSize,geotiffBQA.RasterYSize).astype(np.uint16)\n\t\timgBQA = imgBQA[int(round(bounds.yMinimum())):int(round(bounds.yMaximum())), int(round(bounds.xMinimum())):int(round(bounds.xMaximum()))]\n\t\t# print('Save BQA subset:', subsetPathBQA, resolve('landsat_raw/' + domainLayer.name() + '/' + subsetName + '_bqa.png'))\n\t\tif not DRY_RUN:\n\t\t\t# arrayToRaster(imgBQA, geotiffBQA, bounds, subsetPathBQA)\n\t\t\t# print(fileSourceBQA, geotiffBQA.RasterXSize, geotiffBQA.RasterYSize)\n\t\t\t# print(int(round(bounds.yMinimum())), int(round(bounds.yMaximum())), int(round(bounds.xMinimum())), int(round(bounds.xMaximum())))\n\t\t\timsave(resolve('landsat_raw/' + domainLayer.name() + '/' + subsetName + '_bqa.png'), imgBQA)\n\t\t\n\t\t#Gather MTL info\n\t\tfileSourceMTL = fileSource[:-7] + '_MTL.txt'\n\t\tfileInfoMTL = QFileInfo(fileSourceMTL)\n\t\tfileNameMTL = fileInfoMTL.baseName()\n\t\tsubsetNameMTL = fileNameMTL + '_' + domainLayer.name()\n\t\tsubsetPathMTL = savePaths + '/' + subsetNameMTL + '.txt'\n\t\t#Save MTL subset\t\n\t\tif not DRY_RUN:\n\t\t\timage_feats = ['']*6\n\t\t\twith open(fileSourceMTL, 'r') as image_feats_source_file:\t\n\t\t\t\tlines = image_feats_source_file.readlines()\n\t\t\t\tfor line in lines:\n\t\t\t\t\tif 'SUN_AZIMUTH =' in line:\n\t\t\t\t\t\timage_feats[0] = line.strip()\n\t\t\t\t\telif 'SUN_ELEVATION =' in line:\n\t\t\t\t\t\timage_feats[1] = line.strip()\n\t\t\t\t\telif 'CLOUD_COVER ' in line:\n\t\t\t\t\t\timage_feats[2] = line.strip()\n\t\t\t\t\telif 'CLOUD_COVER_LAND ' in line:\n\t\t\t\t\t\timage_feats[3] = line.strip()\n\t\t\t\t\telif 'DATE_ACQUIRED =' in line:\n\t\t\t\t\t\timage_feats[4] = line.strip()\n\t\t\t\t\telif 'GRID_CELL_SIZE_REFLECTIVE =' in line:\n\t\t\t\t\t\timage_feats[5] = line.strip()\n\t\t\tsavePath = resolve('landsat_raw/' + domainLayer.name() + '/' + subsetName + '_mtl.txt')\n\t\t\twith open(savePath, 'w') as image_feats_dest_file:\n\t\t\t\tfor line in image_feats:\n\t\t\t\t\timage_feats_dest_file.write(str(line) + '\\n')\n\texcept:\n\t\tprint('No BQA/MTL found for:', subsetName)\n\t\n\treturn img.shape\n\ndef perform_subsetting(rasterLayers, rasterPrefix, domainLayers):\n\tprint('Performing subsetting...')\n\t\n\t#Make directories if not already existing\n\tfor domainLayer in domainLayers:\n\t\tdomainLayer = domainLayer.layer()\n\t\traw_path = resolve('landsat_raw/' + domainLayer.name())\n\t\tmask_path = resolve('landsat_preds/' + domainLayer.name())\n\t\tif not os.path.exists(raw_path):\n\t\t\tos.mkdir(raw_path)\n\t\tif not os.path.exists(mask_path):\n\t\t\tos.mkdir(mask_path)\n\t\n\t\t# Clear data from any previous runs\n\t\t# files = glob.glob(raw_path + '/*')\n\t\t# for f in files:\n\t\t\t# if os.path.isfile(f):\n\t\t\t\t# os.remove(f)\n\t\t# files = glob.glob(mask_path + '/*')\n\t\t# for f in files:\n\t\t\t# if os.path.isfile(f):\n\t\t\t\t# os.remove(f)\n\t\n\tresolutions = warpAndSaveSubsets(rasterLayers, rasterPrefix, domainLayers)\n\tresizeandSaveSubsets(rasterLayers, rasterPrefix, domainLayers, resolutions)\n\ndef warpAndSaveSubsets(rasterLayers, rasterPrefix, domainLayers) -> list:\n\t# Perform subsetting for each layer, extracting each subset for every domain in domainGroup\n\tresolutions = defaultdict(list)\n\trasterLen = len(rasterLayers)\n\tdomainLen = len(domainLayers)\n\ttotal = rasterLen * domainLen\n\tfor i in range(rasterLen):\n\t\trasterLayerNode = rasterLayers[i]\n\t\trasterLayer = rasterLayerNode.layer()\n\t\tprint('Raster (number):', rasterLayer.source())#, ' Progress:', str(i) + '/' + str(total), '(' + str((i * rasterLen) / total) + '%)')\n\t\tfor j in range(domainLen):\n\t\t\tdomainLayer = domainLayers[j]\n\t\t\tdomainLayer = domainLayer.layer()\n\t\t\trasterLayers[i] = layerWarp(rasterLayerNode, domainLayer)\n\t\t\trasterLayerNode = rasterLayers[i]\n\t\t\trasterLayer = rasterLayerNode.layer() #Reload layer in case of warping\n\t\t\tsubset_name = domainLayer.name() + \"_\" + rasterLayer.name()\n\t\t\tprint('Domain: ', domainLayer.name())#, ' Progress:', str((i * rasterLen + j) / total) + '%')\n\t\t\tresolution = layerSubsetSave(rasterLayer, domainLayer, subset_name)\n\t\t\tresolutions[domainLayer.name()].append(resolution)\n\t\n\t# Calculate the median resolutions for each domain\t\n\tmedian_resolutions = []\n\tfor i in range(len(domainLayers)):\n\t\tdomainLayer = domainLayers[i]\n\t\tresolution = np.median(resolutions[domainLayer.name()], axis=0)\n\t\tprint('domainLayer (#):', i, domainLayer.name(), ' resolution:', resolution)\n\t\tmedian_resolutions.append(resolution)\n\t\n\treturn median_resolutions\n\ndef resizeandSaveSubsets(rasterLayers, rasterPrefix, domainLayers, resolutions) -> list:\n\t# Resize the images to the median size to account for reprojection differences\n\trasterLen = len(rasterLayers)\n\tdomainLen = len(domainLayers)\n\ttotal = rasterLen * domainLen\n\tfor i in range(rasterLen):\n\t\trasterLayerNode = rasterLayers[i]\n\t\trasterLayer = rasterLayerNode.layer()\n\t\tif rasterLayer.name()[-2:] in rasterPrefix:\n\t\t\tfor j in range(domainLen):\n\t\t\t\tdomainLayer = domainLayers[j]\n\t\t\t\tresolution = resolutions[j]\n\t\t\t\tdomainLayer = domainLayer.layer()\n\t\t\t\tprint('Resizing subsets to', resolution)#, ' Progress:', str((i * rasterLen + j) / total) + '%')\n\t\t\t\tif domainInRaster(rasterLayer, domainLayer):\n\t\t\t\t\tsubset_name = domainLayer.name() + \"_\" + rasterLayer.name()\n\t\t\t\t\tlayerResize(rasterLayer, domainLayer, subset_name, resolution)\n\ndef perform_saving(rasterLayers, rasterPrefix, domainLayers):\n\t#Save for training\n\tfor domainNode in domainLayers:\n\t\tdomainLayer = domainNode.layer()\n\t\tsource_path_base = resolve('landsat_raw/' + domainLayer.name())\n\t\tdest_path_base = r'D:/Daniel/Documents/GitHub/ultrasound-nerve-segmentation/landsat_raw/train_full/' + domainLayer.name()\n\t\tif not os.path.exists(dest_path_base):\n\t\t\tos.mkdir(dest_path_base)\n\t\tfor rasterLayer in rasterLayers:\n\t\t\trasterLayer = rasterLayer.layer()\n\t\t\tif rasterLayer.name()[-2:] in rasterPrefix:\n\t\t\t\tname = domainLayer.name() + \"_\" + rasterLayer.name() + '.png'\n\t\t\t\tsource_path = os.path.join(source_path_base, name)\n\t\t\t\tdest_path = os.path.join(dest_path_base, name)\n\t\t\t\tif not DRY_RUN:\n\t\t\t\t\tshutil.copy2(source_path, dest_path)\n\ndef processLayers(domainLayers, check_masking, check_saving, check_postprocessing):\n\tprint('Performing masking...')\n\tfor domainNode in domainLayers:\n\t\tlaunchcommand = r'C:\\Users\\Daniel\\AppData\\Roaming\\QGIS\\QGIS3\\profiles\\default\\python\\plugins\\calvingfrontmachine\\cfm.bat'\n\t\tcheck_masking = str(int(check_masking))\n\t\tcheck_postprocessing = str(int(check_postprocessing))\n\t\tcheck_saving = str(int(check_saving))\n\t\targuments = [launchcommand, domainNode.name(), check_masking, check_saving, check_postprocessing]\n\t\tprint(arguments)\n\t\tp = subprocess.Popen(arguments, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, creationflags=subprocess.CREATE_NEW_CONSOLE)\n\t\twhile True:\n\t\t\tline = p.stdout.readline()\n\t\t\tprint(str(line))\n\t\t\tif p.poll() != None:\n\t\t\t\tprint('exit code: ', p.poll())\n\t\t\t\tbreak\n\t\tp.kill()\n\ndef perform_vectorization(rasterLayers, domainLayers, rasterPrefix, check_vectorization, check_adding):\n\tfor rasterLayer in rasterLayers:\n\t\tfor domainLayer in domainLayers:\n\t\t\ttry:\n\t\t\t\tif rasterLayer.name()[-2:] in rasterPrefix:\n\t\t\t\t\tif check_vectorization:\n\t\t\t\t\t\tlineLayer, polygonLayer = layerSubsetLoad(rasterLayer.layer(), domainLayer.layer(), rasterGroup, domainLayer.name() + \"_\" + rasterLayer.name())\n\t\t\t\t\tif check_adding:\n\t\t\t\t\t\t# step 1: add the layer to the registry, False indicates not to add to the layer tree\n\t\t\t\t\t\tQgsProject.instance().addMapLayer(lineLayer, False)\n\t\t\t\t\t\tQgsProject.instance().addMapLayer(polygonLayer, False)\n\t\t\t\t\t\t# step 2: append layer to the root group node\n\t\t\t\t\t\trasterLayer.parent().insertLayer(0, lineLayer)\n\t\t\t\t\t\trasterLayer.parent().insertLayer(0, polygonLayer)\n\t\t\t\t\t\t# step 3: Add transparency slider to polygon layers\n\t\t\t\t\t\t#polygonLayer.setCustomProperty(\"embeddedWidgets/count\", 1)\n\t\t\t\t\t\t#polygonLayer.setCustomProperty(\"embeddedWidgets/0/id\", \"transparency\")\n\t\t\t\t\t\t# Alter fill style for vector layers\n\t\t\t\t\t\tpolygonSymbol = polygonLayer.renderer().symbol()\n\t\t\t\t\t\tlineSymbol = lineLayer.renderer().symbol()\n\t\t\t\t\t\tpolygonSymbol.setColor(lineSymbol.color())\n\t\t\t\t\t\tpolygonSymbol.setOpacity(0.25)\n\t\t\t\t\t\t# Redraw canvas and save variable to global context\n\t\t\t\t\t\tiface.layerTreeView().refreshLayerSymbology(lineLayer.id())\n\t\t\t\t\t\tiface.layerTreeView().refreshLayerSymbology(polygonLayer.id())\n\n\t\t\texcept Exception as e:\n\t\t\t\tprint(e)\n\ndef resolve(name, basepath='C:/Users/Daniel/AppData/Roaming/QGIS/QGIS3/profiles/default/python/plugins/calvingfrontmachine'):\n\tif not os.path.exists(basepath):\n\t\tbasepath = os.path.dirname(os.path.realpath(__file__))\n\treturn os.path.join(basepath, name)\n\ndef findGroups(root:QgsLayerTree):\n\t\"\"\"Return a string list of groups.\"\"\"\n\tresult = []\n\tfor child in root.children():\n\t\tif isinstance(child, QgsLayerTreeGroup):\n\t\t\tresult.append(child.name())\n\t\t\tresult.extend(findGroups(child))\n\treturn result\t\n\ndef findChildren(root:QgsLayerTree, matchString:str):\n\t\"\"\"Return a string list of groups.\"\"\"\n\tresult = []\n\tmatchStringParts = matchString.split('/', 1)\n\tfor child in root.children():\n\t\tif fnmatch.fnmatch(child.name(), matchStringParts[0]):\n\t\t\tif isinstance(child, QgsLayerTreeGroup):\n\t\t\t\tif child.name().startswith(('1', '2')): \n\t\t\t\t\t\n\t\t\t\t\tif int(child.name()) < 1985:\n\t\t\t\t\t\tresult.extend(findChildren(child, matchStringParts[1]))\n\t\t\t\telse:\n\t\t\t\t\tprint(child.name())\n\t\t\t\t\tresult.extend(findChildren(child, matchStringParts[1]))\n\t\t\telse:\n\t\t\t\tresult.append(child)\n\treturn result\n\n\nproject = QgsProject.instance()\nroot = project.layerTreeRoot()\nrasterPrefix = ['B5', 'B4', 'B7']\ngroups = findGroups(root)\n\n# Get layer objects based on selection string values\nrasterGroupName = 'CalvingFronts/Rasters/*/*/*'\nrasterGroup = root.findGroup(rasterGroupName)\nrasterLayers = findChildren(root, rasterGroupName)\ndomainGroupName = 'CalvingFronts/Domains/*'\ndomainGroup = root.findGroup(domainGroupName)\ndomainLayers = findChildren(root, domainGroupName)\n\n# Get layer objects based on selection string values\n# rasterGroupName = 'CalvingFronts/Rasters/Upernavik/*/*'\n# rasterGroup = root.findGroup(rasterGroupName)\n# rasterLayers = findChildren(root, rasterGroupName)\n# domainGroupName = 'CalvingFronts/Domains/Upernavik*'\n# domainGroup = root.findGroup(domainGroupName)\n# domainLayers = findChildren(root, domainGroupName)\n\nrasterLayers\ncheck_subsetting = True\ncheck_saving = False\ncheck_masking = False\ncheck_postprocessing = False\ncheck_vectorization = False\ncheck_adding = False\n\n#Save subsets of raster source files using clipping domain\nif check_subsetting:\n\tperform_subsetting(rasterLayers, rasterPrefix, domainLayers)\n", "sub_path": "preprocessing/calvingfrontmachine/calving_front_machine_standalone.py", "file_name": "calving_front_machine_standalone.py", "file_ext": "py", "file_size_in_byte": 31357, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "PyQt5.QtCore.QFileInfo", "line_number": 29, "usage_type": "call"}, {"api_name": "osgeo.gdal.Open", "line_number": 33, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 33, "usage_type": "name"}, {"api_name": "osgeo.osr.SpatialReference", "line_number": 37, "usage_type": "call"}, {"api_name": "osgeo.osr", "line_number": 37, "usage_type": "name"}, {"api_name": "osgeo.gdal.Open", "line_number": 55, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.uint16", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 82, "usage_type": "call"}, {"api_name": "pyproj.Proj", "line_number": 124, "usage_type": "call"}, {"api_name": "pyproj.Proj", "line_number": 125, "usage_type": "call"}, {"api_name": "pyproj.transform", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 147, "usage_type": "attribute"}, {"api_name": "osgeo.gdal.GetDriverByName", "line_number": 177, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 177, "usage_type": "name"}, {"api_name": "osgeo.gdal.GDT_Float32", "line_number": 184, "usage_type": "attribute"}, {"api_name": "osgeo.gdal", "line_number": 184, "usage_type": "name"}, {"api_name": "osgeo.gdal.UseExceptions", "line_number": 210, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 210, "usage_type": "name"}, {"api_name": "osgeo.gdal.Open", "line_number": 213, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 213, "usage_type": "name"}, {"api_name": "osgeo.osr.SpatialReference", "line_number": 216, "usage_type": "call"}, {"api_name": "osgeo.osr", "line_number": 216, "usage_type": "name"}, {"api_name": "osgeo.ogr.GetDriverByName", "line_number": 219, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 219, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 223, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 224, "usage_type": "call"}, {"api_name": "osgeo.ogr.GetDriverByName", "line_number": 227, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 227, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 253, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 254, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 286, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 287, "usage_type": "call"}, {"api_name": "skimage.io.imsave", "line_number": 290, "usage_type": "call"}, {"api_name": "osgeo.gdal.Open", "line_number": 301, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 301, "usage_type": "name"}, {"api_name": "osgeo.osr.SpatialReference", "line_number": 303, "usage_type": "call"}, {"api_name": "osgeo.osr", "line_number": 303, "usage_type": "name"}, {"api_name": "shutil.copy2", "line_number": 345, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 346, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 382, "usage_type": "call"}, {"api_name": "os.path", "line_number": 382, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 383, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 384, "usage_type": "call"}, {"api_name": "os.path", "line_number": 384, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 385, "usage_type": "call"}, {"api_name": "os.path", "line_number": 385, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 386, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 387, "usage_type": "call"}, {"api_name": "os.path", "line_number": 387, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 388, "usage_type": "call"}, {"api_name": "os.path", "line_number": 388, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 389, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 390, "usage_type": "call"}, {"api_name": "os.path", "line_number": 390, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 391, "usage_type": "call"}, {"api_name": "os.path", "line_number": 391, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 396, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 399, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFileInfo", "line_number": 433, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 446, "usage_type": "call"}, {"api_name": "os.path", "line_number": 446, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 451, "usage_type": "call"}, {"api_name": "os.path", "line_number": 451, "usage_type": "attribute"}, {"api_name": "osgeo.gdal.Open", "line_number": 459, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 459, "usage_type": "name"}, {"api_name": "skimage.io.imread", "line_number": 471, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFileInfo", "line_number": 496, "usage_type": "call"}, {"api_name": "osgeo.gdal.Open", "line_number": 502, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 502, "usage_type": "name"}, {"api_name": "osgeo.osr.SpatialReference", "line_number": 506, "usage_type": "call"}, {"api_name": "osgeo.osr", "line_number": 506, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 527, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 527, "usage_type": "attribute"}, {"api_name": "skimage.io.imsave", "line_number": 532, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFileInfo", "line_number": 538, "usage_type": "call"}, {"api_name": "osgeo.gdal.Open", "line_number": 543, "usage_type": "call"}, {"api_name": "osgeo.gdal", "line_number": 543, "usage_type": "name"}, {"api_name": "numpy.uint16", "line_number": 545, "usage_type": "attribute"}, {"api_name": "skimage.io.imsave", "line_number": 552, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFileInfo", "line_number": 556, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 595, "usage_type": "call"}, {"api_name": "os.path", "line_number": 595, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 596, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 597, "usage_type": "call"}, {"api_name": "os.path", "line_number": 597, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 598, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 615, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 638, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 668, "usage_type": "call"}, {"api_name": "os.path", "line_number": 668, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 669, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 674, "usage_type": "call"}, {"api_name": "os.path", "line_number": 674, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 675, "usage_type": "call"}, {"api_name": "os.path", "line_number": 675, "usage_type": "attribute"}, {"api_name": "shutil.copy2", "line_number": 677, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 688, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 688, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 688, "usage_type": "attribute"}, {"api_name": "subprocess.CREATE_NEW_CONSOLE", "line_number": 688, "usage_type": "attribute"}, {"api_name": "qgis.utils.iface.layerTreeView", "line_number": 720, "usage_type": "call"}, {"api_name": "qgis.utils.iface", "line_number": 720, "usage_type": "name"}, {"api_name": "qgis.utils.iface.layerTreeView", "line_number": 721, "usage_type": "call"}, {"api_name": "qgis.utils.iface", "line_number": 721, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 727, "usage_type": "call"}, {"api_name": "os.path", "line_number": 727, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 728, "usage_type": "call"}, {"api_name": "os.path", "line_number": 728, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 728, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 729, "usage_type": "call"}, {"api_name": "os.path", "line_number": 729, "usage_type": "attribute"}, {"api_name": "fnmatch.fnmatch", "line_number": 745, "usage_type": "call"}]} +{"seq_id": "430633660", "text": "from PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtMultimedia import QSound\nfrom settings import *\nimport sys\n\nclass MyWindow(QWidget):\n def __init__(self):\n super(MyWindow, self).__init__()\n self.initGUI()\n\n def NoAccess(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Critical)\n msg.setWindowTitle(\"Windows Security\")\n msg.setText(\"Access is denied.\")\n msg.setInformativeText(\"\"\"\nYou require permission from the computer's administrator to make changes to this program. \nGo to Settings to manage user administrator options.\n\"\"\")\n msg.setStandardButtons(QMessageBox.Ok)\n msg.exec_()\n\n\n def UAC(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"User Account Control\")\n msg.setInformativeText(\"Do you want to allow the program from an unkown publisher to make changes to this computer?\")\n msg.setWindowTitle(\"User Account Control\")\n msg.setDetailedText(\"User Account Control helps prevent potentially harmful programs from making changes to your computer. (We at Windows think the user is stupid.) \")\n msg.setStandardButtons(QMessageBox.Yes | QMessageBox.No)\n msg.buttonClicked.connect(self.NoAccess)\n msg.exec_()\n\n def UAC2(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"User Account Control\")\n msg.setInformativeText(\"Do you really want to run this app?\")\n msg.setWindowTitle(\"User Account Control\")\n msg.setDetailedText(\"User Account Control helps prevent potentially harmful programs from making changes to your computer. (We at Windows think the user is stupid.) \")\n msg.setStandardButtons(QMessageBox.Yes)\n msg.buttonClicked.connect(self.UAC3)\n msg.exec_()\n\n def UAC3(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"User Account Control\")\n msg.setInformativeText(\"Are you sure?\")\n msg.setWindowTitle(\"User Account Control\")\n msg.setDetailedText(\"User Account Control helps prevent potentially harmful programs from making changes to your computer. (We at Windows think the user is stupid.) \")\n msg.setStandardButtons(QMessageBox.Yes)\n msg.buttonClicked.connect(self.UAC4)\n msg.exec_()\n\n def UAC4(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Critical)\n msg.setText(\"User Account Control\")\n msg.setInformativeText(\"Windows does not recommend to run this app.\")\n msg.setWindowTitle(\"User Account Control\")\n msg.setDetailedText(\"User Account Control helps prevent potentially harmful programs from making changes to your computer. (We at Windows think the user is stupid.) \")\n msg.setStandardButtons(QMessageBox.Ok)\n msg.exec_()\n\n def Blocked(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Critical)\n msg.setWindowTitle(\"Windows Security\")\n msg.setText(\"This app has been blocked for you protection.\")\n msg.setInformativeText(\"\"\"\nAn administrator has blocked you from running this app. \nFor more information, contact the administrator:\nwww.windows.com\nGood luck! \n\"\"\")\n msg.setStandardButtons(QMessageBox.Close)\n msg.exec_()\n \n def ShutDown(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Question)\n msg.setWindowTitle(\"Windows Update\")\n msg.setText(\"Your PC needs to finish intstalling updates before shutting down.\")\n msg.setInformativeText(\"Would you like to wait?\")\n msg.setStandardButtons(QMessageBox.Yes)\n msg.exec_()\n\n def Settings(self):\n self.win = MyMainWindow()\n\n def SoundPlay(self):\n self.sound = QSound(\"sounds/pushme.wav\")\n self.sound.play()\n\n def initGUI(self):\n self.setGeometry(0, 30, 1000, 600)\n self.setWindowTitle(\"Start Menu\")\n self.setObjectName(\"main\")\n self.setStyleSheet(\"\"\"\n QWidget#main {\n background-color: #191919\n } \n \"\"\")\n\n\n self.powerbutton = QPushButton(self)\n self.powerbutton.resize(17, 17)\n self.powerbutton.setObjectName(\"powerbutton\")\n self.powerbutton.setStyleSheet(\"\"\"\n QPushButton#powerbutton {\n background-image: url('img/power.png');\n }\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n } \n \"\"\")\n self.powerbutton.move(10, 550)\n self.powerbutton.clicked.connect(self.ShutDown)\n\n self.settings = QPushButton(self)\n self.settings.resize(17, 17)\n self.settings.setObjectName(\"settings\")\n self.settings.setStyleSheet(\"\"\"\n QPushButton#settings {\n background-image: url('img/settings2.png');\n }\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n } \n \"\"\")\n self.settings.move(10, 500)\n self.settings.clicked.connect(self.Settings) \n\n self.explorer = QPushButton(self)\n self.explorer.resize(17, 17)\n self.explorer.setObjectName(\"explorer\")\n self.explorer.setStyleSheet(\"\"\"\n QPushButton#explorer {\n background-image: url('img/explorer.png');\n }\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n } \n \"\"\")\n self.explorer.move(10, 450)\n self.explorer.clicked.connect(self.SoundPlay)\n\n self.header1 = QLabel(self)\n self.header1.setText(\"Leisure\")\n self.header1.setStyleSheet(\"\"\"\n QLabel {\n color: #D9D9D9\n }\n \"\"\")\n self.header1.move(205, 10) #x, y\n\n self.tile11 = QPushButton(self)\n self.tile11.resize(100, 100)\n self.tile11.setObjectName(\"netflix\") #incorrect sRGB\n self.tile11.setStyleSheet(\"\"\"\n QPushButton#netflix {\n background-image: url('img/netflix.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n }\n \"\"\")\n self.tile11.move(200, 30)\n self.tile11.clicked.connect(self.UAC)\n\n self.tile12 = QPushButton(self)\n self.tile12.resize(100, 100)\n self.tile12.setObjectName(\"spotify\")\n self.tile12.setStyleSheet(\"\"\"\n QPushButton#spotify {\n background-image: url('img/spotify.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n }\n\n \"\"\")\n self.tile12.move(310, 30)\n self.tile12.clicked.connect(self.NoAccess)\n\n self.tile13 = QPushButton(self)\n self.tile13.resize(100, 100)\n self.tile13.setObjectName(\"music\") #incorrect sRGB\n self.tile13.setStyleSheet(\"\"\"\n QPushButton#music {\n background-image: url('img/music.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n }\n\n \"\"\")\n self.tile13.move(420, 30)\n self.tile13.clicked.connect(self.UAC2)\n\n self.header2 = QLabel(self)\n self.header2.setText(\"Office\")\n self.header2.setStyleSheet(\"\"\"\n QLabel {\n color: #D9D9D9\n }\n \"\"\")\n self.header2.move(205, 150)\n\n self.tile21 = QPushButton(self)\n self.tile21.resize(100, 100)\n self.tile21.setObjectName(\"word\")\n self.tile21.setStyleSheet(\"\"\"\n QPushButton#word {\n background-image: url('img/word.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n }\n \"\"\")\n self.tile21.move(200, 170)\n self.tile21.clicked.connect(self.Blocked)\n\n self.tile22 = QPushButton(self)\n self.tile22.resize(100, 100)\n self.tile22.setObjectName(\"excel\")\n self.tile22.setStyleSheet(\"\"\"\n QPushButton#excel {\n background-image: url('img/excel.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n }\n \"\"\")\n self.tile22.move(310, 170)\n self.tile22.clicked.connect(self.NoAccess)\n\n self.tile23 = QPushButton(self)\n self.tile23.resize(100, 100)\n self.tile23.setObjectName(\"powerpoint\")\n self.tile23.setStyleSheet(\"\"\"\n QPushButton#powerpoint {\n background-image: url('img/powerpoint.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n }\n \"\"\")\n self.tile23.move(420, 170)\n self.tile23.clicked.connect(self.NoAccess)\n\n self.tile24 = QPushButton(self)\n self.tile24.resize(100, 100)\n self.tile24.setObjectName(\"sway\")\n self.tile24.setStyleSheet(\"\"\"\n QPushButton#sway {\n background-image: url('img/sway.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n }\n \"\"\")\n self.tile24.move(200, 280)\n self.tile24.clicked.connect(self.Blocked)\n\n self.tile25 = QPushButton(self)\n self.tile25.resize(100, 100)\n self.tile25.setObjectName(\"reader\")\n self.tile25.setStyleSheet(\"\"\"\n QPushButton#reader {\n background-image: url('img/reader.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n }\n \"\"\")\n self.tile25.move(310, 280)\n self.tile25.clicked.connect(self.UAC) \n\n self.tile26 = QPushButton(self)\n self.tile26.resize(100, 100)\n self.tile26.setObjectName(\"onenote\")\n self.tile26.setStyleSheet(\"\"\"\n QPushButton#onenote {\n background-image: url('img/onenote.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n }\n \"\"\")\n self.tile26.move(420, 280)\n self.tile26.clicked.connect(self.UAC2) \n\n\n self.header3 = QLabel(self)\n self.header3.setText(\"Creative\")\n self.header3.setStyleSheet(\"\"\"\n QLabel {\n color: #D9D9D9\n }\n \"\"\")\n self.header3.move(565, 10)\n\n self.tile31 = QPushButton(self)\n self.tile31.resize(100, 100)\n self.tile31.setObjectName(\"photoshop\")\n self.tile31.setStyleSheet(\"\"\"\n QPushButton#photoshop {\n background-image: url('img/photoshop.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n } \n \"\"\")\n self.tile31.move(560, 30)\n self.tile31.clicked.connect(self.Blocked) \n\n self.tile32 = QPushButton(self)\n self.tile32.resize(100, 100)\n self.tile32.setObjectName(\"bridge\")\n self.tile32.setStyleSheet(\"\"\"\n QPushButton#bridge {\n background-image: url('img/bridge.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n } \n \"\"\")\n self.tile32.move(670, 30)\n self.tile32.clicked.connect(self.UAC) \n\n self.tile33 = QPushButton(self)\n self.tile33.resize(100, 100)\n self.tile33.setObjectName(\"cubase\")\n self.tile33.setStyleSheet(\"\"\"\n QPushButton#cubase {\n background-image: url('img/cubase.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n } \n \"\"\")\n self.tile33.move(780, 30)\n self.tile33.clicked.connect(self.NoAccess) \n\n self.header4 = QLabel(self)\n self.header4.setText(\"System\")\n self.header4.setStyleSheet(\"\"\"\n QLabel {\n color: #D9D9D9\n }\n \"\"\")\n self.header4.move(565, 150)\n\n self.tile41 = QPushButton(self)\n self.tile41.resize(100, 100)\n self.tile41.setObjectName(\"settings\")\n self.tile41.setStyleSheet(\"\"\"\n QPushButton#settings {\n background-image: url('img/settings.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n } \n \"\"\")\n self.tile41.move(560, 170)\n self.tile41.clicked.connect(self.Settings) \n\n self.tile42 = QPushButton(self)\n self.tile42.resize(100, 100)\n self.tile42.setObjectName(\"explorer\")\n self.tile42.setStyleSheet(\"\"\"\n QPushButton#explorer {\n background-image: url('img/explorer3.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n } \n \"\"\")\n self.tile42.move(670, 170)\n self.tile42.clicked.connect(self.NoAccess) \n\n self.tile43 = QPushButton(self)\n self.tile43.resize(100, 100)\n self.tile43.setObjectName(\"powershell\")\n self.tile43.setStyleSheet(\"\"\"\n QPushButton#powershell {\n background-image: url('img/powershell.png');\n }\n\n QPushButton {\n border: 1px #4682B4;\n }\n QPushButton:hover {\n border: 1px solid #D9D9D9;\n } \n \"\"\")\n self.tile43.move(780, 170)\n self.tile43.clicked.connect(self.Blocked) \n\n self.header5 = QLabel(self)\n self.header5.setText(\"All Applications\")\n self.header5.setStyleSheet(\"\"\"\n QLabel {\n color: #D9D9D9\n }\n \"\"\")\n self.header5.move(45, 10)\n\n self.listwidget = QListWidget(self)\n self.listwidget.addItem(\"A\")\n self.listwidget.addItem(\"Access 2016\")\n self.listwidget.addItem(\"Acrobat Reader\")\n self.listwidget.addItem(\"Adobe Bridge\")\n self.listwidget.addItem(\"Adobe Photoshop\")\n self.listwidget.addItem(\"Alarm & Clock\")\n self.listwidget.addItem(\"Anaconda2\")\n self.listwidget.addItem(\"Arduino\")\n self.listwidget.addItem(\"Audacity\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"B\")\n self.listwidget.addItem(\"Blender\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"C\")\n self.listwidget.addItem(\"Calender\")\n self.listwidget.addItem(\"Camera\")\n self.listwidget.addItem(\"Canon Utilities\")\n self.listwidget.addItem(\"Cortana\")\n self.listwidget.addItem(\"Cubase\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"D\")\n self.listwidget.addItem(\"Dropbox\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"E\")\n self.listwidget.addItem(\"Excel\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"F\")\n self.listwidget.addItem(\"Feedback-Hub\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"G\")\n self.listwidget.addItem(\"Github\")\n self.listwidget.addItem(\"Google Chrome\")\n self.listwidget.addItem(\"Groove-Music\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"M\")\n self.listwidget.addItem(\"Mail\")\n self.listwidget.addItem(\"Microsoft Edge\")\n self.listwidget.addItem(\"Mozilla Firefox\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"N\")\n self.listwidget.addItem(\"Netflix\")\n self.listwidget.addItem(\"News\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"O\")\n self.listwidget.addItem(\"OneNote\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"P\")\n self.listwidget.addItem(\"PowerPoint\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"S\")\n self.listwidget.addItem(\"Spotify\")\n self.listwidget.addItem(\"Sway\")\n self.listwidget.addItem(\"\")\n self.listwidget.addItem(\"W\")\n self.listwidget.addItem(\"Word\")\n\n\n self.listwidget.setStyleSheet(\"\"\"\n QListWidget {\n font-family: \"Monaco\", \"Andale Mono\", monospace;\n font-size: 14px;\n color: #D9D9D9;\n background-color: #191919\n }\n QScrollBar:vertical {\n border: 2px solid white;\n background: solid black;\n width: 40px;\n margin: 60px 0 0 0\n }\n \"\"\")\n self.listwidget.resize(150, 570)\n self.listwidget.move(40, 30)\n\n self.show()\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n win = MyWindow()\n sys.exit(app.exec_())\n", "sub_path": "project.py", "file_name": "project.py", "file_ext": "py", "file_size_in_byte": 17770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "PyQt5.QtMultimedia.QSound", "line_number": 95, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 556, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 558, "usage_type": "call"}]} +{"seq_id": "254586854", "text": "#!/usr/bin/env python3\n\nimport scapy.all as scapy\nimport argparse\n\ndef get_arguments():\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument(\"-t\", \"--target\", dest=\"target\", help=\"Target IP address/range\")\n\toptions = parser.parse_args()\n\tif not options.target:\n\t\tparser.error(\"[-] Please specify target IP/range, use -h or --help for more info.\")\n\treturn options\n\ndef scan(ip):\n\tarp_request = scapy.ARP(pdst=ip)\n\tbroadcast = scapy.Ether(dst=\"ff:ff:ff:ff:ff:ff\")\n\tarp_request_broadcast = broadcast/arp_request\n\tanswered_packets = scapy.srp(arp_request_broadcast, timeout=1, verbose=False)[0]\n\t\n\tclients_list = []\n\t\n\tfor element in answered_packets:\n\t\tclient_dict = {\"ip\":element[1].psrc, \"mac\":element[1].hwsrc}\n\t\tclients_list.append(client_dict)\n\treturn clients_list\n\t\ndef print_result(results_list):\n\tprint(\"IP\\t\\t\\tMAC ADDRESS\\n-------------------------------------------------------\")\n\tfor client in results_list:\n\t\tprint(client[\"ip\"]+\"\\t\\t\"+client[\"mac\"])\n\n# ----------------------- MAIN -----------------\n\nprint(\"\"\" _______ __ __ _________ \n \\ \\ _____/ |___ _ _____________| | __ / _____/ ____ _____ ____ ____ ___________ \n / | \\_/ __ \\ __\\ \\/ \\/ / _ \\_ __ \\ |/ / \\_____ \\_/ ___\\\\\\\\__ \\ / \\ / \\_/ __ \\_ __ \\\\\n/ | \\ ___/| | \\ ( <_> ) | \\/ < / \\ \\___ / __ \\| | \\ | \\ ___/| | \\/\n\\____|__ /\\___ >__| \\/\\_/ \\____/|__| |__|_ \\ /_______ /\\___ >____ /___| /___| /\\___ >__| \n \\/ \\/ \\/ \\/ \\/ \\/ \\/ \\/ \\/ \"\"\")\n\noptions = get_arguments()\nscan_result = scan(options.target)\nprint_result(scan_result)", "sub_path": "network_scanner.py", "file_name": "network_scanner.py", "file_ext": "py", "file_size_in_byte": 1736, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "scapy.all.ARP", "line_number": 15, "usage_type": "call"}, {"api_name": "scapy.all", "line_number": 15, "usage_type": "name"}, {"api_name": "scapy.all.Ether", "line_number": 16, "usage_type": "call"}, {"api_name": "scapy.all", "line_number": 16, "usage_type": "name"}, {"api_name": "scapy.all.srp", "line_number": 18, "usage_type": "call"}, {"api_name": "scapy.all", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "310028120", "text": "import os\nfrom datetime import datetime\n\nfrom django.core.validators import FileExtensionValidator\nfrom django.db import models\n\n# Create your models here.\nfrom django.db.models import Sum\nfrom django.dispatch import receiver\nfrom django.utils.translation import gettext as _\n\nfrom _main.utils import build_filename, rename_file, delete_file, h_encode\nfrom master.models import Pegawai, Status, Provinsi, Kabupaten, Kecamatan, Kelurahan\n\nATAS_NAMA = (\n ('PN Yang Bersangkutan', 'PN Yang Bersangkutan'),\n ('Pasangan / Anak', 'Pasangan / Anak'),\n ('Lainnya', 'Lainnya'),\n)\n\nASAL_USUL = (\n ('Hasil Sendiri', 'Hasil Sendiri'),\n ('Warisan', 'Warisan'),\n ('Hibah dengan Akta', 'Hibah dengan Akta'),\n ('Hibah Tanpa Akta', 'Hibah Tanpa Akta'),\n ('Hadiah', 'Hadiah'),\n ('Lainnya', 'Lainnya'),\n)\n\nNAMA_MODEL = ('HartaSurat', 'HartaKas', 'HartaLainnya', 'PelaporanFile')\n\n\ndef rename_upload_file(instance, filename):\n nip = instance.pelaporan.pegawai.nip\n return os.path.join(f'lhkasn/', build_filename(nip, filename))\n\n\n@receiver(models.signals.pre_save)\ndef auto_delete_file_on_change(sender, instance, **kwargs):\n model = type(instance)\n nama = model.__name__\n\n if nama in NAMA_MODEL:\n if not instance.pk:\n return False\n\n try:\n file_lama = None\n file_baru = None\n obj = model.objects.get(pk=instance.pk)\n if hasattr(obj, 'bukti_dokumen'):\n file_lama = obj.bukti_dokumen\n file_baru = instance.bukti_dokumen\n elif hasattr(obj, 'filepath'):\n file_lama = obj.filepath\n file_baru = instance.filepath\n except model.DoesNotExist:\n return False\n else:\n rename_file(file_lama=file_lama, file_baru=file_baru)\n\n\n@receiver(models.signals.post_delete)\ndef auto_delete_file_on_delete(sender, instance, **kwargs):\n model = type(instance)\n nama = model.__name__\n\n if nama in NAMA_MODEL:\n if hasattr(instance, 'bukti_dokumen'):\n if instance.bukti_dokumen:\n delete_file(path=instance.bukti_dokumen.path)\n elif hasattr(instance, 'filepath'):\n if instance.filepath:\n delete_file(path=instance.filepath.path)\n\n\nclass PelaporanManager(models.Manager):\n def get_no_pelaporan(self):\n lhkasn = 0\n if self.exists():\n lhkasn = self.latest('id').id\n\n nomor = str(lhkasn + 1).zfill(6)\n tahun = datetime.now().year - 1\n return f'{nomor}/LHKASN-{tahun}'\n\n def get_jumlah_pelaporan(self, pegawai):\n jml = self.filter(pegawai=pegawai).count() + 1\n return jml\n\n def get_jumlah_verifikasi(self):\n menunggu = self.filter(isactive=True, isverify=False, status__id=2).count()\n disetujui = self.filter(isactive=True, isverify=True, status__id=3).count()\n dikembalikan = self.filter(isactive=True, isverify=False, status__id=4).count()\n jumlah = {\n 'menunggu': menunggu,\n 'disetujui': disetujui,\n 'dikembalikan': dikembalikan,\n }\n\n return jumlah\n\n\nclass Pelaporan(models.Model):\n no_pelaporan = models.CharField(max_length=100)\n pegawai = models.ForeignKey(to=Pegawai, on_delete=models.CASCADE, related_name='pegawai_pelaporan')\n status = models.ForeignKey(to=Status, on_delete=models.PROTECT, related_name='status_pelaporan')\n tanggal_lapor = models.DateField()\n tanggal_submit = models.DateField(null=True)\n pelaporan_ke = models.PositiveSmallIntegerField(default=1)\n isactive = models.BooleanField(default=True)\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n updated_by = models.CharField(max_length=255, null=True)\n\n isverify = models.BooleanField(default=False)\n verified = models.DateTimeField(null=True)\n verified_by = models.CharField(max_length=255, null=True)\n remarks = models.CharField(max_length=255, verbose_name='Alasan penolakan', null=True)\n h1 = models.BooleanField(default=False, verbose_name='Harta - Tanah dan Bangunan')\n h2 = models.BooleanField(default=False, verbose_name='Harta - Alat Transportasi dan Mesin')\n h3 = models.BooleanField(default=False, verbose_name='Harta - Harta Bergerak Lainnya')\n h4 = models.BooleanField(default=False, verbose_name='Harta - Surat Berharga')\n h5 = models.BooleanField(default=False, verbose_name='Harta - Kas dan Setara Kas')\n h6 = models.BooleanField(default=False, verbose_name='Harta - Harta Lainnya')\n h7 = models.BooleanField(default=False, verbose_name='Hutang')\n h8 = models.BooleanField(default=False, verbose_name='Penerimaan Kas')\n h9 = models.BooleanField(default=False, verbose_name='Pengeluaran Kas')\n\n objects = PelaporanManager()\n\n class Meta:\n permissions = (('verify_pelaporan', 'Can verify LHKASN'), )\n\n @property\n def is_harta(self):\n if self.hartatnbg_set.exists():\n return True\n elif self.hartamesin_set.exists():\n return True\n elif self.hartahbl_set.exists():\n return True\n elif self.hartasurat_set.exists():\n return True\n elif self.hartakas_set.exists():\n return True\n elif self.hartalainnya_set.exists():\n return True\n elif self.hartahutang_set.exists():\n return True\n else:\n return False\n\n @property\n def is_penerimaan(self):\n if hasattr(self, 'penerimaan'):\n return True\n else:\n return False\n\n @property\n def is_pengeluaran(self):\n if hasattr(self, 'pengeluaran'):\n return True\n else:\n return False\n\n def get_rekap_harta(self):\n # ============== harta\n harta_tdkbergerak = self.hartatnbg_set.all().aggregate(jml=Sum('nilai_estimasi')).get('jml') or 0\n harta_bergerak = self.hartamesin_set.all().aggregate(jml=Sum('nilai_estimasi')).get('jml') or 0\n harta_bergerak_lain = self.hartahbl_set.all().aggregate(jml=Sum('nilai_estimasi')).get('jml') or 0\n harta_surat = self.hartasurat_set.all().aggregate(jml=Sum('nilai_estimasi')).get('jml') or 0\n harta_kas = self.hartakas_set.all().aggregate(jml=Sum('nilai_saldo')).get('jml') or 0\n harta_lainnya = self.hartalainnya_set.all().aggregate(jml=Sum('nilai_estimasi')).get('jml') or 0\n harta_hutang = self.hartahutang_set.all().aggregate(jml=Sum('nilai_saldo')).get('jml') or 0\n sub_total_harta = (harta_tdkbergerak + harta_bergerak + harta_bergerak_lain + harta_surat + harta_kas +\n harta_lainnya)\n total_harta = sub_total_harta - harta_hutang\n harta = {\n 'harta_tdkbergerak': harta_tdkbergerak,\n 'harta_bergerak': harta_bergerak,\n 'harta_bergerak_lain': harta_bergerak_lain,\n 'harta_surat': harta_surat,\n 'harta_kas': harta_kas,\n 'harta_lainnya': harta_lainnya,\n 'sub_total_harta': sub_total_harta,\n 'harta_hutang': harta_hutang,\n 'total_harta': total_harta,\n }\n\n # ============== penerimaan\n penerimaan_pekerjaan = penerimaan_usaha = penerimaan_lainnya = 0\n if self.is_penerimaan:\n penerimaan_pekerjaan = (self.penerimaan.pk_gaji_asn + self.penerimaan.pk_gaji_psn +\n self.penerimaan.pk_profesi_asn + self.penerimaan.pk_profesi_psn +\n self.penerimaan.pk_honorarium_asn + self.penerimaan.pk_honorarium_psn +\n self.penerimaan.pk_bonus_asn + self.penerimaan.pk_bonus_psn +\n self.penerimaan.pk_lainnya_asn + self.penerimaan.pk_lainnya_psn)\n penerimaan_usaha = (self.penerimaan.pu_investasi + self.penerimaan.pu_sewa + self.penerimaan.pu_bunga +\n self.penerimaan.pu_penjualan + self.penerimaan.pu_lainnya)\n penerimaan_lainnya = (self.penerimaan.pl_hutang + self.penerimaan.pl_warisan + self.penerimaan.pl_hibah +\n self.penerimaan.pl_lainnya)\n\n total_penerimaan = penerimaan_pekerjaan + penerimaan_usaha + penerimaan_lainnya\n penerimaan = {\n 'pekerjaan': penerimaan_pekerjaan,\n 'usaha': penerimaan_usaha,\n 'lainnya': penerimaan_lainnya,\n 'total': total_penerimaan,\n }\n\n # ============== pengeluaran\n pengeluaran_umum = pengeluaran_harta = pengeluaran_lain = 0\n if self.is_pengeluaran:\n pengeluaran_umum = (self.pengeluaran.rutin_rt + self.pengeluaran.rutin_sosial + self.pengeluaran.rutin_pajak +\n self.pengeluaran.rutin_lainnya)\n pengeluaran_harta = (self.pengeluaran.harta_pembelian + self.pengeluaran.harta_pemeliharaan +\n self.pengeluaran.harta_lainnya)\n pengeluaran_lain = (self.pengeluaran.lainnya_hibah + self.pengeluaran.lainnya_angsuran +\n self.pengeluaran.lainnya_lainnya)\n\n total_pengeluaran = pengeluaran_umum + pengeluaran_harta + pengeluaran_lain\n pengeluaran = {\n 'umum': pengeluaran_umum,\n 'harta': pengeluaran_harta,\n 'lain': pengeluaran_lain,\n 'total': total_pengeluaran,\n }\n\n penerimaan_bersih = total_penerimaan - total_pengeluaran\n rekap = {\n 'harta': harta,\n 'penerimaan': penerimaan,\n 'pengeluaran': pengeluaran,\n 'penerimaan_bersih': penerimaan_bersih,\n }\n\n return rekap\n\n def get_hashid(self):\n return h_encode(self.id)\n\n\nclass HartaTnbg(models.Model):\n JENIS = (\n ('Sertifikat', 'Sertifikat'),\n ('Akta Jual Beli', 'Akta Jual Beli'),\n )\n PEMANFAATAN = (\n ('Tempat Tinggal', 'Tempat Tinggal'),\n ('Disewakan', 'Disewakan'),\n ('Pertanian / Perikanan / Perkebunan / Pertambangan', 'Pertanian / Perikanan / Perkebunan / Pertambangan'),\n ('Lainnya', 'Lainnya'),\n )\n\n pelaporan = models.ForeignKey(to=Pelaporan, on_delete=models.CASCADE, editable=False)\n prov = models.ForeignKey(to=Provinsi, on_delete=models.PROTECT, verbose_name='Provinsi')\n kab = models.ForeignKey(to=Kabupaten, on_delete=models.PROTECT, verbose_name='Kabupaten / kota')\n kec = models.ForeignKey(to=Kecamatan, on_delete=models.PROTECT, verbose_name='Kecamatan')\n kel = models.ForeignKey(to=Kelurahan, on_delete=models.PROTECT, verbose_name='Kelurahan/Desa', null=True,\n blank=True)\n jalan = models.TextField(verbose_name='Nama jalan dan nomor')\n luas_tanah = models.BigIntegerField(verbose_name='Luas tanah (dalam meter persegi)')\n luas_bangunan = models.BigIntegerField(verbose_name='Luas bangunan (dalam meter persegi)')\n jenis_bukti = models.CharField(max_length=100, choices=JENIS, verbose_name='Jenis bukti')\n nomor_bukti = models.CharField(max_length=100)\n atasnama = models.CharField(max_length=100, choices=ATAS_NAMA)\n salsul = models.CharField(max_length=100, choices=ASAL_USUL, verbose_name='Asal usul harta')\n pemanfaatan = models.CharField(max_length=200, choices=PEMANFAATAN)\n nilai_perolehan = models.BigIntegerField(verbose_name='Nilai perolehan (Rp)')\n nilai_estimasi = models.BigIntegerField(verbose_name='Nilai estimasi saat pelaporan (Rp)')\n tahun_perolehan = models.IntegerField()\n isactive = models.BooleanField(default=True, editable=False)\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n updated_by = models.IntegerField(null=True, editable=False)\n\n\nclass HartaMesin(models.Model):\n JENIS = (\n ('Mobil', 'Mobil'),\n ('Motor', 'Motor'),\n ('Alat Produksi', 'Alat Produksi'),\n ('Mesin Lainnya', 'Mesin Lainnya'),\n )\n\n BUKTI = (\n ('BPKB', 'BPKB'),\n ('Kuitansi Pembelian', 'Kuitansi Pembelian'),\n ('Lain-lain', 'Lain-lain'),\n )\n\n PEMANFAATAN = (\n ('Digunakan Sendiri', 'Digunakan Sendiri'),\n ('Disewakan', 'Disewakan'),\n ('Lainnya', 'Lainnya'),\n )\n\n pelaporan = models.ForeignKey(to=Pelaporan, on_delete=models.CASCADE, editable=False)\n jenis = models.CharField(max_length=100, choices=JENIS)\n merek = models.CharField(max_length=255)\n model = models.CharField(max_length=255, verbose_name='Tipe / model')\n tahun_pembuatan = models.IntegerField()\n nomor_registrasi = models.CharField(max_length=100, verbose_name='No pol. / registrasi')\n jenis_bukti = models.CharField(max_length=50, choices=BUKTI)\n tahun_perolehan = models.IntegerField()\n atasnama = models.CharField(max_length=100, choices=ATAS_NAMA)\n salsul = models.CharField(max_length=255, choices=ASAL_USUL, verbose_name='Asal usul harta')\n pemanfaatan = models.CharField(max_length=200, choices=PEMANFAATAN)\n nilai_perolehan = models.BigIntegerField(verbose_name='Nilai perolehan (Rp)')\n nilai_estimasi = models.BigIntegerField(verbose_name='Nilai estimasi saat pelaporan (Rp)')\n isactive = models.BooleanField(default=True, editable=False)\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n updated_by = models.IntegerField(null=True, editable=False)\n\n\nclass HartaHbl(models.Model):\n JENIS = (\n ('Perabotan Rumah Tangga', 'Perabotan Rumah Tangga'),\n )\n\n pelaporan = models.ForeignKey(to=Pelaporan, on_delete=models.CASCADE, editable=False)\n jenis = models.CharField(max_length=200, choices=JENIS)\n jumlah = models.PositiveSmallIntegerField()\n satuan = models.CharField(max_length=255)\n keterangan = models.CharField(max_length=255)\n tahun_perolehan = models.IntegerField()\n salsul = models.CharField(max_length=255, choices=ASAL_USUL, verbose_name='Asal usul harta')\n nilai_perolehan = models.BigIntegerField(verbose_name='Nilai perolehan (Rp)')\n nilai_estimasi = models.BigIntegerField(verbose_name='Nilai estimasi saat pelaporan (Rp)')\n isactive = models.BooleanField(default=True, editable=False)\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n updated_by = models.IntegerField(null=True, editable=False)\n\n\nclass HartaSurat(models.Model):\n JENIS = (\n ('Efek Diperdagangkan di Bursa', 'Efek Diperdagangkan di Bursa'),\n ('Surat Berharga Lainnya', 'Surat Berharga Lainnya'),\n )\n pelaporan = models.ForeignKey(to=Pelaporan, on_delete=models.CASCADE, editable=False)\n nomor_rekening = models.CharField(max_length=100, verbose_name='Nomor rekening / nomor nasabah')\n bukti_dokumen = models.FileField(upload_to=rename_upload_file,\n validators=[FileExtensionValidator(['pdf', 'jpg', 'jpeg', 'png'])],\n verbose_name='Bukti dokumen / rekening (pdf/jpg/png/jpeg)')\n jenis = models.CharField(max_length=100, choices=JENIS)\n atasnama = models.CharField(max_length=100, choices=ATAS_NAMA)\n nilai_perolehan = models.BigIntegerField(verbose_name='Nilai perolehan (Rp)')\n nilai_estimasi = models.BigIntegerField(verbose_name='Nilai estimasi saat pelaporan (Rp)')\n penerbit = models.CharField(max_length=100, verbose_name='Penerbit / perusahaan')\n sekuritas = models.CharField(max_length=100, verbose_name='Custodian / sekuritas')\n tahun_perolehan = models.IntegerField()\n salsul = models.CharField(max_length=100, choices=ASAL_USUL, verbose_name='Asal usul harta')\n isactive = models.BooleanField(default=True, editable=False)\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n updated_by = models.IntegerField(null=True, editable=False)\n\n\nclass HartaKas(models.Model):\n JENIS = (\n ('Tabungan', 'Tabungan'),\n ('Deposito', 'Deposito'),\n ('Kas', 'Kas'),\n )\n MATA_UANG = (\n ('IDR', 'RUPIAH (IDR)'),\n ('USD', 'US DOLLAR (USD)'),\n ('JPY', 'JAPAN YEN (JPY)'),\n )\n\n pelaporan = models.ForeignKey(to=Pelaporan, on_delete=models.CASCADE, editable=False)\n jenis = models.CharField(max_length=100, choices=JENIS)\n bukti_dokumen = models.FileField(upload_to=rename_upload_file,\n validators=[FileExtensionValidator(['pdf', 'jpg', 'jpeg', 'png'])],\n verbose_name='Bukti dokumen / rekening (pdf/jpg/png/jpeg)')\n nama_bank = models.CharField(max_length=100, verbose_name='Nama bank / lembaga keuangan')\n nomor_rekening = models.CharField(max_length=100)\n tahun_buka = models.IntegerField(verbose_name='Tahun buka rekening')\n atasnama = models.CharField(max_length=255, choices=ATAS_NAMA)\n salsul = models.CharField(max_length=255, choices=ASAL_USUL, verbose_name='Asal usul harta')\n jenis_mata_uang = models.CharField(max_length=100, choices=MATA_UANG)\n nilai_saldo = models.BigIntegerField()\n isactive = models.BooleanField(default=True, editable=False)\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n updated_by = models.IntegerField(null=True, editable=False)\n\n\nclass HartaLainnya(models.Model):\n JENIS = (\n ('Piutang', 'Piutang'),\n )\n\n pelaporan = models.ForeignKey(to=Pelaporan, on_delete=models.CASCADE, editable=False)\n jenis = models.CharField(max_length=255, choices=JENIS)\n bukti_dokumen = models.FileField(upload_to=rename_upload_file,\n validators=[FileExtensionValidator(['pdf', 'jpg', 'jpeg', 'png'])],\n verbose_name='Bukti dokumen / rekening (pdf/jpg/png/jpeg)')\n keterangan = models.TextField()\n tahun_perolehan = models.IntegerField()\n nilai_perolehan = models.BigIntegerField(verbose_name='Nilai perolehan (Rp)')\n nilai_estimasi = models.BigIntegerField(verbose_name='Nilai estimasi saat pelaporan (Rp)')\n salsul = models.CharField(max_length=255, choices=ASAL_USUL, verbose_name='Asal usul harta')\n isactive = models.BooleanField(default=True, editable=False)\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n updated_by = models.IntegerField(null=True, editable=False)\n\n\nclass