diff --git "a/1275.jsonl" "b/1275.jsonl"
new file mode 100644--- /dev/null
+++ "b/1275.jsonl"
@@ -0,0 +1,656 @@
+{"seq_id":"367737784","text":"from behave.runner import Context\n\nfrom pages.main_header import MainHeader\nfrom utils.base_page import BasePage\nfrom utils.browsers.browser_selector import browser\nfrom utils.fake_persons.fake_client import FakeClient\n\n\ndef start_driver():\n    driver = browser().driver\n    return driver\n\n\ndef before_all(context):\n    context.stuff = Stuff(context)\n    context.client = context.stuff.client()\n\n\ndef before_scenario(context, scenario):\n    context.driver = start_driver()\n    context.environment = Environment(context, context.driver)\n    pages = context.environment._origin\n    behave = (\"feature\", \"text\", \"table\", \"stdout_capture\", \"stderr_capture\", \"log_capture\", \"fail_on_cleanup_errors\")\n    for behave_item in behave:\n        pages.pop(behave_item, None)\n    for page in pages:\n        setattr(context, page, getattr(context.environment, page))\n\n\ndef after_scenario(context, scenario):\n    context.driver.close()\n\n\ndef after_step(context, step):\n    if step.status == 'failed':\n        step_str = step.name\n        context.driver.save_screenshot(f\"{step_str}.png\")\n\n\nclass Environment(Context):\n\n    def __init__(self, runner, driver):\n        super().__init__(runner)\n        self.base = BasePage(driver)\n        self.main_header = MainHeader(driver)\n\n\nclass Stuff(Context):\n\n    def __init__(self, runner):\n        super().__init__(runner)\n        self.client = FakeClient\n","sub_path":"environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"47391192","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nimport os\nos.environ.update({\"DJANGO_SETTINGS_MODULE\": \"SqlAudit.settings\"})\nfrom django.conf import settings\nfrom django.core.mail import EmailMultiAlternatives\n\ndef sendMail(subject, html_content,to_email):\n    try:\n        ToEmail = []\n        if type(to_email) != list:\n            ToEmail.append(to_email)\n        else:\n            ToEmail = to_email\n        from_email = settings.DEFAULT_FROM_EMAIL\n        # subject = '来自SQL审核系统的通知'\n        # text_content = '这是一封重要的邮件.'\n        # html_content = '<p>这是一封<strong>重要的</strong>邮件.</p>'\n        msg = EmailMultiAlternatives(subject, html_content, from_email, ToEmail)\n        msg.attach_alternative(html_content, \"text/html\")\n        msg.send()\n        print(\"Send Email Successful.\")\n        return \"Send Email Successful.\"\n    except Exception as e:\n        print(e)\n        return e\n\nif __name__ == '__main__':\n    html_content = \"这是一封特别重要的邮件.\"\n    print (sendMail(html_content,'baochengcai@lanjingren.com'))","sub_path":"drf_api/app/celery_tasks/asynchronous/sendmail.py","file_name":"sendmail.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"236392588","text":"# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n\nclass Solution:\n    def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode:\n\n        p = head\n        all_val = []\n        while p:\n            q = ListNode(0)\n            q.val = p.val\n            all_val.append(q)\n            p = p.next\n\n        all_val.remove(all_val[len(all_val) - n])\n        if len(all_val) == 0:\n            return None\n        for i in range(len(all_val) - 1):\n            all_val[i].next = all_val[i + 1]\n\n        return all_val[0]\n\n\na = Solution()\nt1 = ListNode(1)\nt2 = ListNode(2)\n# t3 = ListNode(3)\n# t4 = ListNode(4)\n# t5 = ListNode(5)\nt1.next = t2\n# t2.next = t3\n# t3.next = t4\n# t4.next = t5\n\na.removeNthFromEnd(t1, 1)\n","sub_path":"PythonLearning/OJ/test_all/test_task.py","file_name":"test_task.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"498101313","text":"f = open(\"p079_keylog.txt\", \"r\")\nlogs = []\nfor row in f:\n    logs.append(row[:-1])\n    \ndigits = []\nfor row in logs:\n    for i in row:\n        if i not in digits:\n            digits.append(i)\n","sub_path":"Python/079.py","file_name":"079.py","file_ext":"py","file_size_in_byte":192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"429916942","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse, HttpResponseNotFound\n\nfrom django.contrib.auth.models import User\n\nfrom django.contrib.auth.decorators import login_required\n\nfrom .models import GeneralTopic, UserTopic, BookReference, CourseReference, WebsiteReference\n\nfrom navigatte import wikipedia_api, settings\n\n#Method to get the url from its label\nfrom django.core.urlresolvers import reverse\n\n\nfrom bs4 import BeautifulSoup\nimport urllib.request\n\n\n# Create your views here.\n\n@login_required\ndef addNewUserTopic(request, userpage):\n\n    #ON POST\n\n    #If it was a new topic to be added\n    if request.method == \"POST\" and 'topic_url_title' in request.POST and request.POST['topic_url_title']:\n        \n        queryResult = wikipedia_api.query(request.POST['topic_url_title'])\n        if queryResult == None:\n            return HttpResponse(\"Invalid request\")\n        \n        #Try to get topic, if not found, create a new\n        try: \n            topicReference = GeneralTopic.objects.get(pageid=queryResult['pageid'])\n        except GeneralTopic.DoesNotExist:\n            topicReference = GeneralTopic(title=queryResult['title'], \n                                                pageid=queryResult['pageid'], urlTitle=queryResult['urlTitle'])\n            topicReference.save()\n\n        #Try to get this reference on the current user topics, if it does not exists, create a new\n        try:\n            request.user.usertopic_set.get(generalTopic=topicReference, deleted=False)\n            return HttpResponse(\"User topic already exists.\")\n        except UserTopic.DoesNotExist:\n            #If it does not exists, create it\n            newUserTopic = UserTopic(generalTopic=topicReference, owner=request.user)\n            newUserTopic.save()\n\n        return redirect('display_user_topics', userpage)\n\n    #ON GET        \n\n    if not 'search' in request.GET or not request.GET['search']:\n        return render(request, \"add-new-user-topic.html\", {\n            'noQuery': True,\n            'userpage': userpage\n        })\n\n    resultObject = wikipedia_api.search(request.GET['search'])\n\n    if resultObject == None:\n        return HttpResponse(\"Error while completing the request.\")\n\n    return render(request, \"add-new-user-topic.html\",{\n        'resultTopics': resultObject,\n        'userpage': userpage\n    })\n\n\ndef displayUserTopics(request, userpage):\n    #Check whether the user exists and get it\n    try:\n        userpageRef = User.objects.get_by_natural_key(username=userpage)\n    except User.DoesNotExist:\n        return HttpResponseNotFound(\"User not found.\")\n\n    #return the request userpage, signaling whether this user is the owner or not\n\n    #userpage is the current userpage displayed\n    #Get all the subjects that are not deleted\n    return render(request, 'user-topics-display.html', {\n        'topic_list': userpageRef.usertopic_set.filter(deleted=False),\n        'userpage': userpage,\n        'isOwner': userpageRef == request.user,\n    })\n\ndef userTopicDetails(request, userpage):\n    \n    #Check whether the user exists and get it\n    try:\n        userpageRef = User.objects.get_by_natural_key(username=userpage)\n    except:\n        return HttpResponseNotFound(\"User not found.\")   \n\n    if 'id' in request.GET and request.GET['id']:\n\n        try:\n            topic_details = UserTopic.objects.get(id=request.GET['id'])\n\n            #Get all the references that have not the delete flag set\n            return render(request, 'user-topic-details.html', { \n                'name': topic_details, \n                'books': topic_details.books.all(),\n                'courses': topic_details.courses.all(),    \n                'websites': topic_details.websites.all(),\n                'subject_id': request.GET['id'], \n                'userpage': userpage,\n                'isOwner': userpageRef == request.user,         \n            })\n\n        except UserTopic.DoesNotExist:\n            return HttpResponseNotFound(\"User topic not found\")\n\n    else: #If no id, return the topics list\n        return redirect('display_user_topics')\n\n#View to handle subject delete\n@login_required\ndef deleteUserTopic(request, userpage):\n    if request.method != 'POST':\n        return HttpResponse(\"Invalid Request\")\n\n    #If a subject_id field is present and is valid (not empty)\n    if 'subject_id' in request.POST and request.POST['subject_id']:\n\n        try:\n            targetSubject = UserTopic.objects.get(id=request.POST['subject_id'], owner=request.user)\n\n            #Verifies if this subject does belongs to this user\n            #if request.user != targetSubject.owner:\n                #return invalidRequest(\"Invalid Delete. This subject does not belong to the signed in user.\")\n\n            #If it is the owner, set delete flag in the object and return subjects page\n            targetSubject.deleted = True\n            targetSubject.save()\n\n            return redirect('display_user_topics', userpage=userpage)\n\n        except Exception as e:\n            return invalidRequest(\"Error while deleting: \" + str(e))\n\n    return invalidRequest(\"No subject id or invalid subject id send.\")\n\n\n@login_required\ndef addUserTopicReference(request, userpage):\n    if not \"subject_id\" in request.POST:\n        return invalidRequest(\"Invalid reference add request. No subjectID in POST method.\")\n\n    \n    subjectId = request.POST[\"subject_id\"]\n\n    #Check if the subject_id is valid and belongs to this user\n    try:\n        targetSubject = UserTopic.objects.get(id=subjectId, owner=request.user)\n\n        #If the target object owner is not the current user, finish with error\n        #if request.user != targetSubject.owner:\n            #return invalidRequest(\"Invalid reference add request. The target user owner of the subject is not the authenticated user.\")\n\n        #check which reference is being add\n        if \"course_name\" in request.POST and request.POST[\"course_name\"]:\n            newCourse = CourseReference(name=request.POST[\"course_name\"])\n            newCourse.save()\n            #Add the new course to the target subject\n            targetSubject.courses.add(newCourse)\n\n        elif \"book_title\" in request.POST and request.POST[\"book_title\"]:\n            #Create new book (should be checked whether the desired exists before add a new)\n            newBook = BookReference(name=request.POST[\"book_title\"])\n            newBook.save()\n            targetSubject.books.add(newBook)\n\n        elif \"website_address\" in request.POST and request.POST[\"website_address\"]:\n            \n            #Try to get a reference to the address\n            try:\n                websiteRef = WebsiteReference.objects.get(address=request.POST[\"website_address\"])\n                targetSubject.websites.add(websiteRef)\n            \n            except WebsiteReference.DoesNotExist:\n\n                #If it does not exists, create a new\n                try:\n                    #Get site data\n                    websiteData = urllib.request.urlopen(request.POST[\"website_address\"]).read()\n\n                    #Parse site data to objects\n                    websiteObj = BeautifulSoup(websiteData)\n\n                    #Get a title if there is one on the page\n                    if not websiteObj.title or websiteObj.title.string == None:\n                        websiteName = request.POST[\"website_address\"]\n                    else:\n                        websiteName = websiteObj.title.string\n\n                #If the url is invalid\n                except ValueError:\n                    return HttpResponse(\"Invalid URL.\")\n\n                except Exception:\n                    #If we can not retrieve site url, set the site title as the url\n                    websiteName = request.POST[\"website_address\"]\n\n                newWebsite = WebsiteReference(name=websiteName, address=request.POST[\"website_address\"])\n                newWebsite.save()\n                targetSubject.websites.add(newWebsite)\n\n        else:\n            #If no valid reference, return error\n            return HttpResponse(\"Invalid reference add request (invalid reference).\")\n\n        return redirect(reverse('user_topic_details', kwargs={'userpage': userpage}) + \"?id=\" + subjectId)\n\n    except Exception as e:\n        #return invalidRequest(\"Invalid reference add request. Exception: \" + str(e))\n        raise e\n\n\n#View to handle subject reference delete\n#For now remove the reference from the subject, later set a remove flag and keeps record\ndef deleteUserTopicReference(request, userpage):\n    if request.method != 'POST':\n        return invalidRequest(\"Invalid request. POST method expected.\")\n\n    if not validateEntries(request.POST, [\"subject_id\", \"reference_type\", \"reference_id\"]):\n        return invalidRequest(\"Invalid request. subject_id, reference_type or reference_id missing or not valid.\")\n\n    subjectId = request.POST['subject_id']\n    referenceType = request.POST['reference_type']\n    referenceId = request.POST['reference_id']\n\n    try:\n        #Get the target subject\n        targetSubject = UserTopic.objects.get(id=subjectId, owner=request.user)\n\n        #Verifies if this subject does belongs to this user\n        #if request.user != targetSubject.owner:\n            #return invalidRequest(\"Invalid Delete. This subject does not belong to the signed in user.\")\n        \n        #Get the target reference\n        if referenceType == \"website\":\n            targetReference = WebsiteReference.objects.get(id=referenceId)\n            #remove the reference from the subject\n            targetSubject.websites.remove(targetReference)\n\n        elif referenceType == \"book\":\n            targetReference = BookReference.objects.get(id=referenceId)\n            #remove the reference from the subject\n            targetSubject.books.remove(targetReference)\n        \n        elif referenceType == \"course\":\n            targetReference = CourseReference.objects.get(id=referenceId)\n            #remove the reference from the subject\n            targetSubject.courses.remove(targetReference)\n        else:\n            return invalidRequest(\"Invalid Delete. Invalid type: \" + referenceType)\n\n        #save target subject\n        targetSubject.save()\n\n        return redirect(reverse('user_topic_details', kwargs={'userpage': userpage}) + \"?id=\" + subjectId)\n\n    except Exception as e:\n        return invalidRequest(\"Error while deleting: \" + str(e))\n\n\ndef invalidRequest(debugMsg, nonDebugMsg = \"Invalid Request\"):\n    if settings.DEBUG:\n        return HttpResponse(debugMsg) \n\n    return HttpResponse(nonDebugMsg) \n\ndef validateEntries(collection, entries, blankAllowed = False):\n    #iterate thru the entries\n    for entry in entries:\n        #if the entry does not exist, or is invalid (in case blankAllowed is false), return false\n        if not entry in collection or not (collection[entry] or blankAllowed):\n            return False\n        \n    return True","sub_path":"topics/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"423806636","text":"import unittest\nfrom Controller import Checker\nfrom Models.Student import Student\nfrom Models.BRSPoints import BRSPoints\nfrom Models.EducationYear import EducationYear\nfrom Models.CrossSection import CrossSection\nfrom Models.Group import Group\nfrom Models.Subject import Subject\n\n\nclass CheckPointsTestCase(unittest.TestCase):\n    def test_check_points(self):\n        group1 = Group(\"М-ФИИТ-20\", 2020)\n        group2 = Group(\"М-ФИИТ-19\", 2019)\n\n        year1 = EducationYear(2020, 2021)\n\n        subject1 = Subject(\"321\", \"Психология\")\n        subject3 = Subject(\"323\", \"Английский\")\n\n        cross_section1 = CrossSection.FirstSection\n        cross_section2 = CrossSection.SecondSection\n        cross_section3 = CrossSection.FinalSection\n\n        student1 = Student(code=123, fio=\"Егоров Алексей Васильевич\", birthdate=\"01. 04. 1996\",\n                           email=\"eavamga@gmail.com\", phone=\"+79244669579\", group=group1)\n        student2 = Student(code=124, fio=\"Иванов Иван Иванович\", birthdate=\"30. 12. 2000\", email=\"example@gmail.com\",\n                           phone=\"+79991112233\", group=group2)\n\n        EgorovAV_cs1 = BRSPoints(subject=subject1, year=year1, cross_section=cross_section1, points=33,\n                                 student=student1)\n        EgorovAV_cs2 = BRSPoints(subject=subject1, year=year1, cross_section=cross_section2, points=66,\n                                 student=student1)\n        EgorovAV_cs3 = BRSPoints(subject=subject1, year=year1, cross_section=cross_section3, points=99,\n                                 student=student1)\n\n        IvamovII_cs1 = BRSPoints(subject=subject1, year=year1, cross_section=cross_section1, points=20,\n                                 student=student2)\n        IvamovII_cs2 = BRSPoints(subject=subject1, year=year1, cross_section=cross_section2, points=20,\n                                 student=student2)\n        IvamovII_cs3 = BRSPoints(subject=subject1, year=year1, cross_section=cross_section3, points=20,\n                                 student=student2)\n\n        EgorovAV_cs1_wrong = BRSPoints(subject=subject3, year=year1, cross_section=cross_section1, points=101,\n                                       student=student1)\n        EgorovAV_cs2_wrong = BRSPoints(subject=subject3, year=year1, cross_section=cross_section2, points=0,\n                                       student=student1)\n        EgorovAV_cs3_wrong = BRSPoints(subject=subject3, year=year1, cross_section=cross_section3, points=0,\n                                       student=student1)\n\n        IvamovII_cs1_wrong = BRSPoints(subject=subject3, year=year1, cross_section=cross_section1, points=65,\n                                       student=student2)\n        IvamovII_cs2_wrong = BRSPoints(subject=subject3, year=year1, cross_section=cross_section2, points=60,\n                                       student=student2)\n        IvamovII_cs3_wrong = BRSPoints(subject=subject3, year=year1, cross_section=cross_section3, points=30,\n                                       student=student2)\n\n        Egorov_subject1_2020_2021 = [EgorovAV_cs1, EgorovAV_cs2, EgorovAV_cs3]\n        Egorov_subject3_2020_2021_wrong = [EgorovAV_cs1_wrong, EgorovAV_cs2_wrong, EgorovAV_cs3_wrong]\n\n        Ivanov_subject1_2020_2021 = [IvamovII_cs1, IvamovII_cs2, IvamovII_cs3]\n        Ivanov_subject1_2020_2021_wrong = [IvamovII_cs1_wrong, IvamovII_cs2_wrong, IvamovII_cs3_wrong]\n\n        self.assertEqual(Checker.check_points(Egorov_subject1_2020_2021, cross_section3, EgorovAV_cs3.points), True)\n        self.assertEqual(Checker.check_points(Ivanov_subject1_2020_2021, cross_section3, IvamovII_cs3.points), True)\n\n        self.assertEqual(\n            Checker.check_points(Egorov_subject3_2020_2021_wrong, cross_section1, EgorovAV_cs1_wrong.points), False)\n        self.assertEqual(\n            Checker.check_points(Ivanov_subject1_2020_2021_wrong, cross_section3, IvamovII_cs3_wrong.points), False)\n","sub_path":"Tests/Controller/Checker/test_check_points.py","file_name":"test_check_points.py","file_ext":"py","file_size_in_byte":4001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"511271691","text":"#! /usr/bin/python3\n\nfrom kivy.app import App\nfrom kivy.uix.button import Button\nfrom kivy.uix.scatter import Scatter\nfrom kivy.uix.label import Label\nfrom kivy.uix.floatlayout import FloatLayout\n\nclass TutorialApp(App):\n    def build(self):\n        f = FloatLayout()\n        s = Scatter()\n        l = Label(text='Hello!',\n                  font_size=150)\n        f.add_widget(s)\n        s.add_widget(l)\n        return f\n        # This was a button.\n        #return Button(text='Hello!',\n        #              background_color=(0, 0, 1, 1),  # List of\n        #                                              # rgba components\n        #              font_size=150)\ndef main():\n    print(\"main() function.\")\n    TutorialApp().run()\n\nif __name__ == \"main\":\n    print(\"main.\")\n    main()\nelse:\n    print(\"Not main.\")\n    #TutorialApp().run()\n\n\n#if __name__ == \"__main__\":\n#    TutorialApp().run()","sub_path":"video1.py","file_name":"video1.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"117525545","text":"file_name = 'dataset_3363_3.txt'\nwith open(file_name) as file:\n    dataset = [] \n    for i in file:\n        dataset.append(i.strip())\ndef enc_list(lst):\n    '''func decompose list in string'''\n    result = []\n    for i in lst:\n        result += i.lower().split()\n    return result\ndata = enc_list(dataset)\ndef counter(iterat_obj):\n    d = {}\n    for i in iterat_obj:\n        if i in d:\n            d[i] += 1\n        else:\n            d[i] = 1\n    return d\ndef who_much(dictionary):\n    wold = ''\n    count = 0\n    for i in list(dictionary.items()):\n        if i[1] > count:\n            count = i[1]\n            wold = i[0]\n        elif i[1] == count:\n            if i[0] < wold:\n                wold = i[0]\n    print(wold, end=' ')\n    print(count)\nwho_much(counter(data)) \n#with open(file_name + '_answer.txt', 'w') as file:\n        #file.write(winer)    \n# очень понравилось решение\nwith open('dataset_3363_3.txt') as inf, open('MostPopularWord.txt','w') as ouf:\n    maxc = 0\n    s = inf.read().lower().strip().split()\n    s.sort()\n    for word in s:\n        counter = s.count(word)\n        if counter > maxc:\n            maxc = counter\n            result_word = word\n    ouf.write(result_word +' ' + str(maxc))\n","sub_path":"py4_6.py","file_name":"py4_6.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"33840886","text":"import os\nimport json\nimport logging\nfrom glob import glob\n\nfrom lxml import etree\n\nimport settings as s\nfrom .utils import translate_dir\nfrom .log import set_up_logging\n\nlog = set_up_logging('transform', loglevel=logging.DEBUG)\n\n\ndef transform_sopr(options):\n    def _is_array(element):\n        return element.getchildren() != []\n\n    def _add_element_single(element, json_dict):\n        json_dict[element.tag] = dict(element.attrib)\n\n    def _add_element_array(root_node, json_dict):\n        json_dict[root_node.tag] = []\n        for e in root_node.getchildren():\n            json_dict[root_node.tag].append(dict(e.attrib))\n\n    def _add_element(element, json_dict):\n        if _is_array(element):\n            _add_element_array(element, json_dict)\n        else:\n            _add_element_single(element, json_dict)\n\n    def _write_to_file(xml_filepath, filing):\n        path, destination_dir = translate_dir(xml_filepath,\n                                              from_dir=s.ORIG_DIR,\n                                              to_dir=s.TRANS_DIR)\n        filing_id = json_filing['ID']\n        output_path = os.path.join(destination_dir,\n                                   '{fid}.json'.format(fid=filing_id))\n        if os.path.exists(output_path) and not options['force']:\n            raise OSError(os.errno.EEXIST,\n                          ' '.join([os.strerror(os.errno.EEXIST)+':',\n                                    output_path]))\n\n        with open(output_path, 'w') as output_file:\n            json.dump(json_filing, output_file)\n\n    all_fields = ['AffiliatedOrgs',\n                  'Client',\n                  'ForeignEntities',\n                  'GovernmentEntities',\n                  'Issues',\n                  'Lobbyists',\n                  'Registrant']\n\n    xml_files = glob(os.path.join(s.ORIG_DIR, 'sopr/*/*/*.xml'))\n\n    for xml_filepath in xml_files:\n        for filing in etree.parse(open(xml_filepath)).getroot().iterchildren():\n            json_filing = dict.fromkeys(all_fields)\n            json_filing.update(dict(filing.attrib))\n\n            for element in filing.getchildren():\n                _add_element(element, json_filing)\n\n            _write_to_file(xml_filepath, json_filing)\n","sub_path":"tasks/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":2223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"228479760","text":"from functools import reduce\nfrom pyquery import PyQuery as pq\nfrom random import random\nimport time\nimport json\nfrom pprint import pprint\n\n\ndef fetchPQO(url):\n    \"\"\" URLにアクセスし，HTMLを pyQuery Object にして返す\n    1~4秒スリープ後に処理をする．\n    \"\"\"\n    time.sleep(round(random() * 3.0 + 1.0, 3))\n    return pq(url)\n\n\ndef fetchPQOs(url, max=100):\n    \"\"\" リンクをたどり，page毎に pyQuery Object を取得し，格納したリストを返す\n    \"\"\"\n    def isLastPage(p):\n        return not(p('.pager'))\n\n    pqos = []\n    for i in range(1, max+1):\n        print('page '+str(i)+' fetching...')\n        p = fetchPQO(url + '?page=' + str(i))\n        pqos.append(p)\n        if(isLastPage(p)):\n            break\n    print('num of page = '+str(len(pqos)))\n    return pqos\n\n\ndef getContent(bokeContainer):\n    def extractEl(l=['', '']):\n        if(l[0] == 'boke-text'):\n            return [l[0], pq(l[1])('.boke-text').text()]\n        elif(l[0] == 'boke-meta'):\n            # 配下に'.boke-stars'があるので取得'\n            return ['boke-stars', int(pq(l[1])('.boke-stars').text())]\n        else:\n            return []\n\n    # boke の直接の子要素のうち classを持つもののリスト\n    childl = pq(bokeContainer).children('[class]')\n\n    # [[class名,html],[]....] となるリストを作成\n    datal = [[pq(c).attr('class'), c] for c in childl]\n\n    # htmlから必要なデータを抽出し，辞書を作成．(空リストは削除)\n    return dict(filter(lambda el: el != [], [extractEl(i) for i in datal]))\n\n\ndef getContents(pqo):\n    # pqoから 1つ目の要素(お題), 広告 を除外\n    bokediv = pqo('.boke:gt(0)').filter(lambda i, e:\n                                        pq(e).children().length >= 4)\n    return [getContent(el) for el in bokediv]\n\n\ndef getAllContents(pqos):\n    if(not(len(pqos))):\n        return []\n\n    ll = [getContents(pqo) for pqo in pqos]\n    return list(reduce(lambda tlist, t: tlist+t, ll))\n\n\ndef fetchData(url='', max=100):\n    \"\"\" url にアクセスし，1つのお題に対する全てのデータを取得\n    url : アクセスするページ(お題のトップページ. ex:http://bokete.jp/odai/2227309)\n    max : 取得するbokeのページ数上限\n    \"\"\"\n    # TODO:中 URLにurl末尾のみ入れても動くようにする\n    # TODO:高 fetchしたあと抽出に入る前にお題の画像をとってくる処理、fetchResourceをはさむ\n    # TODO:高 返り値に odaiId(ex. odaiId:2227309 ),\n    #         imgPath(ex. imgPath:'./img/2227309.png')を追加する\n    # TODO:高 返り値を辞書に．   {odaiId:0, imgPath:'' ,boke:[{},{},{},...]}\n    return getAllContents(fetchPQOs(url, max))\n\n\ndef outputJson(obj=[], filename='def.json'):\n    \"\"\" obj を jsonファイル(filename)に出力\n    \"\"\"\n    with open(filename, 'w') as f:\n        f.writelines(json.dumps(obj))\n\n\ndef decodeJson(filename='def.json'):\n    \"\"\"jsonファイル(filename)をロードし，返す\n    \"\"\"\n    with open(filename, 'r', encoding='UTF-8') as f:\n        return json.loads(f.read(), 'UTF-8')\n\n\ndef printJson(filename='def.json'):\n    \"\"\" jsonファイル(filename)をロードし，表示\n    \"\"\"\n    pprint(decodeJson(filename), indent=4)\n\n\n#\n# main\n#\nif __name__ == '__main__':\n    baseUrl = 'http://bokete.jp/odai'\n    odaiId = '2227309'\n    url = baseUrl+'/'+odaiId\n    print('url = \"'+url+'\"')\n\n    # 全ての boke を取得\n    boke = fetchData(url, max=100)\n\n    # 取得データをファイルに保存\n    outputJson(boke, filename='boke.json')\n\n    # jsonの中身確認\n    printJson('boke.json')\n","sub_path":"bokete_scraping/bokescr.py","file_name":"bokescr.py","file_ext":"py","file_size_in_byte":3628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"158535865","text":"\n\nfrom xai.brain.wordbase.nouns._gadabout import _GADABOUT\n\n#calss header\nclass _GADABOUTS(_GADABOUT, ):\n\tdef __init__(self,): \n\t\t_GADABOUT.__init__(self)\n\t\tself.name = \"GADABOUTS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"gadabout\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_gadabouts.py","file_name":"_gadabouts.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"453523928","text":"\"\"\"A configuration file for the school script.\"\"\"\nfrom dataclasses import dataclass\n\n\n@dataclass\nclass CourseType:\n    \"\"\"A class for various types of courses (lectures/labs/whatever).\"\"\"\n\n    color: int\n    has_homework: bool\n\n\n# the relative path to the folder where the courses are stored\ncourses_folder = \"courses/\"\n\n\n# settings regarding course types -- labs/lectures/...\n# the numbers are ANSI colors that the course type will be painted with\n# see https://www.lihaoyi.com/post/BuildyourownCommandLinewithANSIescapecodes.html\ncourse_types = {\n    \"cvičení\": CourseType(118, True),\n    \"přednáška\": CourseType(39, False),\n}\n\n\n# default handlers for opening course folders/websites/notes...\nfile_browser = [\"ranger\"]\nweb_browser = [\"qutebrowser\", \"--target\", \"window\"]\ntext_editor = [\"vim\"]\nnote_handlers = {\".xopp\": \"xournalpp\", \".md\": \"vim\"}\n\n\n# default handler for Cron class notifications\n# the first argument after this command is the body of the notification\nnotify_command = \"DISPLAY=:0 DBUS_SESSION_BUS_ADDRESS=unix:path=/run/user/1000/bus dunstify 'School Schedule'\"\n\nnotify_started_message = \"právě začal předmět\"  # course started message\nnotify_no_more_courses = \"dnes již žádný další předmět není\"  # no more courses today\nnotify_next_course_message = (\n    \"další předmět je <i>{0} ({1})</i>, \"  # {0} is course name, {1} is course type\n    \"který začíná <i>{2} minut</i> po tomto \"  # {2} is minutes till next course\n    \"v učebně <i>{3}</i>\"  # {3} is the location\n)\n","sub_path":"school/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"544636999","text":"import json\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nfrom matplotlib.widgets import Slider\nfrom skimage import io\nfrom skimage.segmentation import find_boundaries\n\n\ndef segid_to_rgb(seg_id):\n    r, seg_id = seg_id % 256, seg_id / 256\n    g, seg_id = seg_id % 256, seg_id / 256\n    b = seg_id % 256\n    return np.array([r, g, b])\n\n\ndef rgb_to_segid(rgb):\n    r, g, b = rgb\n    return r + 256*g + 256*256*b\n\n\ndef get_boundaries(msk):\n    msk_id = msk.astype(np.uint32)\n    msk_id = msk[:, :, 0] + 255*msk[:, :, 1] + 255*255*msk_id[:, :, 2]\n    boundaries = find_boundaries(msk_id, mode='thick')\n    # img[boundaries] = 0\n    return boundaries     \n\n\ndef plot_categories_in_legend(ann, categories):\n    patches = []\n    for seg in ann['segments_info']:\n        color = segid_to_rgb(seg['id']) / 255.\n        label = categories[seg['category_id']]['name']\n\n        patches.append(mpatches.Patch(color=color, label=label))\n\n    plt.subplots_adjust(right=0.7)\n    plt.legend(handles=list(set(patches)),\n               bbox_to_anchor=(1.04,1), loc=\"upper left\", borderaxespad=0)\n\n\ndef plot_img(img, msk, ann, categories):\n    fig = plt.figure()\n\n    opacity = 0.6\n    res = img*opacity + msk*(1 - opacity)\n    \n    boundaries = get_boundaries(msk)\n    res[boundaries] = 0\n\n    plt_img = plt.imshow(res.astype(np.uint8))\n    plt_img.axes.format_coord = lambda x, y: format_coord(x, y,\n                                                          msk, ann, categories)\n    plot_categories_in_legend(ann, categories)\n    slider = add_opacity_slider(img, msk, opacity, boundaries, plt_img, fig)\n\n    # quit by q\n    plt.gcf().canvas.mpl_connect('key_press_event', quit_figure)\n    plt.show()\n\n\ndef main():\n    WDIR = './__data/source'\n    IDIR = os.path.join(WDIR, 'train2017')\n    MDIR = os.path.join(WDIR, 'annotations', 'panoptic_train2017')\n    ANN_PATH = os.path.join(WDIR, 'annotations', 'fix_panoptic_train2017.json')\n    # CAT_PATH = os.path.join(WDIR, 'annotations', 'panoptic_coco_categories.json')\n\n    with open(ANN_PATH, 'r') as f:\n        dataset = json.load(f)\n    # with open(CAT_PATH, 'r') as f:\n    #     categories = json.load(f)\n\n    images = dataset['images']\n    annotations = dataset['annotations']\n    categoriest_cnt = max(cat['id'] for cat in dataset['categories']) + 1\n    categories = [{'id':-1, 'name': 'unknown'}]*categoriest_cnt\n    categories[0] = {'id': 0, 'name': 'void'}\n\n    for cat in dataset['categories']:\n        categories[cat['id']] = cat\n\n    from_idx = np.random.randint(len(images))\n    to_idx = len(images)\n    lst = zip(images[from_idx:to_idx], annotations[from_idx:to_idx])\n    for img, ann in lst:\n        # print(img['file_name'], ann['file_name'])\n        img_path = os.path.join(IDIR, img['file_name'])\n        msk_path = os.path.join(MDIR, ann['file_name'])\n\n        img = io.imread(img_path)\n        msk = io.imread(msk_path)\n\n        plot_img(img, msk, ann, categories)\n\n\ndef add_opacity_slider(img, msk, init_opacity, boundaries, plt_img, fig):\n    def update(opacity):\n        res = img*opacity + msk*(1 - opacity)\n        res[boundaries] = 0\n        plt_img.set_array(res.astype(np.uint8))\n        fig.canvas.draw_idle()\n\n    axcolor = 'lightgoldenrodyellow'\n    axopacity = plt.axes([0.15, 0.95, 0.75, 0.03], facecolor=axcolor)\n    slider = Slider(axopacity, 'Opacity', 0., 1., valinit=init_opacity)\n    slider.on_changed(update)\n\n    return slider\n\n\ndef format_coord(x, y, msk, ann, categories):\n    seg_id = rgb_to_segid(msk[int(y), int(x)])\n    cat_id = 0\n    if seg_id != 0:\n        cat_id = [seg['category_id'] for seg in ann['segments_info'] if seg['id'] == seg_id][0]\n    seg_name = categories[cat_id]['name']\n    return 'x={:.4f}, y={:.4f}   {}'.format(x, y, seg_name)\n\n\ndef quit_figure(event):\n    if event.key == 'q':\n        plt.close(event.canvas.figure)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"run02_inspect_imgs.py","file_name":"run02_inspect_imgs.py","file_ext":"py","file_size_in_byte":3910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"248873510","text":"import turtle\nimport pandas\n\nscreen = turtle.Screen()\nscreen.title(\"U.S. States Game\")\nimage = \"blank_states_img.gif\"\nscreen.addshape(image)\nturtle.shape(image)\n\ncursor = turtle.Turtle()\ncursor.hideturtle()\ncursor.penup()\n\ndata = pandas.read_csv(\"50_states.csv\")\nnum_states = len(data)\ncorrect_answers = 0\ncorrect_guesses = []\nstates_to_learn = []\n\nwhile correct_answers < num_states:\n    answer_state = screen.textinput(title=f\"{correct_answers}/{num_states} States Correct\",\n                                    prompt=\"What's another state's name?\").title()\n    if answer_state == \"Exit\":\n        for state in data.state:\n            if state not in correct_guesses:\n                states_to_learn.append(state)\n        df = pandas.DataFrame(states_to_learn, columns=['state'])\n        df.to_csv(\"states_to_learn.csv\")\n        break\n    for state in data.state:\n        if (answer_state == state) and (answer_state not in correct_guesses):\n            x_pos = float(data.x[data.state == answer_state])\n            y_pos = float(data.y[data.state == answer_state])\n            cursor.setpos(x_pos, y_pos)\n            cursor.write(answer_state, align=\"center\")\n            correct_guesses.append(answer_state)\n            correct_answers += 1\n","sub_path":"us-states-game/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"517145162","text":"#Embedded file name: C:/Users/hovel/Dropbox/packages/studiolibrary/1.6.14/build27/studiolibrary/packages/mutils\\pose.py\n\"\"\"\n# Released subject to the BSD License\n# Please visit http://www.voidspace.org.uk/python/license.shtml\n#\n# Copyright (c) 2014, Kurt Rathjen\n# All rights reserved.\n# Comments, suggestions and bug reports are welcome.\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   # * 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 the\n   # documentation and/or other materials provided with the distribution.\n   # * Neither the name of Kurt Rathjen nor the\n   # objects of its contributors may be used to endorse or promote products\n   # derived from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY KURT RATHJEN  ''AS IS'' AND ANY\n# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL KURT RATHJEN BE LIABLE FOR ANY\n# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\n# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n#\n# pose.py\nimport pose\n\n# Example 1:\n# Create pose from objects\np = mutils.Pose.createFromObjects(objects)\n\n# Example 2:\n# Create from selected objects\nobjects = maya.cmds.ls(selection=True)\np = mutils.Pose.createFromObjects(objects)\n\n# Example 3:\n# Save to file\npath = \"/tmp/pose.json\"\np.save(path)\n\n# Example 4:\n# Load from file\npath = \"/tmp/pose.json\"\np = mutils.Pose.createFromPath(path)\n\n# load to objects from file\np.load()\n\n# load to selected objects\nobjects = maya.cmds.ls(selection=True)\np.load(objects=objects)\n\n# load to namespaces\np.load(namespaces=[\"character1\", \"character2\"])\n\n# load to specified objects\np.load(objects=[\"Character1:Hand_L\", \"Character1:Finger_L\"])\n\n\"\"\"\nimport mutils\nlog = mutils.logger\ntry:\n    import maya.cmds\nexcept ImportError:\n    import traceback\n    traceback.print_exc()\n\n__all__ = ['Pose']\n\nclass Pose(mutils.SelectionSet):\n\n    def __init__(self):\n        \"\"\"\n        \"\"\"\n        mutils.SelectionSet.__init__(self)\n        self._cache = None\n        self._cacheKey = None\n        self._mirrorTable = None\n\n    def cache(self):\n        \"\"\"\n        @rtype: list[(Attribute, Attribute)]\n        \"\"\"\n        return self._cache\n\n    def createObjectData(self, name):\n        \"\"\"\n        @type name: name\n        @rtype: list[Attribute]\n        \"\"\"\n        attrs = maya.cmds.listAttr(name, unlocked=True, keyable=True) or []\n        attrs = list(set(attrs))\n        attrs = [ mutils.Attribute(name, attr) for attr in attrs ]\n        result = {'attrs': self.attrs(name)}\n        for attr in attrs:\n            if attr.isValid():\n                if attr.value() is None:\n                    log.warning('Cannot save the attribute %s with value None.' % attr.fullname())\n                else:\n                    result['attrs'][attr.attr()] = {'type': attr.type(),\n                     'value': attr.value()}\n\n        return result\n\n    def attrs(self, name):\n        \"\"\"\n        @type name: str\n        @rtype: dict[]\n        \"\"\"\n        return self.object(name).get('attrs', {})\n\n    def attr(self, name, attr):\n        \"\"\"\n        @type name: str\n        @rtype: dict[]\n        \"\"\"\n        return self.attrs(name).get(attr, {})\n\n    def attrType(self, name, attr):\n        \"\"\"\n        @type name: str\n        @type attr: str\n        @rtype: str\n        \"\"\"\n        return self.attr(name, attr).get('type', None)\n\n    def attrValue(self, name, attr):\n        \"\"\"\n        @type name: str\n        @type attr: str\n        @rtype: str | int | float\n        \"\"\"\n        return self.attr(name, attr).get('value', None)\n\n    @mutils.timing\n    def load(self, objects = None, namespaces = None, attrs = None, blend = 100, key = False, refresh = False, ignoreConnected = False, onlyConnected = False, cache = True, mirrorTable = None, mirror = False):\n        \"\"\"\n        @type objects: list[str]\n        @type namespaces: list[str]\n        @type attrs: list[str]\n        @type blend: float\n        @type key: bool\n        @type refresh: bool\n        @type mirrorTable: mutils.MirrorTable\n        \"\"\"\n        if mirror and not mirrorTable:\n            log.warning('Cannot mirror pose without a mirror table!')\n            mirror = False\n        self.updateCache(objects=objects, namespaces=namespaces, dstAttrs=attrs, ignoreConnected=ignoreConnected, onlyConnected=onlyConnected, cache=cache, mirrorTable=mirrorTable)\n        self.loadCache(blend=blend, key=key, mirror=mirror)\n        if refresh:\n            maya.cmds.refresh(cv=True)\n\n    def updateCache(self, objects = None, namespaces = None, dstAttrs = None, ignoreConnected = False, onlyConnected = False, cache = True, mirrorTable = None):\n        \"\"\"\n        @type objects: list[str]\n        @type namespaces: list[str]\n        @type dstAttrs: list[str]\n        \"\"\"\n        cacheKey = str(objects) + str(namespaces) + str(dstAttrs) + str(ignoreConnected) + str(maya.cmds.currentTime(query=True))\n        if self._cacheKey != cacheKey or not cache:\n            self._cache = []\n            self._cacheKey = cacheKey\n            dstObjects = objects\n            srcObjects = self.objects()\n            usingNamespaces = not objects and namespaces\n            if mirrorTable:\n                self.setMirrorTable(mirrorTable)\n            mutils.matchObjects(srcObjects, dstObjects=dstObjects, dstNamespaces=namespaces, callback=self.cacheNode, dstAttrs=dstAttrs, ignoreConnected=ignoreConnected, onlyConnected=onlyConnected, usingNamespaces=usingNamespaces)\n        if not self.cache():\n            raise mutils.NoMatchFoundError('No objects match when loading data')\n\n    def setMirrorAxis(self, name, mirrorAxis):\n        \"\"\"\n        @type name: str\n        @type mirrorAxis: list[int]\n        \"\"\"\n        if name in self.objects():\n            self.object(name).setdefault('mirrorAxis', mirrorAxis)\n        else:\n            log.debug('Object does not exist in pose. Cannot set mirror axis for %s' % name)\n\n    def mirrorAxis(self, name):\n        \"\"\"\n        @rtype: list[int] | None\n        \"\"\"\n        result = None\n        if name in self.objects():\n            result = self.object(name).get('mirrorAxis', None)\n        if result is None:\n            log.debug('Cannot find mirror axis in pose for %s' % name)\n        return result\n\n    def mirrorTable(self):\n        \"\"\"\n        @rtype: mutils.MirrorTable\n        \"\"\"\n        return self._mirrorTable\n\n    def setMirrorTable(self, mirrorTable):\n        \"\"\"\n        @type mirrorTable: mutils.MirrorTable\n        \"\"\"\n        self._mirrorTable = mirrorTable\n        mirrorTable.matchObjects(objects=self.objects().keys(), callback=self.updateMirrorAxis)\n\n    def updateMirrorAxis(self, srcNode, dstNode, mirrorAxis):\n        \"\"\"\n        @type srcNode: mutils.Node\n        @type mirrorAxis: list[int]\n        \"\"\"\n        self.setMirrorAxis(dstNode, mirrorAxis)\n\n    def cacheNode(self, srcNode, dstNode, dstAttrs = None, ignoreConnected = None, onlyConnected = None, usingNamespaces = None):\n        \"\"\"\n        @type srcNode: mutils.Node\n        @type dstNode: mutils.Node\n        \"\"\"\n        mirrorAxis = None\n        mirrorObject = None\n        dstNode.stripFirstPipe()\n        if self.mirrorTable():\n            mirrorObject = self.mirrorTable().mirrorObject(srcNode.name())\n            if not mirrorObject:\n                mirrorObject = srcNode.name()\n                log.debug('Cannot find mirror object in pose for %s' % srcNode.name())\n            mirrorAxis = self.mirrorAxis(mirrorObject) or self.mirrorAxis(srcNode.name())\n            if mirrorObject and not maya.cmds.objExists(mirrorObject):\n                log.debug('Mirror object does not exist in the scene %s' % mirrorObject)\n        if usingNamespaces:\n            try:\n                dstNode = dstNode.toShortName()\n            except mutils.NoObjectFoundError as msg:\n                log.debug(msg)\n                return\n            except mutils.MoreThanOneObjectFoundError as msg:\n                log.debug(msg)\n                return\n\n        for attr in self.attrs(srcNode.name()):\n            if dstAttrs and attr not in dstAttrs:\n                continue\n            dstAttribute = mutils.Attribute(dstNode.name(), attr)\n            isConnected = dstAttribute.isConnected()\n            if ignoreConnected and isConnected or onlyConnected and not isConnected:\n                continue\n            type_ = self.attrType(srcNode.name(), attr)\n            value = self.attrValue(srcNode.name(), attr)\n            srcMirrorValue = self.attrMirrorValue(mirrorObject, attr, mirrorAxis=mirrorAxis)\n            srcAttribute = mutils.Attribute(dstNode.name(), attr, value=value, type=type_)\n            dstAttribute.update()\n            self._cache.append((srcAttribute, dstAttribute, srcMirrorValue))\n\n    def attrMirrorValue(self, name, attr, mirrorAxis):\n        \"\"\"\n        @type name: str\n        @type attr: str\n        @type mirrorAxis: list[]\n        @rtype: None | int | float\n        \"\"\"\n        value = None\n        if self.mirrorTable() and name:\n            value = self.attrValue(name, attr)\n            if value is not None:\n                value = self.mirrorTable().formatValue(attr, value, mirrorAxis)\n            else:\n                log.debug('cannot find mirror value for %s.%s' % (name, attr))\n        return value\n\n    def loadCache(self, blend = 100, key = False, mirror = False):\n        \"\"\"\n        @type blend: float\n        @type key: bool\n        \"\"\"\n        cache = self.cache()\n        for i in range(0, len(cache)):\n            srcAttribute, dstAttribute, srcMirrorValue = cache[i]\n            if srcAttribute and dstAttribute:\n                if mirror and srcMirrorValue is not None:\n                    value = srcMirrorValue\n                else:\n                    value = srcAttribute.value()\n                try:\n                    dstAttribute.set(value, blend=blend, key=key)\n                except (ValueError, RuntimeError):\n                    cache[i] = (None, None)\n                    log.debug('Ignoring %s.' % dstAttribute.fullname())\n","sub_path":"packages/mutils/pose.py","file_name":"pose.py","file_ext":"py","file_size_in_byte":10774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"593709502","text":"#!/usr/bin/env python\nimport MySQLdb\nimport yaml\nimport re\nimport os\n\n# import json\n# db = MySQLdb.connect(\"bol-db-products-prod-01.ecmwf.int\", \"ecmwf_ro\", \"ecmwf_ro\", \"param\")\ndb = MySQLdb.connect(\"k8s-bol-webapps-test-worker-016.ecmwf.int\", \"products\", \"products\", \"param\", port=30544)\n# db = MySQLdb.connect(\"k8s-bol-webapps-prod-worker-012.ecmwf.int\", \"products\", \"products\", \"param\", port=30545)\n                     \nPRODGEN = {}\nif os.path.exists(\"prodgen-paramids.yaml\"):\n    with open(\"prodgen-paramids.yaml\") as f:\n        PRODGEN = yaml.load(f.read(), Loader=yaml.FullLoader)\n\n# print(json.dumps(PRODGEN))\n\nPARAMSIDS = {}\nif os.path.exists(\"paramids.yaml\"):\n    with open(\"paramids.yaml\") as f:\n        PARAMSIDS = yaml.load(f.read(), Loader=yaml.FullLoader)\n\ncursor = db.cursor()\n\ncursor.execute(\"select * from param\")\n\nfor data in cursor.fetchall():\n    paramid, abbr, longname = int(data[0]), data[1].lower(), data[2].lower()\n\n    abbr = re.sub(r\"\\W\", \"_\", abbr)\n    abbr = re.sub(r\"_+\", \"_\", abbr)\n    abbr = re.sub(r\"^_\", \"\", abbr)\n    abbr = re.sub(r\"_$\", \"\", abbr)\n\n    if not abbr:\n        abbr = \"_param_%06d\" % (paramid,)\n\n    entry = [abbr.strip(), longname.strip()]\n\n    if paramid in PRODGEN:\n        pgen = [str(x).lower() for x in PRODGEN[paramid]]\n        p = []\n        for n in pgen:\n            if (\n                n not in entry\n            ):  #  and (' ' not in n) and ('.' not in n): #  and ('-' not in n):\n                entry.append(n)\n                p.append(n)\n\n    entry = tuple(entry)\n\n    if paramid in PARAMSIDS:\n        before = tuple(PARAMSIDS[paramid])\n        if before != entry:\n            print(\n                \"WARNING! updated paramid: {},  {} => {}\".format(paramid, before, entry)\n            )\n            PARAMSIDS[paramid] = list(entry)\n    else:\n        print(\"new paramid: {} {}\".format(paramid, entry))\n        PARAMSIDS[paramid] = list(entry)\n\ncursor.close()\ndb.close()\n\n\nfor paramid, entry in PRODGEN.items():\n    if paramid not in PARAMSIDS:\n        print(\"WARNING! adding pseudo-paramid: {},  {}\".format(paramid, tuple(entry)))\n        PARAMSIDS[paramid] = entry\n\nwith open(\"paramids.yaml\", \"w\") as f:\n    f.write(\n        \"# File automatically generated by %s\\n# Do not edit\\n\\n\"\n        % (os.path.basename(__file__))\n    )\n    f.write(yaml.safe_dump(PARAMSIDS, default_flow_style=False))\n","sub_path":"share/metkit/make-paramids-yaml-esuite.py","file_name":"make-paramids-yaml-esuite.py","file_ext":"py","file_size_in_byte":2357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"132709075","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2018/1/16 22:50\n# @Author  : Evescn\n# @Site    : \n# @File    : 多进程queue.py\n# @Software: PyCharm\n\nfrom multiprocessing import Process, Queue\n\n\ndef f(q):\n    q.put([42, None, 'hello'])\n\ndef f2(q):\n    # print('from f2:',q.get())  # prints \"[42, None, 'hello']\"\n    q.put([42, None, 'hello'])\n    print('from f21:', q.get())  # prints \"[42, None, 'hello']\"\n\nif __name__ == '__main__':\n    q = Queue()\n    p = Process(target=f, args=(q,))\n    p.start()\n    print('from parent1:', q.get())  # prints \"[42, None, 'hello']\"\n    p.join()\n\n    p2 = Process(target=f2, args=(q,))\n    p2.start()\n    print('from parent2:',q.get())  # prints \"[42, None, 'hello']\"\n    p2.join()","sub_path":"day8/多进程queue.py","file_name":"多进程queue.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"87720264","text":"import os\n\nimport torch\nfrom torch import optim\nfrom torch.utils.data import sampler\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom common import use_cuda\nfrom widgets import init_pars\nimport os.path as osp\nimport pickle as pkl\nfrom torch.utils.data import Dataset, DataLoader\nimport random\nfrom copy import deepcopy\n\n\nclass LocalDataset(Dataset):\n\n    def __init__(self, root_dir):\n        \"\"\"\n        :param root_dir:<str> the root directory files saved in\n        \"\"\"\n        super()\n        self.root_dir = root_dir\n\n        for i, (root, dirs, files) in enumerate(os.walk(self.root_dir)):\n            if i == 0:\n                self.index_entries = [osp.join(root, filen) for filen in files] \n            else:\n                print('Directory structure not expected!')\n                raise AssertionError()\n        random.shuffle(self.index_entries)\n\n    def __len__(self):\n        return len(self.index_entries)\n\n    def __getitem__(self, idx):\n        \"\"\"\n\n        :param idx:\n        :return: (resized img, class)\n        \"\"\"\n        with open(self.index_entries[idx], 'rb') as f:\n            data = pkl.load(f)\n\n        return data\n\n\nclass Worker:\n    def __init__(self, identifier, dir=osp.join('data', 'debug')):\n        self.data_dir = osp.join(dir, str(identifier))\n        if not osp.exists(self.data_dir):\n            print(f\"Given worker {self.data_dir} doesn't have its data partitioned\")\n            raise AssertionError()\n        self.dataset = LocalDataset(self.data_dir)\n\n    @property\n    def num_samples(self):\n        return len(self.dataset)\n\n    def train(self, model, criterion, current_lr, num_epoches, batch_size=5, verbose=False):\n        \"\"\"\n        new grads are logged in model itself\n        :param num_its: the number of iterations to train locally\n        :param current_lr: current learning rate\n        :param model: model to be trained in place\n        :param criterion:\n        :return: model update\n        \"\"\"\n        running_model = deepcopy(model)\n\n        loader = DataLoader(self.dataset, batch_size=batch_size, shuffle=True)\n\n        if use_cuda:\n            device = torch.device('cuda')\n        else:\n            device = torch.device('cpu')\n\n        running_model = running_model.to(device)\n        optimizer = optim.SGD(running_model.parameters(), lr=current_lr)\n\n        for epoch in range(num_epoches):\n            for it, data in enumerate(loader):  # _ start from 0\n                inputs, labels = data\n                if use_cuda:\n                    inputs = inputs.to(device=torch.device('cuda'))\n                    labels = labels.to(device=torch.device('cuda'))\n\n                # zero the parameter gradients\n                optimizer.zero_grad()\n\n                # forward + backward + optimize\n                outputs = running_model(inputs)\n                loss = criterion(outputs, labels)\n\n                if verbose:\n                    print(f'Epoch: {epoch}, it: {it}, loss: {loss.item()}')\n\n                loss.backward()\n                optimizer.step()\n        \n        return [new_par.data - old_par.data for new_par, old_par in zip(running_model.parameters(), model.parameters())]\n\n\ndef _test():\n    worker = Worker(0)\n    from model import CNNCifar\n    from torch import nn\n\n    net = CNNCifar()\n    worker.train_(net, nn.CrossEntropyLoss(), 0.01, 10, 5, True)\n\n\nif __name__ == '__main__':\n    _test()\n","sub_path":"worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":3391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"91220882","text":"#! python3\n\"\"\"\nAqui serão definidas funções singulares que serão utilizadas como auxílio para outras funções ou classes\ndefinidas\n\"\"\"\nfrom math import pi,radians,sin,cos,atan2, sqrt, modf\nimport re, constantes\n\n\n\"\"\"\nCalcula a distância de linha reta entre 2 pontos.\nParâmetros: coordenadas1 - Tupla com as coordenadas, em graus, de um ponto\n            coordenadas2 - Tupla com as coordenadas, em graus, de outro ponto\n\nretorno: Valor, em quilômetros, da distância entre os pontos\n\nAutor: https://stackoverflow.com/a/365853\n\"\"\"\ndef calcula_distancia(coordenadas1:tuple,coordenadas2:tuple):\n    #Calcula a diferença de latitude e longetude em radianos\n    diferenca_latitude = radians(coordenadas2[0] - coordenadas1[0])\n    diferenca_longetude = radians(coordenadas2[1] - coordenadas1[1])\n    \n    # Calcula o radiano para os graus de latitude dos dois pontos\n    latitude1 = radians(coordenadas1[0])\n    latitude2 = radians(coordenadas2[0])\n\n    # Calculo da distância, Eu particularmente não entendo\n    angulo = sin(diferenca_latitude / 2) * sin(diferenca_latitude / 2) + \\\n             sin(diferenca_longetude / 2) * sin(diferenca_longetude / 2 ) * \\\n             cos(latitude1) * cos(latitude2)\n    calculo = 2 * atan2(sqrt(angulo),sqrt(1-angulo))\n\n    # Retorna o calculo considerando o raio da terra na equação devido a circunferÊncia.\n    return calculo * constantes.RAIO_DA_TERRA\n\n\"\"\"\nFunção para converter os valores GMS para graus\nParâmetros: coordenada - Uma tupla com a latitude e a longetude em GMS (e.g. 0°0'0\"N)\n\nretorno: Uma tupla com os valores em graus\n\"\"\"\ndef converte_GMS_Graus(coordenada:tuple):\n    # salva os valores de latitude e longetude em variáveis para melhor manipulação\n    latitude = coordenada[0]\n    longetude = coordenada[1]\n    \n    # Separa a latitude GMS em tokens com os valores de cada elemento (grau, minuto, segundo e direção)\n    tokens = re.split(\"[° \\' \\\"]\",latitude)\n    \n    # Calcula os graus da latitude\n    grausL = int(tokens[0]) + int(tokens[1]) / 60 + int(tokens[2]) / 3600\n    # Define o sinal de acordo com a direção\n    grausL = grausL if tokens[3] == \"N\" else grausL * -1 \n\n    # Separa a longetude GMS em tokens com os valores de cada elemento (grau, minuto, segundo e direção)\n    tokens = tokens = re.split(\"[° \\' \\\"]\",longetude)\n    \n    # Calcula os graus da longetude\n    grausLo = int(tokens[0]) + int(tokens[1]) / 60 + int(tokens[2]) / 3600\n    # Define o sinal de acordo com a direção\n    grausLo = grausLo if tokens[3] == \"E\" else grausLo * -1 \n\n    # Retorna Tupla com os valores em graus\n    return (grausL,grausLo)\n    \n\"\"\"\nFunção para converter de Graus para o formato GMS (e.g 0°0'0\"N)\nParâmetros: coordenada - Uma tupla com a coordenada em graus\n\nretorno: uma tupla com a coordenada no formato GMS\n\"\"\"\ndef converte_Graus_GMS(coordenada:tuple):\n    # Separa latitude e longetude para maior clareza\n    latitude = coordenada[0]\n    longetude = coordenada[1]\n    \n    # Obtem a direção a partir do sinal e remove-o do valor\n    direcaoL, latitude = (\"N\", latitude) if latitude > 0 else (\"S\", latitude * (-1))\n\n    # Separa parte inteira e decimal do valor original.\n    # O Formato GMS só salva os valores inteiros para os graus e os minutos.\n    # Os segundos mantém a parte decimal\n    separacao = modf(latitude)\n    # Salva a parte inteira dos graus\n    grausL = separacao[1]\n\n    # Os minutos são obtidos multiplicando a parte decimal por 60\n    # Como foi previamente descrito, apenas a parte inteira importa para os minutos\n    separacao = modf(separacao[0]*60)\n    # Salva a parte inteira como minutos\n    minutosL = separacao[1]\n\n    # Os segundos utilizam a parte decimal dos minutos multiplicada por 60\n    # Os segundos mantem todo o valor obtido.\n    segundosL = separacao[0]*60\n    \n    # Obtem a direção a partir do sinal e remove-o do valor\n    direcaoLo, longetude = (\"E\", longetude) if longetude > 0 else (\"W\", longetude * (-1))\n\n    # Separa parte inteira e decimal do valor original.\n    # O Formato GMS só salva os valores inteiros para os graus e os minutos.\n    # Os segundos mantém a parte decimal\n    separacao = modf(longetude)\n    # Salva a parte inteira dos graus\n    grausLo = separacao[1]\n\n    # Os minutos são obtidos multiplicando a parte decimal por 60\n    # Como foi previamente descrito, apenas a parte inteira importa para os minutos\n    separacao = modf(separacao[0]*60)\n    # Salva a parte inteira como minutos\n    minutosLo = separacao[1]\n\n    # Os segundos utilizam a parte decimal dos minutos multiplicada por 60\n    # Os segundos mantem todo o valor obtido.\n    segundosLo = separacao[0]*60\n\n    # Retorna uma tupla no formato GMS\n    return (str(grausL) + \"°\" + str(minutosL) + \"'\" + \"{0:.1f}\".format(segundosL) + \"\\\"\" + direcaoL, str(grausLo) + \"°\" + str(minutosLo) + \"'\" + \"{0:.1f}\".format(segundosLo) + \"\\\"\" + direcaoLo)","sub_path":"funcoes_genericas.py","file_name":"funcoes_genericas.py","file_ext":"py","file_size_in_byte":4885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"228222465","text":"from art.unittest_lib import testflow\n\nfrom reports.reports_base import ReportsTest\nimport config\n\n\nclass LoggingTest(ReportsTest):\n    \"\"\"Base class for logging test\"\"\"\n    @staticmethod\n    def assert_grep_diff_logs(\n            search,\n            log_file=config.DWH_LOG,\n            backup_log_file=config.DWH_LOG_BACKUP,\n            lines=0\n    ):\n        \"\"\"\n        Grep diff of two log files\n\n        Args:\n            search (str): grepped string\n            log_file (str): log file\n            backup_log_file (str): backup of the same log file from past\n            lines (int): append number of lines to grep\n        Returns\n            str: return grepped string from diff of two files\n        \"\"\"\n        testflow.step(\"Grepping dwh log\")\n        cmd = [\n            'diff', '-e', backup_log_file, log_file, '|',\n            'grep', '-F', search, '-A' + str(lines), '|',\n            'grep', '-Fv', 'tWarn'\n        ]\n        result = config.ENGINE_HOST.run_command(command=cmd)\n        assert not result[0]\n\n        return result[1]\n\n    @staticmethod\n    def assert_backup_file(file_name, backup_file):\n        \"\"\"\n        Create a backup for file\n\n        Args:\n            file (str): file to be backed up\n            backup_file (str): service that should run\n        \"\"\"\n        testflow.step(\"Backing up %s log to %s\", file_name, backup_file)\n        cmd = ['cp', file_name, backup_file]\n        assert config.ENGINE_HOST.run_command(command=cmd), (\n            \"Error: Unable to backup {0}\".format(file_name)\n        )\n\n    @staticmethod\n    def assert_remove_backup(file_name):\n        \"\"\"\n        Remove backup file\n\n        Args:\n            file (str): backed up file to be removed\n        \"\"\"\n        testflow.step(\"Removing backup {0}\".format(file_name))\n        assert config.ENGINE_HOST.run_command(['rm', file_name])\n\n    @staticmethod\n    def assert_setting_variable(settings, var, val):\n        \"\"\"\n        Check value of application server variable\n\n        Args:\n            settings (dict): dictionary with settings 'key':value\n            var (str): variable to find\n            val (str): value to check\n        \"\"\"\n        assert var in settings.keys(), \"Variable {0} not found.\".format(var)\n        assert val in settings[var], (\n            \"Option {0} with value {1} not in: {2}\".format(var, val, settings)\n        )\n","sub_path":"art/tests/rhevmtests/integration/reports/logging/logging_base.py","file_name":"logging_base.py","file_ext":"py","file_size_in_byte":2359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"449696871","text":"\n\n#calss header\nclass _REGISTRAR():\n\tdef __init__(self,): \n\t\tself.name = \"REGISTRAR\"\n\t\tself.definitions = [u'an official whose job is to keep official records, especially of births, deaths, and marriages', u'at some colleges, an official in charge of exams, keeping records, and new students', u'a type of hospital doctor: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_registrar.py","file_name":"_registrar.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"153004146","text":"from requests import get\nimport requests.exceptions\nimport logging\nfrom time import sleep\nimport re\n\n\nAPI_URL = \"https://api.freelancehunt.com/v2\"\nEVENT_TEMPLATE = '{type} <b>{login}</b> {message}'\nOK, NETWORK_ERROR, TOKEN_ERROR, TOO_MANY_REQUESTS = range(4)\nlogger = logging.getLogger(__name__)\n\n\ndef _api_get(token, rel_url=\"/\"):\n    return get(API_URL + rel_url, headers={'Authorization': f'Bearer {token}'})\n\n\ndef validate(token):\n    try:\n        response = _api_get(token)\n    except requests.exceptions.RequestException as error:\n        return NETWORK_ERROR\n    res = response.json()\n    if 'error' in res and response.status_code != 404:\n        if response.status_code == 429:\n            logger.warning(\"Server returned 'Too Many Requests' for validation query for user with token %s...\",\n                           token[:len(token) // 2])\n            return TOO_MANY_REQUESTS\n        logger.warning(\"Received error '%s' for token %s...\", res['error'], token[:len(token) // 2])\n        return TOKEN_ERROR\n    else:\n        return OK\n\n\ndef _get_feed(token):\n    try:\n        response = _api_get(token, \"/my/feed\")\n    except requests.exceptions.RequestException as error:\n        logger.critical(\"Request raised error '%s'. Returned 'NETWORK_ERROR'\", error)\n        return NETWORK_ERROR\n    feed = response.json()\n    if 'error' in feed and response.status_code != 404:\n        if response.status_code == 429:\n            logger.warning(\"Server returned 'Too Many Requests' for validation query for user with token %s...\",\n                           token[:len(token) // 2])\n            return TOO_MANY_REQUESTS\n        logger.warning(\"Received error '%s' for token %s...\", feed['error'], token[:len(token) // 2])\n        return TOKEN_ERROR\n    return _prepare_feed(feed)\n\n\ndef _prepare_event_msg(text):\n    msg = re.sub(r\"<img.*/?>\", \"\", text)\n    msg = re.sub(r'<a.* href=\"(.*?)\".*?>', lambda m: f'<a href=\"{m.group(1)}\">', msg)\n    return msg\n\n\ndef _prepare_feed(feed):\n    events = []\n    for i in feed['data']:\n        attrs = i['attributes']\n        if not attrs['is_new']:\n            break\n        from_type = \"закачкик\" if attrs['from']['type'] == \"employer\" else \"исполнитель\"\n        login = attrs['from']['login']\n        msg = _prepare_event_msg(attrs['message'])\n        event = EVENT_TEMPLATE.format(type=from_type, login=login, message=msg)\n        events.append(event)\n    return events[::-1]\n\n\ndef _get_threads(token):\n    try:\n        response = _api_get(token, \"/threads\")\n    except requests.exceptions.RequestException as error:\n        logger.critical(\"Request raised error '%s'. Returned 'NETWORK_ERROR'\", error)\n        return NETWORK_ERROR\n    threads = response.json()\n    if 'error' in threads and response.status_code != 404:\n        if response.status_code == 429:\n            logger.warning(\"Server returned 'Too Many Requests' for validation query for user with token %s...\",\n                           token[:len(token) // 2])\n            return TOO_MANY_REQUESTS\n        logger.warning(\"Received error '%s' for token %s...\", msgs['error'], token[:len(token) // 2])\n        return TOKEN_ERROR\n    return threads['data']\n\n\ndef _get_message(token):\n    return \"some text\"\n\n\ndef _get_messages(token):\n    threads = _get_threads(token)\n    msgs = []\n    for thread in threads:\n        attrs = thread['attributes']\n        if not attrs['is_unread']:\n            continue\n\n\n\ndef get_updates(settings):\n    token = settings['token']\n    logger.debug(\"Starting 'get_updates' for token %s...\", token[:len(token) // 2])\n    validation = validate(token)\n    if validation == TOO_MANY_REQUESTS:\n        sleep(20)\n        return get_updates(settings)\n    elif validation != OK:\n        return validation\n\n    feed = _get_feed(token)\n    if not isinstance(feed, list):\n        return feed\n    return feed\n\n\nif __name__ == \"__main__\":\n    TEST_TOKEN = \"a10207aff6d23d9d735bcd6e36eefa9f2ba2a0d0\"\n    TEST_SETTINGS = {\n        'token': TEST_TOKEN\n    }\n    TEST_MSG = 'От закачкика freelancehunt:\\nИтоги 2019 года Freelancehunt: <a href=\"https://freelancehunt.com/blog/itoghi-ghoda/?utm_source=freelancehunt&utm_medium=feed&utm_campaign=blog-teaser\" target=\"_blank\">рекорды в цифрах!</a> <img src=\"https://freelancehunt.com/static/images/fugu/new-text.png\" width=\"16\" height=\"16\"/>'\n    #print(get_updates(TEST_SETTINGS))\n    #print(_get_feed(TEST_TOKEN))\n    print(_get_threads(TEST_TOKEN))\n    #print(_prepare_msg(TEST_MSG))\n","sub_path":"app/fhapi.py","file_name":"fhapi.py","file_ext":"py","file_size_in_byte":4507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"586549671","text":"'''\nSingle-pole balancing experiment using a continuous-time recurrent neural network (CTRNN).\n'''\n\nfrom __future__ import print_function\n\nimport os\nimport pickle\n\nimport cart_pole\n\nfrom neatsociety import ctrnn, parallel, population, visualize\nfrom neatsociety.config import Config\nfrom neatsociety.math_util import mean\n\nruns_per_net = 5\nnum_steps = 60000 # equivalent to 1 minute of simulation time\n\n\n# Use the CTRNN network phenotype and the discrete actuator force function.