HartaHutang(models.Model):\n JENIS = (\n ('Hutang Konsumtif (KPR, Kendaraan, Kartu Kredit, Multiguna)',\n 'Hutang Konsumtif (KPR, Kendaraan, Kartu Kredit, Multiguna)'),\n )\n\n pelaporan = models.ForeignKey(to=Pelaporan, on_delete=models.CASCADE, editable=False)\n jenis = models.CharField(max_length=100, choices=JENIS, verbose_name='Jenis hutang')\n atasnama = models.CharField(max_length=100, choices=ATAS_NAMA)\n kreditur = models.CharField(max_length=255, verbose_name='Nama kreditur')\n bentuk_agunan = models.CharField(max_length=255)\n nilai_awal = models.BigIntegerField(verbose_name='Nilai awal hutang (Rp)')\n nilai_saldo = models.BigIntegerField(verbose_name='Nilai saldo hutang (Rp)')\n isactive = models.BooleanField(default=True, editable=False)\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n updated_by = models.IntegerField(null=True, editable=False)\n\n\nclass Penerimaan(models.Model):\n pelaporan = models.OneToOneField(to=Pelaporan, on_delete=models.CASCADE, editable=False)\n pk_gaji_asn = models.BigIntegerField(verbose_name='Gaji dan Tunjangan (ASN)')\n pk_gaji_psn = models.BigIntegerField(default=0, verbose_name='Gaji dan Tunjangan (Pasangan)', blank=True)\n pk_profesi_asn = models.BigIntegerField(verbose_name='Penghasilan dari Profesi/Keahlian (ASN)')\n pk_profesi_psn = models.BigIntegerField(default=0, verbose_name='Penghasilan dari Profesi/Keahlian (Pasangan)',\n blank=True)\n pk_honorarium_asn = models.BigIntegerField(verbose_name='Honorarium (ASN)')\n pk_honorarium_psn = models.BigIntegerField(default=0, verbose_name='Honorarium (Pasangan)', blank=True)\n pk_bonus_asn = models.BigIntegerField(verbose_name='Tantiem, Bonus, Jasa Produksi, THR (ASN)')\n pk_bonus_psn = models.BigIntegerField(default=0, verbose_name='Tantiem, Bonus, Jasa Produksi, THR (Pasangan)',\n blank=True)\n pk_lainnya_asn = models.BigIntegerField(verbose_name='Penerimaan Pekerjaan Lainnya (ASN)')\n pk_lainnya_psn = models.BigIntegerField(default=0, verbose_name='Penerimaan Pekerjaan Lainnya (Pasangan)',\n blank=True)\n pu_investasi = models.BigIntegerField(verbose_name='Hasil Investasi dalam Surat Berharga')\n pu_sewa = models.BigIntegerField(verbose_name='Hasil Usaha/Sewa')\n pu_bunga = models.BigIntegerField(verbose_name='Bunga Tabungan/Deposito dan Lainnya')\n pu_penjualan = models.BigIntegerField(verbose_name='Penjualan atau Pelepasan Harta')\n pu_lainnya = models.BigIntegerField(verbose_name='Penerimaan Lainnya')\n pl_hutang = models.BigIntegerField(verbose_name='Penerimaan Hutang')\n pl_warisan = models.BigIntegerField(verbose_name='Penerimaan Warisan')\n pl_hibah = models.BigIntegerField(verbose_name='Penerimaan Hibah/Hadiah')\n pl_lainnya = models.BigIntegerField(verbose_name='Lainnya ')\n isactive = models.BooleanField(default=True, editable=False)\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n updated_by = models.IntegerField(null=True, editable=False)\n\n\nclass Pengeluaran(models.Model):\n pelaporan = models.OneToOneField(to=Pelaporan, on_delete=models.CASCADE, editable=False)\n rutin_rt = models.BigIntegerField(verbose_name='Biaya rumah tangga', help_text=_(\n 'Termasuk transportasi, pendidikan, kesehatan, rekreasi, pembayaran kartu kredit'))\n rutin_sosial = models.BigIntegerField(verbose_name='Biaya sosial',\n help_text=_('Antara lain keagamaan, zakat, infaq, sumbangan lain'))\n rutin_pajak = models.BigIntegerField(verbose_name='Pembayaran pajak',\n help_text=_('Antara lain PBB, Kendaraan, pajak daerah, pajak lain'))\n rutin_lainnya = models.BigIntegerField(verbose_name='Pengeluaran rutin lainnya',\n help_text=_('Pengeluaran rutin akumulasi per-Tahun'))\n harta_pembelian = models.BigIntegerField(verbose_name='Pembelian / Perolehan Harta Baru')\n harta_pemeliharaan = models.BigIntegerField(verbose_name='Pemeliharaan / Modifikasi / Rehabilitasi Harta')\n harta_lainnya = models.BigIntegerField(verbose_name='Pengeluaran Non Rutin Lainnya')\n lainnya_hibah = models.BigIntegerField(verbose_name='Biaya Pengurusan Waris/Hibah/Hadiah')\n lainnya_angsuran = models.BigIntegerField(verbose_name='Pelunasan/Angsuran Hutang')\n lainnya_lainnya = models.BigIntegerField(verbose_name='Pengeluaran Lainnya')\n isactive = models.BooleanField(default=True, editable=False)\n\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n updated_by = models.IntegerField(null=True, editable=False)\n\n\nclass PelaporanFile(models.Model):\n pelaporan = models.ForeignKey(to=Pelaporan, on_delete=models.CASCADE, editable=False)\n deskripsi = models.CharField(max_length=255)\n filepath = models.FileField(upload_to=rename_upload_file,\n validators=[FileExtensionValidator(['pdf', 'jpg', 'jpeg', 'png'])],\n verbose_name='Softcopy file')\n isactive = models.BooleanField(default=True, editable=False)\n\n created = models.DateTimeField(auto_now_add=True)\n", "sub_path": "simpanan_berharga/lhkasn/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 23787, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "_main.utils.build_filename", "line_number": 35, "usage_type": "call"}, {"api_name": "_main.utils.rename_file", "line_number": 60, "usage_type": "call"}, {"api_name": "django.dispatch.receiver", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models.signals", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "_main.utils.delete_file", "line_number": 71, "usage_type": "call"}, {"api_name": "_main.utils.delete_file", "line_number": 74, "usage_type": "call"}, {"api_name": "django.dispatch.receiver", "line_number": 63, "usage_type": "call"}, {"api_name": "django.db.models.signals", "line_number": 63, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 63, "usage_type": "name"}, {"api_name": "django.db.models.Manager", "line_number": 77, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 77, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 84, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 104, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 104, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 105, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 105, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 106, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 106, "usage_type": "name"}, {"api_name": "master.models.Pegawai", "line_number": 106, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 106, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 107, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 107, "usage_type": "name"}, {"api_name": "master.models.Status", "line_number": 107, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 107, "usage_type": "attribute"}, {"api_name": "django.db.models.DateField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 108, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 109, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 109, "usage_type": "name"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 110, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 110, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 111, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 111, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 113, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 113, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 114, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 114, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 115, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 115, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 117, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 117, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 118, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 118, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 119, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 119, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 120, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 120, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 121, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 121, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 122, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 122, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 123, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 123, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 124, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 124, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 125, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 125, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 126, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 126, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 127, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 127, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 128, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 128, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 129, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 129, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 171, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 172, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 173, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 174, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 175, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 176, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 177, "usage_type": "call"}, {"api_name": "_main.utils.h_encode", "line_number": 243, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 246, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 246, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 258, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 258, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 258, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 259, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 259, "usage_type": "name"}, {"api_name": "master.models.Provinsi", "line_number": 259, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 259, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 260, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 260, "usage_type": "name"}, {"api_name": "master.models.Kabupaten", "line_number": 260, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 260, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 261, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 261, "usage_type": "name"}, {"api_name": "master.models.Kecamatan", "line_number": 261, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 261, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 262, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 262, "usage_type": "name"}, {"api_name": "master.models.Kelurahan", "line_number": 262, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 262, "usage_type": "attribute"}, {"api_name": "django.db.models.TextField", "line_number": 264, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 264, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 265, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 265, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 266, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 266, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 267, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 267, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 268, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 268, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 269, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 269, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 270, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 270, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 271, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 271, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 272, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 272, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 273, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 273, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 274, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 274, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 275, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 275, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 277, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 277, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 278, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 278, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 279, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 279, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 282, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 282, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 302, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 302, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 302, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 303, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 303, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 304, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 304, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 305, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 305, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 306, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 306, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 307, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 307, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 308, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 308, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 309, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 309, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 310, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 310, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 311, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 311, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 312, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 312, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 313, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 313, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 314, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 314, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 315, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 315, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 317, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 317, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 318, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 318, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 319, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 319, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 322, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 322, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 327, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 327, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 327, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 328, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 328, "usage_type": "name"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 329, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 329, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 330, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 330, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 331, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 331, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 332, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 332, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 333, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 333, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 334, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 334, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 335, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 335, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 336, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 336, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 338, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 338, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 339, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 339, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 340, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 340, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 343, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 343, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 348, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 348, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 348, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 349, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 349, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 350, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 350, "usage_type": "name"}, {"api_name": "django.core.validators.FileExtensionValidator", "line_number": 351, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 353, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 353, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 354, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 354, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 355, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 355, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 356, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 356, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 357, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 357, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 358, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 358, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 359, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 359, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 360, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 360, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 361, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 361, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 363, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 363, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 364, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 364, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 365, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 365, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 368, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 368, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 380, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 380, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 380, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 381, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 381, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 382, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 382, "usage_type": "name"}, {"api_name": "django.core.validators.FileExtensionValidator", "line_number": 383, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 385, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 385, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 386, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 386, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 387, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 387, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 388, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 388, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 389, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 389, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 390, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 390, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 391, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 391, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 392, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 392, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 394, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 394, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 395, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 395, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 396, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 396, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 399, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 399, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 404, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 404, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 404, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 405, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 405, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 406, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 406, "usage_type": "name"}, {"api_name": "django.core.validators.FileExtensionValidator", "line_number": 407, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 409, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 409, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 410, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 410, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 411, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 411, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 412, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 412, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 413, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 413, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 414, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 414, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 416, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 416, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 417, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 417, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 418, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 418, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 421, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 421, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 427, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 427, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 427, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 428, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 428, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 429, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 429, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 430, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 430, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 431, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 431, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 432, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 432, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 433, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 433, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 434, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 434, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 436, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 436, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 437, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 437, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 438, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 438, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 441, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 441, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 442, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 442, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 442, "usage_type": "attribute"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 443, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 443, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 444, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 444, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 445, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 445, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 446, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 446, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 448, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 448, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 449, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 449, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 450, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 450, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 451, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 451, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 453, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 453, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 454, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 454, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 456, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 456, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 457, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 457, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 458, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 458, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 459, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 459, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 460, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 460, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 461, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 461, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 462, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 462, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 463, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 463, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 464, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 464, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 465, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 465, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 467, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 467, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 468, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 468, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 469, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 469, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 472, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 472, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 473, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 473, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 473, "usage_type": "attribute"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 474, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 474, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 474, "usage_type": "call"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 476, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 476, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 477, "usage_type": "call"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 478, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 478, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 479, "usage_type": "call"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 480, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 480, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 481, "usage_type": "call"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 482, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 482, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 483, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 483, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 484, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 484, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 485, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 485, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 486, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 486, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 487, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 487, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 488, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 488, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 490, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 490, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 491, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 491, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 492, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 492, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 495, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 495, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 496, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 496, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 496, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 497, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 497, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 498, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 498, "usage_type": "name"}, {"api_name": "django.core.validators.FileExtensionValidator", "line_number": 499, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 501, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 501, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 503, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 503, "usage_type": "name"}]} +{"seq_id": "290286572", "text": "#!/usr/bin/python\n#-*-coding:utf-8-*-\n\nfrom flask import Flask, g, request, jsonify\nfrom transaction.bsbdj import BsAPI\n \napp = Flask(__name__)\napp.debug = True\n\n\n@app.route('/app')\ndef hello():\n msg = request.args.get('name', 'bob')\n return \"Hello, %s! - Flask\" % msg\n\n@app.route('/app/joke')\ndef joke():\n bs = BsAPI()\n return jsonify({'joke': bs.getS()})\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', port=5001, debug=False)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "transaction.bsbdj.BsAPI", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "343667206", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nPhotometry\n==========\n\nDefines photometric quantities computation related objects.\n\nReferences\n----------\n- :cite:`Wikipedia2003b` : Wikipedia. (2003). Luminosity function. Retrieved\n October 20, 2014, from\n https://en.wikipedia.org/wiki/Luminosity_function#Details\n- :cite:`Wikipedia2005c` : Wikipedia. (2005). Luminous Efficacy. Retrieved\n April 3, 2016, from https://en.wikipedia.org/wiki/Luminous_efficacy\n\"\"\"\n\nimport numpy as np\n\nfrom colour.colorimetry import SDS_LEFS_PHOTOPIC\nfrom colour.constants import CONSTANT_K_M\nfrom colour.utilities import as_float\n\n__author__ = 'Colour Developers'\n__copyright__ = 'Copyright (C) 2013-2021 - Colour Developers'\n__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'\n__maintainer__ = 'Colour Developers'\n__email__ = 'colour-developers@colour-science.org'\n__status__ = 'Production'\n\n__all__ = ['luminous_flux', 'luminous_efficiency', 'luminous_efficacy']\n\n\ndef luminous_flux(sd,\n lef=SDS_LEFS_PHOTOPIC['CIE 1924 Photopic Standard Observer'],\n K_m=CONSTANT_K_M):\n \"\"\"\n Returns the *luminous flux* for given spectral distribution using given\n luminous efficiency function.\n\n Parameters\n ----------\n sd : SpectralDistribution\n test spectral distribution\n lef : SpectralDistribution, optional\n :math:`V(\\\\lambda)` luminous efficiency function.\n K_m : numeric, optional\n :math:`lm\\\\cdot W^{-1}` maximum photopic luminous efficiency\n\n Returns\n -------\n numeric\n Luminous flux.\n\n References\n ----------\n :cite:`Wikipedia2003b`\n\n Examples\n --------\n >>> from colour import SDS_LIGHT_SOURCES\n >>> sd = SDS_LIGHT_SOURCES['Neodimium Incandescent']\n >>> luminous_flux(sd) # doctest: +ELLIPSIS\n 23807.6555273...\n \"\"\"\n\n lef = lef.copy().align(\n sd.shape,\n extrapolator_kwargs={\n 'method': 'Constant',\n 'left': 0,\n 'right': 0\n })\n sd = sd.copy() * lef\n\n flux = K_m * np.trapz(sd.values, sd.wavelengths)\n\n return as_float(flux)\n\n\ndef luminous_efficiency(\n sd, lef=SDS_LEFS_PHOTOPIC['CIE 1924 Photopic Standard Observer']):\n \"\"\"\n Returns the *luminous efficiency* of given spectral distribution using\n given luminous efficiency function.\n\n Parameters\n ----------\n sd : SpectralDistribution\n test spectral distribution\n lef : SpectralDistribution, optional\n :math:`V(\\\\lambda)` luminous efficiency function.\n\n Returns\n -------\n numeric\n Luminous efficiency.\n\n References\n ----------\n :cite:`Wikipedia2003b`\n\n Examples\n --------\n >>> from colour import SDS_LIGHT_SOURCES\n >>> sd = SDS_LIGHT_SOURCES['Neodimium Incandescent']\n >>> luminous_efficiency(sd) # doctest: +ELLIPSIS\n 0.1994393...\n \"\"\"\n\n lef = lef.copy().align(\n sd.shape,\n extrapolator_kwargs={\n 'method': 'Constant',\n 'left': 0,\n 'right': 0\n })\n sd = sd.copy()\n\n efficiency = (np.trapz(lef.values * sd.values, sd.wavelengths) / np.trapz(\n sd.values, sd.wavelengths))\n\n return efficiency\n\n\ndef luminous_efficacy(\n sd, lef=SDS_LEFS_PHOTOPIC['CIE 1924 Photopic Standard Observer']):\n \"\"\"\n Returns the *luminous efficacy* in :math:`lm\\\\cdot W^{-1}` of given\n spectral distribution using given luminous efficiency function.\n\n Parameters\n ----------\n sd : SpectralDistribution\n test spectral distribution\n lef : SpectralDistribution, optional\n :math:`V(\\\\lambda)` luminous efficiency function.\n\n Returns\n -------\n numeric\n Luminous efficacy in :math:`lm\\\\cdot W^{-1}`.\n\n References\n ----------\n :cite:`Wikipedia2005c`\n\n Examples\n --------\n >>> from colour import SDS_LIGHT_SOURCES\n >>> sd = SDS_LIGHT_SOURCES['Neodimium Incandescent']\n >>> luminous_efficacy(sd) # doctest: +ELLIPSIS\n 136.2170803...\n \"\"\"\n\n efficacy = CONSTANT_K_M * luminous_efficiency(sd, lef)\n\n return as_float(efficacy)\n", "sub_path": "colour/colorimetry/photometry.py", "file_name": "photometry.py", "file_ext": "py", "file_size_in_byte": 4085, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "colour.colorimetry.SDS_LEFS_PHOTOPIC", "line_number": 34, "usage_type": "name"}, {"api_name": "colour.constants.CONSTANT_K_M", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.trapz", "line_number": 75, "usage_type": "call"}, {"api_name": "colour.utilities.as_float", "line_number": 77, "usage_type": "call"}, {"api_name": "colour.colorimetry.SDS_LEFS_PHOTOPIC", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.trapz", "line_number": 119, "usage_type": "call"}, {"api_name": "colour.colorimetry.SDS_LEFS_PHOTOPIC", "line_number": 126, "usage_type": "name"}, {"api_name": "colour.constants.CONSTANT_K_M", "line_number": 155, "usage_type": "name"}, {"api_name": "colour.utilities.as_float", "line_number": 157, "usage_type": "call"}]} +{"seq_id": "642374490", "text": "import unittest\nimport common.mainModule\nfrom selenium import webdriver\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nimport logging\n\nmodule_logger = logging.getLogger(\"mainModule.DefaultTest\")\nclass DefaulTest(unittest.TestCase):\n # logger = logging.getLogger(\"mainModule.sub.Default\")\n # logger.info(\"creating an instance in SubModuleClass\")\n @classmethod\n def setUpClass(cls):\n\n # cls.driver = webdriver.Chrome()\n #使用本地driver\n cls.driver=webdriver.Remote(command_executor='http://47.100.188.71:4444/wd/hub',\n desired_capabilities=DesiredCapabilities.CHROME)\n #调用远程selenium grid的driver\n # opt=webdriver.Chrome.create_options().add_argument('start-maximized')\n cls.url=\"https://console.huilianyi.com/#/login\"\n cls.driver.implicitly_wait(30)\n #chrom需要注销掉\n #\n cls.driver.set_window_size(width=\"1920\", height=\"1080\")\n # cls.driver.maximize_window() 窗口最大化\n # rect = cls.driver.get_window_size()\n # print(rect)\n # logger = logging.getLogger(\"mainModule.sub.module\")\n module_logger.info('Start Testing')\n @classmethod\n def tearDownClass(cls):\n cls.driver.quit()\n module_logger.info('End Testing')\n\n\n\n\n\n", "sub_path": "testcases/DefaultTest.py", "file_name": "DefaultTest.py", "file_ext": "py", "file_size_in_byte": 1321, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 8, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Remote", "line_number": 16, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 16, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities.CHROME", "line_number": 17, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "490292688", "text": "import pygame\r\nimport ntpath\r\n\"\"\"\r\nEach button is a class.\r\nThe button will be in circular in shape.\r\nEquation of circle is then given by x^2+y^2 = r^2\r\n\"\"\"\r\nblack = (255,255,255)\r\nclass Circle:\r\n def __init__(self,screen,x,y,radius,width = 3,color = (255,255,255)): #If width = 0 circle fills\r\n self.screen = screen\r\n self.x = x\r\n self.y = y\r\n self.radius = radius\r\n self.width = width\r\n self.color = color\r\n\r\n def draw(self):\r\n pygame.draw.circle(self.screen,self.color,(self.x,self.y),self.radius,self.width)\r\n\r\n def click(self):\r\n \"\"\"\r\n In general, point x and y must satisfy (x - center_x)^2 + (y - center_y)^2 <= radius^2\r\n \"\"\"\r\n current_mouse_position = pygame.mouse.get_pos()\r\n value_of_equation_at_current_mouse_position = (current_mouse_position[0]-self.x)**2+(current_mouse_position[1]-self.y)**2\r\n if (value_of_equation_at_current_mouse_position <= self.radius**2):\r\n if pygame.mouse.get_pressed()[0]:\r\n return True\r\n else:\r\n return False\r\n\r\nclass Play(Circle):\r\n def draw(self):\r\n pygame.draw.lines(self.screen,black,True, [(self.x+10,self.y),(self.x-10,self.y-10),(self.x-10,self.y+10)],2)\r\n\r\nclass Pause(Circle):\r\n def draw(self):\r\n pygame.draw.lines(self.screen,black,True, [(self.x-10,self.y-10),(self.x-10,self.y+10)],2)\r\n pygame.draw.lines(self.screen,black,True, [(self.x+10,self.y-10),(self.x+10,self.y+10)],2)\r\n\r\nclass Next(Circle):\r\n def draw(self):\r\n pygame.draw.lines(self.screen,black,False,[(self.x,self.y),(self.x-10,self.y-10)],3)\r\n pygame.draw.lines(self.screen,black,False,[(self.x,self.y),(self.x-10,self.y+10)],3)\r\n\r\nclass Previous(Circle):\r\n def draw(self):\r\n pygame.draw.lines(self.screen,black,False,[(self.x,self.y-10),(self.x-10,self.y)],3)\r\n pygame.draw.lines(self.screen,black,False,[(self.x-10,self.y),(self.x,self.y+10)],3)\r\n \r\n\r\nclass Add(Circle):\r\n def draw(self):\r\n pygame.draw.lines(self.screen,black,False,[(self.x,self.y-10),(self.x,self.y+10)],3)\r\n pygame.draw.lines(self.screen,black,False,[(self.x-10,self.y),(self.x+10,self.y)],3)\r\n \r\n\r\nclass Bar:\r\n def __init__(self,screen,width,height):\r\n self.screen = screen\r\n self.width = width\r\n self.height = height\r\n self.white_border = (255,255,255)\r\n #self.width_of_the_playBar = self.width-150\r\n\r\n def draw(self):\r\n x = 50 #Start the bar from 50px \r\n y = self.height - 150 #Based on the other buttons in main.py.\r\n\r\n pygame.draw.rect(self.screen,self.white_border,(x,y,self.width-100,5),2)\r\n\r\nclass BarPlayed(Bar):\r\n def draw(self,dx):\r\n x = 50 #Start the bar from 50px \r\n y = self.height - 150 #Based on the other buttons in main.py.\r\n pygame.draw.rect(self.screen,(255,255,255),(x,y,dx,5),0)\r\n \r\n\r\n \r\n ", "sub_path": "buttons.py", "file_name": "buttons.py", "file_ext": "py", "file_size_in_byte": 2963, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pygame.draw.circle", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 77, "usage_type": "attribute"}]} +{"seq_id": "27355876", "text": "# -*- coding: utf-8 -*-\r\n\r\nfrom django.test import TestCase\r\nfrom models import *\r\nfrom django.http import HttpRequest\r\nfrom views import *\r\nfrom django.test import Client\r\nimport pdb\r\n\r\nclass GameKipTestCase(TestCase):\r\n\r\n\tusers = [\r\n\t\t{ 'u' : 'test1' , 'e' : 'test1@gmail.com' , 'p' : 'test1pass' },\r\n\t\t{ 'u' : 'test2' , 'e' : 'test2@gmail.com' , 'p' : 'test2pass' },\r\n\t]\r\n\r\n\tdef setUp(self):\r\n\r\n\t\tself.group = Group.objects.create(name = 'gamekip' )\r\n\t\tself.imp_group = Group.objects.create(name = 'impostor' )\r\n\r\n\t\t# Creo usuarios de gamekip\r\n\t\tfor u in self.users:\r\n\t\t\tnew_u = User.objects.create_user( u['u'] , u['e'] , u['p'] )\r\n\t\t\tself.group.user_set.add( new_u )\r\n\r\n\t\t# Creo usuario impostor\r\n\t\tself.impostor = User.objects.create_user( 'impostor', 'imp@gmail.com' , 'impostor00' )\r\n\t\tself.imp_group.user_set.add( self.impostor )\r\n\r\n\t\t# Hago login del primer usuario\r\n\t\tself.c = Client()\r\n\t\tself.c.post('/login/', {'email': self.users[0]['e'], 'password': self.users[0]['p']})\r\n\r\n\t\tself.test_user = User.objects.all()[0]\r\n\r\n\t\t# Creando metas\r\n\t\tself.goal = Goal( title = \"Meta 1\" , created_by = self.test_user, completed = False , active_goal = True , assigned_group = self.group )\r\n\t\tself.goal.save()\r\n\t\tself.imp_goal = Goal( title = \"Meta 1 impostor\" , created_by = self.impostor, completed = False , active_goal = True , assigned_group = self.imp_group )\r\n\t\tself.imp_goal.save()\r\n\t\r\n\t# Pruebas de tareas\r\n\r\n\tdef test_create_task(self):\r\n\t\t''' Tareas se crean de forma correcta '''\r\n\r\n\t\t# Creo una tarea\r\n\t\tresponse = self.c.post('/create_new_task', { 'title' : 'Desarrollar modulo de usuarios' , 'assigned_to' : self.test_user.id , 'assigned_goal' : self.goal.id } )\r\n\t\tself.assertEqual( response.content , 'ok' )\r\n\r\n\tdef test_create_task_invalid( self ):\r\n\t\t''' Tareas no se crean si la informacion es invalida '''\r\n\r\n\t\t# Creo una tarea sin titulo\r\n\t\tresponse = self.c.post('/create_new_task', { 'assigned_to' : self.test_user.id , 'assigned_goal' : self.goal.id } )\r\n\t\tself.assertEqual( response.content , 'error' )\r\n\r\n\t\t# Creo una tarea sin asignarla\r\n\t\tresponse = self.c.post('/create_new_task', { 'title' : 'Desarrollar modulo de usuarios' , 'assigned_goal' : self.goal.id } )\r\n\t\tself.assertEqual( response.content , 'error' )\r\n\r\n\t\t# Creo una tarea con titulo pequeno\r\n\t\tresponse = self.c.post('/create_new_task', { 'title' : 'peque' , 'assigned_to' : self.test_user.id , 'assigned_goal' : self.goal.id } )\r\n\t\tself.assertEqual( response.content , 'Título muy corto' )\r\n\r\n\t\t# Creo una tarea con usuario invalido\r\n\t\tresponse = self.c.post('/create_new_task', { 'title' : 'titulo largo' , 'assigned_to' : 10 , 'assigned_goal' : self.goal.id } )\r\n\t\tself.assertEqual( response.content , 'Usuario inválido' )\r\n\r\n\t\t# Creo una tarea con usuario de otro grupo\r\n\t\tresponse = self.c.post('/create_new_task', { 'title' : 'titulo largo' , 'assigned_to' : self.impostor.id , 'assigned_goal' : self.goal.id } )\r\n\t\tself.assertEqual( response.content , 'error' )\r\n\r\n\tdef test_create_repeated_task( self ):\r\n\r\n\t\tassigned_to = self.test_user.id\r\n\r\n\t\t# Creo una tarea\r\n\t\tresponse = self.c.post('/create_new_task', { 'title' : 'Desarrollar modulo de usuarios' , 'assigned_to' : self.test_user.id , 'assigned_goal' : self.goal.id } )\r\n\t\tself.assertEqual( response.content , 'ok' )\r\n\r\n\t\t# Intento crear la misma tarea\r\n\t\tresponse = self.c.post('/create_new_task', { 'title' : 'Desarrollar modulo de usuarios' , 'assigned_to' : assigned_to , 'assigned_goal' : self.goal.id } )\r\n\t\tself.assertEqual( response.content , 'Ya %s tiene asignada esa tarea' % ( User.objects.get( id = assigned_to ).username ) )\r\n\r\n\tdef test_set_task_done( self ):\r\n\r\n\t\t# Creo tarea\r\n\t\tresponse = self.c.post('/create_new_task', { 'title' : 'Desarrollar modulo de usuarios' , 'assigned_to' : self.test_user.id , 'assigned_goal' : self.goal.id } )\r\n\t\ttask = Task.objects.all()[0]\r\n\r\n\t\twas_completed = task.completed\r\n\r\n\t\t# Marco como completada\r\n\t\tresponse = self.c.post('/set_task_done', { 'task_id' : task.id } )\r\n\r\n\t\tself.assertEqual( response.content , 'ok' )\r\n\t\tself.assertEqual( Task.objects.get( id = task.id ).completed , not was_completed )\r\n\r\n\tdef test_set_invalid_task_done( self ):\r\n\r\n\t\tresponse = self.c.post('/set_task_done', { 'task_id' : 10 } )\r\n\t\tself.assertEqual( response.content , 'error' )\r\n\r\n\tdef test_delete_task( self ):\r\n\r\n\t\tself.c.post('/create_new_task', { 'title' : 'Desarrollar modulo de usuarios' , 'assigned_to' : self.test_user.id , 'assigned_goal' : self.goal.id } )\r\n\r\n\t\tnum_tasks = Task.objects.all().count()\r\n\t\ttask = Task.objects.all()[0]\r\n\r\n\t\tresponse = self.c.post('/delete_task', { 'task_id' : task.id } )\r\n\r\n\t\tself.assertEqual( response.content , 'ok' )\r\n\t\tself.assertEqual( Task.objects.all().count() , num_tasks-1 )\r\n\r\n\tdef test_delete_invalid_task( self ):\r\n\r\n\t\t# Tarea no creada\r\n\t\tresponse = self.c.post('/delete_task', { 'task_id' : 10 } )\r\n\t\tself.assertEqual( response.content , 'error' )\r\n\r\n\t\t# Tarea de otro grupo\r\n\t\tt = Task( assigned_to = self.impostor , created_by = self.impostor , title = \"titulo de la tarea\" , completed = False )\r\n\t\tt.save()\r\n\r\n\t\tresponse = self.c.post('/delete_task', { 'task_id' : t.id } )\r\n\t\tself.assertEqual( response.content , 'error' )\r\n\r\n\t# Pruebas de metas\r\n\r\n\tdef test_create_goal( self ):\r\n\r\n\t\t#Creamos Meta\r\n\t\tresponse = self.c.post('/create_goal',{'title': 'proyecto fase 1','assigned_group':self.group.id})\r\n\t\tself.assertEqual( response.content , 'ok' )\r\n\r\n\tdef test_create_invalid_goal( self ):\r\n\r\n\t\t#Creamos Meta sin grupo\r\n\t\tresponse = self.c.post('/create_goal',{'title': 'proyecto fase 1'})\r\n\t\tself.assertEqual( response.content , 'error' )\r\n\r\n\t\t#Creamos Meta titulo corto\r\n\t\tresponse = self.c.post('/create_goal',{'title': 'pro1','assigned_group':self.group})\r\n\t\tself.assertEqual( response.content , 'Título muy corto' )\r\n\r\n\tdef test_edit_goal(self):\r\n\r\n\t\tresponse = self.c.post('/create_goal',{'title': 'proyecto fase 1' , 'assigned_group' : self.group.id })\r\n\t\tself.assertEqual( response.content , 'ok' )\r\n\r\n\t\tg = Goal.objects.filter( title = 'proyecto fase 1' )[0]\r\n\t\ttitle = g.title\r\n\r\n\t\tresponse = self.c.post('/edit_goal',{'title': 'proyecto fase 1 A','goal':g.id })\r\n\r\n\t\tself.assertEqual( response.content , 'ok' )\r\n\t\tself.assertEqual( Goal.objects.get( id = g.id ).title , 'proyecto fase 1 A' )\r\n\r\n\tdef test_edit_invalid_goal(self):\r\n\r\n\t\tresponse = self.c.post('/create_goal',{'title': 'proyecto fase 1' , 'assigned_group' : self.group.id } )\r\n\t\tself.assertEqual( response.content , 'ok' )\r\n\r\n\t\tg = Goal.objects.filter( title = 'proyecto fase 1' )[0]\r\n\t\ttitle = g.title\r\n\t\t \r\n\t\tresponse = self.c.post('/edit_goal',{'title': 'pro' , 'goal':g.id } )\r\n\r\n\t\tself.assertEqual( response.content , 'Título muy corto' )", "sub_path": "gamekip/test_gamekip.py", "file_name": "test_gamekip.py", "file_ext": "py", "file_size_in_byte": 6683, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.test.TestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "546933717", "text": "#!/usr/bin/env python3 -u\n\nimport argparse\nimport fileinput\nimport logging\nimport os\nimport sys\n\nfrom fairseq.models.transformer import TransformerModel\n\n\nlogging.getLogger().setLevel(logging.INFO)\n\n\ndef main():\n src2tgt = TransformerModel.from_pretrained(\n model_name_or_path=\"wmt19.en-ru.ensemble\",\n checkpoint_file=\"model3.pt\",\n tokenizer='moses',\n bpe='fastbpe',\n ).eval()\n\n tgt2src = TransformerModel.from_pretrained(\n model_name_or_path=\"wmt19.ru-en.ensemble\",\n checkpoint_file=\"model3.pt\",\n tokenizer='moses',\n bpe='fastbpe',\n ).eval()\n\n input = [\"Because of the covid, UNC closed down\", \"How are you doing\"]\n\n top_k = 5\n\n tgt_translation = src2tgt.translate(input)\n roundtrip_translation = tgt2src.translate(tgt_translation, beam=2*top_k, top_k=5)\n print(roundtrip_translation)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 909, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 12, "usage_type": "attribute"}, {"api_name": "fairseq.models.transformer.TransformerModel.from_pretrained", "line_number": 16, "usage_type": "call"}, {"api_name": "fairseq.models.transformer.TransformerModel", "line_number": 16, "usage_type": "name"}, {"api_name": "fairseq.models.transformer.TransformerModel.from_pretrained", "line_number": 23, "usage_type": "call"}, {"api_name": "fairseq.models.transformer.TransformerModel", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "499646355", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Categoria',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('titulo_txt', models.CharField(max_length=20, verbose_name=b'Titulo')),\n ],\n ),\n migrations.CreateModel(\n name='Coment',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('fecha_pub', models.DateTimeField(auto_now_add=True, verbose_name=b'date published')),\n ('titulo_txt', models.CharField(max_length=20, verbose_name=b'Titulo del Comentario')),\n ('coment_txt', models.TextField(max_length=100, verbose_name=b'Comentatrio')),\n ('published', models.BooleanField(default=True, verbose_name='Publicado?')),\n ],\n ),\n migrations.CreateModel(\n name='Post',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('titulo_txt', models.CharField(max_length=20, verbose_name=b'Titulo')),\n ('fecha_pub', models.DateTimeField(auto_now_add=True, verbose_name=b'date published')),\n ('txt_resumen', models.CharField(max_length=50, verbose_name=b'Resumen')),\n ('txt_cont', models.TextField(verbose_name=b'Contenido')),\n ('categoria', models.ForeignKey(to='polls.Categoria')),\n ],\n options={\n 'ordering': ['-fecha_pub'],\n },\n ),\n migrations.AddField(\n model_name='coment',\n name='post',\n field=models.ForeignKey(to='polls.Post'),\n ),\n ]\n", "sub_path": "santiblog/polls/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 2014, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "64188938", "text": "import random\nimport json\nimport pickle\nimport numpy as np\nimport nltk\n\n\nfrom nltk.stem import WordNetLemmatizer\nfrom tensorflow.keras.models import load_model\nnltk.download('wordnet')\nlemmatizer = WordNetLemmatizer()\nintents = json.loads(open(\"intents.json\").read())\n\nwords = pickle.load(open('words.pkl', 'rb')) #contains words\nclasses = pickle.load(open('classes.pkl', 'rb')) #contains classes\nmodel = load_model('speakModel.h5')\n\ndef clean_up_sentence(sentence):\n sentence_words = nltk.word_tokenize(sentence)\n sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]\n print(sentence_words)\n return sentence_words\n\ndef bag_of_words(sentence, words):\n sentence_words = clean_up_sentence(sentence)\n bag = [0]*len(words)\n for w in sentence_words:\n for i, words in enumerate(words):\n if words == w:\n bag[i] = 1\n \n return (np.array(bag))\n\ndef predict_class(sentence, model):\n bow = bag_of_words(sentence, words)\n res = model.predict(np.array([bow]))[0]\n ERROR_THERSHOLD = 0.20\n results = [[i, r] for i, r in enumerate(res) if r > ERROR_THERSHOLD]\n\n results.sort(key=lambda x: x[1], reverse=True)\n return_list = []\n for r in results:\n return_list.append({'intent': classes[r[0]], 'probability': str(r[1])})\n print(return_list)\n return return_list\n\ndef get_response(intent_list, intent_json):\n tag = intent_list[0]['intent']\n list_of_intents = intent_json['intents']\n for i in list_of_intents:\n if i['tag'] == tag:\n result = random.choice(i['responses'])\n break\n return result\n\nprint(\"[+] Speak is running.\")\n\nwhile True:\n message = input('')\n ints = predict_class(message, model)\n res = get_response(ints, intents)\n print('[*] '+res)", "sub_path": "chatbot.py", "file_name": "chatbot.py", "file_ext": "py", "file_size_in_byte": 1803, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "nltk.download", "line_number": 10, "usage_type": "call"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 11, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 12, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 14, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "497099085", "text": "from flask import Flask, render_template, jsonify, request\nimport sqlite3\nfrom os.path import isfile\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n if isfile('database.db') == False:\n exec(open('initdb.py').read())\n return render_template('newmovie.html')\n\n@app.route('/newmovie', methods = ['POST'])\ndef newmovie():\n connection = sqlite3.connect('database.db')\n cursor = connection.cursor()\n \n title = request.form['title']\n year = request.form['year']\n \n try:\n cursor.execute('INSERT INTO movies (title,year) VALUES (?,?)', (title,year))\n connection.commit()\n message = 'Success!'\n except:\n connection.rollback()\n message = 'Oops!'\n finally:\n connection.close()\n return render_template('result.html', message = message)\n\n@app.route('/movies')\ndef movies():\n connection = sqlite3.connect('database.db')\n cursor = connection.cursor()\n cursor.execute('SELECT * FROM movies')\n cinematography = cursor.fetchall()\n connection.close()\n return jsonify(cinematography)\n\n@app.route('/search')\ndef serach():\n partial = request.args.get('title')\n connection = sqlite3.connect('database.db')\n cursor = connection.cursor()\n cursor.execute('SELECT * FROM movies WHERE title LIKE \"%{}%\" COLLATE NOCASE'.format(partial))\n foundit = cursor.fetchall()\n connection.close()\n return jsonify(foundit)\n\n@app.route('/favicon.ico')\ndef favicon():\n return app.send_static_file('favicon.ico')\n \napp.run(debug = True)", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1531, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "495520392", "text": "from torch import nn\nfrom torch.nn.modules.activation import GELU\nimport torch \n\nclass Generator(nn.Module):\n def __init__(self, latent_dim: int = 100):\n super(Generator, self).__init__()\n\n self.layers = nn.Sequential(\n Up(latent_dim,75),\n Up(75, 50),\n Up(50, 25),\n Up(25,20),\n Up(20, 15),\n Up(15, 10),\n Up(10, 5),\n Up(5,3)\n )\n\n def forward (self, x):\n return self.layers(x)\n\n\n\n\nclass DoubleConv(nn.Module):\n \"\"\"(convolution => [BN] => ReLU) * 2\"\"\"\n\n def __init__(self, in_channels, out_channels, mid_channels=None):\n super().__init__()\n if not mid_channels:\n mid_channels = out_channels\n self.double_conv = nn.Sequential(\n nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),\n nn.BatchNorm2d(mid_channels),\n nn.ReLU(inplace=True),\n nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),\n nn.BatchNorm2d(out_channels),\n nn.ReLU(inplace=True)\n )\n\n def forward(self, x):\n return self.double_conv(x)\n\nclass Up(nn.Module):\n \"\"\"Upscaling then double conv\"\"\"\n\n def __init__(self, in_channels, out_channels, bilinear=True):\n super().__init__()\n\n # if bilinear, use the normal convolutions to reduce the number of channels\n if bilinear:\n self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)\n self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)\n else:\n self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)\n self.conv = DoubleConv(in_channels, out_channels)\n\n\n def forward(self,x):\n x = self.up(x)\n return self.conv(x)\n", "sub_path": "generator.py", "file_name": "generator.py", "file_ext": "py", "file_size_in_byte": 1839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Upsample", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}]} +{"seq_id": "106641149", "text": "import sqlite3\n\n\n\ndatabaseName = 'example6.db'\nitemsTable = 'items'\ndebtorsTable = 'debtors'\ncreditorsTable = 'creditors'\nusersTable = 'app_users'\n\n\ndef getNewID(id_name):\n conn = sqlite3.connect(databaseName)\n cursor = conn.cursor()\n\n data = cursor.execute(\"select last_val from current_id where id_name='{}'\".format(id_name))\n data = cursor.fetchone()[0]\n if(id_name!='user_id'):\n new_id = data[:2] + \"{:04d}\".format(int(data[2:])+1)\n else:\n new_id = data[:1] + \"{:04d}\".format(int(data[1:])+1)\n print(\"new ID created: \", new_id)\n\n cursor.execute(\"UPDATE current_id set last_val='{}' where id_name='{}'\".format(new_id, id_name))\n conn.commit()\n conn.close()\n return new_id\n\n\ndef insertInvoiceDetails(bill_no,_item_all,_quantity_all,_rate_all,_amount_all,_type,_bill_date):\n for (item,qty,rate,amt) in zip(_item_all,_quantity_all,_rate_all,_amount_all):\n columns = 'bill_no, item_id, qty, rate, amount'\n values = \"'{}', '{}', '{}', '{}', '{}'\".format(bill_no, item, qty, rate, amt)\n insertData('invoice_details', columns, values)\n if(_type == \"sales\"):\n updateQty(item,qty,\"sub\")\n insertData('daily_inventory','item_id, qty, date',\"'{}','{}','{}'\".format(item,-float(qty),_bill_date))\n else:\n updateQty(item,qty,\"add\")\n insertData('daily_inventory','item_id, qty, date',\"'{}','{}','{}'\".format(item,qty,_bill_date))\n\n\ndef updateData(table,columns, values,condition):\n conn = sqlite3.connect(databaseName)\n cursor = conn.cursor()\n updateString=''\n for (c,v) in zip(columns, values):\n updateString += \"{}='{}' \".format(c,v)\n updateString+= condition\n print(\"update SQL Statement: \", repr(\"UPDATE {} set {}\".format(table,updateString)))\n cursor.execute(\"UPDATE {} set {}\".format(table,updateString))\n conn.commit()\n conn.close()\n\ndef insertData(table, columns, values):\n conn = sqlite3.connect(databaseName)\n # conn = mysql.connect()\n cursor = conn.cursor()\n print(\"Insert SQL Statement: insert into {} ({}) values ({})\".format(table, columns, values))\n data = cursor.execute(\"insert into {}({}) values ({})\".format(table, columns, values))\n conn.commit()\n conn.close()\n return data\n\n\ndef getData(table, columns='*',condition='blank',extra='no'):\n conn = sqlite3.connect(databaseName)\n # conn = mysql.connect()\n if(condition != 'blank'):\n finalCondition = \"where {}\".format(condition)\n else:\n finalCondition = ''\n\n if(extra!='no'):\n finalCondition += \" {}\".format(extra)\n cursor = conn.cursor()\n print(\"Get Data SQL statement: \", repr(\"select {} from {} {}\".format(columns, table, finalCondition)))\n data = cursor.execute(\"select {} from {} {}\".format(columns, table, finalCondition))\n data = cursor.fetchall()\n conn.close()\n return data\n\ndef updateBalance(party_type,_id,amount,bill_type):\n table=''\n condition=''\n if(party_type == 'debtor'):\n currBal = getData(debtorsTable, 'balance', \"debtor_id = '{}'\".format(_id))[0][0]\n table=debtorsTable\n condition = \"where debtor_id='{}'\".format(_id)\n else:\n currBal = getData(creditorsTable, 'balance', \"creditor_id = '{}'\".format(_id))[0][0]\n table=creditorsTable\n condition = \"where creditor_id='{}'\".format(_id)\n print(\"current Balance:\", repr(currBal))\n newBal=currBal\n if(bill_type == 'add'):\n newBal=currBal + float(amount)\n else:\n newBal=currBal - float(amount)\n print(\"New Balance:\", newBal)\n updateData(table,['balance'],[newBal],condition)\n\ndef updateQty(item_id, _qty, _type):\n currQty = getData(itemsTable, 'curr_qty', \"item_id = '{}'\".format(item_id))[0][0]\n newQty = currQty\n if(_type == 'add'):\n newQty=currQty + float(_qty)\n else:\n newQty=currQty - float(_qty)\n print(\"new Quantity:\", newQty)\n condition = \"where item_id ='{}'\".format(item_id)\n updateData(itemsTable,['curr_qty'],[newQty],condition)", "sub_path": "DBUtils.py", "file_name": "DBUtils.py", "file_ext": "py", "file_size_in_byte": 4019, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "sqlite3.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "180179530", "text": "import json\nimport requests\nfrom bs4 import BeautifulSoup\nuser_agent='Mozilla/5.0 (compatible;MSIE 5.5;Window NT)'\nheaders={'User-Agent':user_agent}\nr=requests.get('http://seputu.com/',headers=headers)\n# print(r.text)\n#获取标题，章节\nsoup=BeautifulSoup(r.text,'html.parser',from_encoding='utf-8')\ncontent=[]\nfor mulu in soup.find_all(class_=\"mulu\"):\n h2=mulu.find('h2')\n if h2!=None:\n h2_titile=h2.string\n list=[]\n # print(h2_titile)\n for a in mulu.find(class_='box').find_all('a'):#获取所有a的标记中url和章节内容\n href=a.get('href')\n box_title=a.get('title')\n print(href,box_title)\n list.append({'href':href,'box_title':box_title})\n content.append({'title':h2_titile,'content':list})\nwith open('seputu.json','w') as fp:\n json.dump(content,fp,indent=4).encoding('utf-8')\n\n\n'''\n 出现：UserWarning: You provided Unicode markup but also provided a value for from_encoding. \n Your from_encoding will be ignored.warnings.warn\n (\"You provided Unicode markup but also provided a value for from_encoding. Your from_encoding will be ignored.\")\n'''\n", "sub_path": "ch05/5_1.py", "file_name": "5_1.py", "file_ext": "py", "file_size_in_byte": 1178, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "requests.get", "line_number": 6, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 9, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "200854274", "text": "# Rosalind Exercises\n\ndef CountBases(Genome):\n '''\n Count the number of bases, ACGT, in a string as a 4-tuple\n\n Returns\n\n counts: A, C, G, T\n '''\n s = Genome.strip('\\n')\n return s.count('A'), s.count('C'), s.count('G'), s.count('T')\n\ndef reverse(text):\n tmp = []\n for i in range(len(text)-1,-1,-1):\n tmp.append(text[i])\n return ''.join(tmp)\n\ndef complement(nucleotide):\n return {\n 'A':'T',\n 'T':'A',\n 'C':'G',\n 'G':'C'\n }.get(nucleotide, 'X')\n\ndef transcribe(seq):\n nucs = {'A':'U','T':'A','C':'G','G':'C'}\n return ''.join(nucs[n] for n in seq)\n\ndef ReverseComplement(Pattern):\n revComp = []\n comp = dict(zip('ATGC','TACG'))\n for n in reverse(Pattern):\n revComp.append(comp[n])\n return ''.join(revComp)\n\n\ndef translate(seq, start=0, end=len(seq)):\n aa = []\n\n d = {'CUA': 'L', 'UGU': 'C', 'GUA': 'V', 'AUG': 'M',\n 'GCA': 'A', 'CCA': 'P', 'GAC': 'D', 'GUC': 'V',\n 'GCU': 'A', 'UGG': 'W', 'CCU': 'P', 'AGG': 'R',\n 'UUU': 'F', 'AGA': 'R', 'GCG': 'A', 'AGC': 'S',\n 'GAA': 'E', 'ACC': 'T', 'CUG': 'L', 'UCG': 'S',\n 'CGA': 'R', 'UUA': 'L', 'CUC': 'L', 'AGU': 'S',\n 'AUC': 'I', 'GGC': 'G', 'ACA': 'T', 'UGA': 'Stop',\n 'AAU': 'N', 'CAU': 'H', 'CAC': 'H', 'UAG': 'Stop',\n 'GUG': 'V', 'GCC': 'A', 'AUU': 'I', 'CUU': 'L',\n 'ACU': 'T', 'UAA': 'Stop', 'GAG': 'E', 'GGU': 'G',\n 'AAG': 'K', 'UAC': 'Y', 'GGG': 'G', 'ACG': 'T',\n 'GGA': 'G', 'UCU': 'S', 'UCA': 'S', 'GAU': 'D',\n 'CAG': 'Q', 'AAA': 'K', 'UGC': 'C', 'CCC': 'P',\n 'CGG': 'R', 'UAU': 'Y', 'CCG': 'P', 'UUC': 'F',\n 'AAC': 'N', 'UUG': 'L', 'UCC': 'S', 'AUA': 'I',\n 'CGU': 'R', 'GUU': 'V', 'CGC': 'R', 'CAA': 'Q'}\n\n for i in range(start,end,3):\n aa.append(d[seq[i:i+3]])\n\n aa = ''.join(aa).split('Stop')[0]\n\n return aa\n\ndef translateAllORFs(seq):\n orfs = []\n # Forward 3 ORFs\n for i in range(3):\n orfs.append(translate(seq=seq,start=i))\n # Reverse 3 ORFs\n return orfs\n\ndef countGC(seq):\n\n gs = seq.count('G')\n cs = seq.count('C')\n\n return (gs+cs)/len(seq)\n\ndef fibo_slow(n):\n if n < 2:\n return n\n else:\n return fibo_slow(n-2)+fibo_slow(n-1)\n\ndef fib_gen():\n '''Fibonacci generator'''\n a, b = 0,1\n yield a\n yield b\n while True:\n a,b = b, a+b\n yield b\n\ndef fib(n):\n a,b = 1,1\n for i in range(n-1):\n a,b = b, a+b\n return a\n\n# Fast Fibonacci-esque rabbit calculator. Assumes immortal rabbits\ndef immortal_rabbit_fib(n,k):\n a,b = 1,1\n for i in range(n-1):\n b,a=b+k*a,b\n return a\n\n# Fast Fibonacci-esque mortal rabbits using a dictionary\n# to stored previously computed values of the sequence in order to avoid\n# recursive calls\nfrom collections import defaultdict\n__fib_cache = {0:1,1:1,2:1}\n__fib_cache = defaultdict(lambda: 0, __fib_cache)\ndef fib_fast_mortal(n,k,m):\n #print(n,k,m)\n if n in __fib_cache:\n #print('%d is in cache. Returning %d' % (__fib_cache[n],__fib_cache[n]))\n return __fib_cache[n]\n else:\n #print('%d not in cache.' % n)\n __fib_cache[n] = n if n < 2 else k*fib_fast_mortal(n-2,k,m) + \\\n fib_fast_mortal(n-1,k,m) - __fib_cache[n-m-1]\n #print('Added %d:%d to cache' % (n,__fib_cache[n]))\n #print(__fib_cache)\n return __fib_cache[n]\n\n# rosalind.info/fibd top solution\n# Really cool solution that just keeps track of the number of\n# of rabbits in each age group\ndef rosa_fib(n,k=1):\n ages = [1] + [0]*(k-1)\n print(ages)\n for i in range(n-1):\n # of newborns # of mature rabbits\n ages = [sum(ages[1:])] + ages[:-1]\n print(ages)\n return sum(ages)\n\n\ndef calculate_dominance(k,m,n):\n from scipy.misc import comb\n tot = int(comb((k+m+n),2))\n homo = int(comb(k,2))\n homo_cross = k*(m+n)\n hetero = int(comb(m,2))\n hetero_cross = m*n\n\n # Returns the number that create a particular punnet square\n # multiplied by its punnet square derived % of dominance\n return 1/tot*(homo*(4/4)+homo_cross+hetero*.75+hetero_cross*0.5)\n\n# PERM - Enumerating Gene Orders\nimport itertools\ndef permutations(n):\n perms = list(itertools.permutations(range(1,n+1)))\n print(len(perms))\n #[print('%s %s %s' % x) for x in perms]\n\n # From Rosalind solutions\n for line in perms:\n print(' '.join(map(str,line)))\n\n\n# CONS - Consensus and Profiles\ndef consensus(filename):\n from Bio import SeqIO\n import numpy as np\n import pandas as pd\n\n # Initialize containers\n matrix = {'A':[],'C':[],'G':[],'T':[]}\n seqs = []\n\n # Read using SeqIO and add to seqs\n for record in SeqIO.parse(filename,'fasta'):\n seqs.append(record.seq)\n\n seqs = np.array(seqs)\n\n # Count # of nucs in each column\n for i in range(seqs.shape[1]):\n for nuc in list('ACGT'):\n matrix[nuc].append(list(seqs[:,i]).count(nuc))\n\n df = pd.DataFrame(matrix)\n\n # Consensus nuc at each position is the index of the maximum value\n consensus = ''.join(df.idxmax(axis=1).values)\n\n d = df.to_dict(orient='list')\n\n # Output\n return consensus\n #for nuc in list('ACGT'):\n # print('%s:' % nuc,'%r' % ' '.join(list(map(str,d[nuc]))))\n\n\ndef calculateProteinMass(poly):\n import pickle\n d = pickle.load(open('./assignments_misc/mono_table.pkl','rb'))\n mass = 0\n for aa in poly:\n mass+=d[aa]\n print('%0.3f'%mass)\n\ndef printDirectedGraph(seqs):\n '''Prints directed overlap graph given dictionary of sequence ids:seq'''\n for id,seq in seqs.items():\n suf = seq[-3:]\n #print(id,seq,suf)\n for id2,seq2 in seqs.items():\n if id2!=id:\n pre = seq2[0:3]\n if suf==pre:\n print(id,id2)\n\n\ndef countExpectedDominantChildren(s='1 0 0 1 0 1', num_offpsring=2):\n '''\n Returns the number of offpsring that can be expected from\n the population described in s, corresponding to:\n\n 1. AA-AA\n 2. AA-Aa\n 3. AA-aa\n 4. Aa-Aa\n 5. Aa-aa\n 6. aa-aa\n\n '''\n punnets = [4/4,4/4,4/4,3/4,2/4,0/4]\n offspring = [num_offpsring]*6\n nums = s.split()\n\n return sum(int(i)*j*k for i,j,k in zip(nums,punnets,offspring))\n\ndef translateDNA(seq):\n '''\n Given a DNA sequence, returns a string representing\n the resulting polypeptide with '*' for STOP codons\n '''\n from collections import defaultdict\n d = {}\n d = defaultdict(lambda x: [],d)\n with open('./assignments_misc/dna_codon_table_easy.txt','r') as f:\n for line in f.readlines():\n split = line.split()\n d[split[0]] = split[1]\n d = dict(d)\n\n aa = []\n for i in range(0,len(seq),3):\n triplet = seq[i:i+3]\n if len(triplet)==3:\n aa.append(d[triplet])\n\n return ''.join(aa).replace('Stop','*')\n", "sub_path": "rosalind/rosalind.py", "file_name": "rosalind.py", "file_ext": "py", "file_size_in_byte": 6902, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "collections.defaultdict", "line_number": 115, "usage_type": "call"}, {"api_name": "scipy.misc.comb", "line_number": 144, "usage_type": "call"}, {"api_name": "scipy.misc.comb", "line_number": 145, "usage_type": "call"}, {"api_name": "scipy.misc.comb", "line_number": 147, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 157, "usage_type": "call"}, {"api_name": "Bio.SeqIO.parse", "line_number": 177, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 177, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 180, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 187, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 202, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 246, "usage_type": "call"}]} +{"seq_id": "125936820", "text": "\"\"\"\n在经过像素级裁剪的图像中进一步裁剪出包含笔石区域的最小矩形框，尽量保证笔石的长宽比不变，并且在四周随机留白\n\"\"\"\nimport random\nimport cv2 as cv\nimport numpy as np\nfrom pathlib import Path\nimport time\nfrom concurrent.futures import ProcessPoolExecutor\n\ncategory_list1 = [ # set100-10\n '1Dicellograptus bispiralis', # re1\n '1Dicellograptus caduceus', # re1\n '1Dicellograptus divaricatus salopiensis',\n '1Dicellograptus smithi', # re1, re2\n '1Dicellograptus undatus',\n '1Dicranograptus irregularis', # re1\n '1Dicranograptus sinensis', # re1\n '1Didymograptus jiangxiensis', # re1\n '1Didymograptus latus tholiformis', # re1\n '1Didymograptus miserabilis'\n]\ncategory_list2 = [ # set100-20\n '2Amplexograptus acusiformis', # re1\n '2Amplexograptus fusiformis',\n '2Cryptograptus arcticus sinensis', # re1\n '2Cryptograptus gracilicornis', # re1\n '2Dicellograptus divaricatus',\n '2Dicranograptus nicholsoni parvangulus',\n '2Dicranograptus ramosus',\n '2Didymograptus euodus',\n '2Didymograptus linearis longus',\n '2Didymograptus saerganensis'\n]\ncategory_list3 = [ # set100-30\n '3Climacograptus pauperatus', # re1, re2\n '3Cryptograptus arcticus', # re1\n '3Cryptograptus marcidus',\n '3Cryptograptus tricornis', # re1, re2\n '3Glossograptus briaros',\n '3Glossograptus robustus',\n '3Glyptograptus plurithecatus wuningensis', # re1\n '3Glyptograptus teretiusculus siccatus', # re1\n '3Pseudoclimacograptus parvus jiangxiensis', # re1\n '3Pseudoclimacograptus wannanensis' # re1, re2, re3\n]\ncategory_list4 = [ # set100-40\n '4Diplograptus proelongatus', # re1\n '4Glyptograptus teretiusculus', # re1\n '4Jiangxigraptus inculus',\n '4Jishougraptus mui',\n '4Leptograptus flaccidus trentonensis',\n '4Monoclimacis neimengolensis',\n '4Pseudoclimacograptus angulatus',\n '4Pseudoclimacograptus longus',\n '4Pseudoclimacograptus modestus',\n '4Pseudoclimacograptus parvus'\n]\ncategory_list5 = [ # set100-50\n '5Amplexograptus disjunctus yangtzensis',\n '5Amplexograptus suni', # re1\n '5Climacograptus miserabilis', # re1\n '5Climacograptus supernus', # re1\n '5Dicellograptus ornatus', # re1, re2\n '5Diplograptus modestus',\n '5Glyptograptus incertus',\n '5Petalolithus elongatus',\n '5Petalolithus folium',\n '5Streptograptus runcinatus'\n]\ncategory_list6 = [ # set100-60\n '6Dicellograptus szechuanensis',\n '6Diplograptus bohemicus',\n '6Glyptograptus austrodentatus', # re1, re2\n '6Glyptograptus gracilis', # re1\n '6Glyptograptus lungmaensis',\n '6Glyptograptus tamariscus', # re1\n '6Glyptograptus tamariscus linealis',\n '6Glyptograptus tamariscus magnus',\n '6Reteograptus uniformis',\n '6Retiolites geinitzianus'\n]\ncategory_list7 = [ # set100-70\n '7Amplexograptus orientalis',\n '7Climacograptus angustatus', # re1\n '7Climacograptus leptothecalis', # re1\n '7Climacograptus minutus', # re1\n '7Climacograptus normalis',\n '7Climacograptus tianbaensis',\n '7Colonograptus praedeubeli',\n '7Diplograptus angustidens',\n '7Diplograptus diminutus',\n '7Rectograptus pauperatus'\n]\ncategory_list8 = [ # set100-80\n '8Amplexograptus confertus',\n '8Climacograptus angustus', # re1\n '8Climacograptus textilis yichangensis', # re1\n '8Colonograptus deubeli',\n '8Dicellograptus cf. complanatus',\n '8Diplograptus concinnus',\n '8Pristiograptus variabilis',\n '8Pseudoclimacograptus demittolabiosus',\n '8Pseudoclimacograptus formosus',\n '8Rectograptus abbreviatus'\n]\ncategory_list9 = [ # set100-90\n '9Akidograptus ascensus',\n '9Amplexograptus cf. maxwelli',\n '9Cardiograptus amplus',\n '9Climacograptus bellulus',\n '9Climacograptus hastatus',\n '9Glyptograptus dentatus',\n '9Glyptograptus elegans',\n '9Glyptograptus elegantulus',\n '9Orthograptus calcaratus',\n '9Trigonograptus ensiformis'\n]\ncategory_list10 = [ # set100-96\n '10Demirastrites triangulatus',\n '10Dicellograptus tumidus',\n '10Dicellograptus turgidus',\n '10Paraorthograptus pacificus', # re1\n '10Paraorthograptus simplex',\n '10Spirograptus turriculatus'\n]\ncategory_list11 = [ # set100\n '11Appendispinograptus venustus', # re1\n '11Nicholsonograptus fasciculatus',\n '11Nicholsonograptus praelongus',\n '11Paraorthograptus longispinus'\n]\ncategory_list12 = [ # set105\n '12Cryptograptus tricornis (Juvenile)',\n '12Phyllograptus anna',\n '12Rastrites guizhouensis', # re1, re2\n '12Tangyagraptus typicus', # re1\n '12Yinograptus grandis'\n]\ncategory_list13 = [ # set110\n '13Coronograptus cyphus', # re1\n '13Cystograptus vesiculosus', # re1\n '13Normalograptus extraordinarius', # re1, re2\n '13Normalograptus persculptus', # re1, re2\n '13Parakidograptus acuminatus'\n]\ncategory_list14 = [ # set114\n '14Diceratograptus mirus',\n '14Lituigraptus convolutus',\n '14Paraplegmatograptus connectus', # re1\n '14Pararetiograptus regularis',\n]\ncategory_list = category_list1 + category_list2 + category_list3 + category_list4 + category_list5 + category_list6 + \\\n category_list7 + category_list8 + category_list9 + category_list10 + category_list11 + category_list12 +\\\n category_list13 + category_list14\ncategory_paths = [Path(r'D:\\set113_ori\\annotated_images_550') / category for category in category_list]\n\n\n# 在纯色背景图像中裁剪出包含前景信息的最小矩形框，保持长宽比并在四周留白\ndef find_smallest_rectangle(dir_path):\n output_size = 448\n for path in dir_path.iterdir():\n if path.suffix == '.jpg':\n img_path = str(path)\n img_name = path.name\n img = cv.imdecode(np.fromfile(img_path), -1)\n img_gray = cv.cvtColor(img, cv.COLOR_RGB2GRAY)\n h, w = img_gray.shape\n if h == w == output_size:\n continue\n # bk_color = img_gray[0, 0]\n bk_color = 255 # 白色\n\n # 以背景色为阈值，遍历出图像中笔石区域的边界\n step = 5\n leftmost = w\n for i in range(0, h, step):\n for j in range(0, w, step):\n if img_gray[i, j] != bk_color:\n if j < leftmost:\n leftmost = j\n break\n rightmost = 0\n for i in range(0, h, step):\n for j in range(w - 1, -1, -step):\n if img_gray[i, j] != bk_color:\n if j > rightmost:\n rightmost = j\n break\n highest = h\n for i in range(0, w, step):\n for j in range(0, h, step):\n if img_gray[j, i] != bk_color:\n if j < highest:\n highest = j\n break\n lowest = 0\n for i in range(0, w, step):\n for j in range(h - 1, -1, -step):\n if img_gray[j, i] != bk_color:\n if j > lowest:\n lowest = j\n break\n # 笔石区域的高和宽\n new_width = abs(rightmost - leftmost)\n new_high = abs(lowest - highest)\n margin = int(abs(new_width - new_high))\n\n # 笔石区域的高>宽\n if new_high > new_width:\n mid = abs(rightmost - new_width / 2)\n # 左右边距均足，则将margin平分\n if mid - (new_width / 2 + margin / 2) >= 0 and mid + (new_width / 2 + margin / 2) / 2 <= w:\n new_img = img[highest:lowest,\n int(mid - (new_width / 2 + margin / 2)):int(mid + (new_width / 2 + margin / 2))]\n # 左边距不足，则从最左端开始切片，并尽量将剩余margin全部分配给右半部分（若右也不足则取最右）\n elif mid - (new_width / 2 + margin / 2) < 0 and mid + (new_width / 2 + margin / 2) / 2 <= w:\n left_margin = int(mid - new_width / 2)\n right_margin = margin - left_margin\n new_img = img[\n highest:lowest,\n 0:int(mid + (new_width / 2 + right_margin)) if int(\n mid + (new_width / 2 + right_margin)) <= w else w\n ]\n # 右边距不足，则切片至图像最右端，并尽量将剩余margin全部分配给左半部分（若左也不足则取最左端）\n elif mid - (new_width / 2 + margin / 2) >= 0 and mid + (new_width / 2 + margin / 2) / 2 > w:\n right_margin = int(w - rightmost)\n left_margin = margin - right_margin\n new_img = img[\n highest:lowest,\n int(mid - (new_width / 2 + left_margin)) if int(\n mid - (new_width / 2 + left_margin)) >= 0 else 0:w\n ]\n # 左右边距均不足，则横向切片取全部\n else:\n new_img = img[highest:lowest, :]\n\n # 笔石区域的高<宽\n elif new_high < new_width:\n mid = abs(lowest - new_high / 2)\n # 上下边距均足, 则将margin平分\n if mid - (new_high / 2 + margin / 2) >= 0 and mid + (new_high / 2 + margin / 2) <= h:\n new_img = img[int(mid - (new_high / 2 + margin / 2)):int(mid + (new_high / 2 + margin / 2)),\n leftmost:rightmost]\n # 上边距不足，则从最上端开始切片，并尽量将剩余margin全部分配给下半部分（若下也不足则取最下端）\n elif mid - (new_high / 2 + margin / 2) < 0 and mid + (new_high / 2 + margin / 2) <= h:\n up_margin = int(mid - new_high / 2)\n bottom_margin = margin - up_margin\n new_img = img[\n 0:int(mid + (new_high / 2 + bottom_margin)) if int(\n mid + (new_high / 2 + bottom_margin)) <= h else h,\n leftmost:rightmost\n ]\n # 下边距不足，则切片至图像最下端，并尽量将剩余margin全部分配给上半部分（若上也不足则取最上端）\n elif mid - (new_high / 2 + margin / 2) >= 0 and mid + (new_high / 2 + margin / 2) > h:\n bottom_margin = int(h - lowest)\n up_margin = margin - bottom_margin\n new_img = img[\n int(mid - (new_high / 2 + up_margin)) if int(\n mid - (new_high / 2 + up_margin)) >= 0 else 0:h,\n leftmost:rightmost\n ]\n # 上下边距均不足，则纵向切片取全部\n else:\n new_img = img[:, leftmost:rightmost]\n # 笔石区域的高宽刚好相等\n else:\n new_img = img[highest:lowest, leftmost:rightmost]\n\n # 为图像四周留白，大小为笔石区域的边长×0.2\n white_margin = 30\n half_white_margin = int(white_margin / 2)\n new_img = np.pad(new_img, ((half_white_margin, half_white_margin),\n (half_white_margin, half_white_margin),\n (0, 0)), 'constant', constant_values=255)\n\n # 随机缩放至size_list中的任一尺寸\n size_list = [300, 330, 370, 400]\n random_index = random.randint(0, len(size_list) - 1)\n random_size = size_list[random_index]\n new_img = cv.resize(new_img, (random_size, random_size))\n\n # 横纵随机填充至output_size\n padding = output_size - random_size\n w_padding = random.randint(5, padding-5)\n half_w_padding = padding - w_padding\n h_padding = random.randint(5, padding-5)\n half_h_padding = padding - h_padding\n new_img = np.pad(new_img, ((w_padding, half_w_padding),\n (h_padding, half_h_padding),\n (0, 0)), 'constant', constant_values=255)\n\n # new_img_mask = np.zeros((output_size, output_size, 3))\n # new_img = new_img + new_img_mask\n\n new_img = cv.resize(new_img, (output_size, output_size))\n is_success, im_buf_arr = cv.imencode('.jpg', new_img)\n im_buf_arr.tofile(img_path)\n print(\"reshape: \", img_path, new_img.shape)\n assert (new_img.shape[0] == new_img.shape[1] == output_size)\n else:\n continue\n print(\"类别 %s 裁剪完毕\" % str(dir_path).split(\"\\\\\")[-1])\n\n\nif __name__ == '__main__':\n t1 = time.time()\n with ProcessPoolExecutor(2) as executor:\n results = executor.map(find_smallest_rectangle, category_paths)\n print(time.time() - t1)\n", "sub_path": "processing/4_crop_by_square.py", "file_name": "4_crop_by_square.py", "file_ext": "py", "file_size_in_byte": 13560, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "pathlib.Path", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 167, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 167, "usage_type": "attribute"}, {"api_name": "numpy.pad", "line_number": 273, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 279, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 281, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 285, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 289, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 296, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 297, "usage_type": "call"}, {"api_name": "time.time", "line_number": 307, "usage_type": "call"}, {"api_name": "concurrent.futures.ProcessPoolExecutor", "line_number": 308, "usage_type": "call"}, {"api_name": "time.time", "line_number": 310, "usage_type": "call"}]} +{"seq_id": "36764476", "text": "#################################\n##### Name: Yash Kamat ##########\n##### Uniqname: ykamat ##########\n#################################\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport json\nimport secrets as secrets# file that contains your API key\n\n# Global Variables\n# CACHE_PATH = '/Users/yashkamat/Development/si507/projects/project2/Project2Winter2021/cache.json'\n# API_CACHE_PATH = '/Users/yashkamat/Development/si507/projects/project2/Project2Winter2021/api_cache.json'\n\nCURR_CACHE = {}\nCURR_API_CACHE = {}\n\nHOME_URL = 'https://www.nps.gov'\n\ndef save_cache(cache_dic, path):\n\n \"\"\"\n Saves a specific dictionary to local directory as cache.\n\n Parameters\n ----------\n cache_dic: dict\n The dictionary to be cached.\n\n path: string\n The path where the dictionary should be cached.\n\n Returns\n -------\n None\n \"\"\"\n\n with open(path, 'w') as outfile:\n cache = json.dump(cache_dic, outfile)\n\ndef load_cache(path):\n\n \"\"\"\n Returns a dictionary representation of a local cache file.\n\n Parameters\n ----------\n path: string\n The path to the locally saved cache file.\n\n Returns\n -------\n cache_dic: dict\n Dictionary representation of the loaded cache.\n \"\"\"\n\n with open(path) as json_file:\n cache_dic = json.load(json_file)\n return cache_dic\n\ndef get_url(url,cache_dic):\n\n \"\"\"\n Fetches a URL if its repsonse is not in the cache.\n If the URL is in the current cache, it retrieves the response from it.\n\n Parameters\n ----------\n url: string\n The requested URL to be fetched.\n cache_dic: dict\n Dictionary representation of the current cache.\n\n Returns\n -------\n cache_dic[url]: string\n String representation of the response text for the requested url.\n \"\"\"\n\n if url in cache_dic.keys():\n print('Using cache')\n else:\n print(\"Fetching\")\n cache_dic[url] = requests.get(url).text\n return cache_dic[url]\n\ndef get_api(zipcode,cache_dic,params):\n\n \"\"\"\n Makes a call to the MapQuest API if the requested zipcode doesn't exist in the local cache.\n If zipcode exists in local cache, retrieves the API response from cache.\n\n Parameters\n ----------\n zipcode: string\n The requested zipcode to be searched.\n cache_dic: dict\n Current API cache represented as a dictionary.\n params:\n Parameters for MapQuest API request.\n\n Returns\n -------\n cache_dic[zipcode]: JSON representation of API reqest repsonse.\n \"\"\"\n\n if zipcode in cache_dic.keys():\n print('Using cache')\n else:\n print(\"Fetching\")\n cache_dic[zipcode] = requests.get('http://www.mapquestapi.com/search/v2/radius', params=params).text\n\n return cache_dic[zipcode]\n\nclass NationalSite:\n '''a national site\n\n Instance Attributes\n -------------------\n category: string\n the category of a national site (e.g. 'National Park', '')\n some sites have blank category.\n \n name: string\n the name of a national site (e.g. 'Isle Royale')\n\n address: string\n the city and state of a national site (e.g. 'Houghton, MI')\n\n zipcode: string\n the zip-code of a national site (e.g. '49931', '82190-0168')\n\n phone: string\n the phone of a national site (e.g. '(616) 319-7906', '307-344-7381')\n '''\n\n def __init__(self,category=None,name=None,address=None,zipcode=None,phone=None):\n self.category = category\n self.name = name\n self.address = address\n self.zipcode = zipcode\n self.phone = phone\n\n def info(self):\n return f'{self.name} ({self.category}): {self.address} {self.zipcode}'\n\ndef build_state_url_dict():\n ''' Make a dictionary that maps state name to state page url from \"https://www.nps.gov\"\n\n Parameters\n ----------\n None\n\n Returns\n -------\n dict\n key is a state name and value is the url\n e.g. {'michigan':'https://www.nps.gov/state/mi/index.htm', ...}\n '''\n\n response = requests.get(HOME_URL).text\n soup = BeautifulSoup(response,'html.parser')\n state_list = soup.find('ul',class_='dropdown-menu SearchBar-keywordSearch').find_all('li')\n\n links = {}\n\n for item in state_list:\n key = str(item.text).lower()\n value = item.find('a').get('href')\n links[key] = HOME_URL + value\n\n return links\n\ndef get_site_instance(site_url):\n '''Make an instances from a national site URL.\n \n Parameters\n ----------\n site_url: string\n The URL for a national site page in nps.gov\n \n Returns\n -------\n instance\n a national site instance\n '''\n\n response = get_url(site_url,CURR_CACHE)\n soup = BeautifulSoup(response, 'html.parser')\n\n # Category\n try:\n category = soup.find(\"span\", class_=\"Hero-designation\").text.strip()\n except:\n category = ''\n\n # Name\n try:\n name = soup.find(\"a\", class_=\"Hero-title\").text.strip()\n except:\n name = ''\n\n # Address\n try:\n city = soup.find(\"span\", itemprop=\"addressLocality\").text.strip()\n except:\n city = ''\n\n try:\n state = soup.find(\"span\", itemprop='addressRegion').text.strip()\n except:\n state = ''\n address = f'{city}, {state}'\n\n # Zipcode\n try:\n zipcode = soup.find(\"span\", itemprop='postalCode').text.strip()\n except:\n zipcode = ''\n\n # Phone\n try:\n phone = soup.find(\"span\", class_='tel').text.strip()\n except:\n phone = ''\n\n return NationalSite(category=category, name=name,address=address,zipcode=zipcode,phone=phone)\n\ndef get_sites_for_state(state_url):\n '''Make a list of national site instances from a state URL.\n \n Parameters\n ----------\n state_url: string\n The URL for a state page in nps.gov\n \n Returns\n -------\n list\n a list of national site instances\n '''\n sites_list = []\n home_url = 'https://www.nps.gov'\n\n response = requests.get(state_url)\n soup = BeautifulSoup(response.text,'html.parser')\n\n for ref in soup.find('ul', id='list_parks').find_all('h3'):\n site_url = home_url+ref.find('a')['href']\n sites_list.append(get_site_instance(site_url))\n\n return sites_list\n\ndef get_nearby_places(site_object):\n '''Obtain API data from MapQuest API.\n \n Parameters\n ----------\n site_object: object\n an instance of a national site\n \n Returns\n -------\n dict\n a converted API return from MapQuest API\n '''\n\n params = {'key': secrets.API_KEY,\n 'radius': '10',\n 'origin': site_object.zipcode,\n 'maxMatches': '10',\n 'ambiguities': 'ignore',\n 'outFormat': 'json'}\n\n response = get_api(zipcode=site_object.zipcode, cache_dic=CURR_API_CACHE, params=params)\n return json.loads(response)\n\ndef print_sites(site_list):\n\n \"\"\"\n Prints a formatted list of NationalSite objects.\n\n Parameters\n ----------\n site_list: list\n List of NationalSite objects.\n\n Returns\n -------\n None.\n \"\"\"\n\n for site in site_list:\n pos = site_list.index(site)\n print(f'[{pos+1}] {site.info()}')\n\ndef print_nearby(api_dict):\n\n \"\"\"\n Prints a formatted list of 'nearby locations' from an API response.\n\n Parameters\n ----------\n api_dict: dict\n Dictionary represenation of an API reponse for locations near a specific zipcode.\n\n Returns\n -------\n None.\n \"\"\"\n\n for result in api_dict['searchResults']:\n name = result['name']\n address = result['fields']['address']\n category = result['fields']['group_sic_code_name_ext']\n city = result['fields']['city']\n if (len(name) > 0) and (len(category) > 0) and (len(address) > 0):\n print(f'- {name} ({category}): {address}, {city}')\n else:\n if (len(name) == 0):\n name = \"no name\"\n if (len(category) == 0):\n category = \"no category\"\n if (len(address) == 0):\n address = \"no address\"\n if (len(city) == 0):\n city = 'no city'\n print(f'- {name} ({category}): {address}, {city}')\n\ndef user_interface():\n\n \"\"\"\n Runs the user facing loop to fetch sites or nearby locations for specific states/sites.\n Has no parameters or returns.\n\n \"\"\"\n\n try:\n CURR_CACHE = load_cache(CACHE_PATH)\n except:\n CURR_CACHE = {}\n\n try:\n CURR_API_CACHE = load_cache(API_CACHE_PATH)\n except:\n CURR_API_CACHE = {}\n\n state_urls = build_state_url_dict()\n\n while True:\n state = input('Enter a state name (e.g. Michigan, michigan) or \"exit\": ')\n\n if state == \"exit\":\n quit()\n\n elif state.lower() not in state_urls.keys():\n print('[Error] Enter proper state name')\n\n else:\n s_list = get_sites_for_state(state_urls[state.lower()])\n print('\\n-------------------------')\n print(f'List of national sites in {state}')\n print('-------------------------')\n print_sites(s_list)\n\n while True:\n choice = input('Choose the number for detail search or \"exit\" or \"back\": ')\n\n if choice == \"exit\":\n quit()\n\n elif choice == \"back\":\n break\n\n elif choice.isnumeric() == True:\n if(int(choice) <= (len(s_list)+1)):\n print('\\n-------------------------')\n print(f'Places near {s_list[(int(choice) - 1)].name}')\n print('-------------------------')\n print_nearby(get_nearby_places(s_list[(int(choice) - 1)]))\n print('')\n else:\n print('\\n[Error] Invalid input\\n-------------------------')\n else:\n print('\\n[Error] Invalid input\\n-------------------------')\n\nif __name__ == \"__main__\":\n user_interface()", "sub_path": "proj2_nps.py", "file_name": "proj2_nps.py", "file_ext": "py", "file_size_in_byte": 10064, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "json.dump", "line_number": 39, "usage_type": "call"}, {"api_name": "json.load", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 84, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 111, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 161, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 162, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 189, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 245, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 246, "usage_type": "call"}, {"api_name": "secrets.API_KEY", "line_number": 268, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 276, "usage_type": "call"}]} +{"seq_id": "282015720", "text": "\"\"\"\nStudent 1 : Guy Hassan\nID 1:307845032\nStudent 2 : Yahav Mizrahi\nID 2: 305759185\nStudent 3 : Yinon Hirari\nID 3 : 203409024\n\"\"\"\nimport unittest\nimport Feature_Sport_Data\nimport LocalData\nfrom mock import patch\nimport re\nfrom datetime import date\n\n\nclass TDD_Sport_Data(unittest.TestCase):\n @patch('Feature_Sport_Data.get_data_first_feature')\n def mockConnectionFirstFeature(self, mock):\n \"\"\"\n :param mock: Data of feature 1\n :return: func mock\n \"\"\"\n mock.return_value = LocalData.first_feature_data\n return mock()\n\n @patch('Feature_Sport_Data.get_data_second_feature')\n def mockConnectionSecondFeature(self, mock):\n \"\"\"\n :param mock:Data of feature 2\n :return: func mock\n \"\"\"\n mock.return_value = LocalData.second_feature_data\n return mock()\n\n # --------------------------Tests First Feature------------------------------------------\n @patch('Feature_Sport_Data.get_data_first_feature')\n def test_WinnerNameNotNone(self, mock):\n \"\"\"\n Test 1: Check if the winner name team not None.\n \"\"\"\n listofdata = self.mockConnectionFirstFeature()\n for i in range(len(listofdata)):\n self.assertIsNotNone(listofdata[i]['Winner Of League'])\n\n @patch('Feature_Sport_Data.get_data_first_feature')\n def test_StartDateSmallerThanEndDate(self, mock):\n \"\"\"\n Test 2: Check if the start date of the season befor the end date.\n \"\"\"\n listofdata = self.mockConnectionFirstFeature()\n for i in range(len(listofdata)):\n self.assertLess(listofdata[i]['Start Of Season'], listofdata[i]['End Of Season'])\n\n @patch('Feature_Sport_Data.get_data_first_feature')\n def test_AmountOfMatchIsSorted(self, mock):\n \"\"\"\n Test 3: Check Sorted amount of match.\n \"\"\"\n listofdata = self.mockConnectionFirstFeature()\n for i in range(len(listofdata)):\n for j in range(i + 1, len(listofdata)):\n self.assertLessEqual(listofdata[i]['Amount Of Match'], listofdata[j]['Amount Of Match'],\n 'The list is not sorted')\n\n @patch('Feature_Sport_Data.get_data_first_feature')\n def test_NameOfCountryIsUnique(self, mock):\n \"\"\"\n Test 4: The league name appears once.\n \"\"\"\n listofdata = self.mockConnectionFirstFeature()\n for i in range(len(listofdata)):\n for j in range(i + 1, len(listofdata)):\n self.assertNotEqual(listofdata[i]['Name Country'], listofdata[j]['Name Country'])\n\n @patch('Feature_Sport_Data.get_data_first_feature')\n def test_NumberOfWeeksGreaterOfMatches(self, mock):\n \"\"\"\n Test 5: Check if number of weeks greater number of matches.\n \"\"\"\n listofdata = self.mockConnectionFirstFeature()\n for i in range(len(listofdata)):\n start = re.findall('\\d+', (listofdata[i]['Start Of Season']))\n end = re.findall('\\d+', (listofdata[i]['End Of Season']))\n start = date(int(start[0]), int(start[1]), int(start[2]))\n end = date(int(end[0]), int(end[1]), int(end[2]))\n num_of_weeks = (end - start).days // 7\n self.assertGreater(int(num_of_weeks), int(listofdata[i]['Amount Of Match']))\n\n # --------------------------------Tests Second Feature-----------------------------------------------------------------\n @patch('Feature_Sport_Data.get_data_second_feature')\n def test_ScorersIsSorted(self, mock):\n \"\"\"\n Test 6: Check Sorted from best to good at least scores.\n \"\"\"\n listofdata = self.mockConnectionSecondFeature()\n for i in range(len(listofdata)):\n for j in range(i + 1, len(listofdata)):\n self.assertGreaterEqual(listofdata[i]['Players']['Number Of Goals'],\n listofdata[j]['Players']['Number Of Goals'], 'The list is not sorted')\n\n @patch('Feature_Sport_Data.get_data_second_feature')\n def test_NamePlayerNotNone(self, mock):\n \"\"\"\n Test 7: Check that name player not None.\n \"\"\"\n listofdata = self.mockConnectionSecondFeature()\n for i in range(len(listofdata)):\n self.assertIsNotNone(listofdata[i]['Players']['Name Player'])\n\n @patch('Feature_Sport_Data.get_data_second_feature')\n def test_NameTeamIsUnique(self, mock):\n \"\"\"\n Test 8: Check the league appears once.\n \"\"\"\n listofdata = self.mockConnectionSecondFeature()\n for i in range(len(listofdata)):\n for j in range(i + 1, len(listofdata)):\n self.assertNotEqual(listofdata[i]['Name League'], listofdata[j]['Name League'],\n 'Cannot be 2 player in same league')\n\n @patch('Feature_Sport_Data.get_data_second_feature')\n def test_AgeOfPlayerGeatherThanSixteen(self, mock):\n \"\"\"\n Test 9: Check the age of the player greater 16.\n \"\"\"\n listofdata = self.mockConnectionSecondFeature()\n for i in range(len(listofdata)):\n for j in range(i + 1, len(listofdata)):\n today = date.today()\n playerDate = re.findall('\\d+', (listofdata[i]['Players']['Date Of Birth']))\n self.assertGreater(today.year - int(playerDate[0]), 16)\n\n @patch('Feature_Sport_Data.get_data_second_feature')\n def test_NumberOfGoalGreaterThanZero(self, mock):\n \"\"\"\n Test 10: Check amount of golas greater 0.\n \"\"\"\n listofdata = self.mockConnectionSecondFeature()\n for i in range(len(listofdata)):\n self.assertGreater(int(listofdata[i]['Players']['Number Of Goals']), 0)\n\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "TestDrivenDevelopment.py", "file_name": "TestDrivenDevelopment.py", "file_ext": "py", "file_size_in_byte": 5760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "unittest.TestCase", "line_number": 17, "usage_type": "attribute"}, {"api_name": "mock.return_value", "line_number": 24, "usage_type": "attribute"}, {"api_name": "LocalData.first_feature_data", "line_number": 24, "usage_type": "attribute"}, {"api_name": "mock.patch", "line_number": 18, "usage_type": "call"}, {"api_name": "mock.return_value", "line_number": 33, "usage_type": "attribute"}, {"api_name": "LocalData.second_feature_data", "line_number": 33, "usage_type": "attribute"}, {"api_name": "mock.patch", "line_number": 27, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 37, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 46, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 55, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 66, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 83, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 86, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 76, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 91, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 102, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 111, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 130, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 130, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 131, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 122, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 134, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 145, "usage_type": "call"}]} +{"seq_id": "66012504", "text": "from pymongo import MongoClient\nimport sys\nimport json\nimport time\nimport os\nfrom datetime import datetime\nfrom processors import BaseProcessor\nimport resource\nfrom subprocess import PIPE, Popen, STDOUT, TimeoutExpired\n\n\nclass Processor(BaseProcessor):\n def __init__(self, name, config):\n super().__init__(name, config)\n client = MongoClient(config['mongo_host'], config['mongo_port'])\n self.db_courses = client[config['mongo_db_courses']]\n self.db_messages = client[config['mongo_db_messages']]\n self.languages_config = config[\"languages\"]\n\n def process(self, message, config=None): \n if isinstance(config, dict):\n config = {**self.config, **config}\n else:\n config = self.config\n\n try:\n need_keys = ('id', 'mqtt_key', 'user', 'language', 'course', 'problem', 'variant', 'code')\n if not all(k in message for k in need_keys):\n return None\n pr = int(message['problem'])\n var = message['variant']\n code = message['code']\n fname = message['user']\n # Определяем настройки тестов\n try:\n collection = self.db_courses[message[\"course\"]]\n problem_config = list(collection.find({'problem': pr, 'type': 'equal'}))\n # Если не нашлось ничего - выходим\n print(f\"Problem - {problem_config}\") \n if len(problem_config) == 0:\n return None\n tests = problem_config[0]['variants'][var]\n print(f\"Tests - {tests}\")\n except Exception as e:\n self.log(f'Process error: {str(e)}')\n return None\n with open(fname, 'w') as write_file:\n write_file.write(code)\n # Проверка тестов\n results = {}\n success_count = 0\n res_score = 0\n for test_key in tests:\n result = {'score': 0, 'test_out': ''}\n test = tests[test_key]\n test_in = test['in']\n test_out = test['out']\n test_score = test['score']\n p = Popen([self.languages_config[message['language']], fname], stdout=PIPE, stdin=PIPE, stderr=STDOUT) \n try:\n outs, errs = p.communicate(input=str.encode(test_in), timeout=30)\n outs = outs.decode(\"utf-8\").rstrip()\n print(f'Test input - {test_in}')\n print(f'Program output - {outs}')\n print(f'Reference output - {test_out}')\n self.log(f'Test input - {test_in}')\n self.log(f'Program output - {outs}')\n self.log(f'Reference output - {test_out}')\n result['test_out'] = outs\n if test_out == outs:\n result['score'] = test_score\n res_score += test_score\n success_count += 1\n except Exception as e:\n result['test_out'] = str(e)\n p.kill()\n outs, errs = p.communicate()\n results[test_key] = result\n \n collection_date = datetime.today().strftime('%Y-%m-%d')\n # Select problem collection\n collection = self.db_messages[f'{collection_date}']\n results['success_count'] = success_count\n results['res_score'] = res_score\n json_data = {'message': message, 'result': results}\n self.log(f'Save to MongoDB: {json_data}.')\n transaction_id = collection.insert_one(json_data).inserted_id\n self.log(f'MongoDB response: {transaction_id}.')\n del json_data['_id']\n return json_data\n except Exception as err:\n self.log(f'Process error: {str(err)}')\n return None\n", "sub_path": "SGContest/ContestServer/processors/equal_processor.py", "file_name": "equal_processor.py", "file_ext": "py", "file_size_in_byte": 4032, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "processors.BaseProcessor", "line_number": 12, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 15, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 59, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 59, "usage_type": "name"}, {"api_name": "subprocess.STDOUT", "line_number": 59, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "name"}]} +{"seq_id": "246543921", "text": "import os\nimport sys\nimport glob\nimport json\nimport torch\nimport numpy as np\nfrom model import models\nfrom model import metrics\nfrom data import visualize\nfrom data import data_utils\nimport matplotlib.pyplot as plt\nfrom model import dataset_loader\nfrom torchsummary import summary\nimport torch.utils.data as torchdata\nfrom torch.utils.tensorboard import SummaryWriter\n\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nclass Trainer:\n def __init__(self, cfg):\n self.cfg = cfg\n self.configure()\n self.define_loss()\n self.cal_input_channels()\n self.generate_input_mapping()\n self.create_model()\n self.create_dataloader()\n\n\n def configure(self):\n # Bounding volume parameters\n vol_type = self.cfg.vol_type\n self.vol_params = ()\n if vol_type == \"sphere\":\n center = torch.Tensor(self.cfg.vol_params[:3]).to(device)\n radius = self.cfg.vol_params[3]\n self.vol_params = (vol_type, center, radius)\n elif vol_type == \"cube\":\n min_bound = torch.Tensor(self.cfg.vol_params[:3]).to(device)\n max_bound = torch.Tensor(self.cfg.vol_params[3:]).to(device)\n self.vol_params = (vol_type, min_bound, max_bound)\n else:\n sys.exit(\"Unsupported bounding volume\")\n\n # Setup logging\n logdir = os.path.join(os.getcwd(), \"logs\")\n self.modeldir = os.path.join(os.getcwd(), \"models\")\n os.makedirs(logdir, exist_ok=True)\n os.makedirs(self.modeldir, exist_ok=True)\n self.writer = SummaryWriter(logdir)\n \n def define_loss(self):\n if self.cfg.loss == 'l1':\n self.loss_fun = metrics.l1\n elif self.cfg.loss == 'lpips':\n self.loss_fun = metrics.lpips\n elif self.cfg.loss == 'ssim':\n self.loss_fun = metrics.msssim\n elif self.cfg.loss == 'mse':\n self.loss_fun = metrics.mse\n elif self.cfg.loss == 'fft':\n self.loss_fun = metrics.loss_fft\n else:\n self.loss_fun = metrics.mse\n\n\n def cal_input_channels(self):\n dim_points = 3\n dim_viewdir = 0\n\n if self.cfg.points_type == 'spherical':\n dim_points = 2\n \n if self.cfg.use_viewdirs and self.cfg.viewdir_type == 'cartesian':\n dim_viewdir = 3\n elif self.cfg.use_viewdirs and self.cfg.viewdir_type == 'spherical':\n dim_viewdir = 2\n \n self.input_ch = dim_points + dim_viewdir\n\n if self.cfg.feature_mapping != 'none':\n self.input_ch = self.input_ch + (self.cfg.mapping_size * 2) \n # self.input_ch = (self.cfg.mapping_size * 2) \n # if self.cfg.map_points:\n # dim_points = self.cfg.mapping_size * 2\n \n # if self.cfg.use_viewdirs and self.cfg.map_viewdirs:\n # dim_viewdir = 0\n\n def generate_input_mapping(self):\n inp_size = 0\n if self.cfg.map_points:\n if self.cfg.points_type == 'spherical':\n inp_size += 2\n else:\n inp_size += 3\n \n if self.cfg.use_viewdirs and self.cfg.map_viewdirs:\n if self.cfg.viewdir_type == 'cartesian':\n inp_size += 3\n else: \n inp_size += 2\n \n self.B_dict = {}\n # Standard network - no mapping\n self.B_dict['none'] = None\n # Gaussian Fourier feature mapping\n B_gauss = torch.normal(mean=0, std=1.0, size=(self.cfg.mapping_size, inp_size))\n # Three different scales of Gaussian Fourier feature mappings\n for scale in [1., 2., 4., 8., 10., 32.]:\n self.B_dict[f'gauss_{scale}'] = B_gauss * scale\n\n self.input_map = self.B_dict[self.cfg.feature_mapping]\n \n\n def create_dataloader(self):\n datadir = self.cfg.datadir\n\n i_train = []\n i_val = []\n for f in sorted(glob.glob(datadir + \"/train/*.json\")):\n filename = f[f.rfind('_') + 1:f.rfind('.')]