\ndef evaluate_genome(g):\n    net = ctrnn.create_phenotype(g)\n\n    fitnesses = []\n\n    for runs in range(runs_per_net):\n        sim = cart_pole.CartPole()\n\n        # Run the given simulation for up to num_steps time steps.\n        fitness = 0.0\n        for s in range(num_steps):\n            inputs = sim.get_scaled_state()\n            action = net.parallel_activate(inputs)\n\n            # Apply action to the simulated cart-pole\n            force = cart_pole.discrete_actuator_force(action)\n            sim.step(force)\n\n            # Stop if the network fails to keep the cart within the position or angle limits.\n            # The per-run fitness is the number of time steps the network can balance the pole\n            # without exceeding these limits.\n            if abs(sim.x) >= sim.position_limit or abs(sim.theta) >= sim.angle_limit_radians:\n                break\n\n            fitness += 1.0\n\n        fitnesses.append(fitness)\n\n    # The genome's fitness is its worst performance across all runs.\n    return min(fitnesses)\n\n\n# Load the config file, which is assumed to live in\n# the same directory as this script.\nlocal_dir = os.path.dirname(__file__)\nconfig = Config(os.path.join(local_dir, 'ctrnn_config'))\nconfig.node_gene_type = ctrnn.CTNodeGene\n\npop = population.Population(config)\npe = parallel.ParallelEvaluator(evaluate_genome)\npop.run(pe.evaluate, 2000)\n\n# Save the winner.\nprint('Number of evaluations: {0:d}'.format(pop.total_evaluations))\nwinner = pop.statistics.best_genome()\nwith open('ctrnn_winner_genome', 'wb') as f:\n    pickle.dump(winner, f)\n\nprint(winner)\n\n# Plot the evolution of the best/average fitness.\nvisualize.plot_stats(pop.statistics, ylog=True, filename=\"ctrnn_fitness.svg\")\n# Visualizes speciation\nvisualize.plot_species(pop.statistics, filename=\"ctrnn_speciation.svg\")\n# Visualize the best network.\nvisualize.draw_net(winner, view=True, filename=\"ctrnn_winner.gv\")\n","sub_path":"examples/pole_balancing/single_pole/ctrnn_evolve.py","file_name":"ctrnn_evolve.py","file_ext":"py","file_size_in_byte":2383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"223281726","text":"#!/usr/bin/python3\n\"\"\"\nThis module contains Fabric script that creates and distributes an archive\nto the web servers, using the function deploy\n\"\"\"\nfrom fabric.api import *\nenv.user = 'ubuntu'\nenv.hosts = ['52.71.155.152', '184.72.141.74']\n\n\ndef do_clean(number=0):\n    \"\"\"\n    clean local archive and server\n    \"\"\"\n    num = int(number)\n    if num == 0:\n        num += 1\n    files = local(\"ls -tr versions\", capture=True).split(\"\\n\")\n    if len(files) > num:\n        for file in files[:num]:\n            local(\"rm -f versions/{}\".format(file))\n    with cd(\"/data/web_static/releases\"):\n        files = sudo(\"ls -tr\").split(\" \")\n        files = [file for file in files if file != '']\n        print(files)\n        print(len(files))\n        if len(files) > num:\n            for file in files[:num]:\n                print(file)\n                run(\"sudo rm -rf {}\".format(file))\n","sub_path":"100-clean_web_static.py","file_name":"100-clean_web_static.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"311421251","text":"#pylint:disable=W0312\nx=(int(input('Enter number of team:')))\ny=list(range(1,x+1))\nbyes=0\nbyes_from=[2,4,8,16,32]\nbyes_form_index=[1,-1,2,-2,3,-3,4,-4,5,-5]\nprint(\"Team in Fixture\",y)\nupper_half=y[0:int(((len(y)+1)/2))]\nprint(\"Upper Half:\",upper_half)\nupper_half.insert(0,0)\nlower_half=y[int(((len(y)+1)/2)):]\nprint(\"Lower Half:\",lower_half)\nlower_half.insert(0,0)\nfor i in byes_from:\n\tif x>i and x< byes_from[byes_from.index(i)+1]:\n\t \t\tbyes=byes_from[byes_from.index(i)+1]-x\nprint(\"Byes=\",byes)\ndef give_byes(byes):\n\tfor  i in byes_form_index:\n\t\t  if byes !=0:\n\t\t  \tn=-i\n\t\t  \t#lower_half.remove(lower_half[n])\n\t\t  \t#lower_half.insert(n,\"Byes\")\n\t\t  \tlower_half[n]=\"byes\"\n\t\t  \tbyes-=1\n\t\t  else:\n\t\t  \t   return \n\t\t  if byes !=0:\n\t\t  \tj=-(-i)\n\t\t  \t#upper_half.remove(upper_half[j])\n\t\t  \t#upper_half.insert(j,\"byes\")\n\t\t  \tupper_half[j]=\"byes\"\n\t\t  \tbyes-=1\n\t\t  else:\n\t\t    \treturn\ngive_byes(byes)\nupper_half.remove(0)\nlower_half.remove(0)\nprint(\"-\"*15)\nprint(\"Teams\")\nprint(\"-\"*15)\nfor k in upper_half:\n\tprint(k)\nprint(\"-\"*15)\nfor s in lower_half:\n\tprint(s)","sub_path":"corr.py","file_name":"corr.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"638723367","text":"\"\"\"\nCreated by Alex Hopkins\non 10/19/18\n\nElectricity and magnetism model test functions\n\"\"\"\nimport models.em.em_model as emm\nimport unittest\n\n\nclass TestEM(unittest.TestCase):\n\n    def test_coulomb_force(self):\n        \"\"\"\n        Tests the coulomb force between two charges. The asserted values are calculated by hand using the equation\n        F = k * q_1 * q_2 / r ** 2\n        \"\"\"\n        print(emm.coulomb_force(1.6e-19, -1.6e-19, 1))\n        self.assertAlmostEqual(emm.coulomb_force(1.6e-19, -1.6e-19, 1), -2.3e-28, places=3)\n\n    def test_n_electric_field(self):\n        nq = 2\n        emm.n_electric_field(nq)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"models/em/tests/electricity_magnetism_model_test.py","file_name":"electricity_magnetism_model_test.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"619601740","text":"from app.models.endpoint import Endpoint\nfrom bs4 import BeautifulSoup, NavigableString\nimport html\nimport os\nimport urllib.parse as urlparse\nfrom urllib.parse import parse_qs\nimport re\n\nSKIP_ARGS = ['ref_src', 'utm']\nSKIP_PREFIX = ['//www.', '//mobile.', '//m.']\nGOOG_STATIC = 'www.gstatic.com'\nGOOG_IMG = '/images/branding/searchlogo/1x/googlelogo'\nLOGO_URL = GOOG_IMG + '_desk'\nBLANK_B64 = ('data:image/png;base64,'\n             'iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAQAAAAnOwc2AAAAD0lEQVR42mNkw'\n             'AIYh7IgAAVVAAuInjI5AAAAAElFTkSuQmCC')\n\n# Ad keywords\nBLACKLIST = [\n    'ad', 'anuncio', 'annuncio', 'annonce', 'Anzeige', '广告', '廣告', 'Reklama',\n    'Реклама', 'Anunț', '광고', 'annons', 'Annonse', 'Iklan', '広告', 'Augl.',\n    'Mainos', 'Advertentie', 'إعلان', 'Գովազդ', 'विज्ञापन', 'Reklam', 'آگهی',\n    'Reklāma', 'Reklaam', 'Διαφήμιση', 'מודעה', 'Hirdetés', 'Anúncio'\n]\n\nSITE_ALTS = {\n    'twitter.com': os.getenv('WHOOGLE_ALT_TW', 'nitter.net'),\n    'youtube.com': os.getenv('WHOOGLE_ALT_YT', 'invidious.snopyta.org'),\n    'instagram.com': os.getenv('WHOOGLE_ALT_IG', 'bibliogram.art/u'),\n    'reddit.com': os.getenv('WHOOGLE_ALT_RD', 'libredd.it'),\n    **dict.fromkeys([\n        'medium.com',\n        'levelup.gitconnected.com'\n    ], os.getenv('WHOOGLE_ALT_MD', 'scribe.rip'))\n}\n\n\ndef bold_search_terms(response: str, query: str) -> BeautifulSoup:\n    \"\"\"Wraps all search terms in bold tags (<b>). If any terms are wrapped\n    in quotes, only that exact phrase will be made bold.\n\n    Args:\n        response: The initial response body for the query\n        query: The original search query\n\n    Returns:\n        BeautifulSoup: modified soup object with bold items\n    \"\"\"\n    response = BeautifulSoup(response, 'html.parser')\n\n    def replace_any_case(element: NavigableString, target_word: str) -> None:\n        # Replace all instances of the word, but maintaining the same case in\n        # the replacement\n        if len(element) == len(target_word):\n            return\n\n        if not re.match('.*[a-zA-Z0-9].*', target_word) or (\n                element.parent and element.parent.name == 'style'):\n            return\n\n        element.replace_with(BeautifulSoup(\n            re.sub(fr'\\b((?![{{}}<>-]){target_word}(?![{{}}<>-]))\\b',\n                   r'<b>\\1</b>',\n                   html.escape(element),\n                   flags=re.I), 'html.parser')\n        )\n\n    # Split all words out of query, grouping the ones wrapped in quotes\n    for word in re.split(r'\\s+(?=[^\"]*(?:\"[^\"]*\"[^\"]*)*$)', query):\n        word = re.sub(r'[^A-Za-z0-9 ]+', '', word)\n        target = response.find_all(\n            text=re.compile(r'' + re.escape(word), re.I))\n        for nav_str in target:\n            replace_any_case(nav_str, word)\n\n    return response\n\n\ndef has_ad_content(element: str) -> bool:\n    \"\"\"Inspects an HTML element for ad related content\n\n    Args:\n        element: The HTML element to inspect\n\n    Returns:\n        bool: True/False for the element containing an ad\n\n    \"\"\"\n    return (element.upper() in (value.upper() for value in BLACKLIST)\n            or 'ⓘ' in element)\n\n\ndef get_first_link(soup: BeautifulSoup) -> str:\n    \"\"\"Retrieves the first result link from the query response\n\n    Args:\n        soup: The BeautifulSoup response body\n\n    Returns:\n        str: A str link to the first result\n\n    \"\"\"\n    # Replace hrefs with only the intended destination (no \"utm\" type tags)\n    for a in soup.find_all('a', href=True):\n        # Return the first search result URL\n        if 'url?q=' in a['href']:\n            return filter_link_args(a['href'])\n    return ''\n\n\ndef get_site_alt(link: str) -> str:\n    \"\"\"Returns an alternative to a particular site, if one is configured\n\n    Args:\n        link: A string result URL to check against the SITE_ALTS map\n\n    Returns:\n        str: An updated (or ignored) result link\n\n    \"\"\"\n    # Need to replace full hostname with alternative to encapsulate\n    # subdomains as well\n    hostname = urlparse.urlparse(link).hostname\n\n    for site_key in SITE_ALTS.keys():\n        if not hostname or site_key not in hostname:\n            continue\n\n        link = link.replace(hostname, SITE_ALTS[site_key])\n        for prefix in SKIP_PREFIX:\n            link = link.replace(prefix, '//')\n        break\n\n    return link\n\n\ndef filter_link_args(link: str) -> str:\n    \"\"\"Filters out unnecessary URL args from a result link\n\n    Args:\n        link: The string result link to check for extraneous URL params\n\n    Returns:\n        str: An updated (or ignored) result link\n\n    \"\"\"\n    parsed_link = urlparse.urlparse(link)\n    link_args = parse_qs(parsed_link.query)\n    safe_args = {}\n\n    if len(link_args) == 0 and len(parsed_link) > 0:\n        return link\n\n    for arg in link_args.keys():\n        if arg in SKIP_ARGS:\n            continue\n\n        safe_args[arg] = link_args[arg]\n\n    # Remove original link query and replace with filtered args\n    link = link.replace(parsed_link.query, '')\n    if len(safe_args) > 0:\n        link = link + urlparse.urlencode(safe_args, doseq=True)\n    else:\n        link = link.replace('?', '')\n\n    return link\n\n\ndef append_nojs(result: BeautifulSoup) -> None:\n    \"\"\"Appends a no-Javascript alternative for a search result\n\n    Args:\n        result: The search result to append a no-JS link to\n\n    Returns:\n        None\n\n    \"\"\"\n    nojs_link = BeautifulSoup(features='html.parser').new_tag('a')\n    nojs_link['href'] = f'/{Endpoint.window}?location=' + result['href']\n    nojs_link.string = ' NoJS Link'\n    result.append(nojs_link)\n\n\ndef add_ip_card(html_soup: BeautifulSoup, ip: str) -> BeautifulSoup:\n    \"\"\"Adds the client's IP address to the search results\n        if query contains keywords\n\n    Args:\n        html_soup: The parsed search result containing the keywords\n        ip: ip address of the client\n\n    Returns:\n        BeautifulSoup\n\n    \"\"\"\n    if (not html_soup.select_one(\".EY24We\")\n            and html_soup.select_one(\".OXXup\").get_text().lower() == \"all\"):\n        # HTML IP card tag\n        ip_tag = html_soup.new_tag(\"div\")\n        ip_tag[\"class\"] = \"ZINbbc xpd O9g5cc uUPGi\"\n\n        # For IP Address html tag\n        ip_address = html_soup.new_tag(\"div\")\n        ip_address[\"class\"] = \"kCrYT ip-address-div\"\n        ip_address.string = ip\n\n        # Text below the IP address\n        ip_text = html_soup.new_tag(\"div\")\n        ip_text.string = \"Your public IP address\"\n        ip_text[\"class\"] = \"kCrYT ip-text-div\"\n\n        # Adding all the above html tags to the IP card\n        ip_tag.append(ip_address)\n        ip_tag.append(ip_text)\n\n        # Finding the element before which the IP card would be placed\n        f_link = html_soup.select_one(\".BNeawe.vvjwJb.AP7Wnd\")\n        ref_element = f_link.find_parent(class_=\"ZINbbc xpd O9g5cc\" +\n                                                \" uUPGi\")\n\n        # Inserting the element\n        ref_element.insert_before(ip_tag)\n    return html_soup\n","sub_path":"app/utils/results.py","file_name":"results.py","file_ext":"py","file_size_in_byte":7002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"555222650","text":"# Sean McIlvenny\n# CTEC 121 / Winter 2019\n# Module 4 / Problem Set 5\n# Problem 3 (25 points)\n\n\"\"\"\nDevelop a program that draws some sort of substantial face that includes two eyes, a nose, a mouth with some teeth, two ears and some hair.\n\nYou will find faces that were drawn by students in prior classes in a file named faces.png.\n\"\"\"\n\nfrom graphics import *\n\ndef main():\n    win = GraphWin('Faces',800,800)\n\n    face = Circle (Point(400,400),200)\n    face.setFill('yellow')\n    face.draw(win)\n\n    \n\n    hair = Polygon (Point(585,320),Point(585,140),Point(205,150),Point(215,325),Point(260,260),Point(290,270),Point(375,235),Point(385,260),Point(470,235),Point(580,310))\n    hair.setFill('purple')\n    hair.setOutline('green')\n    hair.draw(win)\n\n    eye1 = Circle(Point(300,325),40)\n    eye1.setFill('white')\n    eye1.draw(win)\n\n    eye2 = eye1.clone()\n    eye2.move(170,0)\n    eye2.draw(win)\n    pupil1 = Circle(Point(300,325),15)\n    pupil1.setFill('blue')\n    pupil1.draw(win)\n    pupil2 = pupil1.clone()\n    pupil2.setFill('brown')\n    pupil2.move(170,0)\n    pupil2.draw(win)\n\n    nose = Polygon(Point(380,325),Point(340,410),Point(425,410))\n    nose.setFill('yellow')\n    nose.draw(win)\n\n    mouth = Rectangle(Point(310,455),Point(490,540))\n    mouth.setFill('black')\n    mouth.draw(win)\n\n    teethtoprow = Rectangle(Point(310,455),Point(490,475))\n    teethtoprow.setFill('white')\n    teethtoprow.draw(win)\n    teethbottomrow = teethtoprow.clone()\n    teethbottomrow.move(0,70)\n    teethbottomrow.draw(win)\n\n    ear1 = Polygon(Point(585,395),Point(605,310),Point(625,445))\n    ear1.setFill('yellow')\n    ear1.draw(win)\n    ear2 = Polygon(Point(215,390),Point(190,305),Point(175,440))\n    ear2.setFill('yellow')\n    ear2.draw(win)\n\n\n\n    # for i in range(10):\n    #     point = win.getMouse()\n    #     x = point.getX()\n    #     y = point.getY()\n    #     print(x,y)\n\n\n\n\n\n    input('close program')\n    win.close()\n\n\nmain()","sub_path":"problem-set-5-problem-3.py","file_name":"problem-set-5-problem-3.py","file_ext":"py","file_size_in_byte":1930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"57203765","text":"\"\"\"\nUsage of the Solar PV dataset\n\n1. Download the github repository: https://github.com/zae-bayern/elpv-dataset\n2. Extract the zip file elpv-dataset-master.zip\n3. Make sure the utils folder and this script are outside the extracted folder.\n4. Run this file.\n\n\"\"\"\n\n\nfrom utils.elpv_reader import load_dataset\nimport cv2\nimport numpy as np\nimport shutil\nimport os\nfrom sklearn.model_selection import train_test_split\n\n\ndestination_folder = 'solar_panels_products_V2'\n\nif not os.path.exists(destination_folder):\n\tos.mkdir(destination_folder)\n\n\n# datafolder is the relative path for the contents in the elpv dataset\n\nimages, probs, types = load_dataset(datafolder = 'elpv_dataset')\n\ntrain_images, test_images, train_probs, test_probs, train_types, test_types = train_test_split(images, probs, types, test_size= 0.1)\n\ndef create_dataset(images, probs, types, split):\n\n\tthreshold = 0.6\n\n\tdata_folder = os.path.join(destination_folder, split)\n\n\tif not os.path.exists(data_folder):\n\t\tos.mkdir(data_folder)\n\n\tidx = 0\n\n\tfor image , prob, type_ in zip(images, probs, types):\n\n\n\t\tif prob > threshold:\n\n\n\t\t\timage = cv2.resize(image, (300, 300)).reshape(300,300,1)\n\n\t\t\tcategory_folder = os.path.join(data_folder, type_ + '_defective')\n\n\t\t\tif not os.path.exists(category_folder):\n\t\t\t\tos.mkdir(category_folder)\n\n\t\t\timage_name = os.path.join( category_folder, 'elpv_' + str(idx) + '.jpg')\n\n\n\t\t\tif not os.path.exists(image_name):\n\n\t\t\t\tcv2.imwrite(image_name, image )\n\n\n\t\tif prob < threshold:\n\n\n\t\t\timage = cv2.resize(image, (300, 300)).reshape(300,300,1)\n\n\t\t\tcategory_folder = os.path.join(data_folder, type_ + '_non_defective')\n\n\t\t\tif not os.path.exists(category_folder):\n\t\t\t\tos.mkdir(category_folder)\n\n\t\t\timage_name = os.path.join( category_folder, 'elpv_' + str(idx) + '.jpg')\n\n\n\t\t\tif not os.path.exists(image_name):\n\n\t\t\t\tcv2.imwrite(image_name, image )\n\n\t\t\n\t\tidx += 1\n\t\t\t\n\ncreate_dataset(train_images, train_probs, train_types, \"train\")\ncreate_dataset(test_images, test_probs, test_types, \"test\")\n\n","sub_path":"1.preprocess_dataset/solar_panels_products_v2.py","file_name":"solar_panels_products_v2.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"630673257","text":"import socket\nimport sys\nimport time\n\n# 创建一个套接字\ns = socket.socket()\n\n# 尝试连接 127.0.0.1 的 6666 端口\ns.connect(('127.0.0.1', 6666))\n\nprint('已连接 127.0.0.1')\n\nmessage = '消息: ' + sys.argv[1]\n\nsleep_seconds = int(sys.argv[2])\ntime.sleep(sleep_seconds)\n\n# 发送消息\ns.send(message.encode('utf8'))\n\n# 接受返回消息\nreply_message = s.recv(1024)\n\nprint(reply_message.decode('utf8'))\n\n# 关闭连接\ns.close()\n","sub_path":"03-GUI/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"446774125","text":"def inversion_count(T):\n    def merge(T, temp_T, left, mid, right):\n        i = left\n        j = mid + 1\n        k = left\n        inv_count = 0\n\n        while i <= mid and j <= right:\n            if T[i] <= T[j]:\n                temp_T[k] = T[i]\n                k += 1\n                i += 1\n            else:\n                temp_T[k] = T[j]\n                inv_count += (mid - i + 1)\n                k += 1\n                j += 1\n\n        while i <= mid:\n            temp_T[k] = T[i]\n            k += 1\n            i += 1\n        while j <= right:\n            temp_T[k] = T[j]\n            k += 1\n            j += 1\n\n        for m in range(left, right + 1):\n            T[m] = temp_T[m]\n\n        return inv_count\n\n    def mergesort(T, temp_T, left, right):\n        inv_count = 0\n        \n        if left < right:\n            mid = (left + right)//2\n\n            inv_count += mergesort(T, temp_T, left, mid)\n            inv_count += mergesort(T, temp_T, mid + 1, right)\n            inv_count += merge(T, temp_T, left, mid, right)\n\n        return inv_count\n\n    n = len(T)\n    temp_arr = [0]*n\n\n    return mergesort(T, temp_arr, 0, n-1)\n","sub_path":"zestaw_2/zad2.py","file_name":"zad2.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"404130858","text":"class Solution:\n    def longestCommonPrefix(self, strs):\n        \"\"\"\n        :type strs: List[str]\n        :rtype: str\n        \"\"\"\n        \n        if len(strs) == 0:\n            return \"\"\n        \n        prefix = strs[0]\n        \n        for i in range(1,len(strs)):\n            newprefix = ''\n            #print(len(prefix))\n            for j in range(min(len(prefix),len(strs[i]))):\n                #print(j)\n                newstr =  prefix[j] if strs[i][j] == prefix[j] else ''\n                if newstr == '':\n                    break\n                newprefix = newprefix + newstr\n                \n            prefix = newprefix\n            \n        return prefix","sub_path":"leetcode/14.py","file_name":"14.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"543708111","text":"numlist = list()\r\n\r\nwhile True:\r\n    temp = input('Enter a number: ')\r\n    if temp == 'done' : break\r\n    try:\r\n        numlist.append(float(temp))\r\n    except:\r\n        continue\r\nif len(numlist) > 0:\r\n    print('Maximum:', max(numlist))\r\n    print('Minimum:', min(numlist))\r\n    print('Count:  ', len(numlist))\r\nelse : print('You entered no numbers!')\r\n","sub_path":"ex_08_06.py","file_name":"ex_08_06.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"349807446","text":"#####################################\nimport GAME.core\nimport TILEED.core\nimport config\nimport getopt, sys, os\n#####################################\n\ndef main():\n\ttry:\n\t\topts, args = getopt.getopt(sys.argv[1:], \"hev\", [\"help\"])\n\texcept getopt.GetoptError as err:\n\t\tprint(err)\n\t\tsys.exit(2)\n\tverbose = False\n\tstarteditor = False\n\tfor o, a in opts:\n\t\tif o == \"-v\":\n\t\t\tverbose = True\n\t\telif o in (\"-h\", \"--help\"):\n\t\t\tsys.exit()\n\t\telif o in (\"-e\"):\n\t\t\tstarteditor = True\n\t\telse:\n\t\t\tassert False, \"unhandled option\"\n\tif not starteditor:\n\t\tgame = GAME.core.Game(config.Config())\n\t\tgame.start()\n\telse: \n\t\teditor = TILEED.core.TileEditor(config.Config())\n\t\teditor.start()\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"487275919","text":"# -*- encoding: utf-8 -*-\n##############################################################################\n#\n#    OpenERP, Open Source Management Solution\t\n#    Copyright (C) 2004-2009 Tiny SPRL (<http://tiny.be>). All Rights Reserved\n#    $Id$\n#\n#    This program is free software: you can redistribute it and/or modify\n#    it under the terms of the GNU General Public License as published by\n#    the Free Software Foundation, either version 3 of the License, or\n#    (at your option) any later version.\n#\n#    This program is distributed in the hope that it will be useful,\n#    but WITHOUT ANY WARRANTY; without even the implied warranty of\n#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#    GNU General Public License for more details.\n#\n#    You should have received a copy of the GNU General Public License\n#    along with this program.  If not, see <http://www.gnu.org/licenses/>.\n#\n##############################################################################\n\n\"\"\"wizard that replace completely the wizard on stock with same name,\nbeacause adds the functionally that set zero prodlotas not products\"\"\"\n\nimport wizard\nimport pooler\nfrom tools.translate import _\n\n\nFORM = \"\"\"<?xml version=\"1.0\"?>\n<form string=\"Set Stock to Zero\">\n    <separator colspan=\"4\" string=\"Set Stocks to Zero\" />\n    <field name=\"location_id\"/>\n    <newline/>\n    <label colspan=\"4\" string=\"Do you want to set stocks to zero ?\"/>\n</form>\n\"\"\"\nFIELDS = {\n    'location_id' : {\n        'string':'Location',\n        'type':'many2one',\n        'relation':'stock.location',\n        'required':True\n        }\n}\n\n\ndef do_merge(self, cr, uid, data, context):\n    \"\"\"set to zero the prodlots in a location\"\"\"\n    #gets the objects\n    invent_line_obj = pooler.get_pool(cr.dbname).get('stock.inventory.line')\n    prod_obj =  pooler.get_pool(cr.dbname).get('stock.production.lot')\n\n    if len(data['ids']) != 1:\n        raise wizard.except_wizard(_(\"Warning\"),\n                                   _(\"Please select one and only one inventory !\"))\n    #gets the location from a textbox in the form\n    loc = str(data['form']['location_id'])\n    \n    #deletes any inventory line already exists\n    cr.execute('delete from stock_inventory_line where inventory_id=%s', (data['ids'][0],))\n    #gets the products in this location\n    cr.execute('select distinct product_id from stock_move where (location_dest_id='+loc+') or (location_id='+loc+')')\n    locations_stock = cr.fetchall()\n    for stock in locations_stock:\n        #gets the valid prodlots for the product in location\n        cr.execute('select prodlot_id,name as id from stock_report_prodlots where location_id = %s and product_id = %s and name > 0', (loc, stock[0]))\n        prodlots = cr.fetchall()\n        #creates the inventory lines\n        for prodlot_id in prodlots:\n            obj_prodlot_id = prod_obj.browse(cr, uid, prodlot_id[0])\n            if obj_prodlot_id.product_id.type == 'product':\n                invent_line_obj.create(cr, uid, {\n                                        'inventory_id': data['ids'][0],\n                                        'location_id': loc,\n                                        'product_id': stock[0],\n                                        'product_uom': obj_prodlot_id.uom_id.id,\n                                        'product_qty': 0.0,\n                                        'prodlot_id': obj_prodlot_id.id\n                                    })\n    return {}\n\n\nclass merge_inventory(wizard.interface):\n    \"\"\"wizard that replace completely the wizard on stock with same name,\n    beacause adds the functionally that set zero prodlotas not products\"\"\"\n    states = {\n        'init' : {\n            'actions' : [],\n            'result' : {'type' : 'form',\n                    'arch' : FORM,\n                    'fields' : FIELDS,\n                    'state' : [('end', 'Cancel'),\n                               ('merge', 'Set to Zero') ]}\n        },\n        'merge' : {\n            'actions' : [],\n            'result' : {'type' : 'action',\n                        'action': do_merge,\n                        'state' : 'end'}\n        },\n    }\nmerge_inventory(\"stock.liquid.traceability.merge.stock.zero\")\n\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:\n\n","sub_path":"addons-community/pxgo_full_stock_traceability/wizard/inventory_merge_zero.py","file_name":"inventory_merge_zero.py","file_ext":"py","file_size_in_byte":4274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"491324146","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nAuthor: LIURLN\n\"\"\"\n\nimport json\n\n\nclass CardInformation:\n    thaiFullName = ''\n    engFullName = ''\n    idNumber = ''\n    birthOfDate = ''\n    gender = ''\n    address = ''\n\n    def __init__(self):\n        pass\n\n    def setGender(self, genderData):\n        if(genderData == '1'):\n            self.gender = 'ชาย'\n        elif(genderData == '2'):\n            self.gender = 'หญิง'\n        else:\n            print('Wrong gender data!')\n\n    def clearInformation(self):\n        self.thaiFullName = ''\n        self.engFullName = ''\n        self.idNumber = ''\n        self.birthOfDate = ''\n        self.gender = ''\n        self.address = ''\n\n    def toJson(self):\n        jsonData = {}\n        jsonData['thaiFullName'] = self.thaiFullName\n        jsonData['engFullName'] = self.engFullName\n        jsonData['idNumber'] = self.idNumber\n        jsonData['birthOfDate'] = self.birthOfDate\n        jsonData['gender'] = self.gender\n        jsonData['address'] = self.address\n        return jsonData\n\n\nclass THIDCommand:\n    # THIDCommand is instruction that have to send thought serail port for get\n    # information in the card.\n    getImage = '\\x31\\x0D'                   # portrait image\n    getAddress = '\\x32\\x0D'                 # house number\n    getThaiFullName = '\\x33\\x0D'            # Thai full name include prefix\n    getCardManu = '\\x34\\x0D'                # where card was create\n    getCardCode = '\\x35\\x0D'                # code under the card\n    getCardExpiredDate = '\\x36\\x0D'         # card expired date\n    getEngFullName = '\\x37\\x0D'             # English full name\n    getBirthOfDate = '\\x38\\x0D'             # birth of date in B.E.\n    getGender = '\\x39\\x0D'                  # gender 1 for male 2 for female\n    getIDNumber = '\\x30\\x0D'                # Thai indentification number\n\n\nclass THID:\n    import serial\n    # Open serial port this time THID using ttyUSB1\n    # Config serial port with 115200 buad-rate 8N1\n    # Note! beware wrong serial port\n    thidSerial = serial.Serial('/dev/ttyUSB1', 115200,\n                               bytesize=8, parity='N', stopbits=1)\n    thidIsOpen = False\n    thidIsReadable = False\n    thidIsInserted = False\n    userInformation = CardInformation()\n\n    def __init__(self):\n        # Checking is serial port is open\n        if(self.thidSerial.isOpen()):\n            print('THID is ready!!!')\n            self.thidIsOpen = True\n\n        else:\n            try:\n                # Try to open serial port\n                self.thidSerial.open()\n                self.thidIsOpen = True\n            except Exception as e:\n                print('something wrong : ' + str(e))\n                self.thidIsOpen = False\n\n    def readCard(self):\n        if(self.thidIsReadable is False):\n            print('Insert the card...')\n            if(self.listening() == 'INSERTED'):\n                if(self.listening() == 'READY'):\n                    print('Start read information from card')\n                    self.sendCommand(THIDCommand.getIDNumber)\n                    self.userInformation.idNumber = self.listening()\n                    self.sendCommand(THIDCommand.getEngFullName)\n                    self.userInformation.engFullName = self.listening()\n                    self.sendCommand(THIDCommand.getThaiFullName)\n                    self.userInformation.thaiFullName = self.listening()\n                    self.sendCommand(THIDCommand.getGender)\n                    self.userInformation.setGender(self.listening())\n                    self.sendCommand(THIDCommand.getBirthOfDate)\n                    self.userInformation.birthOfDate = self.listening()\n                    self.sendCommand(THIDCommand.getAddress)\n                    self.userInformation.address = self.listening()\n                    # Call read card again for waiting card removed.\n                    self.readCard()\n        else:\n            print('Remove the card')\n            while(self.listening() != 'REMOVED'):\n                pass\n\n    def sendCommand(self, dataType):\n        if(self.thidIsReadable):\n            print('Sending command...')\n            # Write command to serail port\n            self.thidSerial.write(dataType)\n        else:\n            print('Please check the card!')\n            print('--------------------------------------------------')\n            return '-'\n\n    def listening(self):\n        # listening THID response data\n        respData = []\n        print('Waiting for response...')\n        tmpByte = self.thidSerial.read()\n        while(tmpByte != '\\r'):\n            respData.append(tmpByte)\n            tmpByte = self.thidSerial.read()\n\n        # checking response type\n        respString = ''.join(respData)\n        respLenght = len(respData)\n        if(respLenght == 13 and self.thidIsReadable is False):\n            print('Card inserted.')\n            print('--------------------------------------------------')\n            self.thidIsInserted = True\n            return 'INSERTED'\n        elif(respString == 'READY'):\n            print('Card is ready to use.')\n            self.thidIsReadable = True\n            print('--------------------------------------------------')\n            return 'READY'\n        elif(respString == 'REMOVE'):\n            print('Card removed.')\n            self.thidIsReadable = False\n            self.thidIsInserted = False\n            self.userInformation.clearInformation()\n            print('--------------------------------------------------')\n            return 'REMOVED'\n        elif(respString == 'OK'):\n            print('Card is valid.')\n            self.thidIsReadable = True\n            print('--------------------------------------------------')\n            return 'READY'\n        elif(respString == 'ERROR'):\n            print('Something wrong : invalid instruction!')\n            print('--------------------------------------------------')\n            return 'ERROR'\n        elif(respString == 'CARD'):\n            print('Please insert the card!')\n            self.thidIsReadable = False\n            print('--------------------------------------------------')\n            return 'EMPTY'\n        elif(respString == 'BUSY'):\n            print('THID is busy!')\n            print('--------------------------------------------------')\n            return 'BUSY'\n        elif(respString == 'NO'):\n            print('Check dip switch some has turn on!')\n            print('--------------------------------------------------')\n            return 'DIP'\n        else:\n            print('**This is information from card')\n            print(respString.decode('iso8859_11'))\n            print('--------------------------------------------------')\n            return respString.decode('iso8859_11')\n\n    def isOpen(self):\n        return self.thidIsOpen\n\n    def isReadalbe(self):\n        return self.thidIsReadable\n\n    def isInserted(self):\n        return self.thidIsInserted\n\n    def getInformation(self):\n        return self.userInformation.toJson()\n\n\ndef main():\n    myThid = THID()\n    if(myThid.isOpen()):\n        myThid.readCard()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"src/thid.py","file_name":"thid.py","file_ext":"py","file_size_in_byte":7129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"124012018","text":"from datetime import datetime, timedelta\nimport os\nimport jwt\nimport json\nimport base64\nimport requests\nfrom urllib.parse import parse_qs, parse_qsl, urlencode\nfrom functools import wraps\nfrom flask import Flask, request, url_for, redirect, render_template, jsonify, g\nfrom flask_sqlalchemy import SQLAlchemy\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom requests_oauthlib import OAuth1\nfrom jwt import DecodeError, ExpiredSignature\nfrom flask_restful import Resource, Api\nfrom marshmallow import Schema, fields\n\n# request module python angular sends request to python, python sends request to API.\n\napp = Flask(__name__, static_folder='../client', template_folder='../client/templates',)\napp.config.from_object('config')\napp.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('DATABASE_URL', 'postgres://localhost/mixology')\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napi = Api(app)\ndb = SQLAlchemy(app)\n\ndef create_token(user):\n\tpayload = {\n\t\t'sub': user.id,\n\t\t'iat': datetime.utcnow(),\n\t\t'exp': datetime.utcnow() + timedelta(days=14)\n\t}\n\ttoken = jwt.encode(payload, app.config['TOKEN_SECRET'])\n\treturn token.decode('unicode_escape')\n\ndef parse_token(req):\n\ttoken = req.headers.get('Authorization').split()[1]\n\treturn jwt.decode(token, app.config['TOKEN_SECRET'])\n\ndef login_required(f):\n\t@wraps(f)\n\tdef decorated_function(*args, **kwargs):\n\t\tif not request.headers.get('Authorization'):\n\t\t\tresponse = jsonify(message='Missing authorization header; unauthorized; status code 401')\n\t\t\tresponse.status_code = 401\n\t\t\treturn response\n\t\ttry:\n\t\t\tpayload = parse_token(request)\n\t\texcept DecodeError:\n\t\t\tresponse = jsonify(message='Token is invalid')\n\t\t\tresponse.status_code = 401\n\t\t\treturn response\n\t\texcept ExpiredSignature:\n\t\t\tresponse = jsonify(message='Token has expired')\n\t\t\tresponse.status_code = 401\n\t\t\treturn response\n\t\tg.user_id = payload['sub']\n\t\treturn f(*args, **kwargs)\n\treturn decorated_function\n\n# Root Route to start the application\n@app.route('/')\ndef root():\n\treturn render_template('layout.html');\n\n# Database Schema - USERS\nclass User(db.Model):\n\t__tablename__ = \"users\"\n\n\tid = db.Column(db.Integer, primary_key=True)\n\temail = db.Column(db.String(120), unique=True)\n\tpassword = db.Column(db.String(120))\n\tdisplay_name = db.Column(db.String(120))\n\tfacebook = db.Column(db.String(120))\n\tdrinks = db.relationship('Drink', backref='user', lazy='dynamic')\n\n\tdef __init__(self, email=None, password=None, display_name=None, facebook=None):\n\t\tif email:\n\t\t\tself.email = email.lower()\n\t\tif password:\n\t\t\tself.set_password(password)\n\t\tif display_name:\n\t\t\tself.display_name = display_name\n\t\tif facebook:\n\t\t\tself.facebook = facebook\n\n\tdef set_password(self, password):\n\t\tself.password = generate_password_hash(password)\n\tdef check_password(self, password):\n\t\treturn check_password_hash(self.password, password)\n\tdef to_json(self):\n\t\treturn dict(id=self.id, email=self.email, displayName=self.display_name,\n\t\t\t\t\tfacebook=self.facebook)\n\n# Database Schema - DRINKS\nclass Drink(db.Model):\n\t__tablename__ = \"drinks\"\n\n\tid = db.Column(db.Integer, primary_key=True)\n\tname = db.Column(db.Text())\n\timage_url = db.Column(db.Text())\n\tingredients = db.Column(db.Text())\n\tinstructions = db.Column(db.Text())\n\tuser_id = db.Column(db.Integer, db.ForeignKey('users.id'))\n\n\tdef __init__(self, name, image_url, ingredients, instructions, user_id):\n\t\tself.name = name\n\t\tself.image_url = image_url\n\t\tself.ingredients = ingredients\n\t\tself.instructions = instructions\n\t\tself.user_id = user_id\n\nclass DrinkSchema(Schema):\n\tid = fields.Int()\n\tname = fields.Str()\n\timage_url = fields.Str()\n\tingredients = fields.Str()\n\tinstructions = fields.Str()\n\tuser_id = fields.Int()\n\nschema = DrinkSchema()\n\nclass DrinkListApi(Resource):\n\tdef get(self):\n\t\tdrinks = Drink.query.all()\n\t\treturn schema.dump(drinks, many=True)\n\t@login_required\n\tdef post(self):\n\t\tnew_drink = Drink(**request.get_json())\n\t\tdb.session.add(new_drink)\n\t\tdb.session.commit()\n\t\treturn schema.dump(new_drink)\n\n\nclass DrinkApi(Resource):\n\tdef get(self, id):\n\t\treturn schema.dump(Drink.query.get_or_404(id))\n\t@login_required\n\tdef put(self, id):\n\t\tdrink_to_update = Drink.query.get_or_404(id)\n\t\tdrink = request.get_json();\n\t\tcounter = 1\n\t\tingredients = \"\"\n\t\tdel drink['editing']\n\t\tdel drink['id']\n\t\twhile counter < 10:\n\t\t\tingredient = \"ingredient\" + str(counter)\n\t\t\tif drink.get(ingredient):\n\t\t\t\tingredients += drink[ingredient].replace(\"!\", \"\")\n\t\t\t\tingredients += \"!\"\n\t\t\t\tdel drink[ingredient]\n\t\t\tcounter += 1\n\t\tdrink['ingredients'] = ingredients[0:-1]\n\t\tfor key in drink:\n\t\t\tsetattr(drink_to_update, key, drink[key])\n\t\tdb.session.add(drink_to_update)\n\t\tdb.session.commit()\n\t\treturn schema.dump(drink_to_update)\n\t@login_required\n\tdef delete(self, id):\n\t\tdelete_drink = Drink.query.get_or_404(id)\n\t\tdb.session.delete(delete_drink)\n\t\tdb.session.commit()\n\t\treturn schema.dump(delete_drink)\n\ndb.create_all()\n\n# Login or Sign In Route with Facebook \n\n@app.route('/auth/facebook', methods=['POST'])\ndef facebook():\n\taccess_token_url = 'https://graph.facebook.com/v2.3/oauth/access_token'\n\tgraph_api_url = 'https://graph.facebook.com/v2.3/me'\n\n\tparams = {\n\t\t'client_id': request.json['clientId'],\n\t\t'redirect_uri': request.json['redirectUri'],\n\t\t'client_secret': app.config['FACEBOOK_SECRET'],\n\t\t'code': request.json['code']\n\t}\n\n\t# Step 1. Exchange authorization code for access token.\n\tr = requests.get(access_token_url, params=params)\n\taccess_token = r.json()\n\n\t# Step 2. Retrieve information about the current user.\n\tr = requests.get(graph_api_url, params=access_token)\n\tprofile = json.loads(r.text)\n\n\t# Step 4. Create a new account or return an existing one.\n\tuser = User.query.filter_by(facebook=profile['id']).first()\n\tif user:\n\t\ttoken = create_token(user)\n\t\treturn jsonify(token=token)\n\n\tu = User(facebook=profile['id'], display_name=profile['name'])\n\tdb.session.add(u)\n\tdb.session.commit()\n\ttoken = create_token(u)\n\treturn jsonify(token=token)\n\n# API Routes\n\n@app.route('/api/addb/drinks/<string:keyword>')\ndef get_addb_drinks_by_name(keyword):\n\tr = requests.get('http://addb.absolutdrinks.com/quickSearch/drinks/{}/?apiKey={}'.format(keyword, os.environ.get('API_KEY')))\n\treturn jsonify(r.json())\n\n@app.route('/api/addb/tastes/<string:keyword>')\ndef get_addb_drinks_by_taste(keyword):\n\tr = requests.get('http://addb.absolutdrinks.com/drinks/tasting/{}/?apiKey={}'.format(keyword, os.environ.get('API_KEY')))\n\treturn jsonify(r.json())\n\n@app.route('/api/addb/occasions/<string:keyword>')\ndef get_addb_drinks_by_occasion(keyword):\n\tr = requests.get('http://addb.absolutdrinks.com/drinks/for/{}/?apiKey={}'.format(keyword, os.environ.get('API_KEY')))\n\treturn jsonify(r.json())\n\n@app.route('/api/addb/ingredients/<string:keyword>')\ndef get_addb_drinks_by_ingredients(keyword):\n\tr = requests.get('http://addb.absolutdrinks.com/drinks/with/{}/?apiKey={}'.format(keyword, os.environ.get('API_KEY')))\n\treturn jsonify(r.json())\n\n@app.route('/api/addb/custom/<path:keyword>')\ndef get_addb_drinks_by_custom(keyword):\n\tkeyword = \"/\" + keyword\n\tr = requests.get('http://addb.absolutdrinks.com/drinks{}/?apiKey={}'.format(keyword, os.environ.get('API_KEY')))\n\treturn jsonify(r.json())\n\n@app.route('/api/me')\n@login_required\ndef me():\n\tuser = User.query.filter_by(id=g.user_id).first()\n\treturn jsonify(user.to_json())\n\n@app.route('/api/me/drinks')\n@login_required\ndef me_drink():\n\tdrinks = Drink.query.filter_by(user_id=g.user_id).all()\n\tdata = schema.dump(drinks, many=True).data\n\treturn jsonify(data);\n\n@app.route('/api/ctdb/alcohol')\ndef getAlcoholicDrinks():\n\tr = requests.get(\"http://www.thecocktaildb.com/api/json/v1/1/filter.php?a=Alcoholic\")\n\tdrinks = r.json()\n\treturn jsonify(drinks)\n\n@app.route('/api/ctdb/nonalcohol')\ndef getNonAlcoholicDrinks():\n\tr = requests.get(\"http://www.thecocktaildb.com/api/json/v1/1/filter.php?a=Non_Alcoholic\")\n\tdrinks = r.json()['drinks']\n\treturn jsonify(drinks)\n\n@app.route('/api/ctdb/randomdrink')\ndef randomDrink():\n\tr = requests.get(\"http://www.thecocktaildb.com/api/json/v1/1/random.php\")\n\tdrink = r.json()\n\treturn jsonify(drink)\n\n@app.route('/api/ctdb/drink/<int:id>/')\ndef singleDrink(id):\n\tr = requests.get(\"http://www.thecocktaildb.com/api/json/v1/1/lookup.php?i={}\".format(id))\n\tdrink = r.json()\n\treturn jsonify(drink)\n\napi.add_resource(DrinkListApi, '/api/drinks/')\napi.add_resource(DrinkApi, '/api/drinks/<int:id>/')\n\n@app.after_request\ndef after_request(response):\n\tresponse.headers.add('Access-Control-Allow-Origin', '*')\n\tresponse.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')\n\tresponse.headers.add('Access-Control-Allow-Methods', 'GET,POST,PUT,DELETE,OPTIONS')\n\treturn response\n\nif __name__ == '__main__':\n\tport = int(os.environ.get(\"PORT\", 8000))\n\tapp.run(host='0.0.0.0', port=port)","sub_path":"server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":8730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"433316449","text":"from pylab import *\nimport matplotlib.pyplot as plt\nimport sys\nimport csv\na=input(\"Enter the width of slice \")\nb=input(\"Enter the center of range \")\nfor j in range(1,len(sys.argv)):\n\n\tname=\"wsnapshot_\"+sys.argv[j]+\".csv\"\n\tprint(name);\n\tf=open(name)\n\tr=csv.reader(f)\n\td=list(r)\n\ti=0\n\tprint(\"Plotting \"+sys.argv[j]+\"...\")\n\n\tfor i in range(1,262145):\n\t\tif(float(d[i][2])>b-(a/5) and float(d[i][2])<b+(a/5)):\n\t\t\tplt.plot([float(d[i][0])],[float(d[i][1])],markerfacecolor='blue', marker='o', markersize=1)\n\n\tplt.title(\"X-Y plot\")\n\tplt.grid(True)\n\tsavefig('/home/vee8497/Downloads/Gadget-2.0.7/Project/Plots4/X-Y Plot_'+sys.argv[j]+'.png',bbox_inches='tight')\n\tprint(\"Done.\")\n\tf.close()\n\n","sub_path":"VisualXY.py","file_name":"VisualXY.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"142289091","text":"# -------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n# --------------------------------------------------------------------------\n\nfrom .models import (\n    CommunicationIdentifierModel,\n    CommunicationUserIdentifier,\n    PhoneNumberIdentifier,\n    MicrosoftTeamsUserIdentifier,\n    UnknownIdentifier,\n    CommunicationUserIdentifierModel,\n    PhoneNumberIdentifierModel,\n    MicrosoftTeamsUserIdentifierModel\n)\n\nclass CommunicationUserIdentifierSerializer(object):\n\n    @classmethod\n    def serialize(cls, communicationIdentifier):\n        \"\"\" Serialize the Communication identifier into CommunicationIdentifierModel\n\n        :param identifier: Identifier object\n        :type identifier: Union[CommunicationUserIdentifier,\n           PhoneNumberIdentifier, MicrosoftTeamsUserIdentifier, UnknownIdentifier]\n        :return: CommunicationIdentifierModel\n        :rtype: ~azure.communication.chat.CommunicationIdentifierModel\n        :raises Union[TypeError, ValueError]\n        \"\"\"\n        if isinstance(communicationIdentifier, CommunicationUserIdentifier):\n            return CommunicationIdentifierModel(\n                communication_user=CommunicationUserIdentifierModel(id=communicationIdentifier.identifier)\n            )\n        if isinstance(communicationIdentifier, PhoneNumberIdentifier):\n            return CommunicationIdentifierModel(\n                raw_id=communicationIdentifier.raw_id,\n                phone_number=PhoneNumberIdentifierModel(value=communicationIdentifier.phone_number)\n            )\n        if isinstance(communicationIdentifier, MicrosoftTeamsUserIdentifier):\n            return CommunicationIdentifierModel(\n                raw_id=communicationIdentifier.raw_id,\n                microsoft_teams_user=MicrosoftTeamsUserIdentifierModel(user_id=communicationIdentifier.user_id,\n                is_anonymous=communicationIdentifier.is_anonymous,\n                cloud=communicationIdentifier.cloud)\n            )\n\n        if isinstance(communicationIdentifier, UnknownIdentifier):\n            return CommunicationIdentifierModel(\n                raw_id=communicationIdentifier.identifier\n            )\n\n        raise TypeError(\"Unsupported identifier type \" + communicationIdentifier.__class__.__name__)\n\n    @classmethod\n    def assertMaximumOneNestedModel(cls, identifierModel):\n        presentPropertiesCount = 0\n        if identifierModel.communication_user is not None:\n            presentPropertiesCount += 1\n        if identifierModel.phone_number is not None:\n            presentPropertiesCount += 1\n        if identifierModel.microsoft_teams_user is not None:\n            presentPropertiesCount += 1\n\n        if presentPropertiesCount > 1:\n            raise ValueError(\"Only one of the properties in identifier model should be present.\")\n\n    @classmethod\n    def deserialize(cls, identifierModel):\n        \"\"\"\n        Deserialize the CommunicationIdentifierModel into Communication Identifier\n\n        :param identifierModel: CommunicationIdentifierModel\n        :type identifierModel: CommunicationIdentifierModel\n        :return: Union[CommunicationUserIdentifier, CommunicationPhoneNumberIdentifier]\n        :rtype: Union[CommunicationUserIdentifier, CommunicationPhoneNumberIdentifier]\n        :rasies: ValueError\n        \"\"\"\n\n        raw_id = identifierModel.raw_id\n        if not raw_id:\n            raise ValueError(\"Identifier must have a valid id\")\n\n        CommunicationUserIdentifierSerializer.assertMaximumOneNestedModel(identifierModel)\n\n        if identifierModel.communication_user is not None:\n            return CommunicationUserIdentifier(raw_id)\n        if identifierModel.phone_number is not None:\n            if not identifierModel.phone_number:\n                raise ValueError(\"PhoneNumberIdentifier must have a valid attribute - phone_number\")\n            return PhoneNumberIdentifier(identifierModel.phone_number.value, raw_id=raw_id)\n        if identifierModel.microsoft_teams_user is not None:\n            if identifierModel.microsoft_teams_user.is_anonymous not in [True, False]:\n                raise ValueError(\"MicrosoftTeamsUser must have a valid attribute - is_anonymous\")\n            if not identifierModel.microsoft_teams_user.user_id:\n                raise ValueError(\"MicrosoftTeamsUser must have a valid attribute - user_id\")\n            if not identifierModel.microsoft_teams_user.cloud:\n                raise ValueError(\"MicrosoftTeamsUser must have a valid attribute - cloud\")\n            return MicrosoftTeamsUserIdentifier(\n                raw_id=raw_id,\n                user_id=identifierModel.microsoft_teams_user.user_id,\n                is_anonymous=identifierModel.microsoft_teams_user.is_anonymous,\n                cloud=identifierModel.microsoft_teams_user.cloud\n            )\n\n        return UnknownIdentifier(raw_id)\n","sub_path":"sdk/communication/azure-communication-chat/azure/communication/chat/_shared/communication_identifier_serializer.py","file_name":"communication_identifier_serializer.py","file_ext":"py","file_size_in_byte":4994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"501667693","text":"from nonebot import on_command\nfrom nonebot import CommandSession\nfrom nonebot import on_natural_language\nfrom nonebot import NLPSession\nfrom nonebot import IntentCommand\n\nimport awesome.plugins.update.data_source\n\n\n@on_command('update', aliases=['update', ])\nasync def update(session: CommandSession):\n    problem_name = session.get('problem_name', prompt='没有找到你所描述的问题')\n    method = session.get('method', prompt='没有找到你所描述的问题')\n    await awesome.plugins.update.data_source.update_method(problem_name, method)\n    await session.send('添加成功')\n\n\n@update.args_parser\nasync def _(session: CommandSession):\n    awaited_keyword = ['的解决方法是', '的方法是']\n    for keywords in awaited_keyword:\n        index = session.current_arg_text.find(keywords)\n        if index != -1:\n            session.state['problem_name'] = session.current_arg_text[0:index]\n            session.state['method'] = session.current_arg_text[index + len(keywords):]\n\n\n# @on_natural_language(keywords=['的解决方法是', '的方法是'])\n# async def _(session: NLPSession):\n#     probability = 0.0\n#     awaited_keyword = await const.UPDATE_KEYWORD\n#     one = 100.0 / len(awaited_keyword)\n#     for keywords in awaited_keyword:\n#         if session.msg_text.find(keywords) != -1:\n#             probability += one\n#     return IntentCommand(probability, 'update', current_arg=session.msg_text)\n","sub_path":"awesome/plugins/update/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"609147937","text":"from mesh import Mesh\nfrom scipy.sparse.linalg import lsmr\nfrom scipy.sparse import lil_matrix\nm = Mesh(\"input-face.obj\") # load mesh\n\nA = lil_matrix((m.nverts+m.ncorners, m.nverts))\nfor v in range(m.nverts): # per-vertex attachment to the original geometry\n    A[v,v] = 10 if m.on_border(v) else .29  # hard on the boundary, light for the interior\nfor c in range(m.ncorners): # per-half-edge discretization of the derivative\n    A[m.nverts+c, m.org(c)] = -1\n    A[m.nverts+c, m.dst(c)] =  1\nA = A.tocsr() # sparse row matrix for fast matrix-vector multiplication\n\nfor dim in range(3): # the problem is separable in x,y,z; the matrix A is the same, the right hand side changes\n    b = [m.V[v][dim]*10 if m.on_border(v) else m.V[v][dim]*.29 for v in range(m.nverts)] + \\\n        [2.5*(m.V[m.dst(c)][dim]-m.V[m.org(c)][dim]) for c in range(m.ncorners)]\n    x = lsmr(A, b)[0] # call the least squares solver\n    for v in range(m.nverts): # apply the computed distortion\n        m.V[v][dim] = x[v]\nprint(m) # output the deformed mesh\n\n","sub_path":"presentation/listings/caricature3d.py","file_name":"caricature3d.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"414051544","text":"'''Module for testing functions in utils_common.'''\n\nimport pytest\nimport tensorflow as tf\nimport maddpg.common.utils_common as utils\nfrom gym.spaces import Box\n\n\ndef test_map_zip_all_equal():\n    '''Test map_zip function.'''\n    mappings = [{i: i for i in range(10)} for j in range(10)]\n    for ke_y, values in utils.zip_map(*mappings):\n        assert len(set(values)) == 1\n\n\ndef test_map_zip_all_ascending():\n    '''Test map_zip function.'''\n    mappings = [{i: i for i in range(10+j)} for j in range(10)]\n    for _, values in utils.zip_map(*mappings):\n        assert len(set(values)) == 1\n\n\ndef test_map_zip_all_descending():\n    '''Test map_zip function.'''\n    mappings = [{i: i for i in range(10-j)} for j in range(10)]\n    with pytest.raises(KeyError):\n        for _, values in utils.zip_map(*mappings):\n            assert len(set(values)) == 1\n\n\ndef test_convert_spaces_to_placeholders():\n    '''Test convert_spaces_to_placeholders function.'''\n    spaces = {str(i): Box(0, 1, shape=(4,)) for i in range(10)}\n    placeholders = utils.convert_spaces_to_placeholders(spaces, False)\n    assert spaces.keys() == placeholders.keys()\n    for _, (space, placeholder) in utils.zip_map(spaces, placeholders):\n        assert list(space.shape) == placeholder.shape.as_list()\n\n\nclass SessionStub:\n    def __init__(self):\n        ...\n\n    def run(self, ops, feed_dict=None):\n        return ops\n\n\ndef test_TfFunction():\n    '''Test TfFunction class.'''\n    graph = tf.Graph()\n    with graph.as_default():\n        with tf.variable_scope(\"foo\", reuse=tf.AUTO_REUSE):\n            values = {i: tf.get_variable(name=str(i), initializer=0.)\n                      for i in range(10)}\n            updates = []\n            for key, value in values.items():\n                updates.append(value.assign(value + key))\n        inputs = {i: tf.placeholder(tf.float32, shape=()) for i in range(10)}\n        outputs = {i: ph*i for i, ph in inputs.items()}\n        init_op = tf.global_variables_initializer()\n    session = tf.Session(graph=graph)\n    session.run(init_op)\n    multiply_by_two = utils.TfFunction(session, inputs=inputs, outputs=outputs)\n    update_vars = utils.TfFunction(session, updates=updates)\n    get_vars = utils.TfFunction(session, outputs=values)\n    data = {i: i for i in range(10)}\n    results = multiply_by_two(data)\n    for key, value in results.items():\n        assert key**2 == value\n    for iteration in range(1, 10):\n        update_vars()\n        for key, value in get_vars().items():\n            assert iteration*key == value\n","sub_path":"tests/common/test_utils_common.py","file_name":"test_utils_common.py","file_ext":"py","file_size_in_byte":2535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"56799018","text":"import argparse \nimport tensorflow as tf\n\n\ndef load_graph(frozen_graph_filename):\n    # We load the protobuf file from the disk and parse it to retrieve the \n    # unserialized graph_def\n    with tf.gfile.GFile(frozen_graph_filename, \"rb\") as f:\n        graph_def = tf.GraphDef()\n        graph_def.ParseFromString(f.read())\n\n    # Then, we can use again a convenient built-in function to import a graph_def into the \n    # current default Graph\n    with tf.Graph().as_default() as graph:\n        tf.import_graph_def(\n            graph_def, \n            input_map=None, \n            return_elements=None, \n            name=\"prefix\", \n            op_dict=None, \n            producer_op_list=None\n        )\n    return graph\n\n\nif __name__ == '__main__':\n    # Let's allow the user to pass the filename as an argument\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--frozen_model_filename\", default=\"results/frozen_model.pb\", type=str, help=\"Frozen model file to import\")\n    args = parser.parse_args()\n\n    # We use our \"load_graph\" function\n    graph = load_graph(args.frozen_model_filename)\n\n    # We can verify that we can access the list of operations in the graph\n#    for op in graph.get_operations():\n#        print(op.name)\n        \n    # We access the input and output nodes \n    x = graph.get_tensor_by_name('prefix/Model/v1:0')\n    y = graph.get_tensor_by_name('prefix/Model/v2:0')\n    a= graph.get_tensor_by_name('prefix/Model/add:0')\n\n\n\n    # We launch a Session\n    with tf.Session(graph=graph) as sess:\n        \n        print(sess.run(a, feed_dict={x: 1, y:6}))\n        print(\"Loading is successful!\")","sub_path":"load_simple_model.py","file_name":"load_simple_model.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"591139966","text":"import bottle, grooveshark, tempfile, urllib2, os, thread, time, Queue\n\nimport pygtk\npygtk.require('2.0')\n\nimport gobject\ngobject.threads_init()\n\nimport pygst\npygst.require('0.10')\n\nimport gst\n\n\nclass Server:\t\n\tdef __init__(self):\n\t\tself.queue = []\n\t\tself.position = -1\n\t\tself.player = gst.parse_launch('appsrc name=source ! decodebin2 ! autoaudiosink')\n\t\tself.buffer = Queue.Queue()\n\t\t\n\t\tsrc = self.player.get_by_name('source')\n\t\tself.player.get_bus().connect('message', self.onmessage)\n\t\tsrc.connect('need-data', self.needdata)\n\t\tself.player.set_state(gst.STATE_PLAYING)\n\t\t\n\tdef needdata(self, src, length):\n\t\tbytes = self.buffer.get()\n\t\tsrc.emit('push-buffer', gst.Buffer(bytes))\n\t\n\tdef _play(self, songId):\t\t\n\t\tip, key = grooveshark.getSong(songId)\n\t\tt = tempfile.mktemp(\".mp3\")\t\t\n\t\t\n\t\tresp = urllib2.urlopen(\"http://\" + ip + \"/stream.php\", \"streamKey=\" + key)\n\t\tf = open(t, \"wb\")\n\t\t\n\t\tchunk = resp.read(4096)\n\t\twhile chunk:\n\t\t\tchunk.write(n)\n\t\t\tself.buffer.put(n)\n\t\t\tchunk = resp.read(4096)\n\t\t\n\t\tf.close()\n\t\t\n\t\t# Tag song\n\t\t# Delete it\n\t\n\t@bottle.route('/play')\n\tdef play(self):\n\t\tself.player.play()\n\n\t@bottle.route('/pause')\n\tdef pause(self):\n\t\tself.player.pause()\n\n\t@bottle.route('/set_volume/:volume')\n\tdef set_volume(self, volume):\n\t\tself.player.set_volume(float(volume))\n\n\t@bottle.route('/stop')\n\tdef stop(self):\n\t\tself.player.stop()\n\n\t@bottle.route('/queue/next')\n\tdef queue_next(self):\n\t\tself.position += 1\n\t\tself._play(self.queue[self.position])\n\n\t@bottle.route('/queue/prev')\n\tdef queue_prev(self):\n\t\tself.position -= 1\n\t\tself._play(self.queue[self.position])\n\t\t\n\t@bottle.route('/queue/add/:song')\n\tdef queue_add(self, song):\n\t\tself.queue.append(song)\n\n\t@bottle.route('/queue/clear')\n\tdef queue_clear(self):\n\t\tself.queue = []\n\t\tself.position = 0\n\t\tself.stop()\n\t\n\t# Listen for http connections\n\tdef listen(self):\n\t\tbottle.run(host='localhost', port=9999)\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"397768568","text":"import torch\nimport numpy as np\nimport math\nimport argparse\nfrom augerino import datasets, models, losses\nfrom augerino.models.e2_steerable import C8SteerableCNN, SmallE2\nfrom torch.utils.data import DataLoader\nfrom oil.utils.mytqdm import tqdm\n\ndef main(args):\n    model = SmallE2(channel_in=1, n_classes=10, rot_n=10)\n\n    dataset = datasets.RotMNIST(\"~/datasets/\", train=True)\n    trainloader = DataLoader(dataset, batch_size=args.batch_size)\n\n    optimizer = torch.optim.Adam(model.parameters(),lr=args.lr,\n                                 weight_decay=args.wd)\n    use_cuda = torch.cuda.is_available()\n    if use_cuda:\n        model = model.cuda()\n        print(\"Using Cuda\")\n\n    ## save init model ##\n    fname = \"/e2_init.pt\"\n    torch.save(model.state_dict(), args.dir + fname)\n\n    criterion = torch.nn.CrossEntropyLoss()\n\n    for epoch in tqdm(range(args.epochs)):  # loop over the dataset multiple times\n\n        epoch_loss = 0\n        batches = 0\n        for i, data in enumerate(trainloader, 0):\n            # get the inputs; data is a list of [inputs, labels]\n            inputs, labels = data\n\n            if use_cuda:\n                inputs, labels = inputs.cuda(), labels.cuda()\n\n            # zero the parameter gradients\n            optimizer.zero_grad()\n\n            # forward + backward + optimize\n            # print(inputs.shape)\n            outputs = model(inputs)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n            epoch_loss += loss.detach().item()\n            batches += 1\n            print(loss.item())\n        print(\"Epoch = \", epoch)\n        print(\"\\n\")\n\n        \n    fname = \"/e2_epoch\" + str(epoch+1) + \".pt\"\n    torch.save(model.state_dict(), args.dir + fname)\n\n\n\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser(description=\"olivetti augerino\")\n\n    parser.add_argument(\n        \"--dir\",\n        type=str,\n        default='./saved-outputs',\n        help=\"training directory (default: None)\",\n    )\n    parser.add_argument(\n        \"--batch_size\",\n        type=int,\n        default=50,\n        metavar=\"N\",\n        help=\"input batch size (default: 50)\",\n    )\n    parser.add_argument(\n        \"--lr\",\n        type=float,\n        default=0.001,\n        metavar=\"LR\",\n        help=\"initial learning rate (default: 0.1)\",\n    )\n    parser.add_argument(\n        \"--wd\",\n        type=float,\n        default=1e-4,\n        metavar=\"weight_decay\",\n        help=\"weight decay\",\n    )\n\n    parser.add_argument(\n        \"--epochs\",\n        type=int,\n        default=200,\n        metavar=\"N\",\n        help=\"number of epochs to train (default: 200)\",\n    )\n    args = parser.parse_args()\n\n    main(args)\n","sub_path":"experiments/rotmnist/.ipynb_checkpoints/e2_runner-checkpoint.py","file_name":"e2_runner-checkpoint.py","file_ext":"py","file_size_in_byte":2706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"567869249","text":"f = open('G:/Textbook-Sem5/BT3051 Data Structures and Algorithms/Class Problems/Avengers_ Endgame - Wikipedia.html')\r\nfrom bs4 import BeautifulSoup\r\nsoup = BeautifulSoup(f,'html.parser')\r\n# print(soup.prettify())\r\n\r\n# for table in soup.find_all('table'):\r\n# \ttry:\r\n# \t\tbb = str(table.a.text)\r\n# \texcept Exception as e:\r\n# \t\tpass\r\n\t\r\n# \tprint(bb)\r\nplot = soup.h2.find('span', id='Plot')\r\ntry:\r\n\tsummary = plot.p.text\r\nexcept Exception as e:\r\n\tsummary=''\r\n\r\nprint(summary)","sub_path":"Parsing/parsing_Avengers.py","file_name":"parsing_Avengers.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"314241276","text":"import argparse\n\nparser = argparse.ArgumentParser(\"Add scores from transformer and Bi-LM\")\nparser.add_argument(\"-score_path_1\", type=str, required=True)\nparser.add_argument(\"-score_path_2\", type=str, required=True)\nparser.add_argument(\"-saveto\", type=str, required=True)\n\nargs = parser.parse_args()\n\nwith open(args.score_path_1, 'r') as f_1, open(args.score_path_2, 'r') as f_2:\n    scores_1 = f_1.readlines()\n    scores_2 = f_2.readlines()\n    if len(scores_1) != len(scores_2):\n        raise ValueError(\"Two file should have same line number.\")\n\nwith open(args.saveto, 'w') as f:\n    for s1, s2 in zip(scores_1, scores_2):\n        s = float(s1.strip()) + (-float(s2.strip()))\n        f.write(str(s) + '\\n')\n    \n","sub_path":"thumt/thumt/scripts/ensemble_score.py","file_name":"ensemble_score.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"508242162","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport settings\n\nfrom gensim.models.wrappers import ldamallet\nfrom gensim.corpora import lowcorpus, malletcorpus\n\nif __name__ == '__main__':\n    corpus = malletcorpus.MalletCorpus('parsed.txt')\n    MALLET_PATH = settings.MALLET_PATH\n    # 指定可能なパラメータ及び初期値\n    # malletのpath\n    # corpus=None コーパス\n    # num_topics=100 生成するトピック数\n    # alpha=50 初期パラメータ\n    # id2word=None 辞書データ（トークンIDとのマッピング）\n    # workers=4 スレッド数\n    # prefix=None データ用の接頭辞\n    # optimize_interval=0 N回のイテレーションごとにハイパーパラメータを最適化する。：0=無効\n    # iterations=1000 イテレーション数\n    # topic_threshold=0.0 トピックの分布が疎な場合に使用。トピック選定確率の閾値\n    model = ldamallet.LdaMallet(MALLET_PATH,\n                                corpus=corpus,\n                                num_topics=20,\n                                alpha=1,\n                                id2word=corpus.id2word,\n                                optimize_interval=100)\n    model.save('lda.model')\n","sub_path":"save_model.py","file_name":"save_model.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"607914482","text":"#!/usr/bin/python3\n\nimport json \nimport re\nimport time\n\nimport iota.harness.api as api\nimport iota.harness.infra.store as store\nimport iota.test.athena.utils.misc as utils\nimport iota.test.iris.utils.iperf as iperf\nimport iota.test.athena.testcases.networking.config.flow_gen as flow_gen \nimport iota.test.athena.utils.athena_app as athena_app_utils\nfrom collections import defaultdict\nfrom enum import Enum\n\nICMP_TYPE_ECHO_REQ = '8'\nICMP_CODE_ECHO_REQ = '0'\nICMP_TYPE_ECHO_REPLY = '0'\nICMP_CODE_ECHO_REPLY = '0'\nPING_INTVL = 0.1   # secs\nPING_TIMEOUT = 3   # secs \n\n\nclass FlowState(Enum):\n    UNESTABLISHED = 0       # Connection unestablished\n    SYN_SENT = 1            # TCP SYN sent\n    SYN_RECV = 2            # TCP SYN received\n    SYNACK_SENT = 3         # TCP SYN ACK sent\n    SYNACK_RECV = 4         # TCP SYN ACK received\n    ESTABLISHED = 5         # Established\n    FIN_SENT = 6            # FIN Sent\n    FIN_RECV = 7            # FIN received\n    TIME_WAIT = 8           # Wait\n    RST_CLOSE = 9           # RST close\n    REMOVED = 10            # Connection removed\n    OPEN_CONN_SENT = 11     # TCP OPEN, UDP, ICMP, OTHERS unestablished H2S\n    OPEN_CONN_RECV = 12     # TCP_OPEN, UDP, ICMP, OTHERS unestablished S2H\n\n\ndef parse_args(tc):\n    #==============================================================\n    # init cmd options\n    #==============================================================\n    tc.vnic_type  = 'L3'\n    tc.nat        = 'no'\n    tc.pyld_size  = 64\n    tc.iptype     = 'v4'\n    tc.stateful   = False\n    tc.pkt_cnt    = 2\n    tc.duration   = None\n    tc.proto      = 'ICMP'\n\n    #==============================================================\n    # update non-default cmd options\n    #==============================================================\n    if hasattr(tc.iterators, 'vnic_type'):\n        tc.vnic_type = tc.iterators.vnic_type\n\n    if hasattr(tc.iterators, 'nat'):\n        tc.nat = tc.iterators.nat\n\n    if hasattr(tc.iterators, 'pyld_size'):\n        tc.pyld_size = tc.iterators.pyld_size\n\n    if hasattr(tc.iterators, 'iptype'):\n        tc.iptype = tc.iterators.iptype\n\n    if hasattr(tc.iterators, 'proto'):\n        tc.proto = tc.iterators.proto\n\n    if hasattr(tc.iterators, 'stateful') and \\\n               tc.iterators.stateful == 'yes':\n            tc.stateful = True\n\n    if hasattr(tc.args, 'pkt_cnt'):\n        tc.pkt_cnt = tc.args.pkt_cnt\n\n    if hasattr(tc.args, 'duration'):\n        tc.duration = tc.args.duration\n\n    api.Logger.info('vnic_type: {}, nat: {}, pyld_size: {} '\n            'pkt_cnt: {}, iptype: {}, proto: {}'.format(tc.vnic_type, tc.nat, \n                    tc.pyld_size, tc.pkt_cnt, tc.iptype, tc.proto))\n\n\ndef _get_client_server_info(client_node, tc):\n\n    sintf, dintf = None, None\n    sip, dip = None, None\n    smac, dmac = None, None\n\n    if client_node == 'node1':\n        sintf, dintf = tc.wl[0].interface, tc.wl[1].interface\n        smac, dmac = tc.wl[0].mac_address, tc.wl[1].mac_address\n        if tc.iptype == 'v6':\n            sip, dip = tc.wl[0].ipv6_address, tc.wl[1].ipv6_address\n        else:\n            sip, dip = tc.wl[0].ip_address, tc.wl[1].ip_address\n\n    else:\n        sintf, dintf = tc.wl[1].interface, tc.wl[0].interface\n        smac, dmac = tc.wl[1].mac_address, tc.wl[0].mac_address\n        if tc.iptype == 'v6':\n            sip, dip = tc.wl[1].ipv6_address, tc.wl[0].ipv6_address\n        else:\n            sip, dip = tc.wl[1].ip_address, tc.wl[0].ip_address\n\n    return (sintf, dintf, smac, dmac, sip, dip)\n\n\ndef _get_bitw_flows(cl_node, proto, sip, dip, icmp_req = True,\n                    sport = 0, dport = 0):\n    \"\"\" Return the flows to be tested on node1 and node2 athena nic.