\n i_train.append(filename)\n \n for f in sorted(glob.glob(datadir + \"/val/*.json\")):\n filename = f[f.rfind('_') + 1:f.rfind('.')]\n i_val.append(filename)\n \n self.dataset_train = dataset_loader.DatasetLoad(data_list=i_train, data_dir=os.path.join(datadir, \"train\"),\n cfg=self.cfg, input_map=self.input_map)\n self.dataloader_train = torchdata.DataLoader(self.dataset_train, batch_size=self.cfg.batch_size, \n shuffle=True, num_workers=2, drop_last=False)\n \n self.dataset_val = dataset_loader.DatasetLoad(data_list=i_val, data_dir=os.path.join(datadir, \"val\"),\n cfg=self.cfg, input_map=self.input_map)\n self.dataloader_val = torchdata.DataLoader(self.dataset_val, batch_size=self.cfg.batch_size, \n shuffle=False, num_workers=2, drop_last=False)\n\n\n\n\n def create_model(self):\n \"\"\"\n Create a new model or load model from saved checkpoint\n \"\"\"\n self.model = models.ConvNet(num_layers=self.cfg.num_layers,\n hidden_size=self.cfg.hidden_size,\n skip_connect=self.cfg.skip_connect,\n input_ch= self.input_ch,\n output_ch=self.cfg.output_ch).to(device)\n \n self.optimizer = torch.optim.Adam(self.model.parameters(), \n lr=self.cfg.optimizer.lr,\n weight_decay=self.cfg.optimizer.weight_decay)\n self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size = self.cfg.scheduler.step_decay, gamma = self.cfg.scheduler.lr_decay)\n \n # summary(self.model, (512, 512, 512))\n\n self.start_epoch = 0\n\n # load a model from saved checkpoint if provided\n if self.cfg.checkpoint:\n print('loading model: ', self.cfg.checkpoint)\n checkpoint = torch.load(self.cfg.checkpoint, map_location=device)\n self.start_epoch = checkpoint['epoch']\n self.input_map = checkpoint['input_map'].cpu()\n self.model.load_state_dict(checkpoint['state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n \n def train(self, epoch):\n self.model.train()\n batch_loss = 0.0\n batch_clamped_output = []\n batch_diff = []\n batch_target = []\n batch_input_points = []\n batch_input_viewdirs = []\n\n for idx, sample in enumerate(self.dataloader_train):\n input = sample['input'].to(device)\n mask = sample['input_mask'].to(device)\n target = sample['target'].to(device)\n\n target = target[:,:3,...]\n mask = mask.unsqueeze(1).expand(target.shape)\n\n self.optimizer.zero_grad()\n # forward pass\n output = self.model(input)\n\n # compute losses\n loss_l1 = metrics.l1(target, output, mask)\n loss_ssim = metrics.msssim(target, output, mask)\n loss = loss_l1 + loss_ssim\n # loss = self.loss_fun(target, output, mask)\n\n # backward pass and optimize\n loss.backward()\n self.optimizer.step()\n\n # log\n batch_loss += loss.item()\n\n # visualize images on tensorboard\n if idx in [0, 1] and (epoch+1) % self.cfg.save_every == 0:\n clamped_output = torch.clamp(output.detach(), min=0.0, max=1.0) * mask\n target = target * mask\n batch_target.append(target.cpu())\n batch_diff.append(torch.abs(target-clamped_output).cpu())\n batch_clamped_output.append(clamped_output.cpu())\n\n # if idx in [0,1] and epoch == 0:\n # raw_data = sample['raw_data'].cpu()\n # if self.cfg.use_viewdirs:\n # batch_input_viewdirs.append(raw_data[:,1,...])\n # raw_data = raw_data[:,0,...]\n # batch_input_points.append(raw_data)\n\n # visualize alpha and rgb distribution for first image on tensorboard\n # if idx == 0:\n # self.writer.add_histogram(\"red\", output[0,0,...], epoch+1)\n # self.writer.add_histogram(\"green\", rgb[0,1,...], epoch+1)\n # self.writer.add_histogram(\"blue\", rgb[0,2,...], epoch+1)\n \n # log losses\n self.writer.add_scalar('rgb_loss',batch_loss/(idx+1),epoch+1)\n\n # log input and target images only once\n # if epoch == 0:\n # batch_input_points = torch.cat(batch_input_points)\n # if batch_input_points.shape[1] == 3:\n # batch_input_points = visualize.vis_cartesian_as_matplotfig(batch_input_points)\n # else:\n # batch_input_points = visualize.vis_spherical_as_matplotfig(batch_input_points)\n\n # if self.cfg.use_viewdirs:\n # batch_input_viewdirs = torch.cat(batch_input_viewdirs)\n # if batch_input_viewdirs.shape[1] == 3:\n # batch_input_viewdirs = visualize.vis_cartesian_as_matplotfig(batch_input_viewdirs)\n # else:\n # batch_input_viewdirs = visualize.vis_spherical_as_matplotfig(batch_input_viewdirs)\n \n # self.writer.add_figure('input_points', batch_input_points,epoch+1)\n # if self.cfg.use_viewdirs:\n # self.writer.add_figure('input_viewdirs', batch_input_viewdirs,epoch+1)\n \n if (epoch+1) % self.cfg.save_every == 0:\n self.writer.add_images('rgb_target', torch.cat(batch_target), epoch+1)\n self.writer.add_images('rgb_clamped', torch.cat(batch_clamped_output), epoch+1)\n self.writer.add_images('rgb_diff', torch.cat(batch_diff), epoch+1)\n \n return batch_loss/(idx+1)\n \n\n def val(self, epoch):\n self.model.eval()\n batch_l1 = 0.0\n batch_lpips = 0.0\n batch_psnr = 0.0\n batch_ssim = 0.0\n batch_mse = 0.0\n batch_fft = 0.0\n batch_clamped_output = []\n batch_diff = []\n batch_target = []\n batch_input_points = []\n batch_input_viewdirs = []\n\n with torch.no_grad():\n for idx, sample in enumerate(self.dataloader_val):\n input = sample['input'].to(device)\n mask = sample['input_mask'].to(device)\n target = sample['target'].to(device)\n\n target = target[:,:3,...]\n mask = mask.unsqueeze(1).expand(target.shape)\n\n # forward pass\n output = self.model(input)\n \n # compute losses\n # lpips = metrics.lpips(target, output, mask)\n l1 = metrics.l1(target, output, mask)\n # mse = metrics.mse(target, output, mask)\n psnr = metrics.psnr(target, output, mask)\n ssim = metrics.msssim(target, output, mask)\n # fft = metrics.loss_fft(target, output, mask)\n\n # log\n batch_l1 += l1.item()\n # batch_mse += mse.item()\n # batch_lpips += lpips.item()\n batch_psnr += psnr.item()\n batch_ssim += ssim.item()\n # batch_fft += fft.item()\n\n\n # visualize images on tensorboard\n if idx in [0,1] and (epoch+1) % self.cfg.save_every == 0:\n clamped_output = torch.clamp(output, min=0.0, max=1.0) * mask\n target = target * mask\n batch_diff.append(torch.abs(target-clamped_output).cpu())\n batch_clamped_output.append(clamped_output.cpu())\n\n if epoch == 0 and idx in [0,1]:\n batch_target.append(target.cpu())\n # raw_data = sample['raw_data'].cpu()\n # if self.cfg.use_viewdirs:\n # batch_input_viewdirs.append(raw_data[:,1,...])\n # raw_data = raw_data[:,0,...]\n # batch_input_points.append(raw_data)\n\n # log losses\n self.writer.add_scalar('rgb_val_loss',batch_l1/(idx+1),epoch+1)\n # self.writer.add_scalar('rgb_val_mse',batch_mse/(idx+1),epoch+1)\n # self.writer.add_scalar('rgb_val_lpips',batch_lpips/(idx+1),epoch+1)\n self.writer.add_scalar('rgb_val_psnr',batch_psnr/(idx+1),epoch+1)\n self.writer.add_scalar('rgb_val_ssim',batch_ssim/(idx+1),epoch+1)\n # self.writer.add_scalar('val_fft',batch_fft/(idx+1),epoch+1)\n\n # log input and target images only once\n if epoch == 0:\n # batch_input_points = torch.cat(batch_input_points)\n # if batch_input_points.shape[1] == 3:\n # batch_input_points = visualize.vis_cartesian_as_matplotfig(batch_input_points)\n # else:\n # batch_input_points = visualize.vis_spherical_as_matplotfig(batch_input_points)\n\n # if self.cfg.use_viewdirs:\n # batch_input_viewdirs = torch.cat(batch_input_viewdirs)\n # if batch_input_viewdirs.shape[1] == 3:\n # batch_input_viewdirs = visualize.vis_cartesian_as_matplotfig(batch_input_viewdirs)\n # else:\n # batch_input_viewdirs = visualize.vis_spherical_as_matplotfig(batch_input_viewdirs)\n \n # self.writer.add_figure('test_input_points', batch_input_points,epoch+1)\n # if self.cfg.use_viewdirs:\n # self.writer.add_figure('test_input_viewdirs', batch_input_viewdirs,epoch+1)\n self.writer.add_images('rgb_val_target', torch.cat(batch_target), epoch+1)\n \n if (epoch+1) % self.cfg.save_every == 0:\n self.writer.add_images('rgb_val_clamped', torch.cat(batch_clamped_output), epoch+1)\n self.writer.add_images('rgb_val_diff', torch.cat(batch_diff), epoch+1)\n \n return batch_mse/(idx+1)\n\n def start(self):\n print('Start training')\n\n self.writer.add_text('Summary', self.cfg.text_summary, 1)\n least_val_loss = 50.0\n train_loss_at_best_epoch = 0.0\n best_epoch = 0\n best_model = {}\n for epoch in range(self.start_epoch, self.cfg.max_epochs):\n train_loss = self.train(epoch)\n val_loss = self.val(epoch)\n\n # if val_loss <= least_val_loss:\n # least_val_loss = val_loss\n # train_loss_at_best_epoch = train_loss\n # best_epoch = epoch+1\n # best_model['model_state'] = self.model.state_dict()\n # best_model['optimizer_state'] = self.optimizer.state_dict()\n self.scheduler.step()\n\n if (epoch+1) % 20 == 0:\n torch.save({'epoch': epoch+1, 'input_map':self.input_map, 'state_dict': self.model.state_dict(), \n 'optimizer':self.optimizer.state_dict()},\n os.path.join(self.modeldir, '%02d.pth'%(epoch+1)))\n \n # torch.save({'epoch': best_epoch, 'input_map':self.input_map, 'state_dict': best_model['model_state'], \n # 'optimizer':best_model['optimizer_state']},\n # os.path.join(self.modeldir, 'best_model_%02d.pth'%(best_epoch)))\n\n # print('Train loss of best model: ', train_loss_at_best_epoch)\n # print('Val loss of best model: ', least_val_loss)\n # print('Best epoch: ', best_epoch)\n", "sub_path": "src/model/trainer_ConvNet.py", "file_name": "trainer_ConvNet.py", "file_ext": "py", "file_size_in_byte": 15762, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torch.device", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 48, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 49, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 51, "usage_type": "call"}, {"api_name": "model.metrics.l1", "line_number": 55, "usage_type": "attribute"}, {"api_name": "model.metrics", "line_number": 55, "usage_type": "name"}, {"api_name": "model.metrics.lpips", "line_number": 57, "usage_type": "attribute"}, {"api_name": "model.metrics", "line_number": 57, "usage_type": "name"}, {"api_name": "model.metrics.msssim", "line_number": 59, "usage_type": "attribute"}, {"api_name": "model.metrics", "line_number": 59, "usage_type": "name"}, {"api_name": "model.metrics.mse", "line_number": 61, "usage_type": "attribute"}, {"api_name": "model.metrics", "line_number": 61, "usage_type": "name"}, {"api_name": "model.metrics.loss_fft", "line_number": 63, "usage_type": "attribute"}, {"api_name": "model.metrics", "line_number": 63, "usage_type": "name"}, {"api_name": "model.metrics.mse", "line_number": 65, "usage_type": "attribute"}, {"api_name": "model.metrics", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.normal", "line_number": 109, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 122, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 126, "usage_type": "call"}, {"api_name": "model.dataset_loader.DatasetLoad", "line_number": 130, "usage_type": "call"}, {"api_name": "model.dataset_loader", "line_number": 130, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 132, "usage_type": "name"}, {"api_name": "model.dataset_loader.DatasetLoad", "line_number": 135, "usage_type": "call"}, {"api_name": "model.dataset_loader", "line_number": 135, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 137, "usage_type": "name"}, {"api_name": "model.models.ConvNet", "line_number": 147, "usage_type": "call"}, {"api_name": "model.models", "line_number": 147, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 165, "usage_type": "call"}, {"api_name": "model.metrics.l1", "line_number": 193, "usage_type": "call"}, {"api_name": "model.metrics", "line_number": 193, "usage_type": "name"}, {"api_name": "model.metrics.msssim", "line_number": 194, "usage_type": "call"}, {"api_name": "model.metrics", "line_number": 194, "usage_type": "name"}, {"api_name": "torch.clamp", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 270, "usage_type": "call"}, {"api_name": "model.metrics.l1", "line_number": 284, "usage_type": "call"}, {"api_name": "model.metrics", "line_number": 284, "usage_type": "name"}, {"api_name": "model.metrics.psnr", "line_number": 286, "usage_type": "call"}, {"api_name": "model.metrics", "line_number": 286, "usage_type": "name"}, {"api_name": "model.metrics.msssim", "line_number": 287, "usage_type": "call"}, {"api_name": "model.metrics", "line_number": 287, "usage_type": "name"}, {"api_name": "torch.clamp", "line_number": 301, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 303, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 340, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 343, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 344, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 369, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 371, "usage_type": "call"}, {"api_name": "os.path", "line_number": 371, "usage_type": "attribute"}]} +{"seq_id": "31470552", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom itertools import combinations_with_replacement\n\ndef polynomial_features(X, degree): #Get data and the degree in which we transform them\n n_samples, n_features = np.shape(X) #n_samples = columns\n\n def index_combinations(): #Create a combination of the features given for the specified degree\n combs = [combinations_with_replacement(range(n_features), i) for i in range(0, degree + 1)] #Create the combinations object\n flat_combs = [item for sublist in combs for item in sublist] #Append into a list\n return flat_combs\n \n combinations = index_combinations()\n n_output_features = len(combinations) #Number of possible combinations\n X_new = np.empty((n_samples, n_output_features))\n \n for i, index_combs in enumerate(combinations): \n X_new[:, i] = np.prod(X[:, index_combs], axis=1)\n\n return X_new\n\nclass PolynomialRegression(object):\n def __init__(self, degree=16, iterations=10000, alpha=0.1): #Parent constructor\n self.degree = degree #Degree of polynomial function\n self.iterations = iterations #How many times will (w) get updated\n self.alpha = alpha #How much will w get changed with each step\n \n \n def fit(self, X, y): #Fit for basic multivariate regression X = array of features, y = vector of results \n X = polynomial_features(X, degree = self.degree)\n X=np.insert(X, 0, 1, axis=1)\n \n self.training_errors = [] #With each update the new error will be added so that we can analyze the process\n \n limit= 1/math.sqrt(X.shape[1]) #A fancy way to generate a random number using the number of features of X\n self.w = np.array([np.random.uniform(-limit, limit, (X.shape[1]))]) #Create a vector (w) filled with (n) randomized weights\n print(self.w)\n for i in range(self.iterations): #Start iterating\n y_pred = X @ self.w.T #Multiply each row of feature values of X with w to get predicted result y\n mse = np.mean(0.5 * (y - y_pred)**2) #Calculate mean squared error\n mse = self.training_errors.append(mse) #append error\n self.w -= self.alpha / len(X) * np.sum((X @ self.w.T - y) * X, axis=0) #Subtract old (w) with (learning_rate/len) times the partial derivative\n \n def predict(self, X):\n X = polynomial_features(X, degree=self.degree)\n X=np.insert(X, 0, 1, axis=1)\n y_pred = X.dot(self.w.T)\n return y_pred\n \n \nmy_data = np.genfromtxt('00.2_column_data.csv', delimiter=',')\nmy_data = (my_data - my_data.mean())/(my_data.max() - my_data.min()) #feature scaling\n\nX = my_data[:, :len(my_data[0]) - 1].reshape(-1, len(my_data[0]) - 1) # -1 tells numpy to figure out the dimension by itself\ny = my_data[:, len(my_data[0])-1].reshape(-1,1) #Get y\n\npolynomialRegression = PolynomialRegression()\npolynomialRegression.fit(X, y)\n\nprint('------------------Starting Training Error------------------')\nprint(polynomialRegression.training_errors[0])\nprint('--------------------Final Training Error-------------------')\nprint(polynomialRegression.training_errors[-1])\nprint('-----------------------Final Weights-----------------------')\nprint(polynomialRegression.w)\n\n#Create the plot\nplt.scatter(my_data[:, 0].reshape(-1,1), y)\nplt.title('Plynomial Linear Regression')\nplt.xlabel('sq feet')\nplt.ylabel('price')\nplt.plot(X, polynomialRegression.predict(X))\n", "sub_path": "3.polynomial_regression.py", "file_name": "3.polynomial_regression.py", "file_ext": "py", "file_size_in_byte": 4036, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.shape", "line_number": 6, "usage_type": "call"}, {"api_name": "itertools.combinations_with_replacement", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}]} +{"seq_id": "386060661", "text": "import time\nimport pygal\nimport requests\nimport csv\n\nkey = \"http://coinbase.com/api/v1/prices/historical?page=\"\n\nn=69\n\nwhile (len(requests.get(key+str(n)).content) < 5):\n n+=-1\n\nprint('there are %s pages to get')%n\n\nk = n + 1\n\ndata=\"\"\n\ni=1\n\nfor i in range(1,n+1):\n r = requests.get(key+str(n))\n if r.status_code == 200:\n data += '\\n'+str(r.content)\n else:\n data += '\\n API-ERROR'\n n+=1\n \n\nwith open(\"{}_output.txt\".format(int(time.time())), \"a\") as output:\n output.write(data)\n\nprint('%r') % data\n\nchop = csv.reader(data.split())\n\ncount = 0\n\nfor row in chop:\n count+=1\n #points_for_chart = [(row1),(row2),(row3)...]\n print(row)\n\n#this generates an SVG, as long as points_for_chart exists \n#xy_chart = pygal.XY(stroke=False)\n#xy_chart.title = 'BTC'\n#xy_chart.add('BTC Price, points_for_chart)\n#xy_chart.render_to_file(\"{}_Plot.svg\".format(int(time.time())))\n", "sub_path": "Tests/01InitialTests/3.py", "file_name": "3.py", "file_ext": "py", "file_size_in_byte": 912, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "requests.get", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 30, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "580414580", "text": "import random\nimport xlwt\nimport os\nimport json\n\ndirectory = \"/home/tab/桌面/\"\nfile_name = \"test.xls\"\n\n\n# 将预授信的一条数据作为一个类\nclass PersonalPreApproval(object):\n def __init__(self):\n self.curr_path = os.path.dirname(os.path.dirname(__file__))\n order_number = ''\n user_name = ''\n id_card_no = ''\n phone_no = ''\n ex_account_manager_no = ''\n is_credit_pre_grant = ''\n credit_pre_line = ''\n credit_end_date = ''\n is_using_strategy = ''\n is_man_white_list = ''\n man_credit_pre_line = ''\n is_local_resident = ''\n firm_position = ''\n firm_type = ''\n is_regular_EE = ''\n mean_salary = ''\n level = ''\n acm_haf_amount = ''\n acm_sif_amount = ''\n acm_m1_salary = ''\n acm_m2_salary = ''\n acm_m3_salary = ''\n acm_m4_salary = ''\n acm_m5_salary = ''\n acm_m6_salary = ''\n is_payroll_via_us = ''\n firm_population = ''\n firm_has_contract = ''\n firm_name = ''\n age = ''\n gender = ''\n\n @staticmethod\n def ran(capacity):\n return random() * capacity\n\n # 生成随机姓名：Todo(zhouwentao): 随机两个字或者三个字\n @staticmethod\n def get_name():\n family_name_list = '赵钱孙李周吴郑王冯陈褚卫蒋沈韩杨朱秦尤许何吕施张孔曹严华金魏陶姜戚谢邹喻柏水窦章云苏潘葛奚范彭郎鲁韦昌马苗凤花方' \\\n '俞任袁柳酆鲍史唐费廉岑薛雷贺倪汤滕殷罗毕郝邬安常乐于时傅皮卞齐康伍余元卜顾孟平黄和穆萧尹姚邵湛汪祁毛禹狄米贝明臧' \\\n '计伏成戴谈宋茅庞熊纪舒屈项祝董梁杜阮蓝闵席季麻强贾路娄危江童颜郭梅盛林刁钟徐邱骆高夏蔡田樊胡凌霍虞万支柯咎管卢莫' \\\n '经房裘缪干解应宗宣丁贲邓郁单杭洪包诸左石崔吉钮龚程嵇邢滑裴陆荣翁荀羊於惠甄魏加封芮羿储靳汲邴糜松井段富巫乌焦巴弓' \\\n '牧隗山谷车侯宓蓬全郗班仰秋仲伊宫宁仇栾暴甘钭厉戎祖武符刘姜詹束龙叶幸司韶郜黎蓟薄印宿白怀蒲台从鄂索咸籍赖卓蔺屠蒙' \\\n '池乔阴郁胥能苍双闻莘党翟谭贡劳逄姬申扶堵冉宰郦雍却璩桑桂濮牛寿通边扈燕冀郏浦尚农温别庄晏柴瞿阎充慕连茹习宦艾鱼容' \\\n '向古易慎戈廖庚终暨居衡步都耿满弘匡国文寇广禄阙东殴殳沃利蔚越夔隆师巩厍聂晁勾敖融冷訾辛阚那简饶空曾毋沙乜养鞠须丰' \\\n '巢关蒯相查后江红游竺权逯盖益桓公万俟司马上官欧阳夏侯诸葛闻人东方赫连皇甫尉迟公羊澹台公冶宗政濮阳淳于仲孙太叔申屠' \\\n '公孙乐正轩辕令狐钟离闾丘长孙慕容鲜于宇文司徒司空亓官司寇仉督子车颛孙端木巫马公西漆雕乐正壤驷公良拓拔夹谷宰父谷粱' \\\n '晋楚阎法汝鄢涂钦段干百里东郭南门呼延归海羊舌微生岳帅缑亢况后有琴梁丘左丘东门西门商牟佘佴伯赏南宫墨哈谯笪年爱阳佟' \\\n '第五言福'\n last_name_list = '秀 娟 英 华 慧 巧 美 娜 静 淑 惠 珠 翠 雅 芝 玉 萍 红 娥 玲 芬 芳 燕 彩 春 菊 兰 凤 洁 梅 琳 素 云 莲 真 环' \\\n '雪 荣 爱 妹 霞 香 月 莺 媛 艳 瑞 凡 佳 嘉 琼 勤 珍 贞 莉 桂 娣 叶 璧 璐 娅 琦 晶 妍 茜 秋 珊 莎 锦 黛 青 倩' \\\n '婷姣婉娴瑾颖露瑶怡婵雁蓓纨仪荷丹蓉眉君琴蕊薇菁梦岚苑婕馨瑗琰韵融园艺咏卿聪澜纯毓悦昭冰爽琬茗羽希宁欣飘育滢馥筠柔竹霭' \\\n '凝晓欢霄枫芸菲寒伊亚宜可姬舒影荔枝思丽'\n\n while(True):\n m_key = random.randint(0, len(last_name_list) - 1)\n l_key = random.randint(0, len(last_name_list) - 1)\n if last_name_list[l_key] == ' ' and last_name_list[m_key] == ' ':\n continue\n elif last_name_list[l_key] == ' ':\n name = family_name_list[random.randint(0, len(last_name_list)-1)] + last_name_list[m_key]\n return name\n elif last_name_list[m_key] == ' ':\n name = family_name_list[random.randint(0, len(last_name_list) - 1)] + last_name_list[l_key]\n return name\n else:\n name = family_name_list[random.randint(0, len(last_name_list) - 1)] + last_name_list[m_key] + \\\n last_name_list[l_key]\n return name\n\n @staticmethod\n def get_gender():\n gender_code = random.randint(0, 9)\n return gender_code\n\n @staticmethod\n def get_age():\n birth_year = random.randint(1958, 2007)\n birth_month = random.randint(1, 12)\n if birth_month in [1, 3, 5, 7, 8, 10, 12]:\n birth_date = random.randint(1, 31)\n elif birth_month in [4, 6, 9, 11]:\n birth_date = random.randint(1, 30)\n else:\n if (birth_year % 4 == 0) and (birth_year % 400 != 0):\n birth_date = random.randint(1, 29)\n else:\n birth_date = random.randint(1, 28)\n\n return str(birth_year) + str(\"%02d\" % birth_month) + str(\"%02d\" % birth_date)\n\n def get_identification(self):\n region_f = open(self.curr_path + '/material/region.txt', encoding='utf-8')\n region_no = random.choice(region_f.readlines())\n birth_date = self.get_age()\n self.gender = self.get_gender()\n police_station_id = random.randint(1, 99)\n\n# 取一个多位数各位的值的方法：依次执行 i % 10,i/10从个位向前取\n# birth_year_copy = birth_year\n# region_no_copy = region_no\n# region = [1, 2, 3, 4, 5, 6]\n# year = [1, 2, 3, 4]\n# for i in [0, 1, 2, 3]:\n# year[i] = birth_year_copy % 10\n# birth_year_copy = int(birth_year_copy / 10)\n# for j in [0, 1, 2, 3, 4, 5]:\n# region[j] = int(region_no_copy) % 10\n# region_no_copy = int(region_no_copy / 10)\n# ai = region[0] * 4 + region[1] * 8 + region[2] * 5 + region[3] * 10 + region[4] * 9 + region[5] * 7 + \\\n# year[0]*3 + year[1]*6 + year[2]*1 + year[3]*2 + (birth_month % 10) * 9 + \\\n# int(birth_month/10) * 7 + (birth_date % 10) * 5 + int(birth_date/10) * 10 + \\\n# (police_station_id % 10) * 4 + int(police_station_id/10) * 8 + self.gender * 2\n\n id_without_last = str(region_no).strip() + birth_date + \"%02d\" % police_station_id + str(self.gender)\n k = [7, 9, 10, 5, 8, 4, 2, 1, 6, 3, 7, 9, 10, 5, 8, 4, 2]\n ai = 0\n for i in range(len(k)):\n ai += int(id_without_last[i]) * k[i]\n y = ai % 11\n c = {0: '1', 1: '0', 2: 'X', 3: '9', 4: '8', 5: '7', 6: '6', 7: '5', 8: '4', 9: '3', 10: '2'}\n return str(id_without_last) + c[y]\n #\n # return str(region_no) + str(birth_year) + \\\n # str(\"%02d\" % birth_month) + str(\"%02d\" % birth_date) + \"%02d\" % police_station_id + \\\n # str(self.gender) + c[y]\n\n @staticmethod\n def get_phone_no(operator_index=['dx', 'lt', 'yd']):\n dx = ['133', '149', '153', '173', '177', '180', '181', '189', '199']\n lt = ['130', '131', '132', '145', '155', '156', '166', '171', '175', '176', '185', '186']\n yd = ['134', '135', '136', '137', '138', '139', '147', '150', '151', '152', '157', '158', '159', '178',\n '182', '183', '184', '187', '188', '198']\n operator_list = []\n for i in operator_index:\n if i == 'dx':\n operator_list = operator_list + dx\n elif i == 'lt':\n operator_list = operator_list + lt\n elif i == 'yd':\n operator_list = operator_list + yd\n index = random.randint(0, len(operator_list)-1)\n operator = operator_list[index]\n return str(operator) + str(\"%08d\" % random.randint(1, 99999999))\n\n def get_bank_card(self, bank: str = None) -> str:\n def _rand_digits(length):\n return random.randint(10**(length-1), 10**length-1)\n with open(self.curr_path + '/material/bank.json', encoding='utf-8') as f:\n bank_json = json.load(f)\n sum = 0\n if bank is None:\n head = '6'\n no = head + str(random.randint(10000000000000, 99999999999999))\n else:\n head = bank_json[bank]['BIN']\n no = head + str(_rand_digits(bank_json[bank]['cardNoLength'] - len(head) - 1))\n no_list = ' '.join(no).split(' ')\n no_list.reverse()\n for i in range(0, len(no_list), 2):\n no_list[i] = str(int(no_list[i]) * 2)\n caled_no_list = ' '.join(''.join(no_list)).split(' ')\n for digit in caled_no_list:\n sum += int(digit)\n if sum % 10 == 0:\n verify = 0\n else:\n verify = 10 - (sum % 10)\n return no + str(verify)\n\n\n# 创建表头\nworkbook = xlwt.Workbook(encoding='utf-8')\nworksheet = workbook.add_sheet('预授信白名单样例')\ntitle = ['序号', '姓名', '身份证号', '手机号', '客户经理号（个贷系统）', '是否已预授信', '已预授信额度', '授信结束时间',\n '是否使用系统风控', '是否直接加入白名单', '直接预授信额度', '是否常住本地', '职业', '单位性质', '是否正式员工', '平均月收入',\n '职务级别', '公积金', '社保缴费金额', '过去第1个月实发工资', '过去第2个月实发工资', '过去第3个月实发工资',\n '过去第4个月实发工资', '过去第5个月实发工', '过去第6个月实发工资', '工资是否本行代发', '所在单位人数规模',\n '所在单位是否出具相关书面承诺', '单位名称', '年龄', '性别']\ntitle_en = ['order number', 'username', 'idcardNo', 'phoneNo', 'exAccountManagerNo', 'isCreditPreGrant',\n 'creditPreLine', 'creditEndDate（yyyy-mm-dd）', 'isUsingStrategy', 'isManWhiteList', 'manCreditPreLine',\n 'isLocalResident', 'firmPosition', 'firmType', 'isRegularEE', 'meanSalary', 'level', 'acm_haf_amount',\n 'acm_sif_amount', 'acm_m1_salary', 'acm_m2_salary', 'acm_m3_salary', 'acm_m4_salary', 'acm_m5_salary',\n 'acm_m6_salary', 'isPayrollViaUs', 'firmPopulation', 'firmHasContract', 'firmName', 'age', 'gender']\n\nfor title_column in range(0, len(title)):\n worksheet.write(0, title_column, title[title_column])\n worksheet.write(1, title_column, title_en[title_column])\n\n\nfor line in range(1, 3):\n ppa = PersonalPreApproval()\n ppa.order_number = line\n ppa.user_name = ppa.get_name()\n ppa.id_card_no = ppa.get_identification()\n ppa.phone_no = ppa.get_phone_no()\n ppa.gender = ppa.get_gender()\n ppa.age = str(2019 - int(ppa.get_age()[0: 4]))\n worksheet.write(line + 1, 0, ppa.order_number)\n worksheet.write(line + 1, 1, ppa.user_name)\n worksheet.write(line + 1, 2, ppa.id_card_no)\n worksheet.write(line + 1, 3, ppa.phone_no)\n worksheet.write(line + 1, 29, ppa.age)\n if ppa.gender % 2 == 1:\n worksheet.write(line + 1, 30, '男')\n else:\n worksheet.write(line + 1, 30, '女')\n personal_info = ['', '是', '60000', '2019-11-30', '否', '是', '60000', '是', '专业技术人员', '事业单位', '是', '10000',\n '其他', '1520', '606.86', '17620.5', '6329.51', '11720.77', '20584.69', '11479.71', '11923.81',\n '否', '小于100大于等于50', '是', '遵义市农业科学研究院']\n column = 4\n for personal_info_value in personal_info:\n worksheet.write(line + 1, column, personal_info_value)\n column += 1\n\nworkbook.save(directory + file_name)\n\n\n\n\n", "sub_path": "application/preapproval.py", "file_name": "preapproval.py", "file_ext": "py", "file_size_in_byte": 11902, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 70, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 71, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 75, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 78, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 81, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 87, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 92, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 93, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 95, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 97, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 100, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 102, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 108, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 111, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 156, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 158, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 162, "usage_type": "call"}, {"api_name": "json.load", "line_number": 164, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 168, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 187, "usage_type": "call"}]} +{"seq_id": "189065523", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 6 15:44:05 2018\n\n@author: Labvis\n\"\"\"\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 16 13:23:32 2017\n\n@author: Labvis\n\"\"\"\nimport matplotlib.pyplot as plt\nfrom skimage.io import imread\nfrom skimage.color import rgb2grey \nfrom skimage import filters\nimport numpy as np\nfrom sklearn.cluster import KMeans\nfrom nltk.cluster.kmeans import KMeansClusterer\nfrom skimage import exposure\nfrom kmedias import kmedias\n\n\n#Leitura das imagens\ncameraman = imread('C:/Users/Labvis/Dropbox/mestrado/Processamento Digital de Imagens/Trabalho1/cameraman512.jpg')\n\n#Converte a imagem para níveis de cinza\nlena = rgb2grey(np.asarray(cameraman))*np.max(cameraman)\n\n\n\n#Vetoriza a Imagem\nimage_vector = np.reshape(lena, (1,lena.shape[0]*lena.shape[1]))\n\n#Plota o Histograma\nplt.figure(1)\nplt.xlabel('Níveis de Cinza')\nplt.ylabel('Frequência de Ocorrência')\nplt.title('Histograma')\n\nhist, bins_center = exposure.histogram(lena)\nplt.plot(bins_center, hist)\n\n\n#Calcula o kmedias\npoints,clusters,C = kmedias(image_vector.T,2,'euclidean',10)\n\n\nvector_seg2 = clusters.T\n\n#Transforma o vetor de labels na imagem segmentada\nimage_seg2 = np.reshape(vector_seg2,(lena.shape[0],lena.shape[1]))\n\n#plota a Imagem segmentada pelo kmedias\nplt.figure(2)\nplt.axis('off')\nplt.imshow(image_seg2,cmap = \"gray\")\n\n\n#Calula o limiar do Otsu\nval = filters.threshold_otsu(lena)\n\n#Plota a imagem segmentada pelo otsu\nplt.figure(3)\nplt.axis('off')\nplt.imshow(lena {}'.format(\n state.best_val_ppl, valid_lm_ppl))\n logger.info('- Epoch: {} -> {}'.format(\n state.best_epoch + 1, epoch + 1))\n state.best_val_ppl = valid_lm_ppl\n state.best_epoch = epoch\n ckpt_path = os.path.join(opt_lm.output_dir, \"best_model.ckpt\")\n state_path = os.path.join(opt_lm.output_dir, \"best_state.json\")\n logger.info('- Saving best model to {}'.format(ckpt_path))\n saver.save(sess, ckpt_path)\n with open(state_path, 'w') as ofp:\n json.dump(vars(state), ofp)\n else:\n logger.info('- No improvement!')\n done_training = update_lr(opt_lm, state)\n ckpt_path = os.path.join(opt_lm.output_dir, \"latest_model.ckpt\")\n state_path = os.path.join(opt_lm.output_dir, \"latest_state.json\")\n logger.info('End of epoch {}: '.format(\n epoch + 1))\n logger.info('- Saving model to {}'.format(ckpt_path))\n logger.info('- Epoch time: {}s'.format(time.time() - epoch_time))\n saver.save(sess, ckpt_path)\n with open(state_path, 'w') as ofp:\n json.dump(vars(state), ofp)\n if done_training:\n break\n logger.debug('Updated state:\\n{}'.format(state.__repr__()))\n logger.info('Done training at epoch {}'.format(state.epoch + 1))\n\nif __name__ == \"__main__\":\n global_time = time.time()\n parser = common_utils.get_common_argparse()\n parser.add_argument('--af_mode', type=str, default='gated_state',\n help='additional feature module type')\n parser.add_argument('--def_file', type=str,\n default='t_features.pickle',\n help=('token feature files '\n '(see data_utils.map_vocab_defs)'))\n parser.add_argument('--num_def_samples', type=int, default=64,\n help=('Number of definitions to sample for each batch '\n 'of text (batch size of DM)'))\n parser.add_argument('--lm_burnin', type=int, default=1,\n help=('Number of epochs to run LM before starting DM.'))\n args = parser.parse_args()\n opt_lm = common_utils.Bunch.default_model_options()\n opt_lm.update_from_ns(args)\n opt_dm = common_utils.Bunch.default_model_options()\n opt_dm.update_from_ns(args)\n opt_dm.af_function = 'ex_emb'\n opt_dm.data_dir = \"data/ptb_defs/wordnet/preprocess/\"\n opt_dm.batch_size = opt_dm.num_def_samples\n # With GPU, this will slow us down.\n # A proper weights to the loss function is enough to get correct gradients\n # opt_dm.varied_len = True\n opt_dm.reset_state = True\n logger = common_utils.get_logger(opt_lm.log_file_path)\n if opt_lm.debug:\n logger.setLevel(logging.DEBUG)\n else:\n logger.setLevel(logging.INFO)\n logger.info('Configurations:\\n{}'.format(opt_dm.__repr__()))\n main(opt_lm, opt_dm)\n logger.info('Total time: {}s'.format(time.time() - global_time))\n", "sub_path": "train_joint_lm_dm.py", "file_name": "train_joint_lm_dm.py", "file_ext": "py", "file_size_in_byte": 11331, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "random.seed", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tensorflow.set_random_seed", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.trainable_variables", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.gradients", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_global_norm", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.get_variable", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.zeros_initializer", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.train.GradientDescentOptimizer", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 36, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 83, "usage_type": "call"}, {"api_name": "time.time", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "data_utils.Vocabulary.from_vocab_file", "line_number": 95, "usage_type": "call"}, {"api_name": "data_utils.Vocabulary", "line_number": 95, "usage_type": "attribute"}, {"api_name": "data_utils.Vocabulary.from_vocab_file", "line_number": 98, "usage_type": "call"}, {"api_name": "data_utils.Vocabulary", "line_number": 98, "usage_type": "attribute"}, {"api_name": "cPickle.load", "line_number": 102, "usage_type": "call"}, {"api_name": "data_utils.TokenFeatureIterator", "line_number": 105, "usage_type": "call"}, {"api_name": "data_utils.DataIterator", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform_initializer", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 124, "usage_type": "call"}, {"api_name": "lm.sharded_variable", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 130, "usage_type": "call"}, {"api_name": "lm.LM", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 133, "usage_type": "call"}, {"api_name": "lm.LM", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 136, "usage_type": "call"}, {"api_name": "lm.LMwAF", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.trainable_variables", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 147, "usage_type": "attribute"}, {"api_name": "common_utils.get_initial_training_state", "line_number": 148, "usage_type": "call"}, {"api_name": "common_utils.SUN_BRO", "line_number": 152, "usage_type": "call"}, {"api_name": "time.time", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.assign", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 194, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 197, "usage_type": "call"}, {"api_name": "time.time", "line_number": 204, "usage_type": "call"}, {"api_name": "common_utils.get_common_argparse", "line_number": 205, "usage_type": "call"}, {"api_name": "common_utils.Bunch.default_model_options", "line_number": 218, "usage_type": "call"}, {"api_name": "common_utils.Bunch", "line_number": 218, "usage_type": "attribute"}, {"api_name": "common_utils.Bunch.default_model_options", "line_number": 220, "usage_type": "call"}, {"api_name": "common_utils.Bunch", "line_number": 220, "usage_type": "attribute"}, {"api_name": "common_utils.get_logger", "line_number": 229, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 231, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 233, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 236, "usage_type": "call"}]} +{"seq_id": "587664214", "text": "# filter warnings\nimport warnings\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\n\n# keras imports\nfrom keras.applications.vgg16 import VGG16, preprocess_input\nfrom keras.applications.vgg19 import VGG19, preprocess_input\nfrom keras.applications.xception import Xception, preprocess_input\nfrom keras.applications.resnet50 import ResNet50, preprocess_input\nfrom keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input\nfrom keras.applications.mobilenet import MobileNet, preprocess_input\nfrom keras.applications.inception_v3 import InceptionV3, preprocess_input\nfrom keras.preprocessing import image\nfrom keras.models import Model\nfrom keras.models import model_from_json\nfrom keras.layers import Input\n\n# other imports\nfrom sklearn.preprocessing import LabelEncoder\nimport numpy as np\nimport glob\n#import cv2\nimport h5py\nimport os\nimport json\nimport datetime\nimport time\n\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\nimport pickle\nimport logging\nfrom skimage import color, exposure, transform\nimport pandas as pd\nfrom skimage import io\nfrom sklearn.model_selection import StratifiedKFold\nfrom keras import backend as K\nK.set_image_data_format('channels_last')\nfrom keras import metrics\nfrom keras.optimizers import SGD\nfrom keras.callbacks import LearningRateScheduler, ModelCheckpoint\nfrom keras.models import load_model\n\n# load the user configs\nwith open('conf.json') as f:\n\tconfig = json.load(f)\n\n# config variables\nmodel_name \t\t= config[\"model\"]\nweights \t\t= config[\"weights\"]\ninclude_top \t= config[\"include_top\"]\ntrain_path \t\t= config[\"train_path\"]\nfeatures_path \t= config[\"features_path\"]\nlabels_path \t= config[\"labels_path\"]\ntest_size \t\t= config[\"test_size\"]\nresults \t\t= config[\"results\"]\nmodel_path \t\t= config[\"model_path\"]\nseed \t\t= config[\"seed\"]\nclassifier_path = config[\"classifier_path\"]\nlog_path\t\t= config[\"log_path\"]\n\n#Corleone\ncode_path=\"/home/drobert/tfg/traffic_sign_machine_learning/vgg19/\"\ndataset_path='/home/drobert/tfg/'\n\nfichero_log = (log_path)\n\nprint('Archivo Log en ', fichero_log)\nlogging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s : %(levelname)s : %(message)s',\n filename = fichero_log,\n filemode = 'a',)\n# start time\nprint (\"[STATUS] -------------vgg19 from scratch - start time - {}\".format(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")))\nlogging.info(\" -------------vgg19 from scratch - start time - {}\".format(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")))\nstart = time.time()\n\n# create the pretrained models\n# check for pretrained weight usage or not\n# check for top layers to be included or not\nNUM_CLASSES = 43\nIMG_SIZE = 299\n\n\n\nmodel = VGG19(include_top=True,classes=1000 )\n\n# Funcion para preprocesar las imagenes\ndef preprocess_img(img):\n # normalizacion del histograma en el canal 'v'\n hsv = color.rgb2hsv(img)\n hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])\n img = color.hsv2rgb(hsv)\n\n # recorte del cuadrado central\n min_side = min(img.shape[:-1])\n centre = img.shape[0] // 2, img.shape[1] // 2\n img = img[centre[0] - min_side // 2:centre[0] + min_side // 2,\n centre[1] - min_side // 2:centre[1] + min_side // 2,\n :]\n\n # reescalado de imagenes a tamaño standard\n img = transform.resize(img, (IMG_SIZE, IMG_SIZE), mode='constant')\n\n return img\n\ndef get_class(img_path):\n return int(img_path.split('/')[-2])\n\n\n\n\n\nos.chdir(dataset_path) #direccion local Jupyter Notebooks/pycharm\n#root_dir = 'GTSRB/Final_Training/Images/'\nroot_dir = train_path\n#os.chdir('/home/drobert/tfg/')#direccion en corleone\n#root_dir = 'GTSRB/Final_Training/Images/'\n\n\nimgs = []\nlabels = []\n\nruta_actual = os.getcwd()\nprint(ruta_actual)\n\nall_img_paths = glob.glob(os.path.join(root_dir, '*/*.ppm'))\n\nprint(os.path.join(root_dir, '*/*.ppm'))\nprint(len(all_img_paths))\n\nnp.random.shuffle(all_img_paths)\n\nfor img_path in all_img_paths:\n img = preprocess_img(io.imread(img_path))\n label = get_class(img_path)\n imgs.append(img)\n labels.append(label)\n\nX = np.array(imgs, dtype='float32')\nY = np.asarray(labels)\n\nprint(X.shape)\nprint(Y.shape)\n\nlogging.info(X.shape)\nlogging.info(Y.shape)\n# In[4]:\n\n# Vamos a hacer cross validation con nuestro conjunt de test.\n# En concreto vamos a hacer un Kfold con 10 splits estratificado,\n# de tal manera que cada conjunto tenga aproximadamente el mismo porcentaje\n# de muestras de cada clase que el conjunto de entrenamiento.\n\ntraining_history_list = []\nval_accuracy_list = []\n\nconfusion_matrix_list = []\nclf_list = []\nfilename_clf_list = []\n\n#Contador para saber en que fold estamos\nfold = 1\n\nskf = StratifiedKFold(n_splits=3) # numero de 'trozos' en los que dividimos el dataset de entrenamiento\nprint(skf)\nlogging.info(skf)\n\n\ndef lr_schedule(epoch):\n return lr * (0.1 ** int(epoch / 10))\n\n#Me daba un error.\n#https://stackoverflow.com/questions/46305252/valueerror-dimension-1-must-be-in-the-range-0-2-in-keras\ndef get_categorical_accuracy_keras(y_true, y_pred):\n return K.mean(K.equal(K.argmax(y_true, axis=1), K.argmax(y_pred, axis=1)))\n\nbatch_size = 32\nepochs = 20 #ponemos 5 para que sea mas rapido, normalmente 30\nlr = 0.01\n\nfor train_index, test_index in skf.split(X, Y):\n # conjuntos de train y test(validacion) para cada fold\n x_train, x_test = X[train_index], X[test_index]\n y_train_no_one_hot, y_test_no_one_hot = Y[train_index], Y[test_index]\n\n # Make one hot targets\n y_train = np.eye(NUM_CLASSES, dtype='uint8')[y_train_no_one_hot]\n y_test = np.eye(NUM_CLASSES, dtype='uint8')[y_test_no_one_hot]\n\n #one hot encodig con to_categorical\n #dummy_y = np_utils.to_categorical(y_train_no_one_hot, NUM_CLASSES)\n #dummy_y = np_utils.to_categorical(y_test_no_one_hot, NUM_CLASSES)\n\n\n\n classifier = model\n\n # vamos a entrenar nuestro modelo con SGD + momentum\n sgd = SGD(lr=lr, decay=1e-6, momentum=0.9, nesterov=True)\n classifier.compile(loss='categorical_crossentropy',\n optimizer=sgd,\n #metrics=['accuracy'])\n metrics=[metrics.categorical_accuracy])\n #metrics=[get_categorical_accuracy_keras])#unico que funciona\n\n print(\"tamaños de x_train e y_train\")\n print(x_train.shape)\n print(y_train.shape)\n\n filepath = code_path+\"vgg19-fold\"+str(fold)+\"-epochs\"+str(epochs)+\".h5\"\n\n hist = classifier.fit(x_train, y_train,\n batch_size=batch_size,\n epochs=epochs,\n validation_split=0.2,\n verbose=1,\n callbacks=[LearningRateScheduler(lr_schedule)]\n\n )\n\n\n #Guardar training / validation loss/accuracy en cada epoch\n training_history_list.append(hist.history)\n #print(\"history:\")\n #print(hist.history)\n #logging.info(\"history:\")\n #logging.info(hist.history)\n\n\n val_accuracy = classifier.evaluate(x_test, y_test, verbose=1)\n\n print(\"%s: %.2f%%\" % (classifier.metrics_names[1], val_accuracy[1] * 100))\n logging.info(\"%s: %.2f%%\" % (classifier.metrics_names[1], val_accuracy[1] * 100))\n\n val_accuracy_list.append(val_accuracy[1] * 100)\n\n\n #y_pred = classifier.predict_classes(x_test)\n #test_accuracy = np.sum(y_pred == y_test) / np.size(y_pred)\n\n\n print(\"loss y val accuracy del fold \"+str(fold)+\" :\"+str(val_accuracy))\n logging.info(\"loss y val accuracy del fold \"+str(fold)+\" :\"+str(val_accuracy))\n\n\n\n clf_list.append(classifier) # lista de cada uno de los los clasificadores\n\n #NO hacemos un pickle porque ya lo guardaos en formato h5\n fold = fold +1\n\n\n\nprint('lista de accuracys de los modelos: '+str(val_accuracy_list))\nlogging.info('lista de accuracys de los modelos: '+str(val_accuracy_list))\n\nprecision_media = (np.mean(val_accuracy_list))\ndesviacion_standar = (np.std(val_accuracy_list))\n\n\nprint(\"mean_accuarcy: %.2f%% (+/- %.2f%%)\" % (np.mean(val_accuracy_list), np.std(val_accuracy_list)))\nlogging.info(\"mean_accuarcy: %.2f%% (+/- %.2f%%)\" % (np.mean(val_accuracy_list), np.std(val_accuracy_list)))\n\n\nruta_actual = os.getcwd()\n#print(ruta_actual)\n#print(os.listdir(ruta_actual))\nos.chdir(dataset_path+'/GTSRB')#En local\n#os.chdir('/home/drobert/tfg/GTSRB')#En corleone\n\n# Cargamos el archivo csv con los datos de test y vemos que contienen los 10 primeros\ntest = pd.read_csv('GT-final_test.csv', sep=';')\n#test.head(10)\n\n# In[61]:\n\n# Cargamos el dataset de test\nos.chdir(dataset_path+'/GTSRB/Final_Test/Images/')#en local\n#os.chdir('/home/drobert/tfg/GTSRB/Final_Test/Images/')#en corleone\n\nX_test = []\ny_test = []\ni = 0\n\nfor file_name, class_id in zip(list(test['Filename']), list(test['ClassId'])):\n # img_path = os.path.join('GTSRB/Final_Test/Images/', file_name)\n img_path = os.path.join(os.getcwd(), file_name)\n X_test.append(preprocess_img(io.imread(img_path)))\n y_test.append(class_id)\n\nX_test = np.array(X_test)\ny_test = np.array(y_test)\n\n\n#Los targets tienen que estar en formato one target\ny_test_one_target = np.eye(NUM_CLASSES, dtype='uint8')[y_test]\n\n# Función para encontrar el modelo que está mas proximo a la media\ndef modelo_medio_indx(final, numeros):\n def el_menor(numeros):\n menor = numeros[0]\n retorno = 0\n for x in range(len(numeros)):\n if numeros[x] < menor:\n menor = numeros[x]\n retorno = x\n return retorno\n\n diferencia = []\n for x in range(len(numeros)):\n diferencia.append(abs(final - numeros[x]))\n # devuelve el indice del modelo más próximo a la media\n return numeros.index(numeros[el_menor(diferencia)])\n\n\n\nprint(\"precision media: \"+str(precision_media))\nlogging.info(\"precision media: \"+str(precision_media))\n\nmodel_indx = modelo_medio_indx(precision_media, val_accuracy_list)\n\nprint(\"indice del modelo medio: \"+str(model_indx))\nlogging.info(\"indice del modelo medio: \"+str(model_indx))\n\n# cargamos el modelo medio de disco\nos.chdir(code_path)\nbest_model =clf_list[model_indx]\n\ntest_accuracy = best_model.evaluate(X_test, y_test_one_target, verbose=1)\n\n#Guardar best_model en un pickle\n\n\ntoday_date = datetime.date.today().strftime(\"%d-%m-%Y\")\n\nbest_model_filename= (\"vgg19_epochs%s_test_acc_%.2f%%_%s.h5\" % (epochs,test_accuracy[1] * 100, today_date))\n\n#pickle.dump(best_model, open((code_path + str(best_model_filename)), 'wb'))\n\n#guardar con h5 no funciona por tener un metodo custom de accuracy\nbest_model.save(best_model_filename)\n\nprint(\"Accuracy en test : %s: %.2f%%\" % (best_model.metrics_names[1], test_accuracy[1] * 100))\n\nlogging.info(\"Accuracy en test : %s: %.2f%%\" % (best_model.metrics_names[1], test_accuracy[1] * 100))\n\n\n#Comprobamos que el modelo cargado tiene la misma precision\n\n#loaded_model = pickle.load(open(best_model_filename, 'rb'))\nloaded_model = load_model(best_model_filename)# No funciona con custom metrics\n\nloaded_model_test_accuracy = loaded_model.evaluate(X_test, y_test_one_target, verbose=1)\nprint(\"Loaded_model accuracy en test : %s: %.2f%%\" % (loaded_model.metrics_names[1], loaded_model_test_accuracy[1] * 100))\n#https://github.com/keras-team/keras/issues/3911\n#La solucion propuesta arriba tampoco funciona\n\n#loaded_model = load_model('best_model_filename', custom_objects={'get_categorical_accuracy_keras': get_categorical_accuracy_keras})\n#loaded_model_test_accuracy = loaded_model.evaluate(X_test, y_test_one_target, verbose=1)\n\n# Una técnica muy útil para visualizar el rendimiento de nuestro algoritmo es\n# la matriz de confusión. y la mostramos de varia formas. Solo mostramos\n# la matriz de confusion del modelo medio.\n\n#Para generar la matriz de confusión necesitamos los targets en formato lista\n#No en one hot encoding.\n\n\ny_pred = loaded_model.predict(X_test)\n#pasamos a one hot encoding para que tenga la misma estructura que y_pred\n#No funciona así, tendran que ser los 2 vectores unidimensionales\n#y_test_one_hot = to_categorical(y_test, NUM_CLASSES)\n\n#pasamos y_pred que esta en one hot encoding a un vector plano\ny_pred_no_one_hot= np.argmax(y_pred, axis=1, out=None)\n\nprint(\"shape de y_test , y_pred_no_one_hot :\")\n\nprint(y_test.shape)\nprint(y_pred_no_one_hot.shape)\n\n\n\ncm = pd.DataFrame(confusion_matrix(y_test, y_pred_no_one_hot))\n\n#logging.info(\"matriz de confusión del modelo medio: \")\n#logging.info(cm)\n\n\nprint(\"Fin de la prueba con vgg19 from scratch\")\nlogging.info(\"-----------Fin de la prueba con vgg19 from scratch-----------\")\nlogging.info(\"program ended on - \" + str(datetime.datetime.now))\n\n\n", "sub_path": "vgg19/vgg19_from_scratch.py", "file_name": "vgg19_from_scratch.py", "file_ext": "py", "file_size_in_byte": 12613, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "warnings.simplefilter", "line_number": 3, "usage_type": "call"}, {"api_name": "keras.backend.set_image_data_format", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 42, "usage_type": "name"}, {"api_name": "json.load", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 73, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 79, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.applications.vgg19.VGG19", "line_number": 90, "usage_type": "call"}, {"api_name": "skimage.color.rgb2hsv", "line_number": 95, "usage_type": "call"}, {"api_name": "skimage.color", "line_number": 95, "usage_type": "name"}, {"api_name": "skimage.exposure.equalize_hist", "line_number": 96, "usage_type": "call"}, {"api_name": "skimage.exposure", "line_number": 96, "usage_type": "name"}, {"api_name": "skimage.color.hsv2rgb", "line_number": 97, "usage_type": "call"}, {"api_name": "skimage.color", "line_number": 97, "usage_type": "name"}, {"api_name": "skimage.transform.resize", "line_number": 107, "usage_type": "call"}, {"api_name": "skimage.transform", "line_number": 107, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 118, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 128, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 136, "usage_type": "attribute"}, {"api_name": "skimage.io.imread", "line_number": 139, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 139, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 145, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 150, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 151, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 169, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 171, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 180, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 180, "usage_type": "name"}, {"api_name": "keras.backend.equal", "line_number": 180, "usage_type": "call"}, {"api_name": "keras.backend.argmax", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 193, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 204, "usage_type": "call"}, {"api_name": "keras.metrics.categorical_accuracy", "line_number": 208, "usage_type": "attribute"}, {"api_name": "keras.metrics", "line_number": 208, "usage_type": "name"}, {"api_name": "keras.callbacks.LearningRateScheduler", "line_number": 222, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 238, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 248, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 266, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 267, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 270, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 273, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 277, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 283, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 292, "usage_type": "call"}, {"api_name": "os.path", "line_number": 292, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 292, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 293, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 293, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 301, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 323, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 328, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 331, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 339, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 339, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 350, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 380, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 389, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 389, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 396, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 397, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 397, "usage_type": "attribute"}]} +{"seq_id": "82739887", "text": "\nfrom sklearn.metrics import confusion_matrix, accuracy_score\n\n__author__ = 'dh8835'\n__email__ = 'dasha.herrmannova@open.ac.uk'\n\n\nclass Metrics(object):\n\n @staticmethod\n def metrics(pred, y):\n \"\"\"\n :param pred:\n :param y:\n :return:\n \"\"\"\n tn, fp, fn, tp = confusion_matrix(y, pred).ravel()\n return {\n 'n_samples': len(y),\n 'correct': int(tn + tp),\n 'accuracy': round(100 * accuracy_score(y, pred), 4),\n 'tn': int(tn),\n 'tp': int(tp),\n 'fn': int(fn),\n 'fp': int(fp)\n }\n", "sub_path": "WP3/Task3.3/src/evaluation/metrics.py", "file_name": "metrics.py", "file_ext": "py", "file_size_in_byte": 606, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "sklearn.metrics.confusion_matrix", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "325816988", "text": "#!