\n    Pass client node from where packets are sent to cl_node arg.\n    sip is client ip and dip is server ip.\"\"\"\n    if proto == 'ICMP':\n        fl_n1 = flow_gen.IcmpFlow()         # n1 is node1 athena dev\n        fl_n2 = flow_gen.IcmpFlow()         # n2 is node2 athena dev\n        \n        if cl_node == 'node1':\n            fl_n1.sip, fl_n1.dip = sip, dip\n            fl_n2.sip, fl_n2.dip = dip, sip\n        else:\n            fl_n1.sip, fl_n1.dip = dip, sip\n            fl_n2.sip, fl_n2.dip = sip, dip\n            \n        if icmp_req: \n            fl_n1.icmp_type = fl_n2.icmp_type = ICMP_TYPE_ECHO_REQ\n            fl_n1.icmp_code = fl_n2.icmp_code = ICMP_CODE_ECHO_REQ\n        else:\n            fl_n1.icmp_type = fl_n2.icmp_type = ICMP_TYPE_ECHO_REPLY \n            fl_n1.icmp_code = fl_n2.icmp_code = ICMP_CODE_ECHO_REPLY\n\n    else:\n        if proto == 'TCP':\n            fl_n1 = flow_gen.TcpFlow()         # n1 is node1 athena dev\n            fl_n2 = flow_gen.TcpFlow()         # n2 is node2 athena dev\n        else: \n            fl_n1 = flow_gen.UdpFlow()         # n1 is node1 athena dev\n            fl_n2 = flow_gen.UdpFlow()         # n2 is node2 athena dev\n        \n        if cl_node == 'node1':\n            fl_n1.sip, fl_n1.dip = sip, dip\n            fl_n1.sport, fl_n1.dport = sport, dport\n            \n            fl_n2.sip, fl_n2.dip = dip, sip \n            fl_n2.sport, fl_n2.dport = dport, sport\n        else:\n            fl_n1.sip, fl_n1.dip = dip, sip\n            fl_n1.sport, fl_n1.dport = dport, sport\n            \n            fl_n2.sip, fl_n2.dip = sip, dip\n            fl_n2.sport, fl_n2.dport = sport, dport\n\n    return (fl_n1, fl_n2)\n\n\ndef ping_test(tc):\n    \n    tc.flow_hit_cnt_before = {}\n    tc.flow_hit_cnt_after = {}\n    tc.flow_match_before = {}\n    tc.flow_match_after = {}\n\n    wl_nodes = [pair[0] for pair in tc.wl_node_nic_pairs]\n    node1, node2 = wl_nodes[0], wl_nodes[1]\n    node1_ath_nic = tc.athena_node_nic_pairs[0][1]\n    node2_ath_nic = tc.athena_node_nic_pairs[1][1]\n\n    # cl_node => client node\n    for cl_node in wl_nodes: \n        tc.flow_hit_cnt_before[cl_node] = []\n        tc.flow_hit_cnt_after[cl_node] = []\n        tc.flow_match_before[cl_node] = defaultdict(list)\n        tc.flow_match_after[cl_node] = defaultdict(list)\n\n        sintf, dintf, smac, dmac, sip, dip = _get_client_server_info(\n                                                            cl_node, tc)    \n        \n        icmp_req_n1, icmp_req_n2 = _get_bitw_flows(cl_node, 'ICMP', \n                                                    sip, dip, True)\n        icmp_resp_n1, icmp_resp_n2 = _get_bitw_flows(cl_node, 'ICMP', \n                                                    sip, dip, False)\n       \n\n        # === BEGIN nested fn === #\n        def _get_flow_match_info(flow_match_dict):\n            for node, nic, vnic_id in [(node1, node1_ath_nic, tc.node1_vnic_id),\n                                    (node2, node2_ath_nic, tc.node2_vnic_id)]:\n                \n                if node == node1:\n                    icmp_req, icmp_resp = icmp_req_n1, icmp_resp_n1\n                else:\n                    icmp_req, icmp_resp = icmp_req_n2, icmp_resp_n2\n\n                rc, num_match_ent = utils.match_dynamic_flows(node, vnic_id, \n                                                                icmp_req, nic)\n                if rc != api.types.status.SUCCESS:\n                    return rc\n                \n                flow_match_dict[cl_node]['icmp_req'].append((icmp_req, \n                                                                num_match_ent))\n\n                rc, num_match_ent = utils.match_dynamic_flows(node, vnic_id, \n                                                                icmp_resp, nic)\n                if rc != api.types.status.SUCCESS:\n                    return rc\n                \n                flow_match_dict[cl_node]['icmp_resp'].append((icmp_resp, \n                                                                num_match_ent))\n    \n            return (api.types.status.SUCCESS)        \n        # ==== END nested fn ==== #\n\n\n        # get flow hit count on both athena nics before sending traffic\n        tc.flow_hit_cnt_before[cl_node].append(\n                    utils.get_flow_hit_count(node1, node1_ath_nic))\n        tc.flow_hit_cnt_before[cl_node].append(\n                    utils.get_flow_hit_count(node2, node2_ath_nic))\n        \n        # check if flow installed on both athena nics before sending traffic\n        rc = _get_flow_match_info(tc.flow_match_before) \n        if rc != api.types.status.SUCCESS:\n            return rc\n\n        # Send ping traffic \n        if cl_node == 'node1':\n            wl_name = tc.wl[0].workload_name\n        else:\n            wl_name = tc.wl[1].workload_name\n\n        req = api.Trigger_CreateExecuteCommandsRequest()\n       \n        cmd_descr = 'Pinging %s(%s) from %s(%s)' % (dip, dintf, sip, sintf)\n        tc.cmd_descr.append(cmd_descr)\n        api.Logger.info(cmd_descr)\n\n        cmd = 'ping -c %s -s %s %s -i %s -w %s' % (tc.pkt_cnt, tc.pyld_size, \n                                            dip, PING_INTVL, PING_TIMEOUT)\n        tc.ping_cmds.append(cmd)\n        api.Trigger_AddCommand(req, cl_node, wl_name, cmd)\n\n        tc.ping_client_resp.append(api.Trigger(req))\n\n        # check if flow installed on both athena nics after sending traffic\n        rc = _get_flow_match_info(tc.flow_match_after)\n        if rc != api.types.status.SUCCESS:\n            return rc\n\n        # get flow hit count on both athena nics after sending traffic\n        tc.flow_hit_cnt_after[cl_node].append(\n                    utils.get_flow_hit_count(node1, node1_ath_nic))\n        tc.flow_hit_cnt_after[cl_node].append(\n                    utils.get_flow_hit_count(node2, node2_ath_nic))\n       \n    return (api.types.status.SUCCESS)\n\n\ndef ping_resp_verify(tc):\n    \n    cmd_idx = 0\n    # Verify traffic - check for any packet loss\n    for resp in tc.ping_client_resp:\n        for cmd in resp.commands:\n            api.Logger.info(\"Ping Results for %s\" % (tc.cmd_descr[cmd_idx]))\n            api.Logger.info(\"Ping Command: %s\" % (tc.ping_cmds[cmd_idx]))\n            cmd_idx += 1\n            if cmd.exit_code != 0:\n                api.Logger.error('Ping failed')\n                return api.types.status.FAILURE\n            else:\n                api.Logger.info('Ping successful')\n\n    wl_nodes = [pair[0] for pair in tc.wl_node_nic_pairs]\n    node1, node2 = wl_nodes[0], wl_nodes[1]\n     \n    for cl_node in wl_nodes:\n        # verify flows are installed\n        for icmp_type in ['icmp_req', 'icmp_resp']:\n            _, num_match_before = tc.flow_match_before[cl_node][icmp_type][0]\n            icmp_flow, num_match_after = tc.flow_match_after[\n                                                        cl_node][icmp_type][0]\n            # num of matching flows (sip, dip, proto, type, code) installed \n            # should increment by 1 since every new req/resp has new id field\n            # value and id field is part of the flow cache key \n            if (num_match_after - num_match_before != 1):\n                api.Logger.error(\"For ping from %s, flow not installed on \"\n                                \"node1 athena nic:\" % cl_node)\n                icmp_flow.Display()\n                return api.types.status.FAILURE \n\n            _, num_match_before = tc.flow_match_before[cl_node][icmp_type][1]\n            icmp_flow, num_match_after = tc.flow_match_after[\n                                                        cl_node][icmp_type][1]\n            if (num_match_after - num_match_before != 1):\n                api.Logger.error(\"For ping from %s, flow not installed on \"\n                                \"node2 athena nic:\" % cl_node)\n                icmp_flow.Display()\n                return api.types.status.FAILURE \n\n        # verify flow hit count    \n        hit_cnt_before = tc.flow_hit_cnt_before[cl_node][0]\n        hit_cnt_after = tc.flow_hit_cnt_after[cl_node][0]\n       \n        # every ping req/resp will generate new pair of flows\n        # first pkt goes thru slow path and rest via fast path\n        # hence, fast path req/resp hit count = 2 * (pkts sent - 1)\n        hit_cnt_diff = hit_cnt_after - hit_cnt_before\n        expected_cnt = 2*(tc.pkt_cnt - 1)\n        \n        if hit_cnt_diff != expected_cnt:\n            api.Logger.error(\"For ping from %s, hit cnt verify failed on \"\n                    \"node1 athena nic. Change in hit cnt %d didn't match \"\n                    \"expected %d for: \" % (cl_node, hit_cnt_diff, \n                    expected_cnt))\n            \n            req_flow, _ = tc.flow_match_after[cl_node]['icmp_req'][0]\n            resp_flow, _ = tc.flow_match_after[cl_node]['icmp_resp'][0]\n            req_flow.Display()\n            resp_flow.Display()\n            return api.types.status.FAILURE\n\n        hit_cnt_before = tc.flow_hit_cnt_before[cl_node][1]\n        hit_cnt_after = tc.flow_hit_cnt_after[cl_node][1]\n        hit_cnt_diff = hit_cnt_after - hit_cnt_before\n        expected_cnt = 2*(tc.pkt_cnt - 1)\n        \n        if hit_cnt_diff != expected_cnt:\n            api.Logger.error(\"For ping from %s, hit cnt verify failed on \"\n                    \"node2 athena nic. Change in hit cnt %d didn't match \"\n                    \"expected %d for: \" % (cl_node, hit_cnt_diff, \n                    expected_cnt))\n            \n            req_flow, _ = tc.flow_match_after[cl_node]['icmp_req'][1]\n            resp_flow, _ = tc.flow_match_after[cl_node]['icmp_resp'][1]\n            req_flow.Display()\n            resp_flow.Display()\n            return api.types.status.FAILURE\n\n    return api.types.status.SUCCESS\n\n\ndef iperf_test(tc):\n    \n    tc.flow_hit_cnt_before = {}\n    tc.flow_hit_cnt_after = {}\n    tc.flow_match_before = {}\n    tc.flow_match_after = {}\n    tc.conntrack_state = {}\n\n    tc.is_ct_test = False\n    if tc.duration is not None and tc.stateful:\n        tc.is_ct_test = True\n        api.Logger.info(\"Start Connection Tracking e2e iPerf Test\")\n\n    wl_nodes = [pair[0] for pair in tc.wl_node_nic_pairs]\n    node1, node2 = wl_nodes[0], wl_nodes[1]\n    node1_ath_nic = tc.athena_node_nic_pairs[0][1]\n    node2_ath_nic = tc.athena_node_nic_pairs[1][1]\n    tc.node_info = {'node1': node1, 'node2': node2,\n                    'node1_ath_nic': node1_ath_nic,\n                    'node2_ath_nic': node2_ath_nic}\n\n    # cl_node => client node\n    for cl_node in wl_nodes:\n        tc.flow_hit_cnt_before[cl_node] = []\n        tc.flow_hit_cnt_after[cl_node] = []\n        tc.flow_match_before[cl_node] = []\n        tc.flow_match_after[cl_node] = []\n        tc.conntrack_state[cl_node] = []\n\n        sintf, dintf, smac, dmac, sip, dip = _get_client_server_info(\n                                                            cl_node, tc)    \n        if tc.proto == 'UDP':\n            sport = api.AllocateUdpPort()\n            dport = api.AllocateUdpPort()\n        else:\n            sport = api.AllocateTcpPort()\n            dport = api.AllocateTcpPort()\n\n        flow_n1, flow_n2 = _get_bitw_flows(cl_node, tc.proto, sip, dip, \n                                            sport = sport, dport = dport)\n\n        def _get_flow_match_info(flow_match_dict):\n            for node, nic, vnic_id, flow in [\n                    (node1, node1_ath_nic, tc.node1_vnic_id, flow_n1),\n                    (node2, node2_ath_nic, tc.node2_vnic_id, flow_n2)]:\n                \n                rc, num_match_ent = utils.match_dynamic_flows(node, vnic_id, \n                                                                flow, nic)\n                if rc != api.types.status.SUCCESS:\n                    return rc\n                \n                flow_match_dict[cl_node].append((flow, num_match_ent))\n\n            return (api.types.status.SUCCESS)        \n\n        \n        # check if flow installed on both athena nics before sending traffic\n        rc = _get_flow_match_info(tc.flow_match_before) \n        if rc != api.types.status.SUCCESS:\n            return rc\n\n        # Send iperf traffic \n        if cl_node == 'node1':\n            client_wl = tc.wl[0]\n            server_wl = tc.wl[1]\n        else:\n            client_wl = tc.wl[1]\n            server_wl = tc.wl[0]\n\n        cmd_descr = \"Client %s(%s) <--> Server %s(%s)\" % (sip, sintf, \n                                                        dip, dintf)\n        tc.cmd_descr.append(cmd_descr)\n        api.Logger.info(\"Starting Iperf test: %s\" % cmd_descr)\n        \n        serverCmd = iperf.ServerCmd(dport, server_ip = dip)\n        \n        if tc.proto == 'UDP':\n            clientCmd = iperf.ClientCmd(dip, dport, proto = 'udp',\n                                        jsonOut = True, client_ip = sip,\n                                        client_port = sport, \n                                        pktsize = tc.pyld_size,\n                                        packet_count = tc.pkt_cnt)\n        else:\n            if tc.is_ct_test:\n                clientCmd = iperf.ClientCmd(dip, dport, jsonOut = True,\n                                            client_ip = sip, client_port = sport,\n                                            pktsize = tc.pyld_size,\n                                            time = tc.duration)\n            else:\n                clientCmd = iperf.ClientCmd(dip, dport, jsonOut = True,\n                                            client_ip = sip, client_port = sport,\n                                            pktsize = tc.pyld_size,\n                                            packet_count = tc.pkt_cnt)\n\n        tc.serverCmds.append(serverCmd)\n        tc.clientCmds.append(clientCmd)\n\n        serverReq = api.Trigger_CreateExecuteCommandsRequest()\n        clientReq = api.Trigger_CreateExecuteCommandsRequest()\n        \n        api.Trigger_AddCommand(serverReq, server_wl.node_name, \n                                server_wl.workload_name, serverCmd, \n                                background = True)\n\n        api.Trigger_AddCommand(clientReq, client_wl.node_name, \n                                client_wl.workload_name, clientCmd,\n                                background = True)\n\n        server_resp = api.Trigger(serverReq)\n        # sleep for bg iperf servers to be started\n        time.sleep(3)\n\n        tc.iperf_client_resp.append(api.Trigger(clientReq))\n\n        if tc.is_ct_test:\n            iperf_ct_test(tc, cl_node, flow_n1, flow_n2)\n\n        api.Trigger_TerminateAllCommands(server_resp)\n\n        # check if flow installed on both athena nics after sending traffic\n        rc = _get_flow_match_info(tc.flow_match_after)\n        if rc != api.types.status.SUCCESS:\n            return rc\n\n    return (api.types.status.SUCCESS)\n\n\ndef iperf_ct_test(tc, cl_node, flow_n1, flow_n2):\n    # let client establish the connection\n    utils.Sleep(1)\n\n    node1 = tc.node_info['node1']\n    node2 = tc.node_info['node2']\n    node1_ath_nic = tc.node_info['node1_ath_nic']\n    node2_ath_nic = tc.node_info['node2_ath_nic']\n\n    for node, nic, vnic_id, flow in [\n            (node1, node1_ath_nic, tc.node1_vnic_id, flow_n1),\n            (node2, node2_ath_nic, tc.node2_vnic_id, flow_n2)]:\n        rc = utils.verify_conntrack_state(node, vnic_id, flow, FlowState.ESTABLISHED.value, nic)\n        tc.conntrack_state[cl_node].append((node, \"ESTABLISHED\", rc))\n\n    utils.Sleep(tc.duration)\n\n    for node, nic, vnic_id, flow in [\n            (node1, node1_ath_nic, tc.node1_vnic_id, flow_n1),\n            (node2, node2_ath_nic, tc.node2_vnic_id, flow_n2)]:\n        rc = utils.verify_conntrack_state(node, vnic_id, flow, FlowState.RST_CLOSE.value, nic)\n        tc.conntrack_state[cl_node].append((node, \"RST_CLOSE\", rc))\n        if node == cl_node:\n            rc = utils.verify_conntrack_state(node, vnic_id, flow, FlowState.TIME_WAIT.value, nic, 'client')\n        else:\n            rc = utils.verify_conntrack_state(node, vnic_id, flow, FlowState.TIME_WAIT.value, nic, 'server')\n        tc.conntrack_state[cl_node].append((node, \"TIME_WAIT\", rc))\n\n\ndef iperf_resp_verify(tc):\n    conn_timedout = 0\n    control_socker_err = 0\n    cmd_idx = 0 \n    \n    for resp in tc.iperf_client_resp:\n        for cmd in resp.commands:\n            api.Logger.info(\"Iperf Results for %s\" % (tc.cmd_descr[cmd_idx]))\n            api.Logger.info(\"Iperf Server cmd  %s\" % (tc.serverCmds[cmd_idx]))\n            api.Logger.info(\"Iperf Client cmd %s\" % (tc.clientCmds[cmd_idx]))\n            cmd_idx += 1\n            if cmd.exit_code != 0:\n                api.Logger.error(\"Iperf client exited with error\")\n                if iperf.ConnectionTimedout(cmd.stdout):\n                    api.Logger.error(\"Connection timeout, ignoring for now\")\n                    conn_timedout = conn_timedout + 1\n                    continue\n                if iperf.ControlSocketClosed(cmd.stdout):\n                    api.Logger.error(\"Control socket cloned, ignoring for now\")\n                    control_socker_err = control_socker_err + 1\n                    continue\n                if iperf.ServerTerminated(cmd.stdout):\n                    api.Logger.error(\"Iperf server terminated\")\n                    return api.types.status.FAILURE\n                if not iperf.Success(cmd.stdout):\n                    api.Logger.error(\"Iperf failed\", iperf.Error(cmd.stdout))\n                    return api.types.status.FAILURE\n\n    api.Logger.info(\"Iperf test passed successfully\")\n    api.Logger.info(\"Number of connection timeouts : {}\".format(conn_timedout))\n    api.Logger.info(\"Number of control socket errors : {}\".format(control_socker_err))\n\n    wl_nodes = [pair[0] for pair in tc.wl_node_nic_pairs]\n    node1, node2 = wl_nodes[0], wl_nodes[1]\n     \n    for cl_node in wl_nodes:\n        # verify flows are installed\n        flow, num_match_after = tc.flow_match_after[cl_node][0]\n        \n        if num_match_after == 0:\n            api.Logger.error(\"For %s pkts sent from %s, flow not installed on \"\n                            \"node1 athena nic:\" % (tc.proto, cl_node))\n            flow.Display()\n            return api.types.status.FAILURE \n\n        flow, num_match_after = tc.flow_match_after[cl_node][1]\n        \n        if num_match_after == 0:\n            api.Logger.error(\"For %s pkts sent from %s, flow not installed on \"\n                            \"node2 athena nic:\" % (tc.proto, cl_node))\n            flow.Display()\n            return api.types.status.FAILURE \n        \n        for node, state, rc in tc.conntrack_state[cl_node]:\n            if not rc:\n                api.Logger.error(\"For %s, state verification failed for state %s\"\n                                % (node, state))\n                return api.types.status.FAILURE\n    \n    return api.types.status.SUCCESS\n\n\ndef Setup(tc):\n\n    # parse iterator args\n    parse_args(tc)\n\n    tc.athena_node_nic_pairs = athena_app_utils.get_athena_node_nic_names()\n\n    # Get list of workloads for nodes\n    tc.wl_node_nic_pairs = utils.get_classic_node_nic_pairs()\n    tc.wl_node_nic_pairs.sort(key=lambda x: x[0])    # sort in order of nodes\n\n    # tc.wl_node_nic_pairs[0] -> (node1, naples(classic))\n    # tc.wl_node_nic_pairs[1] -> (node2, naples(classic))\n\n    nodes = [pair[0] for pair in tc.wl_node_nic_pairs]\n    workloads_node1 = api.GetWorkloads(nodes[0])\n    workloads_node2 = api.GetWorkloads(nodes[1])\n\n    # Get index of testcase vnic for policy.json \n    node1_plcy_obj, node2_plcy_obj = None, None\n    node1_vnic_idx, node2_vnic_idx = None, None\n\n    with open(api.GetTestsuiteAttr(\"node1_dp_policy_json_path\")) as fd:\n        node1_plcy_obj = json.load(fd)\n\n    with open(api.GetTestsuiteAttr(\"node2_dp_policy_json_path\")) as fd:\n        node2_plcy_obj = json.load(fd)\n\n    node1_vnic_idx = utils.get_vnic_pos(node1_plcy_obj, tc.vnic_type, tc.nat, _stateful=tc.stateful)\n    node2_vnic_idx = utils.get_vnic_pos(node2_plcy_obj, tc.vnic_type, tc.nat, _stateful=tc.stateful)\n\n    api.Logger.info('node1 vnic idx %d, node2 vnic idx %d' % (\n                    node1_vnic_idx, node2_vnic_idx))\n\n    # Use workloads on up0 for node1 and use workloads \n    # on up1 for node2 since they match switch vlan config\n    node1_wl = workloads_node1[utils.get_wl_idx(0, node1_vnic_idx+1)]\n    node2_wl = workloads_node2[utils.get_wl_idx(1, node2_vnic_idx+1)]\n\n    tc.wl = [node1_wl, node2_wl]    # tc.wl[0] should be node1 wl\n                                    # tc.wl[1] should be node2 wl \n\n    # fetch vnic_id for flow verification later\n    tc.node1_vnic_id = utils.get_vnic_id(node1_plcy_obj, tc.vnic_type, tc.nat, _stateful=tc.stateful) \n    tc.node2_vnic_id = utils.get_vnic_id(node2_plcy_obj, tc.vnic_type, tc.nat, _stateful=tc.stateful) \n\n    return api.types.status.SUCCESS\n\n\ndef Trigger(tc):\n\n    tc.resp = []\n    tc.iperf_client_resp = []\n    tc.ping_client_resp = []\n   \n    # set up static arp on both nodes\n    for node, nic in tc.wl_node_nic_pairs: \n        sintf, dintf, smac, dmac, sip, dip = _get_client_server_info(node, tc)    \n\n        static_arp_exists = False\n        static_route_exists = False\n        # check if arp entry already exists\n        req = api.Trigger_CreateExecuteCommandsRequest()\n\n        api.Trigger_AddHostCommand(req, node, 'arp ' + dip)\n        api.Trigger_AddHostCommand(req, node, 'ip route show ' + dip)\n\n        resp = api.Trigger(req)\n        for cmd in resp.commands:\n            api.PrintCommandResults(cmd)\n            \n            if cmd.exit_code != 0:\n                api.Logger.error('Command failed: %s' % cmd.command)\n                return api.types.status.FAILURE\n\n            if 'arp' in cmd.command and 'no entry' not in cmd.stdout:\n                static_arp_exists = True\n\n            if 'ip' in cmd.command:\n                if cmd.stdout and dip in cmd.stdout:\n                    static_route_exists = True\n\n\n        req = api.Trigger_CreateExecuteCommandsRequest()\n\n        if not static_arp_exists:\n            api.Logger.info(\"Installing static arp: mac addr for host %s -> %s\" % (\n                            dip, dmac))\n            cmd = 'arp -s %s %s -i %s' % (dip, dmac, sintf)\n            api.Trigger_AddHostCommand(req, node, cmd)\n\n        # TODO: for now install static route. To be removed after workload ip\n        # subnet fix is committed by infra team\n        if not static_route_exists:\n            cmd = 'ip route add %s via %s' % (dip, sip)\n            api.Logger.info(\"Installing static route: %s\" % cmd)\n            api.Trigger_AddHostCommand(req, node, cmd)\n\n        if len(req.commands) != 0: \n            resp = api.Trigger(req)\n            tc.resp.append(resp)\n      \n    if tc.proto == 'ICMP':\n        tc.ping_cmds, tc.cmd_descr = [], []\n        rc = ping_test(tc)\n    else:\n        tc.serverCmds, tc.clientCmds, tc.cmd_descr = [], [], []\n        rc = iperf_test(tc)\n\n    if rc != api.types.status.SUCCESS:\n        return rc\n    \n    return api.types.status.SUCCESS\n\n\ndef Verify(tc):\n    if tc.resp is not None: \n        for resp in tc.resp:\n            for idx, cmd in enumerate(resp.commands):\n                api.PrintCommandResults(cmd)\n                \n                if cmd.exit_code != 0:\n                    api.Logger.error('Command failed: %s' % cmd.command)\n                    return api.types.status.FAILURE\n    \n    if tc.ping_client_resp: #ping test\n        rc = ping_resp_verify(tc)\n        if rc != api.types.status.SUCCESS:\n            return rc\n\n    if tc.iperf_client_resp: #iperf test\n        rc = iperf_resp_verify(tc)\n        if rc != api.types.status.SUCCESS:\n            return rc\n\n    return api.types.status.SUCCESS\n\n\ndef Teardown(tc):\n    return api.types.status.SUCCESS\n","sub_path":"iota/test/athena/testcases/networking/e2e_data_path.py","file_name":"e2e_data_path.py","file_ext":"py","file_size_in_byte":27225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"54939255","text":"from django.contrib import admin\n\n# Register your models here.\nfrom . import models\n\nclass TripAdmin(admin.ModelAdmin):\n    search_fields = [\"code\",]\n    list_display = (\"code\",\"file\",\"pilote\")\n    list_per_page = 10\nadmin.site.register(models.Trip,TripAdmin)","sub_path":"Aviation/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"478107082","text":"from ctypes import *\nfrom os import path\n\n__all__ = [ 'Adu2xx' ]\n_AduHidDllpath = path.abspath(path.dirname(path.abspath(__file__)) + '\\AduHidTest64\\AduHid64.dll')\n_AduHidDll = WinDLL(_AduHidDllpath)\n\n\n#void * __stdcall OpenAduDevice(unsigned long iTimeout);\n_OpenAduDevice_Proto = WINFUNCTYPE(c_void_p,c_ulong)\n_OpenAduDevice_Params = ((1,'iTimeout',1),)\n_OpenAduDevice = _OpenAduDevice_Proto(\n\t('OpenAduDevice',_AduHidDll),\n\t_OpenAduDevice_Params)\n\n#void * __stdcall OpenAduDeviceByProductId(int iProductId, \n#\t\t\t\t\t\t\t\t\t\t  unsigned long iTimeout);\n_OpenAduDeviceById_Proto = WINFUNCTYPE(c_void_p,c_int,c_ulong)\n_OpenAduDeviceById_Params = (\n\t(1,'iProductId',0),\n\t(1,'iTimeout',1))\n_OpenAduDeviceById = _OpenAduDeviceById_Proto(\n\t('OpenAduDeviceByProductId',_AduHidDll),\n\t_OpenAduDeviceById_Params)\n\n#void * __stdcall OpenAduDeviceBySerialNumber(const char* psSerialNumber,\n#\t\t\t\t\t\t\t\t\t\t\t unsigned long iTimeout);\n_OpenAduDeviceBySN_Proto = WINFUNCTYPE(c_void_p,c_char_p,c_ulong)\n_OpenAduDeviceBySN_Params = (\n\t(1,'psSerialNumber',0),\n\t(1,'iTimeout',1))\n_OpenAduDeviceBySN = _OpenAduDeviceBySN_Proto(\n\t('OpenAduDeviceBySerialNumber',_AduHidDll),\n\t_OpenAduDeviceBySN_Params)\n\t\n#int __stdcall ReadAduDevice(void * hDevice, \n#\t\t\t\t   void * lpBuffer, \n#\t\t\t\t   unsigned long nNumberOfBytesToRead,\n#\t\t\t\t   unsigned long * lpNumberOfBytesRead,\n#\t\t\t\t   unsigned long iTimeout);\n_ReadAduDevice_Proto = WINFUNCTYPE(\n\tc_int,\n\tc_void_p,\n\tc_void_p,\n\tc_ulong,\n\tPOINTER(c_ulong),\n\tc_ulong)\n_ReadAduDevice_Params = (\n\t(1,'hDevice',0),\n\t(1,'lpBuffer',0),\n\t(1,'nNumberOfBytesToRead',0),\n\t(1,'lpNumberOfBytesRead',0),\n\t(1,'iTimeout',0))\n_ReadAduDevice = _ReadAduDevice_Proto(\n\t('ReadAduDevice',_AduHidDll),\n\t_ReadAduDevice_Params)\n\n#int __stdcall WriteAduDevice(void * hDevice, \n#\t\t\t\t   const void * lpBuffer, \n#\t\t\t\t   unsigned long nNumberOfBytesToWrite,\n#\t\t\t\t   unsigned long * lpNumberOfBytesWritten,\n#\t\t\t\t   unsigned long iTimeout);\n_WriteAduDevice_Proto = WINFUNCTYPE(\n\tc_int,\n\tc_void_p,\n\tc_void_p,\n\tc_ulong,\n\tPOINTER(c_ulong),\n\tc_ulong)\n_WriteAduDevice_Params = (\n\t(1,'hDevice',0),\n\t(1,'lpBuffer',0),\n\t(1,'nNumberOfBytesToWrite',0),\n\t(1,'lpNumberOfBytesWritten',0),\n\t(1,'iTimeout',0))\n_WriteAduDevice = _WriteAduDevice_Proto(\n\t('WriteAduDevice',_AduHidDll),\n\t_WriteAduDevice_Params)\n\n#void __stdcall CloseAduDevice(void * hDevice);\n_CloseAduDevice_Proto = WINFUNCTYPE(None,c_void_p)\n_CloseAduDevice_Params = ((1,'hDevice',0),)\n_CloseAduDevice = _CloseAduDevice_Proto(\n\t('CloseAduDevice',_AduHidDll),\n\t_CloseAduDevice_Params)\n\n\nclass Adu2xx(object):\n\tdef __init__(self,sn=None,prod_id=None,read_timeout=1,write_timeout=1):\n\t\tsuper(Adu2xx,self).__init__()\n\t\tself._handle = None #\n\t\tself._numRelays = 0\n\t\tself._relayStates = {}\n\t\tself._relayNames = {}\n\t\tself._read_timeout = read_timeout\n\t\tself._write_timeout = write_timeout\n\t\tif sn != None:\n\t\t\tprint(\"sn!\",sn)\n\t\t\tself._handle = _OpenAduDeviceBySN(c_char_p(sn.encode('utf-8')),c_ulong(1))\n\t\t\tprint(\"self.handle\", self._handle)\n\t\telif prod_id != None:\n\t\t\tprint(\"prod_id!\",prod_id)\n\t\t\tself._handle = _OpenAduDeviceById(c_int(prod_id),c_ulong(1))\n\t\telse:\n\t\t\tself._handle = _OpenAduDevice(c_ulong(1))\n\t\t\n\tdef __del__(self):\n\t\tself.close()\n\t\n\tdef close(self):\n\t\tif self._handle:\n\t\t\t_CloseAduDevice(c_void_p(self._handle))\n\t\t\tself._handle = None\n\t\t\tself._relayStates = None\n\t\t\tself._relayNames = None\n\t\t\tself._numRelays = None\n\t\t\t\n\tdef get_write_timeout(self):\n\t\treturn self._write_timeout \n\t\t\n\tdef set_write_timeout(self,x):\n\t\tself._write_timeout = x\n\n\tdef get_read_timeout(self):\n\t\treturn self._read_timeout \n\t\t\n\tdef set_read_timeout(self,x):\n\t\tself._read_timeout = x\n\t\t\n\tdef read(self):\n\t\tif not self._handle:\n\t\t\treturn None\n\t\th = c_void_p(self._handle)\n\t\tbuf = create_string_buffer('\\0' * 9)\n\t\tbufLen = c_ulong(8)\n\t\tnRead = c_ulong(0)\n\t\tiTimeout = c_ulong(int(self._read_timeout * 1000 + 0.5))\n\t\t_ReadAduDevice(h,buf,bufLen,byref(nRead),iTimeout)\n\t\treturn buf.value\n\t\t\n\tdef write(self,msg):\n\t\tif not self._handle:\n\t\t\tprint(\"_WriteAduDevice FAILED no _handle\")\n\t\t\treturn 0\n\t\tassert len(msg) <= 7, \\\n\t\t\t'Device only supports messages of 7 or fewer characters'\n\t\th = c_void_p(self._handle)\n\t\tbuffer = c_char_p(msg.encode('utf-8'))\n\t\tnWrite = c_ulong(len(msg))\n\t\tnWritten = c_ulong(0)\n\t\tiTimeout = c_ulong(1000)\n\t\trtn = _WriteAduDevice(h,buffer,nWrite,byref(nWritten),iTimeout)\n\t\tif(rtn == 0) :\n\t\t\tprint(\"_WriteAduDevice FAILED::::\",rtn)\n\t\treturn rtn\n\t\n\tdef getRelayStates(self):\n\t\treturn dict(self._relayStates)\n\t\n\tdef setRelayStates(self, relayStateDict):\n\t\t\"\"\"\n\t\tSets or resets relay specified by n ( n = 0 ,1,2 or 3  ).\n\t\t\"\"\"\n\t\tfor relayNum, relayState in relayStateDict.items():\n\t\t\tself.setRelayState(int(relayNum), relayState)\n\t\treturn True\n\t\t\n\tdef getRelayNames(self):\n\t\treturn dict(self._relayNames)\n\t\n\tdef setRelayNames(self, relayNameDict):\n\t\tfor relayNum, relayName in relayNameDict.items():\n\t\t\tself.setRelayName(int(relayNum), relayName)\n\t\treturn True\n\t\t\n\tdef getRelayName(self, k):\n\t\ttry:\n\t\t\tassert k >= 0\n\t\t\tassert k < self._numRelays\n\t\t\treturn self._relayNames[str(k)]\n\t\texcept:\n\t\t\tprint(\"Not valid relay (getRelayName)\")\n\t\t\treturn False\n\t\n\tdef setRelayName(self, k, name):\n\t\ttry:\n\t\t\tassert k >= 0\n\t\t\tassert k < self._numRelays\n\t\t\tself._relayNames[str(k)] = str(name)\n\t\t\treturn True\n\t\texcept:\n\t\t\tprint(\"Not valid relay (setRelayName)\")\n\t\t\treturn False\n\n\tdef openRelay(self, k):\n\t\t\"\"\" Opens a relay. Example message: self.write('RK0') \"\"\"\n\t\ttry:\n\t\t\tassert k >= 0\n\t\t\tassert k < self._numRelays\n\t\t\tself._relayStates[str(k)] = 0\n\t\t\treturn self.write('RK' + str(k))\n\t\texcept:\n\t\t\tprint(\"Not valid relay (openRelay)\")\n\t\t\treturn False\n\n\tdef closeRelay(self, k):\n\t\t\"\"\" Closes a relay. Example message: self.write('SK0') \"\"\"\n\t\ttry:\n\t\t\tassert k >= 0\n\t\t\tassert k < self._numRelays\n\t\t\tself._relayStates[str(k)] = 1\n\t\t\treturn self.write('SK' + str(k))\n\t\texcept:\n\t\t\tprint(\"Not valid relay (closeRelay)\")\n\t\t\treturn False\n\n\tdef toggleRelay(self, k):\n\t\t\"\"\" Closes a relay. Example message: self.write('SK0') \"\"\"\n\t\ttry:\n\t\t\tassert k >= 0\n\t\t\tassert k < self._numRelays\n\t\t\treturn self.setRelayState(int(k), not self._relayStates[str(k)])\n\t\texcept:\n\t\t\tprint(\"Not valid relay (closeRelay)\")\n\t\t\treturn False\n\n\tdef getRelayState(self, k):\n\t\ttry:\n\t\t\tassert k >= 0\n\t\t\tassert k < self._numRelays\n\t\t\treturn self._relayStates[str(k)]\n\t\texcept:\n\t\t\tprint(\"Not valid relay (getRelayState)\")\n\t\t\treturn False\n\n\tdef setRelayState(self, k, state):\n\t\t\"\"\" Sets a relay to a given state. state : 0 (open) or 1 (closed) \"\"\"\n\t\ttry:\n\t\t\tassert k >= 0\n\t\t\tassert k < self._numRelays\n\t\t\tif(state) :\n\t\t\t\treturn self.closeRelay(k)\n\t\t\telse:\n\t\t\t\treturn self.openRelay(k)\n\t\texcept:\n\t\t\tprint(\"Not valid relay (setRelayState)\")\n\t\t\treturn False\n\nclass Adu218(Adu2xx):\n\tdef __init__(self,sn=None,prod_id=None,read_timeout=1,write_timeout=1):\n\t\tsuper(Adu218,self).__init__(sn,prod_id,read_timeout,write_timeout)\n\t\tif(self._handle is not None) :\n\t\t\tself._numRelays = 8\n\t\t\tfor relay in range(self._numRelays) :\n\t\t\t\tself._relayStates[str(relay)] = \"\"\n\t\t\t\tself._relayNames[str(relay)] = \"\"\n\t\t\t\tself.openRelay(relay)\n\t\telse:\n\t\t\tprint(\"Error opening ADU relay\", prod_id, sn)\n\t\n\tdef k0_open(self):\n\t\treturn self.openRelay(0)\n\n\tdef k0_close(self):\n\t\treturn self.closeRelay(0)\n\n\tdef k1_open(self):\n\t\treturn self.openRelay(1)\n\n\tdef k1_close(self):\n\t\treturn self.closeRelay(1)\n\n\tdef k2_open(self):\n\t\treturn self.openRelay(2)\n\n\tdef k2_close(self):\n\t\treturn self.closeRelay(2)\n\n\tdef k3_open(self):\n\t\treturn self.openRelay(3)\n\n\tdef k3_close(self):\n\t\treturn self.closeRelay(3)\n\n\tdef k4_open(self):\n\t\treturn self.openRelay(4)\n\n\tdef k4_close(self):\n\t\treturn self.closeRelay(4)\n\n\tdef k5_open(self):\n\t\treturn self.openRelay(5)\n\n\tdef k5_close(self):\n\t\treturn self.closeRelay(5)\n\n\tdef k6_open(self):\n\t\treturn self.openRelay(6)\n\n\tdef k6_close(self):\n\t\treturn self.closeRelay(6)\n\n\tdef k7_open(self):\n\t\treturn self.openRelay(7)\n\n\tdef k7_close(self):\n\t\treturn self.closeRelay(7)\n \n","sub_path":"Adu2xx.py","file_name":"Adu2xx.py","file_ext":"py","file_size_in_byte":7840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"211177572","text":"from django.urls import path\nfrom django.conf.urls import url\nfrom webapp import views\n\nurlpatterns = [\n    path('', views.inicio , name='inicio'),\n    path('buscar', views.buscar , name='buscar'),\n    path('herramientas/<slug:slug>', views.info_herramienta , name='herramientas'),\n    path('ejemplos/<slug:slug>', views.info_ejemplo_de_uso , name='ejemplos'),\n    path('personal/<slug:slug>', views.info_persona_de_conectate , name='personal'),\n    path('personal', views.personal , name='personal_dashboard'),\n    path('herramientas/<slug:slug_herramienta>/tutoriales/<slug:slug_tutorial>', views.tutoriales , name='tutoriales'),\n    path('auth/login', views.rest_login, name='login'),\n    path('auth/logout', views.logout_view, name='logout'),\n    path('historial', views.historial, name=\"historial\"),\n]\n","sub_path":"webapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"398586051","text":"from mlp.learning_rules import AdamLearningRuleWithWeightDecay\nfrom mlp.schedulers import CosineAnnealingWithWarmRestarts\nimport numpy as np\n\ndef test_adam_with_weight_decay():\n    weights = np.arange(0., 1000., ).reshape(20, 50)\n    grads = np.arange(-1., 1., step=(2./1000)).reshape(20, 50)\n\n    optimizer = AdamLearningRuleWithWeightDecay()\n    optimizer.initialise(params=weights)\n    optimizer.update_params(grads_wrt_params=grads)\n\n    correct_params = np.load(\"../data/weight_decay_correct_results.npz\")\n    correct_params = correct_params[\"updated_weights\"]\n    check_functionality = np.mean(np.allclose(optimizer.params, correct_params))\n\n    return check_functionality, correct_params, optimizer.params\n\n\ndef test_cosine_scheduler():\n    from mlp.learning_rules import AdamLearningRule\n    loaded = np.load(\"../data/cosine_scheduler_correct_test_results.npz\")\n    learning_rule = AdamLearningRule()\n    correct_learning_rates, epoch_idx = loaded['learning_rates'], loaded['epoch_idx']\n    cosine_scheduler = CosineAnnealingWithWarmRestarts(min_learning_rate=0.0001, max_learning_rate=0.01,\n                                                       total_iters_per_period=100.,\n                                                       max_learning_rate_discount_factor=0.9,\n                                                       period_iteration_expansion_factor=1.0)\n\n\n\n    check_scheduler_functionality_epoch_idx_array = np.array([i for i in range(1000)])\n    check_experiment_continued_functionality_epoch_idx_array = np.array([i for i in range(2000, 3000)])\n\n    learning_rate_array = np.empty(shape=check_scheduler_functionality_epoch_idx_array.shape)\n\n    for idx, epoch in enumerate(check_scheduler_functionality_epoch_idx_array):\n        cur_learning_rate = cosine_scheduler.update_learning_rule(learning_rule=learning_rule, epoch_number=epoch)\n        learning_rate_array[idx] = cur_learning_rate\n\n    check_functionality = np.mean(np.allclose(correct_learning_rates[check_scheduler_functionality_epoch_idx_array],\n                                              learning_rate_array))\n\n    functionality_output = np.copy(learning_rate_array)\n    functionality_correct = np.copy(correct_learning_rates[check_scheduler_functionality_epoch_idx_array])\n\n    learning_rate_array = np.empty(shape=check_scheduler_functionality_epoch_idx_array.shape)\n\n    for idx, epoch in enumerate(check_experiment_continued_functionality_epoch_idx_array):\n        cur_learning_rate = cosine_scheduler.update_learning_rule(learning_rule=learning_rule, epoch_number=epoch)\n        learning_rate_array[idx] = cur_learning_rate\n    \n    check_continuation_feature = np.mean(np.allclose(correct_learning_rates[1000:],\n                                                     learning_rate_array))\n\n    continuation_from_previous_state_output = np.copy(learning_rate_array)\n    continuation_from_previous_state_correct = np.copy(correct_learning_rates[1000:])\n\n    return check_functionality, functionality_correct, functionality_output, check_continuation_feature, continuation_from_previous_state_correct, continuation_from_previous_state_output\n\n","sub_path":"lab6/mlp/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"472069627","text":"import bpy\r\n\r\nfrom bpy.types import Panel\r\n\r\nfrom ... utility import active_tool, addon, names\r\n\r\n\r\nclass HARDFLOW_PT_behavior_settings(Panel):\r\n    bl_label = 'Behavior'\r\n    bl_space_type = 'VIEW_3D'\r\n    bl_region_type = 'UI'\r\n    bl_category = 'Hardflow'\r\n    bl_parent_id = 'HOPS_PT_settings'\r\n    bl_options = {'DEFAULT_CLOSED'}\r\n\r\n    @classmethod\r\n    def poll(cls, context):\r\n        return context.region.type == 'UI'\r\n\r\n    def draw(self, context):\r\n        layout = self.layout\r\n\r\n        preference = addon.preference()\r\n\r\n        option = None\r\n        for tool in context.workspace.tools:\r\n            if tool.idname == 'Hardflow':\r\n                self.label_row(layout.row(), preference.behavior, 'quick_execute')\r\n\r\n            elif tool.idname == 'Hops':\r\n                self.label_row(layout.row(), preference.behavior, 'display_gizmo', label='Hide gizmo on Ctrl')\r\n                self.label_row(layout.row(), preference.behavior, 'display_dots', label='Display dots on Ctrl')\r\n                self.label_row(layout.row(), preference.behavior, 'display_operators', label='Display Operators on Ctrl')\r\n                self.label_row(layout.row(), preference.behavior, 'display_boolshapes', label='Display booleans on Ctrl')\r\n                self.label_row(layout.row(), preference.behavior, 'display_boolshapes_for_all', label='Display booleans for All Objects')\r\n                self.label_row(layout.row(), preference.behavior, 'add_mirror_to_boolshapes', label='Add mirror to boolshapes')\r\n                self.label_row(layout.row(), preference.behavior, 'add_WN_to_boolshapes', label='Add WN to boolshapes')\r\n                self.label_row(layout.row(), preference.behavior, 'cursor_boolshapes', label='Orient boolshapes to cursor')\r\n\r\n    def label_row(self, row, path, prop, label=''):\r\n        row.label(text=label if label else names[prop])\r\n        row.prop(path, prop, text='')\r\n","sub_path":"addon/panel/settings/behavior.py","file_name":"behavior.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"339674004","text":"#!/usr/bin/env python3\n# -*- coding: UTF-8 -*-\n# -----------------------------------------------------------------------------\n#\n#   P A G E B O T  N A N O\n#\n#   Copyright (c) 2020+ Buro Petr van Blokland + Claudia Mens\n#   www.pagebot.io\n#   Licensed under MIT conditions\n#\n#   Supporting DrawBot, www.drawbot.com\n# -----------------------------------------------------------------------------\n#\n#   somethingintheair.py\n#\nimport sys\nsys.path.insert(0, \"../..\") # So we can import pagebotnano without installing.\n\nfrom pagebotnano_050.themes.theme import BaseTheme\nfrom pagebotnano_050.toolbox.color import spotColor\n\nclass SomethingInTheAir(BaseTheme):\n    \"\"\"The SomethingInTheAir theme is ...\"\"\"\n\n    NAME = 'Something in the Air'\n    BASE_COLORS = dict(\n        base0=spotColor('reflexblueu'),\n        base1=spotColor(540),\n        base2=spotColor(542),\n        base3=spotColor(306),\n        base4=spotColor(245), # Supporter1\n        base5=spotColor(190),\n    )\n\nif __name__ == \"__main__\":\n    import doctest\n    import sys\n    sys.exit(doctest.testmod()[0])\n","sub_path":"PageBotNano-050-InDesign/pagebotnano_050/themes/somethingintheair.py","file_name":"somethingintheair.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"76379677","text":"#! /usr/bin/python3\n\n##########################################################################################\n# This is the code used for the video demonstration. Feel free to contact me with any    #\n# any questions at c.wolfe132@gmail.com                                                  #\n#                                                                                        #\n##########################################################################################\n\n\nimport cv2\nimport numpy as np\nimport motion\nimport util_funcs as util\nfrom time import sleep\nfrom picamera.array import PiRGBArray\nfrom picamera import PiCamera\n\n# constants\nBLOCK_AREA = 300\nPICKUP_AREA = 20000\nBOUNDARY_AREA = 12000\nCONTAINER_AREA = 40000\nDROP_OFF_AREA = 120000\nALIGN_MARGIN = 25\nDEAD_END_MARGIN = 10\n\n\n# blue mask limits\nlower_blue = np.array([90,40,40])\nupper_blue = np.array([150,255,255])\n\n# yellow mask limits\nlower_yellow = np.array([15,100,100])\nupper_yellow = np.array([35,255,255])\n\n# green mask limits\nlower_green = np.array([36,0,0])\nupper_green = np.array([86,255,255])\n\n# initialize camera and grab a reference to the raw camera capture\ncamera = PiCamera()\nimage_width = 640\nimage_height = 480\ncamera.resolution = (image_width, image_height)\ncamera.framerate = 32\nrawCapture = PiRGBArray(camera, size=(image_width, image_height))\n\n# allow the camera time to warmup\nsleep(0.1)\n\n# get center of frame\nframe_centerX = int(image_width / 2)\nframe_centerY = int(image_height / 2)\n\n\ntry:\n    # allow the camera time to warmup\n    sleep(0.1)\n    \n    # capture frames from the camera\n    for frame in camera.capture_continuous(rawCapture, format=\"bgr\", use_video_port=True):\n        # grab the raw NumPy array representing the image - this array\n        # will be 3D, representing the width, height, and # of channels\n        image = frame.array\n        \n        # convert image from BGR format to HSV format\n        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n        \n        # create mask for yellow blocks\n        yellow_mask = cv2.inRange(hsv, lower_yellow, upper_yellow)\n        \n        # create a mask for blue boundary\n        blue_mask = cv2.inRange(hsv, lower_blue, upper_blue)\n        \n        # create a mask for green container\n        green_mask = cv2.inRange(hsv, lower_green, upper_green)\n        \n        # do erosion to get rid of white spots\n        kernel = np.ones((5,5),dtype=np.uint8)\n        blocks = cv2.erode(yellow_mask,kernel,iterations=4)\n        boundaries = cv2.erode(blue_mask,kernel,iterations=4)\n        container = cv2.erode(green_mask,kernel,iterations=4)\n        \n        # find contours of blocks\n        yellow_contours, heir = cv2.findContours(blocks, cv2.RETR_CCOMP,\n                                             cv2.CHAIN_APPROX_SIMPLE)\n        \n        # find contours of boundaries\n        blue_contours, heir = cv2.findContours(boundaries, cv2.RETR_CCOMP,\n                                           cv2.CHAIN_APPROX_SIMPLE)\n        \n        # find contours of container\n        green_contours, heir = cv2.findContours(container, cv2.RETR_CCOMP,\n                                           cv2.CHAIN_APPROX_SIMPLE)\n        \n        # reset observed contours\n        block_area = 0\n        boundary_area = 0\n        container_area = 0\n        \n        # reset turn direction and align states\n        turn_dir = 0\n        align_state = 1\n        container_align_state = 1\n        \n        # find the biggest blue contour\n        for cont in blue_contours:\n            blue_area = cv2.contourArea(cont)\n            \n            if (blue_area > boundary_area):\n                boundary_area = blue_area\n                \n                # in case the contour is big enough that the robot must turn,\n                # check if the robot should turn left or right\n                turn_dir = util.check_turn_direction(boundaries, frame_centerX, cont,\n                                                     DEAD_END_MARGIN)        \n        #print(\"boundary area:\")\n        #print(boundary_area)\n                \n        # find biggest yellow contour\n        for cont in yellow_contours:\n            yellow_area = cv2.contourArea(cont)\n            \n            if (yellow_area > block_area):\n                block_area = yellow_area\n                \n                # in case the contour is big enough that the robot pick up block,\n                # check if the robot should align\n                align_state = util.check_align_state(blocks, frame_centerX, cont,\n                                                     ALIGN_MARGIN)\n        #print(\"block area:\")\n        #print(block_area)\n        \n        \n        # find biggest green contour\n        for cont in green_contours:\n            green_area = cv2.contourArea(cont)\n            \n            if (green_area > container_area):\n                container_area = green_area\n                \n                # in case the contour is big enough that the robot put down block,\n                # check if the robot should align\n                container_align_state = util.check_align_state(container, frame_centerX,\n                                                               cont,ALIGN_MARGIN)\n        #print(\"container area:\")\n        #print(container_area)\n        \n        # if contour boundary is bigger than limit, robot is at boundary and must turn \n        if (boundary_area > BOUNDARY_AREA):\n            motion.stop()\n            sleep(0.5)\n            \n            # check turn direction, then backup and turn\n            if (turn_dir == 1):\n                motion.move_backward(0.5)\n                sleep(0.5)\n                motion.turn_right(10)\n                sleep(0.5)\n            elif (turn_dir == 0):\n                motion.move_backward(0.5)\n                sleep(0.5)\n                motion.turn_left(10)\n                sleep(0.5)\n            elif (turn_dir == 2):\n                motion.turn_left(60)\n                sleep(0.5)\n                \n        \n        # if block contour is bigger than limit, align\n        elif ((block_area > BLOCK_AREA) and (block_area < PICKUP_AREA)):\n            motion.stop()\n            sleep(0.1)\n            \n            if (align_state == 1):\n                # block is to the left, so turn slightly left\n                motion.turn_left(2)\n                \n            elif (align_state == 2):\n                # block is to the right, so turn slightly right\n                motion.turn_right(2)\n            \n            else:\n                # block is aligned, so move forward\n                sleep(0.1)\n                motion.move_forward(0.2)\n                sleep(0.1)\n         \n        # if block contour is big enough, pick it up\n        elif (block_area > PICKUP_AREA):\n            sleep(0.1)\n            motion.pickup_item()\n         \n        # if container contour is bigger than limit, align\n        elif ((container_area > CONTAINER_AREA) and (container_area < DROP_OFF_AREA)):\n            motion.stop()\n            sleep(0.5)\n            \n            if (container_align_state == 1):\n                # container is to the left, so turn slightly left\n                motion.turn_left(3)\n                \n            elif (container_align_state == 2):\n                # container is to the right, so turn slightly right\n                motion.turn_right(3)\n            \n            else:\n                # container is aligned, so move forward\n                motion.stop()\n                sleep(0.1)\n                motion.move_forward(0.2)\n                sleep(0.1)\n                \n                \n        # if container contour is big enough, drop off block, back up, and turn around\n        elif (container_area > DROP_OFF_AREA):\n            sleep(0.5)\n            motion.drop_item()\n            sleep(0.5)\n            motion.move_backward(1)\n            sleep(0.5)\n            motion.turn_left(60)\n                \n                \n        \n        # if no contours bigger than limits, robot can go forward\n        else:\n            motion.move_forward(0.5)\n            \n        \n        # show the frame (for debugging purposes)\n        #masked_video = boundaries #+ blocks + container\n        #regular_video = image\n        #cv2.imshow(\"Masked Video\", masked_video)\n        #cv2.imshow(\"Regular video\", regular_video)\n        #key = cv2.waitKey(1) & 0xFF\n        \n        # clear the stream in preparation for the next frame\n        rawCapture.truncate(0)\n       \n                             \n        \nfinally:\n    print(\"\\nturning off motors and exiting program\")\n    cv2.destroyAllWindows()\n    motion.stop()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"auto_mode.py","file_name":"auto_mode.py","file_ext":"py","file_size_in_byte":8593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"393344499","text":"import math\n\n\nwhile True:\n    try:\n        b = int(input(\"How many B bacterias are there? \"))\n        t = int(input(\"How much time in minutes will we wait? \"))\n    except ValueError:\n        print(\"Not a number! Try again!\")\n        continue\n    else:\n        break\n\n\ntotal = b * 2 ** math.floor(t/2)\nprint(\"After\", t, \"minutes, we would have\", total, \"bacterias\")\n","sub_path":"Session-4/Assignment/serious-ex/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"607319301","text":"#!/usr/bin/env python\n# -*- encoding=utf8 -*-\n'''\nFilename: test_handler.py\n'''\n\nimport pytest\n\nimport json\n\nfrom code import handler\nfrom conftest import MODULES, CONFIG, COLLECT_INFO\nfrom conftest import mongodb\n\n@pytest.fixture(scope=\"function\")\ndef http_instance():\n    \"\"\"generate handler instance which upload data by celery\n    \"\"\"\n    instance = handler.Handler(\"http\", CONFIG[\"http\"])\n    return instance\n\n@pytest.fixture(scope=\"function\")\ndef mongodb_instance():\n    \"\"\"generate handler instance which upload data by directly\n       uploading to mongodb\n    \"\"\"\n    instance = handler.Handler(\"mongodb\", CONFIG[\"mongodb\"])\n    return instance\n\ndef test_set_handler(http_instance):\n    \"\"\"test set_handler function\n    \"\"\"\n    ret = http_instance.set_handler(\"mongodb\", CONFIG[\"mongodb\"])\n    assert ret[\"status\"] == 0\n    assert ret[\"ret\"] == \"\"\n\ndef test_http_handle_data(http_instance):\n    \"\"\"test for handle_data function by http, need to start\n       data interface service\n    \"\"\"\n    ret = http_instance.handle_data(MODULES,\n            json.loads(COLLECT_INFO))\n    assert ret[\"status\"] == 0\n\ndef test_mongodb_handle_data(mongodb_instance, mongodb):\n    \"\"\"test for handle_data function by directly uploading\n    \"\"\"\n    ret = mongodb_instance.handle_data(MODULES,\n            json.loads(COLLECT_INFO))\n    assert ret[\"status\"] == 0\n    query = {\"machine_id\":\"3C:97:0E:0E:05:2F\",\n            \"time\":\"2016-02-15 14:35:27\"}\n    for module in MODULES:\n        result = mongodb[module].delete_one(query)\n        assert result.deleted_count == 1\n\n    result = mongodb[\"machine\"].delete_one(\n            {\"_id\":\"3C:97:0E:0E:05:2F\"})\n    assert result.deleted_count == 1\n","sub_path":"agent/tests/unit/test_handler.py","file_name":"test_handler.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"232946389","text":"## Copyright (c) 2012-2016 Aldebaran Robotics. All rights reserved.\n## Use of this source code is governed by a BSD-style license that can be\n## found in the COPYING file.\n\nimport os\n\nimport qitest.conf\n\ndef test_simple(qitest_action, record_messages):\n    testme_proj = qitest_action.add_test_project(\"testme\")\n    qitest_action(\"collect\")\n    pytest_json = os.path.join(testme_proj.path, \"pytest.json\")\n    tests = qitest.conf.parse_tests(pytest_json)\n    names = [x[\"name\"] for x in tests]\n    assert set(names) == set([\n        \"testme.test.test_foo\",\n        \"testme.test.test_bar\",\n        \"testme.test.subfolder.test_spam\"])\n","sub_path":"python/qitest/test/test_qitest_collect.py","file_name":"test_qitest_collect.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"297710504","text":"import rclpy\nfrom example_interfaces.srv import AddTwoInts\n\nfrom rclpy.callback_groups import ReentrantCallbackGroup\n\n\ndef main(args=None):\n    rclpy.init(args=args)\n    node = rclpy.create_node(\"minimal_client\")\n\n    # Node's default callback group is mutually exclusive. This would prevent the client response\n    # from being processed until the timer callback finished, but the timer callback in this\n    # example is waiting for the client response\n    cb_group = ReentrantCallbackGroup()\n\n    client = node.create_client(AddTwoInts, \"add_two_ints\", callback_group=cb_group)\n\n    did_run, did_get_result = False, False\n\n    async def call_service():\n        nonlocal client, node, did_run, did_get_result\n        did_run = True\n\n        try:\n            request = AddTwoInts.Request()\n            request.a = 6\n            request.b = 7\n            future = client.call_async(request)\n            try:\n                result = await future\n            except Exception as e:\n                node.get_logger().error(\"Error to call service: {}\".format(e))\n            else:\n                node.get_logger().info(\"Sum: {}\".format(result.sum))\n\n        finally:  # finally块（如果指定）将被执行，而不管try块是否引发错误。\n            did_get_result = True\n\n    while not client.wait_for_service(timeout_sec=1.0):\n        node.get_logger().info(\"Waiting for service\")\n\n    timer = node.create_timer(0.5, callback=call_service, callback_group=cb_group)\n\n    while rclpy.ok() and not did_run:\n        rclpy.spin_once(node)\n\n    if did_run:\n        # call timer callback only once\n        timer.cancel()\n\n    while rclpy.ok() and not did_get_result:\n        rclpy.spin_once(node)\n\n    node.destroy_node()\n    rclpy.shutdown()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"ROS2/ROS2 Official Doc/Tutorials/Beginner/code/04_Simple_service_server_and_client_Python/src/python_service_server_client/python_service_server_client/client_add_two_ints_async_callback.py","file_name":"client_add_two_ints_async_callback.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"213664705","text":"# Write a program to calculate the age of the user, prompt the user for the current year and the year they were born in.\r\n\r\ncy = input(\"Enter current year(YYYY):\")\r\nif cy.isdigit():\r\n    cy = int(cy)          # converting year to an integer for calculations.\r\nelse:\r\n    print(\"Please enter a valid year\")\r\n    quit()\r\nby = input(\"Enter your birth year(YYYY):\")\r\nif by.isdigit():\r\n    by = int(by)          # converting year to an integer for calculations.\r\n    if cy < by:\r\n        print(f'Please enter a year less than {cy}')    # negative age is not possible.\r\n    else:\r\n        print(f\"The user's birth year is {by}\")\r\n        ca = cy - by            # calculating age.\r\n        if ca<150 :\r\n            print(f\"The user's current age is {ca}\")\r\n        if ca>150 :\r\n            print(f\"Please enter a reasonable age\")\r\n\r\n        if 100<ca<150 :\r\n            print(f\"Congratulations on living more than a hundred years\")\r\nelse:\r\n    print('Please enter a valid year')\r\n","sub_path":"age_calculator.py","file_name":"age_calculator.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"166722310","text":"import logging\n\nAPP_NAME = 'furniture-billing'\nLOGGER_NAME = APP_NAME\nDEBUG = False\nTESTING = False\n# to fix the issue of flask_restful\n# using flask error handlers instead of flask_restful's\nPROPAGATE_EXCEPTIONS = True\nLOGGING_LEVEL = logging.INFO\nSQLALCHEMY_TRACK_MODIFICATIONS = True\nINGEST_TO_DB = True\n","sub_path":"app/settings/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"459292289","text":"import pymongo\nfrom knock65 import conect_db\n\ndef get_artist(co):\n    count = 0\n    for data in co.find({'tags.value':'dance'},sort = [('rating.count',pymongo.DESCENDING)]):\n#        if 'dance' in data.tags[0].value:\n        print(data)\n        print()\n        count += 1\n        if count >=10:\n            break\n\nif __name__ == '__main__':\n    co = conect_db()\n    get_artist(co)\n","sub_path":"kurosawa/chapter07/knock68.py","file_name":"knock68.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"86649563","text":"# Copyright 2023 Iguazio\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.\nimport mlrun\n\n\ndef setup(project: mlrun.projects.MlrunProject):\n    \"\"\"Example for project setup script which modify project metadata and functions\"\"\"\n    project.spec.params[\"test123\"] = \"456\"\n    prep_func = project.set_function(\n        \"prep_data.py\", \"prep-data\", kind=\"job\", image=\"mlrun/mlrun\"\n    )\n    prep_func.set_label(\"tst1\", project.get_param(\"p2\"))\n\n    srv_func = project.set_function(\n        \"serving.py\", \"serving\", kind=\"serving\", image=\"mlrun/mlrun\"\n    )\n    srv_func.add_model(\"x\", \".\", class_name=\"MyCls\")\n    return project\n","sub_path":"tests/projects/assets/new_setup_test/project_setup.py","file_name":"project_setup.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"596648555","text":"#!/usr/bin/env python3\n\nimport re\nimport subprocess\n\nimport utils\n\n\ndef volume():\n    conf = utils.read_conf(\"volume\")\n    color = conf.get(\"color\", \"FFFFFF\")\n    color_inactive = conf.get(\"color_inactive\", \"000000\")\n    emblem = conf.get(\"emblem_default\")\n    label = \"N.A\"\n\n    try:\n        output_raw = subprocess.check_output(\n            [\n                conf.get(\"amixer_path\", \"amixer\"),\n                \"get\", \"Master\"\n            ],\n            timeout=conf.getint(\"timeout\", 1)\n        )\n\n        if re.search('\\[on\\]',  output_raw.decode()):  # volume is on\n            re_output = re.search('(?<=\\[)\\d+(?=%\\])', output_raw.decode())\n            vol_percent = int(re_output.group(0))\n\n            if conf.getboolean(\"dynamic_emblem\", False):\n                if vol_percent >= 65:\n                    emblem = conf.get(\"emblem_high\", \"\")\n                elif vol_percent >= 35:\n                    emblem = conf.get(\"emblem_medium\", \"\")\n                else:\n                    emblem = conf.get(\"emblem_low\", \"\")\n            label = \"{0}%\".format(vol_percent) if vol_percent else \"N.A\"\n        else:  # volume if off\n            color = color_inactive\n            label = \"Off\"\n            emblem = conf.get(\"emblem_muted\", \"\")\n\n    except (subprocess.CalledProcessError, subprocess.CalledProcessError,\n            re.error, ValueError, Exception):\n        color = color_inactive\n\n    if conf.getboolean(\"uppercase\", False):\n        label = label.upper()\n\n    return utils.print_u(emblem, label, color)\n\n\nif __name__ == '__main__':\n    print(volume())\n","sub_path":"src/volume.py","file_name":"volume.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"18796358","text":"'''\nThis module contains celery tasks and helper functions related\nto initial import and scheduled updates for netdb app. To allow\nfor adding vendors without needing additional if/else checking\nas well as functionality beyond basic property population,\nall functions written to extend functionality of these tasks should\nfollow the below naming standard:\n\ncreate_<vendor>_device\n\nIf vendor exists within Vendor table and this function exists with\nproper naming, devices will be processed.\n'''\n\n\nfrom __future__ import absolute_import\nfrom celery import shared_task\nfrom celery.contrib import rdb\nfrom django.conf import settings\nfrom django.db import IntegrityError\nimport datetime\nimport csv\nimport logging\nimport time\n\nfrom .models import Vendor, Customer, ManagedDevice, CSVFile\nfrom .utils import cisco_convert_to_datetime\nfrom cisco_api import api_console\nfrom cisco_api.models import EoXRecord, CiscoBug\nimport cisco_api.tasks\n\nlogger = logging.getLogger('netdb.tasks')\n\n@shared_task\ndef import_from_riverbed(filename, cust_pk):\n    '''\n    Using export of CSV from Riverbed, populate initial \n    customer data into database.\n\n    doc must be filename of CSVFile object.\n    '''\n\n    #grab customer given passed PK\n    customer = Customer.objects.filter(pk=cust_pk).first()\n\n    #grab CSVfile object\n    f = CSVFile.objects.filter(docfile='./' + filename).first()\n\n    #load file for processing\n    proc_file = csv.reader(open(settings.MEDIA_ROOT + '/' + filename))\n\n    for row in proc_file:\n\n        #skip if device exists already. this saves unnecessary API calls\n        if ManagedDevice.objects.filter(name=row[1]):\n            logger.info('Skipping {}, already a managed device'.format(row[1]))\n            continue\n\n        #If it's a supported vendor, then process\n        vendor = Vendor.objects.filter(name=row[2]).first()\n        if vendor:\n            func_name = 'create_' + row[2].split()[0].lower() + '_device'\n            vendor_func = globals().get(func_name)\n            if vendor_func:\n                vendor_func(row, customer, vendor)\n            else:\n                logger.info('Vendor exists in table but no supporting function: {}'.format(row[2]))\n\n        else:\n            logging.debug('[{}:{}] Vendor not recognized. Skipping...'.format(row[2], row[1]))\n\n    #Finally, delete CSV model which will delete local file as well\n    f.delete()\n    logger.debug('Deleted file: {}'.format(filename))\n\n    #Trigger periodic udpates\n    cisco_api.tasks.periodic_bug_update.delay()\n\n@shared_task\ndef import_from_solarwinds(input):\n    '''\n    Initial import from Solarwinds data.\n    '''\n    pass\n\n@shared_task\ndef solarwinds_periodic_discovery(input):\n    '''\n    Periodic data refresh for existing devices from solarwinds.\n    '''\n    pass\n\ndef create_and_return_eox(serial_num):\n    '''\n    Takes a passed in serial number and queries EoX v5 API, saves the\n    record to database, and returns the EoX object to caller.\n    '''\n\n    session = api_console.CiscoEoXApiBySerial()\n    resp = session.eox_api_call(serial_num)['EOXRecord'][0]\n\n    #If EOXError exists, return None\n    if resp.get('EOXError'):\n        logger.warn('EOXError received from EoX API: {}'.format(resp['EOXError']['ErrorDescription']))\n        return None\n\n    #Else create and save record, return to caller\n    else:\n        new_eox = EoXRecord(\n            eox_product_id = resp['EOLProductID'],\n            eox_announcement_date = cisco_convert_to_datetime(resp['EOXExternalAnnouncementDate']['value']),\n            end_of_routine_failure_analysis = cisco_convert_to_datetime(resp['EndOfRoutineFailureAnalysisDate']['value']),\n            end_of_sw_maintenance = cisco_convert_to_datetime(resp['EndOfSWMaintenanceReleases']['value']),\n            end_of_sale = cisco_convert_to_datetime(resp['EndOfSaleDate']['value']),\n            end_of_security_vuln = cisco_convert_to_datetime(resp['EndOfSecurityVulSupportDate']['value']),\n            end_of_contract_renewal = cisco_convert_to_datetime(resp['EndOfServiceContractRenewal']['value']),\n            last_day_of_support = cisco_convert_to_datetime(resp['LastDateOfSupport']['value']),\n            announcement_url = resp['LinkToProductBulletinURL'],\n            product_description = resp['ProductIDDescription'],\n            updated_time_stamp = cisco_convert_to_datetime(resp['UpdatedTimeStamp']['value'])\n        )   \n        new_eox.save()\n        logger.debug('New EoX record {} saved to database.'.format(new_eox.eox_product_id))\n        return new_eox    \n\ndef create_cisco_device(entry, cust, vendor):\n    '''\n    Add Cisco device.