/usr/bin/env python3\nfrom pydarknet import Detector, Image\nimport argparse\nimport cv2\nimport os\n\nimport time\nfrom watchdog.observers import Observer\nfrom watchdog.events import FileSystemEventHandler\n\nclass Watcher:\n def __init__(self):\n self.DIRECTORY_TO_WATCH = './input'\n print('Watching \\'%s\\' directory for images' % self.DIRECTORY_TO_WATCH)\n self.observer = Observer()\n\n def run(self):\n event_handler = Handler()\n self.observer.schedule(event_handler, self.DIRECTORY_TO_WATCH, recursive=True)\n self.observer.start()\n try:\n while True:\n time.sleep(5)\n except:\n self.observer.stop()\n print(\"Error\")\n\n self.observer.join()\n\n\nclass Handler(FileSystemEventHandler):\n @staticmethod\n def on_any_event(event):\n if event.is_directory:\n return None\n\n elif(event.event_type == 'created' and\n event.src_path.endswith(('.png', '.jpg', '.jpeg'))):\n print(\"Received image file %s\" % event.src_path)\n process_image(event.src_path)\n\n\ndef process_image(image_path):\n output_dat = './dat/' + image_path.split('/')[-1].split('.')[0] + '.dat'\n output_img = './output/' + image_path.split('/')[-1]\n img = cv2.imread(image_path)\n img2 = Image(img)\n img_height = float(img.shape[0])\n img_width = float(img.shape[1])\n\n results = net.detect(img2)\n output = open(output_dat, 'w')\n output.write(\"%d\\t%d\\n\" % (img_width, img_height))\n output.write(\"\\n\")\n for cat, score, bounds in results:\n x, y, w, h = bounds\n\n x_scaled = (2*x / img_height) - 1\n y_scaled = (2*(img_height-y) / img_height) - 1\n output.write(\"%d\\t%d\\n\" % (x, y))\n cv2.rectangle(img, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (255, 0, 0), thickness=2)\n cv2.imwrite(output_img, img)\n\n print('%d Detections logged in %s' % (len(results), output_dat))\n\nif __name__ == \"__main__\":\n darknet_path = os.environ['DARKNET_HOME']\n config = os.path.join(darknet_path, 'cfg/yolov3.cfg')\n weights = os.path.join(darknet_path, 'yolov3.weights')\n coco = os.path.join(darknet_path, 'cfg/coco.data')\n\n net = Detector(bytes(config, encoding=\"utf-8\"), bytes(weights, encoding=\"utf-8\"), 0, bytes(coco, encoding=\"utf-8\"))\n\n w = Watcher()\n w.run() \n", "sub_path": "yolo_watcher.py", "file_name": "yolo_watcher.py", "file_ext": "py", "file_size_in_byte": 2379, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "watchdog.observers.Observer", "line_number": 15, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "watchdog.events.FileSystemEventHandler", "line_number": 31, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 46, "usage_type": "call"}, {"api_name": "pydarknet.Image", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 62, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pydarknet.Detector", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "185458987", "text": "from bs4 import BeautifulSoup\nimport json\nimport pandas as pd\nimport requests\nfrom pandas.io.json import json_normalize\n\nclass SpectrumScraper:\n def __init__(self, auction_url_list = None):\n self.auction_url_list = auction_url_list if auction_url_list else self.generate_default_auction_url_list()\n self.spectrum_url_base = 'https://ssl.spectrumwine.com'\n def generate_default_auction_url_list(self):\n pass\n\n def get_auction_raw_html(self, url):\n '''\n Recursive function for hitting all the pages of an auction. Goes to a page, grabs the raw html, attempts to\n find the url for the next button. Recursively calls itself on the next page url\n '''\n print('Getting URL:', url)\n r = requests.get(url)\n next_page_url = self.find_next_page_url(r.text)\n if next_page_url is not None:\n next_page_raw_html = self.get_auction_raw_html(next_page_url)\n return [r.text] + next_page_raw_html\n else:\n return([r.text])\n\n def find_next_page_url(self, text):\n soup = BeautifulSoup(text, 'html.parser')\n next_page_tag = soup.find_all('a', title = 'Next Page')\n #While we can find a next page link\n if(next_page_tag[0].has_attr('href')):\n #next_page_tag only contains the relative link. Need to append to the domain name\n formatted_url = self.spectrum_url_base + next_page_tag[0]['href']\n return(formatted_url)\n else:\n return(None)\n\n def parse_raw_html(self, raw_html_auction_pages):\n '''\n Parse the raw html for a single auction, which contains a list of pages\n '''\n parsed_html_auct = list(map(self.parse_single_auction_page_raw_html, raw_html_auction_pages))\n # List of parsed pages, fold them together\n return parsed_html_auct\n\n def parse_single_auction_page_raw_html(self, raw_html_page):\n soup = BeautifulSoup(raw_html_page, 'html.parser')\n #Only grab the table rows we care about\n rows = soup.find_all('tr', id=lambda id: id and \"ctl00_cphContent_ucAuctionLots1_dgLots_ctl00__\" in id)\n parsed_rows = [[field.text for field in row.findAll(['a', 'span'])] for row in rows]\n\n return(parsed_rows)\n\n#test_url = ['https://ssl.spectrumwine.com/auctions/AuctionLots.aspx?AuctionID=543&SessionID=797']\ntest_url = 'https://ssl.spectrumwine.com/auctions/AuctionLots.aspx?AuctionID=543&SessionID=797&ctl00_cphContent_ucAuctionLots1_dgLotsChangePage=90_50'\n\nspectrum_scraper = SpectrumScraper([test_url])\nprint( 'p1', spectrum_scraper.auction_url_list )\nraw_html_auction_list = spectrum_scraper.get_auction_raw_html(test_url)\nprint(len(raw_html_auction_list))\nparsed_auction_list = spectrum_scraper.parse_raw_html(raw_html_auction_list)\nprint(parsed_auction_list)\n#df_auction = spectrum_scraper.convert_to_dataframe(parsed_auction_list)\n\n", "sub_path": "Scrapers/spectrum_scraper.py", "file_name": "spectrum_scraper.py", "file_ext": "py", "file_size_in_byte": 2903, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 29, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "61670303", "text": "from __future__ import print_function, absolute_import\nimport logging\nimport logging.config\nimport argparse\nimport sys\nimport os\nimport os.path\nimport itertools\n\nfrom mrtarget.modules.Evidences import process_evidences_pipeline\nfrom mrtarget.common.ElasticsearchLoader import Loader\nfrom mrtarget.common.ElasticsearchQuery import ESQuery\nfrom mrtarget.common.connection import RedisManager, new_es_client, new_redis_client\nfrom mrtarget.ElasticsearchConfig import ElasticSearchConfiguration\nfrom mrtarget.modules.Association import ScoringProcess\nfrom mrtarget.modules.DataDrivenRelation import DataDrivenRelationProcess\nfrom mrtarget.modules.ECO import EcoProcess\nfrom mrtarget.modules.EFO import EfoProcess\nfrom mrtarget.modules.Ensembl import EnsemblProcess\nfrom mrtarget.modules.GeneData import GeneManager\nfrom mrtarget.modules.HPA import HPAProcess\nfrom mrtarget.modules.QC import QCMetrics\nfrom mrtarget.modules.Reactome import ReactomeProcess\nfrom mrtarget.modules.SearchObjects import SearchObjectProcess\nfrom mrtarget.modules.Uniprot import UniprotDownloader\nfrom mrtarget.modules.Metrics import Metrics\nfrom mrtarget.Settings import Config, file_or_resource\n\nimport mrtarget.cfg\n\ndef main():\n #parse config file, environment, and command line arguments\n mrtarget.cfg.setup_ops_parser()\n args = mrtarget.cfg.get_ops_args()\n\n #set up logging\n logger = None\n if args.log_config:\n if os.path.isfile(args.log_config) and os.access(args.log_config, os.R_OK):\n #read a log configuration file\n logging.config.fileConfig(args.log_config, disable_existing_loggers=False)\n logger = logging.getLogger(__name__+\".main()\")\n else:\n #unable to read the logging config file, abort\n logging.basicConfig()\n logger = logging.getLogger(__name__+\".main()\")\n logger.error(\"unable to read file {}\".format(args.log_config))\n return 1\n else:\n #no logging config specified, fall back to default\n logging.basicConfig()\n logger = logging.getLogger(__name__+\".main()\")\n\n\n if not args.release_tag:\n logger.error('A [release-tag] has to be specified.')\n print('A [release-tag] has to be specified.', file=sys.stderr)\n return 1\n else:\n Config.RELEASE_VERSION = args.release_tag\n logger.info('setting release version %s' % Config.RELEASE_VERSION)\n\n\n\n \n \n with RedisManager(args.redis_remote,args.redis_host, args.redis_port):\n\n es = new_es_client(args.elasticseach_nodes)\n redis = new_redis_client(args.redis_host, args.redis_port)\n\n #create a single query object for future use\n esquery = ESQuery(es)\n\n #read the data configuration\n data_config = mrtarget.cfg.get_data_config(args.data_config)\n\n #create something to accumulate qc metrics into over various steps\n qc_metrics = QCMetrics()\n\n with Loader(es,\n chunk_size=ElasticSearchConfiguration.bulk_load_chunk,\n dry_run = args.dry_run) as loader:\n\n if args.rea:\n process = ReactomeProcess(loader, \n data_config.reactome_pathway_data, data_config.reactome_pathway_relation)\n if not args.qc_only:\n process.process_all(args.dry_run)\n if not args.skip_qc:\n qc_metrics.update(process.qc(esquery))\n if args.ens:\n process = EnsemblProcess(loader)\n if not args.qc_only:\n process.process(data_config.ensembl_filename, args.dry_run)\n if not args.skip_qc:\n qc_metrics.update(process.qc(esquery))\n if args.unic:\n process = UniprotDownloader(loader)\n if not args.qc_only:\n process.process(data_config.uniprot_uri, args.dry_run)\n if not args.skip_qc:\n qc_metrics.update(process.qc(esquery))\n if args.hpa:\n process = HPAProcess(loader,redis, args.elasticseach_nodes,\n data_config.tissue_translation_map, data_config.tissue_curation_map,\n data_config.hpa_normal_tissue, data_config.hpa_rna_level, \n data_config.hpa_rna_value, data_config.hpa_rna_zscore)\n if not args.qc_only:\n process.process_all(args.dry_run)\n if not args.skip_qc:\n qc_metrics.update(process.qc(esquery)) \n\n if args.gen:\n process = GeneManager(loader, redis,\n args.gen_plugin_places, data_config.gene_data_plugin_names,\n )\n if not args.qc_only:\n process.merge_all(data_config, dry_run=args.dry_run)\n\n if not args.skip_qc:\n qc_metrics.update(process.qc(esquery)) \n \n if args.efo:\n process = EfoProcess(loader, data_config.ontology_efo, data_config.ontology_hpo, \n data_config.ontology_mp, data_config.disease_phenotype)\n if not args.qc_only:\n process.process_all(args.dry_run)\n if not args.skip_qc:\n qc_metrics.update(process.qc(esquery))\n if args.eco:\n process = EcoProcess(loader, data_config.ontology_eco, data_config.ontology_so)\n if not args.qc_only:\n process.process_all(args.dry_run)\n if not args.skip_qc:\n qc_metrics.update(process.qc(esquery))\n\n if args.val:\n es_output_folder = None\n if \"elasticsearch_folder\" in vars(args) and args.elasticsearch_folder is not None:\n es_output_folder = args.elasticsearch_folder\n\n process_evidences_pipeline(filenames=data_config.input_file,\n first_n=args.val_first_n,\n es_client=es,\n redis_client=redis,\n dry_run=args.dry_run,\n output_folder=es_output_folder,\n num_workers=args.val_workers_validator,\n num_writers=args.val_workers_writer,\n max_queued_events=args.val_queue_validator_writer,\n eco_scores_uri=data_config.eco_scores,\n schema_uri = data_config.schema,\n es_hosts=args.elasticseach_nodes,\n excluded_biotypes = data_config.excluded_biotypes,\n datasources_to_datatypes = data_config.datasources_to_datatypes)\n\n #TODO qc\n\n if args.assoc:\n process = ScoringProcess(args.redis_host, args.redis_port,\n args.elasticseach_nodes)\n if not args.qc_only:\n process.process_all(data_config.scoring_weights, \n data_config.is_direct_do_not_propagate,\n data_config.datasources_to_datatypes,\n args.dry_run,\n args.as_workers_production,\n args.as_workers_score,\n args.as_queue_production_score)\n if not args.skip_qc:\n qc_metrics.update(process.qc(esquery))\n pass\n \n if args.ddr:\n process = DataDrivenRelationProcess(es)\n if not args.qc_only:\n process.process_all(args.dry_run,\n args.ddr_workers_production,\n args.ddr_workers_score,\n args.ddr_queue_production_score,\n args.ddr_queue_score_result)\n #TODO qc\n\n if args.sea:\n process = SearchObjectProcess(loader, redis)\n if not args.qc_only:\n process.process_all(\n data_config.chembl_target, \n data_config.chembl_mechanism, \n data_config.chembl_component, \n data_config.chembl_protein, \n data_config.chembl_molecule_set_uri_pattern,\n args.dry_run)\n #TODO qc\n\n if args.metric:\n process = Metrics(es, args.metric_file, \n data_config.datasources_to_datatypes).generate_metrics()\n\n if args.qc_in:\n #handle reading in previous qc from filename provided, and adding comparitive metrics\n qc_metrics.compare_with(args.qc_in)\n\n if args.qc_out:\n #handle writing out to a tsv file\n qc_metrics.write_out(args.qc_out)\n\n logger.info('`'+\" \".join(sys.argv)+'` - finished')\n return 0\n\n\nif __name__ == '__main__':\n sys.exit(main())\n", "sub_path": "mrtarget/CommandLine.py", "file_name": "CommandLine.py", "file_ext": "py", "file_size_in_byte": 8867, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "mrtarget.modules.Evidences.cfg.setup_ops_parser", "line_number": 33, "usage_type": "call"}, {"api_name": "mrtarget.modules.Evidences.cfg", "line_number": 33, "usage_type": "attribute"}, {"api_name": "mrtarget.modules.Evidences", "line_number": 33, "usage_type": "name"}, {"api_name": "mrtarget.modules.Evidences.cfg.get_ops_args", "line_number": 34, "usage_type": "call"}, {"api_name": "mrtarget.modules.Evidences.cfg", "line_number": 34, "usage_type": "attribute"}, {"api_name": "mrtarget.modules.Evidences", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 39, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 39, "usage_type": "attribute"}, {"api_name": "logging.config.fileConfig", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 41, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 45, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 57, "usage_type": "attribute"}, {"api_name": "mrtarget.Settings.Config.RELEASE_VERSION", "line_number": 60, "usage_type": "attribute"}, {"api_name": "mrtarget.Settings.Config", "line_number": 60, "usage_type": "name"}, {"api_name": "mrtarget.Settings.Config.RELEASE_VERSION", "line_number": 61, "usage_type": "attribute"}, {"api_name": "mrtarget.Settings.Config", "line_number": 61, "usage_type": "name"}, {"api_name": "mrtarget.common.connection.RedisManager", "line_number": 67, "usage_type": "call"}, {"api_name": "mrtarget.common.connection.new_es_client", "line_number": 69, "usage_type": "call"}, {"api_name": "mrtarget.common.connection.new_redis_client", "line_number": 70, "usage_type": "call"}, {"api_name": "mrtarget.common.ElasticsearchQuery.ESQuery", "line_number": 73, "usage_type": "call"}, {"api_name": "mrtarget.modules.Evidences.cfg.get_data_config", "line_number": 76, "usage_type": "call"}, {"api_name": "mrtarget.modules.Evidences.cfg", "line_number": 76, "usage_type": "attribute"}, {"api_name": "mrtarget.modules.Evidences", "line_number": 76, "usage_type": "name"}, {"api_name": "mrtarget.modules.QC.QCMetrics", "line_number": 79, "usage_type": "call"}, {"api_name": "mrtarget.common.ElasticsearchLoader.Loader", "line_number": 81, "usage_type": "call"}, {"api_name": "mrtarget.ElasticsearchConfig.ElasticSearchConfiguration.bulk_load_chunk", "line_number": 82, "usage_type": "attribute"}, {"api_name": "mrtarget.ElasticsearchConfig.ElasticSearchConfiguration", "line_number": 82, "usage_type": "name"}, {"api_name": "mrtarget.modules.Reactome.ReactomeProcess", "line_number": 86, "usage_type": "call"}, {"api_name": "mrtarget.modules.Ensembl.EnsemblProcess", "line_number": 93, "usage_type": "call"}, {"api_name": "mrtarget.modules.Uniprot.UniprotDownloader", "line_number": 99, "usage_type": "call"}, {"api_name": "mrtarget.modules.HPA.HPAProcess", "line_number": 105, "usage_type": "call"}, {"api_name": "mrtarget.modules.GeneData.GeneManager", "line_number": 115, "usage_type": "call"}, {"api_name": "mrtarget.modules.EFO.EfoProcess", "line_number": 125, "usage_type": "call"}, {"api_name": "mrtarget.modules.ECO.EcoProcess", "line_number": 132, "usage_type": "call"}, {"api_name": "mrtarget.modules.Evidences.process_evidences_pipeline", "line_number": 143, "usage_type": "call"}, {"api_name": "mrtarget.modules.Association.ScoringProcess", "line_number": 161, "usage_type": "call"}, {"api_name": "mrtarget.modules.DataDrivenRelation.DataDrivenRelationProcess", "line_number": 176, "usage_type": "call"}, {"api_name": "mrtarget.modules.SearchObjects.SearchObjectProcess", "line_number": 186, "usage_type": "call"}, {"api_name": "mrtarget.modules.Metrics.Metrics", "line_number": 198, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 209, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 214, "usage_type": "call"}]} +{"seq_id": "348449373", "text": "import os\r\nimport sys\r\nimport pdb\r\np = os.path.split(os.path.dirname(os.path.abspath(__file__)))[0]\r\nsys.path.append(p)\r\nimport argparse\r\nimport logging\r\nimport torch\r\nimport numpy as np\r\nfrom pprint import pformat, pprint\r\n\r\nfrom datasets import get_dataset\r\nfrom utils.hparams import HParams\r\nfrom utils.test_utils import run_imputation\r\nfrom PIL import Image\r\nfrom torch.utils.tensorboard import SummaryWriter\r\n\r\nfrom transformers import Encoder\r\n#from exnode import ExnodeEncoder\r\n\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\n\r\nckpt_path_dict = dict()\r\nckpt_root_dir = './log/air_quality_min_0.8_miss/ckpt/'\r\nckpt_dir = os.path.join(ckpt_root_dir, 'best_model.pt')\r\n\r\nckpt = torch.load(ckpt_dir)\r\n\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument('--cfg_file', type=str)\r\nparser.add_argument('--num_missing', type=int)\r\nparser.add_argument('--save_fig', type=int)\r\nargs = parser.parse_args()\r\nparams = HParams(args.cfg_file)\r\npprint(params.dict)\r\nnp.random.seed(params.seed)\r\ntorch.manual_seed(params.seed)\r\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\ntorch.autograd.set_detect_anomaly(True)\r\n\r\n\r\n# creat exp dir\r\nif not os.path.exists(params.exp_dir):\r\n os.mkdir(params.exp_dir)\r\nif not os.path.exists(os.path.join(params.exp_dir, 'gen')):\r\n os.mkdir(os.path.join(params.exp_dir, 'gen'))\r\nif not os.path.exists(os.path.join(params.exp_dir, 'ckpt')):\r\n os.mkdir(os.path.join(params.exp_dir, 'ckpt'))\r\nif not os.path.exists(os.path.join(params.exp_dir, 'impute')):\r\n os.mkdir(os.path.join(params.exp_dir, 'impute'))\r\n\r\n\r\ntrain_data, val_data, test_data = get_dataset(params.data_root, params.dataset, False)\r\n\r\ntrain_mean = torch.mean(train_data, 0)\r\ntest_mean = torch.mean(test_data, 0)\r\nmodel = eval(params.model_name)(\r\n max_time_scale=params.max_time_scale,\r\n time_enc_dim=params.time_enc_dim,\r\n time_dim=params.time_dim,\r\n expand_dim=params.expand_dim,\r\n mercer=params.mercer,\r\n n_layers=params.n_layers,\r\n n_head=params.n_heads,\r\n d_k=params.att_dims,\r\n d_v=params.att_dims,\r\n d_model=params.model_dims,\r\n d_inner=params.inner_dims,\r\n d_data=train_data.shape[-1],\r\n dropout=params.dropout,\r\n use_layer_norm=params.layer_norm,\r\n use_gap_encoding=params.use_gap_encoding,\r\n adapter=params.adapter,\r\n use_mask=params.att_mask,\r\n confidence=params.confidence\r\n)\r\nmodel = nn.DataParallel(model).to(device)\r\nprint(model)\r\nprint(\"Start Imputation\")\r\nmodel.load_state_dict(ckpt)\r\nloss = run_imputation(model, params.mode, test_data.repeat(10,1,1), args.num_missing, confidence=params.confidence , max_level=params.max_level, fig_path = os.path.join(params.exp_dir, 'impute'), \r\n save_all_imgs=args.save_fig, dataset=params.dataset, train_mean=train_mean, test_mean=test_mean, gp=params.gp)\r\n\r\noutput_str = 'Testing_Loss: %4f' % (loss)\r\nprint(output_str)\r\n", "sub_path": "codes_partially_observed_dimension/impute.py", "file_name": "impute.py", "file_ext": "py", "file_size_in_byte": 2889, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.split", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 28, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 31, "usage_type": "call"}, {"api_name": "utils.hparams.HParams", "line_number": 36, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.autograd.set_detect_anomaly", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "datasets.get_dataset", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "utils.test_utils.run_imputation", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}]} +{"seq_id": "356742540", "text": "import os\nimport sys\nfrom mininet.topo import Topo\nfrom mininet.net import Mininet\nfrom mininet.node import CPULimitedHost\nfrom mininet.link import TCLink\nfrom mininet.node import OVSController\nfrom mininet.node import Controller\nfrom mininet.node import RemoteController\nfrom mininet.cli import CLI\nfrom mininet.util import pmonitor\nfrom mininet.log import setLogLevel, info\nsys.path.append(\"../../\")\nfrom pox.ext.jelly_pox import JELLYPOX\nfrom subprocess import Popen\nfrom time import sleep, time\nimport networkx as nx\nimport random\nfrom signal import SIGINT\n\nS = 10\nN = 10\nr = 4\n\nclass JellyFishTop(Topo):\n ''' TODO, build your topology here'''\n def build( self, S, N, r):\n \"Create custom topo.\"\n\n # Initialize topology\n # Topo.__init__( self )\n\n hosts = [self.addHost('h%d'%(i,)) for i in range(S)]\n switches = [self.addSwitch('s%d'%(i,)) for i in range(N)]\n\n # make server switch connections\n for i in range(S):\n conn_switch = i%N\n self.addLink(hosts[i], switches[conn_switch], i, conn_switch*1000 + i + 1)\n\n rrg = nx.random_regular_graph(r, N, 100)\n for e in rrg.edges():\n self.addLink(switches[e[0]], switches[e[1]], 1000*e[0] + S + e[1] + 1, 1000*e[1] + S + e[0] + 1)\n\ndef experiment(net):\n for i in range(S):\n h1 = net.get('h%d'%(i,))\n # print h2, h2.IP(), h2.MAC()\n h1.setMAC('00:00:00:00:00:%02d'%(i+1,))\n\n for i in range(S):\n for j in range(S):\n if i == j:\n continue\n h1 = net.get('h%d'%(i,))\n h2 = net.get('h%d'%(j,))\n h1.setARP(h2.IP(), h2.MAC())\n net.start()\n cpopens = {}\n spopens = {}\n speeds = []\n assignments = range(S) \n net.pingAll()\n net.iperf(seconds = 20)\n net.iperf(seconds = 20)\n net.iperf(seconds = 20)\n flows = 1\n while any([a == i for i, a in enumerate(assignments)]):\n random.shuffle(assignments)\n for i, h in enumerate(net.hosts):\n other_h = net.hosts[assignments[i]]\n spopens[other_h] = other_h.popen('iperf', '-s')\n cpopens[h] = h.popen('iperf', '-c', other_h.IP(), '-f', 'm', '-P', str(flows), '-t', '20')\n cclosed = 0\n sclosed = 0\n endtime = time() + 150 # 150 seconds to complete\n\n for h, line in pmonitor(cpopens, timeoutms=10000):\n if h:\n info('%s: %s' % (h.name, line))\n if 'Mbit' in line:\n if 'SUM' not in line:\n speed = float(line.split()[-2])\n speeds.append(speed)\n cclosed += 1\n if time() > endtime:\n # info(\"timeout kill\")\n for p in cpopens.values():\n p.send_signal(SIGINT)\n if cclosed == N*(flows): # +1 for the sum equation\n for p in cpopens.values():\n p.send_signal(SIGINT)\n for h, line in pmonitor(spopens, timeoutms=10000):\n if h:\n info('server %s: %s' % (h.name, line))\n if 'bits/sec' in line and 'SUM' not in line:\n sclosed += 1\n if time() > endtime:\n # info(\"timeout kill\")\n for p in spopens.values():\n p.send_signal(SIGINT)\n if sclosed == N*(flows):\n info(\"shutting down\")\n for p in spopens.values():\n p.send_signal(SIGINT)\n\n # CLI(net)\n info(\"\\n\")\n info(speeds)\n info(\"\\n\")\n info(\"got %d speeds, avg %f Mbits/s\" % (len(speeds), flows*float(sum(speeds)) / len(speeds)))\n net.stop()\n\ndef main():\n topo = JellyFishTop(S, N, r)\n net = Mininet(topo=topo, host=CPULimitedHost, link = TCLink, controller=JELLYPOX)\n experiment(net)\n\nif __name__ == \"__main__\":\n setLogLevel( 'info' )\n main()\n\n", "sub_path": "pox/pox/ext/build_topology.py", "file_name": "build_topology.py", "file_ext": "py", "file_size_in_byte": 4009, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "sys.path.append", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "mininet.topo.Topo", "line_number": 25, "usage_type": "name"}, {"api_name": "networkx.random_regular_graph", "line_number": 41, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 69, "usage_type": "call"}, {"api_name": "time.time", "line_number": 76, "usage_type": "call"}, {"api_name": "mininet.util.pmonitor", "line_number": 78, "usage_type": "call"}, {"api_name": "mininet.log.info", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 86, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 89, "usage_type": "argument"}, {"api_name": "signal.SIGINT", "line_number": 92, "usage_type": "argument"}, {"api_name": "mininet.util.pmonitor", "line_number": 93, "usage_type": "call"}, {"api_name": "mininet.log.info", "line_number": 95, "usage_type": "call"}, {"api_name": "time.time", "line_number": 98, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 101, "usage_type": "argument"}, {"api_name": "mininet.log.info", "line_number": 103, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 105, "usage_type": "argument"}, {"api_name": "mininet.log.info", "line_number": 108, "usage_type": "call"}, {"api_name": "mininet.log.info", "line_number": 109, "usage_type": "call"}, {"api_name": "mininet.log.info", "line_number": 110, "usage_type": "call"}, {"api_name": "mininet.log.info", "line_number": 111, "usage_type": "call"}, {"api_name": "mininet.net.Mininet", "line_number": 116, "usage_type": "call"}, {"api_name": "mininet.node.CPULimitedHost", "line_number": 116, "usage_type": "name"}, {"api_name": "mininet.link.TCLink", "line_number": 116, "usage_type": "name"}, {"api_name": "pox.ext.jelly_pox.JELLYPOX", "line_number": 116, "usage_type": "name"}, {"api_name": "mininet.log.setLogLevel", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "124783949", "text": "import requests\nr = requests.get(\"https://movie.douban.com/chart\")\n\nfrom bs4 import BeautifulSoup\nsoup = BeautifulSoup(r.text, \"html.parser\")\n\nnames = soup.find_all(\"a\", \"nbg\")\n\n\nmarks = soup.find_all(\"span\", \"rating_nums\")\n\nfor name, mark in zip(names, marks):\n data = {\n '电影' : name.get(\"title\"),\n '评分' : mark.get_text()\n }\n print(data)\n", "sub_path": "weekly_newmovie.py", "file_name": "weekly_newmovie.py", "file_ext": "py", "file_size_in_byte": 370, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "requests.get", "line_number": 2, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "92029218", "text": "import argparse\n\nfrom Bio import SeqIO, Seq\nfrom Bio.Alphabet import generic_dna\nfrom Bio.Data.CodonTable import TranslationError\n\nparser = argparse.ArgumentParser(description='Fasta line length normalizer')\n\nparser.add_argument('-n','--fasta_normalizer',\n dest='fasta_normalizer',\n action=\"store_true\"\n)\nparser.add_argument('-p','--peptide-fasta',\n dest='peptide_fasta',\n action=\"store_true\"\n)\nparser.add_argument('-c','--fasta-cleaner',\n dest='fasta_cleaner',\n action=\"store_true\"\n)\nparser.add_argument('-f','--file',\n dest='fasta_file'\n)\nparser.add_argument('-l','--length',\n dest='fasta_line_length'\n)\nparser.add_argument('-o','--overlap',\n dest='fasta_sequences_overlap'\n)\nparser.add_argument('-of','--output_file',\n dest='output_file'\n)\nparser.add_argument('-w','--wrong-bases',\n dest='wrong_bases'\n)\nparser.add_argument('-ol','--one-liner',\n dest='one_liner',\n action=\"store_true\"\n)\n\n\ndef fasta2dict(fasta_file, pyfastalib=True):\n fasta_dict = {}\n\n if pyfastalib:\n fasta_list = [line.strip() for line in open(fasta_file) if line.strip() != \"\"]\n fasta_seq_names = [fasta_list[i].replace(\">\",\"\") for i in range(0, len(fasta_list), 2)]\n fasta_sequences = [fasta_list[i] for i in range(1, len(fasta_list), 2)]\n\n for k, v in zip(fasta_seq_names, fasta_sequences):\n try:\n splited_seq_name = k.split(\"|\")\n seq_name = splited_seq_name[0]\n seq_pos = splited_seq_name[1]\n if seq_name in fasta_dict:\n fasta_dict[seq_name].append((int(seq_pos), v))\n else:\n fasta_dict[seq_name] = []\n fasta_dict[seq_name].append((int(seq_pos), v))\n except:\n print(k)\n\n for k in fasta_dict.keys():\n fasta_dict[k] = sorted(fasta_dict[k])\n\n else:\n fasta = SeqIO.to_dict(SeqIO.parse(fasta_file, format=\"fasta\"))\n fasta_dict = {k:str(v.seq) for k, v in fasta.items()}\n\n return fasta_dict\n\n\ndef dict2fasta(fasta_cleaned, output_fasta=None):\n fasta_list = []\n for k, v in fasta_cleaned.items():\n try:\n for ind, seq in v:\n fasta_list.append(\">{}|{}\\n{}\\n\".format(k, ind, seq))\n except:\n fasta_list.append(\">{}\\n{}\\n\".format(k, v))\n\n if output_fasta != None:\n with open(output_fasta, \"w+\") as f:\n f.write(\"\\n\".join(fasta_list))\n else:\n print(\"\\n\".join(fasta_list))\n\n\ndef fasta_one_liner(fasta, output_fasta=None):\n fasta = SeqIO.to_dict(SeqIO.parse(fasta, format=\"fasta\"))\n one_liner_fasta = {}\n for k, v in fasta.items():\n one_liner_fasta[k] = str(v.seq)\n dict2fasta(one_liner_fasta, output_fasta)\n\n\ndef seq_name_norm(fasta_to_clean, output_fasta=None):\n import re\n fasta = SeqIO.to_dict(SeqIO.parse(fasta_to_clean, format=\"fasta\"))\n fasta_clean = {}\n\n for k, v in fasta.items():\n k_n = re.sub(r\"([0-9])_([0-9])\", r\"\\1|\\2\", k)\n fasta_clean[k_n] = str(v.seq)\n\n dict2fasta(fasta_clean, output_fasta)\n\ndef list_normalizer(sortinglist, overlap_len, desired_len):\n sorted_list = sorted(sortinglist, key=lambda tup: tup[0])\n normalized_list = []\n if len(sorted_list) > 1:\n normalized_list = [x for x in sorted_list[:-2]]\n seq_complete = sorted_list[-2][1]\n seq_incomplete = sorted_list[-1][1]\n if len(seq_incomplete) > int(overlap_len):\n if len(seq_incomplete) < int(desired_len):\n missing_len = int(desired_len) - len(seq_incomplete)\n normalized_list.append((sorted_list[-2][0], sorted_list[-2][1]))\n seq_incomplete = seq_complete[-int(missing_len) - int(overlap_len):-int(overlap_len)] + seq_incomplete\n normalized_list.append((int(sorted_list[-1][0]), seq_incomplete))\n elif len(seq_incomplete) == int(desired_len):\n normalized_list.append((int(sorted_list[-1][0]), seq_incomplete))\n else:\n raise ValueError(\"Alguma parte do script com erro\")\n else:\n normalized_list = [x for x in sorted_list[:-1]]\n return normalized_list\n else:\n for k in sortinglist:\n if len(k[1]) == desired_len:\n normalized_list.append((int(sortinglist[-1][0]), sortinglist[-1][1]))\n return normalized_list\n\n\ndef fasta_normalizer(file, desired_len, overlap_len, output_file = None):\n fasta_dict = fasta2dict(file)\n new_fasta = {k: list_normalizer(v, overlap_len=overlap_len, desired_len=desired_len) for k, v in fasta_dict.items()}\n\n fasta_file_output = []\n estranhos = 0\n for k, v in new_fasta.items():\n for ind, seq in v:\n if len(seq) == int(desired_len):\n fasta_file_output.append(\">{}|{}\\n{}\\n\".format(k, ind, seq))\n else:\n estranhos += 1\n\n if output_file != None:\n with open(output_file, \"w+\") as f:\n f.write(\"\\n\".join(fasta_file_output))\n else:\n print(\"\\n\".join(fasta_file_output))\n\n\ndef fasta_cleaner(input_fasta, output_fasta=None, wrong_bases=\"n\"):\n fasta_dict = fasta2dict(input_fasta, pyfastalib=False)\n fasta_cleaned = {}\n\n for k, v in fasta_dict.items():\n fasta_cleaned[k] = v.replace(wrong_bases, \"\")\n\n if output_fasta == None:\n dict2fasta(fasta_cleaned)\n else:\n dict2fasta(fasta_cleaned, output_fasta)\n\n\ndef pep_fasta(input_fasta, output_fasta=None):\n fasta_dict = fasta2dict(input_fasta)\n fasta_dict_bkp = {}\n for k, v in fasta_dict.items():\n try:\n fasta_dict_bkp[k] = [(ind, Seq.Seq(x, generic_dna).translate()) for ind, x in v]\n except:\n pass\n\n dict2fasta(fasta_dict_bkp, output_fasta)\n\n\nif __name__ == '__main__':\n args = parser.parse_args()\n if args.fasta_normalizer:\n fasta_normalizer(\n args.fasta_file,\n args.fasta_line_length,\n args.fasta_sequences_overlap,\n args.output_file\n )\n\n if args.fasta_cleaner:\n fasta_cleaner(args.fasta_file,\n args.output_file,\n args.wrong_bases\n )\n\n if args.peptide_fasta:\n pep_fasta(\n args.fasta_file,\n args.output_file\n )\n\n if args.one_liner:\n fasta_one_liner(\n args.fasta_file,\n args.output_file\n )\n", "sub_path": "fasta_normalizer.py", "file_name": "fasta_normalizer.py", "file_ext": "py", "file_size_in_byte": 6607, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "Bio.SeqIO.to_dict", "line_number": 67, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 67, "usage_type": "name"}, {"api_name": "Bio.SeqIO.parse", "line_number": 67, "usage_type": "call"}, {"api_name": "Bio.SeqIO.to_dict", "line_number": 90, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 90, "usage_type": "name"}, {"api_name": "Bio.SeqIO.parse", "line_number": 90, "usage_type": "call"}, {"api_name": "Bio.SeqIO.to_dict", "line_number": 99, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 99, "usage_type": "name"}, {"api_name": "Bio.SeqIO.parse", "line_number": 99, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 103, "usage_type": "call"}, {"api_name": "Bio.Seq.Seq", "line_number": 173, "usage_type": "call"}, {"api_name": "Bio.Alphabet.generic_dna", "line_number": 173, "usage_type": "argument"}, {"api_name": "Bio.Seq", "line_number": 173, "usage_type": "name"}]} +{"seq_id": "142813443", "text": "import sqlite3\nimport typing\n\nfrom hydrus.core import HydrusConstants as HC\nfrom hydrus.core import HydrusData\nfrom hydrus.core import HydrusExceptions\nfrom hydrus.core import HydrusTime\n\nfrom hydrus.client import ClientConstants as CC\nfrom hydrus.client import ClientLocation\nfrom hydrus.client import ClientTime\nfrom hydrus.client.db import ClientDBDefinitionsCache\nfrom hydrus.client.db import ClientDBFilesMetadataBasic\nfrom hydrus.client.db import ClientDBFilesStorage\nfrom hydrus.client.db import ClientDBMaster\nfrom hydrus.client.db import ClientDBModule\nfrom hydrus.client.db import ClientDBServices\nfrom hydrus.client.db import ClientDBURLMap\nfrom hydrus.client.importing import ClientImportFiles\nfrom hydrus.client.networking import ClientNetworkingFunctions\n\nclass ClientDBFilesMetadataRich( ClientDBModule.ClientDBModule ):\n \n def __init__(\n self,\n cursor: sqlite3.Cursor,\n modules_services: ClientDBServices,\n modules_hashes: ClientDBMaster.ClientDBMasterHashes,\n modules_files_metadata_basic: ClientDBFilesMetadataBasic.ClientDBFilesMetadataBasic,\n modules_files_storage: ClientDBFilesStorage.ClientDBFilesStorage,\n modules_hashes_local_cache: ClientDBDefinitionsCache.ClientDBCacheLocalHashes,\n modules_url_map: ClientDBURLMap.ClientDBURLMap\n ):\n \n # we could make this guy take urls, tags, ratings, notes, all that, and then make him the MediaResult cache guy\n # he could also probably do file searching too\n \n self.modules_services = modules_services\n self.modules_hashes = modules_hashes\n self.modules_files_metadata_basic = modules_files_metadata_basic\n self.modules_files_storage = modules_files_storage\n self.modules_hashes_local_cache = modules_hashes_local_cache\n self.modules_url_map = modules_url_map\n \n ClientDBModule.ClientDBModule.__init__( self, 'client files rich metadata', cursor )\n \n \n def FilterHashesByService( self, location_context: ClientLocation.LocationContext, hashes: typing.Sequence[ bytes ] ) -> typing.List[ bytes ]:\n \n # returns hashes in order, to be nice to UI\n \n if location_context.IsEmpty():\n \n return []\n \n \n if location_context.IsAllKnownFiles():\n \n return list( hashes )\n \n \n hashes_to_hash_ids = { hash : self.modules_hashes_local_cache.GetHashId( hash ) for hash in hashes if self.modules_hashes.HasHash( hash ) }\n \n valid_hash_ids = self.modules_files_storage.FilterHashIds( location_context, hashes_to_hash_ids.values() )\n \n return [ hash for hash in hashes if hash in hashes_to_hash_ids and hashes_to_hash_ids[ hash ] in valid_hash_ids ]\n \n \n def GetFileHistory( self, num_steps: int ):\n \n # get all sorts of stats and present them in ( timestamp, cumulative_num ) tuple pairs\n \n file_history = {}\n \n # first let's do current files. we increment when added, decrement when we know removed\n \n current_files_table_name = ClientDBFilesStorage.GenerateFilesTableName( self.modules_services.combined_local_media_service_id, HC.CONTENT_STATUS_CURRENT )\n \n current_timestamps = self._STL( self._Execute( 'SELECT timestamp FROM {};'.format( current_files_table_name ) ) )\n \n deleted_files_table_name = ClientDBFilesStorage.GenerateFilesTableName( self.modules_services.combined_local_media_service_id, HC.CONTENT_STATUS_DELETED )\n \n since_deleted = self._STL( self._Execute( 'SELECT original_timestamp FROM {} WHERE original_timestamp IS NOT NULL;'.format( deleted_files_table_name ) ) )\n \n all_known_import_timestamps = list( current_timestamps )\n \n all_known_import_timestamps.extend( since_deleted )\n \n all_known_import_timestamps.sort()\n \n deleted_timestamps = self._STL( self._Execute( 'SELECT timestamp FROM {} WHERE timestamp IS NOT NULL ORDER BY timestamp ASC;'.format( deleted_files_table_name ) ) )\n \n combined_timestamps_with_delta = [ ( timestamp, 1 ) for timestamp in all_known_import_timestamps ]\n combined_timestamps_with_delta.extend( ( ( timestamp, -1 ) for timestamp in deleted_timestamps ) )\n \n combined_timestamps_with_delta.sort()\n \n current_file_history = []\n \n if len( combined_timestamps_with_delta ) > 0:\n \n # set 0 on first file import time\n current_file_history.append( ( combined_timestamps_with_delta[0][0], 0 ) )\n \n if len( combined_timestamps_with_delta ) < 2:\n \n step_gap = 1\n \n else:\n \n step_gap = max( ( combined_timestamps_with_delta[-1][0] - combined_timestamps_with_delta[0][0] ) // num_steps, 1 )\n \n \n total_current_files = 0\n step_timestamp = combined_timestamps_with_delta[0][0]\n \n for ( timestamp, delta ) in combined_timestamps_with_delta:\n \n while timestamp > step_timestamp + step_gap:\n \n current_file_history.append( ( step_timestamp, total_current_files ) )\n \n step_timestamp += step_gap\n \n \n total_current_files += delta\n \n \n \n file_history[ 'current' ] = current_file_history\n \n # now deleted times. we will pre-populate total_num_files with non-timestamped records\n \n ( total_deleted_files, ) = self._Execute( 'SELECT COUNT( * ) FROM {} WHERE timestamp IS NULL;'.format( deleted_files_table_name ) ).fetchone()\n \n deleted_file_history = []\n \n if len( deleted_timestamps ) > 0:\n \n if len( deleted_timestamps ) < 2:\n \n step_gap = 1\n \n else:\n \n step_gap = max( ( deleted_timestamps[-1] - deleted_timestamps[0] ) // num_steps, 1 )\n \n \n step_timestamp = deleted_timestamps[0]\n \n for deleted_timestamp in deleted_timestamps:\n \n while deleted_timestamp > step_timestamp + step_gap:\n \n deleted_file_history.append( ( step_timestamp, total_deleted_files ) )\n \n step_timestamp += step_gap\n \n \n total_deleted_files += 1\n \n \n \n file_history[ 'deleted' ] = deleted_file_history\n \n # and inbox, which will work backwards since we have numbers for archiving. several subtle differences here\n # we know the inbox now and the recent history of archives and file changes\n # working backwards in time (which reverses increment/decrement):\n # - an archive increments\n # - a file import decrements\n # note that we archive right before we delete a file, so file deletes shouldn't change anything for inbox count. all deletes are on archived files, so the increment will already be counted\n # UPDATE: and now we add archived, which is mostly the same deal but we subtract from current files to start and don't care about file imports since they are always inbox but do care about file deletes\n \n inbox_file_history = []\n archive_file_history = []\n \n ( total_inbox_files, ) = self._Execute( 'SELECT COUNT( * ) FROM file_inbox;' ).fetchone()\n total_current_files = len( current_timestamps )\n \n # I now exclude updates and trash my searching 'all my files'\n total_update_files = 0 #self.modules_files_storage.GetCurrentFilesCount( self.modules_services.local_update_service_id, HC.CONTENT_STATUS_CURRENT )\n total_trash_files = 0 #self.modules_files_storage.GetCurrentFilesCount( self.modules_services.trash_service_id, HC.CONTENT_STATUS_CURRENT )\n \n total_archive_files = ( total_current_files - total_update_files - total_trash_files ) - total_inbox_files\n \n # note also that we do not scrub archived time on a file delete, so this upcoming fetch is for all files ever. this is useful, so don't undo it m8\n archive_timestamps = self._STL( self._Execute( 'SELECT archived_timestamp FROM archive_timestamps ORDER BY archived_timestamp ASC;' ) )\n \n if len( archive_timestamps ) > 0:\n \n first_archive_time = archive_timestamps[0]\n \n combined_timestamps_with_deltas = [ ( timestamp, 1, -1 ) for timestamp in archive_timestamps ]\n combined_timestamps_with_deltas.extend( ( ( timestamp, -1, 0 ) for timestamp in all_known_import_timestamps if timestamp >= first_archive_time ) )\n combined_timestamps_with_deltas.extend( ( ( timestamp, 0, 1 ) for timestamp in deleted_timestamps if timestamp >= first_archive_time ) )\n \n combined_timestamps_with_deltas.sort( reverse = True )\n \n if len( combined_timestamps_with_deltas ) > 0:\n \n if len( combined_timestamps_with_deltas ) < 2:\n \n step_gap = 1\n \n else:\n \n # reversed, so first minus last\n step_gap = max( ( combined_timestamps_with_deltas[0][0] - combined_timestamps_with_deltas[-1][0] ) // num_steps, 1 )\n \n \n step_timestamp = combined_timestamps_with_deltas[0][0]\n \n for ( archived_timestamp, inbox_delta, archive_delta ) in combined_timestamps_with_deltas:\n \n while archived_timestamp < step_timestamp - step_gap:\n \n inbox_file_history.append( ( archived_timestamp, total_inbox_files ) )\n archive_file_history.append( ( archived_timestamp, total_archive_files ) )\n \n step_timestamp -= step_gap\n \n \n total_inbox_files += inbox_delta\n total_archive_files += archive_delta\n \n \n inbox_file_history.reverse()\n archive_file_history.reverse()\n \n \n \n file_history[ 'inbox' ] = inbox_file_history\n file_history[ 'archive' ] = archive_file_history\n \n return file_history\n \n \n def GetHashIdStatus( self, hash_id, prefix = '' ) -> ClientImportFiles.FileImportStatus:\n \n if prefix != '':\n \n prefix += ': '\n \n \n hash = self.modules_hashes_local_cache.GetHash( hash_id )\n \n ( is_deleted, timestamp, file_deletion_reason ) = self.modules_files_storage.GetDeletionStatus( self.modules_services.combined_local_file_service_id, hash_id )\n \n if is_deleted:\n \n if timestamp is None:\n \n note = 'Deleted from the client before delete times were tracked ({}).'.format( file_deletion_reason )\n \n else:\n \n note = 'Deleted from the client {} ({}), which was {} before this check.'.format( HydrusTime.TimestampToPrettyTime( timestamp ), file_deletion_reason, HydrusTime.BaseTimestampToPrettyTimeDelta( timestamp ) )\n \n \n return ClientImportFiles.FileImportStatus( CC.STATUS_DELETED, hash, note = prefix + note )\n \n \n result = self.modules_files_storage.GetImportedTimestamp( self.modules_services.trash_service_id, hash_id )\n \n if result is not None:\n \n timestamp = result\n \n note = 'Currently in trash ({}). Sent there at {}, which was {} before this check.'.format( file_deletion_reason, HydrusTime.TimestampToPrettyTime( timestamp ), HydrusTime.BaseTimestampToPrettyTimeDelta( timestamp, just_now_threshold = 0 ) )\n \n return ClientImportFiles.FileImportStatus( CC.STATUS_DELETED, hash, note = prefix + note )\n \n \n result = self.modules_files_storage.GetImportedTimestamp( self.modules_services.combined_local_file_service_id, hash_id )\n \n if result is not None:\n \n timestamp = result\n \n mime = self.modules_files_metadata_basic.GetMime( hash_id )\n \n note = 'Imported at {}, which was {} before this check.'.format( HydrusTime.TimestampToPrettyTime( timestamp ), HydrusTime.BaseTimestampToPrettyTimeDelta( timestamp, just_now_threshold = 0 ) )\n \n return ClientImportFiles.FileImportStatus( CC.STATUS_SUCCESSFUL_BUT_REDUNDANT, hash, mime = mime, note = prefix + note )\n \n \n return ClientImportFiles.FileImportStatus( CC.STATUS_UNKNOWN, hash )\n \n \n def GetHashStatus( self, hash_type, hash, prefix = None ) -> ClientImportFiles.FileImportStatus:\n \n if prefix is None:\n \n prefix = hash_type + ' recognised'\n \n \n if hash_type == 'sha256':\n \n if not self.modules_hashes.HasHash( hash ):\n \n # this used to set the fis.hash = hash here, but that's unhelpful for the callers, who already know the hash and really want to know if there was a good match\n \n return ClientImportFiles.FileImportStatus.STATICGetUnknownStatus()\n \n else:\n \n hash_id = self.modules_hashes_local_cache.GetHashId( hash )\n \n \n else:\n \n try:\n \n hash_id = self.modules_hashes.GetHashIdFromExtraHash( hash_type, hash )\n \n except HydrusExceptions.DataMissing:\n \n return ClientImportFiles.FileImportStatus.STATICGetUnknownStatus()\n \n \n \n return self.GetHashIdStatus( hash_id, prefix = prefix )\n \n \n def GetTablesAndColumnsThatUseDefinitions( self, content_type: int ) -> typing.List[ typing.Tuple[ str, str ] ]:\n \n return []\n \n \n def GetURLStatuses( self, url ) -> typing.List[ ClientImportFiles.FileImportStatus ]:\n \n search_urls = ClientNetworkingFunctions.GetSearchURLs( url )\n \n hash_ids = set()\n \n for search_url in search_urls:\n \n results = self.modules_url_map.GetHashIds( search_url )\n \n hash_ids.update( results )\n \n \n try:\n \n results = [ self.GetHashIdStatus( hash_id, prefix = 'url recognised' ) for hash_id in hash_ids ]\n \n except:\n \n return []\n \n \n return results\n \n \n", "sub_path": "hydrus/client/db/ClientDBFilesMetadataRich.py", "file_name": "ClientDBFilesMetadataRich.py", "file_ext": "py", "file_size_in_byte": 15324, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "hydrus.client.db.ClientDBModule.ClientDBModule", "line_number": 22, "usage_type": "attribute"}, {"api_name": "hydrus.client.db.ClientDBModule", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlite3.Cursor", "line_number": 26, "usage_type": "attribute"}, {"api_name": "hydrus.client.db.ClientDBServices", "line_number": 27, "usage_type": "name"}, {"api_name": "hydrus.client.db.ClientDBMaster.ClientDBMasterHashes", "line_number": 28, "usage_type": "attribute"}, {"api_name": "hydrus.client.db.ClientDBMaster", "line_number": 28, "usage_type": "name"}, {"api_name": "hydrus.client.db.ClientDBFilesMetadataBasic.ClientDBFilesMetadataBasic", "line_number": 29, "usage_type": "attribute"}, {"api_name": "hydrus.client.db.ClientDBFilesMetadataBasic", "line_number": 29, "usage_type": "name"}, {"api_name": "hydrus.client.db.ClientDBFilesStorage.ClientDBFilesStorage", "line_number": 30, "usage_type": "attribute"}, {"api_name": "hydrus.client.db.ClientDBFilesStorage", "line_number": 30, "usage_type": "name"}, {"api_name": "hydrus.client.db.ClientDBDefinitionsCache.ClientDBCacheLocalHashes", "line_number": 31, "usage_type": "attribute"}, {"api_name": "hydrus.client.db.ClientDBDefinitionsCache", "line_number": 31, "usage_type": "name"}, {"api_name": "hydrus.client.db.ClientDBURLMap.ClientDBURLMap", "line_number": 32, "usage_type": "attribute"}, {"api_name": "hydrus.client.db.ClientDBURLMap", "line_number": 32, "usage_type": "name"}, {"api_name": "hydrus.client.db.ClientDBModule.ClientDBModule.