\n    '''\n    new_device = ManagedDevice(\n        name = entry[1],\n        description = entry[9], \n        serial_number = entry[5],\n        current_sw_version = entry[3],\n        customer = cust,\n        mgmt_ip = entry[6],\n        node_type =  entry[4],\n        vendor = vendor\n    )\n\n    session = api_console.CiscoSn2infoApi()\n    resp = session.sn2info_api_call(new_device.serial_number)\n    base_pid = resp['serial_numbers'][0]['orderable_pid_list'][0]['orderable_pid']\n    if resp['serial_numbers'][0]['is_covered'].lower() == 'yes':\n        has_active_support = 1\n    else:\n        has_active_support = 2\n    \n    #set properties from API and save\n    new_device.has_active_support = has_active_support\n    new_device.base_pid = base_pid\n\n    #get coverage end date info if applicable\n    if has_active_support:\n        new_resp = session.sn2info_api_coverage_end_api_call(new_device.serial_number)\n        support_end_date = new_resp['serial_numbers'][0]['coverage_end_date'].split('-')\n        if not support_end_date == ['']:\n            new_device.support_end_date = datetime.datetime(\n                support_end_date[0], \n                support_end_date[1], \n                support_end_date[2]\n            )\n\n    #software suggestions\n    sw_obj = api_console.CiscoSWSuggestionApi()\n    sw_resp = sw_obj.sw_suggestion_api_call(new_device.base_pid)\n    \n    try:\n        new_device.recommended_sw_version = sw_resp['productList'][0]['suggestions'][0]['releaseFormat2']\n    except:\n        new_device.recommended_sw_version = ''\n\n    #EoX record check\n    eox = EoXRecord.objects.filter(eox_product_id=new_device.base_pid).first() \n    if eox:\n        new_device.eox_record = eox\n    else:\n        new_device.eox_record = create_and_return_eox(new_device.serial_number)\n\n    #Save to database, catch unique constraints\n    try:\n        new_device.save()\n        logger.info('Created managed device: {}'.format(new_device.name))\n    except IntegrityError:\n        logger.warn('Unique constraint failed. Duplicate entry: {}'.format(new_device.name))\n\n    #sleep for 3 seconds to avoid rate limiting on API\n    time.sleep(3)\n\n \n","sub_path":"netdevice_database/netdb/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":6731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"248848261","text":"\"\"\"\nThe Graph class contains following information:\n\nnb_nodes: number of nodes in the graph\nnb_edges: number of edges in the graph\nadjacent: adjacent list for each node of the graph\nedges: list of all edges in graph\n\nATTENTION: index of node start from 1, i.e, if graph has V nodes,\nwe will have node 1, ..., V\n\"\"\"\nclass Graph:\n\n  def __init__(self, filename):\n    self.nb_nodes = 0\n    self.nb_edges = 0\n    self.adjacent = []\n    self.edges = []\n    with open(filename) as f:\n      for line in f:\n        if not line:\n          continue\n        tokens = line.split(\" \")\n        if tokens[0] == 'p':\n          self.nb_nodes = int(tokens[2])\n          self.nb_edges = int(tokens[3])\n          self.adjacent = [[] for node in range(0, self.nb_nodes+1)]\n        if tokens[0] == 'e':\n          node1 = int(tokens[1])\n          node2 = int(tokens[2])\n          self.adjacent[node1].append(node2)\n          self.adjacent[node2].append(node1)\n          self.edges.append([node1, node2])\n\n  def __str__(self):\n    return \"Undirected graph \" + str(self.nb_nodes) + \" nodes \" + str(self.nb_edges) + \" edges\"\n","sub_path":"Assignment 4/Graph Coloring/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"154318582","text":"from django.shortcuts import render_to_response, redirect\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.contrib.auth import login\nfrom django.template import RequestContext\n\n\n\ndef index(request):\n    if request.POST:\n        form = AuthenticationForm(request,data=request.POST)\n        if form.is_valid():\n            login(request, form.get_user())\n            if request.session.test_cookie_worked():\n                request.session.delete_test_cookie()            \n            return redirect('device-index')\n    else:\n        form = AuthenticationForm(request)\n    request.session.set_test_cookie()\n    return render_to_response('index.html', {'form': form },\n                                context_instance = RequestContext(request) )\n\n","sub_path":"whitelist/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"472711881","text":"import ROOT\nfrom ROOT import gROOT, gStyle, gPad\nfrom ROOT import TStyle, TPaveStats\nfrom ROOT import TH2F, TF1\n\n\nROOT.gROOT.LoadMacro(\"~/.rootmacro/Belle2Style.C\")\n\nROOT.SetBelle2Style()\ngStyle.SetPadLeftMargin(0.15)\n# gStyle.SetTitleYOffset(1.13)\ngStyle.SetTitleYOffset(1.5)\ngStyle.SetNdivisions(205, 'X')\ngStyle.SetNdivisions(506, 'Y')\ngStyle.SetOptFit(1111)\ncan = ROOT.TCanvas(\"canvas\", \"canvas\", 800, 800)\n\nkey = 'phi0'\n\n\nhistKey = {'z0': 'hdZ0Pt_2;1',\n           'd0': 'hdD0Pt_2;1',\n           'phi0': 'hdPhi0Pt_2;1',\n           'tanl': 'hdtanLPt_2;1',\n           'pt': 'hdPtPt_2;1'}\nyLabel = {'z0': 'z_{0} resolution [cm]',\n          'd0': 'd_{0} resolution [cm]',\n          'phi0': '#phi_{0} resolution',\n          'tanl': 'tan#lambda resolution',\n          'pt': 'P_{t} resolution [%]'}\n\nyRange = {'z0': 0.5,\n          'd0': 0.1,\n          'phi0': 2.0,\n          'tanl': 0.3,\n          'pt': 1.2}\nf = ROOT.TFile('result_aug_rel00-09-02.root')\n# f = ROOT.TFile('result_aug_head.root')\n\n\nh2 = TH2F('h2', 'h2', 10, 0, 5, 12, 0, yRange[key])\nh2.GetXaxis().SetTitle('P_{t} [GeV/c]')\nh2.GetYaxis().SetTitle(yLabel[key])\nhist = f.Get(histKey[key])\nhist.Draw()\nif key == 'pt':\n    fun = hist.FindObject('f1')\n    fun.SetLineWidth(2)\n    fun.SetLineStyle(2)\n\n    st = hist.FindObject(\"stats\")\n    st.SetOptFit(1011)\n    st.SetX1NDC(0.2)\n    st.SetX2NDC(0.45)\n    st.SetY1NDC(0.70)\n    st.SetY2NDC(0.9)\n    st.SetBorderSize(0)\n\n\n'''\nh2 = TH2F('h2','h2',10,-1.5,1.5,100,0,2500)\nh2.GetXaxis().SetTitle('\\Delta z_{0}  [cm]')\nhist = f.Get('hdZ0;1')\n'''\nfmc = ROOT.TFile('result_mc.root')\nhistmc = fmc.Get(histKey[key])\nhistmc.SetStats(0)\nif key == 'pt':\n    histmc.GetFunction('f1').SetBit(TF1.kNotDraw)\n\nh2.Draw()\nhist.SetMarkerColor(2)\n# hist.SetMarkerSize(1)\nhist.SetLineColor(2)\nhist.Draw('same')\nhistmc.SetMarkerColor(4)\nhistmc.SetLineColor(4)\n# histmc.SetMarkerSize(1)\nhistmc.Draw('same')\n\n\nf3 = ROOT.TFile('result_jul_rel00-09-01.root')\n# f3 = ROOT.TFile('result_jul_head.root')\nhist3 = f3.Get(histKey[key])\nhist3.SetMarkerColor(3)\nhist3.SetLineColor(3)\n# hist3.SetMarkerSize(1)\nhist3.SetStats(0)\nif key == 'pt':\n    hist3.GetFunction('f1').SetBit(TF1.kNotDraw)\nhist3.Draw('same')\n\nfmc2 = ROOT.TFile('result_mc_gt277.root')\nhistmc2 = fmc2.Get(histKey[key])\nhistmc2.SetStats(0)\nif key == 'pt':\n    histmc2.GetFunction('f1').SetBit(TF1.kNotDraw)\nhistmc2.SetMarkerColor(7)\nhistmc2.SetLineColor(7)\nhistmc2.Draw('same')\n\n\n'''\nf4 = ROOT.TFile('result_aug_rel00-09-02.root')\nhist4 = f4.Get('hdPtPt_2;1')\nhist4.SetMarkerColor(5)\nhist4.SetLineColor(5)\nhist4.SetStats(0)\nhist4.GetFunction('f1').SetBit(TF1.kNotDraw)\nhist4.Draw('same')\n'''\nleg = ROOT.TLegend(0.5, 0.68, 0.93, 0.9, '', 'NDC')\n# leg.AddEntry(hist3,'Data, Jul. head','lep')\n# leg.AddEntry(hist,'Data, Aug. head','lep')\nleg.AddEntry(hist3, 'Data (July), rel-00-09-01', 'lep')\nleg.AddEntry(hist, 'Data (Aug.), pre-rel-00-09-02', 'lep')\nleg.AddEntry(histmc, 'MC, pre-rel-00-09-02 (prod.)', 'lep')\nleg.AddEntry(histmc2, 'MC, pre-rel-00-09-02 (GT277)', 'lep')\nleg.SetBorderSize(0)\n\n\nleg.Draw()\ncan.Modified()\n","sub_path":"release/cdc/test/plot_performance.py","file_name":"plot_performance.py","file_ext":"py","file_size_in_byte":3059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"427904811","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@created on: 4/18/20,\n@author: Shreesha N,\n@version: v0.0.1\n@system name: badgod\nDescription:\n\n..todo::\n\n\"\"\"\nimport numpy as np\nfrom sklearn.svm import OneClassSVM\nfrom sklearn.metrics import recall_score\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch\nfrom torch import tensor\nimport tqdm\n\n\ndef norm(data):\n    min_val = data.min()\n    max_val = data.max()\n    data = (data - min_val) / (max_val - min_val)\n    return np.mean(data, axis=2)\n\n\ndef uar(y, yhat):\n    return recall_score(y, yhat, average='macro')\n\n\n#\n# class Encoder(nn.Module):\n#     def __init__(self):\n#         super(Encoder, self).__init__()\n#         self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=[1, 2])\n#         self.conv1_bn = nn.BatchNorm2d(64)\n#\n#         self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=[1, 2])\n#         self.conv2_bn = nn.BatchNorm2d(128)\n#         self.pool1 = nn.MaxPool2d(kernel_size=4, stride=1, return_indices=True)\n#         self.dropout0 = nn.Dropout(p=0.4)\n#\n#         self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2)\n#         self.conv3_bn = nn.BatchNorm2d(256)\n#         self.conv4 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=3, stride=[1, 2])\n#         self.conv4_bn = nn.BatchNorm2d(128)\n#         self.pool2 = nn.MaxPool2d(kernel_size=4, stride=1, return_indices=True)\n#\n#         self.conv5 = nn.Conv2d(in_channels=128, out_channels=32, kernel_size=3, stride=[1, 2])\n#         self.conv5_bn = nn.BatchNorm2d(32)\n#\n#     def forward(self, x):\n#         x = x.unsqueeze(1)\n#         # print('x.size() ', x.size())\n#         encoder_op1 = F.relu(self.conv1(x))\n#         # print('conv 1', encoder_op1.size())\n#         encoder_op2 = F.relu(self.conv2(encoder_op1))\n#         # print('conv 2', encoder_op2.size())\n#         encoder_op2_pool, pool1_indices = self.pool1(encoder_op2)\n#         # print('pool1', encoder_op2_pool.size())\n#         encoder_op2_pool = self.dropout0(encoder_op2_pool)\n#\n#         encoder_op3 = F.relu(self.conv3(encoder_op2_pool))\n#         # print('conv 3', encoder_op3.size())\n#         encoder_op4 = F.relu(self.conv4(encoder_op3))\n#         # print('conv 4', encoder_op4.size())\n#         encoder_op4_pool, pool2_indices = self.pool2(encoder_op4)\n#         # print('pool2 ', encoder_op4_pool.size(), pool2_indices.shape)\n#\n#         encoder_op5 = F.relu(self.conv5(encoder_op4_pool))\n#         # print('after conv net 5 ', encoder_op5.size())\n#\n#         return encoder_op5, pool1_indices, pool2_indices\n#\n#\n# class Decoder(nn.Module):\n#     def __init__(self):\n#         super(Decoder, self).__init__()\n#\n#         self.decoder1 = nn.ConvTranspose2d(in_channels=32, out_channels=128, kernel_size=3, stride=[1, 2],\n#                                            output_padding=[0, 1])\n#         self.decoder1_bn = nn.BatchNorm2d(128)\n#         self.unpool1 = nn.MaxUnpool2d(4, stride=1)\n#         self.decoder2 = nn.ConvTranspose2d(in_channels=128, out_channels=256, kernel_size=3, stride=[1, 2])\n#         self.decoder2_bn = nn.BatchNorm2d(256)\n#         self.decoder3 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=3, stride=[2, 2],\n#                                            output_padding=[0, 1])\n#         self.decoder3_bn = nn.BatchNorm2d(128)\n#         self.unpool2 = nn.MaxUnpool2d(4, stride=1)\n#         self.decoder4 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=3, stride=[1, 2],\n#                                            output_padding=[0, 1])\n#         self.decoder4_bn = nn.BatchNorm2d(64)\n#         self.decoder5 = nn.ConvTranspose2d(in_channels=64, out_channels=1, kernel_size=3, stride=[1, 2])\n#         self.decoder5_bn = nn.BatchNorm2d(1)\n#\n#     def forward(self, x, pool1_indices, pool2_indices, final_op_shape):\n#         # decoder\n#         decoder_op1 = F.relu(self.decoder1_bn(self.decoder1(x)))  # , output_size=encoder_op4_pool.size()\n#         # print('decoder1', decoder_op1.size())\n#         decoder_op1_unpool1 = self.unpool1(decoder_op1, indices=pool2_indices)\n#         # print(\"decoder_op1_unpool1\", decoder_op1_unpool1.size())\n#         decoder_op2 = F.relu(self.decoder2_bn(self.decoder2(decoder_op1_unpool1)))  # , output_size=encoder_op3.size()\n#         # print('decoder2', decoder_op2.size())\n#         decoder_op3 = F.relu(self.decoder3_bn(self.decoder3(decoder_op2)))\n#         # print('decoder3', decoder_op3.size())\n#         decoder_op3_unpool2 = self.unpool2(decoder_op3, indices=pool1_indices)\n#         # print(\"decoder_op3_unpool2\", decoder_op3_unpool2.size())\n#\n#         decoder_op4 = F.relu(self.decoder4_bn(self.decoder4(decoder_op3_unpool2)))\n#         # print('decoder4', decoder_op4.size())\n#         reconstructed_x = self.decoder5_bn(self.decoder5(decoder_op4, output_size=final_op_shape))\n#         # print('decoder5', reconstructed_x.size())\n#         return reconstructed_x\n#\n#\n# class ConvAutoEncoder(nn.Module):\n#\n#     def __init__(self):\n#         super(ConvAutoEncoder, self).__init__()\n#         self.encoder = Encoder()\n#         self.decoder = Decoder()\n#\n#     def forward(self, x):\n#         latent_filter_maps, pool1_indices, pool2_indices = self.encoder(x)\n#         reconstructed_x = self.decoder(latent_filter_maps, pool1_indices=pool1_indices, pool2_indices=pool2_indices,\n#                                        final_op_shape=x.size())\n#         return reconstructed_x\n#\n#\n# def get_predcitions(data):\n#     batched_input = [data[pos:pos + batch_size] for pos in\n#                      range(0, len(data), batch_size)]\n#     train_ae_op = []\n#     for batch in tqdm.tqdm(batched_input):\n#         batch = tensor(batch)\n#         pred, _, _ = network.encoder(batch)\n#         pred = pred.view(-1, pred.size()[1:].numel()).detach().numpy()\n#         train_ae_op.extend(pred)\n#     return tensor(train_ae_op)\n#\n#\n# batch_size = 32\n# is_cuda_available = torch.cuda.is_available()\n# device = torch.device(\"cuda\" if is_cuda_available else \"cpu\")\n# network = ConvAutoEncoder().to(device)\n# restore_path = \"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/emotion_alco_audio_challenge_data_cae_bo\" \\\n#                \"th_classes_1587421865/alco_trained_models/emotion_alco_audio_challenge_data_cae_both_classes_1587421865_27.pt\"\n# network.load_state_dict(torch.load(restore_path, map_location=device))\n# read = 10000000\n# train_data = np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/train_challenge_with_d1_data.npy\")[:read]\n# train_labels = np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/train_challenge_with_d1_labels.npy\")[\n#                :read]\n# train_data = norm(train_data)\n#\n# dev_data = np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/dev_challenge_with_d1_data.npy\")[:read]\n# dev_labels = np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/dev_challenge_with_d1_labels.npy\")[:read]\n# dev_data = norm(dev_data)\n#\n# test_data = np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/test_challenge_data.npy\")[:read]\n# test_labels = np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/test_challenge_labels.npy\")[:read]\n# test_data = norm(test_data)\n#\n#\n# print('Running CAE on train')\n# train_data = get_predcitions(tensor(train_data))\n# print('Running CAE on dev')\n# dev_data = get_predcitions(tensor(dev_data))\n# print('Running CAE on test')\n# test_data = get_predcitions(tensor(test_data))\n\ntrain_data = norm(np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/train_challenge_with_d1_data.npy\"))\ntrain_labels = np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/train_challenge_with_d1_labels.npy\")\ndev_data = norm(np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/dev_challenge_with_d1_data.npy\"))\ndev_labels = np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/dev_challenge_with_d1_labels.npy\")\ntest_data = norm(np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/test_challenge_data.npy\"))\ntest_labels = np.load(\"/Users/badgod/badgod_documents/Alco_audio/server_data/2d/test_challenge_labels.npy\")\n\ninliers_ids, outliers_ids = [i for i, x in enumerate(train_labels) if x == 0], [i for i, x in enumerate(train_labels) if\n                                                                                x == 1]\ntrain_inliers, train_outliers = train_data[inliers_ids], train_data[outliers_ids]\n# outlier_prop = len(train_outliers) / len(train_inliers)\nsvm = OneClassSVM(kernel='rbf', nu=0.2, gamma='auto', cache_size=7000)\nsvm.fit(train_inliers)\ndev_y_pred = svm.predict(dev_data)\ntest_y_pred = svm.predict(test_data)\n\ndev_y_pred = [max(0, x) for x in -dev_y_pred]\nprint(uar(dev_labels, dev_y_pred))\n\ntest_y_pred = [max(0, x) for x in -test_y_pred]\nprint(uar(test_labels, test_y_pred))\n","sub_path":"alcoaudio/experiments/oc_svm.py","file_name":"oc_svm.py","file_ext":"py","file_size_in_byte":8888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"279954711","text":"import os, sys\nimport pickle\nimport numpy as np\nimport scipy as sp\nimport scipy.stats as stats\nimport sklearn.metrics\n\n    \ndef pearson(targets, predict):\n    r = stats.pearsonr(targets, predict)[0]\n    return r\n    \ndef spearman(targets, predict):\n    rho = stats.spearmanr(targets, predict)[0]\n    return rho\n    \ndef kendall(targets, predict):\n    tau = stats.kendalltau(targets, predict)[0]\n    return tau\n\ndef r2(targets, predict):\n    r = pearson(targets, predict)\n    return r**2\n        \ndef mae(targets, predict):\n    abs_err = np.abs(targets - predict)\n    return np.mean(abs_err)\n\ndef rmse(targets, predict):\n    sq_err = ((predict - targets) ** 2)\n    return np.sqrt(np.mean(sq_err))\n    \ndef auroc(targets, predict):\n    auc = sklearn.metrics.roc_auc_score(targets, predict)\n    return auc\n    \ndef auprc(targets, predict):\n    ap = sklearn.metrics.average_precision_score(targets, predict)\n    return ap\n\ndef accuracy(targets, predict):\n    ac = sklearn.metrics.accuracy_score(targets, predict)\n    return ac\n    \n    \ndef evaluate_average(results, metric=r2, verbose=True, select=None):\n    \"\"\"\n    Calculate metric for training, validation and test data, averaged over all replicates.\n    \"\"\"\n    # Initialization\n    reps = results.keys()\n    metric_tr = np.empty(len(reps))\n    metric_va = np.empty(len(reps))\n    metric_te = np.empty(len(reps))\n    # Go through training repetitions\n    for r, rep in enumerate(results.keys()):\n        # Load the predictions\n        targets_tr, predict_tr = results[rep]['targets']['train'], results[rep]['predict']['train']\n        targets_va, predict_va = results[rep]['targets']['valid'], results[rep]['predict']['valid']\n        targets_te, predict_te = results[rep]['targets']['test'],  results[rep]['predict']['test']\n        # Calculate Statistics\n        metric_tr[r] = metric(targets_tr, predict_tr)\n        metric_va[r] = metric(targets_va, predict_va)\n        metric_te[r] = metric(targets_te, predict_te)\n        if verbose: print(' - %s  -  Training: %7.3f  -  Validation: %7.3f  -  Test: %7.3f'%(rep, metric_tr[r], metric_va[r], metric_te[r]))\n    # Optionally select training runs with lowest/highest validation metrics\n    if select is not None:\n        order = np.argsort(metric_va)\n        if select<0: \n            order = order[select:]\n        else:\n            order = order[:select]\n        metric_tr = metric_tr[order]\n        metric_va = metric_va[order]\n        metric_te = metric_te[order]\n    # Mean and corresponding standard deviations\n    summary_tr = (np.mean(metric_tr), np.std(metric_tr))\n    summary_va = (np.mean(metric_va), np.std(metric_va))\n    summary_te = (np.mean(metric_te), np.std(metric_te))\n    if verbose: \n        print('---')\n        print(' Training:   %7.3f +/- %7.3f'%summary_tr)\n        print(' Validation: %7.3f +/- %7.3f'%summary_va)\n        print(' Test:       %7.3f +/- %7.3f'%summary_te)\n    return summary_tr, summary_va, summary_te\n\ndef _per_target_mean(res, metric):\n    all_res = []\n    for targets, predictions in res:\n        all_res.append(metric(targets, predictions))\n    return np.mean(all_res)\n\ndef evaluate_per_target_average(results, metric=r2, verbose=True):\n    \"\"\"\n    Calculate metric for training, validation and test data, averaged over all replicates.\n    \"\"\"\n    # Initialization\n    reps = results.keys()\n    metric_tr = np.empty(len(reps))\n    metric_va = np.empty(len(reps))\n    metric_te = np.empty(len(reps))\n    # Go through training repetitions\n    for r, rep in enumerate(results.keys()):\n        # Load the predictions\n        train = results[rep]['train']\n        val = results[rep]['valid']\n        test = results[rep]['test']\n            \n        # Calculate Statistics\n        metric_tr[r] = _per_target_mean(train, metric)\n        metric_va[r] = _per_target_mean(val, metric)\n        metric_te[r] = _per_target_mean(test, metric)\n        \n        if verbose: print(' - %s  -  Training: %7.3f  -  Validation: %7.3f  -  Test: %7.3f'%(rep, metric_tr[r], metric_va[r], metric_te[r]))\n    # Mean and corresponding standard deviations\n    summary_tr = (np.mean(metric_tr), np.std(metric_tr))\n    summary_va = (np.mean(metric_va), np.std(metric_va))\n    summary_te = (np.mean(metric_te), np.std(metric_te))\n    if verbose: \n        print('---')\n        print(' Training:   %7.3f +/- %7.3f'%summary_tr)\n        print(' Validation: %7.3f +/- %7.3f'%summary_va)\n        print(' Test:       %7.3f +/- %7.3f'%summary_te)\n    return summary_tr, summary_va, summary_te\n","sub_path":"atom3d/util/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":4501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"373555610","text":"#! /powerapps/share/python-anaconda-3.6/bin/python\n\nimport sys\nsys.path.insert(0,'/sternadi/home/volume1/taliakustin/SternLab')\nfrom optparse import OptionParser\nimport os\nimport glob\nimport argparse\nfrom Bio import SeqIO\nfrom Bio.Seq import Seq\nimport re\nfrom file_utilities import check_filename, check_dirname\nimport seqFileAnalyzer\nimport pandas as pd\n\ndef main(args):\n    gb_file = check_filename(args.gb)\n    fasta_file = check_filename(args.fasta)\n    gb = list(SeqIO.parse(gb_file, \"gb\"))\n    fasta = list(SeqIO.parse(fasta_file, \"fasta\"))\n    print(len(gb), len(fasta))\n    new_seqs = []\n    j = 0\n    for i in range(len(fasta)):\n        while not gb[j].id in fasta[i].id:\n            j += 1\n\n        s = gb[i]\n        for feature in s.features:\n            if feature.type == \"source\":\n                mol_type = feature.qualifiers[\"mol_type\"][0]\n                if mol_type == \"genomic RNA\" or mol_type == \"genomic DNA\":\n                    temp_seq = fasta[i]\n                    temp_seq.seq = temp_seq.seq.reverse_complement()\n                    new_seqs.append(temp_seq)\n                elif mol_type == \"mRNA\" or mol_type == \"viral cRNA\":\n                    new_seqs.append(fasta[i])\n                else:\n                    print(mol_type)\n                    new_seqs.append(fasta[i])\n    output = fasta_file.split(\".fasta\")[0] + \"_correctly_oriented.fasta\"\n    SeqIO.write(new_seqs, output, \"fasta\")\n\n\n\n\n\n\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-g\", \"--gb\", type=str, help=\"gb file\", required=True)\n    parser.add_argument(\"-f\", \"--fasta\", type=str, help=\"fasta file\", required=True)\n    args = parser.parse_args()\n    main(args)\n\n","sub_path":"scripts/arrange_orientation_of_sequence_files.py","file_name":"arrange_orientation_of_sequence_files.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"208597406","text":"import pygame\n\n# initialize the pygame\npygame.init()\n\n# create screen\n\nSCREEN_HEIGHT = 600\nSCREEN_WIDTH = 800\nGAMEOVER = False\ntext = pygame.font.Font('freesansbold.ttf', 32)\nPOINTS = 0\n\nSCREEN = pygame.display.set_mode([SCREEN_WIDTH, SCREEN_HEIGHT])  # pass a list\n\n# Title and Icon\npygame.display.set_caption(\"space invaders by Taru\")\nicon = pygame.image.load('src/images/icon.png')\npygame.display.set_icon(icon)\n\n# background\nbackgroundImg = pygame.image.load('src/images/BG.png').convert()\n\n# boundary\nBOUNDARY_HEIGHT = 7 * (SCREEN_HEIGHT / 8) - 60\n","sub_path":"defaults.py","file_name":"defaults.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"289805933","text":"#!/usr/bin/env python3\n\n__author__ = \"Dickins Lab\"\n__version__ = \"0.2.0\"\n\n# importing packages\nimport numpy as np\nimport scipy as sp\nimport scipy.stats as stats\nimport scipy.special as special\nimport scipy.constants as constants\nimport matplotlib.pyplot as plt\nplt.style.use(\"ggplot\")\n\n# constants\nkt = float(0.001987204118 * (273.15+37))\ndd_mean, dd_std = 1, 1.7\n\n# user-defined variables\nUd = float(input(\"Please enter Ud [2.4] \") or 2.4) # non-synonymous mutations per genome per replication\nN = int(input(\"Please enter N [1000] \") or 1000) # carrying capacity\nR = int(input(\"Please enter R [2] \") or 2) # effective burst size\nreplicate = int(input(\"Please enter the replicate # [1] \") or 1)\n\n# program-defined variables\nL = 0.1 # proportion of lethal mutations\nG = 10 # number of genes\nsimlen = 50 # number of generations - change me back to 200 please\nF = -5 # initial free energy of all proteins\n\n# viral class\nclass Virus:\n    def __init__(self, genes, deltag):\n        self.genes = genes # stores mutation counts\n        self.deltag = deltag # records current free energy\n        self.fitness = self._fitness() # calculates fitness from free energy\n        \n    def _mutate(self):\n        genes_int, deltag_int = self.genes, self.deltag # lists for mutations and free energies\n        new_mutations = stats.poisson.rvs(Ud, size=1) # no. of new mutations\n        if np.random.rand(1) > np.power((1-L), new_mutations): # fraction L of mutations are lethal\n            return None, None # so don't bother calculating, just return nothing\n        else:\n            ddG_values = stats.norm.rvs(loc=dd_mean, scale=dd_std, size=new_mutations) # deltadeltaG from Gaussian\n            mutgene_coord = np.random.randint(0, G, new_mutations) # picking gene destined to have these mutations\n            for index, gene_id in enumerate(mutgene_coord):\n                genes_int[gene_id] += 1\n                deltag_int[gene_id] = self.deltag[gene_id] + ddG_values[index]\n                if deltag_int[gene_id] > 0: #in this case the fitness is zero\n                    return None, None # so just return nothing and break out of function\n            return genes_int, deltag_int\n\n    def _fitness(self): # calculate fitness from product function\n        return np.prod([ 1 / (1 + np.exp(dg/kt)) for dg in np.nditer(self.deltag) ])\n    \n    def reproduce(self):\n        sprog = Virus(np.copy(self.genes), np.copy(self.deltag))\n        sprog.genes, sprog.deltag = sprog._mutate()\n        try:\n            if sprog.genes.any(): # if lethal mutate() will have returned a None value\n                sprog.fitness = sprog._fitness()\n                return sprog\n        except:\n            return None # if Nones were returned, kill the sprog\n        \n    def __repr__(self): # for diagnostic printingS\n        return \"M\"+str(np.sum(self.genes))+\" F\"+str(self.fitness)\n\n# initialise population\ndef initialise():\n    population = [ Virus(np.zeros(G, dtype=int), np.array([F]*G, dtype=float)) for _ in range(N) ]\n    return population\n\n# function to help with multinomial picks\ndef get_weights(population):\n    weights = [v.fitness for v in population]\n    return weights/sum(weights)\n\n# reporting function\ndef report_update(psiz, fit, free, mut):\n    psiz.append( len(population) )\n    fit.append( np.mean([v.fitness for v in population]) )\n    free.append( np.mean([np.min(v.deltag) for v in population]) )\n    mut.append( np.mean([sum(v.genes) for v in population]) )\n    \n# main simulation\npopulation = initialise()\npopsize = N\nreport_psiz, report_fit, report_free, report_mut = [], [], [], []\n\nwhile simlen > 0:\n    simlen -= 1\n    for n in range(N):\n        if popsize > N:\n            population = list( np.random.choice(population, N, p = get_weights(population), replace=False) )\n            popsize = N # why calculate when you can set?\n        elif popsize == 0:\n            simlen = 0 # they're all dead so let's end early (this is why I chose while loop)\n            \n        if popsize != 0: # pull an individual out of the population\n            parent_index = np.random.choice(range(popsize), p=get_weights(population))\n            parent = population.pop(parent_index)\n\n            for r in range(R): # not sure whether to do this Moran process for R>2\n                child = parent.reproduce()\n                if child: # might be stillborn\n                    population.append(child)\n                    \n        popsize = len(population)\n                    \n    if popsize > 0:\n        report_update(report_psiz, report_fit, report_free, report_mut)\n\nwith open(\"simres_Ud({})_N({})_R({})_rep({}).csv\".format(Ud, N, R, replicate), \"w\") as outfile:\n    for a, b, c, d in zip(report_psiz, report_fit, report_free, report_mut):\n        print(\"{},{},{},{}\".format(a, b, c, d), file = outfile)\n\n# Now let's graph the changes\nfig, axs = plt.subplots(2, 2, sharex=True, figsize=(16, 12))  # dpi= 150\n\naxs[0, 0].plot(report_fit)\naxs[0, 0].set(ylim=[0, 1], ylabel=\"mean fitness\", title=\"Fitness\")\n\naxs[0, 1].plot(report_psiz)\naxs[0, 1].set(ylim=[0, max(report_psiz)+10], ylabel=\"# viruses\", title=\"Population Size\")\n\naxs[1, 0].plot(report_free)\naxs[1, 0].set(ylabel=\"mean of (min \\u0394G)\", title=\"Free Energy\")\n\naxs[1, 1].plot(report_mut)\naxs[1, 1].set(ylabel=\"mean # mutations\", title=\"Number of Mutations\")\nplt.savefig(\"simres_Ud({})_N({})_R({})_rep({}).png\".format(Ud, N, R, replicate))\n","sub_path":"dfem_runsolo_v_0_2.py","file_name":"dfem_runsolo_v_0_2.py","file_ext":"py","file_size_in_byte":5391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"470161489","text":"import json\nimport os\nfrom datetime import datetime\n\nTEMPLATE = []\n\n# VALIDATORS\n\ndef validate_datetime(date):\n    try:\n        datetime.strptime(date, '%Y-%m-%d %H:%M:%S')\n        return True\n    except ValueError:\n        return False\n\ndef validate_array(array):\n    if len(array) == 0:\n        return True\n\n    for item in array:\n        if all([isinstance(item, list), len(item) == 2,\n                 isinstance(item[0], str), validate_datetime(item[1])]):\n            return True\n    return False\n\ndef validate_json(file):\n    if os.path.isfile(file):\n        try:\n            with open(file, 'r') as f:\n                json_file = json.load(f)\n                f.close()\n            return json_file\n\n        except ValueError:\n            print(\"Could not read events.json file\")\n\n    return False\n\n# MERGESORT\n\ndef merge(left, right):\n    new_array = []\n\n    while len(left) > 0 and len(right) > 0:\n        if datetime.strptime(left[0][1], '%Y-%m-%d %H:%M:%S') <= datetime.strptime(right[0][1], '%Y-%m-%d %H:%M:%S'):\n            new_array.append(left.pop(0))\n        else:\n            new_array.append(right.pop(0))\n\n    while len(left) > 0:\n        new_array.append(left.pop(0))\n\n    while len(right) > 0:\n        new_array.append(right.pop(0))\n\n    return new_array\n\ndef mergesort(array):\n    if len(array) <= 1:\n        return array\n\n    half_len = len(array) // 2\n    l_array = array[:half_len]\n    r_array = array[half_len:]\n\n    l_array = mergesort(l_array)\n    r_array = mergesort(r_array)\n\n    return merge(l_array, r_array)\n\n# THE LIST\n\nclass EventList:\n    def __init__(self):\n        self.filename = 'events.json'\n        self.list = self.load_from_file()\n\n    def print_event_list(self):\n        for index, event in enumerate(self.list):\n            print(\"{}: {} at {}\".format(index, event[0], event[1]))\n\n    def load_from_file(self):\n        events = validate_json(self.filename)\n        if events is False:\n            with open(filename, 'w') as f:\n                json.dump(TEMPLATE, f)\n                f.close()\n\n        return mergesort(events)\n\n    def save_file(self):\n        with open(self.filename, 'w') as f:\n            json.dump(self.list, f)\n            f.close()\n        self.list = self.load_from_file()\n\n    def add_event(self, event):\n        self.list.append(event)\n        self.save_file()\n        print(\"Event added with success\")\n\n    def edit_event(self, name, new_name, new_date):\n        if validate_array([[new_name, new_date]]):\n            self.del_event(name)\n            self.add_event([new_name, new_date])\n            self.save_file()\n        else:\n            print(\"Invalid name and/or date\")\n\n    def del_event(self, name):\n        for e in self.list:\n            if e[0] == name:\n                self.list.remove(e)\n                self.save_file()\n\n    def event_exists_by_name(self, e_name):\n        for e in self.list:\n            if e_name == e[0]:\n                return True\n        return False\n\n    def __str__(self):\n        return str(self.list)\n\n\ndef interface(schedule):\n    while True:\n        schedule.print_event_list()\n        action = input(\"Add, Edit, Del or Exit: \")\n\n        if action == \"Add\":\n            new_ev = [input(\"Choose a name for the event: \"),\n                       input(\"Choose a date for the event: \")]\n\n            if (not schedule.event_exists_by_name(new_ev[0]) and\n                validate_array([new_ev])):\n                    schedule.add_event(new_ev)\n            else:\n                print(\"Invalid name and/or date\")\n\n\n        elif action == \"Edit\":\n            e_name = input(\"Insert the name of the event to edit: \")\n            if schedule.event_exists_by_name(e_name):\n                new_name = input(\"Choose a new name for the event: \")\n                new_date = input(\"Choose a new date for the event: \")\n                schedule.edit_event(e_name, new_name, new_date)\n            else:\n                print(\"{} event does not exist\".format(e_name))\n\n        elif action == \"Del\":\n            e_name = input(\"Insert the name of the event to delete: \")\n            if schedule.event_exists_by_name(e_name):\n                schedule.del_event(e_name)\n            else:\n                print(\"{} event does not exist\".format(e_name))\n\n        elif action == \"Exit\":\n            exit()\n\nif __name__ == '__main__':\n    schedule = EventList()\n    interface(schedule)\n\n\n\n","sub_path":"intermediate/challenge #1/event_organizer.py","file_name":"event_organizer.py","file_ext":"py","file_size_in_byte":4379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"217297477","text":"# uncompyle6 version 3.6.7\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: build/bdist.macosx-10.7-x86_64/egg/airflow/models/skipmixin.py\n# Compiled at: 2019-09-11 03:47:34\n# Size of source mod 2**32: 4136 bytes\nfrom airflow.models.taskinstance import TaskInstance\nfrom airflow.utils import timezone\nfrom airflow.utils.db import provide_session\nfrom airflow.utils.log.logging_mixin import LoggingMixin\nfrom airflow.utils.state import State\nimport six\nfrom typing import Union, Iterable, Set\n\nclass SkipMixin(LoggingMixin):\n\n    @provide_session\n    def skip(self, dag_run, execution_date, tasks, session=None):\n        \"\"\"\n        Sets tasks instances to skipped from the same dag run.\n\n        :param dag_run: the DagRun for which to set the tasks to skipped\n        :param execution_date: execution_date\n        :param tasks: tasks to skip (not task_ids)\n        :param session: db session to use\n        \"\"\"\n        if not tasks:\n            return\n        task_ids = [d.task_id for d in tasks]\n        now = timezone.utcnow()\n        if dag_run:\n            session.query(TaskInstance).filter(TaskInstance.dag_id == dag_run.dag_id, TaskInstance.execution_date == dag_run.execution_date, TaskInstance.task_id.in_(task_ids)).update({TaskInstance.state: State.SKIPPED, \n             TaskInstance.start_date: now, \n             TaskInstance.end_date: now},\n              synchronize_session=False)\n            session.commit()\n        else:\n            assert execution_date is not None, 'Execution date is None and no dag run'\n            self.log.warning('No DAG RUN present this should not happen')\n            for task in tasks:\n                ti = TaskInstance(task, execution_date=execution_date)\n                ti.state = State.SKIPPED\n                ti.start_date = now\n                ti.end_date = now\n                session.merge(ti)\n\n            session.commit()\n\n    def skip_all_except(self, ti, branch_task_ids):\n        \"\"\"\n        This method implements the logic for a branching operator; given a single\n        task ID or list of task IDs to follow, this skips all other tasks\n        immediately downstream of this operator.\n        \"\"\"\n        self.log.info('Following branch %s', branch_task_ids)\n        if isinstance(branch_task_ids, six.string_types):\n            branch_task_ids = [\n             branch_task_ids]\n        dag_run = ti.get_dagrun()\n        task = ti.task\n        dag = task.dag\n        downstream_tasks = task.downstream_list\n        if downstream_tasks:\n            branch_downstream_task_ids = set()\n            for b in branch_task_ids:\n                branch_downstream_task_ids.update(dag.get_task(b).get_flat_relative_ids(upstream=False))\n\n            skip_tasks = [t for t in downstream_tasks if t.task_id not in branch_task_ids if t.task_id not in branch_downstream_task_ids]\n            self.log.info('Skipping tasks %s', [t.task_id for t in skip_tasks])\n            self.skip(dag_run, ti.execution_date, skip_tasks)","sub_path":"pycfiles/apache_beam-2.20.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64/skipmixin.cpython-36.py","file_name":"skipmixin.cpython-36.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"190398140","text":"\"\"\"bin_noise_maps_agave.py\n\nThis version of the script has hard-coding to facilitate\nparallelization on Agave cluster.\n\n\"\"\"\nimport os\nimport itertools\nimport argparse\nfrom datetime import datetime\n\nimport numpy as np\nimport xarray as xr\nimport dask\nfrom dask.distributed import Client, LocalCluster\n\n\nFILTER_TRIM_PIX = {'1': [6, -5], '2': [12, -11], '4': [25, -24], '8': [50, -49]}\nBIN_WIDTH_PIX = {'1': 12, '2': 25, '4': 50, '8': 100}\n\n\ndef bin_data(params):\n    start_time = datetime.now()\n\n    bw, fbw, t, s, f = params\n\n    # Get input and output file path\n    # Get input and output file path\n    input_file = '/scratch/pkittiwi/fg1p/noise_map/' \\\n        'bin0.08/fbw{:.2f}/theta{:.1f}/shift{:d}/' \\\n        'noise_map_bin0.08_fbw{:.2f}_theta{:.1f}_shift{:d}_{:03d}.nc' \\\n        .format(fbw, t, s, fbw, t, s, f)\n    output_file = '/scratch/pkittiwi/fg1p/noise_map/' \\\n        'bin{:.2f}/fbw{:.2f}/theta{:.1f}/shift{:d}/noise_map_' \\\n        'bin{:.2f}_fbw{:.2f}_theta{:.1f}_shift{:d}_{:03d}.nc' \\\n        .format(bw, fbw, t, s, bw, fbw, t, s, f)\n\n    # Check output file directory\n    output_dir = output_file.rsplit('/', 1)[0]\n    if not os.path.exists(output_dir):\n        os.makedirs(output_dir)\n\n    # Read in actual data\n    noise = xr.open_dataarray(input_file)\n\n    # Change units of noise coordinates from (Hz, deg, deg)\n    # to (Hz, rad, rad) to match noise coordinates\n    noise.coords['y'] = noise.coords['y'] * np.pi / 180\n    noise.coords['x'] = noise.coords['x'] * np.pi / 180\n\n    # Set pixels outside filter boundary to NaN values.\n    # Indexing object must be DataArray for XArray broadcasting to work\n    fcut = FILTER_TRIM_PIX[str(int(bw))]\n    noise_masked = noise.where(\n        (noise['f'] > noise['f'][fcut[0]]) &\n        (noise['f'] < noise['f'][fcut[1]])\n    )\n\n    # Perform frequency binning\n    bin_size = BIN_WIDTH_PIX[str(int(bw))]\n    roll = noise_masked.rolling(f=bin_size, center=True)\n    binned_data = roll.mean()\n\n    # Add some attributes to the output DataArray to keep track of parameters\n    binned_data.attrs['noise_file'] = input_file\n    binned_data.attrs['bin_width'] = bw\n    binned_data.attrs['bin_width_unit'] = 'MHz'\n    binned_data.attrs['channel_width'] = 0.08\n    binned_data.attrs['channel_width_unit'] = 'MHz'\n    binned_data.attrs['xunit'] = 'rad'\n    binned_data.attrs['yunit'] = 'rad'\n    binned_data.attrs['funit'] = 'Hz'\n\n    # Save output\n    binned_data.to_netcdf(output_file)\n\n    # Print some info\n    print('Finish: noise_file={:s}, bin_width={:.2f}, '\n          'output_file{:s}. Time spent: {:.5f}.'\n          .format(input_file, bw, output_file,\n                  (datetime.now() - start_time).total_seconds()))\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        '--bin_width', nargs='+', type=float, required=True,\n        help='Frequency binning bin width [MHz]'\n    )\n    parser.add_argument(\n        '--filter_bandwidth', nargs='+', type=float, required=True,\n        help='Filter bandwidth [MHz]'\n    )\n    parser.add_argument(\n        '--theta', nargs='+', type=float, required=True,\n        help='Instrument max FoV from the field center. [deg]'\n    )\n    parser.add_argument(\n        '--shift', nargs='+', type=int, required=True,\n        help='Wedge shift in kz [pixel]'\n    )\n    parser.add_argument(\n        '--field', nargs='+', type=int, required=True,\n        help='Field number [0, 20]'\n    )\n    dask_options = parser.add_argument_group('Dask parameters')\n    dask_options.add_argument(\n        '--n_workers', type=int, default=None,\n        help='Number of workers to start in a LocalCluster. '\n             'Default will use all available cpus.'\n    )\n    dask_options.add_argument(\n        '--processes', action='store_true',\n        help='Start workers as processes instead of as threads.'\n    )\n    dask_options.add_argument(\n        '--threads_per_worker', type=int, default=1,\n        help='Number of threads per each worker. Default is 1'\n    )\n    dask_options.add_argument(\n        '--scheduler_port', type=int, default=0,\n        help='Port of Dask scheduler. Default (0) will choose a random port.'\n    )\n    dask_options.add_argument(\n        '--diagnostics_port', type=int, default=0,\n        help='Port of Dask dashboard. Default (0) will choose a random port.'\n    )\n    dask_options.add_argument(\n        '--worker_local_dir', type=str,\n        help='Directory to place local worker resources.'\n    )\n    args = parser.parse_args()\n\n    cluster = LocalCluster(n_workers=args.n_workers, processes=args.processes,\n                           threads_per_worker=args.threads_per_worker,\n                           scheduler_port=args.scheduler_port,\n                           diagnostics_port=args.diagnostics_port,\n                           local_dir=args.worker_local_dir)\n    client = Client(cluster)\n\n    print(cluster.dashboard_link)\n\n    results = []\n    for p in itertools.product(\n        args.bin_width, args.filter_bandwidth, args.theta,\n        args.shift, args.field\n    ):\n        d = dask.delayed(bin_data)(p)\n        results.append(d)\n    result = dask.compute(*results)\n    client.close()\n    cluster.close()\n","sub_path":"scripts/fg1p/bin_noise_maps_agave.py","file_name":"bin_noise_maps_agave.py","file_ext":"py","file_size_in_byte":5181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"483632310","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.5 (3351)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/www.django/makarthy/codenerix_cms/migrations/0004_auto_20170428_0850.py\n# Compiled at: 2017-11-28 07:16:52\n# Size of source mod 2**32: 1164 bytes\nfrom __future__ import unicode_literals\nfrom django.db import migrations, models\n\nclass Migration(migrations.Migration):\n    dependencies = [\n     ('codenerix_cms', '0003_auto_20170418_1515')]\n    operations = [\n     migrations.AddField(model_name='sliderelementtexten', name='name_file', field=models.CharField(blank=True, max_length=254, null=True, verbose_name='Name')),\n     migrations.AddField(model_name='sliderelementtextes', name='name_file', field=models.CharField(blank=True, max_length=254, null=True, verbose_name='Name')),\n     migrations.AddField(model_name='staticheaderelementtexten', name='name_file', field=models.CharField(blank=True, max_length=254, null=True, verbose_name='Name')),\n     migrations.AddField(model_name='staticheaderelementtextes', name='name_file', field=models.CharField(blank=True, max_length=254, null=True, verbose_name='Name'))]","sub_path":"pycfiles/django_codenerix_cms-1.0.16-py2.py3-none-any/0004_auto_20170428_0850.cpython-35.py","file_name":"0004_auto_20170428_0850.cpython-35.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"628669265","text":"# search.py\n# ---------\n# Licensing Information: Please do not distribute or publish solutions to this\n# project. You are free to use and extend these projects for educational\n# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by\n# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).\n# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html\n\n\"\"\"\nIn search.py, you will implement generic search algorithms which are called \nby Pacman agents (in searchAgents.py).\n\"\"\"\n\nimport util\n\nclass SearchProblem:\n  \"\"\"\n  This class outlines the structure of a search problem, but doesn't implement\n  any of the methods (in object-oriented terminology: an abstract class).\n  \n  You do not need to change anything in this class, ever.\n  \"\"\"\n  \n  def getStartState(self):\n     \"\"\"\n     Returns the start state for the search problem \n     \"\"\"\n     util.raiseNotDefined()\n    \n  def isGoalState(self, state):\n     \"\"\"\n       state: Search state\n    \n     Returns True if and only if the state is a valid goal state\n     \"\"\"\n     util.raiseNotDefined()\n\n  def getSuccessors(self, state):\n     \"\"\"\n       state: Search state\n     \n     For a given state, this should return a list of triples, \n     (successor, action, stepCost), where 'successor' is a \n     successor to the current state, 'action' is the action\n     required to get there, and 'stepCost' is the incremental \n     cost of expanding to that successor\n     \"\"\"\n     util.raiseNotDefined()\n\n  def getCostOfActions(self, actions):\n     \"\"\"\n      actions: A list of actions to take\n \n     This method returns the total cost of a particular sequence of actions.  The sequence must\n     be composed of legal moves\n     \"\"\"\n     util.raiseNotDefined()\n           \n\ndef tinyMazeSearch(problem):\n  \"\"\"\n  Returns a sequence of moves that solves tinyMaze.  For any other\n  maze, the sequence of moves will be incorrect, so only use this for tinyMaze\n  \"\"\"\n  from game import Directions\n  s = Directions.SOUTH\n  w = Directions.WEST\n  return  [s,s,w,s,w,w,s,w]\n\ndef depthFirstSearch(problem):\n  \"\"\"\n  Search the deepest nodes in the search tree first [p 85].\n  \n  Your search algorithm needs to return a list of actions that reaches\n  the goal.  Make sure to implement a graph search algorithm [Fig. 3.7].\n  \n  To get started, you might want to try some of these simple commands to\n  understand the search problem that is being passed in:\n  \n  print \"Start:\", problem.getStartState()\n  print \"Is the start a goal?\", problem.isGoalState(problem.getStartState())\n  print \"Start's successors:\", problem.getSuccessors(problem.getStartState())\n  \"\"\"\n  \"*** YOUR CODE HERE ***\"\n  #util.raiseNotDefined()\n\n  #create a stack to expand nodes\n  fringe = []\n  #create a list to store visited nodes\n  explored= []\n  #create a dictionary to store (node a : [a's previous node, the direction])\n  #we will use this relationship to trace back the solution route\n  previous = dict()\n\n  # map-version depthFirstSearch starts here\n  startState = problem.getStartState()\n  fringe.append(startState)\n  fringe.append(startState)\n\n  while (len(fringe)):\n    currentNode=fringe.pop()\n    \n    if (problem.isGoalState(currentNode)):\n      #track back to get the solution\n      return traceBackSolution(currentNode,startState,previous)\n    else:\n      #expand nodes\n      expandNodes(currentNode,explored,problem,previous,fringe)\n        \ndef traceBackSolution(currentNode,startState,previous):\n  \"a help-function designed specifically to trace back the solution route\"\n  solution=[]\n  from game import Directions\n  while ((currentNode in previous) and (currentNode!=startState)):\n    solution.insert(0,previous[currentNode][1])\n    currentNode=previous[currentNode][0]\n  return solution\n\ndef expandNodes(currentNode,explored,problem,previous,fringe):\n  \"a help function designed specifically to expand nodes\"\n  successors = problem.getSuccessors(currentNode)\n  for i in successors:\n    if (not(i[0] in explored)):\n      fringe.append(i[0])\n      previous[i[0]]=[currentNode,i[1]]\n      explored.append(i[0])\n\n          \ndef breadthFirstSearch(problem):\n  \"Search the shallowest nodes in the search tree first. [p 81]\"\n  \"*** YOUR CODE HERE ***\"\n  #create a queue to exand nodes\n  fringe = []\n  #create a list to store visited nodes\n  explored= []\n  #create a dictionary to store (node a : [a's previous node, the direction])\n  #we will use this relationship to trace back the solution route\n  previous = dict()\n\n  #map-version Breath-first Search starts here\n  startState=problem.getStartState()\n  fringe.append(startState)\n  explored.append(startState)\n\n  while (len(fringe)):\n    currentNode = fringe.pop(0)\n    if (problem.isGoalState(currentNode)):\n      #trace back to get the solution route\n      return traceBackSolution(currentNode,startState,previous)\n    else:\n      #expand nodes\n      expandNodes(currentNode,explored,problem,previous,fringe)        \n      \n    \n\n      \ndef uniformCostSearch(problem):\n  \"Search the node of least total cost first. \"\n  \"*** YOUR CODE HERE ***\"\n  #create a min-heap to record and expand nodes:   heap[0] is always the lowest cost\n  heap=[]\n  #create a list to record what is on the heap: pos[0] is the state that is on heap[0]\n  pos=[]\n  #create a list to store visited nodes\n  explored= []\n  #create a dictionary to store (node a: [a's previous node, the direction])\n  #we will use this relationship to trace back the solution route\n  previous=dict()\n\n  #map-version uniform-cost search starts here\n  startState= problem.getStartState()\n  heap.append(0)\n  pos.append(startState)\n\n  while (len(heap)):\n    #extract the state with lowest cost\n    currentNode=pos[0]\n    currentNodeCost=heap[0]\n    deleteMinFromHeap(heap,pos)\n    explored.append(currentNode)\n\n    if (problem.isGoalState(currentNode)):\n      #track back to get the solution\n      return traceBackSolution(currentNode,startState,previous)\n    else:\n      #expand nodes\n      successors=problem.getSuccessors(currentNode)\n      for i in successors:\n        if (not(i[0] in explored)):\n          if (i[0] in pos):\n            #the successor is already in the heap\n            positionOfSuccessorInHeap=pos.index(i[0])\n            if (i[2]+currentNodeCost<heap[positionOfSuccessorInHeap]):\n              #decrease the successor's cost in the heap\n              heap[positionOfSuccessorInHeap]=i[2]+currentNodeCost\n              bubbleUp(heap,pos,positionOfSuccessorInHeap)\n              previous[i[0]]=[currentNode,i[1]]\n          else:\n            #the successor is not in the heap\n            heap.append(i[2]+currentNodeCost)\n            pos.append(i[0])\n            bubbleUp(heap,pos,len(heap)-1)\n            previous[i[0]]=[currentNode,i[1]]\n    \n    \n\ndef deleteMinFromHeap(heap,pos):\n  \"A help function designed specifically to delete the min from heap, and maintain its heap property\"\n  heap[0]=heap[-1]\n  pos[0]=pos[-1]\n  heap.pop()\n  pos.pop()\n\n  current=0\n  leftChild=2*current+1\n  rightChild=2*current+2\n  while ((leftChild<len(heap) and heap[current]>heap[leftChild]) or (rightChild<len(heap) and heap[current]>heap[rightChild])):\n    if (rightChild>=len(heap) or heap[rightChild]>heap[leftChild]):\n      heap[current],heap[leftChild]=heap[leftChild],heap[current]\n      pos[current],pos[leftChild]=pos[leftChild],pos[current]\n      current=leftChild\n    else:\n      heap[current],heap[rightChild] = heap[rightChild],heap[current]\n      pos[current],pos[rightChild] = pos[rightChild],pos[current]\n      current=rightChild\n    leftChild=2*current+1\n    rightChild=2*current+2\n\ndef bubbleUp(heap,pos,positionOfSuccessorInHeap):\n  \"A help function designed specifically to main heap property, after the priority of some element in the heap is updated or some element is added into the heap\"\n  parent = (positionOfSuccessorInHeap-1)/2\n  while (parent>=0 and heap[positionOfSuccessorInHeap]<=heap[parent]):\n    heap[parent],heap[positionOfSuccessorInHeap]=heap[positionOfSuccessorInHeap],heap[parent]\n    pos[parent],pos[positionOfSuccessorInHeap]=pos[positionOfSuccessorInHeap],pos[parent]\n    positionOfSuccessorInHeap=parent\n    parent=(positionOfSuccessorInHeap-1)/2\n\ndef nullHeuristic(state, problem=None):\n  \"\"\"\n  A heuristic function estimates the cost from the current state to the nearest\n  goal in the provided SearchProblem.  This heuristic is trivial.\n  \"\"\"\n  return 0\n\ndef aStarSearch(problem, heuristic=nullHeuristic):\n  \"Search the node that has the lowest combined cost and heuristic first.\"\n  \"*** YOUR CODE HERE ***\"\n  #create a min-heap to record and expand nodes:   heap[0] is always the lowest cost\n  heap=[]\n  #create a list to record what is on the heap: pos[0] is the state that is on heap[0]\n  pos=[]\n  #create a list to store visited nodes\n  explored= []\n  #create a dictionary to store (node a: [a's previous node, the direction])\n  #we will use this relationship to trace back the solution route\n  previous=dict()\n\n  #map-version uniform-cost search starts here\n  startState= problem.getStartState()\n  heap.append(0+heuristic(startState,problem))\n  pos.append(startState)\n\n  while (len(heap)):\n    #extract the state with lowest cost\n    currentNode=pos[0]\n    currentNodeCost=heap[0]-heuristic(currentNode,problem) #heap[0] includes the heuristic value of the node on heap[0]; we need to get rid of the value from currentNodeCost. So currentNodeCost= the cost from startState to the currentNode\n    deleteMinFromHeap(heap,pos)\n    explored.append(currentNode)\n\n    if (problem.isGoalState(currentNode)):\n      #track back to get the solution\n      return traceBackSolution(currentNode,startState,previous)\n    else:\n      #expand nodes\n      successors=problem.getSuccessors(currentNode)\n      for i in successors:\n        if (not(i[0] in explored)):\n          estimatedCostToGoal=heuristic(i[0],problem)\n          if (i[0] in pos):\n            #the successor is already in the heap\n            positionOfSuccessorInHeap=pos.index(i[0])\n            if (i[2]+currentNodeCost+estimatedCostToGoal<heap[positionOfSuccessorInHeap]):\n              #decrease the successor's cost in the heap\n              heap[positionOfSuccessorInHeap]=i[2]+currentNodeCost+estimatedCostToGoal\n              bubbleUp(heap,pos,positionOfSuccessorInHeap)\n              previous[i[0]]=[currentNode,i[1]]\n          else:\n            #the successor is not in the heap\n            heap.append(i[2]+currentNodeCost+estimatedCostToGoal)\n            pos.append(i[0])\n            bubbleUp(heap,pos,len(heap)-1)\n            previous[i[0]]=[currentNode,i[1]]\n  \n    \n  \n# Abbreviations\nbfs = breadthFirstSearch\ndfs = depthFirstSearch\nastar = aStarSearch\nucs = uniformCostSearch","sub_path":"layouts/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":10665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"214122103","text":"import sys\nfrom functools import reduce\n\nmultDict = {\n    ('i', 'j'): 'k',\n    ('j', 'i'): '-k',\n    ('j', 'k'): 'i',\n    ('k', 'j'): '-i',\n    ('i', 'k'): '-j',\n    ('k', 'i'): 'j'\n}\n\ndef neg(s):\n    ''' Negate a string. '''\n    return '-' + s if len(s) == 1 else s[1]\n\ndef mult(a, b):\n    ''' Quaternion multiplication of a, b (for single strings). '''\n    if a == '1': return b\n    if b == '1': return a\n    if a == '-1': return neg(b)\n    if b == '-1': return neg(a)\n    if a == b: return '-1'\n    if a[0] == '-': return neg(mult(neg(a), b))\n    if b[0] == '-': return neg(mult(a, neg(b)))\n    return multDict[(a, b)]\n\ndef test():\n    # For any s in {ijk}*, s^4 = 1\n    # Thus we can take the multiple of the string modulo 12\n    # (For each of i, j k in the end result)\n    # Also since the result must be ijk = -1 then the string repeated (X mod 4)\n    # times should multiply out to be -1 as well\n    l, x = map(int, sys.stdin.readline().strip().split())\n    s = sys.stdin.readline().strip()\n\n    # Check that the string should result in -1\n    product = '1'\n    repeats = x % 4\n    if repeats == 0: return 'NO'\n    product = reduce(mult, s, '1')\n    if product == '1':\n        # Product of 1 is 1 repeated any number of times\n        return 'NO'\n    elif product == '-1':\n        if x % 2 == 0:\n            # Product was -1 and repeated even number of times is 1\n            return 'NO'\n    elif x % 4 != 2:\n        # Product of i, j, k -i, -j, -k is -1 iff repeated 2 mod 4 times\n        return 'NO'\n\n    # Well now every multiple of 4 past 12 is useless\n    if x > 12:\n        x = 12 + x % 4\n\n    # Idea: if we find the product is `i` at position a, then the product\n    # is later `i` at position b it means that [a+1:b+1] is 1 so there\n    # is no difference if that part is included in the `k` either.\n    product = '1'\n    searching = 'i'\n    for c in s * x:\n        product = mult(product, c)\n        if product != 'k' and product == searching:\n            if searching == 'i':\n                # Start looking for j now\n                searching = 'j'\n                product = '1'\n            elif searching == 'j':\n                # Start looking for k now\n                searching = 'k'\n                product = '1'\n    if searching == product == 'k':\n        return 'YES'\n    return 'NO'\n\n\ndef main(n):\n    ''' Read and perform n test cases. '''\n\n    for i in range(n):\n        answer = test()\n        print('Case #{}: {}'.format(i+1, answer))\n\nif __name__ == '__main__':\n    n = int(sys.stdin.readline())\n    main(n)\n\n","sub_path":"solutions_5670465267826688_0/Python/WalrusCow/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":2540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"394123793","text":"# -*- coding: utf-8 -*-\n\n\ndef get_max_num(nums):\n    \"\"\"\n    letcode: 最大子序和\n    用动态规划法实现：\n     求局部最优解\n    :param nums:\n    :return:\n    \"\"\"\n    compare_num_list = []\n    for i in range(1, len(nums)):\n        if nums[i - 1] > 0:\n            compare_num = nums[i - 1] + nums[i]\n            compare_num_list.append(compare_num)\n            nums[i] = compare_num\n    return max(nums) if len(nums) > 0 else 0\n\n\ndef del_rep_num(nums):\n    \"\"\"\n    letcode: 删除排序数组中的重复项\n    快慢指针\n    :param nums:\n    :return:\n    \"\"\"\n    pre_index = 0\n    for _back_index in range(1, len(nums)):\n        if nums[_back_index] > nums[pre_index]:\n            nums[pre_index + 1] = nums[_back_index]\n            pre_index += 1\n    return pre_index + 1\n\n\ndef reverse_list(k, nums):\n    \"\"\"\n    letcode: 反转数组\n    快慢指针\n    :param nums:\n    :return:\n    \"\"\"\n    n = len(nums)\n    k %= n\n    nums[:] = nums[::-1]\n    nums[:k] = nums[:k][::-1]\n    # print(nums)\n    nums[k:] = nums[k:][::-1]\n    # print(nums)\n\n\nif __name__ == \"__main__\":\n\n    # nums = [-2, 1, -3, 4, -1, 2, 1, -5, 4]\n    # max_sum = get_max_num(nums)\n    # print(max_sum)\n\n   nums = [1, 1, 2]\n   list_num = del_rep_num(nums)\n   print(list_num)\n","sub_path":"Week_01/practise1.py","file_name":"practise1.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"260505002","text":"import os\nimport argparse\nfrom tensorflow.keras import optimizers, metrics\nfrom backbone.losses import get_losses_weights, create_losses\nfrom backbone.model import create_model, create_training_step, create_validate_step, calc_loop\nfrom utils.data_generator import ClassifyGenerator\nfrom utils.dataframe import read_csv, train_val_split\nimport time\n\n\ndef reset_states():\n    global train_mean_losses\n    global val_mean_losses\n    global training_metrics\n    global val_metrics\n    train_mean_losses.reset_states()\n    val_mean_losses.reset_states()\n    for i in range(len(training_metrics)):\n        training_metrics[i].reset_states()\n    for i in range(len(val_metrics)):\n        val_metrics[i].reset_states()\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--batch_size', type=int, default=16)\n    parser.add_argument('--epochs', type=int, default=50)\n    parser.add_argument('--csv_file', type=str)\n    parser.add_argument('--image_dir', type=str, default='images')\n    parser.add_argument('--lr', type=float, default=2e-4)\n    parser.add_argument('--output_dir', type=str, default='model')\n    args = vars(parser.parse_args())\n    return args\n\n\nif __name__ == '__main__':\n    args = parse_args()\n    print(args)\n    if not os.path.exists(args['output_dir']):\n        os.mkdir(args['output_dir'])\n    images_dir = args['image_dir']\n    # Load csv\n    X_train_val_df, y_train_val_df = read_csv(args['csv_file'])\n    # Split train, val\n    (X_train, y_train), (X_val, y_val) = train_val_split(X_train_val_df.values, y_train_val_df.values, log=False)\n    # Flatten X\n    X_train = X_train.reshape(-1)\n    X_val = X_val.reshape(-1)\n    # Create ds\n    # train_ds = create_ds(X_train, y_train, images_dir, training=True)\n    # val_ds = create_ds(X_val, y_val, images_dir)\n    train_ds = ClassifyGenerator(X_train, y_train, images_dir, training=True)\n    val_ds = ClassifyGenerator(X_val, y_val, images_dir, training=False)\n    # Create model\n    model = create_model()\n    # Compile with loss\n    losses_weights = get_losses_weights(y_train)\n    l_losses = create_losses(losses_weights)\n    # training\n    EPOCHS = args['epochs']\n    lr = args['lr']\n    lr_decay = 0.97\n    train_mean_losses = metrics.Mean('losses')\n    val_mean_losses = metrics.Mean(name='val_losses')\n    training_metrics = [metrics.BinaryAccuracy(name='acc') for _ in range(len(l_losses))]\n    val_metrics = [metrics.BinaryAccuracy(name='val_acc') for _ in range(len(l_losses))]\n\n    best_val = 0.\n    for epoch in range(EPOCHS):\n        # Reset state\n        reset_states()\n        # Training\n        if epoch > 5 and epoch % 3 == 0:\n            lr = lr_decay * lr\n        optimizer = optimizers.Adam(lr)\n        print(\"\\n===============================================================\")\n        print(f\"Epoch: {epoch + 1}\")\n        start_time = time.time()\n        training_fn = create_training_step(model, l_losses, training_metrics, optimizer)\n        calc_loop(train_ds, training_fn, train_mean_losses, training_metrics)\n        # Validation\n        validate_fn = create_validate_step(model, l_losses, val_metrics)\n        val_loss, val_acc = calc_loop(val_ds, validate_fn, val_mean_losses, val_metrics, mode='val')\n        # Update weight\n        if val_acc > best_val:\n            best_val = val_acc\n            model.save_weights(f'{args[\"output_dir\"]}/checkpoint')\n        end_time = time.time()\n        print(f\"After {end_time - start_time}s\")\n","sub_path":"classification_training.py","file_name":"classification_training.py","file_ext":"py","file_size_in_byte":3460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"329934957","text":"from p5 import *\nfrom random import randint\nfrom Ball import *\n\n\ndef setup():\n\n    global ball\n    size(640, 360)\n    no_stroke()\n    ball = Ball(width, height)\n\n\ndef draw():\n    background(0)\n    ball.displayBall()\n    ball.checkEdges()\n\n    ball.updateBall()\n\n\ndef key_pressed(event):\n    ball.moving = not ball.moving\n    print(\"key_pressed\")\n\n\nrun()\n","sub_path":"boilerplate/bounce_ball/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"133310491","text":"# -*- coding: utf-8 -*-\n\nimport datetime\nimport collections\n\nfrom celery.utils.log import get_task_logger\nfrom flask import g\n\nfrom drift.utils import get_tier_name\nfrom drift.flaskfactory import load_config, TenantNotFoundError\nfrom drift.tenant import get_connection_string\nfrom drift.orm import sqlalchemy_session\nfrom drift.core.extensions.celery import celery\nfrom drift.rediscache import RedisCache\n\nfrom driftbase.db.models import Match, MatchQueuePlayer, Client, Server, Machine\nimport driftbase.tasks\n\nimport logging\nlog = logging.getLogger(__name__)\n\n\ndef utcnow():\n    return datetime.datetime.utcnow()\n\n\ndef lock(redis):\n    # Moving this into a separate function so systems test can mock it out.\n    return redis.lock(\"process_match_queue\")\n\n\ndef check_ref_and_placement(player, match, server, machine):\n    if player.ref and player.ref != server.ref:\n        log.debug(\"player %s wants a match in ref '%s' but match %s is in '%s'\" %\n                  (player.player_id, player.ref, match.match_id, server.ref))\n        return False\n    if player.placement and player.placement != machine.placement:\n        log.debug(\"player %s wants a match in placement '%s' but match %s is in '%s'\" %\n                  (player.player_id, player.placement, match.match_id, machine.placement))\n        return False\n    return True\n\n\ndef process_match_queue(redis=None, db_session=None):\n    log.info(\"process_match_queue...\")\n    if redis is None:\n        redis = g.redis\n    if db_session is None:\n        db_session = g.db\n    with lock(redis):\n        # find all valid players waiting in the queue\n        queued_players = db_session.query(MatchQueuePlayer, Client) \\\n                                   .filter(Client.client_id == MatchQueuePlayer.client_id,\n                                           MatchQueuePlayer.status == \"waiting\",\n                                           MatchQueuePlayer.match_id == None) \\\n                                   .order_by(MatchQueuePlayer.id) \\\n                                   .all()\n\n        query = db_session.query(Machine, Server, Match)\n        query = query.filter(Match.server_id == Server.server_id,\n                             Server.machine_id == Machine.machine_id,\n                             Match.num_players == 0,\n                             Match.status == \"idle\",\n                             Server.server_id == Match.server_id,\n                             Server.heartbeat_date >= utcnow() - datetime.timedelta(seconds=60))\n        idle_matches = query.all()\n\n        eligible_players = []\n        challenge_players = collections.defaultdict(list)\n        for r in queued_players:\n            player, client = r\n            log.debug(\"Found %s in the queue\", r[0].player_id)\n            if client.heartbeat < utcnow() - datetime.timedelta(seconds=60):\n                log.info(\"Player %s is in the queue but has missed his heartbeat. \"\n                         \"Removing him from the queue\", player.player_id)\n                db_session.delete(player)\n            elif not player.token:\n                eligible_players.append(player)\n            else:\n                challenge_players[player.token].append(player)\n        db_session.commit()\n\n        matched_players = set()\n        for machine, server, match in idle_matches:\n            possibly_matched_players = []\n            # start by processing player challenges\n            for token, players in challenge_players.items():\n                if len(players) < match.max_players:\n                    continue\n\n                # if either player specifies a ref or placement, use that for picking match\n                check_player = players[0]\n                if players[1].ref or players[1].placement:\n                    check_player = players[1]\n                if not check_ref_and_placement(check_player, match, server, machine):\n                    continue\n\n                possibly_matched_players.extend(players)\n                del challenge_players[token]\n\n            if len(possibly_matched_players) == 0:\n                for p in eligible_players:\n                    if p.player_id in matched_players:\n                        continue\n\n                    if not check_ref_and_placement(p, match, server, machine):\n                        continue\n\n                    possibly_matched_players.append(p)\n\n                    log.debug(\"Possibly adding player %s to match %s\",\n                              player.player_id, match.match_id)\n                    if len(possibly_matched_players) == match.max_players:\n                        break\n\n            # if we found enough players to populate this match, mark them as matched and add them\n            # to the match. Also set the match to the 'queue' status.\n            if len(possibly_matched_players) >= match.max_players:\n                for p in possibly_matched_players:\n                    matched_players.add(p.player_id)\n                    p.match_id = match.match_id\n                    p.status = \"matched\"\n                    log.info(\"Adding player %s to match %s\", p.player_id, match.match_id)\n\n                match.status = \"queue\"\n                match.status_date = utcnow()\n                db_session.commit()\n            elif len(possibly_matched_players) > 0:\n                log.info(\"Only found %s players for match %s which needs %s players \"\n                         \"so I cannot populate it\",\n                         len(possibly_matched_players), match.match_id, match.max_players)\n\n\n@celery.task\ndef cleanup_orphaned_matchqueues():\n    \"\"\"\n    Find matches who have been reserved by the match queue but not joined\n    for 10 minutes and make them available to other players\n    \"\"\"\n    logger = get_task_logger(\"cleanup_orphaned_matchqueues\")\n\n    tier_name = get_tier_name()\n    tenants = driftbase.tasks.get_tenants()\n    logger.info(\"Cleaning up match queues for %s tenants...\", len(tenants))\n    for tenant_config in tenants:\n        tenant_name = tenant_config[\"name\"]\n        if tenant_config.get(\"name\", \"*\") == \"*\":\n            continue\n        try:\n            this_conn_string = get_connection_string(tenant_config, None, tier_name=tier_name)\n\n        except TenantNotFoundError:\n            continue\n\n        with sqlalchemy_session(this_conn_string) as session:\n            sql = \"\"\"\n            SELECT m.* FROM gs_matches m\n                INNER JOIN gs_servers s ON s.server_id = m.server_id\n            WHERE m.status = 'queue' AND\n                  m.status_date::timestamp < now()::timestamp - interval '5 minutes' AND\n                  s.heartbeat_date::timestamp >= now()::timestamp - interval '2 minutes'\n             ORDER BY m.match_id DESC\n            \"\"\"\n            result = session.execute(sql)\n            orphaned_matches = set()\n            match = result.fetchone()\n            while match:\n                orphaned_matches.add(match.match_id)\n                match = result.fetchone()\n            if orphaned_matches:\n                log.info(\"Tenant '%s' has %s orphaned matches\", tenant_name, len(orphaned_matches))\n            for match_id in orphaned_matches:\n                match = session.query(Match).get(match_id)\n                match.status = \"idle\"\n                match.status_date = utcnow()\n                matchqueueplayers = session.query(MatchQueuePlayer) \\\n                                           .filter(MatchQueuePlayer.match_id == match_id)\n                for p in matchqueueplayers:\n                    session.delete(p)\n                logger.info(\"Cleaning up orphaned match '%s' in tenant '%s' and putting it \"\n                            \"back into the pool\", match_id, tenant_name)\n            session.commit()\n\n            # if we cleaned up any matches we should process the match queue in\n            # case there are any players waiting\n            if orphaned_matches:\n                logger.info(\"Processing match queue\")\n                redis = RedisCache(tenant=tenant_config[\"name\"],\n                                   redis_server=tenant_config['redis_server'])\n                process_match_queue(redis, session)\n","sub_path":"driftbase/matchqueue/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":8115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"543949783","text":"############################\n# String Lists\n# by RestlessPuppet\n# 11-9-19\n############################\n\nstr = input(\"please enter any random string: \")\nrts = str[::-1]\nif str == rts:\n    print(\"Your string is a palindrome!\")\nelse:\n    print(\"Not a palindrome\")\n","sub_path":"06-StringLists.py","file_name":"06-StringLists.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"9893005","text":"import time\nimport sys\nimport json\nimport logging\nimport re\nimport multiprocessing\nimport traceback\nfrom datetime import datetime\nimport selenium \nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.common.exceptions import WebDriverException, NoSuchElementException, \\\n    TimeoutException, UnexpectedAlertPresentException, NoAlertPresentException, StaleElementReferenceException\n\nfrom module_wrappers import handle_element, find, get_handling_logs, element_handling_logs\n\n# print(multiprocessing.current_process().name, str(traceback.print_exc()))\n\nform_test_arument = None\n#===========================================================================================\n    #form_render_test() function\n#===========================================================================================\ndef form_rendered_test(webdriver, form_definition , before, form_visible):\n    '''\n    To test if the form is present and the form elements as described in the form definition,\n    any invalid form definition and incorrect detail about the form will stop the test not proceed for \n    testing the presence of form elements\n\n    Arguments\n    ---------\n    webdriver           -   object of selenium.webdriver.chrome.webdriver.WebDriver .\n    form_definition     -   dictionary having data of the form and the form elments\n                            required keys:- form_selector\n    before              -   function given by user to reach to the form and perform the test\n    \n    Return\n    ---------\n    result              -   result of the form_render_test a json object\n\n    '''\n    \n    # to store the result\n    result = {}                             \n    # form definition\n    definition = form_definition\n    \n    # to the result of the elements fo the form later update to result \n    form_elements_test_result ={}\n    # to store the details fo the element if found, later update to form_elements_test_result\n    elements_found = []\n    # to store the details fo the element if not found, later update to form_elements_test_result\n    elements_missing = []\n    # no. of element found, later updated to result\n    elements_found_count = 0\n    # no. of elements not found, later updated to result\n    elements_missing_count =0\n    # to log any detail which is regarding the test\n    logs=[]\n    #===========================================================================================\n        # if form selector is provided, if not provided terminate,\n    #===========================================================================================\n    try:\n        form_selector_test = {}\n        #===========================================================================================\n            #checking if form has selector key\n        #===========================================================================================\n        if definition.get('form_selectors'):\n            result.update( { \"form_selector_provided\": \"true\" } )\n            # if type( definition.get('form_selectors')) != dict:\n            #     result.update( { \"failure_message\": \" object form_selectors of form_definition is not dictionary type\" } )\n            #     return result \n            #===========================================================================================\n            #checking if form has selector key has length 2\n        #===========================================================================================\n            if len(definition.get('form_selectors')) < 1 :\n                # css_selector = definition.get('form_selector')[0]\n                # css_selector_value = definition.get('form_selector')[1]\n                # logging.info( result )\n                # else:\n                result.update( { \"failure_message\":\" value error: selector key must have atleast one key value pair i.e {'selector','selector_value'}\" } )\n                # logging.info( result )\n                return result\n        #===========================================================================================\n            # if form does not has selector key\n        #===========================================================================================\n        else:\n            result.update( { \"failure_message\":\"key error: form_selectors object missing in form definition \" } )\n            # logging.info( result )\n            return result\n\n        if form_visible == False:\n            before()\n        else:\n            pass\n\n        #===========================================================================================\n            #checking if form with given selector is present or not\n        #===========================================================================================\n        valid_form_selector_present = 0\n        for selector, selector_val in definition.get('form_selectors').items():\n            # print( 'controle is hrere ')\n            try:\n                if find(webdriver, selector , selector_val ):\n                    result.update({ \"is_form_rendered\": \"true\" })\n                    valid_form_selector_present += 1\n                # logging.info( result )\n                else:\n                    logs.append('[FormNotFound] : no form found with form selector= \"{}\", selector_value= \"{}\" '.format( selector , selector_val ))\n            except:\n                # logging.info( \"failure_message :\" +\"form with selector [{}='{}'] is not rendered\".format( css_selector, css_selector_value) )\n                logs.append('[FormNotFound] : no form found with form selector= \"{}\", selector_value= \"{}\" '.format( selector , selector_val ))\n        \n        if valid_form_selector_present == 0:\n            result.update( { \"failure_message\" : \"No form found using any of the provided form_selectors in from_definition\"})\n            return result\n\n        #=======================================================================================\n            #for loop for looping over all elments of the form \n        #=======================================================================================\n        for element in definition.get(\"form_elements\"):\n            # all the test details will regarding to one element will be stroed in this variable like it, locator, locator_value, attributed_found, attribute_missing(attributes_test)\n            element_result={}\n            element_result_logs=[]\n            #=======================================================================================\n            # for loop for all selectors given for an elment if first selector as key and its value does not return any element\n            # next selector will be considered and if returns valid element, rest of the option will be ignored\n            #=======================================================================================\n            for el_selector, el_selector_val in element.get(\"element_selectors\").items():\n        \n                try:\n                    #===========================================================================================\n                        #checking if elment exists , else log will be genereted for encountering invalid selectors in else\n                    #===========================================================================================\n                    if find(webdriver,  el_selector , el_selector_val ):\n                        # print(el_selector_val)\n                        #=======================================================================================\n                            # oversvation variable\n                        #=======================================================================================\n                        form_element = find(webdriver, el_selector,el_selector_val )\n                        elements_found_count += 1\n                        # selector = el_selector\n                        # selector_value = el_selector_val\n                        #=======================================================================================\n                            # testing expected attributes \n                        #=======================================================================================\n                        \n                        attribute_found={}\n                        attribute_missing=[]\n                        if type( element.get('expect_attribute')) != list:\n                            element_result_logs.append(\"Attribute test is skipped : provided object expect_attribute is not list type \")\n                            # element_result.update( { \"locator\": el_selector, \"locator_value\": el_selector_val , \"element_render_test_log\" : element_result_logs} )\n                        else:\n                            try:\n                                for attr in element.get(\"expect_attribute\"):\n                                    # print(attr)\n                                    if type(attr) == dict:\n                                        for attribute, attribute_val in attr.items():\n                                            #=======================================================================================\n                                                #if attribute is present\n                                            #=======================================================================================\n                                            try:\n                                                if form_element.get_attribute(attribute):\n                                                    recieved_attr_val = form_element.get_attribute(attribute)\n                                                    if attribute_val == recieved_attr_val:\n                                                        attribute_found.update( { attribute : {  \"value_matched\":\"true\" , \"provided_attr_value\":attribute_val, \"recieved_attr_value\": recieved_attr_val } } )\n                                                        \n                                                        continue\n                                                    else:\n                                                        attribute_found.update( { attribute : {  \"value_matched\":\"false\" , \"provided_attr_value\":attribute_val, \"recieved_attr_value\": recieved_attr_val } } )\n                                                        continue                                           \n                                            except NoSuchElementException as err:\n                                                logging.info('element not found : {}'.format(err))\n                                            #=======================================================================================\n                                                #if attribute is not present\n                                            #=======================================================================================\n                                            else:\n                                                attribute_missing.append( attribute )\n                                                continue\n                                        \n                                    elif type(attr) == str:\n                                        # if \n                                        if form_element.get_attribute(attr):\n                                            recieved_attr_val = form_element.get_attribute(attr)\n                                            attribute_found.update( { attr :   { \"provided_attr_value\":\"not provided\", \"recieved_attr_value\": recieved_attr_val } })\n                                            continue\n                                        else:\n                                            attribute_missing.append( attr )\n                                            continue                       \n                            #=======================================================================\n                            # control will come here only if not attribute are provided for testing\n                            #=======================================================================\n                            except :\n                                # control comes here when there will be no expect_attribute: passed for any element so either log it or pass\n                                pass\n                                # logging.info('expect_attribute: not given for element with selector [{}=\"{}\"]'.format( selector, selector_value))\n                                \n                                # print(multiprocessing.current_process().name, str(traceback.print_exc()))\n                            #=======================================================================================\n                                #putting element in element_found\n                            #=======================================================================================\n                    else:\n                        element_result_logs.append('[{}=\"{}\"]: not valid selector, next selector will be processed from the elemet_selectors object and if it returns valid element others will be skipped'.format( el_selector, el_selector_val ))\n                        continue\n\n                    element_result.update( { \"locator\": el_selector, \"locator_value\": el_selector_val ,  \"attribute_found\" : attribute_found, \"attribute_missing\": attribute_missing , \"element_render_test_log\" : element_result_logs } )\n                except NoSuchElementException as err:\n                    #=======================================================================================\n                        # when element with its selector given by user is not found error is thrown and here store the result\n                    #=======================================================================================\n                    elements_missing_count +=1\n                    # print( el_selector,el_selector_val +\":: Not Found===========================================================\")\n                    elements_missing.append( { \"locator\": el_selector, \"locator_value\":el_selector_val, \"failure_message\": str(err) } )\n                    continue\n                #=======================================================================================\n                    #updating result of this element in elements_found\n                #=======================================================================================\n                elements_found.append( element_result )\n                #=======================================================================================\n                    #update result on test of each input type\n                #=======================================================================================\n                form_elements_test_result.update( { \"elements_found\": elements_found,\"elements_missing\":elements_missing } )\n                break\n        #=======================================================================================\n        #update form_elements_test_result result completely first\n        #=======================================================================================\n        form_elements_test_result.update({ \"elements_missing_count\": elements_missing_count, \"elements_found_count\": elements_found_count ,\"logs\":logs })\n        result.update(form_elements_test_result)\n        \n    # control will come here when locator value is not correct element will not be found\n    except NoSuchElementException as err:\n        # print( \"error from outer most loop\",err)\n        result.update( form_selector_test )\n    except:\n        # logging.info( \"error from outer most loop\")\n        # print(multiprocessing.current_process().name, str(traceback.print_exc()))\n        result.update( form_selector_test )\n    \n    # result = json.dumps(result)\n    # logging.info( result)\n    return result\n\n#===========================================================================================\n#   input test for elements of the form to be tested with values \n#===========================================================================================\ndef form_elements_input_test( webdriver, inputs_definition, form_definition ,before , api):\n    '''\n    To test inputs to the form elements are submits the form\n\n    Arguments\n    ---------\n    webdriver           -   object of selenium.webdriver.chrome.webdriver.WebDriver .\n    inputs_definition   -   details from the user with required some required constraints\n    before   -   this is function passed by the user in order to reach to form for processing test\n\n\n    Returns\n    ---------\n    result              -   json string with details about the test \n\n\n    '''\n    # to store the result of entire test\n    result = {}\n    form_rendered_test_report = []\n    # to store any detail, for any info for the user\n    logs = []\n    keys_of_inputs_definition = inputs_definition.keys()\n    form_selectors = None\n    # input_array = inputs_definition.get( \"inputs\", None )\n    # the list of child elements of the form \n    # form_elements = None\n    # result for individual set inputs form form at a time\n    input_set_result = {}\n    #===========================================================================================\n        # this function will be called when a form is found in the document using xpath so that all child elements could be traced \n        # the function will take the argument case acc to way of finding child elements of form \n    #===========================================================================================\n    \n\n    def check_invalid_elements(form_elements):\n        '''\n        To iterate over all elements of form, see if it returns a validation message,\n        Object of elements returning validation message are returned\n\n        Arguments\n        ---------\n        form_elements   -   list of all the elements present in form\n\n        Return\n        ---------\n        elements        -   object containing the info of the elements returned validationMessage.\n\n        '''\n        elements = {}\n        others = []\n        locator = None\n        for el in form_elements:\n            try:\n                message = el.get_attribute('validationMessage')\n                if message:\n                    locator = el.get_attribute('id') or el.get_attribute('name') or el.get_attribute('class') \n                    tag_name = el.get_attribute('tagName')\n                    if locator :\n                        elements.update( { tag_name+'<'+locator +'>' : message })\n                    else:\n                        others.append({ 'element_text' : el.get_attribute('outerHTML'),'validationMessage': message  })\n            except:\n                print( ' error from check_invalid_elements() ')\n                pass\n        if len( others ) != 0 :\n            elements.update( { 'others': others })\n            return elements\n        else:\n            return elements\n            \n\n    def provide_inputs():\n        # print('executing provide_inputs(case)')\n        # form_elements_list = form_elements\n        form_selectors_filtered = False\n        selectors_to_pop = []\n        # form_rendered_test_report = None\n        \n        for index , input_set in enumerate(inputs_definition.get('inputs')):  # here i get the inputs array\n           \n            try:\n                before()\n            except NoSuchElementException as err:\n                result.update({ 'failure_message':'error from function assigned to before in form_test_args, [ERROR] => {}'.format( err ) } )\n                return result\n            except WebDriverException as err  :\n                result.update({ 'failure_message':'error from function assigned to before in form_test_args, [ERROR] => {}'.format( err ) } )\n                return result\n            except:\n                result.update({ 'failure_message': 'error from function assigned to before in form_test_args, unknown error'})\n                # print(multiprocessing.current_process().name, str(traceback.print_exc()))\n                return\n\n            #===========================================================================================\n                # filtering the form_selectors if the pair of selectors does not return a form element the will be popped out of the form_selectors \n                # will run only once if there is no valid pair of selector and selector value this test will be skipped from filling inputs \n            #===========================================================================================\n            if form_selectors_filtered == False:\n                for selector, selector_val in form_selectors.items():\n                    try:\n                        if find( webdriver,selector, selector_val):\n                            if form_definition is not None:\n                                form_rendered_report = form_rendered_test(webdriver, form_definition, before, True)\n                                # logging.info( form_rendered_report ) \n                                form_rendered_test_report.append( form_rendered_report )\n                            else:\n                                form_rendered_test_report.append( None )\n                                # form_rendered_test_report.update( { form_rendered_report })\n                                # result.update( { \"form_rendered_test_report\" : form_render_test_result } )\n                            break\n                        else:\n                            logs.append('form not found with this form_selector [{}=\"{}\"] provided in form_selectors of input_definition : [MESSAGE] => locator in not appropriate generic locators are [ id, name, xpath, link_text, partial_link_text,tag_name, class_name, css_selector]'.format( selector, selector_val))\n                            selectors_to_pop.append(selector)\n                            continue\n                    except NoSuchElementException as err:\n                        logs.append('NoSuchElementFound: form not found using this selector value {}, next selector will if considered, if form is found inputs will be inserted else this test will be terminated here'.format(selector_val) )\n                        continue\n                    except :\n                        logs.append('form not found with this form_selector [{}=\"{}\"] provided in form_selectors of input_definition : [MESSAGE] => locator in not appropriate generic locators are [ id, name, xpath, link_text, partial_link_text,tag_name, class_name, css_selector]'.format( selector, selector_val))\n                        continue\n                for selector in selectors_to_pop:\n                    form_selectors.pop(selector)\n                if len( form_selectors ) == 0:\n                    result.update({ 'failure_message':'none form_selectors provided in input definition is valid to find form see logs for more info.' })\n                    return\n                \n                form_selectors_filtered = True\n\n            \n            for selector, selector_val in form_selectors.items():\n                try:\n                    # if find( webdriver, selector, selector_val):\n                    form = find( webdriver, selector, selector_val)\n                    form_elements = form.find_elements_by_xpath('.//*').copy()\n                    # else:\n                        # form_selectors.pop(selector)        unable to pop due to size connot be changed during iteration\n                        # logs.append('form not found with this form_selector [{}=\"{}\"] provided in form_selectors of input_definition : [MESSAGE] => locator in not appropriate generic locators are [ id, name, xpath, link_text, partial_link_text,tag_name, class_name, css_selector]'.format( selector, selector_val))\n                except NoSuchElementException as err:\n                    logs.append('NoSuchElementFound: form not found using this selector value {}, next selector will if considered, if form is found inputs will be inserted else this test will be terminated here'.format(selector_val) )\n                    continue\n                except:\n                    logs.append(\"not able to find form or form child element with given selectors[{}='{}'], next selector will if considered, if form is found inputs will be inserted else this test will be terminated here\".format(selector, selector_val) )\n                    continue\n            \n            form_elements_accessed = []\n            form_submitted = []\n\n            for el in form_elements:\n                if len(form_submitted) != 0:\n                    continue\n                for locator_val, input_val in input_set.items():  \n                    el_html = el.get_attribute('outerHTML')\n\n                    if locator_val in el_html[:el_html.index('>')+1]:\n                        form_elements_accessed.append( locator_val )\n\n                        if el.get_attribute('type') == 'submit':\n                            handle_element(webdriver, el,input_val)                #here the formelement is handled\n                            time.sleep(3)\n                            # =======================================================================================\n                                # name for gettingEntries need to be input by user, \n                                # name is the usrl using which the user will post details ( see if the logic will be same for the get method also    )\n                            #========================================================================================\n                            resources = webdriver.execute_script('return performance.getEntriesByName(\"{}\")'.format(api))\n                            # print( resources )\n                            resource_found  = 0\n                            for resource in resources:\n                                # print('aaaaaaaaaaaaaa')\n                                if resource.get(\"name\",None) == api:\n                                    input_set_result.update( { index : { \"status\":'Form Submitted',\"form_element_accessed\": form_elements_accessed,  } })\n                                    resource_found += 1\n                                    form_submitted.append('true')\n                            if resource_found == 0:\n                                input_set_result.update( { index : { \"status\":'Form Not Submitted',\"form_element_accessed\": form_elements_accessed, 'invalid_elements' : check_invalid_elements( form_elements ) } })\n                        else:\n                            handle_element(webdriver, el, input_val)                       #here the formelement is handled\n            input_set_result.get(index).update( { 'element_handling_logs' : get_handling_logs() }  )\n            # print( element_handling_logs )\n    \n    if \"form_selectors\" not in keys_of_inputs_definition:\n        result.update( { \"input_form_selectors_provided\": \"false\" } )\n        # print( result )\n        # return  result \n        return { \"result\" : result, \"form_rendered_test_report\": form_rendered_test_report[0] }   #%\n    else:\n        form_selectors = inputs_definition.get( \"form_selectors\",None )\n        if type( form_selectors ) == dict and form_selectors is not None :\n            result.update( { \"input_form_selector_provided\" : \"true\" })\n            # print( form_selectors)\n            if len(form_selectors) == 0  :\n                result.update({ \"failure_message\": \"form_selectors in input_definition does not containe any 'selector':'slector_value' \"})\n            else:\n                #====================================================================\n                    # assigning provide_inputs()  \n                #====================================================================\n                provide_inputs()\n\n    # print( form_rendered_test_report )\n    result.update({ \"inputs_result\": input_set_result })\n    result.update({ \"logs\": logs })  \n    # print('executing from_element_input_test(): ends ')\n    # result = json.dumps(result)\n    # return  result \n    return { \"result\" : result, \"form_rendered_test_report\": form_rendered_test_report[0] }   #%\n\n#=======================================================================================\n    # this function will perform test according to the argument object passed\n#=======================================================================================\n\ndef create_test_report(data):\n    '''\n    To write test report as json in a file, if any error occurs the test report will be logged in log file\n\n    Arguments\n    ---------\n    data        - data to write in file \n\n    Return\n    ---------\n\n    (none)\n    '''\n    try:\n        with open('test_report.json', 'w') as file:\n            file.write( data )\n    except IOError as err:\n        print('IOERROR: the content will be logged in the log file',err)\n        logging.info( data )\n\n\ndef json_result(dict_object):\n    '''\n    Helper function to return convert and return dictionary object as json format string\n\n    Arguments\n    ---------\n    dict_object     - an object of dict type\n\n    Resturn\n    ---------\n    converted json string of dictionary object\n    \n    '''\n    return json.dumps(dict_object)\n\ndef form_tests( form_test_args ):\n    '''\n    To process the test type like to perforn form_rendered_test() or form_elements_input_test \n\n    Arguments\n    ---------\n\n    form_test_args  -   a dictionary object with details according to which the test to be perform \n\n    Return\n    ---------\n\n    (none)          \n\n    [NOT IMPLEMENTED CURRENTLY]\n    This function will finally write the file with the result of the test\n\n    '''\n    # print('control is here')\n    arguments       = form_test_args  \n    result          = {}\n    # fail_and_exit   = False\n\n    # test value must be present\n    possible_args = [ 'test', 'web_driver', 'form_definition', 'inputs_definition', 'before','api' ]\n\n    for arg in form_test_args.keys():\n        if arg not in possible_args:\n            result.update( { 'failure_message': 'Received an unknown argument: \"{}\"'.format( arg ) } )\n            logging.info( json_result(result) )\n            return\n\n    test            = arguments.get('test', None ) \n    webdriver       = arguments.get('web_driver', None )\n    before          = arguments.get('before', None )\n    form_definition    = arguments.get('form_definition', None )\n    inputs_definition  = arguments.get('inputs_definition', None )\n    api_endpoint       = arguments.get('api',None)\n\n\n    #   webdriver sanity check\n    def web_driver_sanity_check(webdriver):\n        if type(webdriver) != selenium.webdriver.chrome.webdriver.WebDriver:\n            # fail_and_exit = True\n            result.update( { \"failure_message\": \"Object passed as value of key 'web_driver' is not of type 'selenium.webdriver.chrome.webdriver.WebDriver'\" } )\n            return 0\n        else:\n            return 1\n    # form_definition sanity check \n    def form_definition_sanity_check( form_definition ):\n        if type( form_definition) != dict:\n            result.update( { \"failure_message\": \"Object passed as value of key 'form_definition' is not of type 'dict' \" } )\n            return 0\n        if type( form_definition.get('form_selectors') ) != dict :\n            result.update( { \"failure_message\": \" object form_selectors of form_definition is not dictionary type\" } )\n            return 0\n        if type(form_definition.get('form_elements')) != list:\n            result.update( { \"failure_message\": \"Object passed as value of key 'form_elements' in form_definition is not 'list' type \"})\n            return 0\n        if len( form_definition.get('form_elements')) >0:\n            for  index, item in enumerate(form_definition.get('form_elements')):\n                if type(item) != dict:\n                    result.update( { \"failure_message\": \"Object at index= '{}' stored in list object 'form_elements' of form_definition is not dict type\".format(index) } )\n                    return 0\n        else:\n            return 1\n    \n     # inputs_definition sanity check\n    def inputs_definition_sanity_check( inputs_definition ):\n        if type( inputs_definition ) != dict:\n            result.update( { \"failure_message\": \"Object passed as value of key 'inputs_definition' is not of type 'dict' \" } )\n            return 0\n        if type( inputs_definition.get('form_selectors')) != dict:\n            result.update( { \"failure_message\": \"Object passed as value of key 'form_selectors' in inputs_definition is not 'dict' type \"})\n            return 0\n        if type( inputs_definition.get('inputs')) != list:\n            result.update( { \"failure_message\": \"Object passed as value of key 'inputs' in inputs_definition is not 'list' type \"})\n            return 0\n        if len( inputs_definition.get('inputs')) > 0:\n            for index, item in enumerate(inputs_definition.get('inputs')):\n                if type(item) != dict:\n                    result.update( { \"failure\":\"Object stored at index= '{}' in list object 'inputs' of inputs_definition is not dict type\".format(index)})\n                    return 0\n            \n        else:\n            return 1\n\n    # before sanity check \n    def before_sanity_check( before ):\n        if hasattr( before, '__call__'):\n            return 1\n        else:\n            result.update( { \"failure_message\":\"Object passes as value of key 'before' is not of type function\" } )\n            return 0\n\n    def api_sanity_check( api ):\n        valid_url_exp = re.compile(\n        r'^(?:http|ftp)s?://' # http:// or https://\n        r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+(?:[A-Z]{2,6}\\.?|[A-Z0-9-]{2,}\\.?)|' #domain...\n        r'localhost|' #localhost...\n        r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})' # ...or ip\n        r'(?::\\d+)?' # optional port\n        r'(?:/?|[/?]\\S+)$', re.IGNORECASE)\n\n        if type(api) != str :\n            result.update( { \"failure_message\": \"value of form_test.arg is not string\"})\n            return 0\n        elif len(api) == 0:\n            result.update( { \"failure_message\":\"value of form_test_arg.api is not acceptable as empty string\"})\n            return 0\n        elif re.match( valid_url_exp, api) is None:\n            result.update( { \"failure_message\":\"value of api is not valid url\" } )\n            return 0\n        else:\n            return 1\n\n    if test is None:\n        result.update( { \"failure_message\": \"Required key value 'test' not provided, please mention the test to perform\" } )\n        logging.info( json_result(result) )\n\n        create_test_report( json_result( result ) )\n        return\n    \n    #=======================================================================================\n     # selecting function \n    #=======================================================================================\n\n    #=======================================================================================\n        # \"test\":\"form_render\"\n    #=======================================================================================\n    if test == 'form_render':\n        result.update( { 'test_name': 'form_render' } )\n        arguments_list = form_test_args.keys()\n        required_args = [ 'test', 'web_driver', 'form_definition', 'before' ]\n        # checking if all required argument for this test is passed or not\n        for arg in required_args:\n            if arg not in arguments_list:\n                result.update( { \"failure_message\": \"Required argument'{}' for test: '{}' is missing\".format( arg, test ) } )\n                logging.info( result )\n                create_test_report( json_result( result ) )\n                return  \n            else:\n                continue\n        \n        # now if all req args are present we can implement sanity check and if all is well perform test\n\n        # sanity checking webdriver object\n        if web_driver_sanity_check( webdriver ) == 0 :\n            logging.info( json_result(result) )\n            create_test_report( json_result( result ) )\n            return\n                    \n        # sanity checking form_definition\n        if form_definition_sanity_check( form_definition ) == 0 :\n            logging.info( json_result( result ) )\n            create_test_report( json_result( result ) )\n            return\n\n        # sanity checking for before function\n        if before_sanity_check( before ) == 0:\n            logging.info( json_result(result) )\n            create_test_report( json_result( result ) )\n            return\n\n        form_render_test_result = form_rendered_test( webdriver, form_definition, before, False )\n        result.update( { 'form_render_test_report': form_render_test_result })\n        logging.info( '[final form_render result]:'+ json_result( result ) )\n\n        create_test_report( json_result( result ) )\n        return\n\n    #=======================================================================================\n        # \"test\":\"form_elements_inputs\"\n    #=======================================================================================\n    if test == 'form_elements_inputs':\n        \n        result.update( { 'test_name': 'form_elements_inputs' } )\n        arguments_list  = form_test_args.keys()\n        required_args   = [ 'test',  'web_driver', 'inputs_definition','api', 'before']\n        # checking if all required argument for this test is passed or not\n        for arg in required_args:\n            if arg not in arguments_list:\n                # print(arg)\n                result.update( { \"failure_message\": \"Required argument'{}' for test: '{}' is missing\".format( arg, test ) } )\n                logging.info( result )\n                create_test_report( json_result( result ) )\n                return  \n            else:\n                continue\n\n\n        # sanity checking webdriver object\n        if web_driver_sanity_check( webdriver ) == 0 :\n            logging.info( json_result(result) )\n            create_test_report( json_result( result ) )\n            return\n\n        # sanity checking for before function\n        if before_sanity_check( before ) == 0:\n            logging.info( json_result(result) )\n            create_test_report( json_result( result ) )\n            return\n\n         # sanity checking inputs_definition\n        if inputs_definition_sanity_check( inputs_definition ) == 0 :\n            logging.info( json_result( result ) )\n            create_test_report( json_result( result ) )\n            return\n\n        if api_sanity_check(  api_endpoint ) == 0:\n            logging.info( json_result(result))\n            create_test_report( json_result( result ) )\n            create_test_report( json_result( result ) )\n            return\n\n        # print( type( form_definition ))\n        # return\n        form_elements_input_test_result = form_elements_input_test( webdriver, inputs_definition, form_definition, before , api_endpoint )\n        result.update( { 'form_elements_inputs_test_report': form_elements_input_test_result.get(\"result\") })\n        logging.info( \"[final form_elements_input_test_result]:\"+ json_result(result) )\n        \n        create_test_report( json_result( result ) )\n        return\n        \n\n    #=======================================================================================\n        # \"test\":\"default\"\n    #=======================================================================================\n\n    if test == 'default':\n        result.update( { 'test_name':'default' } )\n        arguments_list  = form_test_args.keys()\n        required_args   = [ 'test',  'web_driver', 'inputs_definition','form_definition','api', 'before' ]\n\n        for arg in required_args:\n            if arg not in arguments_list :\n                print(arg)\n                result.update( { \"failure_message\": \"Required argument'{}' for test: '{}' is missing\".format( arg, test ) } )\n                logging.info( result )\n                create_test_report( json_result( result ) )\n                return  \n            else:\n                continue\n        \n        # sanity checking webdriver object\n        if web_driver_sanity_check( webdriver ) == 0 :\n            logging.info( json_result(result) )\n            create_test_report( json_result( result ) )\n            return\n\n        # sanity checking for before function\n        if before_sanity_check( before ) == 0:\n            logging.info( json_result(result) )\n            create_test_report( json_result( result ) )\n            return\n\n         # sanity checking inputs_definition\n        if inputs_definition_sanity_check( inputs_definition ) == 0 :\n            logging.info( json_result( result ) )\n            create_test_report( json_result( result ) )\n            return\n\n        # sanity checking form_definition\n        if form_definition_sanity_check( form_definition ) == 0 :\n            logging.info( json_result( result ) )\n            create_test_report( json_result( result ) )\n            return\n\n        if api_sanity_check(  api_endpoint ) == 0:\n            logging.info( json_result(result))\n            create_test_report( json_result( result ) )\n            return\n        # print( type( form_definition ))\n        # return\n        report = form_elements_input_test( webdriver, inputs_definition, form_definition, before,api_endpoint )\n        form_elements_input_test_report = report.get('result')\n        form_rendered_test_report = report.get( 'form_rendered_test_report')\n        result.update( { 'form_elements_input_test_report': form_elements_input_test_report,\"form_rendered_test_report\": form_rendered_test_report })\n        logging.info( \"[final default_test_result]:\" + json_result( result ) )\n        \n        \n        create_test_report( json_result( result ) )\n        return\n\n","sub_path":"common-library/frontend/testing/e2e/src/Tests/E2E/module_tests.py","file_name":"module_tests.py","file_ext":"py","file_size_in_byte":42052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"337937872","text":"#!/usr/bin/env python\n\nfrom Queue import Queue\nfrom warnings import warn\nimport functools\nimport json\nimport os\nimport re\nimport struct\nimport subprocess\nimport sys\nimport threading\nimport traceback\nimport weakref\n\n\nfrom sgactions.dispatch import dispatch as _dispatch\n\n\ndef log(*args):\n    sys.stderr.write('[SGActions] %s\\n' % ' '.join(str(x) for x in args))\n    sys.stderr.flush()\n\n# We need to keep checking for this as long as the old Firefox plugin is\n# in the wild.\n_line_based = os.environ.get('SGACTIONS_HOST') == 'Firefox'\n\n_capabilities = {}\n_handlers = {}\n_threads = weakref.WeakValueDictionary()\n_local = threading.local()\n\n\ndef reply(orig, **msg):\n    msg['dst'] = orig.get('src') or orig\n    send(**msg)\n\ndef send(**msg):\n    msg['src'] = 'native'\n    encoded_msg = json.dumps(msg)\n    log('send', len(encoded_msg), encoded_msg)\n    if _line_based:\n        sys.__stdout__.write(encoded_msg + '\\n')\n    else:\n        sys.__stdout__.write(struct.pack('I', len(encoded_msg)))\n        sys.__stdout__.write(encoded_msg)\n    sys.__stdout__.flush()\n\ndef format_exception(e):\n    return dict(type='error', error_type=e.__class__.__name__, error=str(e))\n\ndef reply_exception(orig, e):\n    reply(orig, **format_exception(e))\n\n\ndef handler(func, name=None):\n    if isinstance(func, basestring):\n        return functools.partial(handler, name=func)\n    _handlers[name or func.__name__] = func\n    return func\n\n\n@handler\ndef hello(capabilities=None, **kw):\n    _capabilities.update(capabilities or {})\n    reply(kw,\n        type='elloh',\n        capabilities={'dispatch': True},\n        executable=sys.executable,\n        script=__file__,\n        native=os.environ.get('SGACTIONS_NATIVE'),\n        origin=os.environ.get('SGACTIONS_ORIGIN'),\n        host=os.environ.get('SGACTIONS_HOST'),\n    )\n\n@handler\ndef elloh(**kw):\n    pass\n\n\n@handler\ndef ping(**req):\n    res = req.copy()\n    res['type'] = 'pong'\n    reply(req, res)\n\n@handler\ndef pong(**kw):\n    pass\n\n\n@handler\ndef dispatch(entrypoint=None, kwargs=None, url=None, **kw):\n\n    # We must respect the old URL method until the legacy Chrome/Firefox addon is gone.\n\n    log('dispatching:', entrypoint or url)\n\n    res = _dispatch(entrypoint=entrypoint, kwargs=kwargs, url=url, reload=None)\n\n    if isinstance(res, Exception):\n        reply_exception(kw, res)\n    else:\n        reply(kw, type='result', result=res)\n\n\ndef send_and_recv(**kwargs):\n    session = current_session()\n    queue = session.get('result_queue')\n    if not queue:\n        queue = session['result_queue'] = Queue(1)\n    timeout = kwargs.pop('timeout', 300)\n    send(dst=session['src'], session_token=session['token'], **kwargs)\n    reply = queue.get(timeout=timeout)\n    log('async response:', repr(reply))\n    return reply\n\n\n@handler\ndef user_response(session_token, **kw):\n    thread = _threads.get(session_token)\n    if not thread:\n        raise ValueError('no matching thread', session_token)\n    session = thread.session\n    queue = session.get('result_queue')\n    if not queue:\n        raise ValueError('session not expecting result', session_token)\n    queue.put(kw, block=False)\n\n\ndef main():\n\n    # We need to take over stdout so that print statements don't result in the\n    # browser thinking it is getting a message back.\n    sys.stdout = open('/tmp/sgactions.native.log', 'a')\n\n    dispatch_counter = 0\n\n    log('entering main loop')\n\n    while True:\n\n        try:\n\n            if _line_based:\n                raw_msg = sys.stdin.readline()\n                if not raw_msg:\n                    log('native port closed')\n                    break\n\n            else:\n                raw_size = sys.stdin.read(4)\n                if not raw_size:\n                    log('native port closed')\n                    break\n                size, = struct.unpack('I', raw_size)\n                log('reading message of size', raw_size)\n                raw_msg = sys.stdin.read(size)\n\n            msg = json.loads(raw_msg)\n\n        except Exception as e:\n            traceback.print_exc()\n            send(**format_exception(e))\n            continue\n\n        if len(_threads):\n            log('%d sessions already open' % len(_threads))\n\n        if msg.get('type') not in _handlers:\n            reply(msg, type='error', error='unknown message type %r' % msg.get('type'))\n            log('unknown message type: %s' % msg.get('type'))\n\n        dispatch_counter += 1\n\n        thread = _threads[dispatch_counter] = threading.Thread(target=_dispatch_target, args=[msg])\n        thread.daemon = True\n        thread.session = {\n            'type': msg['type'],\n            'src': msg.get('src'),\n            'token': dispatch_counter,\n        }\n        thread.start()\n        del thread # Kill this reference immediately.\n\n\ndef current_session(strict=True):\n    try:\n        return threading.current_thread().session\n    except AttributeError:\n        if strict:\n            raise RuntimeError('no current native handler')\n\n\ndef _dispatch_target(msg):\n    try:\n        _handlers[msg['type']](**msg)\n    except Exception as e:\n        traceback.print_exc()\n        try:\n            reply_exception(msg, e)\n        except Exception as e:\n            # Just in case it is the exception reporting mechanism...\n            log('exception during reply_exception')\n            traceback.print_exc()\n\n","sub_path":"sgactions/browsers/native/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":5312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"571901137","text":"import serial.tools.list_ports\n\n\ndef IsNewCardPresent():\n    Serial.flushOutput()\n    Serial.write(b'IsNewCardPresent')\n    _ans = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n    if 'IsNewCardPresent' in _ans:\n        return Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n\n    else:\n        return -1\n\n\ndef getId():\n    Serial.flushOutput()\n    Serial.flushInput()\n    Serial.write(b'getID')\n    _ans = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n    if 'getID' in _ans:\n        ID = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n        Serial.flushOutput()\n        Serial.flushInput()\n        return ID\n    else:\n        Serial.flushOutput()\n        Serial.flushInput()\n        return -1\n\n\ndef ReadCredit():\n    Serial.flushOutput()\n    Serial.flushInput()\n    Serial.write(b'ReadCredit')\n    _ans = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n    if 'ReadCredit' in _ans:\n        credit = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n        Serial.flushOutput()\n        Serial.flushInput()\n        return credit\n    else:\n        Serial.flushOutput()\n        Serial.flushInput()\n        return -1\n\n\ndef WriteCredit(number):\n    Serial.flushOutput()\n    Serial.flushInput()\n    Serial.write(b'WriteCredit')\n\n    _ans = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n    if 'WriteCredit' in _ans:\n        reply = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n        if \"Input:\" in reply:\n            print(\"Writer is wating for input...\")\n            Serial.flushOutput()\n            Serial.flushInput()\n            Serial.write(number.encode())\n            res_write = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n            #print(\"Wrote \" + str(res_write) + \" to card\")\n            return res_write\n    else:\n        Serial.flushOutput()\n        Serial.flushInput()\n        return -1\n\n\ndef flush_serial():\n    Serial.flushInput()\n    Serial.flushOutput()\n\n\ndef reset_card():\n    Serial.flushOutput()\n    Serial.flushInput()\n    Serial.write(b'Reset')\n    _ans = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n    print(_ans)\n    if 'Reset' in _ans:\n        Ans = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n        if 'Reset' in Ans: print(\"Trying to reset the chip!\")\n        State = Serial.readline().decode().replace(\"\\n\", \"\").replace(\"\\r\", \"\")\n        print(State)\n        if 'Err' in State:\n            print(\"Reset Failed !\")\n            return -1\n\n        Serial.flushOutput()\n        Serial.flushInput()\n        return 1\n    else:\n        Serial.flushOutput()\n        Serial.flushInput()\n        return -1\n\n\ndef read_default():\n    Serial.readline().decode()\n    print(\"Clear input and output buffer\")\n\ncredit = 0\n\n\nprint(\"K20 - Terminal\\n\")\n\nports = serial.tools.list_ports.comports()\nPortCount = 0\n\nfor port in ports:\n    print(str(PortCount) + \": \" + port.device)\n    PortCount += 1\n\nCount = int(input(\"\\nChoose: \"))\n\nprint(\"connect to: \" + ports[Count].device + \"\\n\")\nSerial = serial.Serial(ports[Count].device)\nread_default()\n\nprint(\"## Please place chip on reader ##\")\n\nwhile True:\n\n    Serial.flushInput()\n    Serial.flushOutput()\n    state = IsNewCardPresent()\n    credit = 0\n\n    if state == (\" \" or \"\" or not \"1\"):\n        continue\n\n    if state == 'Unknown cmd':\n        print(\"Unkown serial answer - Reset loop\")\n        flush_serial()\n        continue\n\n    if state == \"1\":\n\n        User_ID = getId()\n        current_credit = ReadCredit()\n\n        print(\"------------------------\")\n        print(\"ID:     \" + str(User_ID))\n        print(\"Credit: \" + str(current_credit))\n        print(\"------------------------\")\n\n        if str(current_credit) == \"-1\":\n            print(\"Card not initialized !\")\n            ini = int(input(\"Initialize [0] // Try again [1]: \"))\n            if ini != 1:\n                if reset_card() != 1:\n                    print(\"## Could not reset placed chip ##\")\n                    continue\n                else:\n                    print(\"## Chip reset successful ##\")\n                    continue\n            else:\n                continue\n\n        while True:\n\n            mode = input('1 - 30ct\\n2 - 50ct\\n3 - Setup new chip\\n4 - Service\\nSelect: ')\n\n            if mode == \"1\":\n                money = float(input(\"Money: \"))\n                credit = int(money / 0.30 + int(current_credit))\n                break\n\n            elif mode == \"2\":\n                money = float(input(\"Money: \"))\n                credit = int(money / 0.50 + int(current_credit))\n                break\n\n            elif mode == \"3\":\n                select = \"Y\"\n                if int(current_credit) != 0:\n                    print(\"Credit found on chip!\")\n                    print(\"Reset and write 0 to card?\")\n                    select = str(input(\"[Y]es [N]o :\"))\n                if select.upper() == \"Y\":\n                    print(\"## Reset card ##\")\n                    while reset_card() != 1:\n                        flush_serial()\n                        print(\"Trying to reset you chip !\")\n                        continue\n                else:\n                    credit = 0\n                break\n\n            elif mode == \"4\":\n                credit = int(input(\"Service Credit: \"))\n                break\n\n            else:\n                print(\"## Err - Try again ##\")\n                continue\n\n        flush_serial()\n\n        print(\"Write \" + str(credit) + \" to card\")\n\n        write_state = WriteCredit(str(credit))\n\n        flush_serial()\n        i = 0\n        while True:\n\n            if IsNewCardPresent() == \"1\":\n                break\n\n            i = i + 1\n\n            print(\"Checking :\" + str(i))\n            flush_serial()\n            if (i > 5):\n                break\n\n        flush_serial()\n\n        print(\"Read new credit: \" + ReadCredit())\n\n        if input(\"Add more? [Y]es [N]o: \").upper() == \"Y\":\n            print(\"## Please place chip on reader ##\")\n            continue\n        else:\n            break\n\nSerial.close()\n","sub_path":"python/write_only.py","file_name":"write_only.py","file_ext":"py","file_size_in_byte":6086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"362591244","text":"# encoding: utf-8\n\nu'''MCL — Specimen Type Folder'''\n\nfrom ._base import IIngestableFolder, Ingestor, IngestableFolderView\nfrom .interfaces import ISpecimenType\nfrom five import grok\n\n\nclass ISpecimenTypeFolder(IIngestableFolder):\n    u'''Folder containing specimentype.'''\n\n\nclass SpecimenTypeIngestor(Ingestor):\n    u'''RDF ingestor for specimentype.'''\n    grok.context(ISpecimenTypeFolder)\n    def getContainedObjectInterface(self):\n        return ISpecimenType\n\n\nclass View(IngestableFolderView):\n    u'''View for an specimentype folder'''\n    grok.context(ISpecimenTypeFolder)\n","sub_path":"src/jpl.mcl.site.knowledge/src/jpl/mcl/site/knowledge/specimentypefolder.py","file_name":"specimentypefolder.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"503427285","text":"import json\n\nfilename = \"username.json\"\ntry:\n    with open(filename, \"r\") as file_obj:\n        name = json.load(file_obj)\n        print(\"Hey there, Joshua!\")\n    file_obj.close()\nexcept FileNotFoundError:\n    name = input(\"Could not find your name in the system. Please give your name! \")\n    with open(filename, \"w\") as file_obj:\n        json.dump(name, file_obj)\n    file_obj.close()\n    print(\"Ok, \" + name + \", your name has been successfully added to the system!\")\n","sub_path":"venv/bin/remember_me.py","file_name":"remember_me.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"547975217","text":"import numpy as np\r\nimport torch\r\nfrom torch import nn\r\nimport time\r\n\r\n\r\nepochs = 2000\r\nSEED = 16\r\nnp.random.seed(SEED)\r\ncuda = torch.cuda.is_available()\r\nif cuda:\r\n    torch.cuda.manual_seed(SEED)\r\n\r\n\r\nclass CF(nn.Module):\r\n    def __init__(self, input_dim, hidden_dim, layer_num):\r\n        super(CF, self).__init__()\r\n\r\n        self.input_dim = input_dim\r\n        self.hidden_dim = hidden_dim\r\n        self.gru = torch.nn.GRU(input_size=self.input_dim, hidden_size=self.hidden_dim,\r\n                                num_layers=layer_num)\r\n        self.out = torch.nn.Linear(self.hidden_dim, 2)  # output layer\r\n\r\n    def forward(self, miu):\r\n        output, hn = self.gru(miu)\r\n        output_in_last_timestep = hn[-1, :, :]\r\n        x = self.out(output_in_last_timestep)\r\n        return x\r\n\r\n\r\ndata_death = []\r\nwith open(\"./data/7/hadm_death_20_7.txt\", \"r\") as file_death:\r\n    for line in file_death.readlines():\r\n        is_death = float(str(line.strip('\\n')).split('\\t')[1])\r\n        data_death.append(is_death)\r\ndata_death_Tensor = torch.LongTensor(data_death)\r\n\r\ngroup = 776\r\n# cf_record = np.load(\"./data/7/CF_node_polynomial_eff_pro.npy\")\r\n# cf_record = np.load(\"./data/7/CF_node_is_fail.npy\")\r\ncf_record = np.load(\"./data/7/CF_node_eff_pro.npy\")\r\n\r\nhidden_dim_all = [32, 64, 128, 256]\r\nweight_CRs = [0.3, 0.5, 0.7, 0.9, 1.0]\r\nMAX_AUC_list = []\r\nfor index in range(5):\r\n    # with open(\"./data/7/AUC_result_GRU_polynomial_eff_pro.txt\", \"a\") as f:\r\n    with open(\"./data/7/AUC_result_GRU_node_eff_pro.txt\", \"a\") as f:\r\n    # with open(\"./data/7/AUC_result_GRU_node_is_fail.txt\", \"a\") as f:\r\n        f.write(str(index+1)+'\\n')\r\n        max_AUC = 0\r\n        for weight_CR in weight_CRs:\r\n            for hidden_dim in hidden_dim_all: \r\n                length = 60\r\n                input_dim = 451\r\n                layer_num = 2\r\n                learning_rate = 0.0005\r\n\r\n                if index == 0:\r\n                    cal_r_is_fail_train = cf_record[group * 1:]\r\n                    cal_r_is_fail_test = cf_record[:group * 1]\r\n                elif index == 4:\r\n                    cal_r_is_fail_train = cf_record[:group * 4]\r\n                    cal_r_is_fail_test = cf_record[group * 4:]\r\n                else:\r\n                    cal_r_is_fail_train = np.concatenate((cf_record[group * 0:group * index], cf_record[group * (index + 1):]))\r\n                    cal_r_is_fail_test = cf_record[group * index:group * (index + 1)]\r\n\r\n                miu_train_new = []\r\n                for i in range(cal_r_is_fail_train.shape[1]):\r\n                    miu_train_new.append(cal_r_is_fail_train[:, i, :])\r\n                miu_train_new = torch.Tensor(miu_train_new)\r\n\r\n                miu_test_new = []\r\n                for i in range(cal_r_is_fail_test.shape[1]):\r\n                    miu_test_new.append(cal_r_is_fail_test[:, i, :])\r\n                miu_test_new = torch.Tensor(miu_test_new)\r\n\r\n                net = CF(input_dim=input_dim, hidden_dim=hidden_dim, layer_num=layer_num)  # d\r\n                optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)\r\n                loss_func = torch.nn.CrossEntropyLoss(weight=torch.Tensor([weight_CR, 1.0]).cuda())\r\n\r\n                if cuda:\r\n                    net.cuda()\r\n                    miu_train_new = miu_train_new.cuda()\r\n                    miu_test_new = miu_test_new.cuda()\r\n                    data_death_Tensor = data_death_Tensor.cuda()\r\n\r\n                best_epoch = 0\r\n                best_loss = 10000\r\n                bad_count = 0\r\n                patience = 100\r\n                start = time.time()\r\n                for epoch in range(epochs):\r\n                    now = time.time()\r\n                    net.train()\r\n                    optimizer.zero_grad()\r\n                    out_train = net(miu_train_new)\r\n\r\n                    if index == 0:\r\n                        loss = loss_func(out_train, data_death_Tensor[group*1:])\r\n                    elif index == 4:\r\n                        loss = loss_func(out_train, data_death_Tensor[:group*4])\r\n                    else:\r\n                        loss = loss_func(out_train, torch.cat((data_death_Tensor[:group*index], data_death_Tensor[group*(index+1):])))\r\n                    loss.backward()\r\n                    optimizer.step()\r\n\r\n                    net.eval()\r\n                    out_test = net(miu_test_new)\r\n                    if index == 0:\r\n                        loss_test = loss_func(out_test, data_death_Tensor[:group*1])\r\n                        target_y = data_death_Tensor[:group*1].cpu().data.numpy()\r\n                    elif index == 4:\r\n                        loss_test = loss_func(out_test, data_death_Tensor[group*4:])\r\n                        target_y = data_death_Tensor[group*4:].cpu().data.numpy()\r\n                    else:\r\n                        loss_test = loss_func(out_test, data_death_Tensor[group*index:group*(index+1)])\r\n                        target_y = data_death_Tensor[group*index:group*(index+1)].cpu().data.numpy()\r\n\r\n                    if loss_test < best_loss:\r\n                        best_loss = loss_test\r\n                        best_epoch = epoch\r\n                        bad_count = 0\r\n                        best_out_test = out_test.cpu()\r\n                    else:\r\n                        bad_count += 1\r\n\r\n                    if bad_count == patience:\r\n                        break\r\n\r\n                True_sample_pro = best_out_test[:, 0].detach().numpy().tolist()\r\n                index_dic = {}\r\n                for id, pro in enumerate(True_sample_pro):\r\n                    index_dic[id] = pro\r\n                index_dic = sorted(index_dic.items(), key=lambda item:item[1], reverse=True)\r\n\r\n                FPR_list = [0.0]\r\n                TPR_list = [0.0]\r\n                TP = 0\r\n                FN = 0\r\n                FP = 0\r\n                TN = 0\r\n\r\n                for id in index_dic:\r\n                    if target_y[id[0]] == 0:\r\n                        TP += 1\r\n                    else:\r\n                        FP += 1\r\n                    if index == 4:\r\n                        FN = group + 4 - target_y.sum() - TP\r\n                    else:\r\n                        FN = group - target_y.sum() - TP\r\n\r\n                    TN = target_y.sum() - FP\r\n                    FPR = FP / (TN + FP)\r\n                    TPR = TP / (TP + FN)\r\n                    FPR_list.append(FPR)\r\n                    TPR_list.append(TPR)\r\n\r\n                AUC = 0\r\n                for id, x in enumerate(FPR_list[:-1]):\r\n                    AUC += (FPR_list[id+1] - x) * (TPR_list[id] + TPR_list[id+1])\r\n                AUC = AUC/2\r\n                if AUC > max_AUC:\r\n                    max_AUC = AUC\r\n\r\n                f.write(\"seq_len=\" + str(length) + '  ')\r\n                f.write(\"input=\" + str(input_dim) + '  ')\r\n                f.write(\"hidden=\" + str(hidden_dim) + '  ')\r\n                f.write(\"layer_num=\" + str(layer_num) + '  ')\r\n                f.write(\"w_CR=\" + str(weight_CR) + '  ')\r\n                f.write(\"lr=\" + str(learning_rate) + '  ')\r\n                f.write(\"AUC=\"+str(AUC)+'\\n')\r\n        f.write(\"MAX_AUC=\"+str(max_AUC)+'\\n')\r\n        MAX_AUC_list.append(max_AUC)\r\n        if index == 4:\r\n            sum = 0\r\n            f.write('(')\r\n            for i, auc in enumerate(MAX_AUC_list):\r\n                sum += auc\r\n                if i < 4:              \r\n                    f.write(str(auc) +'+')\r\n                else:\r\n                    f.write(str(auc))\r\n                    f.write(')/5=')\r\n                    f.write(str(sum/5) + '\\n')\r\n","sub_path":"predict_GRU_CUDA.py","file_name":"predict_GRU_CUDA.py","file_ext":"py","file_size_in_byte":7589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"455730868","text":"\"\"\"\nThis file handles the details of the loss function during training.