__init__", "line_number": 45, "usage_type": "call"}, {"api_name": "hydrus.client.db.ClientDBModule.ClientDBModule", "line_number": 45, "usage_type": "attribute"}, {"api_name": "hydrus.client.db.ClientDBModule", "line_number": 45, "usage_type": "name"}, {"api_name": "hydrus.client.ClientLocation.LocationContext", "line_number": 48, "usage_type": "attribute"}, {"api_name": "hydrus.client.ClientLocation", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 48, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 48, "usage_type": "attribute"}, {"api_name": "hydrus.client.db.ClientDBFilesStorage.GenerateFilesTableName", "line_number": 77, "usage_type": "call"}, {"api_name": "hydrus.client.db.ClientDBFilesStorage", "line_number": 77, "usage_type": "name"}, {"api_name": "hydrus.core.HydrusConstants.CONTENT_STATUS_CURRENT", "line_number": 77, "usage_type": "attribute"}, {"api_name": "hydrus.core.HydrusConstants", "line_number": 77, "usage_type": "name"}, {"api_name": "hydrus.client.db.ClientDBFilesStorage.GenerateFilesTableName", "line_number": 81, "usage_type": "call"}, {"api_name": "hydrus.client.db.ClientDBFilesStorage", "line_number": 81, "usage_type": "name"}, {"api_name": "hydrus.core.HydrusConstants.CONTENT_STATUS_DELETED", "line_number": 81, "usage_type": "attribute"}, {"api_name": "hydrus.core.HydrusConstants", "line_number": 81, "usage_type": "name"}, {"api_name": "hydrus.core.HydrusTime.TimestampToPrettyTime", "line_number": 257, "usage_type": "call"}, {"api_name": "hydrus.core.HydrusTime", "line_number": 257, "usage_type": "name"}, {"api_name": "hydrus.core.HydrusTime.BaseTimestampToPrettyTimeDelta", "line_number": 257, "usage_type": "call"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus", "line_number": 260, "usage_type": "call"}, {"api_name": "hydrus.client.importing.ClientImportFiles", "line_number": 260, "usage_type": "name"}, {"api_name": "hydrus.client.ClientConstants.STATUS_DELETED", "line_number": 260, "usage_type": "attribute"}, {"api_name": "hydrus.client.ClientConstants", "line_number": 260, "usage_type": "name"}, {"api_name": "hydrus.core.HydrusTime.TimestampToPrettyTime", "line_number": 269, "usage_type": "call"}, {"api_name": "hydrus.core.HydrusTime", "line_number": 269, "usage_type": "name"}, {"api_name": "hydrus.core.HydrusTime.BaseTimestampToPrettyTimeDelta", "line_number": 269, "usage_type": "call"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus", "line_number": 271, "usage_type": "call"}, {"api_name": "hydrus.client.importing.ClientImportFiles", "line_number": 271, "usage_type": "name"}, {"api_name": "hydrus.client.ClientConstants.STATUS_DELETED", "line_number": 271, "usage_type": "attribute"}, {"api_name": "hydrus.client.ClientConstants", "line_number": 271, "usage_type": "name"}, {"api_name": "hydrus.core.HydrusTime.TimestampToPrettyTime", "line_number": 282, "usage_type": "call"}, {"api_name": "hydrus.core.HydrusTime", "line_number": 282, "usage_type": "name"}, {"api_name": "hydrus.core.HydrusTime.BaseTimestampToPrettyTimeDelta", "line_number": 282, "usage_type": "call"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus", "line_number": 284, "usage_type": "call"}, {"api_name": "hydrus.client.importing.ClientImportFiles", "line_number": 284, "usage_type": "name"}, {"api_name": "hydrus.client.ClientConstants.STATUS_SUCCESSFUL_BUT_REDUNDANT", "line_number": 284, "usage_type": "attribute"}, {"api_name": "hydrus.client.ClientConstants", "line_number": 284, "usage_type": "name"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus", "line_number": 287, "usage_type": "call"}, {"api_name": "hydrus.client.importing.ClientImportFiles", "line_number": 287, "usage_type": "name"}, {"api_name": "hydrus.client.ClientConstants.STATUS_UNKNOWN", "line_number": 287, "usage_type": "attribute"}, {"api_name": "hydrus.client.ClientConstants", "line_number": 287, "usage_type": "name"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus", "line_number": 238, "usage_type": "attribute"}, {"api_name": "hydrus.client.importing.ClientImportFiles", "line_number": 238, "usage_type": "name"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus.STATICGetUnknownStatus", "line_number": 303, "usage_type": "call"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus", "line_number": 303, "usage_type": "attribute"}, {"api_name": "hydrus.client.importing.ClientImportFiles", "line_number": 303, "usage_type": "name"}, {"api_name": "hydrus.core.HydrusExceptions.DataMissing", "line_number": 316, "usage_type": "attribute"}, {"api_name": "hydrus.core.HydrusExceptions", "line_number": 316, "usage_type": "name"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus.STATICGetUnknownStatus", "line_number": 318, "usage_type": "call"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus", "line_number": 318, "usage_type": "attribute"}, {"api_name": "hydrus.client.importing.ClientImportFiles", "line_number": 318, "usage_type": "name"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus", "line_number": 290, "usage_type": "attribute"}, {"api_name": "hydrus.client.importing.ClientImportFiles", "line_number": 290, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 325, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 325, "usage_type": "attribute"}, {"api_name": "hydrus.client.networking.ClientNetworkingFunctions.GetSearchURLs", "line_number": 332, "usage_type": "call"}, {"api_name": "hydrus.client.networking.ClientNetworkingFunctions", "line_number": 332, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 330, "usage_type": "attribute"}, {"api_name": "hydrus.client.importing.ClientImportFiles.FileImportStatus", "line_number": 330, "usage_type": "attribute"}, {"api_name": "hydrus.client.importing.ClientImportFiles", "line_number": 330, "usage_type": "name"}]} +{"seq_id": "633603313", "text": "import os\nimport lockfile\nimport posixpath\nclass FileSystemKeyQueue(object):\n def __init__(self, dirname):\n os.path.exists(dirname) or os.mkdir(dirname)\n self.lock = lockfile.FileLock(dirname + \".lock\")\n self.dirname = dirname\n\n def _files(self):\n files = os.listdir(self.dirname)\n files = map(int, files)\n files.sort()\n return files\n\n def _old_filepath(self):\n files = self._files()\n if not files:\n return None\n return os.path.join(self.dirname, str(files[0]))\n\n def _new_filepath(self):\n files = self._files()\n if not files:\n files = [0]\n return os.path.join(self.dirname, str(files[-1]+1))\n\n def _check_key(self, key):\n key = posixpath.normpath(key)\n if not key.startswith(self.dirname): return None\n if not os.path.exists(key): return None\n return key\n\n def get(self):\n with self.lock:\n filepath = self._old_filepath()\n if not filepath: return None\n\n with open(filepath, 'rb') as f:\n data = f.read()\n newfilepath = self._new_filepath()\n os.rename(filepath, newfilepath)\n return newfilepath, data\n\n def put(self, data):\n with self.lock:\n filepath = self._new_filepath()\n with open(filepath, 'wb') as f:\n f.write(data)\n\n def remove(self, key):\n with self.lock:\n key = self._check_key(key)\n if not key: return False\n os.remove(key)\n return True\n", "sub_path": "keyqueue.py", "file_name": "keyqueue.py", "file_ext": "py", "file_size_in_byte": 1594, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.exists", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 6, "usage_type": "call"}, {"api_name": "lockfile.FileLock", "line_number": 7, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "posixpath.normpath", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 42, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "7729377", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nAlgebraic Quaternion Algorithm\n==============================\n\nRoberto Valenti's Algebraic Quaterion Algorithm (AQUA) [Valenti2015]_ estimates\na quaternion with the algebraic solution of a system from inertial/magnetic\nobservations.\n\nAQUA computes the \"tilt\" quaternion and the \"heading\" quaternion separately in\ntwo sub-parts. This avoids the impact of the magnetic disturbances on the roll\nand pitch components of the orientation.\n\nIt uses a complementary filter that fuses together gyroscope data with\naccelerometer and magnetic field readings. The correction part of the filter is\nbased on the independently estimated quaternions and works for both IMU\n(Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity)\nsensors [Valenti2016]_.\n\nReferences\n----------\n.. [Valenti2015] Valenti, R.G.; Dryanovski, I.; Xiao, J. Keeping a Good\n Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs. Sensors\n 2015, 15, 19302-19330.\n (https://res.mdpi.com/sensors/sensors-15-19302/article_deploy/sensors-15-19302.pdf)\n.. [Valenti2016] R. G. Valenti, I. Dryanovski and J. Xiao, \"A Linear Kalman\n Filter for MARG Orientation Estimation Using the Algebraic Quaternion\n Algorithm,\" in IEEE Transactions on Instrumentation and Measurement, vol.\n 65, no. 2, pp. 467-481, 2016.\n (https://ieeexplore.ieee.org/document/7345567)\n\n\"\"\"\n\nimport numpy as np\nfrom ..common.orientation import q_prod, q2R\nfrom ..common.constants import MUNICH_LATITUDE, MUNICH_HEIGHT\n\n# Reference Observations in Munich, Germany\nfrom ..utils.wgs84 import WGS\nGRAVITY = WGS().normal_gravity(MUNICH_LATITUDE, MUNICH_HEIGHT)\n\ndef slerp_I(q: np.ndarray, ratio: float, t: float) -> np.ndarray:\n \"\"\"\n Interpolation with identity quaternion\n\n Interpolate a given quaternion with the identity quaternion\n :math:`\\\\mathbf{q}_I=\\\\begin{pmatrix}1 & 0 & 0 & 0\\\\end{pmatrix}` to\n scale it to closest versor.\n\n The interpolation can be with either LERP (Linear) or SLERP (Spherical\n Linear) methods, decided by a threshold value :math:`t`, which lies\n between ``0.0`` and ``1.0``.\n\n .. math::\n \\\\mathrm{method} = \\\\left\\\\{\n \\\\begin{array}{ll}\n \\\\mathrm{LERP} & \\\\: q_w > t \\\\\\\\\n \\\\mathrm{SLERP} & \\\\: \\\\mathrm{otherwise}\n \\\\end{array}\n \\\\right.\n\n For LERP, a simple equation is implemented:\n\n .. math::\n \\\\hat{\\\\mathbf{q}} = (1-\\\\alpha)\\\\mathbf{q}_I + \\\\alpha\\\\Delta \\\\mathbf{q}\n\n where :math:`\\\\alpha\\\\in [0, 1]` is the gain characterizing the cut-off\n frequency of the filter. It basically decides how \"close\" to the given\n quaternion or to the identity quaternion the interpolation is.\n\n If the scalar part :math:`q_w` of the given quaternion is below the\n threshold :math:`t`, SLERP is used:\n\n .. math::\n \\\\hat{\\\\mathbf{q}} = \\\\frac{\\\\sin([1-\\\\alpha]\\\\Omega)}{\\\\sin\\\\Omega} \\\\mathbf{q}_I + \\\\frac{\\\\sin(\\\\alpha\\\\Omega)}{\\\\sin\\\\Omega} \\\\mathbf{q}\n\n where :math:`\\\\Omega=\\\\arccos(q_w)` is the subtended arc between the\n quaternions.\n\n Parameters\n ----------\n q : numpy.array\n Quaternion to inerpolate with.\n ratio : float\n Gain characterizing the cut-off frequency of the filter.\n t : float\n Threshold deciding interpolation method. LERP when qw>t, otherwise\n SLERP.\n\n Returns\n -------\n q : numpy.array\n Interpolated quaternion\n \"\"\"\n q_I = np.array([1.0, 0.0, 0.0, 0.0])\n if q[0]>t: # LERP\n q = (1.0-ratio)*q_I + ratio*q # (eq. 50)\n else: # SLERP\n angle = np.arccos(q[0])\n q = q_I*np.sin(abs(1.0-ratio)*angle)/np.sin(angle) + q*np.sin(ratio*angle)/np.sin(angle) # (eq. 52)\n q /= np.linalg.norm(q) # (eq. 51)\n return q\n\ndef adaptive_gain(a: float, a_norm: float, t1: float = 0.1, t2: float = 0.2, g: float = GRAVITY) -> float:\n \"\"\"\n Adaptive filter gain factor\n\n The estimated gain :math:`\\\\alpha` is dependent on the gain factor\n :math:`f` determined by the magnitude error :math:`e_m`:\n\n .. math::\n \\\\alpha = a f(e_m)\n\n where the magnitude error is defined by the measured acceleration\n :math:`\\\\mathbf{a}=\\\\begin{bmatrix}a_x & a_y & a_z\\\\end{bmatrix}^T` and the\n reference gravity :math:`g\\\\approx 9.809196 \\\\frac{m}{s^2}`:\n\n .. math::\n e_m = \\\\frac{|\\\\|\\\\mathbf{a}\\\\|-g|}{g}\n\n The gain factor is constant and equal to 1 when the magnitude of the\n nongravitational acceleration is not high enough to overcome gravity.\n\n If nongravitational acceleration rises and :math:`e_m` exceeds the\n first threshold, the gain factor :math:`f` decreases linearly with the\n increase of the magnitude until reaching zero at the second threshold\n and above it.\n\n Empirically, both thresholds have been defined at ``0.1`` and ``0.2``,\n respectively. They can be, however, changed by setting the values of\n input parameters ``t1`` and ``t2``.\n\n Parameters\n ----------\n a : float\n Constant gain yielding best results in static conditions.\n a_norm : float\n Norm of measured local acceleration vector.\n t1 : float, default: 0.1\n First threshold\n t2 : float, default: 0.2\n Second threshold\n g : float, default: 9.809196\n Reference gravitational acceleration in m/s^2. The estimated gravity in\n Munich, Germany (``9.809196``) is used as default reference value.\n\n Returns\n -------\n alpha : float\n Gain factor\n\n Examples\n --------\n >>> alpha = adaptive_gain(a, 9.71)\n \"\"\"\n em = abs(a_norm-GRAVITY)/GRAVITY # Magnitude error (eq. 60)\n f = 0.0\n if e1 np.ndarray:\n \"\"\"\n Quaternion from Earth-Field Observations\n\n Algebraic estimation of a quaternion as a function of an observation of\n the Earth's gravitational and magnetic fields.\n\n It decomposes the quaternion :math:`\\\\mathbf{q}` into two auxiliary\n quaternions :math:`\\\\mathbf{q}_{\\\\mathrm{acc}}` and\n :math:`\\\\mathbf{q}_{\\\\mathrm{mag}}`, such that:\n\n .. math::\n \\\\mathbf{q} = \\\\mathbf{q}_{\\\\mathrm{acc}}\\\\mathbf{q}_{\\\\mathrm{mag}}\n\n Parameters\n ----------\n acc : numpy.ndarray, default: None\n Sample of tri-axial Accelerometer in m/s^2\n mag : numpy.ndarray, default: None\n Sample of tri-axial Magnetometer in mT\n\n Returns\n -------\n q : numpy.ndarray\n Estimated quaternion.\n \"\"\"\n ax, ay, az = acc/np.linalg.norm(acc)\n # Quaternion from Accelerometer Readings (eq. 25)\n if az>=0:\n q_acc = np.array([np.sqrt((az+1)/2), -ay/np.sqrt(2*(1-ax)), ax/np.sqrt(2*(az+1)), 0.0])\n else:\n q_acc = np.array([-ay/np.sqrt(2*(1-az)), np.sqrt((1-az)/2.0), 0.0, ax/np.sqrt(2*(1-az))])\n q_acc /= np.linalg.norm(q_acc)\n # m_norm = np.linalg.norm(mag)\n if mag is not None and not (np.linalg.norm(mag)>0):\n lx, ly, lz = q2R(q_acc).T@(mag/np.linalg.norm(mag)) # (eq. 26)\n Gamma = lx**2 + ly**2 # (eq. 28)\n # Quaternion from Magnetometer Readings (eq. 35)\n if lx>=0:\n q_mag = np.array([np.sqrt(Gamma+lx*np.sqrt(Gamma))/np.sqrt(2*Gamma), 0.0, 0.0, ly/np.sqrt(2)*np.sqrt(Gamma+lx*np.sqrt(Gamma))])\n else:\n q_mag = np.array([ly/np.sqrt(2)*np.sqrt(Gamma-lx*np.sqrt(Gamma)), 0.0, 0.0, np.sqrt(Gamma-lx*np.sqrt(Gamma))/np.sqrt(2*Gamma)])\n # Generalized Quaternion Orientation (eq. 36)\n q = q_prod(q_acc, q_mag)\n return q/np.linalg.norm(q)\n return q_acc\n\n def updateIMU(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray) -> np.ndarray:\n \"\"\"\n Quaternion Estimation with a IMU architecture.\n\n The estimation is made in two steps: a *prediction* is done with the\n angular rate (gyroscope) to integrate and estimate the current\n orientation; then a *correction* step uses the measured accelerometer\n to infer the expected gravity vector and use it to correct the\n predicted quaternion.\n\n If the gyroscope data is invalid, it returns the given a-priori\n quaternion. Secondly, if the accelerometer data is invalid the\n predicted quaternion (using gyroscopes) is returned.\n\n Parameters\n ----------\n q : numpy.ndarray\n A-priori quaternion.\n gyr : numpy.ndarray\n Sample of tri-axial Gyroscope in rad/s.\n acc : numpy.ndarray\n Sample of tri-axial Accelerometer in m/s^2\n\n Returns\n -------\n q : numpy.ndarray\n Estimated quaternion.\n\n \"\"\"\n if gyr is None or not np.linalg.norm(gyr)>0:\n return q\n # PREDICTION\n qDot = -0.5*q_prod([0, *gyr], q) # Quaternion derivative (eq. 38)\n qInt = q + qDot*self.Dt # Quaternion integration (eq. 42)\n qInt /= np.linalg.norm(qInt)\n # CORRECTION\n a_norm = np.linalg.norm(acc)\n if not a_norm>0:\n return qInt\n a = acc/a_norm\n gx, gy, gz = q2R(qInt).T@a # Predicted gravity (eq. 44)\n q_acc = np.array([np.sqrt((gz+1)/2.0), -gy/np.sqrt(2.0*(gz+1)), gx/np.sqrt(2.0*(gz+1)), 0.0]) # Delta Quaternion (eq. 47)\n if self.adaptive:\n self.alpha = self.adaptive_gain(self.alpha, a_norm)\n q_acc = slerp_I(q_acc, self.alpha, self.threshold)\n q_prime = q_prod(qInt, q_acc) # (eq. 53)\n return q_prime/np.linalg.norm(q_prime)\n\n def updateMARG(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray, mag: np.ndarray) -> np.ndarray:\n \"\"\"\n Quaternion Estimation with a MARG architecture.\n\n The estimation is made in two steps: a *prediction* is done with the\n angular rate (gyroscope) to integrate and estimate the current\n orientation; then a *correction* step uses the measured accelerometer\n and magnetic field to infer the expected geodetic values. Its\n divergence is used to correct the predicted quaternion.\n\n If the gyroscope data is invalid, it returns the given a-priori\n quaternion. Secondly, if the accelerometer data is invalid the\n predicted quaternion (using gyroscopes) is returned. Finally, if the\n magnetometer measurements are invalid, returns a quaternion corrected\n by the accelerometer only.\n\n Parameters\n ----------\n q : numpy.ndarray\n A-priori quaternion.\n gyr : numpy.ndarray\n Sample of tri-axial Gyroscope in rad/s.\n acc : numpy.ndarray\n Sample of tri-axial Accelerometer in m/s^2\n mag : numpy.ndarray\n Sample of tri-axial Magnetometer in mT\n\n Returns\n -------\n q : numpy.ndarray\n Estimated quaternion.\n\n \"\"\"\n if gyr is None or not np.linalg.norm(gyr)>0:\n return q\n # PREDICTION\n qDot = -0.5*q_prod([0, *gyr], q) # Quaternion derivative (eq. 38)\n qInt = q + qDot*self.Dt # Quaternion integration (eq. 42)\n qInt /= np.linalg.norm(qInt)\n # CORRECTION\n a_norm = np.linalg.norm(acc)\n if not a_norm>0:\n return qInt\n a = acc/a_norm\n gx, gy, gz = q2R(qInt).T@a # Predicted gravity (eq. 44)\n # Accelerometer-Based Quaternion\n q_acc = np.array([np.sqrt((gz+1)/2.0), -gy/np.sqrt(2.0*(gz+1)), gx/np.sqrt(2.0*(gz+1)), 0.0]) # Delta Quaternion (eq. 47)\n if self.adaptive:\n self.alpha = self.adaptive_gain(self.alpha, a_norm)\n q_acc = slerp_I(q_acc, self.alpha, self.threshold)\n q_prime = q_prod(qInt, q_acc) # (eq. 53)\n q_prime /= np.linalg.norm(q_prime)\n # Magnetometer-Based Quaternion\n m_norm = np.linalg.norm(mag)\n if not m_norm>0:\n return q_prime\n lx, ly, lz = q2R(q_prime).T@(mag/m_norm) # World frame magnetic vector (eq. 54)\n Gamma = lx**2 + ly**2 # (eq. 28)\n q_mag = np.array([np.sqrt(Gamma+lx*np.sqrt(Gamma))/np.sqrt(2*Gamma), 0.0, 0.0, ly/np.sqrt(2*(Gamma+lx*np.sqrt(Gamma)))]) # (eq. 58)\n q_mag = slerp_I(q_mag, self.beta, self.threshold)\n # Generalized Quaternion\n q = q_prod(q_prime, q_mag) # (eq. 59)\n return q/np.linalg.norm(q)\n", "sub_path": "ahrs/filters/aqua.py", "file_name": "aqua.py", "file_ext": "py", "file_size_in_byte": 16314, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "common.constants.MUNICH_LATITUDE", "line_number": 40, "usage_type": "argument"}, {"api_name": "common.constants.MUNICH_HEIGHT", "line_number": 40, "usage_type": "argument"}, {"api_name": "utils.wgs84.WGS", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 216, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 251, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 277, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 283, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 285, "usage_type": "attribute"}, {"api_name": "common.orientation.q2R", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 286, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 292, "usage_type": "call"}, {"api_name": "common.orientation.q_prod", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 295, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 298, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 327, "usage_type": "attribute"}, {"api_name": "common.orientation.q_prod", "line_number": 330, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 332, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 334, "usage_type": "attribute"}, {"api_name": "common.orientation.q2R", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 339, "usage_type": "call"}, {"api_name": "common.orientation.q_prod", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 344, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 346, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 379, "usage_type": "attribute"}, {"api_name": "common.orientation.q_prod", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 384, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 386, "usage_type": "attribute"}, {"api_name": "common.orientation.q2R", "line_number": 390, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 392, "usage_type": "call"}, {"api_name": "common.orientation.q_prod", "line_number": 396, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 397, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 397, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 399, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 399, "usage_type": "attribute"}, {"api_name": "common.orientation.q2R", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 404, "usage_type": "call"}, {"api_name": "common.orientation.q_prod", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 408, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 408, "usage_type": "attribute"}]} +{"seq_id": "349518789", "text": "from django.conf.urls import url\nfrom tasks import views\n\nurlpatterns = [\n url(r'^(?P[0-9]+)/$', views.task_detail),\n url(r'^deal/(?P[0-9]+)/(?P[0-9]+)/$', views.task_list_deal),\n url(r'^contact/(?P[0-9]+)/(?P[0-9]+)/$', views.task_list_cont),\n url(r'^finish/$', views.task_finish),\n url(r'^types/$', views.task_type_list),\n url(r'^delete/$', views.delete_tasks),\n url(r'^change_responsible/$', views.change_responsible),\n url(r'^for_birthday_script/$', views.for_birthday_script),\n url(r'^(?P\\w+)/$', views.task_list),\n]\n", "sub_path": "tasks/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 588, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "tasks.views.task_detail", "line_number": 5, "usage_type": "attribute"}, {"api_name": "tasks.views", "line_number": 5, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "tasks.views.task_list_deal", "line_number": 6, "usage_type": "attribute"}, {"api_name": "tasks.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "tasks.views.task_list_cont", "line_number": 7, "usage_type": "attribute"}, {"api_name": "tasks.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "tasks.views.task_finish", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tasks.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "tasks.views.task_type_list", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tasks.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "tasks.views.delete_tasks", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tasks.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "tasks.views.change_responsible", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tasks.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "tasks.views.for_birthday_script", "line_number": 12, "usage_type": "attribute"}, {"api_name": "tasks.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "tasks.views.task_list", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tasks.views", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "288858436", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm, gaussian_kde\nfrom scipy.special import softmax, logsumexp\n\nraw_col = '#029386'\ncluster_cols = {0: '#0343df', 1: '#e50000', 2:'#f97306', 3: '#15b01a'} \n\n\ndef simulate_data(plot = True):\n\n\tnsamples = 5000\n\n\ta_sd = 1\n\ta_mean = 3\n\ta_prop = 2\n\n\tb_mean = -2\n\tb_sd = 2\n\tb_prop = 1\n\tproportions = [a_prop, b_prop]\n\tproportions /= np.sum(proportions, axis = 0)\n\t#print(props)\n\ta = np.random.randn(int(nsamples*proportions[0]))*a_sd + a_mean\n\tb = np.random.randn(int(nsamples*proportions[1]))*b_sd + b_mean\n\n\tX = np.concatenate( (a,b) )\n\n\t\n\tif plot: \n\t\tdensity = gaussian_kde(X)\n\t\trg = np.linspace(min(X),max(X),500)\n\t\tplt.plot(rg,density(rg), raw_col, linewidth = 1) #row=0, col=0\n\t\tplt.plot(rg, norm.pdf(rg, a_mean, a_sd)*proportions[0], color = cluster_cols[0], linewidth = 1) \n\t\tplt.plot(rg, norm.pdf(rg, b_mean, b_sd)*proportions[1], color = cluster_cols[1], linewidth = 1)\n\t\tplt.show()\n\n\treturn(X)\n\ndef gmm_em(x, n_clust, plot = True, iter = 100, tol = 1e-4):\n\n\t\"\"\"fits a guassian mixture model through expectation-maximisation\"\"\"\n\n\t#initialise means and sds and props\n\tmeans = np.random.randn(n_clust)\n\tsds = np.ones(n_clust)\n\tprops = np.full(n_clust, 1/n_clust) #this has an array of size n_clust summing to one\n\tn_obs = x.shape[0]\n\tlik_hist = [] #for convergence\n\n\tfor i in range(iter):\n\n\t\tif plot:\n\t\t\tplt.cla()\n\t\t\tdensity = gaussian_kde(x)\n\t\t\trg = np.linspace(min(x),max(x),500)\n\t\t\tplt.plot(rg, density(rg), color = raw_col)\n\t\t\tfor c, (m, s, p) in enumerate(zip(means, sds, props)):\n\t\t\t\tplt.plot(rg, norm.pdf(rg, m, s)*p, color = cluster_cols[c]) \t\t\t\n\t\t\tplt.draw()\n\t\t\tplt.pause(.2)\n\n\t\t#EXPECTATION STEP\n\t\t#compute likelihood of latent labels (i.e. belonging to each cluster)\t\t\n\t\tliks = np.zeros([n_clust,n_obs])\n\t\tfor c in range(n_clust): \n\t\t\tprior = props[c] #props is cluster size\n\t\t\tloglik = norm.logpdf(x, means[c], sds[c]) + np.log(props[c])\n\t\t\tliks[c] = loglik\n\t\t\t \t\t\t\n\t\ttotal_lik = np.sum(logsumexp(liks, axis=0)) \n\t\tlik_hist.append(total_lik) \n\n\t\tresps = softmax(liks, axis=0) #responsibility vector for each sample\t\t\n\t\t\n\t\tprops = np.sum(resps, axis = 1) #to update estimates of cluster size take sum of total weights and divide by n observations\n\t\tprops /= n_obs\t\t\t\n\n\t\t#check convergence\n\t\tif i > 0: \n\t\t\tlik_change = total_lik - lik_hist[-2]\n\t\t\t# print(lik_change)\n\t\t\tassert(lik_change > 0)\n\t\t\tif lik_change < tol: \n\t\t\t\tfit = {\n\t\t\t\t\t'data':X,\n\t\t\t\t\t'means':means,\n\t\t\t\t\t'sds':sds,\n\t\t\t\t\t'props':props,\n\t\t\t\t\t'lik_hist':lik_hist,\n\t\t\t\t\t'resps':resps \n\t\t\t\t}\n\t\t\t\tprint(\"converged on step: \", i) \n\t\t\t\treturn (fit) \n\n\t\t#MAXIMISATION STEP to obtain new means and sds, given the expectations/responsibilities\n\t\tmeans = [np.average(x, weights = r) for r in resps] #weighted average \n\t\tsds = [ np.average((x-m)**2, weights = r) for m, r in zip(means, resps)] #weighted variance\n\t\tsds = np.sqrt(sds)\n\nif __name__ == '__main__':\n\tX = simulate_data(plot = True)\n\tgmm_em(X, 2, plot = True)\n\n", "sub_path": "Processing/gmm_1d_example.py", "file_name": "gmm_1d_example.py", "file_ext": "py", "file_size_in_byte": 3073, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "numpy.sum", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.stats.gaussian_kde", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.random.randn", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cla", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "scipy.stats.gaussian_kde", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.stats.norm.logpdf", "line_number": 68, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 71, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 71, "usage_type": "call"}, {"api_name": "scipy.special.softmax", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "229195725", "text": "import sys \nfrom trainer import model_cnn\n\n# from model_cnn import *\nimport argparse\nimport hypertune\n\nnum_files = range(0,15)\nGC_PATHS = []\n\nfor num_file in num_files:\n file_path = 'gs://mfccs/mfccs200_' + str(num_file) + '.tfrecords'\n GC_PATHS.append(file_path)\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\n '--alpha',\n default=0.001,\n type=float)\nparser.add_argument(\n '--job-dir',\n required=False)\n \nargs = parser.parse_args()\narguments = args.__dict__\njob_dir = arguments.pop('job_dir')\n\nprint('ALPHA',args.alpha)\nepochs = 2\n\nmodel = model_cnn.start_training(GC_PATHS[0:4], GC_PATHS[5], args.alpha, epochs)\n\nval = model_cnn.get_dataset(GC_PATHS[5])\nloss = model.evaluate(val)[0]\n\n# Calling the hypertune library and setting our metric\nhpt = hypertune.HyperTune()\nhpt.report_hyperparameter_tuning_metric(\n hyperparameter_metric_tag='loss',\n metric_value=loss,\n global_step=epochs)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "json_main.py", "file_name": "json_main.py", "file_ext": "py", "file_size_in_byte": 959, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "trainer.model_cnn.start_training", "line_number": 31, "usage_type": "call"}, {"api_name": "trainer.model_cnn", "line_number": 31, "usage_type": "name"}, {"api_name": "trainer.model_cnn.get_dataset", "line_number": 33, "usage_type": "call"}, {"api_name": "trainer.model_cnn", "line_number": 33, "usage_type": "name"}, {"api_name": "hypertune.HyperTune", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "497538814", "text": "# _________________________________________________________________________\n#\n# Pyomo: Python Optimization Modeling Objects\n# Copyright (c) 2014 Sandia Corporation.\n# Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,\n# the U.S. Government retains certain rights in this software.\n# This software is distributed under the BSD License.\n# _________________________________________________________________________\n\nimport sys\nimport logging\n\nimport pyomo.core\nfrom pyomo.core.base import Constraint, \\\n Objective, \\\n ComponentMap, \\\n active_components_data\nfrom pyomo.repn import generate_ampl_repn\n\n\ndef preprocess_block_objectives(block):\n\n # Get/Create the ComponentMap for the repn\n if not hasattr(block,'_ampl_repn'):\n block._ampl_repn = ComponentMap()\n block_ampl_repn = block._ampl_repn\n\n for objective_data in active_components_data(block, Objective): #recursive = False\n\n if objective_data.expr is None:\n raise ValueError(\"No expression has been defined for objective %s\" % str(key))\n\n try:\n ampl_repn = generate_ampl_repn(objective_data.expr)\n except Exception:\n err = sys.exc_info()[1]\n logging.getLogger('pyomo.core').error\\\n ( \"exception generating a ampl representation for objective %s: %s\" \\\n % (objective_data.cname(True), str(err)) )\n raise\n\n block_ampl_repn[objective_data] = ampl_repn\n\ndef preprocess_block_constraints(block):\n\n # Get/Create the ComponentMap for the repn\n if not hasattr(block,'_ampl_repn'):\n block._ampl_repn = ComponentMap()\n block_ampl_repn = block._ampl_repn\n\n for constraint_data in active_components_data(block, Constraint): #recursive = False\n\n if constraint_data.body is None:\n raise ValueError(\"No expression has been defined for the body of constraint %s, index=%s\" % (str(constraint.name), str(index)))\n\n try:\n ampl_repn = generate_ampl_repn(constraint_data.body)\n except Exception:\n err = sys.exc_info()[1]\n logging.getLogger('pyomo.core').error\\\n ( \"exception generating a ampl representation for constraint %s: %s\" \\\n % (constraint_data.cname(True), str(err)) )\n raise\n\n block_ampl_repn[constraint_data] = ampl_repn\n\n@pyomo.util.pyomo_api(namespace='pyomo.repn')\ndef compute_ampl_repn(data, model=None):\n \"\"\"\n This plugin computes the ampl representation for all objectives\n and constraints. All results are stored in a ComponentMap named\n \"_ampl_repn\" at the block level.\n\n NOTE: this does not check for trivial constraints\n\n We break out preprocessing of the objectives and constraints\n in order to avoid redundant and unnecessary work, specifically\n in contexts where a model is iteratively solved and modified.\n we don't have finer-grained resolution, but we could easily\n pass in a Constraint and an Objective if warranted.\n\n Required:\n model: A concrete model instance.\n \"\"\"\n for block in model.all_blocks(active=True):\n preprocess_block_constraints(block)\n preprocess_block_objectives(block)\n\n", "sub_path": "pyomo/repn/compute_ampl_repn.py", "file_name": "compute_ampl_repn.py", "file_ext": "py", "file_size_in_byte": 3300, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pyomo.core.base.ComponentMap", "line_number": 25, "usage_type": "call"}, {"api_name": "pyomo.core.base.active_components_data", "line_number": 28, "usage_type": "call"}, {"api_name": "pyomo.core.base.Objective", "line_number": 28, "usage_type": "argument"}, {"api_name": "pyomo.repn.generate_ampl_repn", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 37, "usage_type": "call"}, {"api_name": "pyomo.core.base.ComponentMap", "line_number": 48, "usage_type": "call"}, {"api_name": "pyomo.core.base.active_components_data", "line_number": 51, "usage_type": "call"}, {"api_name": "pyomo.core.base.Constraint", "line_number": 51, "usage_type": "argument"}, {"api_name": "pyomo.repn.generate_ampl_repn", "line_number": 57, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 60, "usage_type": "call"}, {"api_name": "pyomo.core.util.pyomo_api", "line_number": 67, "usage_type": "call"}, {"api_name": "pyomo.core.util", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pyomo.core", "line_number": 67, "usage_type": "name"}]} +{"seq_id": "372203912", "text": "from __future__ import print_function\n\nimport os, sys, glob, time, re, gc\n\nmodule_path = os.path.abspath(os.path.join('.'))\nif module_path not in sys.path:\n sys.path.append(module_path)\nb = sys.path\nsys.path = [module_path] + b\n\nimport pathlib as p\nimport copy, warnings\nfrom collections import OrderedDict\nimport time\n\nimport numpy\n\nimport joblib as jl\n\nfrom libtbx.math_utils import ifloor, iceil\nfrom scitbx.array_family import flex\n\nfrom bamboo.common.status import status_bar, status_bar_2\n\nfrom pandda.analyse.classes import PanddaStatMapList, MapHolderList\nfrom pandda.analyse.functions import DatasetAligner, MapLoader, DensityStatistics, UncertaintyCalculator, wrapper_run\n\nfrom dask.distributed import worker_client\n\n\ndef load(model):\n # Get truncated datasets\n model.dataset.sample_loader.truncate_datasets(model.dataset.datasets)\n model.dataset.datasets = model.dataset.sample_loader.truncated_datasets\n\n # Get cur resolution for dataset\n test_datasets = model.dataset.partition_datasets(\"test\")\n train_datasets = model.dataset.partition_datasets(\"train\")\n test_datasets.update(train_datasets)\n resolutions = [d.data.summary.high_res for dtag, d in test_datasets.items()]\n max_res = max(resolutions)\n\n # Get ref map\n model.dataset.sample_loader.get_reference(max_res)\n\n # Attach grid to\n model.map_maker.attach_grid(model.grid)\n\n # Attach grid to event table maker\n model.event_table_maker.grid = model.grid\n\n return model\n\n\ndef fit(model):\n\n model.fit()\n\n return model\n\n\ndef evaluate(model):\n\n model.evaluate()\n\n return model\n\n\ndef criticise(model):\n\n event_table = model.criticise()\n\n return event_table\n\n\nclass PanDDAEventModelDistributed:\n\n def __init__(self, statistical_model, clusterer, event_finder, bdc_calculator=None, statistics=[], dataset=None,\n reference=None, grid=None, map_maker=None, event_table_maker=None, cpus=1, tree=None, name=None):\n\n self.trace = None\n self.cpus = cpus\n self.tree = tree\n self.name = name\n\n self.statistical_model = statistical_model\n self.clusterer = clusterer\n self.event_finder = event_finder\n self.bdc_calculator = bdc_calculator\n self.statistics = statistics\n self.map_maker = map_maker\n self.event_table_maker = event_table_maker\n\n self.parameters = None\n\n self.statistical_maps = None\n self.clusters = None\n self.events = None\n self.bdcs = None\n self.dataset = None\n self.grid = None\n\n self.dataset = dataset\n self.reference = reference\n self.grid = grid\n\n def instantiate(self, reference, tree):\n self.reference = reference\n self.tree = tree\n\n self.grid = self.dataset.sample_loader.get_grid(reference=self.reference)\n\n # Get truncated datasets\n self.dataset.sample_loader.truncate_datasets(self.dataset.datasets)\n self.dataset.datasets = self.dataset.sample_loader.truncated_datasets\n\n # Get cur resolution for dataset\n test_datasets = self.dataset.partition_datasets(\"test\")\n train_datasets = self.dataset.partition_datasets(\"train\")\n test_datasets.update(train_datasets)\n resolutions = [d.data.summary.high_res for dtag, d in test_datasets.items()]\n max_res = max(resolutions)\n\n # Get ref map\n self.dataset.sample_loader.get_reference(max_res)\n\n # Attach grid to\n self.map_maker.attach_grid(self.grid)\n\n # Attach grid to event table maker\n self.event_table_maker.grid = self.grid\n\n return self\n\n def clone(self, name=None, dataset=None):\n\n # Create a clone with new dataset and name\n model = PanDDAEventModelDistributed(self.statistical_model,\n self.clusterer,\n self.event_finder,\n dataset=dataset,\n reference=self.reference,\n grid=self.grid,\n bdc_calculator=self.bdc_calculator,\n statistics=self.statistics,\n map_maker=self.map_maker,\n event_table_maker=self.event_table_maker,\n cpus=self.cpus,\n tree=self.tree,\n name=name)\n\n model.tree.update({str(model.name): {\"dummy\": None}})\n\n # Attach grid to clusterer and event finder\n model.clusterer.grid = self.grid\n model.event_finder.grid = self.grid\n model.bdc_calculator.grid = self.grid\n\n return model\n\n def fit(self, samples_train, samples_test):\n\n self.parameters = self.statistical_model.fit(samples_train, samples_test)\n\n return self\n\n def evaluate_single(self, sample, dataset, ref_map):\n\n print(\"Making statistical map\")\n statistical_map = self.statistical_model.evaluate(sample)\n\n print(\"Clustering\")\n clusters = self.clusterer(dataset, statistical_map)\n\n print(\"evaluating\")\n events = self.event_finder(dataset, clusters[0], clusters[1])\n\n print(\"Finding bdcs\")\n bdcs = self.bdc_calculator(dataset, sample, ref_map, events)\n\n return statistical_map, clusters, events, bdcs\n\n def evaluate(self):\n dtags = set(self.dataset.partition_datasets(\"test\").keys()\n + self.dataset.partition_datasets(\"train\").keys()\n )\n truncated_datasets = self.dataset.sample_loader.truncated_datasets\n res = max([d.data.summary.high_res for dtag, d in self.dataset.datasets.items()])\n print(res)\n sample_loaders = {dtag: lambda d: self.dataset.sample_loader.get_sample(res, d)\n for dtag\n in dtags}\n gc.collect()\n results = jl.Parallel(n_jobs=int(self.cpus),\n verbose=10)(jl.delayed(self.evaluate_single)(sample_loaders[dtag],\n truncated_datasets[dtag],\n self.dataset.sample_loader.ref_map)\n for dtag\n in dtags)\n # with worker_client() as client:\n # results = client.map(self.evaluate_single, [(sample_loaders[dtag],\n # truncated_datasets[dtag],\n # self.dataset.sample_loader.ref_map)\n # for dtag\n # in dtags])\n\n\n # self.statistical_maps = {dtag: res[0]\n # for dtag, res\n # in zip(dtags, results)}\n self.clusters = {dtag: res[1]\n for dtag, res\n in zip(dtags, results)}\n self.events = {dtag: res[2]\n for dtag, res\n in zip(dtags, results)}\n\n self.bdcs = {dtag: res[3]\n for dtag, res\n in zip(dtags, results)}\n\n return self\n\n def criticise_single(self, sample_loader, truncated_dataset, ref_map, events, bdcs, tree):\n\n dataset_path = p.Path(tree((\"processed_datasets\", truncated_dataset.tag))[0])\n\n self.map_maker.process_single(sample_loader, truncated_dataset, ref_map, events, bdcs, dataset_path)\n\n def criticise_all(self, tree):\n # dtags = set(self.dataset.partition_datasets(\"test\").keys()\n # + self.dataset.partition_datasets(\"train\").keys()\n # )\n #\n # res = max([d.data.summary.high_res for dtag, d in self.dataset.datasets.items()])\n # sample_loaders = {dtag: lambda d: self.dataset.sample_loader.get_sample(res, d)\n # for dtag\n # in dtags}\n #\n # self.map_maker.statistical_model = self.statistical_model\n #\n # gc.collect()\n # # jl.Parallel(n_jobs=int(self.cpus),\n # # verbose=10)(jl.delayed(self.criticise_single)(sample_loaders[dtag],\n # # self.dataset.sample_loader.truncated_datasets[dtag],\n # # self.dataset.sample_loader.ref_map,\n # # self.events[dtag],\n # # self.bdcs[dtag],\n # # self.tree)\n # # for dtag\n # # in dtags)\n\n # Produce maps that are shared by iteration\n dir_path = p.Path(tree([str(self.name)])[0])\n dir_path_string = str(dir_path)\n self.map_maker.process_shell(self.dataset.sample_loader.reference,\n self.dataset.sample_loader.ref_map,\n dir_path_string)\n\n # Produce the event table\n event_table_path = dir_path / \"event_table.csv\"\n\n event_table = self.event_table_maker(self.dataset.partition_datasets(\"test\"),\n self.events,\n event_table_path)\n\n return event_table\n\n def log(self):\n log = OrderedDict()\n return log\n\n\nclass PanDDANormalModel:\n\n def __init__(self, method=\"adjusted+uncertainty\", cpus=1):\n\n self.method = method\n self.cpus = cpus\n\n self.mu = 0\n self.sigma_uncertainty = {}\n self.sigma_adjusted = 0\n\n # TODO: legacy requirement\n self.statistical_maps = PanddaStatMapList()\n\n def fit(self, samples_train, samples_test):\n\n # TODO: move into fit\n map_data_size = 0\n for dtag, sample in samples_train.items():\n map_data_size = sample.data.size()\n break\n\n characterisation_maps = [sample for dtag, sample in samples_train.items()]\n\n analysis_maps = [sample for dtag, sample in samples_test.items()]\n\n self.mu = self.fit_mu(characterisation_maps, map_data_size)\n\n self.sigma_uncertainty = self.fit_sigma_uncertainty(analysis_maps, map_data_size, cpus=self.cpus)\n\n self.sigma_adjusted = self.fit_sigma_adjusted(analysis_maps, self.sigma_uncertainty, map_data_size,\n cpus=self.cpus)\n\n def fit_mu(self, dataset_maps, map_data_size):\n \"\"\"Calculate the average map from all of the different observations\"\"\"\n print(\"\\t### Fitting mu!\")\n\n # Extract the maps to be used for averaging\n if len(dataset_maps) == 1:\n\n # Extract the map from the list\n m = dataset_maps[0]\n # Mean and median are simply the map value -- copy directly to the statistical maps\n mean_map_vals = medn_map_vals = numpy.array(m.data)\n\n else:\n\n # Chunk the points into groups - Compromise between cpu time and memory usage - ~200 dataset -> chunksize of 5000\n chunk_size = 500 * iceil(1000.0 / len(dataset_maps))\n chunk_idxs = [i for i in range(0, map_data_size, chunk_size)]\n num_chunks = len(chunk_idxs)\n\n t1 = time.time()\n\n mean_map_vals = numpy.zeros(map_data_size)\n medn_map_vals = numpy.zeros(map_data_size)\n\n for i_chunk, chunk_start in enumerate(chunk_idxs):\n status_bar_2(n=i_chunk, n_max=num_chunks)\n\n tmp_map_vals = numpy.array([m.data[chunk_start:chunk_start + chunk_size] for m in dataset_maps])\n\n # Check that the output values are the expected dimensions\n if i_chunk + 1 < num_chunks:\n assert len(tmp_map_vals) == len(dataset_maps)\n assert len(tmp_map_vals.T) == chunk_size\n\n tmp_map_means = numpy.mean(tmp_map_vals, axis=0)\n mean_map_vals[chunk_start:chunk_start + chunk_size] = tmp_map_means\n tmp_map_medns = numpy.median(tmp_map_vals, axis=0)\n medn_map_vals[chunk_start:chunk_start + chunk_size] = tmp_map_medns\n\n status_bar_2(n=num_chunks, n_max=num_chunks)\n\n t2 = time.time()\n\n mu = m.new_from_template(map_data=flex.double(mean_map_vals.flatten()),\n sparse=m.is_sparse())\n\n return mu\n\n def fit_sigma_uncertainty(self, analysis_maps, map_data_size, masked_idxs=None, mask_name=None, q_cut=1.5, cpus=1):\n \"\"\"Calculate the uncertainty in each of the different maps\"\"\"\n\n print(\"\\t### Fitting sigma_uncertainty!\")\n\n if masked_idxs is None:\n masked_idxs = flex.size_t(range(0, map_data_size))\n else:\n assert max(masked_idxs) < map_data_size, 'masked_idxs out of range of map'\n masked_idxs = flex.size_t(masked_idxs)\n\n # Extract masked map values from the average map... and sort them\n comp_vals = self.mu.data.select(masked_idxs)\n\n arg_list = []\n\n # for i_m, m in enumerate(self.dataset_maps.mask(mask_name=mask_name)):\n for i_m, m in enumerate(analysis_maps):\n\n if m.meta.map_uncertainty is not None:\n arg_list.append(None)\n continue\n\n u = UncertaintyCalculator(query_values=m.data.select(masked_idxs),\n ref_values=comp_vals)\n arg_list.append(u)\n\n t1 = time.time()\n num_to_process = len(arg_list) - arg_list.count(None)\n print('1' + ''.join(['{:<5}'.format(i) for i in range(0, num_to_process + 5, 5)])[2:])\n print(' ' * num_to_process + '|\\r', end='')\n sys.stdout.flush()\n # TODO: use joblib instead\n # map_uncertainties = easy_mp.pool_map(func=wrapper_run, args=arg_list, processes=cpus, chunksize=1)\n map_uncertainties = jl.Parallel(n_jobs=self.cpus,\n verbose=5)(jl.delayed(wrapper_run)(arg)\n for arg\n in arg_list)\n # with worker_client() as client:\n # map_uncertainties_futures = client.map(wrapper_run, arg_list)\n # map_uncertainties = client.gather(map_uncertainties_futures)\n print('|')\n\n for i_m, m in enumerate(analysis_maps):\n\n map_unc = map_uncertainties[i_m]\n\n if m.meta.map_uncertainty is not None:\n assert map_unc is None\n else:\n # TODO: remove this print\n # print(\"Adding map uncertainty for {}\".format(m.meta.tag))\n assert map_unc is not None\n m.meta.map_uncertainty = map_unc\n # TODO: Not sure why this is breaking - probably to do with futures print\n\n # return [m.meta.map_uncertainty for m in self.dataset_maps.mask(mask_name=mask_name)]\n return {m.meta.tag: m.meta.map_uncertainty for m in analysis_maps}\n\n def fit_sigma_adjusted(self, analysis_maps, uncertainties, map_data_size, cpus=1):\n\n print(\"\\t### Fitting sigma_adjusted!\")\n\n uncertainties_ordered = [uncertainties[edmap.meta.tag]\n for edmap\n in analysis_maps]\n\n self.calculate_statistical_maps(analysis_maps, uncertainties_ordered, map_data_size, cpus=self.cpus)\n\n return self.statistical_maps.sadj_map\n\n def calculate_statistical_maps(self, dataset_maps, uncertainties, map_data_size, mask_name=None, ignore_warnings=True, cpus=1):\n \"\"\"Take the sampled maps and calculate statistics for each grid point across the datasets\"\"\"\n\n # Extract the maps to be used for averaging\n\n if len(dataset_maps) == 1:\n\n self._set_statistical_maps_from_array(template_map=self.mu,\n map_array=numpy.zeros((map_data_size, 5)))\n\n return self.statistical_maps\n else:\n\n # Create statistics objects for each grid point\n if ignore_warnings:\n warnings.simplefilter('ignore', category=RuntimeWarning)\n\n # Extract the map uncertainties\n # uncertainties = [m.meta.map_uncertainty for m in dataset_maps]\n assert uncertainties.count(None) == 0, 'some maps have not got associated uncertainties'\n\n # Chunk the points into groups - Compromise between cpu time and memory usage - 1000 per cpu at 50 datasets\n chunk_size = iceil(1000.0 * cpus * 50.0 / len(dataset_maps))\n chunk_idxs = [i for i in range(0, map_data_size, chunk_size)]\n num_chunks = len(chunk_idxs)\n\n # Second level of iteration - split the first chunk level between the cpus\n chunk_size_2 = iceil(1.0 * chunk_size / cpus)\n chunk_idxs_2 = [i for i in range(0, chunk_size, chunk_size_2)]\n num_chunks_2 = len(chunk_idxs_2)\n\n t1 = time.time()\n\n # Output array of the 5 statistics for each map point\n point_statistics = numpy.zeros((map_data_size, 5))\n\n tot = 0\n for i_chunk, chunk_start in enumerate(chunk_idxs):\n status_bar_2(n=i_chunk, n_max=num_chunks)\n\n # Argument list for multiprocessing\n arg_list = []\n\n # Loop through the secondary chunks and send for multi-core processing\n for i_chunk_2, chunk_start_2 in enumerate(chunk_idxs_2):\n\n # Lower limit - always the beginning of the chunk\n l1 = chunk_start + chunk_start_2\n # Upper limit - full chunk size, limited by the larger chunk size, or by map size\n l2 = min(chunk_start + chunk_start_2 + chunk_size_2, chunk_start + chunk_size,\n map_data_size)\n\n if l1 >= l2:\n continue\n\n # Extract map values from the maps\n map_vals = [m.data[l1:l2] for m in dataset_maps]\n # Want to iterate over grid points not datasets\n map_vals = numpy.transpose(map_vals)\n assert map_vals.shape[1] == len(dataset_maps)\n\n # Create DensityStatistics object for analysis of the density variation\n arg_list.append(DensityStatistics(observations_array=map_vals, uncertainties=uncertainties))\n\n if not arg_list: continue\n\n # Calculate the statistis of the grid points\n # TODO: use joblib instead\n # tmp_point_statistics = easy_mp.pool_map(func=wrapper_run, args=arg_list, processes=cpus)\n tmp_point_statistics = jl.Parallel(n_jobs=self.cpus)(jl.delayed(wrapper_run)(arg)\n for arg\n in arg_list)\n # with worker_client() as client:\n # tmp_point_statistics_futures = client.map(wrapper_run, arg_list)\n # tmp_point_statistics = client.gather(tmp_point_statistics_futures)\n\n # Put values into the output array\n offset = 0\n for point_vals in tmp_point_statistics:\n assert point_vals.shape[1] == 5\n l1 = chunk_start + offset\n l2 = l1 + point_vals.shape[0]\n if not (point_statistics[l1:l2, :] == 0.0).all():\n print('Overwriting data?!')\n print(point_statistics[l1 - 10:l2 + 10, :])\n assert point_statistics[l1:l2, :].shape == point_vals.shape, '{} != {}'.format(\n point_statistics[l1:l2, :].shape, point_vals.shape)\n point_statistics[l1:l2, :] = point_vals\n offset += point_vals.shape[0]\n tot += offset\n\n status_bar_2(n=num_chunks, n_max=num_chunks)\n\n # Check that we've calculated the right number of things\n assert tot == map_data_size, 'tot {}, map size {}'.format(tot, map_data_size)\n\n t2 = time.time()\n\n self._set_statistical_maps_from_array(template_map=self.mu,\n map_array=point_statistics,\n map_data_size=map_data_size)\n\n def _set_statistical_maps_from_array(self, template_map, map_array, map_data_size):\n \"\"\"Set the five non-average-based statistical maps from an array\"\"\"\n\n assert map_array.shape == (map_data_size, 5)\n\n # Create the other statistical maps\n self.statistical_maps.stds_map = template_map.new_from_template(\n map_data=flex.double(map_array[:, 0].tolist()), sparse=template_map.is_sparse())\n self.statistical_maps.sadj_map = template_map.new_from_template(\n map_data=flex.double(map_array[:, 1].tolist()), sparse=template_map.is_sparse())\n self.statistical_maps.skew_map = template_map.new_from_template(\n map_data=flex.double(map_array[:, 2].tolist()), sparse=template_map.is_sparse())\n self.statistical_maps.kurt_map = template_map.new_from_template(\n map_data=flex.double(map_array[:, 3].tolist()), sparse=template_map.is_sparse())\n self.statistical_maps.bimo_map = template_map.new_from_template(\n map_data=flex.double(map_array[:, 4].tolist()), sparse=template_map.is_sparse())\n\n def evaluate(self, m):\n \"\"\"Calculate the z-map relative to the mean and std map\"\"\"\n\n assert self.method in ['none', 'adjusted', 'uncertainty', 'adjusted+uncertainty']\n uncertainty = self.sigma_uncertainty[m.meta.tag]\n\n # Check that a value has been found/supplied\n if 'uncertainty' in self.method:\n assert uncertainty is not None\n\n # Extract maps in the right sparseness\n is_sparse = m.is_sparse()\n # Extract mean values (for subtraction)\n comp_vals = self.mu.get_map_data(sparse=is_sparse)\n\n # Extract the normalisation values (for division)\n if self.method == 'none':\n norm_vals = 1.0\n # elif method == 'naive':\n # norm_vals = self.statistical_maps.stds_map.get_map_data(sparse=is_sparse)\n elif self.method == 'adjusted':\n norm_vals = self.sigma_adjusted.get_map_data(sparse=is_sparse)\n elif self.method == 'uncertainty':\n norm_vals = uncertainty\n elif self.method == 'adjusted+uncertainty':\n norm_vals = flex.sqrt(\n self.sigma_adjusted.get_map_data(sparse=is_sparse) ** 2 + uncertainty ** 2)\n else:\n raise Exception('method not found: {!s}'.format(self.method))\n\n return (m - comp_vals) * (1.0 / norm_vals)\n", "sub_path": "pandda_2/pandda_analyse/event_model_distributed.py", "file_name": "event_model_distributed.py", "file_ext": "py", "file_size_in_byte": 23431, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 193, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 194, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 195, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 226, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 254, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 270, "usage_type": "call"}, {"api_name": "pandda.analyse.classes.PanddaStatMapList", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 317, "usage_type": "call"}, {"api_name": "libtbx.math_utils.iceil", "line_number": 322, "usage_type": "call"}, {"api_name": "time.time", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 329, "usage_type": "call"}, {"api_name": "bamboo.common.status.status_bar_2", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 343, "usage_type": "call"}, {"api_name": "bamboo.common.status.status_bar_2", "line_number": 346, "usage_type": "call"}, {"api_name": "time.time", "line_number": 348, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex.double", "line_number": 350, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex", "line_number": 350, "usage_type": "name"}, {"api_name": "scitbx.array_family.flex.size_t", "line_number": 361, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex", "line_number": 361, "usage_type": "name"}, {"api_name": "scitbx.array_family.flex.size_t", "line_number": 364, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex", "line_number": 364, "usage_type": "name"}, {"api_name": "pandda.analyse.functions.UncertaintyCalculator", "line_number": 378, "usage_type": "call"}, {"api_name": "time.time", "line_number": 382, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 386, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 386, "usage_type": "attribute"}, {"api_name": "joblib.Parallel", "line_number": 389, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 390, "usage_type": "call"}, {"api_name": "pandda.analyse.functions.wrapper_run", "line_number": 390, "usage_type": "argument"}, {"api_name": "numpy.zeros", "line_number": 434, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 441, "usage_type": "call"}, {"api_name": "libtbx.math_utils.iceil", "line_number": 448, "usage_type": "call"}, {"api_name": "libtbx.math_utils.iceil", "line_number": 453, "usage_type": "call"}, {"api_name": "time.time", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 460, "usage_type": "call"}, {"api_name": "bamboo.common.status.status_bar_2", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 484, "usage_type": "call"}, {"api_name": "pandda.analyse.functions.DensityStatistics", "line_number": 488, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 495, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 495, "usage_type": "call"}, {"api_name": "pandda.analyse.functions.wrapper_run", "line_number": 495, "usage_type": "argument"}, {"api_name": "bamboo.common.status.status_bar_2", "line_number": 517, "usage_type": "call"}, {"api_name": "time.time", "line_number": 522, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex.double", "line_number": 535, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex", "line_number": 535, "usage_type": "name"}, {"api_name": "scitbx.array_family.flex.double", "line_number": 537, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex", "line_number": 537, "usage_type": "name"}, {"api_name": "scitbx.array_family.flex.double", "line_number": 539, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex", "line_number": 539, "usage_type": "name"}, {"api_name": "scitbx.array_family.flex.double", "line_number": 541, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex", "line_number": 541, "usage_type": "name"}, {"api_name": "scitbx.array_family.flex.double", "line_number": 543, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex", "line_number": 543, "usage_type": "name"}, {"api_name": "scitbx.array_family.flex.sqrt", "line_number": 570, "usage_type": "call"}, {"api_name": "scitbx.array_family.flex", "line_number": 570, "usage_type": "name"}]} +{"seq_id": "70977719", "text": "from django.conf.urls import url, include\nfrom django.contrib import admin\n\nfrom qa.views import popular, index, answer, test, question, ask, signup, user_login\n\nurlpatterns = [\n url(r'^$', index, name='index'),\n url(r'^popular/.