\n\nThis includes: LossComputeBase and the standard NMTLossCompute, and\n               sharded loss compute stuff.\n\"\"\"\nfrom __future__ import division\nimport torch\nimport torch.nn as nn\n#This two libraries were added for max-margin loss\n#################################\nfrom torch.autograd import Variable\nfrom torch.nn import Parameter\n#################################\nimport onmt\nimport onmt.io\nimport tensorflow as tf\nimport tensorflow_hub as hub\nfrom sentence_transformers import SentenceTransformer\nimport re\nfrom onmt.io.DatasetBase import PAD_WORD\n\nclass LossComputeBase(nn.Module):\n    \"\"\"\n    Class for managing efficient loss computation. Handles\n    sharding next step predictions and accumulating mutiple\n    loss computations\n\n\n    Users can implement their own loss computation strategy by making\n    subclass of this one.  Users need to implement the _compute_loss()\n    and make_shard_state() methods.\n\n    Args:\n        generator (:obj:`nn.Module`) :\n             module that maps the output of the decoder to a\n             distribution over the target vocabulary.\n        tgt_vocab (:obj:`Vocab`) :\n             torchtext vocab object representing the target output\n        normalzation (str): normalize by \"sents\" or \"tokens\"\n    \"\"\"\n\n    def __init__(self, generator, tgt_vocab):\n        super(LossComputeBase, self).__init__()\n        self.generator = generator\n        self.tgt_vocab = tgt_vocab\n        self.padding_idx = tgt_vocab.stoi[onmt.io.PAD_WORD]\n\n    def _make_shard_state(self, batch, output, range_, attns=None):\n        \"\"\"\n        Make shard state dictionary for shards() to return iterable\n        shards for efficient loss computation. Subclass must define\n        this method to match its own _compute_loss() interface.\n        Args:\n            batch: the current batch.\n            output: the predict output from the model.\n            range_: the range of examples for computing, the whole\n                    batch or a trunc of it?\n            attns: the attns dictionary returned from the model.\n        \"\"\"\n        return NotImplementedError\n\n    def _compute_loss(self, batch, output, target, **kwargs):\n        \"\"\"\n        Compute the loss. Subclass must define this method.\n\n        Args:\n\n            batch: the current batch.\n            output: the predict output from the model.\n            target: the validate target to compare output with.\n            **kwargs(optional): additional info for computing loss.\n        \"\"\"\n        return NotImplementedError\n\n    def monolithic_compute_loss(self, batch, output, attns):\n        \"\"\"\n        Compute the forward loss for the batch.\n\n        Args:\n          batch (batch): batch of labeled examples\n          output (:obj:`FloatTensor`):\n              output of decoder model `[tgt_len x batch x hidden]`\n          attns (dict of :obj:`FloatTensor`) :\n              dictionary of attention distributions\n              `[tgt_len x batch x src_len]`\n        Returns:\n            :obj:`onmt.Statistics`: loss statistics\n        \"\"\"\n        range_ = (0, batch.tgt.size(0))\n        shard_state = self._make_shard_state(batch, output, range_, attns)\n        _, batch_stats = self._compute_loss(batch, **shard_state)\n\n        return batch_stats\n\n    def sharded_compute_loss(self, batch, output, attns,\n                             cur_trunc, trunc_size, shard_size,\n                             normalization):\n        \"\"\"Compute the forward loss and backpropagate.  Computation is done\n        with shards and optionally truncation for memory efficiency.\n\n        Also supports truncated BPTT for long sequences by taking a\n        range in the decoder output sequence to back propagate in.\n        Range is from `(cur_trunc, cur_trunc + trunc_size)`.\n\n        Note sharding is an exact efficiency trick to relieve memory\n        required for the generation buffers. Truncation is an\n        approximate efficiency trick to relieve the memory required\n        in the RNN buffers.\n\n        Args:\n          batch (batch) : batch of labeled examples\n          output (:obj:`FloatTensor`) :\n              output of decoder model `[tgt_len x batch x hidden]`\n          attns (dict) : dictionary of attention distributions\n              `[tgt_len x batch x src_len]`\n          cur_trunc (int) : starting position of truncation window\n          trunc_size (int) : length of truncation window\n          shard_size (int) : maximum number of examples in a shard\n          normalization (int) : Loss is divided by this number\n\n        Returns:\n            :obj:`onmt.Statistics`: validation loss statistics\n\n        \"\"\"\n        batch_stats = onmt.Statistics()\n        range_ = (cur_trunc, cur_trunc + trunc_size)\n        shard_state = self._make_shard_state(batch, output, range_, attns)\n\n        for shard in shards(shard_state, shard_size):\n            loss, stats = self._compute_loss(batch, **shard)\n            loss.div(float(normalization)).backward()\n            batch_stats.update(stats)\n\n        return batch_stats\n\n    def _stats(self, loss, scores, target):\n        \"\"\"\n        Args:\n            loss (:obj:`FloatTensor`): the loss computed by the loss criterion.\n            scores (:obj:`FloatTensor`): a score for each possible output\n            target (:obj:`FloatTensor`): true targets\n\n        Returns:\n            :obj:`Statistics` : statistics for this batch.\n        \"\"\"\n        pred = scores.max(1)[1]\n        non_padding = target.ne(self.padding_idx)\n        num_correct = pred.eq(target) \\\n                          .masked_select(non_padding) \\\n                          .long().sum()\n        return onmt.Statistics(loss.data.cpu().numpy(),\n                               non_padding.long().sum().data.cpu().numpy(),\n                               num_correct.data.cpu().numpy())\n\n    def _bottle(self, v):\n        return v.view(-1, v.size(2))\n\n    def _unbottle(self, v, batch_size):\n        return v.view(-1, batch_size, v.size(1))\n\n\nclass NMTLossCompute(LossComputeBase):\n    \"\"\"\n    Standard NMT Loss Computation.\n    \"\"\"\n\n    def __init__(self, generator, tgt_vocab, normalization=\"sents\",\n                 label_smoothing=0.0, ReWE=False, ReSE=False, generator_ReWE=None, generator_ReSE=None, dec_embeddings=None,\n                 alpha_loss_ReWE=None, alpha_loss_ReSE=None, tgt_emb_size=None, ReSE_type='USE', device=-1):\n        super(NMTLossCompute, self).__init__(generator, tgt_vocab)\n        assert (label_smoothing >= 0.0 and label_smoothing <= 1.0)\n        if label_smoothing > 0:\n            # When label smoothing is turned on,\n            # KL-divergence between q_{smoothed ground truth prob.}(w)\n            # and p_{prob. computed by model}(w) is minimized.\n            # If label smoothing value is set to zero, the losshhhhhhh\n            # is equivalent to NLLLoss or CrossEntropyLoss.hhhh\n            # All non-true labels are uniformly set to low-confidence.\n            self.criterion = nn.KLDivLoss(size_average=False)\n            one_hot = torch.randn(1, len(tgt_vocab))\n            one_hot.fill_(label_smoothing / (len(tgt_vocab) - 2))\n            one_hot[0][self.padding_idx] = 0\n            self.register_buffer('one_hot', one_hot)\n        else:\n            weight = torch.ones(len(tgt_vocab))\n            weight[self.padding_idx] = 0\n            self.criterion = nn.NLLLoss(weight, size_average=False)\n        self.confidence = 1.0 - label_smoothing\n        if device >= 0:\n            self.device = 'cuda:'+str(device)\n        else:\n            self.device = 'cpu'\n        self.ReWE=ReWE\n        self.ReSE=ReSE\n        self.ReSE_type = ReSE_type\n        if ReWE:\n            self.generator_ReWE = generator_ReWE\n            self.dec_embeddings = dec_embeddings\n            self.tgt_emb_size = tgt_emb_size\n            self.alpha_loss_ReWE = alpha_loss_ReWE\n            self.criterion_ReWE = nn.CosineEmbeddingLoss(size_average=False)\n        if ReSE:\n            self.generator_ReSE = generator_ReSE\n            if self.ReSE_type == 'USE':\n                g = tf.Graph()\n                with g.as_default():\n                    self.messages = tf.placeholder(dtype=tf.string, shape=[None])\n                    self.embed = hub.Module(\"universal_sentence_encoder\")\n                    self.embeddings = self.embed(self.messages)\n                    init_op = tf.group([tf.global_variables_initializer(), tf.tables_initializer()])\n                g.finalize()\n                self.sess = tf.Session(graph=g)\n                self.sess.run(init_op)\n            elif self.ReSE_type == 'sBERT':\n                self.sBERT_model = SentenceTransformer('bert-base-nli-mean-tokens')\n            self.dec_embeddings = dec_embeddings\n            self.tgt_emb_size = tgt_emb_size\n            self.alpha_loss_ReSE = alpha_loss_ReSE\n            # self.criterion_continous = nn.MSELoss(size_average=False)\n            self.criterion_ReSE = nn.CosineEmbeddingLoss(size_average=False)\n\n\n    def _make_shard_state(self, batch, output, range_, attns=None):\n\n        return {\n            \"output\": output,\n            \"target\": batch.tgt[range_[0] + 1: range_[1]],\n        }\n\n    def idSEQ2strSEQ(self,target_IDs):\n        list_batch=[]\n        sen_len, batch_len = target_IDs.size()\n        for i in range(batch_len):\n            list_sent = []\n            for j in range(sen_len):\n                word = self.tgt_vocab.itos[target_IDs[j, i]]\n                if word == PAD_WORD:\n                    break\n                list_sent.append(word)\n            list_sent = \" \".join(list_sent)\n            list_batch.append(list_sent)\n        return list_batch\n\n    def process_bpe(self,sent):\n        new_list = []\n        for s in sent:\n            #print ('trueeeeeeeee')\n            sen = re.sub(r'(@@ )|(@@ ?$)','',s)\n            new_list.append(sen)\n        return new_list\n\n    def _compute_loss(self, batch, output, target):\n        scores = self.generator(self._bottle(output))\n        if self.ReWE:\n            scores_reg = self.generator_ReWE(self._bottle(output))\n\n        # print (target.size())\n\n        if self.ReWE:\n            target_dim=target.unsqueeze(2)\n            # print (target_dim.size())\n            with torch.no_grad():\n                embeddings_tgt=self.dec_embeddings(target_dim)\n            # print (embeddings_tgt)\n            # print (embeddings_tgt.size())\n\n            gtruth_emb = embeddings_tgt.view(-1, self.tgt_emb_size)\n            #print (gtruth_emb.requires_grad)\n\n        gtruth = target.view(-1)\n\n        if self.ReSE:\n            sen_vecs = self.generator_ReSE(output.transpose(0,1))\n            target_dim = target.unsqueeze(2)\n            with torch.no_grad():\n                embeddings_tgt=self.dec_embeddings(target_dim)\n            # print (embeddings_tgt.size())\n            # with torch.no_grad():\n            #     embeddings_tgt = self.dec_embeddings(target_dim)\n            # sen_gt_embs = self.ReSE_gt(embeddings_tgt.transpose(0,1))\n            if self.ReSE_type == 'USE' or self.ReSE_type == 'sBERT':\n                sentString = self.idSEQ2strSEQ(target)\n                sentString = self.process_bpe(sentString)\n                list_embs = []\n                for sen in sentString:\n                    if self.ReSE_type == 'USE':\n                        new_embedding = self.sess.run(self.embeddings, feed_dict={self.messages: [sen]})\n                        new_embedding = torch.from_numpy(new_embedding[0])\n                    elif self.ReSE_type == 'sBERT':\n                        new_embedding = self.sBERT_model.encode(sen, show_progress_bar=False)\n                        new_embedding = torch.from_numpy(new_embedding[0])\n                    list_embs.append(new_embedding)\n                sen_gt_embs = torch.stack(list_embs).squeeze().cuda()\n            elif self.ReSE_type == 'avgEmbs':\n                sen_gt_embs = embeddings_tgt.mean(0)\n            elif self.ReSE_type == 'maxpoolEmbs':\n                embeddings_tgt_int = embeddings_tgt.permute(1, 2, 0)\n                _, _, dim = embeddings_tgt_int.size()\n                pool_layer = nn.MaxPool1d(dim)\n                sen_gt_embs = pool_layer(embeddings_tgt_int).squeeze()\n            if len(sen_gt_embs.size())<2:\n                sen_gt_embs = sen_gt_embs.unsqueeze(dim=0)\n            #print (sen_gt_embs.size())\n\n        #Convert IDs to embeddings\n\n\n        if self.confidence < 1:\n            tdata = gtruth.data\n            mask = torch.nonzero(tdata.eq(self.padding_idx)).squeeze()\n            log_likelihood = torch.gather(scores.data, 1, tdata.unsqueeze(1))\n            tmp_ = self.one_hot.repeat(gtruth.size(0), 1)\n            tmp_.scatter_(1, tdata.unsqueeze(1), self.confidence)\n            if mask.numel() > 0:\n                log_likelihood.index_fill_(0, mask, 0)\n                tmp_.index_fill_(0, mask, 0)\n            gtruth = tmp_\n\n        if self.ReWE==True and self.ReSE==False:\n            #Loss 1\n            loss_NLL= self.criterion(scores, gtruth)\n            #Loss 2\n            loss_ReWE = self.criterion_ReWE(scores_reg, gtruth_emb,torch.tensor(1, dtype=torch.float,device=self.device)) #Cosine loss\n            #Combined loss\n            loss = loss_NLL + self.alpha_loss_ReWE*loss_ReWE\n        elif self.ReWE==False and self.ReSE==True:\n            # Loss 1\n            loss_NLL = self.criterion(scores, gtruth)\n            # Loss 2\n            loss_ReSE = self.criterion_ReSE(sen_vecs, sen_gt_embs, torch.tensor(1, dtype=torch.float,device=self.device))  # Cosine loss\n            #Combined loss\n            loss = loss_NLL + self.alpha_loss_ReSE * loss_ReSE\n        elif self.ReWE==True and self.ReSE==True:\n            #Loss 1\n            loss_NLL = self.criterion(scores, gtruth)\n            #Loss 2\n            loss_ReWE = self.criterion_ReWE(scores_reg, gtruth_emb,torch.tensor(1, dtype=torch.float, device=self.device))  # Cosine loss\n            #Loss 3\n            #print (sen_vecs.size())\n            #print (sen_gt_embs.size())\n            loss_ReSE = self.criterion_ReSE(sen_vecs, sen_gt_embs,torch.tensor(1, dtype=torch.float, device=self.device))  # Cosine loss\n            #Combined loss\n            loss = loss_NLL + self.alpha_loss_ReWE*loss_ReWE + self.alpha_loss_ReSE * loss_ReSE\n        else:\n            loss = self.criterion(scores, gtruth)\n\n\n        if self.confidence < 1:\n            # Default: report smoothed ppl.\n            # loss_data = -log_likelihood.sum(0)\n            loss_data = loss.data.clone()\n        else:\n            loss_data = loss.data.clone()\n\n        stats = self._stats(loss_data, scores.data, target.view(-1).data)\n\n        return loss, stats\n\n\ndef filter_shard_state(state, shard_size=None):\n    for k, v in state.items():\n        if shard_size is None:\n            yield k, v\n\n        if v is not None:\n            v_split = []\n            if isinstance(v, torch.Tensor):\n                for v_chunk in torch.split(v, shard_size):\n                    v_chunk = v_chunk.data.clone()\n                    v_chunk.requires_grad = v.requires_grad\n                    v_split.append(v_chunk)\n            yield k, (v, v_split)\n\n\ndef shards(state, shard_size, eval=False):\n    \"\"\"\n    Args:\n        state: A dictionary which corresponds to the output of\n               *LossCompute._make_shard_state(). The values for\n               those keys are Tensor-like or None.\n        shard_size: The maximum size of the shards yielded by the model.\n        eval: If True, only yield the state, nothing else.\n              Otherwise, yield shards.\n\n    Yields:\n        Each yielded shard is a dict.\n\n    Side effect:\n        After the last shard, this function does back-propagation.\n    \"\"\"\n    if eval:\n        yield filter_shard_state(state)\n    else:\n        # non_none: the subdict of the state dictionary where the values\n        # are not None.\n        non_none = dict(filter_shard_state(state, shard_size))\n\n        # Now, the iteration:\n        # state is a dictionary of sequences of tensor-like but we\n        # want a sequence of dictionaries of tensors.\n        # First, unzip the dictionary into a sequence of keys and a\n        # sequence of tensor-like sequences.\n        keys, values = zip(*((k, [v_chunk for v_chunk in v_split])\n                             for k, (_, v_split) in non_none.items()))\n\n        # Now, yield a dictionary for each shard. The keys are always\n        # the same. values is a sequence of length #keys where each\n        # element is a sequence of length #shards. We want to iterate\n        # over the shards, not over the keys: therefore, the values need\n        # to be re-zipped by shard and then each shard can be paired\n        # with the keys.\n        for shard_tensors in zip(*values):\n            yield dict(zip(keys, shard_tensors))\n\n        # Assumed backprop'd\n        variables = []\n        for k, (v, v_split) in non_none.items():\n            if isinstance(v, torch.Tensor) and state[k].requires_grad:\n                variables.extend(zip(torch.split(state[k], shard_size),\n                                     [v_chunk.grad for v_chunk in v_split]))\n        inputs, grads = zip(*variables)\n        torch.autograd.backward(inputs, grads)\n\n\nclass max_margin_loss(nn.Module):\n    def __init__(self, num_classes,decoder_embeddings, margin=0.1, weights=None):\n        \"\"\"\n        :param num_classes: An int. The number of possible classes.\n        :param embed_size: An int. EmbeddingLockup size\n        :param num_sampled: An int. The number of sampled from noise examples\n        :param weights: A list of non negative floats. Class weights. None if\n            using uniform sampling. The weights are calculated prior to\n            estimation and can be of any form, e.g equation (5) in [1]\n        \"\"\"\n\n        super(max_margin_loss, self).__init__()\n\n        self.vocab_size = num_classes\n        self.decoder_embeddings= decoder_embeddings\n        self.margin=margin\n\n\n        self.weights = weights\n        if self.weights is not None:\n            assert min(self.weights) >= 0, \"Each weight should be >= 0\"\n\n            self.weights = Variable(torch.from_numpy(weights)).float()\n\n    def sample(self, num_sample):\n        \"\"\"\n        draws a sample from classes based on weights\n        \"\"\"\n        return torch.multinomial(self.weights, num_sample, True)\n\n    def forward(self, pred_embs, ground_truth_embs, num_sampled):\n        \"\"\"\n        :param pred_embs: Tensor with shape of [predictions_num x emb_length]\n        :param ground_truth_embs: Tensor with shape of [predictions_num x emb_length]\n        :param num_sampled: An int. The number of sampled from noise examples\n        :return: Loss estimation with shape of [1]\n        \"\"\"\n\n        [predictions_num, emb_length] = ground_truth_embs.size()\n\n\n\n        # if self.weights is not None:\n        #     noise_sample_count = batch_size * window_size * num_sampled\n        #     draw = self.sample(noise_sample_count)\n        #     noise = draw.view(batch_size * window_size, num_sampled)\n        # else:\n        noise = Variable(torch.Tensor(num_sampled,predictions_num,1).uniform_(0, self.vocab_size - 1).long()) #Size: [predictions_num x num_sampled ]\n        noise=noise.cuda()\n        #print (noise.size())\n\n        with torch.no_grad():\n            sampled_embeddings = self.decoder_embeddings(noise)\n        #sampled_embeddings = noise.view(-1, emb_length)  #Size: [predictions_num x num_sampled x embedding_size]\n\n        #Compute all the cosines\n        cos_truth=nn.functional.cosine_similarity(pred_embs,ground_truth_embs) #Not sure if it is well defined\n        cos_neg_samples= torch.zeros(predictions_num, dtype=torch.float,device='cuda:0')#ALERT! it needs to be a scalar an it is a vector!\n        for i in range(num_sampled):\n            cos_neg_samples+=nn.functional.cosine_similarity(pred_embs,sampled_embeddings[i,:,:])\n        loss_almost=cos_neg_samples - cos_truth  #ALERT!!! margin is a scalar and needs to be a vector!!!!!!! Size: [predictions_num]\n        loss=torch.add(loss_almost,self.margin)\n\n        #Hinge loss\n        #Select the maximum between zero and the margin\n        vector_zeros=torch.zeros(predictions_num, dtype=torch.float,device='cuda:0')  #ALERT!!! margin is a scalar and needs to be a vector!!!!!!!  Size: [predictions_num]\n        concatenated=torch.stack((vector_zeros,loss),dim=1) #Size: [predictions_num x 2]\n        final_loss=torch.max(concatenated,dim=1)[0] #Size: [predictions_num]\n\n        #Sum all the loss\n        final_loss_sum=final_loss.sum()  #Size: ESCALAR\n\n        return final_loss_sum\n\n\n        # #OLD CODE\n        # log_target = (pred_embs * output).sum(1).squeeze().sigmoid().log()\n        #\n        # ''' ∑[batch_size * window_size, num_sampled, embed_size] * [batch_size * window_size, embed_size, 1] ->\n        #     ∑[batch_size, num_sampled, 1] -> [batch_size] '''\n        # sum_log_sampled = torch.bmm(sampled_embeddings, pred_embs.unsqueeze(2)).sigmoid().log().sum(1).squeeze()\n        #\n        # loss = log_target + sum_log_sampled\n        #\n        # return -loss.sum() / batch_size\n\n    def input_embeddings(self):\n        return self.in_embed.weight.data.cpu().numpy()\n","sub_path":"Supervised/onmt/Loss.py","file_name":"Loss.py","file_ext":"py","file_size_in_byte":21156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"47537246","text":"import math\r\nimport bisect\r\n\r\ninputfile = open('testdata.txt')\r\n\r\nNumbers=[]\r\ntot = 0\r\nfor line in inputfile:\r\n    num = int(line)\r\n    tot += 1\r\n        \r\n    bisect.insort(Numbers,num) # insert the incoming number in sorted order: takes O(N)\r\n    \r\n    \r\n    if tot % 2 == 0:\r\n        index = int(tot/2)\r\n        index -=1\r\n        median = (Numbers[index] + Numbers[index + 1])/2\r\n    else:\r\n        index = int((tot+1)/2)\r\n        index -=1\r\n        median = Numbers[index]\r\n\r\n    # The following if block prints the total number of integers read so far (after every 10,000\r\n    # This is useful when you are testing naive.py for very large input file as it will show you the progress.\r\n    if tot % 10000 == 0:\r\n        print(tot)\r\n\r\n    # You may want to comment the following print command if you are using large input file\r\n    print(int(median))\r\n    \r\n","sub_path":"Week 4/median/naive.py","file_name":"naive.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"625650264","text":"\"\"\"\nDiagnostics module for the turboPy computational physics simulation framework.\n\nDiagnostics can access :class:`PhysicsModule` data. \nThey are called every time step, or every N steps.\nThey can write to file, cache for later, update plots, etc, and they\ncan halt the simulation if conditions require.\n\"\"\"\nfrom abc import ABC, abstractmethod\nimport numpy as np\nimport xarray as xr\n\nfrom .core import Diagnostic, Simulation\n\n\nclass OutputUtility(ABC):\n    \"\"\"Abstract base class for output utility\n\n    An instance of an OutputUtility can (optionally) be used by diagnostic\n    classes to assist with the implementation details needed for outputing\n    the diagnostic information.\n    \"\"\"\n    def __init__(self, input_data):\n        pass\n\n    @abstractmethod\n    def diagnose(self, data):\n        \"\"\"Perform the diagnostic\"\"\"\n        pass\n\n    @abstractmethod\n    def finalize(self):\n        \"\"\"Perform any finalization steps when the simulation is complete\"\"\"\n        pass\n\n    @abstractmethod\n    def write_data(self):\n        \"\"\"Optional function for writting buffer to file etc.\"\"\"\n        pass\n\n\nclass PrintOutputUtility(OutputUtility):\n    \"\"\"OutputUtility which writes to the screen\"\"\"\n    def diagnose(self, data):\n        \"\"\"\n        Prints out data to standard output.\n\n        Parameters\n        ----------\n        data : :class:`numpy.ndarray`\n            1D numpy array of values.\n        \"\"\"\n        print(data)\n\n\nclass CSVOutputUtility(OutputUtility):\n    \"\"\"Comma separated value (CSV) diagnostic output helper class\n\n    Provides routines for writing data to a file in CSV format. This\n    class can be used by Diagnostics subclassses to handle output to\n    csv format.\n\n    Parameters\n    ----------\n    filename : str\n       File name for CSV data file.\n    diagnostic_size : (int, int)\n       Size of data set to be written to CSV file. First value is the\n       number of time points. Second value is number of spatial points.\n\n    Attributes\n    ----------\n    filename: str\n        File name for CSV data file.\n    buffer: :class:`numpy.ndarray`\n        Buffer for storing data before it is written to file.\n    buffer_index: int\n        Position in buffer.\n    \"\"\"\n\n    def __init__(self, filename, diagnostic_size, **kwargs):\n        self._filename = filename\n        self._buffer = np.zeros(diagnostic_size)\n        self._buffer_index = 0\n\n    def diagnose(self, data):\n        \"\"\"\n        Adds 'data' into csv output buffer.\n\n        Parameters\n        ----------\n        data : :class:`numpy.ndarray`\n            1D numpy array of values to be added to the buffer.\n        \"\"\"\n        self._append(data)\n\n    def finalize(self):\n        \"\"\"Write the CSV data to file.\n        \"\"\"\n        self._write_buffer()\n\n    def write_data(self):\n        \"\"\"Write buffer to file\"\"\"\n        self._write_buffer()\n\n    def append(self, data):\n        \"\"\"Append data to the buffer.\n\n        .. deprecated::\n            `append` has been removed from the public API. Use `diagnose`\n            instead.\n        \"\"\"\n        self._append(data)\n\n    def _append(self, data):\n        \"\"\"Append data to the buffer.\n\n        Parameters\n        ----------\n        data : :class:`numpy.ndarray`\n            1D numpy array of values to be added to the buffer.\n        \"\"\"\n        self._buffer[self._buffer_index, :] = data\n        self._buffer_index += 1\n\n    def _write_buffer(self):\n        \"\"\"Write the CSV data to file.\n        \"\"\"\n        with open(self._filename, 'wb') as f:\n            np.savetxt(f, self._buffer, delimiter=\",\")\n\n\nclass NPYOutputUtility(OutputUtility):\n    \"\"\"NumPy formatted binary file (.npy) diagnostic output helper class\n\n    Provides routines for writing data to a file in NumPy format. This\n    class can be used by Diagnostics subclassses to handle output to\n    .npy format.\n\n    Parameters\n    ----------\n    filename : str\n       File name for .npy data file.\n    diagnostic_size : (int, int)\n       Size of data set to be written to .npy file. First value is the\n       number of time points. Second value is number of spatial points.\n\n    Attributes\n    ----------\n    filename: str\n        File name for .npy data file.\n    buffer: :class:`numpy.ndarray`\n        Buffer for storing data before it is written to file.\n    buffer_index: int\n        Position in buffer.\n    \"\"\"\n\n    def __init__(self, filename, diagnostic_size, **kwargs):\n        self._filename = filename\n        self._buffer = np.zeros(diagnostic_size)\n        self._buffer_index = 0\n\n    def diagnose(self, data):\n        \"\"\"\n        Adds 'data' into npy output buffer.\n\n        Parameters\n        ----------\n        data : :class:`numpy.ndarray`\n            1D numpy array of values to be added to the buffer.\n        \"\"\"\n        self._append(data)\n\n    def finalize(self):\n        \"\"\"Write the npy data to file.\n        \"\"\"\n        self._write_buffer()\n\n    def write_data(self):\n        \"\"\"Write buffer to file\"\"\"\n        self._write_buffer()\n\n    def _append(self, data):\n        \"\"\"Append data to the buffer.\n\n        Parameters\n        ----------\n        data : :class:`numpy.ndarray`\n            1D numpy array of values to be added to the buffer.\n        \"\"\"\n        self._buffer[self._buffer_index, :] = data\n        self._buffer_index += 1\n    \n    def _write_buffer(self):\n        \"\"\"Write the npy data to file.\n        \"\"\"\n        with open(self._filename, 'wb') as f:\n            np.save(f, self._buffer)\n\n\nutilities = {\n    \"stdout\": PrintOutputUtility,\n    \"csv\": CSVOutputUtility,\n    \"npy\": NPYOutputUtility\n}\n\n\nclass IntervalHandler:\n    \"\"\"Calls a function (action) if a given interval has passed\n\n    Parameters\n    ----------\n    interval : float, None\n        The time interval to wait in between actions. If interval is None,\n        then the action will be called every time.\n    action : callable\n        The function to call when the interval has passed\n    \"\"\"\n    def __init__(self, interval, action):\n        self._interval = interval\n        self._action = action\n        self._last_action = None\n        self.current_step = 0\n\n        if interval is None:\n            self.perform_action = self._action_every_time\n\n    def _action_every_time(self, time):\n        self._action()\n        self.current_step += 1\n\n    def perform_action(self, time):\n        \"\"\"Perform the action if an interval has passed\"\"\"\n        if self._check_step(time):\n            self._action()\n            self._last_action = time\n            self.current_step += 1\n\n    def _check_step(self, time):\n        \"\"\"Check if an interval has passed since last action\"\"\"\n        if self._last_action is None:\n            # Always run the action the first time\n            return True\n        return time >= self._last_action + self._interval\n\n\nclass PointDiagnostic(Diagnostic):\n    \"\"\"\n    Parameters\n    ----------\n    owner : Simulation\n       Simulation object containing current object.\n    input_data : dict\n       Dictionary that contains information regarding location, field,\n       and output type.\n\n    Attributes\n    ----------\n    location : str\n        Location.\n    field_name : str\n        Field name.\n    output : str\n        Output type.\n    get_value : function, None\n        Function to get value given the field.\n    field : str, None\n        Field as dictated by resource.\n    output_function : function, None\n        Function for assigned output method: standard output or csv.\n    csv : :class:`numpy.ndarray`, None\n        numpy.ndarray being written as a csv file.\n    \"\"\"\n    def __init__(self, owner: Simulation, input_data: dict):\n        super().__init__(owner, input_data)\n        self.location = input_data[\"location\"]\n        self.field_name = input_data[\"field\"]\n        self.output = input_data[\"output_type\"]  # \"stdout\"\n        self.get_value = None\n        self.field = None\n        self.outputter = None\n        self.interval = self._input_data.get('write_interval', None)\n        self.handler = None\n        self._needed_resources = {self.field_name: \"field\"}\n\n    def diagnose(self):\n        \"\"\"\n        Run output function given the value of the field.\n        \"\"\"\n        self.outputter.diagnose(self.get_value(self.field))\n        if self.handler:\n            self.handler.perform_action(self._owner.clock.time)\n\n    def initialize(self):\n        \"\"\"\n        Initialize output function if provided as csv, and self.csv\n        as an instance of the :class:`CSVOuputUtility` class.\n        \"\"\"\n        # set up function to interpolate the field value\n        super().initialize()\n        self.get_value = self._owner.grid.create_interpolator(\n                                self.location)\n\n        # setup output method\n        diagnostic_size = (self._owner.clock.num_steps + 1, 1)\n        self._input_data[\"diagnostic_size\"] = diagnostic_size\n\n        # Use composition to provide i/o functionality\n        self.outputter = utilities[self._input_data[\"output_type\"]](**self._input_data)\n\n        # set up interval handler\n        if self.interval:\n            self.handler = IntervalHandler(self.interval, self.outputter.write_data)\n\n    def finalize(self):\n        \"\"\"\n        Write the CSV data to file if CSV is the proper output type.\n        \"\"\"\n        self.diagnose()\n        self.outputter.finalize()\n\n\nclass FieldDiagnostic(Diagnostic):\n    \"\"\"\n    Parameters\n    ----------\n    owner : Simulation\n       Simulation object containing current object.\n    input_data : dict\n       Dictionary that contains information regarding location, field,\n       and output type.\n\n    Attributes\n    ----------\n    component : str\n    field_name : str\n        Field.\n    output : str\n        Output type.\n    field : str, None\n        Field as dictated by resource.\n    dump_interval : int, None\n        Time interval at which the diagnostic is run.\n    write_interval : int, None\n        Time interval at which the diagnostic buffer is written to file. If\n        this is None, then the buffer is not written out until the end of\n        the simulation.\n    diagnose : method\n        Uses the dump and write handlers to perform the diagnostic actions.\n    diagnostic_size : (int, int), None\n        Size of data set to be written to CSV file. First value is the\n        number of time points. Second value is number of spatial points.\n    \"\"\"\n    def __init__(self, owner: Simulation, input_data: dict):\n        super().__init__(owner, input_data)\n\n        self.component = input_data[\"component\"]\n        self.field_name = input_data[\"field\"]\n        self.output = input_data[\"output_type\"]  # \"stdout\"\n        self.field = None\n\n        # Set up handler for the diagnostic interval\n        self.dump_handler = None\n        self.dump_interval = self._input_data.get('dump_interval', None)\n\n        self.outputter = None\n        self.diagnostic_size = None\n\n        # Set up handler for writing to file during the simulation\n        self.write_handler = None\n        self.write_interval = self._input_data.get('write_interval', None)\n\n        # Set up resource sharing\n        self._needed_resources = {self.field_name: \"field\"}\n\n    def diagnose(self):\n        self.dump_handler.perform_action(self._owner.clock.time)\n        if self.write_handler:\n            self.write_handler.perform_action(self._owner.clock.time)\n\n    def do_diagnostic(self):\n        \"\"\"\n        Run output_function depending on field.shape.\n        \"\"\"\n        if len(self.field.shape) > 1:\n            self.outputter.diagnose(self.field[:, self.component])\n        else:\n            self.outputter.diagnose(self.field)\n\n    def initialize(self):\n        \"\"\"\n        Initialize diagnostic_size and output function if provided as\n        csv, and self.csv as an instance of the\n        :class:`CSVOutputUtility` class.\n        \"\"\"\n        super().initialize()\n        self.diagnostic_size = (self._owner.clock.num_steps + 1,\n                                self.field.shape[0])\n\n        if \"dump_interval\" in self._input_data:\n            dump_interval = self._input_data[\"dump_interval\"]\n            self.diagnostic_size = (int(np.ceil(\n                self._owner.clock.end_time / dump_interval) + 1),\n                self.field.shape[0])\n\n        self._input_data['diagnostic_size'] = self.diagnostic_size\n\n        # Use composition to provide i/o functionality\n        self.outputter = utilities[self._input_data[\"output_type\"]](**self._input_data)\n\n        # Set up write interval handler\n        if self.write_interval:\n            self.write_handler = IntervalHandler(\n                self.write_interval,\n                self.outputter.write_data)\n\n        # Set up the dump handler:\n        self.dump_handler = IntervalHandler(\n            self.dump_interval,\n            self.do_diagnostic)\n\n    def finalize(self):\n        \"\"\"\n        Write the CSV data to file if CSV is the proper output type.\n        \"\"\"\n        self.do_diagnostic()\n        self.outputter.finalize()\n\n\nclass GridDiagnostic(Diagnostic):\n    \"\"\"Diagnostic subclass used to store and save grid data\n    into a CSV file\n\n    Parameters\n    ----------\n    owner : Simulation\n        The 'Simulation' object that contains this object\n    input_data : dict\n        Dictionary containing information about this diagnostic such as\n        its name\n\n    Attributes\n    ----------\n    owner : Simulation\n        The 'Simulation' object that contains this object\n    input_data : dict\n        Dictionary containing information about this diagnostic such as\n        its name\n    filename : str\n        File name for CSV grid file\n    \"\"\"\n\n    def __init__(self, owner: Simulation, input_data: dict):\n        super().__init__(owner, input_data)\n        self.filename = input_data[\"filename\"]\n\n    def diagnose(self):\n        \"\"\"Grid diagnotic only runs at startup\"\"\"\n        pass\n\n    def initialize(self):\n        \"\"\"Save grid data into CSV file\"\"\"\n        super().initialize()\n        with open(self.filename, 'wb') as f:\n            np.savetxt(f, self._owner.grid.r, delimiter=\",\")\n\n\nclass ClockDiagnostic(Diagnostic):\n    \"\"\"Diagnostic subclass used to store and save time data into a CSV\n    file using the CSVOutputUtility class.\n\n    Parameters\n    ----------\n    owner : Simulation\n        The :class:`Simulation` object that contains this object\n    input_data : dict\n        Dictionary containing information about this diagnostic such as\n        its name\n\n    Attributes\n    ----------\n    owner : Simulation\n        The :class:`Simulation` object that contains this object\n    input_data : dict\n        Dictionary containing information about this diagnostic such as\n        its name\n    filename : str\n        File name for CSV time file\n    csv : :class:`numpy.ndarray`\n        Array to store values to be written into a CSV file\n    interval : float, None\n        The time interval to wait in between writing to output file. If interval is None,\n        then the outputs are written only at the end of the simulation.\n    handler : IntervalHandler\n        The :class:`IntervalHandler` object that handles writing to output files while\n        the simulation is running. Is None if the interval parameter is not specified\n    \"\"\"\n\n    def __init__(self, owner: Simulation, input_data: dict):\n        super().__init__(owner, input_data)\n        self.filename = input_data[\"filename\"]\n        self.csv = None\n        self.interval = self._input_data.get('write_interval', None)\n        self.handler = None\n\n    def diagnose(self):\n        \"\"\"Append time into the csv buffer.\"\"\"\n        if self.handler:\n            self.handler.perform_action(self._owner.clock.time)\n        self.csv.diagnose(self._owner.clock.time)\n\n    def initialize(self):\n        \"\"\"Initialize `self.csv` as an instance of the\n        :class:`CSVOuputUtility` class.\"\"\"\n        super().initialize()\n        diagnostic_size = (self._owner.clock.num_steps + 1, 1)\n        self.csv = CSVOutputUtility(self._input_data[\"filename\"],\n                                    diagnostic_size)\n        if self.interval:\n            self.handler = IntervalHandler(self.interval, self.csv.write_data)\n\n    def finalize(self):\n        \"\"\"Write time into self.csv and saves as a CSV file.\"\"\"\n        self.diagnose()\n        self.csv.finalize()\n\n\nclass HistoryDiagnostic(Diagnostic):\n    \"\"\"Outputs histories/traces as functions of time\n\n    This diagnostic assists in outputting 1D history traces. Multiple time-\n    dependant quantities can be selected, and are output to a NetCDF file\n    using the xarray python package.\n\n    Examples\n    --------\n    When using a python dictionary to define the turboPy simulation, the\n    history diagnostics can be added as in this example. Each item in the\n    \"traces\" list has several key: value pairs. The \"name\" key corresponds\n    to a turboPy resource that is shared by another module. The \"coords\"\n    key is used in cases where the shared resource is more than just a\n    scalar quantitiy. In this example, the position and momentum are\n    length-3 vectors, with the three entries corresponding to the three\n    vector components. In the case where a resources is a quantity on the\n    grid, then something like ``'coords': ['x'], 'units': 'm'`` might be\n    appropriate.\n\n    Note that the 'coords' list has two items, because the shape of the\n    shared numpy array is ``(1, 3)`` in this example. The first item is\n    basically just a placeholder, and is called \"dim0\".\n\n    >>> simulation_parameters = {\"Diagnostics\": {\n                \"histories\": {\n                    \"filename\": \"output.nc\",\n                    \"traces\": [\n                        {'name': 'EMField:E'},\n                        {'name': 'ChargedParticle:momentum',\n                        'units': 'kg m/s',\n                        'coords': [\"dim0\", \"vector component\"],\n                        'long_name': 'Particle Momentum'\n                        },\n                        {'name': 'ChargedParticle:position',\n                        'units': 'm',\n                        'coords': [\"dim0\", \"vector component\"],\n                        'long_name': 'Particle Position'\n                        },\n                    ]\n                }\n            }\n        }\n\n    This is another example of a similar history setup, but in the format\n    expected for a ``toml`` input file. ::\n\n        [Diagnostics.histories]\n        filename = \"history.nc\"\n\n        [[Diagnostics.histories.traces]]\n        name = 'ChargedParticle:momentum'\n        units = 'kg m/s'\n        coords = [\"dim0\", \"vector component\"]\n        long_name = 'Particle Momentum'\n\n        [[Diagnostics.histories.traces]]\n        name = 'ChargedParticle:position'\n        units = 'm'\n        coords = [\"dim0\", \"vector component\"]\n        long_name = 'Particle Position'\n\n        [[Diagnostics.histories.traces]]\n        name = 'EMField:E'\n\n\n    References\n    ----------\n    [1] C. Birdsall and A. Langdon. Plasma Physics via Computer Simulation.\n    Institute of Physics Series in Plasma Physics and Fluid Dynamics.\n    Taylor & Francis, 2004. Page 382.\n    \"\"\"\n    def __init__(self, owner: Simulation, input_data: dict) -> None:\n        super().__init__(owner, input_data)\n        self._filename = input_data['filename']\n        self._traces = xr.Dataset()\n        self._history_key_list = [t['name'] for t in input_data['traces']]\n        self._handler = None\n\n        # set up the interval handler\n        self._interval = self._input_data.get('interval', None)\n        self._handler = IntervalHandler(self._interval,\n                                        self.do_diagnostic)\n        self._num_outputs = self._owner.clock.num_steps\n        if self._interval:\n            self._num_outputs = int(np.ceil(\n                self._owner.clock.end_time / self._interval))\n\n        # get shared resources\n        self._needed_resources = {k: f'_data_{k}' for k in self._history_key_list}\n\n    def diagnose(self):\n        self._handler.perform_action(self._owner.clock.time)\n\n    def do_diagnostic(self):\n        this_step = self._handler.current_step\n        self._traces['time']._variable._data[this_step] = self._owner.clock.time\n\n        for name in self._history_key_list:\n            # Note, use the ellipsis here to handle multidimensional data\n            self._traces[name]._variable._data[this_step, ...] = self.__dict__[f'_data_{name}']\n\n    def initialize(self):\n        # set up the time coordinate\n        self._traces.coords['time'] = ('timestep', np.zeros(self._num_outputs))\n        self._traces.coords['time'].attrs['units'] = 's'\n        self._traces.coords['time'].attrs['long_name'] = 'Time'\n\n        # set up the grid coordinate\n        self._traces.coords['r'] = ('grid', self._owner.grid.r)\n        self._traces.coords['r'].attrs['units'] = 'm'\n        self._traces.coords['r'].attrs['long_name'] = 'Radius'\n\n        # set up the history traces\n        for trace in self._input_data['traces']:\n            trace_data = self.__dict__[f'_data_{trace[\"name\"]}']\n\n            if isinstance(trace_data, xr.Dataset):\n                for item in trace_data:\n                    # use the xarray API to add this to the dataset\n                    self._traces[item] = trace_data[item].expand_dims(\n                        {'timestep': self._traces.coords['timestep']}).copy(deep=True)\n                    self.__dict__[f'_data_{item}'] = trace_data[item]\n                    self._history_key_list.append(item)\n                self._history_key_list.remove(trace['name'])\n            else:\n                # Convert data into DataArray\n                if not isinstance(trace_data, xr.DataArray):\n                    trace_data = xr.DataArray(trace_data, dims=trace['coords'])\n\n                # use the xarray API to add this to the dataset\n                self._traces[trace['name']] = trace_data.expand_dims(\n                    {'timestep': self._traces.coords['timestep']}).copy(deep=True)\n\n                # add attributes\n                if 'units' in trace:\n                    self._traces[trace['name']].attrs['units'] = trace['units']\n                if 'long_name' in trace:\n                    self._traces[trace['name']].attrs['long_name'] = trace['long_name']\n\n    def finalize(self):\n        self._traces = self._traces.squeeze()  # remove unused dimensions\n        self._traces.to_netcdf(self._filename, 'w')\n\n\nDiagnostic.register(\"point\", PointDiagnostic)\nDiagnostic.register(\"field\", FieldDiagnostic)\nDiagnostic.register(\"grid\", GridDiagnostic)\nDiagnostic.register(\"clock\", ClockDiagnostic)\nDiagnostic.register(\"histories\", HistoryDiagnostic)\n\n\n\n# TODO: add tests for plotting\n# class FieldPlottingDiagnostic(FieldDiagnostic):\n#     \"\"\"Extend the FieldDiagnostic to also create plots of the data\"\"\"\n#     def __init__(self, owner: Simulation, input_data: dict):\n#         super().__init__(owner, input_data)\n# \n#     def do_diagnostic(self):\n#         super().do_diagnostic()\n#         plt.clf()\n#         self.field.plot()\n#         plt.title(f\"Time: {self._owner.clock.time:0.3e} s\")\n#         plt.pause(0.01)\n# \n#     def finalize(self):\n#         super().finalize()\n#         # Call show to keep the plot open\n#         plt.show()\n\n\n\n# sample = {\"Diagnostics\": {\n#         # default values come first\n#         \"directory\": \"block_on_spring/output_leapfrog/\",\n#         \"output_type\": \"netcdf\",\n#         \"histories\": {\n#             \"filename\": \"test.nc\",\n#             \"traces\": [\n#             {'name': 'ChargedParticle:momentum',\n#              'units': 'kg m/s',\n#              'coords': [\"vector component\"],\n#              'long_name': 'Particle Momentum'\n#             },\n#             {'name': 'ChargedParticle:position', \n#              'units': 'm',\n#              'coords': [\"vector component\"],\n#              'long_name': 'Particle Position'\n#             },\n#             {'name': 'EMField:E'}\n#             ]\n#         }\n#     }\n# }\n","sub_path":"turbopy/diagnostics.py","file_name":"diagnostics.py","file_ext":"py","file_size_in_byte":23838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"218444951","text":"\"\"\"\r\n\r\nEDB  Analysis\r\n--------------------------------------------\r\nThis Example shows how to use EDB co to interact with a layout\r\n\"\"\"\r\n\r\nimport os\r\nimport sys\r\nimport pathlib\r\nimport glob\r\nimport shutil\r\nlocal_path = os.path.abspath('')\r\nmodule_path = pathlib.Path(local_path)\r\naedt_lib_path = module_path.parent.parent.parent\r\nsys.path.append(os.path.join(aedt_lib_path))\r\nimport os\r\nimport time\r\nfrom pyaedt import generate_unique_name, examples\r\ntemp_folder = os.path.join(os.environ[\"TEMP\"], generate_unique_name(\"Example\"))\r\nexample_path = examples.download_aedb()\r\ntargetfolder = os.path.join(temp_folder,'Galileo.aedb')\r\nif os.path.exists(targetfolder):\r\n    shutil.rmtree(targetfolder)\r\nshutil.copytree(example_path[:-8], targetfolder)\r\ntargetfile=os.path.join(targetfolder)\r\nprint(targetfile)\r\naedt_file = targetfile[:-12]+\"aedt\"\r\n\r\n\r\n#################################\r\n\r\nfrom pyaedt import Edb\r\n\r\n###############################################################################\r\n# Launch Edb Class\r\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n# This examples will use Edb 2021.1\r\n\r\n# This examples will use SI units.\r\n\r\nif os.path.exists(aedt_file): os.remove(aedt_file)\r\nedb = Edb(edbpath=targetfile)\r\n\r\n#################################\r\n# Compute Nets and Components\r\n# there are queries for nets, stackup, layer, components, geometries\r\n\r\nprint(\"Nets {}\".format(len(edb.core_nets.nets.keys())))\r\nstart = time.time()\r\nprint(\"Components {}\".format(len(edb.core_components.components.keys())))\r\nprint(\"elapsed time = \", time.time() - start)\r\n\r\n#################################\r\n# Get Pin Position\r\n# with next instructions user can get all pins for a specific component and get position of each of them\r\n# every pin position will be a list of x, y postions\r\n\r\npins = edb.core_components.get_pin_from_component(\"U2\")\r\nfor pin in pins:\r\n    print(edb.core_components.get_pin_position(pin))\r\n\r\n\r\n#################################\r\n# Get all Nets connected to a specific Component\r\n\r\nedb.core_components.get_component_net_connection_info(\"U2\")\r\n\r\n#################################\r\n# Compute Rats\r\n\r\nrats = edb.core_components.get_rats()\r\n\r\n#################################\r\n# Get all dc connected netlist through inductance\r\n# Input needed are ground nets list. it will return all the list of nets connected to groun thorugh an inductor\r\n\r\nGROUND_NETS = [\"GND\", \"PGND\"]\r\ndc_connected_net_list = edb.core_nets.get_dcconnected_net_list(GROUND_NETS)\r\nprint(dc_connected_net_list)\r\n\r\n#################################\r\n# Get Power Tree based on a speficifi net\r\n\r\nVRM = \"U3A1\"\r\nOUTPUT_NET = \"BST_V1P0_S0\"\r\npowertree_df, power_nets = edb.core_nets.get_powertree(OUTPUT_NET, GROUND_NETS)\r\nfor el in powertree_df:\r\n    print(el)\r\n\r\n\r\n\r\n#################################\r\n# Delete all RLC with 1 pin only\r\n# this method is useful to remove components not needed into the simulation\r\n\r\n\r\nedb.core_components.delete_single_pin_rlc()\r\n\r\n#################################\r\n# Delete component\r\n# User can still delete manually one or multiple components\r\n\r\nedb.core_components.delete_component(\"C3B17\")\r\n\r\n#################################\r\n# Delete one or more net\r\n# User can still delete manually one or multiple net\r\n\r\nedb.core_nets.delete_nets(\"A0_N\")\r\n\r\n#################################\r\n# Save Modification\r\n\r\nedb.save_edb()\r\n\r\n#################################\r\n# Close edb. After edb is closed it can be opened by AEDT\r\n\r\n\r\nedb.close_edb()\r\n\r\n#################################\r\n\r\n\r\n","sub_path":"_downloads/d642228f27e3a4426bb7de4cf0e4bdde/edb_example.py","file_name":"edb_example.py","file_ext":"py","file_size_in_byte":3486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"578414217","text":"# COLLECT SUBLET INFORMATION FROM WOKO.CH\n\n# IMPORT LIBRARIES\nfrom bs4 import BeautifulSoup as soup\nfrom urllib import request\nimport csv\nimport time\n\n# get the page and\nhome = \"http://www.woko.ch\"\nurl = \"http://www.woko.ch/en/untermieter-gesucht\"\npage = request.urlopen(url)\nscrap = soup(page,'html.parser')\n\n# find result rows for region Zurich\nzurich = scrap.find('div',{'id':'GruppeID_98'})\nitems = zurich.find_all('div',{'class':'inserat'})\n\n# set titles\nrows = []\nrow = ['Title','PostedTime','Duration','Address','Rent (CHF/Month)','Link','Room Info']\nrows.append(row)\n\n# loop through items and get info for each one\nfor item in items:\n\theader = item.find('div',{'class':'titel'})\n\ttitle = header.find('h3').getText().replace('ä','ae').replace('ö','oe').replace('ü','ue')\n\tpostedtime = header.find('span').getText()\n\tlink = home+item.find('a').get('href')\n\tdata = item.find_all('td')\n\tduration = data[1].getText().strip('as from ').strip().replace('until','-')\n\taddress = data[3].getText().replace('ä','ae').replace('ö','oe').replace('ü','ue')\n\trent = item.find('div',{'class':'preis'}).getText().replace('.--','')\n\tdetailpage = soup(request.urlopen(link),'html.parser')\n\troominfo = home+detailpage.find('a',{'class':'btn btn-primary'}).get('href')\n\trows.append([title,postedtime,duration,address,rent,link,roominfo])\n\n# output to csv file\ndate = time.strftime(\"%m%d\",time.localtime())\nfilename = \"sublet_info_\"+date+\".csv\"\nwith open(filename,\"w\",newline = \"\",encoding=\"utf-8\") as output:\n\twriter = csv.writer(output)\n\twriter.writerows(rows)\n\nprint(\"finished scrapying \"+filename)\n\n\n","sub_path":"woko-info/sublet.py","file_name":"sublet.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"480429271","text":"# google microsoft\n\"\"\"\nIn this problem, a tree is an undirected graph that is connected and has no cycles.\n\nThe given input is a graph that started as a tree with N nodes (with distinct values 1, 2, ..., N), with one additional edge added. The\nadded edge has two different vertices chosen from 1 to N, and was not an edge that already existed.\n\nThe resulting graph is given as a 2D-array of edges. Each element of edges is a pair [u, v] with u < v, that represents an undirected edge\nconnecting nodes u and v.\n\nReturn an edge that can be removed so that the resulting graph is a tree of N nodes. If there are multiple answers, return the answer that\noccurs last in the given 2D-array. The answer edge [u, v] should be in the same format, with u < v.\n\nExample 1:\nInput: [[1,2], [1,3], [2,3]]\nOutput: [2,3]\nExplanation: The given undirected graph will be like this:\n  1\n / \\\n2 - 3\nExample 2:\nInput: [[1,2], [2,3], [3,4], [1,4], [1,5]]\nOutput: [1,4]\nExplanation: The given undirected graph will be like this:\n5 - 1 - 2\n    |   |\n    4 - 3\nNote:\nThe size of the input 2D-array will be between 3 and 1000.\nEvery integer represented in the 2D-array will be between 1 and N, where N is the size of the input array.\n\"\"\"\nclass Solution:\n    def findRedundantConnection(self, edges):\n        \"\"\"\n        :type edges: List[List[int]]\n        :rtype: List[int]\n        \"\"\"\n        def find(v):\n            if v not in parent:\n                parent[v] = v\n                rank[v] = 0\n            elif parent[v] != v:\n                parent[v] = find(parent[v])\n            return parent[v]\n\n        parent = dict()\n        rank = dict()\n        for u, v in edges:\n            pu, pv = find(u), find(v)\n            if pu == pv:\n                return [u, v]\n            ru, rv = rank[pu], rank[pv]\n            if ru <= rv:\n                parent[pu] = pv\n                rank[pv] = rv + 1\n            else:\n                parent[pv] = pu\n                rank[pu] = ru + 1\n        return None\n\nsolution = Solution()\nprint(solution.findRedundantConnection([[1,2], [1,3], [2,3]]))\nprint(solution.findRedundantConnection([[1,2], [2,3], [3,4], [1,4], [1,5]]))\n","sub_path":"leetcode/redundantConnection/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":2143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"216858570","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\n@package cgsn_parsers.parsers.parse_spkir\n@file cgsn_parsers/parsers/parse_spkir.py\n@author Christopher Wingard\n@brief Parses SPKIR data logged by the custom built WHOI data loggers.\n'''\nimport os\nimport re\n\nfrom struct import Struct\n\n# Import common utilites and base classes\nfrom cgsn_parsers.parsers.common import ParserCommon\nfrom cgsn_parsers.parsers.common import dcl_to_epoch, inputs, DCL_TIMESTAMP, FLOAT, NEWLINE\n\n# Regex pattern for a line with a DCL time stamp and the OCR-507 data sample\nPATTERN = (\n    DCL_TIMESTAMP + r'\\s+' +     # DCL Time-Stamp\n    r'SATDI7' + r'([\\d]{4})' +   # Serial number\n    FLOAT +                      # Timer (seconds)\n    b'([\\x00-\\xFF]{38})' +       # binary data packet\n    NEWLINE\n)\nREGEX = re.compile(PATTERN, re.DOTALL)\n\n# Set the format for the binary packet for later unpacking\nSPKIR = Struct('<h7I3HBB')\n\n_parameter_names_spkir = [\n        'date_time_string',\n        'serial_number',\n        'timer',\n        'sample_delay',\n        'raw_channels',\n        'input_voltage',\n        'analog_rail_voltage',\n        'frame_counter',\n        'internal_temperature'\n    ]\n\n\nclass Parser(ParserCommon):\n    '''\n    A Parser subclass that calls the Parser base class, adds the SPKIR specific\n    methods to parse the data, and extracts the SPKIR data records from the DCL\n    daily log files.\n    '''\n    def __init__(self, infile):\n        self.initialize(infile, _parameter_names_spkir)\n\n    def parse_data(self):\n        '''\n        Iterate through the record lines (defined via the regex expression\n        above) in the data object, and parse the data into a pre-defined\n        dictionary object created using the Bunch class.\n        '''\n        for line in self.raw:\n            match = REGEX.match(line)\n            if match:\n                self._build_parsed_values(match)\n\n    def _build_parsed_values(self, match):\n        '''\n        Extract the data from the relevant regex groups and assign to elements\n        of the data dictionary.\n        '''\n        # Use the date_time_string to calculate an epoch timestamp (seconds since\n        # 1970-01-01)\n        epts = dcl_to_epoch(match.group(1))\n        self.data.time.append(epts)\n\n        self.data.date_time_string.append(str(match.group(1)))\n        self.data.serial_number.append(int(match.group(2)))\n        self.data.timer.append(float(match.group(3)))\n\n        # Unpack the binary data packet\n        (delay, ch1, ch2, ch3, ch4, ch5, ch6, ch7,\n         Vin, Va, temp, count, check) = SPKIR.unpack(match.group(4))\n\n        # Assign the remaining SPKIR data to the named parameters\n        self.data.sample_delay.append(delay)\n        self.data.raw_channels.append([ch1, ch2, ch3, ch4, ch5, ch6, ch7])\n        self.data.input_voltage.append(Vin)\n        self.data.analog_rail_voltage.append(Va)\n        self.data.frame_counter.append(count)\n        self.data.internal_temperature.append(temp)\n\nif __name__ == '__main__':\n    # load the input arguments\n    args = inputs()\n    infile = os.path.abspath(args.infile)\n    outfile = os.path.abspath(args.outfile)\n\n    # initialize the Parser object for SPKIR\n    spkir = Parser(infile)\n\n    # load the data into a buffered object and parse the data into a dictionary\n    spkir.load_ascii()\n    spkir.parse_data()\n\n    # write the resulting Bunch object via the toJSON method to a JSON\n    # formatted data file (note, no pretty-printing keeping things compact)\n    with open(outfile, 'w') as f:\n        f.write(spkir.data.toJSON())\n","sub_path":"cgsn_parsers/parsers/parse_spkir.py","file_name":"parse_spkir.py","file_ext":"py","file_size_in_byte":3544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"284455584","text":"#!/usr/bin/env python3\nimport sys\nimport os\n\nkmer_len = 20\nmax_mismatch = 2\n\ndirname_kmer = 'kmer_SPECIES.GTDB_r89'\nfilename_out = '%d%s.target_freq' % (kmer_len, dirname_kmer)\n\nkmer_freq = dict()\nfor tmp_filename in os.listdir(dirname_kmer):\n    if not tmp_filename.endswith('%dmer' % kmer_len):\n        continue\n\n    sys.stderr.write('Read %s\\n' % tmp_filename)\n    tmp_genus = tmp_filename.split('.')[0]\n\n    f = open(os.path.join(dirname_kmer, tmp_filename), 'r')\n    for line in f:\n        tokens = line.strip().split()\n        tmp_oligo = tokens[0]\n        if tmp_oligo not in kmer_freq:\n            kmer_freq[tmp_oligo] = []\n        kmer_freq[tmp_oligo].append(tmp_genus)\n    f.close()\n\noligo_sorted = sorted(kmer_freq.keys())\noligo_matches = dict()\ntmp_word_len = int(kmer_len / 4)\nfor i in range(0, len(oligo_sorted)):\n    oligo_i = oligo_sorted[i]\n    sys.stderr.write('Oligo %s\\n' % oligo_i)\n\n    for j in range(i+1, len(oligo_sorted)):\n        oligo_j = oligo_sorted[j]\n        count_mismatch = 0\n\n        for k in range(0, kmer_len, tmp_word_len):\n            if oligo_i[k:k+tmp_word_len] != oligo_j[k:k+tmp_word_len]:\n                count_mismatch += 1\n\n        if count_mismatch > max_mismatch:\n            continue\n        \n        count_mismatch2 = 0\n        for p in range(0, kmer_len):\n            if oligo_i[p] != oligo_j[p]:\n                count_mismatch2 += 1\n            if count_mismatch2 > max_mismatch:\n                count_mismatch2 = max_mismatch + 1\n                break\n        \n        if count_mismatch2 <= max_mismatch :\n            if oligo_i not in oligo_matches:\n                oligo_matches[oligo_i] = dict()\n            oligo_matches[oligo_i][oligo_j] = max_mismatch\n            if oligo_j not in oligo_matches:\n                oligo_matches[oligo_j] = dict()\n            oligo_matches[oligo_j][oligo_i] = max_mismatch\n\nf_out = open(filename_out, 'w')\nfor tmp_oligo in oligo_sorted:\n    tmp_count = len(kmer_freq[tmp_oligo])\n    tmp_list = ','.join(sorted((kmer_freq[tmp_oligo])))\n    tmp_match_count = 0\n    tmp_match_list = 'NA'\n    if tmp_oligo in oligo_matches:\n        tmp_match_count = len(oligo_matches[tmp_oligo].keys())\n        tmp_match_list = ','.join(sorted(oligo_matches[tmp_oligo].keys()))\n    f_out.write(\"%s\\t%d\\t%s\\t%d\\t%s\\n\" % (tmp_oligo, tmp_count, tmp_match_list, tmp_match_count, tmp_list))\nf_out.close()\n","sub_path":"ProkFISHdb/scripts/kmer-to-target_freq.py","file_name":"kmer-to-target_freq.py","file_ext":"py","file_size_in_byte":2369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"428525928","text":"from django.contrib import admin\nfrom .models import (\n    InteractiveMap,\n    AnalysisMap,\n    DownloadMap,\n    DownloadCategory\n    )\n\n\nclass InteractiveMapAdmin(admin.ModelAdmin):\n    list_display = ('title_es', 'short_description_es' )\n    search_fields = ['title_es']\n\n    fieldsets = [\n        ('Content Info',  {'fields': [ 'datePublished',\n                                        'section',\n                                        ('title_es',\n                                        'title_en'),\n                                        ('description_es',\n                                        'description_en')]},),\n\n        ('General',  {'fields': ['thumb_img',\n                                'mapIframe']}),\n    ]\n\nclass AnalysisMapAdmin(admin.ModelAdmin):\n    list_display = ('title_es', 'short_description_es' , 'id')\n    search_fields = ['title_es']\n\n    fieldsets = [\n        ('Content Info',  {'fields': [ 'datePublished',\n                                        'section',\n                                        'thumb_img',\n                                        ('title_es',\n                                        'title_en'),\n                                        ('description_es',\n                                        'description_en'),\n                                        ('content_es',\n                                        'content_en')]},),\n    ]\n\n\nclass DownloadMapAdmin(admin.ModelAdmin):\n    list_display = ('title_es', 'short_description_es' )\n    search_fields = ['title_es']\n\n    fieldsets = [\n        ('Content Info',  {'fields': [ 'datePublished',\n                                        'section',\n                                        'category',\n                                        ('title_es',\n                                        'title_en'),\n                                        'thumb_img',\n                                        'map_PDF',\n                                        ('description_es',\n                                        'description_en')]},),\n\n    ]\n\nadmin.site.register(DownloadMap, DownloadMapAdmin)\nadmin.site.register(InteractiveMap, InteractiveMapAdmin)\nadmin.site.register(AnalysisMap, AnalysisMapAdmin)\n\n# Register your models here.\n","sub_path":"gaia/gaia/maps/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"462406937","text":"import urllib.request, urllib.parse, urllib.error\nimport json\n\n# API key\n#https://cloud.google.com/maps-platform/user-guide/account-changes/#no-plan\n\nserviceurl='http://maps.googleapis.com/maps/api/geocode/json?'\n\nwhile True:\n    address=input('Enter location : ')\n    if(len(address)<1):break\n    \n    url = serviceurl + urllib.parse.urlencode({'address':address})\n    \n    print('Retrieving',url)\n    \n    uh = urllib.request.urlopen(url)\n    \n    data = uh.read().decode()\n    \n    print('Retrieved', len(data),'characters')\n    \n    try:\n        js = data.loads(data)\n    except:\n        js = None\n    \n    if not js or 'status' not in js or js['status'] !='OK':\n        print('========Failure to retrieve=====')\n        print(data)\n        continue\n    \n    print(json.dumps(js,inent=4))\n    \n    lat = js[\"result\"][0][\"gemometry\"][\"location\"][\"lat\"]","sub_path":"PythonForEveryone/ex_13/137.py","file_name":"137.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"85873621","text":"with open(\"A-large.in\", 'r') as f_in:\n    cases = int(f_in.readline())\n    output = \"\"\n\n    for case in range(cases):\n        n = int(f_in.readline())\n        if n == 0:\n            res = 'INSOMNIA'\n        else:\n            seen = set()\n            m = 0\n            while len(seen) != 10:\n                m += 1\n                seen.update(list(str(m * n)))\n            res = str(m * n)\n\n        output += \"Case #{}: {}\\n\".format(str(case + 1), res)\n        with open(\"output.txt\", 'w') as f:\n            f.write(output)\n","sub_path":"codes/CodeJamCrawler/16_0_1_neat/16_0_1_smur232_sheep.py","file_name":"16_0_1_smur232_sheep.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"21986689","text":"import cv2 as opencv\n\nWINDOW_NAME = 'MobileNet'\n\n# Colors (BGR)\nGREEN = (0, 255, 0)\nYELLOW = (0, 255, 255)\nRED = (0, 0, 255)\n\n# Thickness\nLINE_THICKNESS = 3\nTHICKNESS_FILL = -1\n\n# Radius\nCIRCLE_RADIUS = 15\n\n# Image labeling parameters\nFONT_FACE = opencv.FONT_HERSHEY_PLAIN\nFONT_SCALE = 2\nFONT_THICKNESS = 2\nFONT_LINE = opencv.LINE_AA\n\nTEXT_OFFSET = 4\n","sub_path":"Part_03/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"285994171","text":"import cv2\nimport numpy as np\nimport os\n\n\npath = 'C:/Users/Sivo/Desktop/Python/ML/files/logloss_1'\n\nX = None\nY = np.array([])\nw = np.zeros(65536)\nb = 0\n\ndef read_files(X, Y, path, ans):\n    files = os.listdir(path)\n    for name in files:\n        img = cv2.imread(path + '/' + name, 0)\n        if img.shape != 0:\n            img = cv2.resize(img, (256, 256))\n            vect = img.reshape(1, 256 ** 2)\n            vect = vect / 255.\n            X = vect if (X is None) else np.vstack((X, vect)) \n            Y = np.append(Y, ans)\n    return X, Y\n\nX, Y = read_files(X, Y, path, 1)\n\n\ndef sigmoid(z):\n    s = np.array([])\n    for i in z:\n        s = np.append(s, 1/(1- np.exp(-i)))    \n    return s\n\n\ndef propagate(w, b, X, Y):   \n    z = np.array([])\n    WX = np.array([])\n    A = np.array([])\n    L = np.array([])\n    dw = np.array([])\n    db, J = 0, 0\n\n\n    for j, i in enumerate(X):\n        print(\"Сигмоида:\", j)\n        WX = np.append(WX, 0)\n        for e in i:\n            WX += w[j] * e\n        z = np.append(z, (WX[j] + b))\n\n    A = np.append(A, sigmoid(z))\n\n    for j, i in enumerate(A):\n        print(\"J\")\n        L = np.append(L, (-Y[j]*np.log(i) - (1 - Y[j])*np.log(1 - i)))\n        J += L[j]\n    J = J / 42\n\n    for e, i in enumerate(X):\n        print(\"dw\")\n        dw = np.append(dw, 0)\n        for j in i:\n            dw[e] += j * (A[e] - Y[e]) / 42\n    \n    for j, i in enumerate(Y):\n        print(\"db\")\n        db += A[j] + i\n    db = db / 42\n\n    grads = {\"dw\": dw, \"db\": db}\n\n    return grads, J\n\npropagate(w, b, X, Y)\n\n\ndef optimize(w, b, X, Y, num_iterations, learning_rate, print_cost=False):  \n    costs = []  \n    for i in range(num_iterations):\n\n        ###Напишите значения для градиентов и функции ошибки\n        grads, cost = propagate(w, b, X, Y)\n\n        # Retrieve derivatives from grads\n        dw = grads[\"dw\"]\n        db = grads[\"db\"]\n   \n        ### START CODE HERE ###\n        for j, i in enumerate(W):\n            w[i] = j - l_l*dw    \n        b = b - l_l*db\n        ### END CODE HERE ###\n        \n        # Record the costs\n        if i % 100 == 0:\n            costs.append(cost)\n        \n        # Print the cost every 100 training iterations\n        if print_cost and i % 100 == 0:\n            print(\"Cost after iteration %i: %f\" % (i, cost))\n    \n    params = {\"w\": w,\n              \"b\": b}\n    \n    grads = {\"dw\": dw,\n             \"db\": db}\n    \n    return params, grads, costs\n\n\ndef predict(w, b, X):\n    \n    m = X.shape[1]\n    Y_prediction = np.zeros((1,m))\n    w = w.reshape(X.shape[0], 1)\n    \n    \n    ### START CODE HERE ### (≈ 1 line of code)\n    A = sigmoid()\n    ### END CODE HERE ###\n    \n    for i in range(A.shape[1]):\n        \n        # Установите порог, выше которого считаем, что модель выдает 1, а ниже - ноль\n        ### START CODE HERE ###\n        if i >= 0.8:\n            Y_prediction.append(i)\n        else:\n            pass\n        ### END CODE HERE ###   \n    return Y_prediction\n\nprint(predict(w, b, X))\n","sub_path":"hw1.py","file_name":"hw1.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"132486222","text":"from perfrunner.helpers.remote import RemoteHelper\nimport logger\n\nclass Spring(object):\n    def __init__(self, settings):\n        logger.info(\"Initializing spring class\")\n        self.settings = settings\n        self.remote = settings.remote\n\n    def run(self):\n        load_settings = self.settings\n        creates = load_settings.creates\n        reads = load_settings.reads\n        updates = load_settings.updates\n        deletes = load_settings.deletes\n        expires = load_settings.expiration\n        operations = load_settings.items\n        throughput = int(load_settings.throughput) if load_settings.throughput != float('inf') \\\n            else load_settings.throughput\n        size = load_settings.size\n        existing_items = load_settings.existing_items\n        items_in_working_set = int(load_settings.working_set)\n        operations_to_hit_working_set = load_settings.working_set_access\n        workers = load_settings.spring_workers\n        logger.info(\"Inside Spring class to run workload gen via celery. {}\".format(load_settings))\n        self.remote.run_spring_on_kv(creates=creates, reads=reads, updates=updates, deletes=deletes,\n                                     expires=expires, operations=operations, throughput=throughput, size=size,\n                                     existing_items=existing_items, items_in_working_set=items_in_working_set,\n                                     operations_to_hit_working_set=operations_to_hit_working_set, workers=workers)\n","sub_path":"perfrunner/workloads/spring.py","file_name":"spring.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"464602987","text":"'''\n\n'''\n\nx = [3,6,21,1,5,98,4,23,1,6]\nx.sort()\nprint(x)\n\nwords = [\"Be\",\"Car\",\"Always\",\"Door\",\"Eat\" ]\nwords.sort()\nprint(words)\n\nx = [3,6,21,1,5,98,4,23,1,6]\nx.sort()\nx = list(reversed(x))\nprint(x)\n\nwords = words[::-1]\nprint(words)\n\n#Given a list with pairs, sort on the first element\n# while do just one when I can run through it all\nx = [ (3,6),(4,7),(5,9),(8,4),(3,1)]\nprint(x)\nfor z in range(len(x)):\n    y = list(x[z])\n    y.sort()\n    x[z] = y\n\nprint(x)","sub_path":"Learning/DataAndOperations/sort-list-example.py","file_name":"sort-list-example.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"590779419","text":"# woodhouse.ics.uci.edu:4444\n\nfrom collections import namedtuple\nimport socket\n\n\nServerConnection = namedtuple(\"ServerConnection\", [\"socket\", \"input\", \"output\"])\n\n\ndef print_welcome():\n    print(\"Welcome to Connect Four network program.\")\n    print()\n\n\n\ndef read_host():\n    '''\n    Asks the user to specify what host they'd like to connect to,\n    continuing to ask until a valid answer is given.  An answer is\n    considered valid when it consists of something other than just\n    spaces.\n    '''\n    while True:\n        h = str(input(\"Host: \"))\n\n        if not h:\n            print(\"Input can't be empty\")\n        else:\n            return h\n\n\n\ndef read_port():\n    '''\n    Asks the user to specify what port they'd like to connect to,\n    continuing to ask until a valid answer is given.  A port must be an\n    integer between 0 and 65535.\n    '''\n    while True:\n        try:\n            p = int(input(\"Port: \"))\n            if not p:\n                print(\"Input can't be empty\")\n            elif p < 0 or p > 65535:\n                print(\"Port must be between 0-65535\")\n            else:\n                return p\n\n        except ValueError:\n            print(\"Not an integer\")\n\n\n\ndef read_username() -> str:\n    ''' Asks the user for a username and returns it if valid '''\n    while True:\n        username = input(\"Username: \")\n        if \" \" in username or \"\\t\" in username:\n            print(\"Can't have spaces in username\")\n        else:\n            return username\n\n\n\ndef read_msg(username: str) -> str:\n    '''\n    Asks the user what message they'd like to send to the echo server.\n    Any message is legitimate, including the empty string, so no validation\n    is done, but this function still serves the purpose of encapsulating\n    our decision about how to prompt the user.\n    '''\n    return str(input(\"{}: \".format(username)))\n\n\n\ndef connect(host: str, port: int) -> ServerConnection:\n    '''\n    Connects to the server, which is assumed to be running on the\n    given host and listening on the given port.  If successful, a\n    connection object is returned.\n    '''\n    echo_socket = socket.socket()\n    echo_socket.connect((host, port))\n\n    echo_socket_in = echo_socket.makefile(\"r\")\n    echo_socket_out = echo_socket.makefile(\"w\")\n\n    return ServerConnection(echo_socket, echo_socket_in, echo_socket_out)\n\n\n\ndef close(connection: ServerConnection) -> None:\n    ''' Closes a connection '''\n    connection.socket.close()\n    connection.input.close()\n    connection.output.close()\n\n\n\ndef send_message(connection: ServerConnection, message) -> None:\n    '''\n    Sends a message to the server via a connection that is already\n    assumed to have been opened (and not yet closed).\n    '''\n    connection.output.write(message + \"\\r\\n\")\n    connection.output.flush()\n\n\n\ndef receive_message(connection: ServerConnection) -> str:\n    '''\n    Receives a response from the server via a connection that is\n    already assumed to have been opened (and not yet closed).\n    '''\n    return connection.input.readline()[:-1]\n","sub_path":"connectfour/connectfour_network.py","file_name":"connectfour_network.py","file_ext":"py","file_size_in_byte":3030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"168160029","text":"#coding:utf-8\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport argparse\nimport h5py\nimport pandas as pd\nparser = argparse.ArgumentParser()\nparser.add_argument('--batch_size',\n                    type = int,\n                    default = 16)\nparser.add_argument('--image_size',\n                    type = int,\n                    default = 32)\nparser.add_argument('--class_num',\n                    type = int,\n                    default = 17)\nparser.add_argument('--img_channels',\n                    type = int,\n                    default = 10)\nflags = parser.parse_args()\n\nclass datagen_tra():\n    def __init__(self, bs):\n        # 初始化所需的参数\n        self.path = './data/'\n        \"\"\"change\"\"\"\n        self.names = [str(i) for i in range(0, 9 + 1, 1)]\n        np.random.shuffle(self.names)\n        # self.names += '9'\n        self.names_idx = 0\n        self.N = 0\n        self.bs = bs\n        self.X = np.load(self.path + 'X_' + self.names[self.names_idx] + '.npy')\n        self.Y = np.load(self.path + 'Y_' + self.names[self.names_idx] + '.npy')\n        print('self.X.shape[0]=',self.X.shape[0])\n        idx_random = np.arange(self.X.shape[0] - 1)\n\n        np.random.shuffle(idx_random)\n        self.X[:-1] = self.X[idx_random]\n        self.Y[:-1] = self.Y[idx_random]\n        print(self.X.shape)\n        print(self.Y.shape)\n\n    def __len__(self):\n        # 让代码知道这个序列的长度\n        return self.X.shape[0] // self.bs\n\n    def __getitem__(self, idx):\n        X_item = self.X[self.N: self.N + self.bs]\n        Y_item = self.Y[self.N: self.N + self.bs]\n\n        self.N += self.bs\n        if self.N >= len(self.X):\n            self.N = 0\n        print('.........', self.N, 'X_' + self.names[self.names_idx] + '.npy', len(self.X))\n        # return X_item, Y_item\n        return X_item[:, :, :, 8:], Y_item\n\n    def on_epoch_end(self):\n        # print('X_' + self.names[self.names_idx] + '.npy')\n        self.N = 0\n        self.names_idx += 1\n        if self.names_idx >= len(self.names):\n            self.names_idx = 0\n            self.names = [str(i) for i in range(0, 8 + 1, 1)]\n            np.random.shuffle(self.names)\n            # self.names += '9'\n        self.X = np.load(self.path + 'X_' + self.names[self.names_idx] + '.npy')\n        self.Y = np.load(self.path + 'Y_' + self.names[self.names_idx] + '.npy')\n        idx_random = np.arange(self.X.shape[0] - 1)\n        np.random.shuffle(idx_random)\n        self.X[:-1] = self.X[idx_random]\n        self.Y[:-1] = self.Y[idx_random]\n\ndef del_certain_num(x):\n    \"\"\"change\"\"\"\n    y = [i for i in range(0, 12)]\n    y.remove(x)\n    return y\n\ndef get_batch(X,Y,N,length):\n    if N + flags.batch_size < length:\n        X_batch = X[N: N + flags.batch_size]\n        Y_batch = Y[N: N + flags.batch_size]\n    else:\n        X_batch = X[N: ]\n        Y_batch = Y[N: ]\n    # return X_batch, Y_batch\n    return X_batch[...,8:],Y_batch\ndef shuffle_(X,Y,shuffle=True):\n    # idx_random = np.arange(X.shape[0] - 1)\n    idx_random = np.arange(X.shape[0])\n    if shuffle:\n        np.random.shuffle(idx_random)\n        # X[:-1] = X[idx_random]\n        # Y[:-1] = Y[idx_random]\n        X = X[idx_random]\n        Y = Y[idx_random]\n    return X,Y\ndef data_loader(i,shuffle):\n    val_imgs = np.load('./data/'+'X_'+str(i)+'.npy')\n    val_labels = np.load('./data/' + 'Y_' + str(i) + '.npy')\n\n    train_imgs_list = []\n    train_labels_list = []\n    for train_name in del_certain_num(i):\n        X = np.load('./data/'+'X_'+str(train_name)+'.npy')\n        train_imgs_list.append(X)\n\n        Y = np.load('./data/' + 'Y_' + str(train_name) + '.npy')\n        train_labels_list.append(Y)\n\n    train_imgs=np.concatenate(train_imgs_list)\n\n    train_labels = np.concatenate(train_labels_list)\n\n    train_imgs, train_labels=shuffle_(train_imgs,train_labels,shuffle=shuffle)\n\n    num_train = train_imgs.shape[0]\n    num_val = val_imgs.shape[0]\n\n    return num_train,num_val,train_imgs,train_labels,val_imgs,val_labels\n# def data_loader(name,shuffle):\n#     imgs = np.load('./data/'+'X_'+str(name)+'.npy')\n#     labels = np.load('./data/' + 'Y_' + str(name) + '.npy')\n#\n#     imgs, labels=shuffle_(imgs,labels,shuffle=shuffle)\n#\n#     num_ = imgs.shape[0]\n\n    # return num_,imgs,labels\ndef load_npy():\n    X_10 = np.load('./data/' + 'X_' + str(11) + '.npy')\n    Y_10 = np.load('./data/' + 'Y_' + str(11) + '.npy')\n    print(X_10.shape)\n    print(Y_10.shape)\n    print(Y_10[:2])\n    # \"\"\"test data .h5->.npy\"\"\"\n    # path = './data/confidence_round1_test_ab.h5'\n    # fid = h5py.File(path, 'r')\n    # print(fid)\n    # print(type(fid))\n    # print(fid.keys())\n    # s1 = np.array(fid['sen1'])\n    # s2 = np.array(fid['sen2'])\n    # print(s1.shape)\n    # print(s2.shape)\n    # label=np.array(fid['label'])\n    # print(label.shape)\n    # print('label_quanti={}'.format(np.sum(label,axis=0)))\n    #\n    # test_data=np.concatenate([s1,s2],axis=-1)\n    # print(test_data.shape)\n    # np.save('./data/X_10.npy',test_data)\n    # np.save('./data/Y_10.npy',label)\n    #\n    # path = './data/round1_test_a_20181109.h5'\n    # print('=====round1 testa=========')\n    # fid = h5py.File(path, 'r')\n    # s1 = np.array(fid['sen1'])\n    # s2 = np.array(fid['sen2'])\n    # print(s1.shape)\n    # print(s2.shape)\n    #\n    # path = './data/round1_test_b_20190104.h5'\n    # print('=====round1 testb=========')\n    # fid = h5py.File(path, 'r')\n    # s1 = np.array(fid['sen1'])\n    # s2 = np.array(fid['sen2'])\n    # print(s1.shape)\n    # print(s2.shape)\n    #\n    # path = './data/round2_test_a_20190121.h5'\n    # print('=====round2 testa=========')\n    # fid = h5py.File(path, 'r')\n    # s1 = np.array(fid['sen1'])\n    # s2 = np.array(fid['sen2'])\n    # print(s1.shape)\n    # print(s2.shape)\n\n\n    #\n    # df=pd.read_csv('./result_one_hot.csv',header=None).copy()\n    # print(df.values.shape)\n    # df_new_values=df.values*0.829\n    # print(df_new_values)\n    # np.save('./data/Y_10.npy',np.array(df_new_values))\n\ndef read_all():\n    path = './data/validation.h5'\n\n    out_path='./data/visual_data'\n    if not os.path.exists(out_path):\n        os.mkdir(out_path)\n    fid = h5py.File(path, 'r')\n    print(fid)\n    print(type(fid))\n    print(fid.keys())\n    s1 = np.array(fid['sen1'])\n    s2 = np.array(fid['sen2'])\n    labels = np.array(fid['label'])\n    print(s2[:2])\n    # print(labels[:1])\n    # (24119, 32, 32, 8) (24119, 32, 32, 10) (24119, 17)\n    # cv2.imwrite(out_path)\n\ndef check_train_test():\n    import matplotlib\n    matplotlib.use('Agg')\n    import matplotlib.pyplot as plt\n    all_label_qty=[]\n    for i in range(10):\n        Y = np.load('./data/' + 'Y_' + str(i) + '.npy')\n        print('Y{}.shape={}'.format(i,Y.shape))\n        label_qty=np.sum(Y, axis=0)\n        all_label_qty.append(label_qty)\n        print('label_qty{}={}'.format(i, label_qty))\n    all_label=np.sum(np.array(all_label_qty),axis=0)\n    print('all_label_qty={}'.format(all_label))\n    plt.bar(range(all_label.shape[0]), all_label)\n    plt.savefig('./data/visual_data/train_Y_bar.jpg')\n\n    plt.figure()\n    path = './data/confidence_round1_test_ab.h5'\n    fid = h5py.File(path, 'r')\n    test_Y = np.array(fid['label'])\n    test_Y=np.sum(test_Y,axis=0)\n    print('round1_test_Y.shape=', test_Y.shape)\n    plt.bar(range(test_Y.shape[0]), test_Y)\n    plt.savefig('./data/visual_data/round1_test_Y.jpg')\n\ndef sns_train_test():\n    train_Y_list=[]\n    for i in range(10):\n        Y = np.load('./data/' + 'Y_' + str(i) + '.npy')\n        train_Y_list.append(Y)\n    train_Y = np.concatenate(train_Y_list, axis=0)\n    print('train_Y.shape',train_Y.shape)\n\n    import matplotlib\n    matplotlib.use('Agg')\n    import matplotlib.pyplot as plt\n    import seaborn as sns\n    train_Y = pd.DataFrame(train_Y)\n\n    print('======train_y=============')\n    # 利用map方法可视化直方图\n    for i in range(train_Y.shape[1]):\n        plt.figure()\n        g = sns.FacetGrid(train_Y)\n        g.map(sns.distplot, i)\n        # sns.kdeplot(train_Y[:,i], color=\"Red\", shade=True)\n        plt.savefig('./data/visual_data/train_Y{}.jpg'.format(i))\n\n    path = './data/confidence_round1_test_ab.h5'\n    fid = h5py.File(path, 'r')\n    test_Y = np.array(fid['label'])\n    print('round1_test_Y.shape=',test_Y.shape)\n    test_Y = pd.DataFrame(test_Y)\n    print('======round1_testy=============')\n    # 利用map方法可视化直方图\n    for i in range(test_Y.shape[1]):\n        plt.figure()\n        g = sns.FacetGrid(test_Y)\n        g.map(sns.distplot, i)\n        # sns.kdeplot(train_Y[:,i], color=\"Red\", shade=True)\n        plt.savefig('./data/visual_data/test_Y{}.jpg'.format(i))\ndef transform_npy():\n    # path = './data/round2_test_a_20190121.h5'\n    # fid = h5py.File(path, 'r')\n    # print(fid.keys())\n    # # test_Y = np.array(fid['label'])\n    # # print('round1_test_Y.shape=', test_Y.shape)\n    # # print(test_Y)\n    # s1 = np.array(fid['sen1'])\n    # s2 = np.array(fid['sen2'])\n    # test_data = np.concatenate([s1, s2], axis=-1)\n    # print(test_data.shape)\n    # np.save('./data/X_11.npy', test_data)\n    # np.save('./data/Y_10.npy', test_Y)\n\n    csv_path='result_no_one_hot_germany.csv'\n    df=pd.read_csv(csv_path,header=None).copy()\n    df_value=df.values\n    print(df_value.shape)\n    print(type(df_value))\n    print(df_value[:2,:5])\n    df_value=np.around(df_value,3)\n    print(df_value[:2, :5])\n\n    # np.save('./data/Y_11.npy', df_value)\n\ndef compute_balance_class():\n    all_label_qty = []\n    for i in range(11):\n        Y = np.load('./data/' + 'Y_' + str(i) + '.npy')\n        label_qty = np.sum(Y, axis=0)\n        all_label_qty.append(label_qty)\n    f_class = np.sum(np.array(all_label_qty), axis=0)\n    # print('all_label_qty={}'.format(f_class))\n    f_class_median = np.median(f_class)\n    # print(f_class_median)\n    balance_lcass=f_class_median / np.array(f_class)\n    return balance_lcass\nif __name__ == '__main__':\n    # check_train_test()\n    # transform_npy()\n    # sns_train_test()\n    load_npy()\n    # compute_balance_class()\n    # read_all()\n    # num_train, num_val, train_imgs, train_labels, val_imgs, val_labels=data_loader(0)\n    # print(num_train)\n    # print(num_val)\n    # print(train_imgs.shape)\n    # print(train_labels.shape)\n    # print(val_imgs.shape)\n    # print(val_labels.shape)\n    # for step in range(0,num_train,flags.batch_size):\n    #     X_train_batch,Y_train_batch=get_batch(train_imgs,train_labels,step)\n    #     print(X_train_batch.shape)\n    #     print(Y_train_batch.shape)\n    #\n    # for step in range(0,num_val,flags.batch_size):\n    #     X_val_batch,Y_val_batch=get_batch(val_imgs,val_labels,step)\n    #     print(X_val_batch.shape)\n    #     print(Y_val_batch.shape)\n    # import seaborn as sns\n    # import matplotlib.pyplot as plt\n    # #\n    # a=np.array([1.,2.,3,0,2,3,4])\n    # plt.bar(range(a.shape[0]),a)\n    # plt.show()\n\n","sub_path":"data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":10799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"446760880","text":"# -------------------------------------------------------\n\n########################################################################\n#\n# Functions for downloading the COCO data-set from the internet\n# and loading it into memory. This data-set contains images and\n# various associated data such as text-captions describing the images.\n#\n# http://cocodataset.org\n#\n# Implemented in Python 3.6\n#\n# Usage:\n# 1) Call set_data_dir() to set the desired storage directory.\n# 2) Call maybe_download_and_extract() to download the data-set\n#    if it is not already located in the given data_dir.\n# 3) Call load_records(train=True) and load_records(train=False)\n#    to load the data-records for the training- and validation sets.\n# 5) Use the returned data in your own program.\n#\n# Format:\n# The COCO data-set contains a large number of images and various\n# data for each image stored in a JSON-file.\n# Functionality is provided for getting a list of image-filenames\n# (but not actually loading the images) along with their associated\n# data such as text-captions describing the contents of the images.\n#\n########################################################################\n#\n# This file is part of the TensorFlow Tutorials available at:\n#\n# https://github.com/Hvass-Labs/TensorFlow-Tutorials\n#\n# Published under the MIT License. See the file LICENSE for details.\n#\n# Copyright 2018 by Magnus Erik Hvass Pedersen\n#\n########################################################################\n\nimport json\nimport os\n# from cache import cache\n# import cache\n\n########################################################################\n\n# Directory where you want to download and save the data-set.\n# Set this before you start calling any of the functions below.\n# Use the function set_data_dir() to also update train_dir and val_dir.\ndata_dir = \"../coco_2017/\"\n\n# Sub-directories for the training- and validation-sets.\ntrain_dir = \"../coco_2017/train2017\"\nval_dir = \"../coco_2017/val2017\"\n\n########################################################################\n# Private helper-functions.\n\ndef _load_records(train=True):\n    \"\"\"\n    Load the image-filenames and captions\n    for either the training-set or the validation-set.\n    \"\"\"\n\n    if train:\n        # Training-set.\n        filename = \"captions_train2017.json\"\n    else:\n        # Validation-set.\n        filename = \"captions_val2017.json\"\n\n    # Full path for the data-file.\n    path = os.path.join(data_dir, \"annotations\", filename)\n\n    # Load the file.\n    with open(path, \"r\", encoding=\"utf-8\") as file:\n        data_raw = json.load(file)\n\n    # Convenience variables.\n    images = data_raw['images']\n    annotations = data_raw['annotations']\n\n    # Initialize the dict for holding our data.\n    # The lookup-key is the image-id.\n    records = dict()\n\n    # Collect all the filenames for the images.\n    for image in images:\n        # Get the id and filename for this image.\n        image_id = image['id']\n        filename = image['file_name']\n\n        # Initialize a new data-record.\n        record = dict()\n\n        # Set the image-filename in the data-record.\n        record['filename'] = filename\n\n        # Initialize an empty list of image-captions\n        # which will be filled further below.\n        record['captions'] = list()\n\n        # Save the record using the the image-id as the lookup-key.\n        records[image_id] = record\n\n    # Collect all the captions for the images.\n    for ann in annotations:\n        # Get the id and caption for an image.\n        image_id = ann['image_id']\n        caption = ann['caption']\n\n        # Lookup the data-record for this image-id.\n        # This data-record should already exist from the loop above.\n        record = records[image_id]\n\n        # Append the current caption to the list of captions in the\n        # data-record that was initialized in the loop above.\n        record['captions'].append(caption)\n\n    # Convert the records-dict to a list of tuples.\n    records_list = [(key, record['filename'], record['captions'])\n                    for key, record in sorted(records.items())]\n\n    # Convert the list of tuples to separate tuples with the data.\n    ids, filenames, captions = zip(*records_list)\n\n    return ids, filenames, captions\n\n\n########################################################################\n# Public functions that you may call to download the data-set from\n# the internet and load the data into memory.\n\n\ndef set_data_dir(new_data_dir):\n    \"\"\"\n    Set the base-directory for data-files and then\n    set the sub-dirs for training and validation data.\n    \"\"\"\n\n    # Ensure we update the global variables.\n    global data_dir, train_dir, val_dir\n\n    data_dir = new_data_dir\n    train_dir = os.path.join(new_data_dir, \"train2017\")\n    val_dir = os.path.join(new_data_dir, \"val2017\")\n\n\ndef load_records(train=True):\n    \"\"\"\n    Load the data-records for the data-set. This returns the image ids,\n    filenames and text-captions for either the training-set or validation-set.\n    \n    This wraps _load_records() above with a cache, so if the cache-file already\n    exists then it is loaded instead of processing the original data-file.\n    \n    :param train:\n        Bool whether to load the training-set (True) or validation-set (False).\n    :return: \n        ids, filenames, captions for the images in the data-set.\n    \"\"\"\n\n    if train:\n        # Cache-file for the training-set data.\n        cache_filename = \"records_train.pkl\"\n    else:\n        # Cache-file for the validation-set data.\n        cache_filename = \"records_val.pkl\"\n\n    # Path for the cache-file.\n#     cache_path = os.path.join(data_dir, cache_filename)\n    cache_dir = \"cache\"\n    cache_path = os.path.join(cache_dir, cache_filename)\n\n\n    # If the data-records already exist in a cache-file then load it,\n    # otherwise call the _load_records() function and save its\n    # return-values to the cache-file so it can be loaded the next time.\n    records = cache(cache_path=cache_path,\n                    fn=_load_records,\n                    train=train)\n\n    return records\n\n########################################################################\n\n########################################################################\n#\n# Cache-wrapper for a function or class.\n#\n# Save the result of calling a function or creating an object-instance\n# to harddisk. This is used to persist the data so it can be reloaded\n# very quickly and easily.\n#\n# Implemented in Python 3.5\n#\n########################################################################\n#\n# This file is part of the TensorFlow Tutorials available at:\n#\n# https://github.com/Hvass-Labs/TensorFlow-Tutorials\n#\n# Published under the MIT License. See the file LICENSE for details.\n#\n# Copyright 2016 by Magnus Erik Hvass Pedersen\n#\n########################################################################\n\nimport os\nimport pickle\nimport numpy as np\n\n########################################################################\n\n\ndef cache(cache_path, fn, *args, **kwargs):\n    \"\"\"\n    Cache-wrapper for a function or class. If the cache-file exists\n    then the data is reloaded and returned, otherwise the function\n    is called and the result is saved to cache. The fn-argument can\n    also be a class instead, in which case an object-instance is\n    created and saved to the cache-file.\n    :param cache_path:\n        File-path for the cache-file.\n    :param fn:\n        Function or class to be called.\n    :param args:\n        Arguments to the function or class-init.\n    :param kwargs:\n        Keyword arguments to the function or class-init.\n    :return:\n        The result of calling the function or creating the object-instance.\n    \"\"\"\n\n    # If the cache-file exists.\n    if os.path.exists(cache_path):\n        # Load the cached data from the file.\n        with open(cache_path, mode='rb') as file:\n            obj = pickle.load(file)\n\n        print(\"- Data loaded from cache-file: \" + cache_path)\n    else:\n        # The cache-file does not exist.\n\n        # Call the function / class-init with the supplied arguments.\n        obj = fn(*args, **kwargs)\n\n        # Save the data to a cache-file.\n        with open(cache_path, mode='wb') as file:\n            pickle.dump(obj, file)\n\n        print(\"- Data saved to cache-file: \" + cache_path)\n\n    return obj\n\n\n########################################################################\n\n\ndef convert_numpy2pickle(in_path, out_path):\n    \"\"\"\n    Convert a numpy-file to pickle-file.\n    The first version of the cache-function used numpy for saving the data.\n    Instead of re-calculating all the data, you can just convert the\n    cache-file using this function.\n    :param in_path:\n        Input file in numpy-format written using numpy.save().\n    :param out_path:\n        Output file written as a pickle-file.\n    :return:\n        Nothing.\n    \"\"\"\n\n    # Load the data using numpy.\n    data = np.load(in_path)\n\n    # Save the data using pickle.\n    with open(out_path, mode='wb') as file:\n        pickle.dump(data, file)\n\n\n########################################################################\n\nif __name__ == '__main__':\n    # This is a short example of using a cache-file.\n\n    # This is the function that will only get called if the result\n    # is not already saved in the cache-file. This would normally\n    # be a function that takes a long time to compute, or if you\n    # need persistent data for some other reason.\n    def expensive_function(a, b):\n        return a * b\n\n    print('Computing expensive_function() ...')\n\n    # Either load the result from a cache-file if it already exists,\n    # otherwise calculate expensive_function(a=123, b=456) and\n    # save the result to the cache-file for next time.\n    result = cache(cache_path='cache_expensive_function.pkl',\n                   fn=expensive_function, a=123, b=456)\n\n    print('result =', result)\n\n    # Newline.\n    print()\n\n    # This is another example which saves an object to a cache-file.\n\n    # We want to cache an object-instance of this class.\n    # The motivation is to do an expensive computation only once,\n    # or if we need to persist the data for some other reason.\n    class ExpensiveClass:\n        def __init__(self, c, d):\n            self.c = c\n            self.d = d\n            self.result = c * d\n\n        def print_result(self):\n            print('c =', self.c)\n            print('d =', self.d)\n            print('result = c * d =', self.result)\n\n    print('Creating object from ExpensiveClass() ...')\n\n    # Either load the object from a cache-file if it already exists,\n    # otherwise make an object-instance ExpensiveClass(c=123, d=456)\n    # and save the object to the cache-file for the next time.\n    obj = cache(cache_path='cache_ExpensiveClass.pkl',\n                fn=ExpensiveClass, c=123, d=456)\n\n    obj.print_result()\n\n########################################################################","sub_path":"cocoa.py","file_name":"cocoa.py","file_ext":"py","file_size_in_byte":10910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"164916426","text":"#!/usr/bin/python\nimport os\n\nvendorJS = [];\n\nbuildFolder = \"./dist/\"\n\nfor file in os.listdir(buildFolder):\n\n  if file.endswith(\".bundle.js\") & file.startswith(\"vendor\"):\n    vendorJS.append(buildFolder + file)\n\nos.system(\"uglifyjs \" +\n          \"--compress \" +\n          \"--mangle  \" +\n          \"--output \" +\n          vendorJS[0] +\n          \" -- \" +\n          vendorJS[0])\n\n","sub_path":"optimize/uglify-js.py","file_name":"uglify-js.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"237310639","text":"import json\nimport subprocess\nimport os\nimport time\n\nfrom flask import Flask, render_template, request, flash, jsonify\nimport pathlib\nimport requests\n\n# App config\n# from test.traffic_rennes_elasticsearch_utils import jprint\nfrom traffic_rennes_elasticsearch_utils import connect_elasticsearch, read_json, Config, loadData, test\n\nDEBUG = True\napp = Flask(__name__)\napp.secret_key = b'_5#y2L\"F4Q8z\\n\\xec]/'\n\njsonFile = \"param.json\"\n\n\n@app.route('/_stuff/<task>', methods=['GET'])\ndef stuff(task):\n    param = read_json()\n    # flash('You were successfully logged in!!!!!!!!!!')\n    # connect to elasticsearch\n    if task == 'elastic':\n        result = connect_elasticsearch()\n        # print(result.ping())\n        flash('elastic ==')\n        return jsonify(result.ping())\n        # send message with param default\n    if task == 'resume':\n        loadData()\n        flash('resume ==')\n        return jsonify(\"load data\")\n\n    if task == 'test':\n        test()\n        flash('test ==')\n        return jsonify(\"test\")\n\n    #  index create or update\n    if task == 'index':\n        if (param.index_init):\n            result = connect_elasticsearch()\n            print(result.ping())\n            return jsonify(result.ping())\n\n\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\ndef results():\n    title = \"Tests\"\n    file_data = \"\"\n    submission_successful = False\n    if request.method == \"POST\":\n        print(request.form)\n        with open(jsonFile, \"w+\") as f:\n            json.dump(request.form, f)\n        submission_successful = True\n        flash('You were successfully logged in')\n\n    #  filepath = \".run_traffic_elasticsearch.bat\"\n    #  p = subprocess.Popen(filepath, shell=True, stdout=subprocess.PIPE)\n    #  stdout, stderr = p.communicate()\n    #  print(p.returncode)  # is 0 if success\n\n    try:\n        with open(jsonFile) as f:\n            file_data = json.load(f)\n            f.close()\n            flash('Config default')\n            flash('Done')\n    except:\n        file_data = Config.query.all()\n        submission_successful = \"could not read file\"\n\n    return render_template('index.html', data=file_data, submission=submission_successful)\n\n@app.route(\"/test\", methods=[\"GET\", \"POST\"])\ndef loadData():\n    param = read_json()\n    print(\"== param :\", param['traffic_nb_rows'])\n    # url\n    traffic_url = \"https://data.rennesmetropole.fr/api/records/1.0/search/?dataset=etat-du-trafic-en-temps-reel&q=&rows=\" + str(\n        param[\"traffic_nb_rows\"])\n\n    flash(\"\\n- Api traffic:\")\n    flash(\"\\n--> url:\" + str(traffic_url))\n    flash(\"\\n--> nombre de lignes à prendre:\" + str(param[\"traffic_nb_rows\"]))\n    flash(\"\\n--> reliability: >=\" + str(format(param[\"traffic_reliability\"])))\n    flash(\n        \"\\n--> rafraichissement: tous les {}s (max de {}s)\".format(param[\"traffic_time_interval\"],\n                                                                   param[\"traffic_time_max\"]))\n    flash(\"\\n\\n- Elasticsearch index:\", param[\"index_name\"])\n\n    # %% ### Connection elastic search\n    time.sleep(1)\n    flash(\"\\n\\nConnection à elasticsearch\")\n    # crée une connection elasticsearch\n    es = connect_elasticsearch()\n\n    # ### Initialisation de l'index et mapping\n    time.sleep(1)\n    flash(f\"\\n\\nGestion de l'index '{param['index_name']}'\")\n\n    index_create = False\n    print(\"== param init :\", param)\n    print(\"== param init :\", param['index_init'])\n    if param[\"index_init\"]:\n        index_create = True\n        if es.indices.exists(index=param[\"index_name\"]):\n            index_init_text = \"remplacement (après suppression)\"\n            es.indices.delete(index=param[\"index_name\"])\n        else:\n            index_init_text = \"création\"\n    else:\n        if es.indices.exists(index=param[\"index_name\"]):\n            index_init_text = \"mise à jour\"\n        else:\n            index_create = True\n            index_init_text = \"création (car index non existant pour maj)\"\n\n    flash(\"--> opéation à réaliser sur l'index:\", index_init_text)\n\n    # mappings pour les coordonnées geospatiales\n    req_body = {\n        \"mappings\": {\n            \"properties\": {\n                \"fields\": {\n                    \"properties\": {\n                        # fields.geo_point_2d\n                        \"geo_point_2d\": {\"type\": \"geo_point\"},\n\n                        # fields.geo_shape\n                        \"geo_shape\": {\"type\": \"geo_shape\"}\n                    }\n                },\n\n                \"geometry\": {\n                    \"properties\": {\n                        # geometry.coordinates\n                        \"coordinates\": {\"type\": \"geo_point\"}\n                    }\n                }\n            }\n        }\n    }\n\n    # crée l'índex vide\n    if index_create:\n        es.indices.create(index=param[\"index_name\"], body=req_body)\n        flash(\"--> index bien créé!\")\n\n    nb_rows_elastic1 = es.count(index=param[\"index_name\"])[\"count\"]\n    flash(\"--> nombre de documents dans l'index:\", nb_rows_elastic1)\n\n    # %% ### Loop\n    time.sleep(1)\n    flash(\"\\n\\nApi traffic to Elasticsearch: stream-processing\")\n    traffic_nb_requete = int(param[\"traffic_time_max\"] / param['traffic_time_interval'])\n    flash(\n        f\"- stream-config: il y aura {traffic_nb_requete} requêtes à faire tous les {param['traffic_time_interval']}s\")\n\n    flash(\"- stream-starts:\", time.strftime(\"%Y/%m/%d %H:%M:%S\"))\n    cpt = 1\n    while cpt <= traffic_nb_requete:\n        flash(f\"\\n--- Requête {cpt}/{traffic_nb_requete} ---\")\n        flash(\"- lancement:\", time.strftime(\"%Y/%m/%d %H:%M:%S\"))\n\n        ### Import depuis l'api traffic\n        flash(\"\\n- API traffic\")\n\n        # get\n        traffic = requests.get(traffic_url)\n        if traffic.status_code == 200:\n            flash(\"--> requête ok!\")\n        else:\n            flash(\"--> requête non-ok!\")\n            flash(\"----> erreur :\", traffic.status_code)\n\n        # les données\n        data_json = traffic.json()\n        data = data_json[\"records\"]\n        nb_rows1 = len(data)\n        flash(f\"--> nombre de documents importés: {nb_rows1}/{param['traffic_nb_rows']}\")\n\n        ### Data processing\n        flash(\"\\n- Qualité des données\")\n\n        flash(\"--> reliability...\")\n        data = [i for i in data if i[\"fields\"][\"traveltimereliability\"] >= param[\"traffic_reliability\"]]\n\n        nb_rows2 = len(data)\n        nb_rows3 = nb_rows1 - nb_rows2\n        flash(f\"----> nombre de documents restants: {nb_rows2}/{nb_rows1} ({nb_rows3} supprimés)\")\n\n        ### Export vers elasticsearch\n        flash(\"\\n- Export vers elasticsearch\")\n\n        # export vers elastic\n        flash(\"--> export en cours...\")\n        i = es.count(index=param[\"index_name\"])['count'] + 1\n        j = 0\n        for doc_body in data:\n            req = es.index(index=param[\"index_name\"], id=i, doc_type='_doc', body=doc_body)\n            i = i + 1\n            j = j + 1\n        flash(f\"----> nombre de documents exportés: {j}/{nb_rows2}\")\n\n        ### attente / rafraichissement\n        if cpt < traffic_nb_requete:\n            flash(f\"\\n(--- attente-de-{param['traffic_time_interval']}s-avant-nouvel-appel-api ---)\\n\")\n            time.sleep(param[\"traffic_time_interval\"])\n        cpt = cpt + 1\n    nb_rows_elastic2 = es.count(index=param[\"index_name\"])[\"count\"]\n    flash(\"\\n- stream-ends:\", time.strftime(\"%Y/%m/%d %H:%M:%S\"))\n    # %% check elasticsearch\n    time.sleep(3)\n    flash(\"\\n\\nElasticsearch\")\n    nb_rows_elastic2 = es.count(index=param[\"index_name\"])[\"count\"]\n    flash(f\"--> nombre total de documents dans l'index: {nb_rows_elastic2}/{nb_rows_elastic1}\")\n    # flash(\"\\n\\nFin-programme-python:\", time.strftime(\"%Y/%m/%d %H:%M:%S\"), \"\\n\")\n    return render_template('statut.html')\n\nif __name__ == \"__main__\":\n    app.run()\n","sub_path":"releases/python/v4.1/home.py","file_name":"home.py","file_ext":"py","file_size_in_byte":7708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"169600266","text":"import tensorflow as tf\nimport csv\nimport numpy as np\ndata = np.genfromtxt('input.csv',delimiter=',',skip_header=0, dtype='int')\ndatain = []\n\nclassesDict = {}\nclasses = [3,4,5,6,7,8,9,12,14,15,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,999]\nfor i in range(len(classes)):\n\tclassesDict[classes[i]] = len(classesDict)\n\ndataout = [[0 for col in range(len(classes))] for row in range(len(data))]\n\nfor i in range(len(data)):\n\tdatain.insert(len(datain),data[i][:-1])\n\tdataout[i][classesDict[data[i][-1]]] = 1\n\nindexes = np.arange(len(data))\n# definir les donnees comme un un tableau\nx = tf.placeholder(tf.float32, [None, len(datain[0])], name=\"x-nodes\")\n# on definit les differents poids\n# variable est un type de tenseur qui peut etre modifie pendant l apprentissage\nW = tf.Variable(tf.zeros([len(datain[0]), len(classes)], name=\"weights\"))\nb = tf.Variable(tf.zeros([len(classes)]), name=\"bias\")\n# on implemente le modele (resultat predit)\ny = tf.nn.softmax(tf.matmul(x, W) + b)\n# on definit l histogramme de visualisation de l'apprentissage\nw_hist = tf.histogram_summary(\"weights\", W)\nb_hist = tf.histogram_summary(\"biases\", b)\ny_hist = tf.histogram_summary(\"y\", y)\n# y reels\ny_ = tf.placeholder(tf.float32, [None, len(classes)], name=\"y-input\")\nwith tf.name_scope(\"xent\") as scope:\n\t# definition cross entropy\n\t#cross_entropy = -tf.reduce_sum(y_*tf.log(y)) #bug nan see http://stackoverflow.com/questions/33712178/tensorflow-nan-bug\n\tcross_entropy = -tf.reduce_sum(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))\n\t#VISU\n\tce_summ = tf.scalar_summary(\"cross entropy\", cross_entropy)\nwith tf.name_scope(\"train\") as scope:\n\t# on minimise l'erreur par descente du gradient\n\ttrain_step = tf.train.GradientDescentOptimizer(0.005).minimize(cross_entropy)\nwith tf.name_scope(\"test\") as scope:\n\t# on recupere les bonnes predictions (tableau de booleens)\n\tcorrect_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n\t# on calcule la fraction de predictions correctes\n\taccuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n\t#VISU\n\taccuracy_summary = tf.scalar_summary(\"accuracy\", accuracy)\n#creation session\nsess = tf.Session()\n#VISU * 2\nmerged = tf.merge_all_summaries()\nwriter = tf.train.SummaryWriter(\"/tmp/mnist_logs\", sess.graph_def)\n\n# initialiser les variables creees\ninit = tf.initialize_all_variables()\n\n# demarrage de la session\nsess.run(init)\n# 1000 loops de 100 vecteurs\nfor i in range(1000):\n\tif i % 100 == 0:  # Record summary data, and the accuracy\n\t\t#VISU\n\t\tfeed = {x: datain, y_: dataout}\n\t\tresult = sess.run([merged, accuracy], feed_dict=feed)\n\t\tsummary_str = result[0]\n\t\tacc = result[1]\n\t\twriter.add_summary(summary_str, i)\n\t\tprint(\"Accuracy at step %s: %s\" % (i, acc))\n\telse:\n\t\t#DATA ensemble d apprentissage, renvoyer deux tableaux de tableaux\n\t\tnp.random.shuffle(indexes)\n\t\tbatch_xs, batch_ys = np.array(datain)[indexes][0:100], np.array(dataout)[indexes][0:100]\n\t\tsess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n# on ecrit le nombre de bonnes predictions \t\n#DATA ensemble de test, renvoyer un tableau de deux tableaux de tableaux\nprint(sess.run(accuracy, feed_dict={x: datain, y_: dataout}))\n\n#ATTHEEND\npredictions = sess.run([tf.argmax(y,1)], feed_dict={x: datain})\ndistr = {}\nfor i in range(len(predictions[0])):\n\tkey = classes[predictions[0][i]]\n\tif distr.has_key(key):\n\t\tdistr[key] = distr[key] + 1\n\telse:\n\t\tdistr[key] = 1\nprint(len(distr))\nprint(distr)\n","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":3422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"195518193","text":"from django.urls import path\n\nfrom Pro_App import views\n\napp_name = \"pro_app\"\nurlpatterns = [\n    path('appcase/<int:pid>', views.AppCaseView.as_view(), name='appcase'),\n    path('appcasedetail/<int:aid>',views.AppCaseStepView.as_view(),name='appcasedetail'),\n\n\n\n]","sub_path":"Pro_App/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"594201395","text":"\nclass Cargar:\n    def cargarModelos(objetos,cursor):\n        for objeto in list(reversed(objetos)):\n            drop=\"DELETE FROM \"+objeto.nombre\n            cursor.execute(drop)\n            print(drop)\n        #print(len(objetos[0].datos))\n        for objeto in objetos:\n            #print(objeto.datos)\n            #print(str(objeto[1]))\n            print('==Insert== \\n'+\"==========\"+objeto.nombre+\"==========\")\n            for i in range(len(objeto.datos)):\n                #print(objeto.datos[0][0].tipo)\n                #print (i)\n                query = 'INSERT INTO ' + objeto.nombre + ' VALUES ('\n                for j in range(len(objeto.datos[i])):\n                    #print (i)\n                    if(j!=0):\n                        query=query+','\n                    if objeto.datos[i][j]==None:\n                        query = query + 'Null'\n                    elif objeto.atributos[j].tipo=='string' and objeto.datos[i][j] is not None:\n                        query=query+\"'\"+str(objeto.datos[i][j])+\"'\"\n                    else:\n                        query = query + str(objeto.datos[i][j])\n\n\n\n                query=query+');\\n'\n                print (query)\n                cursor.execute(query)\n\n\n","sub_path":"etl/cargar/cargar.py","file_name":"cargar.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"141730471","text":"class DataSet(object):\n    def __init__(self, filename, preprocessor=None, max_iter=None):\n        self.filename = filename\n        self.preprocessor = preprocessor\n        self.max_iter = max_iter\n        self.length = None\n\n    def __iter__(self):\n        niter = 0\n        with open(self.filename) as f:\n            words, tags = [], []\n            for line in f:\n                line = line.strip()\n                if (len(line) == 0 or line.startswith(\"-DOCSTART-\")):\n                    if len(words) != 0:\n                        niter += 1\n                        if self.max_iter is not None and niter > self.max_iter:\n                            break\n                        yield words, tags\n                        words, tags = [], []\n                else:\n                    ls = line.split(' ')\n                    word, tag = ls[0], ls[-1]\n                    if self.preprocessor is not None:\n                        word = self.preprocessor.word(word)\n                        tag = self.preprocessor.tag(tag)\n                    words += [word]\n                    tags += [tag]\n\n    def __len__(self):\n        if self.length is None:\n            self.length = 0\n            for _ in self:\n                self.length += 1\n\n        return self.length\n","sub_path":"PIE/data_set.py","file_name":"data_set.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"267750920","text":"from flask import Flask, render_template, redirect, url_for, flash, request, abort\nfrom flask_bootstrap import Bootstrap\nfrom flask_ckeditor import CKEditor\nfrom datetime import date\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy.orm import relationship\nfrom flask_login import UserMixin, login_user, LoginManager, login_required, current_user, logout_user\nfrom forms import CreatePostForm, RegisterForm, LoginForm, CommentForm\nfrom flask_gravatar import Gravatar\nfrom functools import wraps\nimport bleach\nimport os\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = os.environ.get(\"SECRET_KEY\")\nckeditor = CKEditor(app)\nBootstrap(app)\n\n# CONNECT TO DB\napp.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get(\"DATABASE_URL\", \"sqlite:///blog.db\")\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\n\n\n# CONFIGURE TABLES\n\nclass BlogPost(db.Model):\n    __tablename__ = \"blog_posts\"\n    id = db.Column(db.Integer, primary_key=True)\n    title = db.Column(db.String(250), unique=True, nullable=False)\n    subtitle = db.Column(db.String(250), nullable=False)\n    date = db.Column(db.String(250), nullable=False)\n    body = db.Column(db.Text, nullable=False)\n    img_url = db.Column(db.String(250), nullable=False)\n    author_id = db.Column(db.Integer, db.ForeignKey(\"users.id\"))\n    author = relationship(\"User\", back_populates=\"posts\")\n    comments = relationship(\"Comment\", back_populates=\"parent_post\")\n\n\nclass User(UserMixin, db.Model):\n    __tablename__ = \"users\"\n    id = db.Column(db.Integer, primary_key=True)\n    email = db.Column(db.String(100), unique=True, nullable=False)\n    name = db.Column(db.String(250), nullable=False)\n    hash = db.Column(db.String(500), nullable=False)\n    posts = relationship(\"BlogPost\", back_populates=\"author\")\n    comments = relationship(\"Comment\", back_populates=\"author\")\n\n\nclass Comment(db.Model):\n    __tablename__ = \"comments\"\n    id = db.Column(db.Integer, primary_key=True)\n    text = db.Column(db.Text, nullable=False)\n    author = relationship(\"User\", back_populates=\"comments\")\n    author_id = db.Column(db.Integer, db.ForeignKey(\"users.id\"))\n    parent_post = relationship(\"BlogPost\", back_populates=\"comments\")\n    parent_post_id = db.Column(db.Integer, db.ForeignKey(\"blog_posts.id\"))\n\n\ndb.create_all()\n\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\n\ngravatar = Gravatar(\n    app,\n    size=300,\n    rating='g',\n    default='robohash',\n    force_default=False,\n    force_lower=False,\n    use_ssl=False,\n    base_url=None\n)\n\n\ndef admin_only(func):\n    @wraps(func)\n    def wrapper_func(*args, **kwargs):\n        if current_user.id == 1:\n            return func(*args, **kwargs)\n        else:\n            abort(status=403, description=\"Not authorised\")\n\n    return wrapper_func\n\n\n@app.route('/')\ndef get_all_posts():\n    posts = BlogPost.query.all()\n    return render_template(\"index.html\", all_posts=posts, logged_in=current_user.is_authenticated)\n\n\n@app.route('/register', methods=[\"GET\", \"POST\"])\ndef register():\n    form = RegisterForm()\n    if form.validate_on_submit():\n        email = request.form.get(\"email\")\n        name = request.form.get(\"name\")\n        password = request.form.get(\"password\")\n        if User.query.filter_by(email=email).first() is not None:\n            flash(\"That e-mail is already registered. Please log in instead.\")\n            return redirect(url_for(\"login\"))\n        else:\n            new_user = User(\n                email=email,\n                name=name,\n                hash=generate_password_hash(password=password,\n                                            method=\"pbkdf2:sha256\",\n                                            salt_length=8)\n            )\n            db.session.add(new_user)\n            db.session.commit()\n            login_user(new_user)\n            return redirect(url_for(\"get_all_posts\"))\n    return render_template(\"register.html\", form=form, logged_in=current_user.is_authenticated)\n\n\n@app.route('/login', methods=[\"GET\", \"POST\"])\ndef login():\n    form = LoginForm()\n    if form.validate_on_submit():\n        email = request.form.get(\"email\")\n        password = request.form.get(\"password\")\n        user = User.query.filter_by(email=email).first()\n        if user is not None:\n            if check_password_hash(user.hash, password):\n                login_user(user)\n                return redirect(url_for(\"get_all_posts\"))\n            else:\n                flash(\"Wrong password\")\n        else:\n            flash(\"This email is not registered\")\n    return render_template(\"login.html\", form=form, logged_in=current_user.is_authenticated)\n\n\n@app.route('/logout')\ndef logout():\n    logout_user()\n    return redirect(url_for('get_all_posts'))\n\n\n@app.route(\"/post/<int:post_id>\", methods=[\"GET\", \"POST\"])\ndef show_post(post_id):\n    requested_post = BlogPost.query.get(post_id)\n    form = CommentForm()\n    if form.validate_on_submit():\n        if current_user.is_authenticated:\n            new_comment = Comment(\n                text=clean_text(request.form.get(\"comment\")),\n                author=current_user,\n                parent_post=requested_post,\n            )\n            db.session.add(new_comment)\n            db.session.commit()\n        else:\n            flash(\"You need to log in to leave a comment\")\n            return redirect(url_for(\"login\"))\n    return render_template(\"post.html\",\n                           post=requested_post,\n                           form=form,\n                           all_comments=requested_post.comments,\n                           logged_in=current_user.is_authenticated)\n\n\n@app.route(\"/about\")\ndef about():\n    return render_template(\"about.html\", logged_in=current_user.is_authenticated)\n\n\n@app.route(\"/contact\")\ndef contact():\n    return render_template(\"contact.html\", logged_in=current_user.is_authenticated)\n\n\n@app.route(\"/new-post\", methods=[\"GET\", \"POST\"])\n@login_required\n@admin_only\ndef add_new_post():\n    form = CreatePostForm()\n    if form.validate_on_submit():\n        new_post = BlogPost(\n            title=form.title.data,\n            subtitle=form.subtitle.data,\n            body=clean_text(form.body.data),\n            img_url=form.img_url.data,\n            author=current_user,\n            date=date.today().strftime(\"%B %d, %Y\")\n        )\n        db.session.add(new_post)\n        db.session.commit()\n        return redirect(url_for(\"get_all_posts\"))\n    return render_template(\"make-post.html\", form=form, logged_in=current_user.is_authenticated)\n\n\n@app.route(\"/edit-post/<int:post_id>\")\n@login_required\n@admin_only\ndef edit_post(post_id):\n    post = BlogPost.query.get(post_id)\n    edit_form = CreatePostForm(\n        title=post.title,\n        subtitle=post.subtitle,\n        img_url=post.img_url,\n        author=post.author,\n        body=post.body\n    )\n    if edit_form.validate_on_submit():\n        post.title = edit_form.title.data\n        post.subtitle = edit_form.subtitle.data\n        post.img_url = edit_form.img_url.data\n        post.author = edit_form.author.data\n        post.body = clean_text(edit_form.body.data)\n        db.session.commit()\n        return redirect(url_for(\"show_post\", post_id=post.id))\n\n    return render_template(\"make-post.html\", form=edit_form, logged_in=current_user.is_authenticated)\n\n\n@app.route(\"/delete/<int:post_id>\")\n@login_required\n@admin_only\ndef delete_post(post_id):\n    post_to_delete = BlogPost.query.get(post_id)\n    db.session.delete(post_to_delete)\n    db.session.commit()\n    return redirect(url_for('get_all_posts'))\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n    return User.query.get(user_id)\n\n\ndef clean_text(text):\n    allowed_tags = ['a', 'abbr', 'acronym', 'address', 'b', 'br', 'div', 'dl', 'dt',\n                    'em', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'hr', 'i', 'img',\n                    'li', 'ol', 'p', 'pre', 'q', 's', 'small', 'strike',\n                    'span', 'sub', 'sup', 'table', 'tbody', 'td', 'tfoot', 'th',\n                    'thead', 'tr', 'tt', 'u', 'ul']\n\n    allowed_attrs = {\n        'a': ['href', 'target', 'title'],\n        'img': ['src', 'alt', 'width', 'height'],\n    }\n\n    cleaned_text = bleach.clean(text,\n                                tags=allowed_tags,\n                                attributes=allowed_attrs,\n                                strip=True)\n\n    return cleaned_text\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"512206641","text":"from django.shortcuts import render, redirect, reverse\nfrom django.views import generic\nfrom django.http import HttpResponseRedirect\nfrom .models import * \n#import pandas as pd\n#from django_pandas.io import read_frame\nimport datetime\nfrom math import ceil\n\n# Create your views here.\n\nclass ClientView(generic.ListView):\n    template_name = 'GILTI/clientview.html'\n    \n    def get_queryset(self):\n        client_list = Client.objects.all()\n        \n        return client_list\n\ndef ClientCreate(*args, **kwargs):\n    c = Client(name='')\n    c.save()\n    return HttpResponseRedirect(reverse('GILTI:updateclient', args=[c.id]))\n\n\nclass ClientUpdate(generic.UpdateView):\n    model = Client\n    fields = '__all__'\n    template_name = 'GILTI/updateclient.html'\n\n\n\ndef ClientDelete(*args, **kwargs):\n\n    Client.objects.filter(id=kwargs['client_id']).delete()\n    return HttpResponseRedirect('/GILTI')\n    \n\n    \nclass SubView(generic.DetailView):\n    model = Client\n    template_name = 'GILTI/subview.html'\n\n    def get_context_data(self, **kwargs):\n        client = kwargs['object']\n\n\n        sub_list = Subsidiary.objects.filter(client_id=client.id)\n\n\n        data_fields = [\n            'Gross Income over deductions (including taxes)',\n            'CFC Exclusions - ECI',\n            'CFC Exclusions - Subpart F',\n            'CFC Exclusions - Related Party Dividends',\n            'CFC Subtotal * Ownership',\n            'Tested Loss',\n            'Net CFC Tested Income',\n            'Tested Foreign Income Taxes',\n            'Qualified Business Asset Investment',\n            '10% of QBAI',\n            'Interest Expense that reduced tested income and corresponding income not included in tested income',\n            'Tested Income',\n            'CFC Exclusions - ECI',\n            'CFC Exclusions - Subpart F',\n            'CFC Exclusions - Related Party Dividends',\n            'CFC Subtotal * Ownership',\n            'Tested Loss',\n            'Net CFC Tested Income',\n            'Tested Foreign Income Taxes',\n            'Qualified Business Asset Investment',\n            '10% of QBAI',\n            'Interest Expense that reduced tested income and corresponding income not included in tested income',\n            'Net deemed tangible income',\n            'GILTI',\n            'Inclusion percentage',\n            'Deemed paid credit before 20% haircut',\n            'Deemed paid credit after 20% haircut',\n            'Taxable Income before GILTI and NOLs',\n            'Grossed up GILTI',\n            'Net Operating Loss Deduction',\n            'Net GILTI',\n            '50% Deduction',\n            'Taxable income before credit and expense allocation',\n            'Expense allocation to GILTI basket',\n            'GILTI for FTC Limitation',\n            'FTC Limitation',\n            'US tax before credit',\n            'Foreign Tax Credit',\n            'US Tax on GILTI',\n            'Foreign Effective Tax rate on tested income',\n            'U.S. effective rate',\n            'Taxable Income before GILTI and NOLs',\n            'Net Operating Loss Available',\n            'Amount used for Taxable Income + GILTI',\n            'Net Operating Loss Carryforward',\n        ]\n\n        context = {\n            'sub_list':sub_list,\n            'client':client,\n            'fields':data_fields,\n        }\n\n        return context\n\nclass SubDetail(generic.DetailView):\n    model = Client\n    template_name = 'GILTI/subdetail.html'\n\n    def get_context_data(self, **kwargs):\n        client = kwargs['object']\n        numFields = []\n        sub_data=[]\n\n        data_fields = [\n            'Gross Income over deductions (including taxes)',\n            'CFC Exclusions - ECI',\n            'CFC Exclusions - Subpart F',\n            'CFC Exclusions - Related Party Dividends',\n            'CFC Subtotal * Ownership',\n            'Tested Loss',\n            'Net CFC Tested Income',\n            'Tested Foreign Income Taxes',\n            'Qualified Business Asset Investment',\n            '10% of QBAI',\n            'Interest Expense that reduced tested income and corresponding income not included in tested income',\n            'Tested Income',\n            'CFC Exclusions - ECI',\n            'CFC Exclusions - Subpart F',\n            'CFC Exclusions - Related Party Dividends',\n            'CFC Subtotal * Ownership',\n            'Tested Loss',\n            'Net CFC Tested Income',\n            'Tested Foreign Income Taxes',\n            'Qualified Business Asset Investment',\n            '10% of QBAI',\n            'Interest Expense that reduced tested income and corresponding income not included in tested income',\n            'Net deemed tangible income',\n            'GILTI',\n            'Inclusion percentage',\n            'Deemed paid credit before 20% haircut',\n            'Deemed paid credit after 20% haircut',\n            'Taxable Income before GILTI and NOLs',\n            'Grossed up GILTI',\n            'Net Operating Loss Deduction',\n            'Net GILTI',\n            '50% Deduction',\n            'Taxable income before credit and expense allocation',\n            'Expense allocation to GILTI basket',\n            'GILTI for FTC Limitation',\n            'FTC Limitation',\n            'US tax before credit',\n            'Foreign Tax Credit',\n            'US Tax on GILTI',\n            'Foreign Effective Tax rate on tested income',\n            'U.S. effective rate',\n            'Taxable Income before GILTI and NOLs',\n            'Net Operating Loss Available',\n            'Amount used for Taxable Income + GILTI',\n            'Net Operating Loss Carryforward',\n        ]\n\n        for n in range(0, len(data_fields)):\n            data_fields[n] += ':'\n            numFields.append(n)\n\n        \n        subs = Subsidiary.objects.filter(client_id=client.id)\n\n        us_calc = []\n        \n        if len(subs) > 0:\n\n            for sub in subs:\n                slist = [\n                    sub.gross_income,   #0\n                    sub.eci,    #1\n                    sub.sub_f,  #2\n                    sub.rp_div  #3\n                ]\n\n                slist.append(slist[0] + slist[1] + slist[2] + slist[3]),    #4\n                slist += [\n                    0,  #5\n                ]\n\n                if sub.gross_income < 0:\n                    for i in range(0, 5):\n                        slist[i] = 0\n\n                slist += [\n                    slist[0] + slist[1] + slist[2] + slist[3],  #6\n                    sub.for_tax,    #7\n                    sub.qbai    #8\n                ]\n                slist += [\n                    '', #9\n                    sub.int_exp,    #10\n                ]\n                slist += [\n                    slist[0]*(sub.ownership/100),  #11\n                    slist[1]*(sub.ownership/100),   #12\n                    slist[2]*(sub.ownership/100),   #13\n                    slist[3]*(sub.ownership/100)   #14\n                ]\n\n                \n\n                if sub.gross_income < 0:\n                    for i in range(11, 15):\n                        slist[i] = 0\n\n                    slist[5] = sub.gross_income\n                    slist[6] = slist[5]\n                    #slist[16] = slist[11]\n                    #slist[17] = slist[11]\n\n                slist += [\n                    slist[14] + slist[13] + slist[12] + slist[11],   #15\n                    sub.gross_income *(sub.ownership/100),    #16\n                ]\n\n                if sub.gross_income < 0:\n                    slist[15] = 0\n                else:\n                    slist[16] = 0\n\n                slist += [\n                    slist[15] + slist[16], #17\n                    slist[7] * (sub.ownership/100),   #18\n                    #slist[2]*(sub.ownership/100) + slist[3]*(sub.ownership/100),    #19\n                    #slist[7]*(sub.ownership/100),    #20\n                    slist[8]*(sub.ownership/100),   #19\n                    '', #20\n                    slist[10]*(sub.ownership/100),    #21\n\n                ]\n\n                \n                sub_data.append(slist)\n\n            gross = 0\n            eci = 0\n            subf = 0\n            rpdiv = 0\n            subtot = 0\n            loss = 0\n            CFCTInc = 0\n            forinc = 0\n            qbai = 0\n            int_exp = 0\n            tinc = 0\n            a = 0\n            b = 0\n            c = 0\n            d = 0\n            e = 0\n            f = 0\n            g = 0\n            h = 0\n\n            for sub in sub_data:\n                gross += sub[0] #0\n                eci += sub[1]   #1\n                subf += sub[2]  #2\n                rpdiv += sub[3] #3\n                subtot += sub[4]    #4\n                loss += sub[5]  #5\n                CFCTInc += sub[6]   #6\n                forinc = 0  #7\n                qbai += sub[8]  #8\n                int_exp += sub[10]  #10\n                tinc += sub[11] #11\n                a += sub[12]    #12\n                b += sub[13]    #13\n                c += sub[14]    #14\n                d += sub[15]    #15\n                e += sub[16]    #16\n                f += sub[17]    #17\n                g += sub[19]    #19\n                h += sub[21]    #21\n\n            us_calc.extend((gross, eci, subf, rpdiv, subtot, loss, CFCTInc, forinc,\n                            qbai, (qbai/10), int_exp, tinc, a, b, c, d, e, f, 0, g, g/10, h))\n\n            if us_calc[20] - us_calc[21] <= 1:  #22\n                us_calc.append(0)\n            else:\n                us_Calc.append(us_calc[20] - us_calc[21])\n\n            if us_calc[17] - us_calc[22] < 0:   #23\n                us_calc.append(0)\n            else:\n                us_calc.append(us_calc[17] - us_calc[22])\n            if us_calc[15] == 0:\n                us_calc.append(0)\n            elif us_calc[23]/us_calc[15] <= 1:    #24\n                us_calc.append((us_calc[23]/us_calc[15]) * 100)\n            else:\n                us_calc.append('ERROR')\n\n            us_calc.extend((0, 0, client.tax_inc, us_calc[23], 0))\n\n            if (us_calc[27] + us_calc[28]) <= 0: #30\n                us_calc.append(0)\n            else:\n                us_calc.append(us_calc[27] + us_calc[28])\n\n            us_calc.append(0)   #31\n\n            if us_calc[30] < 0: #32\n                us_calc.append(0)\n            else:\n                us_calc.append(us_calc[30])\n\n            us_calc.extend((client.exp_alloc, (us_calc[30] + client.exp_alloc)))\n            us_calc.append(us_calc[34] * 0.21)\n            us_calc.append(us_calc[32] * 0.37)\n\n            if us_calc[35] < 0:\n                us_calc.append(35)\n            else:\n                us_calc.append(0)\n\n            us_calc.extend((us_calc[36] - us_calc[37], 0,\n                            client.US_Rate, client.tax_inc, 0, 0, 0))\n\n            #, 0, us_calc[23], client.exp_alloc))\n\n            sub_data.append(us_calc)\n            lst = []\n            for i in range(0, len(sub_data[len(sub_data)-1])):\n                lst.append(i)\n\n            #sub_data.append(lst)\n\n            for i in range(0, len(sub_data)):\n                for l in range(0, len(sub_data[i])):\n                    if l == 24:\n                        sub_data[i][l] = round(sub_data[i][l], 2)\n                    elif type(sub_data[i][l]) == float:\n                        \n                        if abs(sub_data[i][l] - int(sub_data[i][l])) == 0.5:\n\n                            if sub_data[i][l] < 0:\n                                sub_data[i][l] = ceil(abs((sub_data[i][l]))) * -1\n                            else:\n                                sub_data[i][l] = ceil(sub_data[i][l])\n                        else:\n\n                            sub_data[i][l] = round(sub_data[i][l])\n                    \n\n        \n\n        context = {\n            'sub_data':sub_data,\n            'fields':data_fields,\n            'subs':subs,\n            'client':client\n        }  \n        return context\n        \ndef SubCreate(*args, **kwargs):\n    cid = kwargs['client_id']\n    c = Client.objects.filter(id=cid)[0]\n    s = Subsidiary(client=c, country='', ref='')\n    s.save()\n    return HttpResponseRedirect(reverse('GILTI:updatesub', kwargs={'pk':s.id}))\n\nclass SubUpdate(generic.UpdateView):\n    model = Subsidiary\n    fields = '__all__'\n    template_name = 'GILTI/updateclient.html'\n    client = None\n\n\n\ndef SubDelete(*args, **kwargs):\n\n    sub_id = kwargs['pk']\n\n    sub = Subsidiary.objects.filter(id=sub_id)\n    id = sub[0].client_id\n    sub.delete()\n    return HttpResponseRedirect('/GILTI/sub-' + str(id) +'/detail')\n\n\n\n\n\n","sub_path":"MATRIX/GILTI/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"66362275","text":"from selenium.webdriver import Chrome\npath = '../chromedriver.exe'\ndriver = Chrome(executable_path=path)\ndriver.maximize_window()\n#driver.get('https://www.thetestingworld.com/testings')\n#driver.execute_script('window.scrollTo(0,400);')\nmainWin=''\ndriver.get('https://www.naukri.com')\nAllwindow = driver.window_handles\nfor win in Allwindow:\n    driver.switch_to.window(win)\n    if (driver.current_url == 'https://www.naukri.com/'):\n        mainWin = win\n    else:\n        driver.close() \ndriver.switch_to.window(mainWin)\nprint(driver.current_url)","sub_path":"sections/Advance_selenium/jsrutine.py","file_name":"jsrutine.