*$', popular, name='popular'),\n url(r'^ask/.*$', ask, name='ask'),\n url(r'^answer/.*$', answer, name='answer'),\n url(r'^new/.*$', test),\n url(r'^question/(?P[\\d]+)/$', question, name='question'),\n url(r'^signup/.*$', signup, name='signup'),\n url(r'^login/.*$', user_login, name='login'),\n]\n", "sub_path": "urls1.py", "file_name": "urls1.py", "file_ext": "py", "file_size_in_byte": 556, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "qa.views.index", "line_number": 7, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "qa.views.popular", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "qa.views.ask", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "qa.views.answer", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "qa.views.test", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "qa.views.question", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "qa.views.signup", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "qa.views.user_login", "line_number": 14, "usage_type": "argument"}]} +{"seq_id": "359060291", "text": "\"\"\"\nFile watch commercial remover\n\"\"\"\nimport os\nimport subprocess\nimport logging\nimport shutil\nimport sys\nimport time\nfrom threading import Thread\nfrom queue import Queue\n\nWORK_ROOT = \"/config/\"\n\n_LOGGER = logging.getLogger(__name__)\nlogging.basicConfig(filename=WORK_ROOT+'watcher.log', filemode='a', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')\n\nIN_PROCESS = set()\n\n\nclass CommercialWorker(Thread):\n \"\"\"Commercial process queue\"\"\"\n def __init__(self, queue):\n Thread.__init__(self)\n self.queue = queue\n\n def run(self):\n while True:\n # Get paths\n pid_path, file_path = self.queue.get()\n try:\n find_commercials(pid_path, file_path)\n finally:\n self.queue.task_done()\n\n\ndef find_commercials(pid_path, file_path):\n \"\"\"Call comchap to find commercials\"\"\"\n # file_path = file_path.rstrip()\n\n # print(pid_path)\n # print(file_path)\n # return\n\n # Check to make sure file exists first\n if os.path.isfile(file_path):\n _LOGGER.info(\"Processing: \" + file_path)\n\n name = os.path.splitext(os.path.basename(file_path))[0]\n path = os.path.dirname(file_path)\n\n # Make backup of original in case something goes wrong and\n # store it's size for comparison later\n backup = os.path.join(path, name + \".mkv.bak\")\n shutil.copy(file_path, backup)\n backup_size = os.path.getsize(backup)\n _LOGGER.info(\"Backup Created (%s): %s\", backup_size, backup)\n\n # Start commercial processing\n cmd = ['/opt/comchap/comchap',\n '--keep-edl',\n '--cuvid',\n '--comskip=/opt/Comskip/comskip',\n '--comskip-ini=/opt/Comskip/comskip.ini',\n file_path]\n try:\n result = subprocess.run(cmd, stdout=subprocess.DEVNULL, timeout=5400)\n _LOGGER.debug(\"Subprocess finished (code: %s) for: %s\", result.returncode, file_path)\n except subprocess.TimeoutExpired as err:\n # Timeout expired before we had a result\n _LOGGER.debug(\"1:30hr timeout expired for: %s, code: %s\", file_path, result.returncode)\n # If we end up here we need to make sure the backup is restored\n shutil.move(backup, file_path)\n # Remove working indicator\n os.remove(pid_path)\n IN_PROCESS.remove(file_path)\n\n if result.returncode == 0:\n _LOGGER.info(\"Commercial chapters inserted into: \" + file_path)\n # Explicitly set new file permissions\n shutil.chown(file_path, 99, 100)\n os.chmod(file_path, 0o644)\n\n # Make sure new file exists and is in the size ballpark\n if os.path.isfile(file_path):\n new_size = os.path.getsize(file_path)\n if new_size > (backup_size*.9):\n # New is at least 90% of backup, we can move on\n # Remove path from process set and delete file\n try:\n os.remove(pid_path)\n IN_PROCESS.remove(file_path)\n os.remove(backup)\n except OSError as err:\n _LOGGER.error(\"File removal error: \" + err)\n else:\n _LOGGER.error(\"New file size incorrect (B: %s, N: %s) Restoring Backup.\", backup_size, new_size)\n # New file size isn't what we expect, restore the backup\n shutil.move(backup, file_path)\n # Remove working indicators\n os.remove(pid_path)\n IN_PROCESS.remove(file_path) # Only removing this would allow a retry\n else:\n _LOGGER.error(\"New file doesn't exist, restoring backup.\")\n shutil.move(backup, file_path)\n # Remove working indicator from set to try again\n IN_PROCESS.remove(file_path)\n else:\n if result.stderr:\n # Something went wrong in commercial processing\n _LOGGER.error(\"Comchap error: %s\", result.stderr)\n else:\n _LOGGER.error(\"Unknown Comchap error (%s) for file: %s, Restoring backup.\", result.returncode, file_path)\n # If we end up here we need to make sure the backup is restored\n shutil.move(backup, file_path)\n # Remove working indicator\n os.remove(pid_path)\n IN_PROCESS.remove(file_path)\n else:\n # File doesn't exist, we can't do anything\n _LOGGER.info(\"%s does not exist, nothing to do...\", file_path)\n # Remove working indicator\n os.remove(pid_path)\n IN_PROCESS.remove(file_path)\n\n\ndef main():\n \"\"\"Main function.\"\"\"\n watch_path = os.fsencode(sys.argv[1])\n\n queue = Queue()\n\n for xwork in range(5):\n worker = CommercialWorker(queue)\n worker.daemon = True\n worker.start()\n\n queue.join()\n\n _LOGGER.info(\"Starting Loop...\")\n\n while True:\n # Check folder for new file tasks\n for item in os.scandir(watch_path):\n if item.is_file():\n pid = item.path.decode('utf-8')\n if pid.endswith(\".comm\"):\n # New comm task to process\n with open(pid) as fop:\n fpath = fop.readline().rstrip()\n if fpath not in IN_PROCESS:\n IN_PROCESS.add(fpath)\n queue.put((pid, fpath))\n\n # Check every 5s to limit I/O\n time.sleep(5)\n\nif __name__ == '__main__':\n main()\n", "sub_path": "file_watch.py", "file_name": "file_watch.py", "file_ext": "py", "file_size_in_byte": 5695, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 16, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 21, "usage_type": "name"}, {"api_name": "threading.Thread.__init__", "line_number": 24, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 67, "usage_type": "call"}, {"api_name": "subprocess.DEVNULL", "line_number": 67, "usage_type": "attribute"}, {"api_name": "subprocess.TimeoutExpired", "line_number": 69, "usage_type": "attribute"}, {"api_name": "shutil.move", "line_number": 73, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 75, "usage_type": "call"}, {"api_name": "shutil.chown", "line_number": 81, "usage_type": "call"}, {"api_name": "os.chmod", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 91, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 93, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 99, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 101, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 105, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 115, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 117, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 123, "usage_type": "call"}, {"api_name": "os.fsencode", "line_number": 129, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 129, "usage_type": "attribute"}, {"api_name": "queue.Queue", "line_number": 131, "usage_type": "call"}, {"api_name": "queue.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.scandir", "line_number": 144, "usage_type": "call"}, {"api_name": "queue.put", "line_number": 153, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "53489231", "text": "from keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.layers.normalization import BatchNormalization\nimport numpy as np\n\n\nclass MyNeuralNetwork(Sequential):\n def __init__(self):\n # instantiate model\n Sequential.__init__(self)\n\n # input layer\n self.add(Dense(64, input_dim=1, kernel_initializer='uniform'))\n self.add(Dropout(rate=0.5))\n self.add(BatchNormalization())\n self.add(Activation('relu'))\n\n # # hidden layer\n self.add(Dense(64, kernel_initializer='uniform'))\n self.add(Dropout(rate=0.5))\n self.add(BatchNormalization())\n self.add(Activation('relu'))\n\n # output layer\n self.add(Dense(1, kernel_initializer='uniform', activation='linear'))\n\n self.compile(loss='mse', optimizer='adam', metrics=['mae', 'accuracy'])\n\n def init_NN_params(self):\n # If kernel_initializer='uniform', minval and maxval used by keras are -0.05 and 0.05 respectively\n minval = -0.05\n maxval = 0.05\n layers = self.layers\n for layer in layers:\n if len(layer.weights) != 0:\n if (layer.output_shape == layer.input_shape) and (len(layer.weights) == 4): # in case of BatchNormalization layer, always 4 \"weight\" parameters\n dim = layer.input_shape[1]\n gamma_init = np.ones(dim)\n beta_init = np.zeros(dim)\n moving_mean_init = np.zeros(dim)\n moving_variance_init = np.ones(dim)\n w_list = (gamma_init, beta_init, moving_mean_init, moving_variance_init) # list with total weight parameters (according to BatchNormalization doc)\n else: # otherwise (i.e, a normal layer sucha as Dense)\n in_dim = layer.input_shape[1] # input dimension\n out_dim = layer.output_shape[1] # output dimension\n link_w = np.random.uniform(minval, maxval, (in_dim, out_dim)) # weight term\n b = np.zeros(out_dim) # bias term\n w_list = (link_w, b) # list with total weight parameters (bias 'b' and weight 'w')\n layer.set_weights(w_list)\n", "sub_path": "Implementaciones/BDANN-MF/PycharmProjects/DQN_DAFA/NeuralNetworkOperations.py", "file_name": "NeuralNetworkOperations.py", "file_ext": "py", "file_size_in_byte": 2242, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "keras.models.Sequential", "line_number": 7, "usage_type": "name"}, {"api_name": "keras.models.Sequential.__init__", "line_number": 10, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 10, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 13, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 14, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "197904592", "text": "#!/usr/local/bin/python3\n\"\"\"\nThis package has the following function:\n\nget_main_color():\n If receives a path to an image file and returns the main color.\n It will also reduce the number of colors in the image if wanted.\n\nnamed_color():\n Returns the closest named color from the dictionary below.\n Also returns the dominant channel (r or g or b)\n\ncolor_summary():\n Summarizes the image colors into a dictionary of helpful \n values for comparing colors. See function comment.\n\n\"\"\"\nimport glob\nimport os,sys\nimport cv2\nfrom PIL import Image\nimport numpy as np\nimport pprint\nfrom matplotlib import pyplot as plt\nfrom sklearn.cluster import MiniBatchKMeans\n\ncolors_dict = {\n 'red':[255,0,0],\n 'orange':[255,128,0],\n 'yellow':[255,255,0],\n 'green':[0,255,0],\n 'teal':[0,128,128],\n 'blue':[0,0,255],\n 'purple':[128,0,128],\n 'pink':[255,192,203],\n 'white':[255,255,255],\n 'gray':[128,128,128],\n 'black':[0,0,0],\n 'brown':[165,42,42]\n}\n\n\n\"\"\"\nFunction: get_main_color\n Finds the \"main\" color or dominant color.\nParams:\n path [string] : path to image \n reduce [bool] : reduce colors or not\n num_colors [int] : number of colors to reduce to\nReturns:\n summary [dict] : see below\n\"\"\"\n\ndef get_main_color(file,reduce=False,num_colors=8):\n\n if reduce:\n img = reduce_colors(file,num_colors)\n else:\n img = Image.open(file)\n\n width,height = im.size\n\n colors = img.getcolors(width*height) #put a higher value if there are many colors in your image\n \n max_occurence, most_present = 0, 0\n\n try:\n for c in colors:\n if c[0] > max_occurence:\n (max_occurence, most_present) = c\n return most_present\n except TypeError:\n raise Exception(\"Too many colors in the image\")\n\n\"\"\"\nReturns the closest named color from dict above.\nAlso returns the dominant r,g,b \n\nExamples: \n (99, 136, 95) returns ('gray', 'g')\n (216, 166, 9) returns ('orange', 'r')\n\n\"\"\"\ndef named_color(color):\n closest_rgb = 99999\n closest_name = None\n highest_rgb = None\n highest_val = 0\n \n for name,rgb in colors_dict.items():\n val = 0\n for i in range(3):\n val += abs(color[i] - rgb[i])\n if val < closest_rgb:\n closest_rgb = val\n closest_name = name\n \n if color[0] > color[1]:\n highest_rgb = 'r'\n highest_val = color[0]\n else:\n highest_rgb = 'g'\n highest_val = color[1]\n \n if color[2] > highest_val:\n highest_rgb = 'b'\n highest_val = color[2]\n\n return closest_name,highest_rgb\n\n\"\"\"\nFunction: \n color_summary\nParams:\n im [pil image]\nReturns:\n summary [dict] : see below\n'named_colors': {'counts': {'black': 11057,\n 'brown': 6907,\n 'gray': 19187,\n 'green': 117,\n 'pink': 313,\n 'teal': 12648,\n 'white': 94,\n 'yellow': 2},\n 'ratios': {'black': 0.22,\n 'brown': 0.14,\n 'gray': 0.38,\n 'green': 0.0,\n 'pink': 0.01,\n 'teal': 0.25,\n 'white': 0.0,\n 'yellow': 0.0}},\n 'rgb': {'counts': {'b': 415, 'g': 47123, 'r': 2787},\n 'ratios': {'b': 0.01, 'g': 0.94, 'r': 0.06}},\n 'total_colors': 50325}\n\"\"\"\ndef color_summary(im):\n color_count = {\n 'rgb':{\n 'counts':{},\n 'ratios':{}\n },\n 'named_colors':{\n 'counts':{},\n 'ratios':{}\n },\n 'total_colors':0\n }\n for c in list(im.getdata()):\n color_count['total_colors'] += 1\n color,rgb = named_color(c)\n if not color in color_count['named_colors']['counts']:\n color_count['named_colors']['counts'][color] = 0\n color_count['named_colors']['counts'][color] += 1\n\n if not rgb in color_count['rgb']['counts']:\n color_count['rgb']['counts'][rgb] = 0\n color_count['rgb']['counts'][rgb] += 1\n\n\n for c,count in color_count['named_colors']['counts'].items():\n color_count['named_colors']['ratios'][c] = round(count / color_count['total_colors'],2)\n\n for c,count in color_count['rgb']['counts'].items():\n color_count['rgb']['ratios'][c] = round(count / color_count['total_colors'],2)\n\n return color_count\n\n\n\"\"\"\nFunction: color_summary\n Returns a pil image with reduced colors using kmeans clustering\n by opencv\nParams:\n path [string] : path to image \n numcolors [int] : num colors to reduce to\n show [bool] : display image in gui\nReturns:\n summary [dict] : see below\n\"\"\"\ndef reduce_colors(path,numcolors,show=False):\n\n tmpfile = '/tmp/tmpimage.jpg'\n\n img = cv2.imread(path)\n Z = img.reshape((-1,3))\n\n # convert to np.float32\n Z = np.float32(Z)\n\n # define criteria, number of clusters(K) and apply kmeans()\n criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)\n K = numcolors\n ret,labels,centers=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)\n\n # Now convert back into uint8, and make original image\n centers = np.uint8(centers)\n res = centers[labels.flatten()]\n res2 = res.reshape((img.shape))\n\n # save opencv version to tmp dir\n cv2.imwrite(tmpfile,res2)\n\n if show:\n cv2.imshow('res2',res2)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\n return Image.open(tmpfile)\n\n\"\"\"\nabove uses python pil to some extent, this one sticks with opencv\n\"\"\"\ndef reduce_colors2(image,K=4):\n\n if isinstance(image,str):\n if os.path.isfile(image):\n image = cv2.imread(image)\n else:\n print(\"Error: image arg is string but not valid file.\")\n sys.exit()\n\n if not isinstance(image, np.ndarray):\n print(\"Error: image arg is not a string but not a valid numpy array either.\")\n sys.exit()\n\n print(K)\n \n (h, w) = image.shape[:2]\n \n # convert the image from the RGB color space to the L*a*b*\n # color space -- since we will be clustering using k-means\n # which is based on the euclidean distance, we'll use the\n # L*a*b* color space where the euclidean distance implies\n # perceptual meaning\n image = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)\n \n # reshape the image into a feature vector so that k-means\n # can be applied\n image = image.reshape((image.shape[0] * image.shape[1], 3))\n \n # apply k-means using the specified number of clusters and\n # then create the quantized image based on the predictions\n clt = MiniBatchKMeans(n_clusters = K)\n labels = clt.fit_predict(image)\n quant = clt.cluster_centers_.astype(\"uint8\")[labels]\n \n # reshape the feature vectors to images\n quant = quant.reshape((h, w, 3))\n image = image.reshape((h, w, 3))\n \n # convert from L*a*b* to RGB\n quant = cv2.cvtColor(quant, cv2.COLOR_LAB2BGR)\n image = cv2.cvtColor(image, cv2.COLOR_LAB2BGR)\n\n return quant\n\ndef matchShapes(img1,img2):\n image2name = img2\n if isinstance(img1,str):\n if os.path.isfile(img1):\n img1 = cv2.imread(img1,0)\n else:\n print(\"Error: image arg is string but not valid file.\")\n sys.exit()\n\n if isinstance(img2,str):\n if os.path.isfile(img2):\n img2 = cv2.imread(img2,0)\n else:\n print(\"Error: image arg is string but not valid file.\")\n sys.exit()\n\n if not isinstance(img1, np.ndarray):\n print(\"Error: image arg1 is not a string but not a valid numpy array either.\")\n sys.exit()\n\n if not isinstance(img2, np.ndarray):\n print(\"Error: image arg2 is not a string but not a valid numpy array either.\")\n sys.exit()\n\n ret, thresh = cv2.threshold(img1, 127, 255,0)\n ret2, thresh2 = cv2.threshold(img2, 127, 255,0)\n contours,hierarchy = cv2.findContours(thresh,2,1)\n cnt1 = contours[0]\n contours,hierarchy = cv2.findContours(thresh2,2,1)\n if len(contours) == 0:\n return 1000000\n cnt2 = contours[0]\n\n ret = cv2.matchShapes(cnt1,cnt2,1,0.0)\n if ret == 1.7976931348623157e+308:\n return 1000000\n\n return ret\n\ndef color_distance(im1,im2,size=(128,128)):\n\n im1 = cv2.imread(im1)\n im1 = cv2.resize(im1,size)\n\n im2 = cv2.imread(im2)\n im2 = cv2.resize(im2,size)\n\n colors = ('b','g','r')\n\n comparisons = {\n 'correlation':cv2.HISTCMP_CORREL,\n 'chisquare':cv2.HISTCMP_CHISQR,\n 'intersect':cv2.HISTCMP_INTERSECT,\n 'bhattacharyya':cv2.HISTCMP_BHATTACHARYYA\n }\n\n hists = [{},{}]\n \n for i,col in enumerate(colors):\n hists[0][col] = cv2.calcHist([im1],[i],None,[256],[0,256])\n # plt.plot(hists[0][col],color = col)\n # plt.xlim([0,256])\n # plt.show()\n\n for i,col in enumerate(colors):\n hists[1][col] = cv2.calcHist([im2],[i],None,[256],[0,256])\n # plt.plot(hists[0][col],color = col)\n # plt.xlim([0,256])\n # plt.show()\n\n\n d = {}\n for key,comp in comparisons.items():\n d[key] = {}\n for c in colors:\n d[key][c] = cv2.compareHist(hists[0][c], hists[1][c],comp) \n pprint.pprint(d)\n\n\n\n\nif __name__=='__main__':\n # im = reduce_colors2(\"/Users/griffin/Dropbox/Scripts-random/image_projects/downloads/corn-blue/3. mp,550x550,matte,ffffff,t.3u5.jpg\",5)\n # cv2.imshow(\"image\", im)\n # cv2.waitKey(0)\n\n results = {}\n images = {}\n imagesList = glob.glob('/Users/griffin/Dropbox/Scripts-random/image_projects/EmojiColors/emojis_64x64'+'/*.png')\n\n img1 = '/Users/griffin/Dropbox/Scripts-random/image_projects/EmojiColors/emojis_64x64/lollipop.png'\n for img2 in imagesList:\n results[os.path.basename(img2)] = matchShapes(img1,img2)\n #images[os.path.basename(img2)] = bw\n \n results = sorted([(v, k) for (k, v) in results.items()])\n print(results[:25])\n\n bw1 = cv2.imread('/Users/griffin/Dropbox/Scripts-random/image_projects/EmojiColors/emojis_64x64/lollipop.png',0)\n ret1, thresh1 = cv2.threshold(bw1, 127, 255,0)\n cv2.imshow('res2',thresh1)\n cv2.waitKey(0)\n \n bw2 = cv2.imread('/Users/griffin/Dropbox/Scripts-random/image_projects/EmojiColors/emojis_64x64/'+results[0][1],0)\n ret2, thresh2 = cv2.threshold(bw2, 127, 255,0)\n cv2.imshow('res2',thresh2)\n cv2.waitKey(0)\n\n # i = 0\n # for name,val in results.items():\n # cv2.imshow('img',images[name])\n # i += 1\n # if i >= 5:\n # sys.exit()\n\n\n #im = Image.open(\"./downloads/forest-red/5.jpg\")\n # im = Image.open(\"/Users/griffin/Dropbox/Scripts-random/image_projects/AsciiArt/original_images/lilly_400x.jpg\")\n # width,height = im.size\n # print(width,height)\n # histogram = im.histogram()\n # print(histogram)\n\n # pprint.pprint(color_summary(im))\n\n # abe1 = '/Users/griffin/Dropbox/Scripts-random/image_projects/image_collage/downloads/forest-red/4.jpg'\n # abe2 = '/Users/griffin/Dropbox/Scripts-random/image_projects/image_collage/downloads/forest-red/5.jpg'\n # abe2 = '/Users/griffin/Dropbox/Scripts-random/image_projects/image_collage/downloads/forest1/4.jpg'\n # abe1 = '/Users/griffin/Dropbox/Scripts-random/image_projects/image_collage/downloads/forest1/4.jpg'\n\n # color_distance(abe1,abe2)\n", "sub_path": "Assignments/A08/image_package/color_functions.py", "file_name": "color_functions.py", "file_ext": "py", "file_size_in_byte": 11507, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "PIL.Image.open", "line_number": 59, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 59, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 190, "usage_type": "call"}, {"api_name": "cv2.TERM_CRITERIA_EPS", "line_number": 193, "usage_type": "attribute"}, {"api_name": "cv2.TERM_CRITERIA_MAX_ITER", "line_number": 193, "usage_type": "attribute"}, {"api_name": "cv2.kmeans", "line_number": 195, "usage_type": "call"}, {"api_name": "cv2.KMEANS_RANDOM_CENTERS", "line_number": 195, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 198, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 203, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 206, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 207, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 208, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 210, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 210, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path", "line_number": 218, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 219, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 224, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 226, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 237, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2LAB", "line_number": 237, "usage_type": "attribute"}, {"api_name": "sklearn.cluster.MiniBatchKMeans", "line_number": 245, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 254, "usage_type": "call"}, {"api_name": "cv2.COLOR_LAB2BGR", "line_number": 254, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 255, "usage_type": "call"}, {"api_name": "cv2.COLOR_LAB2BGR", "line_number": 255, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 262, "usage_type": "call"}, {"api_name": "os.path", "line_number": 262, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 263, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 269, "usage_type": "call"}, {"api_name": "os.path", "line_number": 269, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 270, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 275, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 279, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 281, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 283, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 284, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 285, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 287, "usage_type": "call"}, {"api_name": "cv2.matchShapes", "line_number": 292, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 300, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 301, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 303, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 304, "usage_type": "call"}, {"api_name": "cv2.HISTCMP_CORREL", "line_number": 309, "usage_type": "attribute"}, {"api_name": "cv2.HISTCMP_CHISQR", "line_number": 310, "usage_type": "attribute"}, {"api_name": "cv2.HISTCMP_INTERSECT", "line_number": 311, "usage_type": "attribute"}, {"api_name": "cv2.HISTCMP_BHATTACHARYYA", "line_number": 312, "usage_type": "attribute"}, {"api_name": "cv2.calcHist", "line_number": 318, "usage_type": "call"}, {"api_name": "cv2.calcHist", "line_number": 324, "usage_type": "call"}, {"api_name": "cv2.compareHist", "line_number": 334, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 335, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 347, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 351, "usage_type": "call"}, {"api_name": "os.path", "line_number": 351, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 357, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 358, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 359, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 360, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 362, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 363, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 364, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 365, "usage_type": "call"}]} +{"seq_id": "236229976", "text": "from flask import session, request, redirect\n\nfrom app.server import server\n\n\n@server.route(\"/theme/light\", methods=['POST'])\ndef set_light_theme():\n session['dark_theme'] = False\n redirect_url = request.args.get('redirect') or '/'\n return redirect(redirect_url, code=303)\n\n\n@server.route(\"/theme/dark\", methods=['POST'])\ndef set_dark_theme():\n session['dark_theme'] = True\n redirect_url = request.args.get('redirect') or '/'\n return redirect(redirect_url, code=303)\n", "sub_path": "app/routes/theme.py", "file_name": "theme.py", "file_ext": "py", "file_size_in_byte": 485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.session", "line_number": 8, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 9, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 10, "usage_type": "call"}, {"api_name": "app.server.server.route", "line_number": 6, "usage_type": "call"}, {"api_name": "app.server.server", "line_number": 6, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 17, "usage_type": "call"}, {"api_name": "app.server.server.route", "line_number": 13, "usage_type": "call"}, {"api_name": "app.server.server", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "271928488", "text": "\"\"\"Database Layer for the Emmission API.\n\"\"\"\n\nfrom functools import wraps\n\nfrom sqlalchemy import create_engine, Column, DateTime, Integer, Float, String\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import sessionmaker\n\nimport geoalchemy2\n\nfrom emissionsapi.config import config\nimport emissionsapi.logger\n\n# Logger\nlogger = emissionsapi.logger.getLogger('emission-api.db')\n\n# Database uri as described in\n# https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls\n# Retrieved as environment variable.\ndatabase = config('database') or 'postgresql://user:user@localhost/db'\n\n# Global session variable. Set on initialization.\n__session__ = None\n\n# Base Class of all ORM objects.\nBase = declarative_base()\n\n\nclass File(Base):\n \"\"\"ORM Object for the nc files.\n \"\"\"\n # Tablename\n __tablename__ = 'file'\n filename = Column(String, primary_key=True)\n\n\nclass Carbonmonoxide(Base):\n \"\"\"ORM Object for Carbonmonoxide Point\n \"\"\"\n # Tablename\n __tablename__ = 'carbonmonoxide'\n # Primary Key\n id = Column(Integer, primary_key=True)\n # Carbonmonoxide Value\n value = Column(Float)\n # Longitude\n longitude = Column(Float)\n # Latitude\n latitude = Column(Float)\n # timestamp\n timestamp = Column(DateTime)\n # PostGis type\n geom = Column(geoalchemy2.Geometry(geometry_type=\"POINT\"))\n\n def __init__(self, value, longitude, latitude, timestamp):\n self.value = value\n self.longitude = longitude\n self.latitude = latitude\n self.timestamp = timestamp\n self.geom = geoalchemy2.elements.WKTElement(\n f\"POINT({longitude} {latitude})\")\n\n\ndef with_session(f):\n \"\"\"Wrapper for f to make a SQLAlchemy session present within the function\n\n :param f: function to call\n :type f: function\n :raises e: Possible Exception of f\n :return: result of f\n \"\"\"\n @wraps(f)\n def decorated(*args, **kwargs):\n # Get new session\n session = get_session()\n try:\n # Call f with the session and all the other arguments\n result = f(session, *args, **kwargs)\n except Exception as e:\n # Rollback session, something bad happend.\n session.rollback()\n session.close()\n raise e\n # Close session and return the result of f\n session.close()\n return result\n return decorated\n\n\ndef get_session():\n \"\"\"Get a new session.\n\n Lazy load the database connection and create the tables.\n\n Returns:\n sqlalchemy.orm.session.Session -- SQLAlchemy Session Object\n \"\"\"\n global __session__\n # Create Database Connection, Tables and Sessionmaker if neccessary.\n if not __session__:\n Engine = create_engine(database)\n __session__ = sessionmaker(bind=Engine)\n Base.metadata.create_all(Engine)\n\n # Return new session object\n return __session__()\n\n\ndef get_points_in_polygon(session, polygon):\n \"\"\"Get all points from within the specified polygon.\n\n :param session: SQL Alchemy Session\n :type session: sqlalchemy.orm.session.Session\n :param polygon: Polygon where to search for points\n :type polygon: geoalchemy2.WKTElement\n :return: SQLAlchemy Query Object with the points from within the polygon.\n :rtype: sqlalchemy.orm.query.Query\n \"\"\"\n return session.query(Carbonmonoxide).filter(\n geoalchemy2.func.ST_WITHIN(Carbonmonoxide.geom, polygon))\n\n\ndef get_points_in_rectangle(session, upper_left, lower_right):\n \"\"\"Get all points from within a rectangle.\n\n :param session: SQL Alchemy Session\n :type session: sqlalchemy.orm.session.Session\n :param polygon: Polygon where to search for points\n :type polygon: geoalchemy2.WKTElement\n :param upper_left: Upper left point of the rectangle\n :type upper_left: tuple\n :param lower_right: Lower right point of the rectangle\n :type lower_right: tuple\n :return: SQLAlchemy Query Object with the points from within the polygon.\n :rtype: sqlalchemy.orm.query.Query\n \"\"\"\n # Defining the rectangle\n rectangle = geoalchemy2.elements.WKTElement(\n f'POLYGON(({upper_left[0]} {upper_left[1]},'\n f' {lower_right[0]} {upper_left[1]},'\n f' {lower_right[0]} {lower_right[1]},'\n f' {upper_left[0]} {lower_right[1]},'\n f' {upper_left[0]} {upper_left[1]}))')\n return get_points_in_polygon(session, rectangle)\n", "sub_path": "emissionsapi/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 4413, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "emissionsapi.config.logger.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "emissionsapi.config.logger", "line_number": 16, "usage_type": "attribute"}, {"api_name": "emissionsapi.config", "line_number": 16, "usage_type": "name"}, {"api_name": "emissionsapi.config.config", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 35, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 44, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 46, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 48, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 50, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 52, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 54, "usage_type": "call"}, {"api_name": "geoalchemy2.Geometry", "line_number": 54, "usage_type": "call"}, {"api_name": "geoalchemy2.elements.WKTElement", "line_number": 61, "usage_type": "call"}, {"api_name": "geoalchemy2.elements", "line_number": 61, "usage_type": "attribute"}, {"api_name": "functools.wraps", "line_number": 73, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 102, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 103, "usage_type": "call"}, {"api_name": "geoalchemy2.func.ST_WITHIN", "line_number": 121, "usage_type": "call"}, {"api_name": "geoalchemy2.func", "line_number": 121, "usage_type": "attribute"}, {"api_name": "geoalchemy2.elements.WKTElement", "line_number": 139, "usage_type": "call"}, {"api_name": "geoalchemy2.elements", "line_number": 139, "usage_type": "attribute"}]} +{"seq_id": "601779999", "text": "import os\nimport argparse\nimport json\nCOMMON_VIDEO_ETX = set([\n \".webm\", \".mpg\", \".mpeg\", \".mpv\", \".ogg\",\n \".mp4\", \".m4p\", \".mpv\", \".avi\", \".wmv\", \".qt\",\n \".mov\", \".flv\", \".swf\"])\n\n\ndef main(opts):\n videopath = opts.video_path\n feature_path = opts.feature_path\n csv_folder = opts.csv_folder\n if not os.path.exists(csv_folder):\n os.mkdir(csv_folder)\n if not os.path.exists(feature_path):\n os.mkdir(feature_path)\n if os.path.exists(opts.corrupted_id_file):\n corrupted_ids = set(json.load(\n open(opts.corrupted_id_file, 'r')))\n else:\n corrupted_ids = None\n\n outputFile = f\"{csv_folder}/slowfast_info.csv\"\n with open(outputFile, \"w\") as fw:\n fw.write(\"video_path,feature_path\\n\")\n fileList = []\n for dirpath, _, files in os.walk(videopath):\n for fname in files:\n input_file = os.path.join(dirpath, fname)\n if os.path.isfile(input_file):\n _, ext = os.path.splitext(fname)\n if ext.lower() in COMMON_VIDEO_ETX:\n fileList.append(input_file)\n\n for input_filename in fileList:\n filename = os.path.basename(input_filename)\n fileId, _ = os.path.splitext(filename)\n\n output_filename = os.path.join(\n feature_path, fileId+\".npz\")\n if not os.path.exists(output_filename):\n fw.write(input_filename+\",\"+output_filename+\"\\n\")\n if corrupted_ids is not None and fileId in corrupted_ids:\n fw.write(input_filename+\",\"+output_filename+\"\\n\")\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--video_path\", default=\"/video/\", type=str,\n help=\"The input video path.\")\n parser.add_argument(\"--feature_path\", default=\"/output/slowfast_features\",\n type=str, help=\"output feature path.\")\n parser.add_argument(\n '--csv_folder', type=str, default=\"/output/csv\",\n help='output csv folder')\n parser.add_argument(\n '--corrupted_id_file', type=str, default=\"\",\n help='corrupted id file')\n args = parser.parse_args()\n main(args)\n", "sub_path": "slowfast/extract_feature/gather_video_paths.py", "file_name": "gather_video_paths.py", "file_ext": "py", "file_size_in_byte": 2230, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 19, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "174638015", "text": "'''\nImplements the automation services\n'''\n\n\nimport sys\nimport time\nimport schedule\nfrom datetime import datetime\nfrom daemon import Daemon\nfrom d1_metrics.metricsreporter import MetricsReporter\nfrom d1_metrics.metricsdatabase import MetricsDatabase\n\n\n\nclass MetricsServiceManager(object):\n\n def __init__(self):\n pass\n\n def job(self):\n with open(\"test.txt\", \"a\") as myfile:\n myfile.write(\"I'm still working at\")\n myfile.write(datetime.now().strftime('%m/%d/%Y'))\n print(\"I'm working...\")\n\n def run(self):\n metrics_database = MetricsDatabase()\n metrics_reporter = MetricsReporter()\n schedule.every(1).minute.do(job_func=self.job)\n schedule.every().hour.do(job_func=self.job)\n schedule.every().day.at(\"00:30\").do(job_func=self.job)\n # schedule.every().day.at(\"01:30\").do(job_func=metrics_reporter.scheduler())\n # schedule.every().day.at(\"02:30\").do(job_func=metrics_database.getCitations())\n while True:\n schedule.run_pending()\n time.sleep(1)\n\n\nclass MyDaemon(Daemon):\n def run(self):\n service_manager_object = MetricsServiceManager()\n service_manager_object.run()\n\n\nif __name__ == \"__main__\":\n # daemon = MyDaemon('/tmp/daemon-example.pid')\n # if len(sys.argv) == 2:\n # if 'start' == sys.argv[1]:\n # daemon.start()\n # elif 'stop' == sys.argv[1]:\n # daemon.stop()\n # elif 'restart' == sys.argv[1]:\n # daemon.restart()\n # else:\n # print(\"Unknown command\")\n # sys.exit(2)\n # sys.exit(0)\n # else:\n # print(\"usage: %s start|stop|restart\" % sys.argv[0])\n # sys.exit(2)\n service_manager_object = MetricsServiceManager()\n service_manager_object.run()", "sub_path": "src/d1_metrics/d1_metrics/metricsservicemanager.py", "file_name": "metricsservicemanager.py", "file_ext": "py", "file_size_in_byte": 1808, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "90", "api": [{"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "name"}, {"api_name": "d1_metrics.metricsdatabase.MetricsDatabase", "line_number": 28, "usage_type": "call"}, {"api_name": "d1_metrics.metricsreporter.MetricsReporter", "line_number": 29, "usage_type": "call"}, {"api_name": "schedule.every", "line_number": 30, "usage_type": "call"}, {"api_name": "schedule.every", "line_number": 31, "usage_type": "call"}, {"api_name": "schedule.every", "line_number": 32, "usage_type": "call"}, {"api_name": "schedule.run_pending", "line_number": 36, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 37, "usage_type": "call"}, {"api_name": "daemon.Daemon", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "15064685", "text": "from flask import Flask, render_template\r\nfrom data import db_session\r\nfrom data.jobs import Jobs\r\nfrom data.users import User\r\n\r\n\r\napp = Flask(__name__)\r\napp.config['SECRET_KEY'] = 'f509a688-46b1-492f-911e-a20939a0a875'\r\n\r\n\r\n@app.route('/')\r\ndef work():\r\n db_session.global_init('db/blogs.db')\r\n db_sess = db_session.create_session()\r\n all_data = []\r\n for job in db_sess.query(Jobs).all():\r\n user = job.team_leader\r\n for getting in db_sess.query(User).filter(User.id == user):\r\n user = getting.surname + ' ' + getting.name\r\n break\r\n title = job.job\r\n time = job.work_size\r\n people = job.collaborators\r\n is_finished = job.is_finished\r\n all_data.append((title, user, time, people, is_finished))\r\n return render_template(\"work.html\", all_data=all_data)\r\n\r\n\r\nif __name__ == '__main__':\r\n app.run(port=8080, host='127.0.0.1')\r\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 912, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "data.db_session.global_init", "line_number": 13, "usage_type": "call"}, {"api_name": "data.db_session", "line_number": 13, "usage_type": "name"}, {"api_name": "data.db_session.create_session", "line_number": 14, "usage_type": "call"}, {"api_name": "data.db_session", "line_number": 14, "usage_type": "name"}, {"api_name": "data.jobs.Jobs", "line_number": 16, "usage_type": "argument"}, {"api_name": "data.users.User", "line_number": 18, "usage_type": "argument"}, {"api_name": "data.users.User.id", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "228146797", "text": "\"\"\"Train an ICNet Model on ADE20K Data.\"\"\"\n\nimport argparse\nimport keras\nimport logging\nimport sys\nimport setup\n\nfrom keras import backend as K\nfrom keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, TensorBoard, Callback\nimport tensorflow as tf\n\nfrom image_segmentation.icnet2 import ICNetModelFactory\nfrom image_segmentation.data_generator import ADE20KGenerator\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger('train')\n\n#from callbacks import TensorBoardImage\nfrom metrics import mean_IoU,categorical_crossentropy_logits\nfrom utils import random_crop,one_hot_it,colour_code_segmentation,prepare_data,ATRSequence,get_label_info\n\n#########################\n##Create a global session\n#########################\ns=tf.Session()\nK.set_session(s)\n\ndef train(argv):\n \"\"\"Train an ICNet model.\"\"\"\n parser = argparse.ArgumentParser(\n description='Train an ICNet model.'\n )\n # Data options\n parser.add_argument(\n '-d', '--data-directory', type=str, required=False,\n help='The top level directory containing AED2K data.'\n )\n parser.add_argument(\n '-s', '--image-size', type=int, default=384,\n help=('The pixel dimension of model input and output. Images '\n 'will be square.')\n )\n parser.add_argument(\n '-a', '--augment-images', type=bool, default=True,\n help='turn on image augmentation.'\n )\n parser.add_argument(\n '-w', '--whitelist-labels', type=str,\n help=('A pipe | separated list of object labels to whitelist. To see a'\n ' full list of allowed labels run with --list-labels.')\n )\n parser.add_argument(\n '-t', '--whitelist-threshold', type=float, default=0.7,\n help=('The fraction of whitelisted labels an image must contain to be '\n 'used for training.')\n )\n parser.add_argument(\n '--list-labels', action='store_true',\n help='If true, print a full list of object labels.'\n )\n # Training options\n parser.add_argument(\n '-b', '--batch-size', type=int, default=8,\n help='The training batch_size.'\n )\n parser.add_argument(\n '--lr', type=float, default=0.001, help='The learning rate.'\n )\n parser.add_argument(\n '-e', '--epochs', type=int, default=1000,\n help='Number of training epochs'\n )\n parser.add_argument(\n '-o', '--output', type=str, required=False,\n help='An output file to save the trained model.')\n parser.add_argument(\n '--checkpoint', type=str,\n help='A Keras model checkpoint to load and continue training.'\n )\n\n args = parser.parse_args(argv)\n\n # if args.list_labels:\n # logger.info('Labels:')\n # labels = ''\n # for label in ADE20KGenerator.load_class_labels( setup.dataset_dir):\n # labels += '%s\\n' % label\n # logger.info(labels)\n # sys.exit()\n\n whitelist_labels = None\n if args.whitelist_labels:\n whitelist_labels = args.whitelist_labels.split('|')\n\n # generator = ADE20KGenerator(\n # setup.dataset_dir,\n # batch_size=setup.batch_size,\n # image_size=(args.image_size, args.image_size)\n # )\n\n\n icnet = ICNetModelFactory.build(\n args.image_size,\n setup.classes,\n train=True\n )\n\n optimizer = keras.optimizers.Adam(lr=args.lr)\n icnet.compile(\n optimizer,\n loss=keras.losses.categorical_crossentropy,\n loss_weights=[1.0, 0.8, 0.4, 0.16],\n metrics=[mean_IoU],\n )\n icnet.summary()\n ###########\n ##callbacks\n ###########\n sess = K.get_session()\n callbacks = [ModelCheckpoint('models/ATR_ICNet.h5',\n verbose=0,\n mode='auto',\n period=1),\n TensorBoard(log_dir=setup.logdir),\n #TensorBoardImage(sess, \"Segmentation\", \"Overlay\")\n ]\n # callbacks = [\n # keras.callbacks.ModelCheckpoint(\n # args.output,\n # verbose=0,\n # mode='auto',\n # period=1\n # ),\n # ]\n\n\n\n train_input_names, train_output_names, val_input_names, val_output_names, test_input_names, test_output_names = prepare_data(setup.dataset_dir)\n\n generator = ATRSequence(\"train\", train_input_names, train_output_names, setup.batch_size, setup.csv_path)\n validation_data = ATRSequence(\"validation\", val_input_names, val_output_names, setup.batch_size, setup.csv_path)\n\n icnet.fit_generator(generator,\n len(generator),\n epochs=setup.epochs,\n verbose=1,\n shuffle=True,\n callbacks=callbacks,\n validation_data=validation_data,\n )\nif __name__ == '__main__':\n train(sys.argv[1:])\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 4886, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.basicConfig", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.backend.set_session", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 27, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 31, "usage_type": "call"}, {"api_name": "image_segmentation.icnet2.ICNetModelFactory.build", "line_number": 103, "usage_type": "call"}, {"api_name": "image_segmentation.icnet2.ICNetModelFactory", "line_number": 103, "usage_type": "name"}, {"api_name": "setup.classes", "line_number": 105, "usage_type": "attribute"}, {"api_name": "keras.optimizers.Adam", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 109, "usage_type": "attribute"}, {"api_name": "keras.losses", "line_number": 112, "usage_type": "attribute"}, {"api_name": "metrics.mean_IoU", "line_number": 114, "usage_type": "name"}, {"api_name": "keras.backend.get_session", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 120, "usage_type": "name"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 121, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 125, "usage_type": "call"}, {"api_name": "setup.logdir", "line_number": 125, "usage_type": "attribute"}, {"api_name": "utils.prepare_data", "line_number": 139, "usage_type": "call"}, {"api_name": "setup.dataset_dir", "line_number": 139, "usage_type": "attribute"}, {"api_name": "utils.ATRSequence", "line_number": 141, "usage_type": "call"}, {"api_name": "setup.batch_size", "line_number": 141, "usage_type": "attribute"}, {"api_name": "setup.csv_path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "utils.ATRSequence", "line_number": 142, "usage_type": "call"}, {"api_name": "setup.batch_size", "line_number": 142, "usage_type": "attribute"}, {"api_name": "setup.csv_path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "setup.epochs", "line_number": 146, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 153, "usage_type": "attribute"}]} +{"seq_id": "61601254", "text": "import re\nimport json\nimport urllib\nimport requests\nimport os\nimport time\nimport selenium.webdriver as wd\nfrom PIL import Image as im\n\n\nclass Douban(object):\n\n def __init__(self, url):\n \"\"\"\n 初始化item页信息\n :param url: 需要抓取的豆瓣电影页\n \"\"\"\n self.target_url = url\n if not os.path.exists('./'):\n os.mkdir('./src')\n self.driver = wd.Chrome()\n self.driver.maximize_window()\n self.driver.get(url)\n with open('./src/item_page.html', 'w', encoding='utf-8') as f:\n f.write(self.driver.page_source)\n # self.driver.close()\n with open('./src/item_page.html', 'r', encoding='utf-8') as f:\n self.html = f.read()\n self.data = {}\n self.basic_dir = os.path.abspath('./')\n self.header = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36\"}\n\n def init_data(self):\n \"\"\"\n 初始化部分电影信息\n :return:\n \"\"\"\n # 获取item名称\n _name_reg = re.compile(\n r'(.*?)', re.S)\n self.data['movie_name'] = re.findall(_name_reg, self.html)[0]\n\n # 获取item发布年份\n _released_year_reg = re.compile(\n r'\\((.*?)\\)', re.S)\n self.data['released_year'] = re.findall(\n _released_year_reg, self.html)[0]\n\n # 获取item海报url\n _posters_url_reg = re.compile(\n r'', re.S)\n self.data['posters'] = re.findall(_posters_url_reg, self.html)\n # 获取item主创姓名\n _main_team_reg = re.compile(r'(.*?)', re.S)\n for line in self.html.split('\\n'):\n if '导演' in line:\n self.data['director'] = re.findall(_main_team_reg, line)\n elif '编剧' in line:\n self.data['screenwriter'] = re.findall(_main_team_reg, line)\n elif '主演' in line:\n self.data['actor'] = re.findall(_main_team_reg, line)[:-1]\n\n # 获取item类型\n _type_reg = re.compile(r'(.*?)', re.S)\n self.data['type'] = re.findall(_type_reg, self.html)\n\n # 获取item发行地\n _state_reg = re.compile(\n r'制片国家/地区:(.*?)
', re.S)\n self.data['state'] = [item.strip()\n for item in re.findall(_state_reg, self.html)]\n\n # 获取item发行时间\n _released_time_reg = re.compile(\n r'initialReleaseDate.*?>([\\d]{4}-[\\d]{2}-[\\d]{2})\\((.*?)\\).*?>', re.S)\n self.data['released_time'] = [{item[-1]: item[0]}\n for item in re.findall(_released_time_reg, self.html)]\n\n # 获取item简介\n _summary_reg = re.compile(r'v:summary.*?>(.*?)
(.*?)', re.S)\n self.data['summary'] = '\\t' + '\\n\\t'.join([''.join(element) for element in [re.split(\n r'[\\s]', item) for item in re.findall(_summary_reg, self.html)[0]]])\n\n # 获取item主创名\n _main_team_name_reg = re.compile(\n r'
  • .*?
    (.*?)<.*?class=\"role\".*?>(.*?)<',\n re.S)\n self.data['main_team_info'] = [{'name': item[1], 'role': ' '.join(item[-1].split(' ')[1:]) if len(\n item[-1].split(' ')) != 1 else item[-1], 'avatar': item[0]} for item in\n re.findall(_main_team_name_reg, self.html)]\n\n # 获取item剧照url\n # _poster_url_reg = re.compile(\n # r'
    图片', re.S)\n # self.data['stills'] = re.findall(_poster_url_reg, self.html)\n self.data['stills'] = self.data['posters'][0][:-1] + 'S'\n\n def build_dir(self):\n \"\"\"\n 为电影创建目录结构\n .\n +-- movie_item.py\n +-- movie_name/\n | +-- main_team/\n | +-- posters/\n | +-- stills/\n | +-- movie_name_info.json\n\n :return:\n \"\"\"\n if not os.path.exists(f'./{self.data[\"movie_name\"]}'):\n os.mkdir(self.data['movie_name'])\n os.chdir(os.path.abspath(f'./{self.data[\"movie_name\"]}'))\n self.item_abs_path = os.path.abspath('./')\n for dir_name in ['./posters', './main_team', './stills']:\n if not os.path.exists(dir_name):\n os.mkdir(dir_name)\n # if not os.path.exists('./posters'):\n # os.mkdir('./posters')\n # elif not os.path.exists('./main_team'):\n # os.mkdir('./main_team')\n # elif not os.path.exists('./stills'):\n # os.mkdir('./stills')\n\n def webp_2_jpeg(self, bname, aname):\n \"\"\"\n webp 转 jpg\n :param bname: 转换前文件名，可以包含路径\n :param aname: 转换后文件名，可以包含路径\n :return:\n \"\"\"\n im.open(bname).save(aname)\n os.remove(bname)\n\n def download(self, type, name, url):\n \"\"\"\n 下载页面图片\n :param type: 图片分类\n :param name: 图片名\n :param url: 图片url\n :return:\n \"\"\"\n res = requests.get(url, headers=self.header)\n if res.status_code == 200:\n with open(f'./{type}/{name}.webp', 'wb') as w:\n w.write(res.content)\n self.webp_2_jpeg(f'./{type}/{name}.webp', f'./{type}/{name}.jpg')\n\n def get_element_page(self, url):\n \"\"\"\n 获取跳转页html\n :param url: 需要获取的跳转页url\n :return:\n \"\"\"\n self.driver.get(url)\n return self.driver.page_source\n\n def download_poster(self):\n \"\"\"\n 下载电影海报（部分）\n :return:\n \"\"\"\n _count = 0\n _poster_url = self.data['posters'][0]\n _poster_page = self.get_element_page(_poster_url)\n _poster_url_reg = re.compile(\n r'.*?class=\"prop\".*?(\\d+x\\d+).*?
    ', re.S)\n self.data['poster_urls'] = [{'url': item[0], 'pixel': item[-1]}\n for item in re.findall(_poster_url_reg, _poster_page)]\n for item in self.data['poster_urls']:\n _count += 1\n self.download(\n 'posters', f'poster-{str(_count)}-{item[\"pixel\"]}', item['url'])\n\n def download_main_team_pic(self):\n \"\"\"\n 下载电影主创人员图片\n :return:\n \"\"\"\n for item in self.data['main_team_info']:\n self.download('main_team', item['name'], item['avatar'])\n\n def download_stills(self):\n \"\"\"\n 下载电影剧照（部分）\n :return:\n \"\"\"\n _count = 0\n _stills_url = self.data['stills']\n _stills_page = self.get_element_page(_stills_url)\n _stills_url_reg = re.compile(\n r'.*?class=\"prop\".*?(\\d+x\\d+).*?
    ', re.S)\n self.data['stills_urls'] = [{'url': item[0], 'pixel': item[-1]}\n for item in re.findall(_stills_url_reg, _stills_page)]\n for item in self.data['stills_urls']:\n _count += 1\n self.download(\n 'stills', f'stills-{str(_count)}-{item[\"pixel\"]}', item['url'])\n\n def store_item_info(self):\n \"\"\"\n 保存电影信息\n :return:\n \"\"\"\n with open(f'./{self.data[\"movie_name\"]}.json', 'w', encoding='utf-8') as j:\n j.write(json.dumps(self.data))\n\n def run(self):\n \"\"\"\n 启动\n :return:\n \"\"\"\n self.init_data()\n self.build_dir()\n self.download_poster()\n self.download_main_team_pic()\n self.download_stills()\n self.store_item_info()\n self.driver.close()\n\n\nif __name__ == '__main__':\n tmp = Douban(\n r'https://movie.douban.com/subject/27060077/?tag=%E7%83%AD%E9%97%A8&from=gaia_video')\n tmp.run()\n print(json.dumps(tmp.data, indent=4))\n\n # print(os.path.abspath('./'))\n", "sub_path": "tmp_1.py", "file_name": "tmp_1.py", "file_ext": "py", "file_size_in_byte": 8397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 20, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 21, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 21, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 40, "usage_type": "call"}, {"api_name": "re.S", "line_number": 41, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 42, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 45, "usage_type": "call"}, {"api_name": "re.S", "line_number": 46, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 47, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 51, "usage_type": "call"}, {"api_name": "re.S", "line_number": 52, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 53, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 55, "usage_type": "call"}, {"api_name": "re.S", "line_number": 55, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 58, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 60, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 62, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 65, "usage_type": "call"}, {"api_name": "re.S", "line_number": 65, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 66, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 69, "usage_type": "call"}, {"api_name": "re.S", "line_number": 70, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 72, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 75, "usage_type": "call"}, {"api_name": "re.S", "line_number": 76, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 78, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 81, "usage_type": "call"}, {"api_name": "re.S", "line_number": 81, "usage_type": "attribute"}, {"api_name": "re.split", "line_number": 82, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 83, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 86, "usage_type": "call"}, {"api_name": "re.S", "line_number": 88, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 113, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 118, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 133, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 133, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 134, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 144, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 167, "usage_type": "call"}, {"api_name": "re.S", "line_number": 168, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 170, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 192, "usage_type": "call"}, {"api_name": "re.S", "line_number": 193, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 195, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 207, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 227, "usage_type": "call"}]} +{"seq_id": "485479042", "text": "# coding: utf-8\n# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department\n# Distributed under the terms of \"New BSD License\", see the LICENSE file.\n\nimport os\nimport sys\nfrom io import StringIO\nimport numpy as np\nfrom pyiron_base.generic.hdfio import FileHDFio\nfrom pyiron_base._tests import PyironTestCase\nimport unittest\n\n\nclass TestFileHDFio(PyironTestCase):\n @classmethod\n def setUpClass(cls):\n cls.current_dir = os.path.dirname(os.path.abspath(__file__)).replace(\"\\\\\", \"/\")\n cls.empty_hdf5 = FileHDFio(file_name=cls.current_dir + \"/filehdfio_empty.h5\")\n cls.full_hdf5 = FileHDFio(file_name=cls.current_dir + \"/filehdfio_full.h5\")\n cls.i_o_hdf5 = FileHDFio(file_name=cls.current_dir + \"/filehdfio_io.h5\")\n cls.es_hdf5 = FileHDFio(\n file_name=cls.current_dir + \"/../static/dft/es_hdf.h5\"\n )\n with cls.full_hdf5.open(\"content\") as hdf:\n hdf[\"array\"] = np.array([1, 2, 3, 4, 5, 6])\n hdf[\"array_3d\"] = np.array([[1, 2, 3], [4, 5, 6]])\n hdf[\"traj\"] = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9]]], dtype=object)\n hdf[\"dict\"] = {\"key_1\": 1, \"key_2\": \"hallo\"}\n hdf[\"dict_numpy\"] = {\"key_1\": 1, \"key_2\": np.array([1, 2, 3, 4, 5, 6])}\n with hdf.open('group') as grp:\n grp['some_entry'] = 'present'\n with cls.i_o_hdf5.open(\"content\") as hdf:\n hdf[\"exists\"] = True\n # Open and store value in a hdf file to use test_remove_file on it, do not use otherwise\n cls.to_be_removed_hdf = FileHDFio(file_name=cls.current_dir + '/filehdfio_tbr.h5')\n with cls.to_be_removed_hdf.open('content') as hdf:\n hdf['value'] = 1\n # Remains open to be closed by test_close, do not use otherwise\n cls.opened_hdf = cls.full_hdf5.open(\"content\")\n\n @classmethod\n def tearDownClass(cls):\n cls.current_dir = os.path.dirname(os.path.abspath(__file__)).replace(\"\\\\\", \"/\")\n os.remove(cls.current_dir + \"/filehdfio_full.h5\")\n os.remove(cls.current_dir + \"/filehdfio_io.h5\")\n\n def _check_full_hdf_values(self, hdf):\n self.assertTrue(\n all(np.equal(hdf[\"content/array\"], np.array([1, 2, 3, 4, 5, 6])))\n )\n self.assertTrue(\n all(\n np.equal(\n hdf[\"content\"][\"array_3d\"],\n np.array([[1, 2, 3], [4, 5, 6]]),\n ).flatten()\n )\n )\n self.assertTrue(\n all(\n np.equal(\n hdf[\"content/traj\"][0], np.array([[1, 2, 3], [4, 5, 6]])\n ).flatten()\n )\n )\n self.assertTrue(\n all(\n np.equal(\n hdf[\"content/traj\"][1], np.array([[7, 8, 9]])\n ).flatten()\n )\n )\n self.assertEqual(hdf[\"content/dict\"][\"key_1\"], 1)\n self.assertEqual(hdf[\"content/dict\"][\"key_2\"], \"hallo\")\n self.assertEqual(hdf[\"content/dict_numpy\"][\"key_1\"], 1)\n self.assertTrue(\n all(\n np.equal(\n hdf[\"content/dict_numpy\"][\"key_2\"],\n np.array([1, 2, 3, 4, 5, 6]),\n )\n )\n )\n self.assertEqual(hdf['content/group/some_entry'], 'present')\n\n def test_get_item(self):\n self._check_full_hdf_values(self.full_hdf5)\n # Test leaving to pyiron Project at hdf file location:\n pr = self.full_hdf5['content/..']\n from pyiron_base import Project\n self.assertIsInstance(pr, Project)\n self.assertEqual(pr.path, self.full_hdf5.file_path + '/')\n # Test leaving to pyiron Project at other than hdf file location:\n pr = self.full_hdf5['..']\n self.assertIsInstance(pr, Project)\n self.assertEqual(pr.path.replace(\"\\\\\", \"/\"),\n os.path.normpath(\n os.path.join(self.full_hdf5.file_path, '..')\n ).replace(\"\\\\\", \"/\") + '/'\n )\n # Test getting a new FileHDFio object:\n group_hdf = self.full_hdf5['content/group']\n self.assertIsInstance(group_hdf, FileHDFio)\n self.assertEqual(group_hdf.h5_path, '/content/group')\n # Test getting the parent FileHDFio object:\n content_hdf = group_hdf['..']\n self.assertIsInstance(content_hdf, FileHDFio)\n self.assertEqual(content_hdf.h5_path, self.full_hdf5.h5_path + 'content')\n # Getting the '/' of the hdf would result in a path which already belongs to the project.\n # Therefore, the project is returned instead.\n pr = content_hdf['..']\n self.assertIsInstance(pr, Project)\n self.assertEqual(pr.path, self.full_hdf5.file_path + '/')\n # Test getting the same object directly:\n pr = group_hdf['../..']\n self.assertIsInstance(pr, Project)\n self.assertEqual(pr.path, self.full_hdf5.file_path + '/')\n\n def test_file_name(self):\n self.assertEqual(\n self.empty_hdf5.file_name, self.current_dir + \"/filehdfio_empty.h5\"\n )\n self.assertEqual(\n self.full_hdf5.file_name, self.current_dir + \"/filehdfio_full.h5\"\n )\n\n def test_h5_path(self):\n self.assertEqual(self.full_hdf5.h5_path, '/')\n\n def test_open(self):\n opened_hdf = self.full_hdf5.open('content')\n self.assertEqual(opened_hdf.h5_path, '/content')\n self.assertEqual(opened_hdf.history[-1], 'content')\n\n def test_close(self):\n self.opened_hdf.close()\n self.assertEqual(self.opened_hdf.h5_path, '/')\n\n def test_remove_file(self):\n path = self.to_be_removed_hdf.file_name\n self.to_be_removed_hdf.remove_file()\n self.assertFalse(os.path.isfile(path))\n\n def test_get_from_table(self):\n pass\n\n def test_get_pandas(self):\n pass\n\n def test_get(self):\n self.assertEqual(self.full_hdf5.get(\"doesnotexist\", default=42), 42,\n \"default value not returned when value doesn't exist.\")\n self.assertTrue(np.array_equal(\n self.full_hdf5.get(\"content/array\", default=42),\n np.array([1, 2, 3, 4, 5, 6])\n ), \"default value returned when value does exist.\")\n with self.assertRaises(ValueError):\n self.empty_hdf5.get('non_existing_key')\n\n def test_hd_copy(self):\n new_hdf_file = os.path.join(self.current_dir, 'copy_full.h5')\n new_hdf = FileHDFio(file_name=new_hdf_file)\n new_hdf = self.full_hdf5.hd_copy(self.full_hdf5, new_hdf)\n self._check_full_hdf_values(new_hdf)\n os.remove(new_hdf_file)\n\n def test_groups(self):\n groups = self.full_hdf5.groups()\n # _filter is actually relies on the _filter property of the Project, thus groups does not do anything.\n self.assertIsInstance(groups, FileHDFio)\n\n def test_rewrite_hdf5(self):\n pass\n\n def test_to_object(self):\n pass\n\n def test_put(self):\n self.i_o_hdf5.put('answer', 42)\n self.assertEqual(self.i_o_hdf5['answer'], 42)\n\n def test_list_all(self):\n empty_file_dict = self.empty_hdf5.list_all()\n self.assertEqual(empty_file_dict[\"groups\"], [])\n self.assertEqual(empty_file_dict[\"nodes\"], [])\n es_file_dict = self.es_hdf5.list_all()\n self.assertEqual(es_file_dict[\"groups\"], [\"es_new\", \"es_old\"])\n self.assertEqual(es_file_dict[\"nodes\"], [])\n es_group_dict = self.es_hdf5[\"es_new\"].list_all()\n self.assertEqual(es_group_dict[\"groups\"], [\"dos\"])\n self.assertEqual(\n es_group_dict[\"nodes\"],\n [\"TYPE\", \"efermi\", \"eig_matrix\", \"k_points\", \"k_weights\", \"occ_matrix\"],\n )\n\n def test_list_nodes(self):\n self.assertEqual(self.empty_hdf5.list_nodes(), [])\n self.assertEqual(\n self.es_hdf5[\"es_new\"].list_nodes(),\n [\"TYPE\", \"efermi\", \"eig_matrix\", \"k_points\", \"k_weights\", \"occ_matrix\"],\n )\n\n def test_list_groups(self):\n self.assertEqual(self.empty_hdf5.list_groups(), [])\n self.assertEqual(self.es_hdf5.list_groups(), [\"es_new\", \"es_old\"])\n\n def test_listdirs(self):\n self.assertEqual(self.empty_hdf5.listdirs(), [])\n self.assertEqual(self.es_hdf5.listdirs(), [\"es_new\", \"es_old\"])\n\n def test_show_hdf(self):\n sys_stdout = sys.stdout\n result = StringIO()\n sys.stdout = result\n self.full_hdf5.show_hdf()\n result_string = result.getvalue()\n sys.stdout = sys_stdout\n self.assertEqual(result_string,\n 'group: content\\n node array\\n node array_3d\\n node dict\\n node dict_numpy\\n' +\n ' node traj\\n group: group\\n node some_entry\\n'\n )\n\n def test_is_empty(self):\n self.assertTrue(self.empty_hdf5.is_empty)\n self.assertFalse(self.full_hdf5.is_empty)\n\n def test_is_root(self):\n self.assertTrue(self.full_hdf5.is_root)\n hdf = self.full_hdf5['content']\n self.assertFalse(hdf.is_root)\n\n def test_base_name(self):\n self.assertEqual(self.full_hdf5.base_name, 'filehdfio_full')\n self.assertEqual(self.empty_hdf5.base_name, 'filehdfio_empty')\n self.assertEqual(self.i_o_hdf5.base_name, 'filehdfio_io')\n\n def test_file_size(self):\n self.assertTrue(self.es_hdf5.file_size(self.es_hdf5) > 0)\n\n def test_get_size(self):\n self.assertTrue(self.es_hdf5.get_size(self.es_hdf5) > 0)\n\n def test_copy(self):\n copy = self.es_hdf5.copy()\n self.assertIsInstance(copy, FileHDFio)\n self.assertEqual(copy.h5_path, self.es_hdf5.h5_path)\n\n # results in an Error\n # File \"C:\\Users\\Siemer\\pyiron_git\\pyiron_base\\tests\\generic\\test_fileHDFio.py\", line 249, in test_copy_to\n # copy = self.full_hdf5.copy_to(destination)\n # File \"C:\\Users\\Siemer\\pyiron_git\\pyiron_base\\pyiron_base\\generic\\hdfio.py\", line 355, in copy_to\n # _internal_copy(source=f_source, source_path=self._h5_path, target=f_target,\n # File \"C:\\Users\\Siemer\\pyiron_git\\pyiron_base\\pyiron_base\\generic\\hdfio.py\", line 332, in _internal_copy\n # source.copy(source_path, target, name=target_path)\n # File \"C:\\Users\\Siemer\\anaconda3\\envs\\pyiron_git\\lib\\site-packages\\h5py\\_hl\\group.py\", line 494, in copy\n # h5o.copy(source.id, self._e(source_path), dest.id, self._e(dest_path),\n # File \"h5py\\_objects.pyx\", line 54, in h5py._objects.with_phil.wrapper\n # File \"h5py\\_objects.pyx\", line 55, in h5py._objects.with_phil.wrapper\n # File \"h5py\\h5o.pyx\", line 217, in h5py.h5o.copy\n # ValueError: No destination name specified (no destination name specified)\n #def test_copy_to(self):\n # file_name = self.current_dir + '/filehdfio_tmp'\n # destination = FileHDFio(file_name=file_name)\n # copy = self.full_hdf5.copy_to(destination)\n # self._check_full_hdf_values(copy)\n # os.remove(file_name)\n\n def test_remove_group(self):\n grp = 'group_to_be_removed'\n hdf = self.i_o_hdf5.create_group(grp)\n # If nothing is written to the group, the creation is not reflected by the HDF5 file\n hdf['key'] = 1\n self.assertTrue(grp in self.i_o_hdf5.list_groups())\n hdf.remove_group()\n self.assertFalse(grp in self.i_o_hdf5.list_nodes())\n # This should not raise an error, albeit the group of hdf is removed\n hdf.remove_group()\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "sub_path": "tests/generic/test_fileHDFio.py", "file_name": "test_fileHDFio.py", "file_ext": "py", "file_size_in_byte": 11590, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "pyiron_base._tests.PyironTestCase", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "pyiron_base.generic.hdfio.FileHDFio", "line_number": 18, "usage_type": "call"}, {"api_name": "pyiron_base.generic.hdfio.FileHDFio", "line_number": 19, "usage_type": "call"}, {"api_name": "pyiron_base.generic.hdfio.FileHDFio", "line_number": 20, "usage_type": "call"}, {"api_name": "pyiron_base.generic.hdfio.FileHDFio", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "pyiron_base.generic.hdfio.FileHDFio", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 43, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 44, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "pyiron_base.Project", "line_number": 91, "usage_type": "name"}, {"api_name": "pyiron_base.Project", "line_number": 95, "usage_type": "name"}, {"api_name": "os.path.normpath", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pyiron_base.generic.hdfio.FileHDFio", "line_number": 103, "usage_type": "argument"}, {"api_name": "pyiron_base.generic.hdfio.FileHDFio", "line_number": 107, "usage_type": "argument"}, {"api_name": "pyiron_base.Project", "line_number": 112, "usage_type": "name"}, {"api_name": "pyiron_base.Project", "line_number": 116, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pyiron_base.generic.hdfio.FileHDFio", "line_number": 162, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 165, "usage_type": "call"}, {"api_name": "pyiron_base.generic.hdfio.FileHDFio", "line_number": 170, "usage_type": "argument"}, {"api_name": "sys.stdout", "line_number": 212, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 213, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 214, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 217, "usage_type": "attribute"}, {"api_name": "pyiron_base.generic.hdfio.FileHDFio", "line_number": 245, "usage_type": "argument"}, {"api_name": "unittest.main", "line_number": 281, "usage_type": "call"}]} +{"seq_id": "558401328", "text": "from django.contrib import messages\r\nfrom django.contrib.contenttypes.models import ContentType\r\nfrom django.http import HttpResponse\r\nfrom django.shortcuts import render, get_object_or_404, HttpResponseRedirect, redirect, Http404\r\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\r\nfrom urllib.parse import quote_plus\r\nfrom django.utils import timezone\r\nfrom django.db.models import Q\r\nfrom django.core.mail import send_mail\r\nfrom django.conf import settings\r\n\r\nfrom .models import Post\r\nfrom .forms import PostForm, ClientSubscribeForm\r\nfrom comments.models import Comment\r\nfrom comments.forms import CommentForm\r\n# from .utils import get_read_time\r\n\r\n\r\ndef post_list(request):\r\n today = timezone.now().date()\r\n queryset_list = Post.objects.active().order_by('-created')\r\n if request.user.is_staff or request.user.is_superuser:\r\n queryset_list = Post.objects.all().order_by('-created')\r\n queryset_list_last_items = queryset_list.order_by('-created')[:3]\r\n\r\n query = request.GET.get('q')\r\n\r\n if query:\r\n queryset_list = queryset_list.filter(\r\n Q(title__icontains=query) |\r\n Q(content__icontains=query) |\r\n Q(user__first_name__icontains=query) |\r\n Q(user__last_name__icontains=query)\r\n ).distinct()\r\n page = request.GET.get('page')\r\n paginator = Paginator(queryset_list, 5)\r\n try:\r\n queryset = paginator.page(page)\r\n except PageNotAnInteger:\r\n queryset = paginator.page(1)\r\n except EmptyPage:\r\n queryset = paginator.page(paginator.num_pages)\r\n\r\n form_contact = ClientSubscribeForm(request.POST or None)\r\n if request.method == 'POST' and form_contact.is_valid():\r\n # for key, value in form.cleaned_data.items():\r\n # print(key, value)\r\n\r\n form_full_name = form_contact.cleaned_data.get('full_name')\r\n form_email = form_contact.cleaned_data.get('email')\r\n form_skype = form_contact.cleaned_data.get('skype')\r\n form_telegram = form_contact.cleaned_data.get('telegram')\r\n\r\n subject = 'Take your bonus motherfucker!!!'\r\n contact_message = \"Hi %s: skype(%s), telegram(%s), email(%s). You fucking idiot!\" % (form_full_name, form_skype, form_telegram, form_email)\r\n from_email = settings.EMAIL_HOST_USER\r\n to_email = [form_email, from_email]\r\n send_mail(\r\n subject,\r\n contact_message,\r\n from_email,\r\n to_email,\r\n fail_silently=False,\r\n )\r\n form_contact.save()\r\n\r\n return HttpResponseRedirect(request.META.get('HTTP_REFERER'))\r\n context = {\r\n 'title': 'List',\r\n 'object_list': queryset,\r\n 'object_list_3': queryset_list_last_items,\r\n 'today': today,\r\n 'form_contact': form_contact,\r\n\r\n }\r\n\r\n\r\n return render(request, 'posts/post_list.html', context)\r\n\r\n\r\ndef post_detail(request, slug=None):\r\n instance = get_object_or_404(Post, slug=slug)\r\n if instance.draft or instance.publish > timezone.now().date():\r\n if not request.user.is_staff or not request.user.is_superuser:\r\n raise Http404\r\n share_string = quote_plus(instance.content)\r\n # print(get_read_time(instance.get_markdown()))\r\n initial_data = {\r\n 'content_type': instance.get_content_type,\r\n 'object_id': instance.id\r\n }\r\n form_comment = CommentForm(request.POST or None, initial=initial_data)\r\n if form_comment.is_valid():\r\n c_type = form_comment.cleaned_data.get('content_type')\r\n content_type = ContentType.objects.get(model=c_type)\r\n obj_id = form_comment.cleaned_data.get('object_id')\r\n content_data = form_comment.cleaned_data.get('content')\r\n parent_obj = None\r\n try:\r\n parent_id = int(request.POST.get('parent_id'))\r\n except:\r\n parent_id = None\r\n\r\n if parent_id:\r\n parent_qs = Comment.objects.filter(id=parent_id)\r\n if parent_qs.exists() and parent_qs.count() == 1:\r\n parent_obj = parent_qs.first()\r\n\r\n new_comment, created = Comment.objects.get_or_create(\r\n user = request.user,\r\n content_type = content_type,\r\n object_id = obj_id,\r\n content = content_data,\r\n parent = parent_obj,\r\n )\r\n return HttpResponseRedirect(new_comment.content_object.get_absolute_url())\r\n\r\n comments = instance.comments\r\n\r\n queryset_list = Post.objects.active().order_by('-created')\r\n if request.user.is_staff or request.user.is_superuser:\r\n queryset_list = Post.objects.all().order_by('-created')\r\n queryset_list_last_items = queryset_list.order_by('-created')[:3]\r\n\r\n form_contact = ClientSubscribeForm(request.POST or None)\r\n if request.method == 'POST' and form_contact.is_valid():\r\n # for key, value in form.cleaned_data.items():\r\n # print(key, value)\r\n form_full_name = form_contact.cleaned_data.get('full_name')\r\n form_email = form_contact.cleaned_data.get('email')\r\n form_skype = form_contact.cleaned_data.get('skype')\r\n form_telegram = form_contact.cleaned_data.get('telegram')\r\n subject = 'Your Free Deposit!!!'\r\n contact_message = \"%s: %s %s via %s\" % (form_full_name, form_skype, form_telegram, form_email)\r\n from_email = settings.EMAIL_HOST_USER\r\n to_email = [form_email, from_email]\r\n send_mail(\r\n subject,\r\n contact_message,\r\n from_email,\r\n to_email,\r\n fail_silently=False,\r\n )\r\n return HttpResponseRedirect(request.META.get('HTTP_REFERER'))\r\n\r\n context = {\r\n 'title': instance.title,\r\n 'instance': instance,\r\n 'share_string': share_string,\r\n 'object_list_3': queryset_list_last_items,\r\n 'form_contact': form_contact,\r\n 'comments': comments,\r\n 'form_comment': form_comment,\r\n }\r\n\r\n return render(request, 'posts/post_detail.html', context)\r\n\r\n\r\ndef post_create(request):\r\n if not request.user.is_staff or not request.user.is_superuser:\r\n raise Http404\r\n form = PostForm(request.POST or None, request.FILES or None)\r\n if form.is_valid():\r\n instance = form.save(commit=False)\r\n instance.user = request.user\r\n instance.save()\r\n messages.success(request, 'Created')\r\n return HttpResponseRedirect(instance.get_absolute_url())\r\n elif form.errors:\r\n messages.error(request, \"Not Successfully Created\")\r\n else: pass\r\n\r\n context = {\r\n 'form': form\r\n }\r\n return render(request, 'posts/post_form.html', context)\r\n\r\n\r\ndef post_update(request, slug=None):\r\n if not request.user.is_staff or not request.user.is_superuser:\r\n raise Http404\r\n instance = get_object_or_404(Post, slug=slug)\r\n form = PostForm(request.POST or None, request.FILES or None, instance=instance)\r\n if form.is_valid():\r\n instance = form.save(commit=False)\r\n instance.save()\r\n messages.success(request, 'Updated')\r\n return HttpResponseRedirect(instance.get_absolute_url())\r\n\r\n\r\n context = {\r\n 'title': instance.title,\r\n 'instance': instance,\r\n 'form': form\r\n }\r\n return render(request, 'posts/post_form.html', context)\r\n\r\n\r\ndef post_delete(request, slug=None):\r\n if not request.user.is_staff or not request.user.is_superuser:\r\n raise Http404\r\n instance = get_object_or_404(Post, slug=slug)\r\n instance.delete()\r\n messages.success(request, \"Deleted\")\r\n\r\n return redirect(\"posts:post_list\")", "sub_path": "posts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7598, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.utils.timezone.now", "line_number": 20, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 20, "usage_type": "name"}, {"api_name": "models.Post.objects.active", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 21, "usage_type": "name"}, {"api_name": "models.Post.objects.all", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 33, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 36, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 39, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 41, "usage_type": "name"}, {"api_name": "forms.ClientSubscribeForm", "line_number": 44, "usage_type": "call"}, {"api_name": "django.conf.settings.EMAIL_HOST_USER", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 56, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 58, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 67, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 78, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 82, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 82, "usage_type": "argument"}, {"api_name": "django.utils.timezone.now", "line_number": 83, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 83, "usage_type": "name"}, {"api_name": "django.shortcuts.Http404", "line_number": 85, "usage_type": "name"}, {"api_name": "urllib.parse.quote_plus", "line_number": 86, "usage_type": "call"}, {"api_name": "comments.forms.CommentForm", "line_number": 92, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get", "line_number": 95, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 95, "usage_type": "name"}, {"api_name": "comments.models.Comment.objects.filter", "line_number": 105, "usage_type": "call"}, {"api_name": "comments.models.Comment.objects", "line_number": 105, "usage_type": "attribute"}, {"api_name": "comments.models.Comment", "line_number": 105, "usage_type": "name"}, {"api_name": "comments.models.Comment.objects.get_or_create", "line_number": 109, "usage_type": "call"}, {"api_name": "comments.models.Comment.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "comments.models.Comment", "line_number": 109, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 116, "usage_type": "call"}, {"api_name": "comments.models", "line_number": 118, "usage_type": "name"}, {"api_name": "models.Post.objects.active", "line_number": 120, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 120, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 120, "usage_type": "name"}, {"api_name": "models.Post.objects.all", "line_number": 122, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 122, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 122, "usage_type": "name"}, {"api_name": "forms.ClientSubscribeForm", "line_number": 125, "usage_type": "call"}, {"api_name": "django.conf.settings.EMAIL_HOST_USER", "line_number": 135, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 135, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 137, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 144, "usage_type": "call"}, {"api_name": "comments.models", "line_number": 152, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 156, "usage_type": "call"}, {"api_name": "django.shortcuts.Http404", "line_number": 161, "usage_type": "name"}, {"api_name": "forms.PostForm", "line_number": 162, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 167, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 167, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 168, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 170, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 170, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 176, "usage_type": "call"}, {"api_name": "django.shortcuts.Http404", "line_number": 181, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 182, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 182, "usage_type": "argument"}, {"api_name": "forms.PostForm", "line_number": 183, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 187, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 187, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 188, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 196, "usage_type": "call"}, {"api_name": "django.shortcuts.Http404", "line_number": 201, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 202, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 202, "usage_type": "argument"}, {"api_name": "django.contrib.messages.success", "line_number": 204, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 204, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 206, "usage_type": "call"}]} +{"seq_id": "83085029", "text": "import pandas as pd\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_selection import SelectKBest, chi2\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import BaggingClassifier\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.utils.multiclass import unique_labels\nimport numpy as np\n\n\n# READ DATA INTO LISTS\ntraining = open(\"processed_train_raw.tsv\")\ndev = open(\"processed_dev_raw.tsv\")\nx_train = []\ny_train = []\nx_dev = []\ny_dev = []\n\nfor line in training:\n line = line.split(\"\\t\")\n x_train.append(line[2])\n y_train.append(line[1])\n\nfor line in dev:\n line = line.split(\"\\t\")\n x_train.append(line[2])\n y_train.append(line[1])\n\n# CREATE A TRAINING DATA VECTOR\nvectorizer_t = TfidfVectorizer(stop_words=\"english\")\nv_train = vectorizer_t.fit_transform(x_train)\n\n\n# TOKENIZE THE TEST DATA\nx_dev_t = []\nnltk.download('stopwords')\nnltk.download('punkt')\nstop_words = set(stopwords.words('english'))\nfor str in x_dev:\n tokens = word_tokenize(str)\n tokens = [w for w in tokens if not w in stop_words]\n x_dev_t.append(tokens)\n\n# FIND THE BEST FEATURES\nkbest_t = SelectKBest(chi2, k=400)\nkbest_t.fit(v_train, y_train)\nmask = kbest_t.get_support()\nxk_train = kbest_t.transform(v_train)\nxk_train = xk_train.toarray()\n\nbest_features = []\nfor i in range(len(mask)):\n if (mask[i]):\n best_features.append(vectorizer_t.get_feature_names()[i])\n\n\ndev_data = []\nfor line in x_dev_t:\n instance = []\n for f in best_features:\n instance.append(line.count(f))\n dev_data.append(instance)\n\ndev_data = pd.DataFrame(dev_data)\ndev_data = dev_data.values\n\n\ny_dev_i = []\nfor i in range(len(y_dev)):\n if y_dev[i] == 'Melbourne':\n y_dev_i.append(0)\n elif y_dev[i] == 'Sydney':\n y_dev_i.append(1)\n elif y_dev[i] == 'Brisbane':\n y_dev_i.append(2)\n else:\n y_dev_i.append(3)\n\nclass_names = np.array(['Melbourne', 'Sydney', 'Brisbane', 'Perth'])\n\n\nclassifier = MultinomialNB(())\ny_pred = classifier.fit(xk_train, y_train).predict(dev_data)\n\ny_pred_i = []\nfor i in range(len(y_pred)):\n if y_pred[i] == 'Melbourne':\n y_pred_i.append(0)\n elif y_pred[i] == 'Sydney':\n y_pred_i.append(1)\n elif y_pred[i] == 'Brisbane':\n y_pred_i.append(2)\n else:\n y_pred_i.append(3)\n\n\n# class definition used from:\n# https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py\ndef plot_confusion_matrix(y_true, y_pred, classes,\n normalize=False,\n title=None,\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if not title:\n if normalize:\n title = 'Normalized confusion matrix'\n else:\n title = 'Confusion matrix, without normalization'\n\n # Compute confusion matrix\n cm = confusion_matrix(y_true, y_pred)\n # Only use the labels that appear in the data\n\n classes = classes[unique_labels(y_true, y_pred)]\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n fig, ax = plt.subplots()\n im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n # We want to show all ticks...\n ax.set(xticks=np.arange(cm.shape[1]),\n yticks=np.arange(cm.shape[0]),\n # ... and label them with the respective list entries\n xticklabels=classes, yticklabels=classes,\n title=title,\n ylabel='True label',\n xlabel='Predicted label')\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Loop over data dimensions and create text annotations.\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i in range(cm.shape[0]):\n for j in range(cm.shape[1]):\n ax.text(j, i, format(cm[i, j], fmt),\n ha=\"center\", va=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n fig.tight_layout()\n return ax\n\n\nnp.set_printoptions(precision=2)\n# Plot non-normalized confusion matrix\nplot_confusion_matrix(y_dev_i, y_pred_i, classes=class_names,\n title='Confusion matrix, without normalization')\n\n# Plot normalized confusion matrix\nplot_confusion_matrix(y_dev_i, y_pred_i, classes=class_names, normalize=True,\n title='MNB Normalized Confusion Matrix')\n\nplt.show()", "sub_path": "confusion-matrix.py", "file_name": "confusion-matrix.py", "file_ext": "py", "file_size_in_byte": 4989, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 36, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 42, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 43, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 44, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 44, "usage_type": "name"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.SelectKBest", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.chi2", "line_number": 51, "usage_type": "argument"}, {"api_name": "pandas.DataFrame", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.MultinomialNB", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 108, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 120, "usage_type": "call"}, {"api_name": "sklearn.utils.multiclass.unique_labels", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 125, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "numpy.set_printoptions", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}]} +{"seq_id": "189525518", "text": "from django.urls import path\nfrom . import views\n\napp_name = 'students'\nurlpatterns = [\n path('', views.StudentListView.as_view(), name='list_view'),\n path('/', views.StudentDetailView.as_view(), name='detail'),\n path('add/', views.StudentCreateView.as_view(), name='add'),\n path('edit//', views.StudentUpdateView.as_view(), name='edit'),\n path('remove//', views.StudentDeleteView.as_view(), name='remove'),]\n", "sub_path": "students/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 448, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "116716516", "text": "import torch\nprint(torch.__version__)\nprint(torch.cuda.is_available())\nimport os\nimport glob\nfrom PIL import Image\nimport numpy as np\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch import optim\nfrom visdom import Visdom\n\n# 自定义数据集 ：二分类\nclass myDataset(Dataset):\n def __init__(self, class1_dir, class2_dir):\n self.x, self.y = self.preprocess(class1_dir, class2_dir)\n\n def __getitem__(self, index):\n return self.x[index], self.y[index]\n\n def __len__(self):\n return self.x.shape[0]\n\n def _read_data(self, dataset_dir):\n data_dir = os.path.join('./dataset/', dataset_dir)\n data_dir = data_dir + '/*.jpg'\n eye_data = glob.glob(data_dir)\n x = []\n for img in eye_data:\n img = Image.open(img)\n img = np.asarray(img)\n x.append(img)\n x = np.array(x)\n x = torch.from_numpy(x).float() / 255.\n x = torch.unsqueeze(x, dim=3)\n return x\n\n # 预处理\n def preprocess(self, class1_dir, class2_dir):\n x1 = self._read_data(class1_dir)\n y1 = torch.ones(x1.shape[0])\n\n x2 = self._read_data(class2_dir)\n y2 = torch.zeros(x2.shape[0])\n\n x = torch.cat([x1, x2], dim=0)\n y = torch.cat([y1, y2], dim=0)\n return x, y\n\n\ntrain_dataset = myDataset('closedEyes', 'openEyes')\ntest_dataset = myDataset('close_test', 'open_test')\n# print(train_x.type(), train_y.type(), test_x.type(), test_y.type())\n\ntrain_db = DataLoader(train_dataset, batch_size=64, shuffle=True)\ntest_db = DataLoader(test_dataset, batch_size=64, shuffle=True)\n\n# x, y = iter(train_db).next()\n# print(x.shape, y.shape)\n\nclass Basenet(nn.Module):\n def __init__(self, input, output):\n super(Basenet, self).__init__()\n\n self.conv1 = nn.Conv2d(input, 64, kernel_size=[3, 3], padding=1)\n self.relu1 = nn.ReLU()\n\n self.conv2 = nn.Conv2d(64, 64, kernel_size=[3, 3], padding=1)\n self.relu2 = nn.ReLU()\n self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n\n\n self.conv3 = nn.Conv2d(64, 128, kernel_size=[3, 3], padding=1)\n self.relu3 = nn.ReLU()\n self.conv4 = nn.Conv2d(128, 128, kernel_size=[3, 3], padding=1)\n self.relu4 = nn.ReLU()\n self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n\n self.conv5 = nn.Conv2d(128, 256, kernel_size=[3, 3], padding=1)\n self.relu5 = nn.ReLU()\n self.conv6 = nn.Conv2d(256, 256, kernel_size=[3, 3], padding=1)\n self.relu6 = nn.ReLU()\n self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n\n self.conv7 = nn.Conv2d(256, 512, kernel_size=[3, 3], padding=1)\n self.relu7 = nn.ReLU()\n self.conv8 = nn.Conv2d(512, 512, kernel_size=[3, 3], padding=1)\n self.relu8 = nn.ReLU()\n self.maxpool4 = nn.MaxPool2d(kernel_size=4, stride=4)\n\n self.dense1 = nn.Linear(512, 256)\n self.relu9 = nn.ReLU()\n self.dense2 = nn.Linear(256, 128)\n self.relu10 = nn.ReLU()\n self.dense3 = nn.Linear(128, output)\n\n def forward(self, inputs):\n x = self.conv1(inputs)\n x = self.relu1(x)\n x = self.conv2(x)\n x = self.relu2(x)\n\n x = self.maxpool1(x)\n\n x = self.conv3(x)\n x = self.relu3(x)\n\n x = self.conv4(x)\n\n x = self.relu4(x)\n x = self.maxpool2(x)\n\n x = self.conv5(x)\n x = self.relu5(x)\n x = self.conv6(x)\n x = self.relu6(x)\n x = self.maxpool3(x)\n\n x = self.conv7(x)\n x = self.relu7(x)\n x = self.conv8(x)\n x = self.relu8(x)\n x = self.maxpool4(x)\n\n x = torch.reshape(x, [-1, 512])\n x = self.dense1(x)\n x = self.relu9(x)\n x = self.dense2(x)\n x = self.relu10(x)\n x = self.dense3(x)\n\n\n return x\n\n\ndevice = torch.device('cuda')\nbasenet = Basenet(24, 2).to(device)\n# x, _ = iter(train_db).next()\n# test = basenet(x.to(device))\n# print(basenet)\n\ncriteon = nn.CrossEntropyLoss().to(device)\noptimizer = optim.Adam(basenet.parameters(), lr=1e-4)\n\nviz = Visdom()\nviz.line([0.], [0.], win='train_loss', opts=dict(title='train_loss'))\n\nglobals_step = 0\nfor epoch in range(10):\n # train mode\n basenet.train()\n for step, (x, y) in enumerate(train_db):\n x, y = x.to(device), y.to(device)\n # forward\n logit = basenet(x)\n # loss\n # print(logit.type(), y.type())\n # logit = logit.long()\n loss = criteon(logit, y.long())\n # grads\n optimizer.zero_grad()\n loss.backward()\n # update\n optimizer.step()\n if step % 10 == 0:\n print('epoch:', epoch, 'loss:', loss.item())\n\n globals_step += 1\n viz.line([loss.item()], [globals_step], win='train_loss', update='append')\n\n # turn to eval mode\n basenet.eval()\n with torch.no_grad():\n total_num = 0\n total_correct = 0\n for x, y in test_db:\n x, y = x.to(device), y.to(device)\n logit = basenet(x)\n prob = F.softmax(logit, dim=1)\n pred = torch.argmax(prob, dim=1)\n correct = torch.eq(pred, y.long()).sum().item()\n\n total_num += x.shape[0]\n total_correct +=correct\n acc = total_correct / total_num\n print('epoch:', epoch, 'acc:', acc)\n\ntorch.save(basenet.state_dict(), 'eyes.pkl')\n\ndel basenet\n\nbasenet = Basenet(24, 2).to(device)\nbasenet.load_state_dict(torch.load('eyes.pkl'))\n\nbasenet.eval()\nwith torch.no_grad():\n total_num = 0\n total_correct = 0\n for x, y in test_db:\n x, y = x.to(device), y.to(device)\n logit = basenet(x)\n prob = F.softmax(logit, dim=1)\n pred = torch.argmax(prob, dim=1)\n correct = torch.eq(pred, y.long()).sum().item()\n\n total_num += x.shape[0]\n total_correct += correct\n acc = total_correct / total_num\n print('epoch:', epoch, 'acc:', acc)\n\nparams = basenet.state_dict()\nfor k, v in params.items():\n print(k) # 打印网络中的变量名\nprint(params['conv1.weight']) # 打印conv1的weight\nprint(params['conv1.bias']) # 打印conv1的bias\n", "sub_path": "example_pytorch1.0/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6177, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "torch.__version__", "line_number": 2, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 3, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 3, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 89, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.reshape", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 144, "usage_type": "name"}, {"api_name": "visdom.Visdom", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.argmax", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.eq", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 197, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 203, "usage_type": "name"}, {"api_name": "torch.argmax", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.eq", "line_number": 205, "usage_type": "call"}]} +{"seq_id": "440094266", "text": "# Copyright 2015 The Swarming Authors. All rights reserved.\n# Use of this source code is governed by the Apache v2.0 license that can be\n# found in the LICENSE file.\n\n\"\"\"Models and functions to build and query Auth DB change log.\"\"\"\n\nimport logging\nimport webapp2\n\nfrom google.appengine.api import modules\nfrom google.appengine.api import taskqueue\nfrom google.appengine.ext import ndb\n\nfrom components import decorators\n\nfrom . import config\nfrom . import model\nfrom . import utils\n\n\ndef process_change(auth_db_rev):\n \"\"\"Called asynchronously (via task queue) on AuthDB changes.\"\"\"\n logging.info('Processing AuthDB change rev %d', auth_db_rev)\n\n # We need an initial snapshot of all groups to be able to reconstruct any\n # historical snapshot later. It's important only for applications that existed\n # before change log functionality was added.\n ensure_initial_snapshot(auth_db_rev)\n\n # TODO(vadimsh): Get *History entities in historical_revision_key(auth_db_rev)\n # and diff them against previous versions to produce a set of\n # \"change log entry\" entities (displayed later in UI).\n\n\n### Code to snapshot initial state of AuthDB into *History.\n\n\nclass _AuthDBSnapshotMarker(ndb.Model):\n # AuthDB rev of the snapshot.\n auth_db_rev = ndb.IntegerProperty(indexed=False)\n\n @staticmethod\n def marker_key():\n \"\"\"Returns ndb.Key of entity that exists only if initial snapshot was done.\n\n Bump key ID to redo the snapshot.\n \"\"\"\n return ndb.Key(_AuthDBSnapshotMarker, 1, parent=model.root_key())\n\n\ndef ensure_initial_snapshot(auth_db_rev):\n \"\"\"Makes sure all current AuthDB entities are represented in the history.\n\n It's important only for applications that existed before change log\n functionality was added.\n\n It generates a new AuthDB revision by \"touching\" all existing entities. That\n way we reuse logic of generating *History entities already present in\n model.py. Note that original entities will also be updated ('auth_db_rev'\n property is modified), so it's indeed a true new AuthDB revision.\n \"\"\"\n # Already done?\n key = _AuthDBSnapshotMarker.marker_key()\n if key.get() is not None:\n return\n\n # Is it a fresh application that had change log from the very beginning?\n # No need to snapshot existing groups (they all end up in the history by usual\n # means).\n if auth_db_rev == 1:\n _AuthDBSnapshotMarker(key=key, auth_db_rev=1).put()\n return\n\n @ndb.transactional\n def touch_auth_db():\n # Recheck under transaction.\n if key.get() is not None:\n return\n to_process = []\n\n # Start slow queries in parallel.\n groups_future = model.AuthGroup.query(\n ancestor=model.root_key()).fetch_async()\n whitelists_future = model.AuthIPWhitelist.query(\n ancestor=model.root_key()).fetch_async()\n\n # Singleton entities.\n to_process.append(model.root_key().get())\n to_process.append(model.ip_whitelist_assignments_key().get())\n\n # Finish queries.\n to_process.extend(groups_future.get_result())\n to_process.extend(whitelists_future.get_result())\n\n # Update auth_db_rev properties, make *History entities. Keep modified_by\n # and modified_ts as they were.\n to_put = []\n for ent in to_process:\n if not ent:\n continue\n ent.record_revision(\n modified_by=ent.modified_by,\n modified_ts=ent.modified_ts,\n comment='Initial snapshot')\n to_put.append(ent)\n\n # Store changes, update the marker to make sure this won't run again.\n ndb.put_multi(to_put)\n auth_db_rev = model.replicate_auth_db()\n _AuthDBSnapshotMarker(key=key, auth_db_rev=auth_db_rev).put()\n\n logging.warning('Snapshotting all existing AuthDB entities for history')\n touch_auth_db()\n\n\n### Task queue plumbing.\n\n\n@model.commit_callback\ndef on_auth_db_change(auth_db_rev):\n \"\"\"Called in a transaction that updated AuthDB.\"\"\"\n # Avoid adding task queues in unit tests, since there are many-many unit tests\n # (in multiple project and repos) that indirectly make AuthDB transactions\n # and mocking out 'enqueue_process_change_task' in all of them is stupid\n # unscalable work. So be evil and detect unit tests right here.\n if not utils.is_unit_test():\n enqueue_process_change_task(auth_db_rev)\n\n\ndef enqueue_process_change_task(auth_db_rev):\n \"\"\"Transactionally adds a call to 'process_change' to the task queue.\n\n Pins the task to currently executing version of BACKEND_MODULE module\n (defined in config.py).\n\n Added as AuthDB commit callback in get_backend_routes() below.\n \"\"\"\n assert ndb.in_transaction()\n conf = config.ensure_configured()\n try:\n # Pin the task to the module and version.\n taskqueue.add(\n url='/internal/auth/taskqueue/process-change/%d' % auth_db_rev,\n queue_name=conf.PROCESS_CHANGE_TASK_QUEUE,\n headers={'Host': modules.get_hostname(module=conf.BACKEND_MODULE)},\n transactional=True)\n except Exception as e:\n logging.error(\n 'Problem adding \"process-change\" task to the task queue (%s): %s',\n e.__class__.__name__, e)\n raise\n\n\nclass InternalProcessChangeHandler(webapp2.RequestHandler):\n def post(self, auth_db_rev):\n # We don't know task queue name during module loading time, so delay\n # decorator application until the actual call.\n queue_name = config.ensure_configured().PROCESS_CHANGE_TASK_QUEUE\n @decorators.require_taskqueue(queue_name)\n def call_me(_self):\n process_change(int(auth_db_rev))\n call_me(self)\n\n\ndef get_backend_routes():\n \"\"\"Returns a list of routes with task queue handlers.\n\n Used from ui/app.py (since it's where WSGI module is defined) and directly\n from auth_service backend module.\n \"\"\"\n return [\n webapp2.Route(\n r'/internal/auth/taskqueue/process-change/',\n InternalProcessChangeHandler),\n ]\n", "sub_path": "appengine/components/components/auth/change_log.py", "file_name": "change_log.py", "file_ext": "py", "file_size_in_byte": 5795, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "logging.info", "line_number": 23, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb.Model", "line_number": 38, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 38, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.IntegerProperty", "line_number": 40, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 40, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Key", "line_number": 48, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 48, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.put_multi", "line_number": 108, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 108, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.transactional", "line_number": 74, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 74, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 112, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb.in_transaction", "line_number": 138, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 138, "usage_type": "name"}, {"api_name": "google.appengine.api.taskqueue.add", "line_number": 142, "usage_type": "call"}, {"api_name": "google.appengine.api.taskqueue", "line_number": 142, "usage_type": "name"}, {"api_name": "google.appengine.api.modules.get_hostname", "line_number": 145, "usage_type": "call"}, {"api_name": "google.appengine.api.modules", "line_number": 145, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 148, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 154, "usage_type": "attribute"}, {"api_name": "components.decorators.require_taskqueue", "line_number": 159, "usage_type": "call"}, {"api_name": "components.decorators", "line_number": 159, "usage_type": "name"}, {"api_name": "webapp2.Route", "line_number": 172, "usage_type": "call"}]} +{"seq_id": "154029424", "text": "import logging\nimport os\nimport pickle\n\nfrom telegram.ext import Updater, CommandHandler, MessageHandler, Filters\nfrom telegram import ParseMode\n\nfrom typing import Set, Dict\n\n_PICKLE_FILE = 'chats_and_terms.db'\n\n\nclass Bot():\n def __init__(self,\n token: str,\n pickle_path=os.path.dirname(os.path.realpath(__file__))\n ) -> None:\n logging.info('Creating bot')\n self.token = token\n self.pickle_path = pickle_path\n self.chat_ids: Dict[str, str] = self._read_picked_data()\n\n self.updater = Updater(token, use_context=True)\n self.dispatcher = self.updater.dispatcher\n self.bot = self.updater.bot\n\n self.dispatcher.add_handler(CommandHandler('start', self._start))\n self.dispatcher.add_handler(\n MessageHandler(Filters.text, self._register))\n\n if len(self.chat_ids) > 0:\n for user in self.chat_ids.keys():\n self.bot.send_message(\n text=\n 'Hi there, just wanted to say I\\'m still on the lookout for your gear. Have a nice day :)',\n chat_id=user,\n parse_mode=ParseMode.MARKDOWN)\n\n self.updater.start_polling()\n\n def get_terms(self) -> Set[str]:\n return set(self.chat_ids.values())\n\n def _register(self, update, context) -> None:\n self.chat_ids[update.message.chat_id] = update.message.text\n update.message.reply_markdown(\n f'You will receive updates for *{update.message.text}*')\n self._update_pickled_data()\n\n def _start(self, update, context) -> None:\n self.chat_ids[update.message.chat_id] = ''\n update.message.reply_markdown(\n \"# Hi\\nI'm the friendly reverb search bot! Send me a seach query\" +\n \" and I will notify you of new listings for used electric guitars matching your query 🎸🎸🎸\"\n )\n self._update_pickled_data()\n\n def _read_picked_data(self) -> Dict[str, str]:\n try:\n with open(os.path.join(self.pickle_path, _PICKLE_FILE),\n 'rb') as handle:\n d = pickle.load(handle)\n return d if d is not None else {}\n except FileNotFoundError:\n logging.warn(\n 'No pickled data found. This is normal on the first start.')\n return {}\n\n def _update_pickled_data(self) -> None:\n with open(os.path.join(self.pickle_path, _PICKLE_FILE),\n 'wb') as handle:\n pickle.dump(self.chat_ids,\n handle,\n protocol=pickle.HIGHEST_PROTOCOL)\n\n def send_update(self, term, listing_messages) -> None:\n for user in self.chat_ids.keys():\n if term in self.chat_ids[user]:\n text = f'There are new listings for your search \\'{term}\\'! \\n'\n text += '\\n'.join(listing_messages)\n self.bot.send_message(text=text,\n chat_id=user,\n parse_mode=ParseMode.MARKDOWN)\n", "sub_path": "telegram_bot.py", "file_name": "telegram_bot.py", "file_ext": "py", "file_size_in_byte": 3105, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "87", "api": [{"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 21, "usage_type": "name"}, {"api_name": "telegram.ext.Updater", "line_number": 23, "usage_type": "call"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 27, "usage_type": "call"}, {"api_name": "telegram.ext.MessageHandler", "line_number": 29, "usage_type": "call"}, {"api_name": "telegram.ext.Filters.text", "line_number": 29, "usage_type": "attribute"}, {"api_name": "telegram.ext.Filters", "line_number": 29, "usage_type": "name"}, {"api_name": "telegram.ParseMode.MARKDOWN", "line_number": 37, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 62, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 65, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 58, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 72, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 74, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode.MARKDOWN", "line_number": 83, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 83, "usage_type": "name"}]} +{"seq_id": "533220193", "text": "#!/usr/bin/env python\n# encoding: utf-8\n\nfrom flask import Flask,request\n#from requests import get\nfrom sh import wget\nimport re\napp=Flask(__name__)\n\n@app.route(\"/\")\ndef index():\n return \"\"\"\n

    Video Downloader

    \n
    \n \n \n \n
    \n\"\"\"\n\n@app.route('/down',methods=['POST','GET'])\ndef down():\n print(request.method)\n if request.method== \"POST\":\n url = request.form[\"videosrc\"]\n wget(url)\n name = re.findall(r\"\\/(\\w*?)\\.mp4\",url,re.S)[0]\n return \"\"\"\n