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"132499336","text":"import csv\nimport glob\nimport os\n\nimport numpy\n\nframe_base = 50\nmarker_path = \"/Users/manuelleonhardt/Desktop/Label_Premiere/*.csv\"\nitem_path = \"/Users/manuelleonhardt/Desktop/Neuer Ordner 5/{0:s}.txt\"\nto_path = \"/Users/manuelleonhardt/Desktop/Label_Premiere/Prepared\"\n\n\ndef convert_timecode_to_frames(timecode):\n  timecode = numpy.array(timecode.split(':')).astype(numpy.uint32)\n  return numpy.sum(numpy.multiply(timecode, [24 * 60 * frame_base, 60 * frame_base, frame_base, 1]))\n\n\ndef prepare(filename):\n\n  basepath, extension = os.path.splitext(filename)\n  basename = os.path.basename(basepath)\n\n  with open(filename, \"rb\") as f:\n    contents = f.read()\n\n  with open(item_path.format(basename), \"r\") as f:\n    items = f.readlines()\n\n  items.insert(0, \"calibration\")\n\n  contents = contents.decode('utf-16').replace(\"\\r\\n\", \"\\n\").splitlines()\n  contents = list(csv.DictReader(contents, delimiter=\"\\t\"))\n\n  new_contents = []\n\n  index = 0\n  for stepper_index, entry in enumerate(contents[::2]):\n    in_ = convert_timecode_to_frames(entry[\"In\"])\n    out = convert_timecode_to_frames(contents[index + 1][\"In\"])\n\n    new_entry = {'item': items[stepper_index].strip(),\n                 'out': out,\n                 'in': in_,\n                 'duration': out - in_}\n    new_contents.append(new_entry)\n    index += 2\n\n  with open(os.path.join(to_path, basename + extension), \"wb\") as f:\n    writer = csv.DictWriter(f, new_contents[0].keys())\n    writer.writeheader()\n    writer.writerows(new_contents)\n\n\nif __name__ == \"__main__\":\n  # Session.sequence.marker = [list(x) for x in zip(*[iter(keyframes)] * 2)]\n  # content = list(csv.reader(f, delimiter=\"\\t\"))\n  # try:\n    marker_files = glob.glob(marker_path)\n    for file in marker_files:\n      prepare(file)\n  # except:\n  #   pass\n\n  #\n  # filename = \"/Users/manuelleonhardt/Desktop/Label_Premiere/proband_020.csv\"\n  # prepare(filename)","sub_path":"scriptdrive/prepare_marker_csv_for_excel.py","file_name":"prepare_marker_csv_for_excel.py","file_ext":"py","file_size_in_byte":1881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"650102155","text":"from __future__ import division\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt \nimport math\nimport seaborn as sns\n\nrc = {'lines.linewidth': 2, \n      'axes.labelsize': 18, \n      'axes.titlesize': 18, \n      'axes.facecolor': 'DFDFE5'}\nsns.set_context('notebook', rc=rc)\nsns.set_style('darkgrid', rc=rc)\n\nnumTrials = 1000\nnItems = 4\n\nfname = 'ddm_n' + str(nItems) + '_output_file.csv'\ndf = pd.read_csv(fname)\n\nitem1Value = np.array(df['valueItem1'])\nitem2Value = np.array(df['valueItem2'])\nitem3Value = np.array(df['valueItem3'])\nitem4Value = np.array(df['valueItem4'])\n\nsimulChoice = np.array(df['choice'])\nsimulRT = np.array(df['RT'])\nfixItem = np.array(df['fixItem'])\nfixTime = np.array(['fixTime'])\n\nnumberOfFixations = np.zeros(numTrials)\nfirstFixation = np.zeros(numTrials)\nlastFixation = np.zeros(numTrials)\nmaxMinusOther = np.zeros(numTrials)\nlastMinusOther = np.zeros(numTrials)\n\nfor i in range(numTrials):\n\tfixationOrder = fixItem[i]\n\ttotalFixations = 0\n\tfor j in range(len(fixationOrder)):\n\t\tif fixationOrder[j] == '1' or fixationOrder[j] == '2' or fixationOrder[j] == '3' or\\\n\t\tfixationOrder[j] == '4':\n\t\t\ttotalFixations += 1\n\tnumberOfFixations[i] = totalFixations\n\tif fixationOrder[1] == '0':\n\t\tfirstFixation[i] = int(fixationOrder[4])\n\telse:\n\t\tfirstFixation[i] = int(fixationOrder[1])\n\n\tif fixationOrder[-2] == '0':\n\t\tlastFixation[i] = int(fixationOrder[-5])\n\telse:\n\t\tlastFixation[i] = int(fixationOrder[-2])\n\n\titemValues = np.zeros(nItems)\n\titemValues[0] = item1Value[i]\n\titemValues[1] = item2Value[i]\n\titemValues[2] = item3Value[i]\n\titemValues[3] = item4Value[i]\n\n\tmaxMinusOther[i] = max(itemValues) - min(itemValues)\n\n\tlastFix = lastFixation[i]\n\tlastMinusOther[i] = itemValues[lastFix - 1] - ((sum(itemValues) - itemValues[lastFix - 1]) / (nItems - 1))\n\nsumOfAllValues = np.zeros(numTrials)\nfor i in range(nItems):\n\tname = 'valueItem' + str(i + 1)\n\tsumOfAllValues += np.array(df[name])\n\nleftMinusOther = item1Value - ((sumOfAllValues - item1Value) / (nItems - 1))\ndifference_in_value = np.arange(-15, 6, 0.5)\ndifference_in_value = list(difference_in_value)\n\nleft_chosen1 = np.zeros(len(difference_in_value))\ntotal1 = np.zeros(len(difference_in_value))\n\nreaction_time_sum = np.zeros(len(difference_in_value))\ntotal2 = np.zeros(len(difference_in_value))\n\nfixations_sum = np.zeros(len(difference_in_value))\ntotal3 = np.zeros(len(difference_in_value))\n\nlast_fixation = np.zeros(len(difference_in_value))\ntotal4 = np.zeros(len(difference_in_value))\n\nfor i in range(numTrials):\n\tdifference1 = leftMinusOther[i]\n\tdifference1 = round(2 * difference1) / 2\n\tdifferenceIndex1 = difference_in_value.index(difference1)\n\n\tdifference2 = maxMinusOther[i]\n\tdifference2 = round(2 * difference2) / 2\n\tdifferenceIndex2 = difference_in_value.index(difference2)\n\treaction_time_sum[differenceIndex2] += simulRT[i]\n\ttotal2[differenceIndex2] += 1\n\n\tfixations_sum[differenceIndex2] += numberOfFixations[i]\n\ttotal3[differenceIndex2] += 1\n\n\tdifference4 = lastMinusOther[i]\n\tdifference4 = round(2 * difference4) / 2\n\tdifferenceIndex4 = difference_in_value.index(difference4)\n\n\tif simulChoice[i] == 1:\n\t\tleft_chosen1[differenceIndex1] += 1\n\ttotal1[differenceIndex1] += 1\n\n\t# if (lastFixation[i] == 1 and simulChoice[i] == -1):\n\t# \tlast_fixation[differenceIndex4] += 1\n\t# elif (lastFixation[i] == 2 and simulChoice[i] == 1):\n\t# \tlast_fixation[differenceIndex4] += 1\n\tif lastFixation[i] == simulChoice[i]:\n\t\tlast_fixation[differenceIndex4] += 1\n\ttotal4[differenceIndex4] += 1\n\nprobability_left_chosen1 = left_chosen1 / total1\nnormalized_reaction_time = reaction_time_sum / total2\nnormalized_fixations = fixations_sum / total3\nnormalized_last_item_chosen = last_fixation / total4\n\ndef graph1():\n\t\"\"\"\n\tPlot: p(item1) vs leftMinusOther\n\t\"\"\"\n\tplt.scatter(difference_in_value, probability_left_chosen1)\n\tplt.xlabel('leftMinusOther')\n\tplt.ylabel('p(Left chosen)')\nfixationOrder\ndef graph2():\n\t\"\"\"\n\tReaction time vs max - other\n\t\"\"\"\n\tplt.scatter(difference_in_value, normalized_reaction_time)\n\tplt.xlabel('Max - Other')\n\tplt.ylabel('Reaction time')\n\ndef graph3():\n\t\"\"\"\n\tNumber of fixations vs Max - Other\n\t\"\"\"\n\tplt.scatter(difference_in_value, normalized_fixations)\n\tplt.xlabel('Max - Other')\n\tplt.ylabel('Number of Fixations')\n\ndef graph4():\n\t\"\"\"\n\tp(last fixation chosen) vs last_fixation - other\n\t\"\"\"\n\tplt.scatter(difference_in_value, normalized_last_item_chosen)\n\tplt.xlabel('last_fixation - other')\n\tplt.ylabel('p(last fixation chosen)')","sub_path":"addm_old_code/analysis_nItems.py","file_name":"analysis_nItems.py","file_ext":"py","file_size_in_byte":4443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"325102248","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 19 18:42:54 2018\n\n@author: gabriel\n\"\"\"\n\n#dtr for the kc_house dataset\nimport pandas as pd\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nimport matplotlib.pyplot as plt \n\ndataset = pd.read_csv('kc_house_data.csv')\n\n#filter missing values\ndataset = dataset.dropna(axis=0)\n\n#splitting into the relevant atributes\ntarget = dataset.price #o que queremos prever\n\natribute_list = ['bedrooms', 'bathrooms', 'sqft_living',\\\n       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\\\n       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\\\n       'lat', 'long', 'sqft_living15', 'sqft_lot15'] #os atributos que me parecem relevantes\n\natribute_data = dataset[atribute_list] #ir buscar os dados destes atributos\n\nX_train, X_test, y_train, y_test = train_test_split(atribute_data, target, test_size = 0.2)\n\n#criar o regressor\n\n\"\"\"regressor = DecisionTreeRegressor()\nregressor.fit(atribute_data, target)\n\n#imprimir a data original e a predicted\nprint('Original data:')\nprint(atribute_data.head())\nprint('Predicted data:')\nprint(regressor.predict(atribute_data.head()))\n\n#calculo do mean absolute error\npredicted_target = regressor.predict(atribute_data)\n\nmean_absolute_error(target, predicted_target)\n\"\"\"\n#separar o dataset em 4 campos para comparar out of range values\nregressor2 = DecisionTreeRegressor()\nregressor2.fit(X_train, y_train)\n\nreg2_prediction = regressor2.predict(X_test)\nmse = mean_absolute_error(y_test, reg2_prediction)\nprint (mse)\n\n#regressor2.score(X_test, y_test)\n\nX_grid = np.arange(1,4324)\nplt.plot(X_grid,y_test)\nplt.plot(X_grid,reg2_prediction)\n\n\n\n","sub_path":"Projetos/kc_house/DTR.py","file_name":"DTR.py","file_ext":"py","file_size_in_byte":1786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"350967580","text":"\n\"\"\"\nUse a convex solver to complete the full matrix of all pMHC affinities\n\"\"\"\n\nimport mhcflurry\nimport dataset_paths\nimport numpy as np\nimport cvxpy\n\n\ndef compute_overlaps_similarities(datasets, min_weight=2.0):\n    sims = {}\n    overlaps = {}\n    weights = {}\n    for a, da in datasets.items():\n        if len(a) != 5:\n            continue\n        y_dict_a = {peptide: y for (peptide, y) in zip(da.peptides, da.Y)}\n        seta = set(da.peptides)\n        for b, db in train_datasets.items():\n            if len(b) != 5:\n                continue\n\n            y_dict_b = {peptide: y for (peptide, y) in zip(db.peptides, db.Y)}\n            setb = set(db.peptides)\n            intersection = seta.intersection(setb)\n            overlaps[(a, b)] = len(intersection)\n            total = 0.0\n            weight = 0.0\n            for peptide in intersection:\n                ya = y_dict_a[peptide]\n                yb = y_dict_b[peptide]\n                minval = min(ya, yb)\n                maxval = max(ya, yb)\n                total += minval\n                weight += maxval\n            weights[(a, b)] = weight\n            if weight > min_weight:\n                sims[(a, b)] = total / weight\n            else:\n                sims[(a, b)] = np.nan\n    return sims, overlaps, weights\n\n\ndef create_sims_array(sims_dict):\n    allele_names_set = {a for (a, _) in sims_dict.keys()}\n    allele_names_list = list(sorted(allele_names_set))\n    n_alleles = len(allele_names_list)\n    sims_array = np.zeros((n_alleles, n_alleles), dtype=float)\n    for i, a in enumerate(allele_names_list):\n        for j, b in enumerate(allele_names_list):\n            sims_array[i, j] = sims_dict[(a, b)]\n    return sims_array, allele_names_list\n\n\ndef prepare_incomplete_affinity_matrix(datasets_dict, prefix=None, allele_name_length=5):\n    if allele_name_length:\n        datasets_dict = {k: d for (k, d) in datasets_dict.items() if len(k) == allele_name_length}\n    if prefix:\n        datasets_dict = {k: d for (k, d) in datasets_dict.items() if k.startswith(prefix)}\n    if len(datasets_dict) == 0:\n        raise ValueError(\"No alleles matched criteria\")\n    all_peptides = set([])\n    allele_list = []\n    for k, d in sorted(datasets_dict.items()):\n        print(k, len(d.peptides))\n        allele_list.append(k)\n        for p in d.peptides:\n            all_peptides.add(p)\n    peptide_list = list(sorted(all_peptides))\n    allele_order = {a: i for (i, a) in enumerate(allele_list)}\n    peptide_order = {p: i for (i, p) in enumerate(peptide_list)}\n    n_alleles = len(allele_order)\n    n_peptides = len(peptide_order)\n    incomplete_affinity_matrix = np.ones((n_alleles, n_peptides), dtype=float) * np.nan\n    for k, d in datasets_dict.items():\n        if k not in allele_order:\n            continue\n        i = allele_order[k]\n        for p, y in zip(d.peptides, d.Y):\n            j = peptide_order[p]\n            incomplete_affinity_matrix[i, j] = y\n    print(\"Total # alleles = %d, peptides = %d\" % (n_alleles, n_peptides))\n    return incomplete_affinity_matrix, allele_list, allele_order, peptide_list, peptide_order\n\n\ndef create_matrix_completion_convex_problem(\n        n_alleles,\n        n_peptides,\n        known_mask,\n        known_values,\n        tolerance=0.001):\n\n    A = cvxpy.Variable(n_alleles, n_peptides, name=\"A\")\n    # nuclear norm minimization should create a low-rank matrix\n    objective = cvxpy.Minimize(cvxpy.norm(A, \"nuc\"))\n\n    masked_A = cvxpy.mul_elemwise(known_mask, A)\n    masked_known = cvxpy.mul_elemwise(known_mask, known_values)\n    diff = masked_A - masked_known\n    close_to_data = diff ** 2 <= tolerance\n    lower_bound = (A >= 0)\n    upper_bound = (A <= 1)\n    constraints = [close_to_data, lower_bound, upper_bound]\n    problem = cvxpy.Problem(objective, constraints)\n    return A, problem\n\nif __name__ == \"__main__\":\n    train_datasets = mhcflurry.data_helpers.load_allele_datasets(\n        dataset_paths.PETERS2009_CSV_PATH,\n        binary_encoding=False,\n        max_ic50=20000)\n\n    allele_sims, allele_overlap_counts, allele_overlap_weights = \\\n        compute_overlaps_similarities(train_datasets)\n    allele_sims_array, allele_names_list = create_sims_array(allele_sims)\n    incomplete_affinity_matrix, allele_list, allele_order, peptide_list, peptide_order = \\\n        prepare_incomplete_affinity_matrix(train_datasets)\n    n_peptides = len(peptide_order)\n    n_alleles = len(allele_order)\n\n    affinity_nan_mask = np.isnan(incomplete_affinity_matrix)\n    print(\"# missing entries: %d/%d\" % (\n        affinity_nan_mask.sum(),\n        n_peptides * n_alleles))\n    affinity_ok_mask = (~affinity_nan_mask).astype(int)\n    zeroed_affinity_matrix = incomplete_affinity_matrix.copy()\n    zeroed_affinity_matrix[affinity_nan_mask] = 0.0\n\n    completed_matrix_variable, convex_problem = \\\n        create_matrix_completion_convex_problem(\n            n_alleles=n_alleles,\n            n_peptides=n_peptides,\n            known_mask=affinity_ok_mask,\n            known_values=zeroed_affinity_matrix,\n            tolerance=0.001)\n    convex_problem.solve(verbose=True, solver=cvxpy.SCS)\n    completed_affinities = completed_matrix_variable.value\n    np.save(\"completed_pmhc_affinity_matrix.npy\", completed_affinities)\n    with open(\"completed_pmhc_affinity_matrix_rows.txt\", \"w\") as f:\n        for allele in allele_names_list:\n            f.write(\"%s\\n\" % allele)\n    with open(\"completed_pmhc_affinity_matrix_columns.txt\", \"w\") as f:\n        for peptide in peptide_list:\n            f.write(\"%s\\n\" % peptide)\n","sub_path":"experiments/complete_binding_affinities.py","file_name":"complete_binding_affinities.py","file_ext":"py","file_size_in_byte":5520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"610049943","text":"import numpy\nfrom models.database import database as db\nfrom models.misc import utilities\n\n\nclass PlotDatabase(db.Database):\n\n    def __init__(self, model_type, model_region, buffer,\n                 model_year, summary_level, image_source,\n                 image_version, dsn='rocky2lemma'):\n        \"\"\"\n        Initializes a ModelDatabase instance and sets up the metadata for\n        the tables that it defines\n\n        Parameters\n        ----------\n        model_type : str\n            Model type.  Should be one of ('sppsz', 'trecov', 'wdycov', etc.)\n        model_region : str\n            LEMMA modeling region\n        buffer : bool\n            indicates whether model covers MR buffer\n        model_year : int\n            modeling year\n        summary_level : str\n            Summary level for scaling plot data (PNT, CC, FC, or PLT)\n        image_source : str\n            source of imagery (i.e. LARSE, RSAC)\n        image_version : float\n            version of imagery\n        dsn : str\n            Name of DSN for model database\n\n        Returns\n        -------\n        None\n        \"\"\"\n\n        self.model_type = model_type\n        self.model_region = model_region\n        self.buffer = buffer\n        self.model_year = model_year\n        self.summary_level = summary_level\n        self.image_source = image_source\n        self.image_version = image_version\n        self.dsn = dsn\n\n    def get_area_estimates(self, comparison_year):\n        \"\"\"\n        Get estimates of acres separated into forest, nonforest\n        and nonsampled areas\n\n        Parameters\n        ----------\n        comparison_year : int\n            Year for which plot assessment years will be compared.  If more\n            than one plot exists at a location, the plot that is temporally\n            closer to this parameter will be chosen\n\n        Returns\n        -------\n        observed_data: numpy.recarray\n            structure and species data and # forest hectares\n        nf_hectares: float\n            # nonforest hectares\n        ns_hectares: float\n            # nonsampled hectares\n        \"\"\"\n\n        sql_base = \"\"\"\n           EXEC lemma.GET_AREA_EXPANSIONS\n            @model_region = %d,\n            @model_type = '%s',\n            @glc_group = '%s',\n            @model_year = %d\n        \"\"\"\n\n        # Forested area\n        sql = sql_base % (self.model_region, self.model_type, 'F',\n            comparison_year)\n        (records, descr) = self.get_data(sql)\n        observed_data = utilities.pyodbc2rec(records, descr)\n\n        # Nonforest area\n        sql = sql_base % (self.model_region, self.model_type, 'NF',\n            comparison_year)\n        (records, descr) = self.get_data(sql)\n        nf_hectares = records[0][0]\n\n        # Non-sampled area\n        sql = sql_base % (self.model_region, self.model_type, 'NS',\n            comparison_year)\n        (records, descr) = self.get_data(sql)\n        ns_hectares = records[0][0]\n\n        return observed_data, nf_hectares, ns_hectares\n\n    def get_attribute_data(self, id_str):\n        \"\"\"\n        Get an attribute table which includes both continuous stand\n        variables and species by abundance measure (basal area, percent\n        cover, etc.) for use in accuracy assessment for NN models.\n\n        Parameters\n        ----------\n        id_str: str\n            Comma-delimited list of IDs to include\n\n        Returns\n        -------\n        attibute_data: numpy.recarray\n            Structure and species attribute data summarized at\n                the requested summary level\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_MODEL_ATTRIBUTES\n            @ids = '%s',\n            @summary_level = '%s',\n            @model_region = %d,\n            @model_type = '%s'\n        \"\"\"\n\n        sql = sql % (\n            id_str, self.summary_level, self.model_region, self.model_type)\n        (records, descr) = self.get_data(sql)\n        stand_attribute_data = utilities.pyodbc2rec(records, descr)\n        return stand_attribute_data\n\n    def get_environmental_matrix(self, id_str, plot_years, image_years,\n                                 spatial_vars):\n        \"\"\"\n        Get a matrix of plots by environmental variables for use as\n        input to NN models\n\n        Parameters\n        ----------\n        id_str: str\n            comma-delimited list of IDs to include\n        plot_years: str\n            comma-delimited list of plot assessment years\n        image_years: str\n            comma-delimited list of image years\n        spatial_vars: str\n            comma-delimited list of spatial variables to return\n\n        Returns\n        -------\n        env_matrix: numpy.recarray\n            **ID: unique plot identifier as specified by summary_level\n            ordination variables (names correspond to spatial_vars list)\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_ENVIRONMENTAL_MATRIX\n              @ids = '%s',\n              @summary_level = '%s',\n              @model_region = %d,\n              @model_type = '%s',\n              @plot_years = '%s',\n              @image_years = '%s',\n              @image_source = '%s',\n              @image_version = %f,\n              @spatial_vars = '%s'\n        \"\"\"\n        sql = sql % (id_str, self.summary_level, self.model_region,\n                     self.model_type, plot_years, image_years,\n                     self.image_source, self.image_version, spatial_vars)\n        records, descr = self.get_data(sql)\n        env_matrix = utilities.pyodbc2rec(records, descr)\n        return env_matrix\n\n    def get_field_names(self, table_name):\n        \"\"\"\n        Get field names for this table\n\n        Parameters\n        ----------\n        table_name: str\n            name of table for which to get field names\n\n        Returns\n        -------\n        field_names: numpy.array\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_FIELD_NAMES\n                @table_name = '%s'\n        \"\"\"\n\n        sql = sql % (table_name)\n        records, descr = self.get_data(sql)\n        field_names = [str(x[0]) for x in records]\n        return numpy.array(field_names)\n\n    def get_hex_attributes(self, comparison_year, plot_years, image_years):\n        \"\"\"\n        Get hexagon IDs and continuous stand attributes for forested\n        Annual plots to be used in Riemann accuracy diagnostics\n\n        Parameters\n        ----------\n        comparison_year: int\n            Year for which plot assessment years will be compared.  If more\n            than one plot exists at a location, the plot that is temporally\n            closer to this parameter will be chosen\n        plot_years: str\n            comma-delimited list of plot assessment years\n        image_years: str\n            comma-delimited list of image years\n\n        Returns\n        -------\n        hex_attributes: numpy.recarray\n\n        \"\"\"\n        sql = \"\"\"\n            EXEC lemma.GET_HEX_ATTRIBUTES\n                @model_region = %d,\n                @model_type = '%s',\n                @model_year = %d,\n                @summary_level = '%s',\n                @plot_years = '%s',\n                @image_years = '%s',\n                @image_source = '%s',\n                @image_version = %f\n        \"\"\"\n        sql = sql % (self.model_region, self.model_type,\n                     comparison_year, self.summary_level,\n                     plot_years, image_years, self.image_source,\n                     self.image_version)\n        records, descr = self.get_data(sql)\n        hex_attributes = utilities.pyodbc2rec(records, descr)\n        return hex_attributes\n\n    def get_image_years(self):\n        \"\"\"\n        Return all image years for this model region\n\n        Parameters\n        ----------\n        None\n\n        Returns\n        -------\n        image_years: numpy.array\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_IMAGE_YEARS\n                @model_region = %d,\n                @buffer = %d,\n                @image_source = '%s',\n                @image_version = %f\n        \"\"\"\n        sql = sql % (self.model_region, self.buffer,\n                     self.image_source, self.image_version)\n        records, descr = self.get_data(sql)\n        image_years = [int(x[0]) for x in records]\n        return numpy.array(image_years)\n\n    def get_metadata(self, table_names):\n        \"\"\"\n        Get metadata for the fields and codes in the tables passed in\n\n        Parameters\n        ----------\n        table_names: str\n            comma-delimited list of table names to get metadata for\n\n        Returns\n        -------\n        metadata_fields: numpy.recarray\n            field names and descriptions\n        metadata_codes: numpy.recarray\n            code names and descriptions\n        \"\"\"\n\n        metadata_fields = self.get_metadata_fields(table_names)\n        metadata_codes = self.get_metadata_codes(table_names)\n        return metadata_fields, metadata_codes\n\n    def get_metadata_field_dictionary(self, table_names):\n        metadata = {}\n        metadata_table = self.get_metadata_fields(table_names)\n\n        # Load the metadata into a dictionary\n        for row in metadata_table:\n            # First get the name of the field the metadata is describing\n            # This will be the key to the metadata dictionary\n            field_name = row.FIELD_NAME\n            field_dict = {}\n            for key in row.dtype.names:\n                if key == 'CODED':\n                    if row[key] == 0:\n                        field_dict['CODES'] = ''\n                    else:\n                        # Build the code dictionary and insert\n                        code_dict = \\\n                            self.get_metadata_code_dictionary(field_name)\n                        field_dict['CODES'] = code_dict\n                else:\n                    field_dict[key] = row[key]\n\n            # Delete the FIELD_NAME entry from the inner dictionary\n            # del(field_dict['FIELD_NAME'])\n            metadata[field_name] = field_dict\n        return(metadata)\n\n    def get_metadata_code_dictionary(self, field_name):\n        \"\"\"\n        Build a dictionary of codes for a given field\n\n        Parameters\n        ----------\n        table_name : str\n            The database object name\n        field_name : str\n            The field name within the table_name\n\n        Returns\n        -------\n        code_dict : dict\n            A dictionary of all codes associated with this field\n        \"\"\"\n\n        sql = \"EXEC lemma.GET_METADATA_CODES '\"\n        sql += field_name + \"'\"\n        code_dict = {}\n        (records, desc) = self.get_data(sql)\n        code_table = utilities.pyodbc2rec(records, desc)\n        for row in code_table:\n            code_value = row.CODE_VALUE\n            inner_dict = {}\n            for key in row.dtype.names:\n                # if key <> 'CODE_VALUE':\n                inner_dict[key] = row[key]\n            code_dict[code_value] = inner_dict\n        return code_dict\n\n    def get_metadata_fields(self, table_names):\n        \"\"\"\n        Get metadata for just the fields in the tables passed in\n\n        Parameters\n        ----------\n        table_names: str\n            comma-delimited list of table names to get metadata for\n\n        Returns\n        -------\n        metadata_fields: numpy.recarray\n            field names and descriptions\n        \"\"\"\n        sql = \"\"\"\n            EXEC lemma.GET_METADATA_TABLE_FIELDS\n                @table_names = '%s'\n        \"\"\"\n        sql = sql % (table_names)\n        (records, desc) = self.get_data(sql)\n        metadata_fields = utilities.pyodbc2rec(records, desc)\n        return metadata_fields\n\n    def get_metadata_codes(self, table_names):\n        \"\"\"\n        Get metadata for just the codes in the tables passed in\n\n        Parameters\n        ----------\n        table_names: str\n            comma-delimited list of table names to get metadata for\n\n        Returns\n        -------\n        metadata_codes: numpy.recarray\n            code names and descriptions\n        \"\"\"\n        sql = \"\"\"\n            EXEC lemma.GET_METADATA_TABLE_CODES\n                @table_names = '%s'\n        \"\"\"\n        sql = sql % (table_names)\n        try:\n            (records, desc) = self.get_data(sql)\n        except IndexError:\n            # If the query returned no records, return None\n            # this means there were no coded fields in the table(s)\n            metadata_codes = None\n        else:\n            metadata_codes = utilities.pyodbc2rec(records, desc)\n\n        return metadata_codes\n\n    def get_model_region_window(self):\n        \"\"\"\n        Get the bounding coordinates for this model region\n\n        Parameters\n        ----------\n        None\n\n        Returns\n        -------\n        mr_bounds : numpy.recarray\n            X_MIN, Y_MIN, X_MAX, Y_MAX, BOUNDARY_RASTER (file location)\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_MODEL_REGION_WINDOW\n                @model_region = %d\n        \"\"\"\n        sql = sql % (self.model_region)\n        (records, descr) = self.get_data(sql)\n        bounds = utilities.pyodbc2rec(records, descr)\n        return bounds\n\n    def get_ordination_variable_descriptions(self, ordination_vars):\n        \"\"\"\n        \"\"\"\n        sql = \"\"\"\n            EXEC lemma.GET_ORDINATION_VARIABLE_DESCRIPTIONS\n                @ordination_vars = '%s'\n            \"\"\"\n        sql = sql % (ordination_vars)\n        (records, descr) = self.get_data(sql)\n        ordination_vars = utilities.pyodbc2rec(records, descr)\n        return ordination_vars\n\n    def get_ordination_variable_list(self, var_types, variable_filter):\n        \"\"\"\n        Query the database to get a list of ordination variables and their\n        file locations for this model region\n\n        Parameters\n        ----------\n        var_types: str\n            keyword to specify which variables to return\n            Values:\n                ALL = all variables that have complete coverage for this MR\n                DEFAULT = subset of variables chosen for this MR\n        variable_filter: str\n            method of filtering ordination variables: RAW or FOCAL\n        Returns\n        -------\n        ordination_table : numpy.recarray\n            VARIABLE_NAME, VARIABLE_PATH\n        \"\"\"\n\n        # Create the parameter signature and get the possible ordination\n        # variables\n        sql = \"\"\"\n            EXEC lemma.GET_ORDINATION_VARIABLE_LIST\n              @model_region = %d,\n              @model_type = '%s',\n              @model_year = %d,\n              @buffer = %d,\n              @var_types = '%s',\n              @image_source = '%s',\n              @image_version = %f,\n              @variable_filter = '%s'\n        \"\"\"\n        sql = sql % (self.model_region, self.model_type, self.model_year,\n                     self.buffer, var_types, self.image_source,\n                     self.image_version, variable_filter)\n        (records, descr) = self.get_data(sql)\n        ordination_table = utilities.pyodbc2rec(records, descr)\n        return ordination_table\n\n    def get_plot_data_source_summary(self, ids):\n        \"\"\"\n        Get plot counts grouped by data sources and assessment years\n\n        Parameters\n        ----------\n        ids : str\n            comma-delimited list of IDs for plots to include\n            (should be PNTID, CCID, FCID or PLTID depending on\n            self.summary_level)\n\n        Returns\n        -------\n        plot_data_sources : numpy.recarray\n            ID, DATA_SOURCE, ASSESSMENT_YEAR\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_PLOT_DATA_SOURCE_SUMMARY\n            @ids = '%s',\n            @summary_level = '%s'\n        \"\"\"\n        sql = sql % (ids, self.summary_level)\n        (records, descr) = self.get_data(sql)\n        plot_data_sources = utilities.pyodbc2rec(records, descr)\n        return plot_data_sources\n\n    def get_plot_image_pairs(self, keyword):\n        \"\"\"\n        Query the database to get all plot assessment years and available\n        imagery years and crosswalk the returned information using the\n        logic associated with each keyword\n\n        Parameters\n        ----------\n        keyword : str\n            Keyword to determine the logic of how to match plot assessment\n            years to imagery years. The only keyword implemented now is\n            'DEFAULT', which matches plots to the closest year of imagery\n            available.\n\n        Returns\n        -------\n        plot_image_years : numpy.recarray\n            PLOT_YEAR, IMAGE_YEAR\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_PLOT_IMAGE_PAIRS\n              @model_region = %d,\n              @buffer = %d,\n              @image_source = '%s',\n              @image_version = %f,\n              @keyword = '%s'\n        \"\"\"\n        sql = sql % (self.model_region, self.buffer, self.image_source,\n                     self.image_version, keyword)\n        (records, descr) = self.get_data(sql)\n        plot_image_years = utilities.pyodbc2rec(records, descr)\n        return plot_image_years\n\n    def get_plot_list(self, coincident_plots, lump_table, plot_years,\n            image_years, plot_types, exclusion_codes):\n        \"\"\"\n        Get a list of plot IDs that match the criteria of the parameters\n\n        Parameters\n        ----------\n        coincident_plots: bool\n            indicates whether to include multiple assessments at each location\n        plot_years: str\n            comma-delimited list of plot assessment years\n        image_years: str\n            comma-delimited list of image years\n        plot_types: str\n            comma-delimited list of plot types\n            Values: ANNUAL, PERIODIC, ECOPLOT, JACKJO, FIA_SPECIAL\n        exclusion_codes: str\n            comma-delimited list of attributes for excluding plots\n            Values: COORDS, INEXACT_COORDS, QUESTIONABLE_COORDS,\n                DUPLICATE_COORDS, MISMATCH, DISTURB, STRUCEDGE,\n                PVT_GLC_MISMATCH, UNUSUAL_PLCOM_SPP, MULTGLC,\n                MULTFORCC, MULTPAG, MULTSERIES, FOR_MINORITY, ESLF_ONLY\n\n        Returns\n        -------\n        id_str: str\n            Comma-delimited string of integer IDs corresponding to PNTID,\n            CCID, FCID or PLTID as specified by summary_level param\n        \"\"\"\n\n        # lemma.GET_PLOT_LIST is the regular proc to call\n        # Use lemma.GET_PLOT_LIST_OLD_FOREST to subset to plots that\n        # are within the LT stable mask and have a brightness value\n        # between 1000-2000 (1000 <= TC1 <= 2000)\n        sql = \"\"\"\n            EXEC lemma.GET_PLOT_LIST\n              @summary_level = '%s',\n              @model_region = %d,\n              @model_year = %d,\n              @model_type = '%s',\n              @buffer = %d,\n              @coincident_plots = %d,\n              @lump_table = %d,\n              @plot_types = '%s',\n              @exclusion_codes = '%s',\n              @plot_years = '%s',\n              @image_years = '%s',\n              @image_source = '%s',\n              @image_version = %f\n        \"\"\"\n        sql = sql % (self.summary_level, self.model_region, self.model_year,\n            self.model_type, self.buffer, coincident_plots, lump_table,\n            plot_types, exclusion_codes, plot_years, image_years,\n            self.image_source, self.image_version)\n        (id_table, descr) = self.get_data(sql)\n        id_str = ','.join([str(x.ID) for x in id_table])\n        return id_str\n\n    def get_plot_years(self):\n        \"\"\"\n        Return all plot assessment years for this model region\n\n        Returns\n        -------\n        plot_years: numpy.array\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_PLOT_YEARS\n              @model_region = %d\n        \"\"\"\n        sql = sql % (self.model_region)\n        (records, descr) = self.get_data(sql)\n        plot_years = [int(x[0]) for x in records]\n        return numpy.array(plot_years)\n\n    def get_species_names(self, id_str, lump_table):\n        \"\"\"\n        Get common names and scientific names for species codes\n\n        Parameters\n        ----------\n        id_str: str\n            comma-delimited list of IDs to include\n\n        Returns\n        -------\n        species_names : numpy.recarray\n            SPP_SYMBOL, SCIENTIFIC_NAME, COMMON_NAME\n        \"\"\"\n\n        sql = \"\"\"\n        EXEC lemma.GET_SPECIES_NAMES\n            @ids = '%s',\n            @summary_level = '%s',\n            @model_type = '%s',\n            @lump_table = %d\n        \"\"\"\n\n        sql = sql % (id_str, self.summary_level, self.model_type, lump_table)\n        (records, descr) = self.get_data(sql)\n        species_info = utilities.pyodbc2rec(records, descr)\n        return species_info\n\n    def get_species_matrix(self, id_str, purpose, lump_table):\n        \"\"\"\n        Get a species data in the format of a crosstab table of\n        species by abundance measure (basal area, percent cover, etc.)\n        for use as input to NN models or for attaching to model output\n        or for using in model accuracy assessment\n\n        Parameters\n        ----------\n        id_str: str\n            comma-delimited list of IDs to include\n        purpose: str\n            ORDINATION: for use as input to model ordination\n            ATTRIBUTE: for joining attributes to model output\n            ACCURACY: for use in accuracy assessment\n        lump_table: bool\n            indicates whether to lump species using this model region's\n            lump table in the model database\n\n        Returns\n        -------\n        species_matrix: numpy.recarray\n            **ID: unique plot identifier as specified by summary_level\n            species abundance (names differ by which species occur in\n                model region)\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_SPECIES_MATRIX\n              @ids = '%s',\n              @summary_level = '%s',\n              @model_region = %d,\n              @model_type = '%s',\n              @purpose = '%s',\n              @lump_table = %d\n        \"\"\"\n        sql = sql % (id_str, self.summary_level, self.model_region,\n                     self.model_type, purpose, lump_table)\n        (records, descr) = self.get_data(sql)\n        species_matrix = utilities.pyodbc2rec(records, descr)\n        return species_matrix\n\n    def get_species_metadata(self):\n        \"\"\"\n        Get metadata for all of the species attributes\n\n        Returns\n        -------\n            fields: numpy.recarray\n                field names and definitions\n            codes: numpy.recarray\n                code names and definitions\n        \"\"\"\n        sql = \"\"\"\n            EXEC lemma.GET_SPECIES_METADATA\n              @model_region = %d,\n              @model_type = '%s'\n        \"\"\"\n        sql = sql % (self.model_region, self.model_type)\n        records, descr = self.get_data(sql)\n        species_metadata = utilities.pyodbc2rec(records, descr)\n        return species_metadata\n\n    def get_species_plot_counts(self, id_str):\n        sql = \"\"\"\n            EXEC lemma.GET_SPECIES_PLOT_COUNTS\n              @ids = '%s',\n              @summary_level = '%s',\n              @model_region = %d,\n              @model_type = '%s'\n        \"\"\"\n\n        sql = sql % (id_str, self.summary_level,\n                     self.model_region, self.model_type)\n\n        records, descr = self.get_data(sql)\n        species_plot_counts = utilities.pyodbc2rec(records, descr)\n        return species_plot_counts\n\n    def get_species_table_names(self):\n        \"\"\"\n        Get the name of the generic species tables in MS-SQL\n        For wdycov models there are two tables, one for trees\n        and one for shrubs\n        Other model types have just one species table\n\n        Parameters\n        ----------\n        none\n\n        Returns\n        -------\n        table_names: numpy.array\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_SPECIES_TABLE_NAME\n                @model_type = '%s'\n        \"\"\"\n        sql = sql % (self.model_type)\n        records, descr = self.get_data(sql)\n        #recarray = utilities.pyodbc2rec(records, descr)\n        #return recarray\n        table_names = ''\n        for x in records:\n            table_names += str(x[0]) + ','\n        return table_names\n        #table_names = [str(x[0]) for x in records]\n        #return numpy.array(table_names)\n\n    def get_structure_table_name(self):\n        \"\"\"\n        Get the name of the structure attribute table in MS-SQL\n\n        Returns\n        -------\n        table_name: String\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_STRUCTURE_TABLE_NAME\n                @model_type = '%s',\n                @summary_level = '%s'\n        \"\"\"\n        sql = sql % (self.model_type, self.summary_level)\n        records, descr = self.get_data(sql)\n        table_name = records[0][0]\n        return table_name\n\n    def get_structure_metadata(self, project_id):\n        \"\"\"\n        Get metadata for all of the structure attributes\n        \n        Parameters\n        ----------\n        None\n            \n        Returns\n        -------\n            fields: numpy.recarray\n                field names and definitions\n            codes: numpy.recarray\n                code names and definitions\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_STRUCTURE_METADATA_FIELDS\n                @model_type = '%s',\n                @summary_level = '%s',\n                @model_region = %d,\n                @project_id = '%s'\n        \"\"\"\n        sql = sql % (self.model_type, self.summary_level, \\\n                     self.model_region, project_id)\n        records, descr = self.get_data(sql)\n        metadata_fields = utilities.pyodbc2rec(records, descr)\n\n        structure_table = self.get_structure_table_name()\n        metadata_codes = self.get_metadata_codes(structure_table)\n        return metadata_fields, metadata_codes\n\n    def get_validation_attributes(self):\n        \"\"\"\n        Get structure and species attributes for validation plots\n\n        Returns\n        -------\n            validation_attributes: numpy.recarray\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.GET_VALIDATION_ATTRIBUTES\n                @summary_level = '%s',\n                @model_region = %d,\n                @model_type = '%s'\n        \"\"\"\n        sql = sql % (self.summary_level, self.model_region, self.model_type)\n        records, descr = self.get_data(sql)\n        validation_attributes = utilities.pyodbc2rec(records, descr)\n        return validation_attributes\n\n    def get_duplicate_plots_to_remove(self, id_str):\n        \"\"\"\n        Get a list of plots to remove from the plot pool after\n        ordination. We only want to keep one plot at locations with\n        fixed spectal values for imagery (i.e. the location has been\n        stabilized through LandTrendr procedures).\n\n        Returns\n        -------\n        id_list: list of plot IDs to delete\n        \"\"\"\n        sql = \"\"\"\n            EXEC lemma.DUPLICATE_PLOTS_TO_REMOVE\n                @fcids = '%s',\n                @image_source = '%s',\n                @image_version = %f,\n                @model_year = %d\n         \"\"\"\n\n        sql = sql % (id_str, self.image_source, self.image_version,\n            self.model_year)\n        records, descr = self.get_data(sql)\n        id_list = []\n        for r in records:\n            id_list.append(r.FCID)\n        return id_list\n\n    def load_outlier(self, fcid, rule_id, prediction_type,\n                     variable_filter, observed_value,\n                     predicted_value, vc_outlier_type, average_position):\n        \"\"\"\n        Load a plot outlier into dbo.PLOT_SCREEN_OUTLIERS table\n\n        Returns\n        -------\n        None (function inserts records into DB table)\n        \"\"\"\n\n        sql = \"\"\"\n            EXEC lemma.LOAD_OUTLIER\n                @model_region = %d,\n                @fcid = %d,\n                @rule_id = '%s',\n                @prediction_type = '%s',\n                @variable_filter = '%s',\n                @image_version = %f,\n                @observed_value = %d,\n                @predicted_value = %d,\n                @vc_outlier_type = '%s',\n                @average_position = %d\n        \"\"\"\n\n        sql = sql % (self.model_region, fcid, rule_id, prediction_type,\n                     variable_filter, self.image_version, observed_value,\n                     predicted_value, vc_outlier_type, average_position)\n        self.update_data(sql)\n","sub_path":"pynnmap/models/database/plot_database.py","file_name":"plot_database.py","file_ext":"py","file_size_in_byte":28210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"418361543","text":"from flask import Flask, render_template, request, redirect, url_for, abort\nimport sqlite3\nimport re\nimport datetime\nimport model\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET'])\ndef main():\n    return render_template('main.html')\n\n@app.route('/init', methods=['GET'])\ndef init():\n    model.init()\n    return redirect(url_for('main'))\n\n@app.route('/legs/', methods=['GET'])\ndef legs():\n    legs = model.getLegs()\n    return render_template('legs/legs.html', legs=legs)\n\n@app.route('/legs/<int:leg_id>', methods=['GET'])\ndef leg(leg_id):\n    leg = model.getLegById(leg_id)\n    players = model.getPlayers()\n    if not leg: abort(404)\n    return render_template('legs/leg.html', leg=leg, players=players)\n\n@app.route('/legs/add', methods=['GET', 'POST'])\ndef add_leg():\n    if request.method == 'GET':\n        players = model.getPlayers()\n        return render_template('legs/add_leg.html', players=players)\n    if request.method == 'POST':\n        players = model.getPlayers()\n        if int(request.form['player_1']) not in players or int(request.form['player_2']) not in players or request.form['player_1'] == request.form['player_2']:\n            return render_template('legs/add_leg.html', players=players)\n        model.addLeg(request.form['player_1'], request.form['player_2'])\n        return redirect(url_for('legs'))\n\n@app.route('/legs/edit/<int:leg_id>', methods=['GET', 'POST'])\ndef edit_leg(leg_id):\n    return render_template('legs/edit_leg.html')\n\n@app.route('/legs/delete/<int:leg_id>', methods=['GET', 'POST'])\ndef delete_leg(leg_id):\n    model.deleteLegById(leg_id)\n    return redirect(url_for('legs'))\n\n@app.route('/legs/<int:leg_id>/add_approach', methods=['GET', 'POST'])\ndef add_approach(leg_id):\n    if request.method == 'GET':\n        return render_template('approaches/add_approach.html')\n    if request.method == 'POST':\n        player_id = request.args.get('player_id', None)\n        if player_id is None:\n            return redirect(url_for('leg', leg_id=leg_id))\n        model.addApproach(leg_id, player_id, request.form['throw_1'], request.form['throw_2'], request.form['throw_3'])\n        return redirect(url_for('leg', leg_id=leg_id))\n\n@app.route('/legs/quick_leg', methods=['GET'])\ndef quick_leg():\n    return render_template('legs/quick_leg.html')\n\n@app.route('/players/', methods=['GET'])\ndef players():\n    players = model.getPlayers()\n    return render_template('players/players.html', players=players)\n\n@app.route('/players/<int:player_id>', methods=['GET'])\ndef player(player_id):\n    player = model.getPlayerById(player_id)\n    return render_template('players/player.html', player=player)\n\n@app.route('/players/add', methods=['GET', 'POST'])\ndef add_player():\n    if request.method == 'GET':\n        return render_template('players/add_player.html')\n    if request.method == 'POST':\n        if re.match('^[a-zA-Zа-яА-ЯїЇіІєЄъЪёЁ]{2,32}$', request.form['name']) is None: return render_template('players/add_player.html')\n        model.addPlayer(request.form['name'])\n        return redirect(url_for('players'))\n\n@app.route('/players/edit/<int:player_id>', methods=['GET', 'POST'])\ndef edit_player(player_id):\n    if request.method == 'GET':\n        player = model.getPlayerById(player_id)\n        return render_template('players/edit_player.html', name=player['name'])\n    if request.method == 'POST':\n        if re.match('^[a-zA-Zа-яА-ЯїЇіІєЄъЪёЁ]{2,32}$', request.form['name']) is None: return render_template('players/edit_player.html', name='')\n        model.updatePlayer(player_id, {'name': request.form['name']})\n        return redirect(url_for('players'))\n\n@app.route('/players/delete/<int:player_id>', methods=['GET', 'POST'])\ndef delete_player(player_id):\n    model.deletePlayerById(player_id)\n    return redirect(url_for('players'))\n\n\nif __name__ == '__main__':\n    app.run(debug=True)","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"249867699","text":"# -*- coding: utf-8 -*-\nimport os, sys\nimport time\n\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\nsys.path.append(os.path.join(os.path.split(os.path.realpath(__file__))[0], '../../../util'))\nsys.path.append(os.path.join(os.path.split(os.path.realpath(__file__))[0], '../../support'))\nimport loghelper\nimport db\n\nsys.path.append(os.path.join(os.path.split(os.path.realpath(__file__))[0], '../../crawler/company/itjuzi'))\nimport itjuzi_company_3_search\n\n# sys.path.append(os.path.join(os.path.split(os.path.realpath(__file__))[0], '../../crawler/company/kr36'))\n# import kr36_company_2_search\n\n#logger\nloghelper.init_logger(\"refresh_source_company\", stream=True)\nlogger = loghelper.get_logger(\"refresh_source_company\")\n\n# #Mark for user info efficient\n# companycrawler = kr36_company_2_search.kr36Crawler()\n# listcrawler = kr36_company_2_search.ListCrawler()\n\ndef refresh_company(companyId):\n    conn = db.connect_torndb()\n    company = conn.get(\"select * from company where id=%s\", companyId)\n    corporate = conn.get(\"select * from corporate where id=%s\", company[\"corporateId\"])\n    scs_itjuzi = conn.query(\"select * from source_company where source=13030 and companyId=%s and \"\n                            \"(active is null or active='Y')\", companyId)\n    # scs_kr36 = conn.query(\"select * from source_company where source=13022 and companyId=%s and \"\n    #                       \"(active is null or active='Y')\", companyId)\n\n    if len(scs_itjuzi) == 0:\n        if company[\"name\"] is not None and company[\"name\"].strip() != \"\":\n            itjuzi_company_3_search.start_run(company[\"name\"], None)\n        if corporate[\"fullName\"] is not None and corporate[\"fullName\"].strip() != \"\":\n            itjuzi_company_3_search.start_run(corporate[\"fullName\"], None)\n    else:\n        for sc_itjuzi in scs_itjuzi:\n            if sc_itjuzi[\"sourceId\"] is not None and sc_itjuzi[\"sourceId\"].strip() != \"\":\n                itjuzi_company_3_search.start_run(company[\"name\"], sc_itjuzi[\"sourceId\"])\n\n    # if len(scs_kr36) == 0:\n    #     if company[\"name\"] is not None and company[\"name\"].strip() != \"\":\n    #         kr36_company_2_search.start_run(company[\"name\"], None, listcrawler,companycrawler)\n    #     if corporate[\"fullName\"] is not None and corporate[\"fullName\"].strip() != \"\":\n    #         kr36_company_2_search.start_run(corporate[\"fullName\"], None, listcrawler,companycrawler)\n    # else:\n    #     for sc_kr36 in scs_kr36:\n    #         if sc_kr36[\"sourceId\"] is not None and sc_kr36[\"sourceId\"].strip() != \"\":\n    #             kr36_company_2_search.start_run(company[\"name\"], sc_kr36[\"sourceId\"], listcrawler,companycrawler)\n\n    conn.close()\n    pass\n\n\nif __name__ == '__main__':\n\n    logger.info(\"python refresh_source_company\")\n    while True:\n        conn = db.connect_torndb()\n        mongo = db.connect_mongo()\n        collection_raw = mongo.raw.projectdata\n        collection = mongo.task.company_refresh\n\n        tasks = list(collection.find({\"status\":0, \"extendType\":0}))\n        for t in tasks:\n            if t.has_key(\"subStatus\") is True:\n\n                if t[\"subStatus\"].has_key(\"itjuzi\") is True:\n\n                    logger.info(\"refresh source company done %s\", t[\"companyId\"])\n\n                elif t[\"subStatus\"].has_key(\"rawCrawler\") is True:\n\n                    rawCompanies = list(collection_raw.find(\n                        {\"source\": {'$in': [13030]}, \"type\": 36001, \"processed\": {\"$ne\": True}}))\n                    rawCompanies2 = conn.query(\"select * from source_company where processStatus in (0,1) and \"\n                                               \"source in (13030) and (active is null or active='Y')\")\n\n                    if len(rawCompanies) == 0 and len(rawCompanies2) == 0:\n                        collection.update_one({\"_id\": t[\"_id\"]},\n                                              {\"$set\": {\"subStatus.itjuzi\": 1}})\n                        logger.info(\"refresh source company done %s\", t[\"companyId\"])\n                    else:\n                        logger.info(\"refresh source company wait parser: %s\", t[\"companyId\"])\n\n                else:\n                    logger.info(\"refresh source company start: %s\", t[\"companyId\"])\n                    refresh_company(t[\"companyId\"])\n                    collection.update_one({\"_id\": t[\"_id\"]}, {\"$set\": {\"subStatus.rawCrawler\": 1}})\n                    # in order to enhance efficient\n                    break\n\n            else:\n                logger.info(\"refresh source company start: %s\", t[\"companyId\"])\n                refresh_company(t[\"companyId\"])\n                collection.update_one({\"_id\": t[\"_id\"]}, {\"$set\": {\"subStatus.rawCrawler\": 1}})\n\n        conn.close()\n        mongo.close()\n        time.sleep(10)\n\n","sub_path":"data/spider2/aggregator/refresh/refresh_source_company.py","file_name":"refresh_source_company.py","file_ext":"py","file_size_in_byte":4721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"273552065","text":"from django.urls import path, include\r\nfrom django.conf.urls import url\r\nfrom rest_framework import routers\r\nfrom . import views\r\n\r\napp_name = 'database'\r\nrouter = routers.DefaultRouter()\r\nrouter.register('Discipline', views.DisciplineView)\r\nrouter.register('Function', views.FunctionView)\r\nrouter.register('Discriminator', views.DiscriminatorView)\r\nrouter.register('Performance_Parameter', views.Performance_ParameterView)\r\nrouter.register('Technology', views.TechnologyView)\r\nrouter.register('Application_Reference', views.Application_ReferenceView)\r\nrouter.register('Application_Value', views.Application_ValueView)\r\n\r\nurlpatterns=[\r\n    #url(r'search/$', views.search, name='search'),\r\n    path('', include(router.urls)),\r\n    path('Discipline/', views.DisciplineListView.as_view(), name='Discipline'),\r\n    #path('Discipline/<int:pk>', views.DisciplineDetailView.as_view(), name='DisciplineDetail'),\r\n    path('Function', views.FunctionListView.as_view(), name='Function'),\r\n    #path('Function/<int:pk>', views.FunctionDetailView.as_view(), name='FunctionDetail'),\r\n    path('Discriminator/', views.DiscriminatorListView.as_view(), name='Discriminator'),\r\n    #path('Discriminator/<int:pk>', views.DiscriminatorDetailView.as_viw(), name='DiscriminatorDetail'),\r\n    path('Performance_Parameter/', views.Performance_ParameterListView.as_view(), name='Performance_Parameter'),\r\n    #path('Performance_Parameter/<int:pk>', views.performance_ParameterDetailView.as_view(), name='Performance_ParameterDetail'),\r\n    path('Technology/', views.TechnologyListView.as_view(), name='Technology'),\r\n    #path('Technology/<int:pk>', views.TechnologyDetailView.as_view(), name='TechnologyDetail'),\r\n    path('Application_Reference/', views.Application_ReferenceListView.as_view(), name='Application_Reference'),\r\n    #path('ApplicationReference/<int:pk>', views.Application_ReferenceDetailView.as_view(), name='Application_ReferenceDetail'),\r\n    path('Application_Value/', views.Application_ValueListView.as_view(), name='Application_Value'),\r\n    #path('Application_Value/<int:pk>', views.Application_ValueDetailView.as_view(), name='Application_ValueDetail')\r\n]\r\n","sub_path":"techDb/database/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"3774060","text":"import json\nimport os\nimport shutil\nfrom subprocess import Popen, PIPE\nimport subprocess\nimport time\nfrom typing import List, Dict\n\n\nfrom test_information import TestInformation\n\nBUILD_PATH = \"build\"\n\nclass RunResult:\n    def __init__(self, success, msg, stderr, stdout):\n        self.success = success\n        self.msg = msg\n        self.stderr = stderr\n        self.stdout = stdout\n\n\nclass StopClock:\n    def __init__(self):\n        self.start_time = time.time()\n\n    def time_elapsed_s(self):\n        return time.time() - self.start_time\n\n\ndef process_proc(proc_handle) -> RunResult:\n    try:\n        # anything over 2 seconds will be considered a failure\n        result = proc_handle.wait(2)\n        success = proc_handle.returncode == 0\n        msg = \"Succeed\" if success else \"Failed with exit code of : \" + proc_handle.return_code\n    except subprocess.TimeoutExpired as timeout:\n        success = False\n        msg = \"Timed out\"\n    except Exception as ex:\n        success = False\n        msg = \"Encountered exception: \" + str(ex)\n    return RunResult(success, msg, proc_handle.stderr, proc_handle.stdout)\n        \n\ndef compile(base_path: str) -> None:\n    result = subprocess.run([\"cmake\", f\"{base_path}/code\", f\"-B{BUILD_PATH}\"])\n    print(\"Configuring Result: {}\".format(result.returncode))\n    if result.returncode != 0:\n        raise Exception(\"Configuring Failed\")\n\n    result = subprocess.run([\"cmake\", \"--build\", \"build\"])\n    print(\"Compilation Result: {}\".format(result.returncode))\n    if result.returncode != 0:\n        raise Exception(\"Compilation Failed\")\n\ndef dump_results(output: Dict) -> None:\n    with open(\"results/results.json\", \"w\") as result_file:\n            result_file.write(json.dumps(output))\n\n\ndef copy_files(to_copy: List, base_path: str) -> None:\n    for ori, dst in to_copy:\n        shutil.copyfile(ori, \"{}/{}\".format(base_path, dst))\n\n\ndef run_tests(to_copy: List, all_tests: List) -> None:\n    base = (os.getenv(\"AUTOGRADER_SOURCE\") or \".\") + \"/source\"\n    run_tests_impl(to_copy, all_tests, base)\n\n\ndef blank_output(stop_clock) -> Dict:\n    output = {}\n    output[\"execution_time\"] = stop_clock.time_elapsed_s()\n    output[\"visibility\"] = \"hidden\"\n    output[\"stdout_visibility\"] = \"hidden\"\n    output[\"extra_data\"] = None \n    return output\n\n\ndef run_tests_impl(to_copy: List, all_tests: List, base_path: str) -> None:\n    start = StopClock()\n    try:\n        copy_files(to_copy, base_path)\n    except Exception as ex:\n        print(str(ex))\n        output = blank_output(start)\n        output[\"score\"] = 0\n        output[\"output\"] = str(ex)\n        dump_results(output)\n        return\n\n    try:\n        compile(base_path)\n    except Exception as ex:\n        print(str(ex))\n        output = blank_output(start)\n        output[\"score\"] = 0\n        output[\"output\"] = str(ex)\n        dump_results(output)\n        return\n\n    procs = {\n        test:\n        Popen(\n            [f\"{BUILD_PATH}/{test.binary_name}\", f\"\\\"{test.test_name}\\\"\"],\n            stdout=PIPE, \n            stderr=PIPE,\n        )\n        for test in all_tests\n    }\n\n    run_results = {\n        test: process_proc(proc_handle)\n        for test, proc_handle in procs.items()\n    }\n\n    for test, result in run_results.items():\n        print(\"Test {}: {}\".format(\n            test.full_name(),\n            \"succeed\" if result.success else \"failed\",\n        ))\n        if not result.success:\n            print(result.stdout.read(10000))\n            print(result.stderr.read(10000))\n\n    execution_time = 10\n    output = blank_output(start)\n    output[\"output\"] = \"Text relevant to the entire submission\"\n    output[\"tests\"] = [\n        {\n            \"score\": test.score if run_results[test].success else 0,\n            \"max_score\": test.score,\n            \"name\": test.full_name(),\n        }\n        for test in all_tests\n    ]\n    dump_results(output)\n    ","sub_path":"project3autograder/grader/test_runner.py","file_name":"test_runner.py","file_ext":"py","file_size_in_byte":3866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"401622652","text":"#!/usr/bin/env python\r\nimport os\r\nfrom shutil import copy\r\nfrom subprocess import call\r\n\r\n\r\ndef create_dirs():\r\n    with open('sample_host_list', 'r') as host_list:\r\n        for line in host_list:\r\n            if not os.path.exists('./certs/%s' % line.strip()):\r\n                os.makedirs('./certs/%s' % line.strip())\r\n\r\n\r\ndef copy_config_files():\r\n    with open('sample_host_list', 'r') as host_list:\r\n        for line in host_list:\r\n            copy('req.cfg', './certs/%s/req.cfg' % line.strip())\r\n            config = open('./certs/%s/req.cfg' % line.strip()).read()\r\n            config = config.replace('CHANGEME', line.strip())\r\n            config_write = open('./certs/%s/req.cfg' % line.strip(), 'w')\r\n            config_write.write(config)\r\n            config_write.close()\r\n\r\n\r\ndef generate_certs():\r\n    with open('sample_host_list', 'r') as host_list:\r\n        for line in host_list:\r\n            call(\r\n                'cd ./certs/%s && openssl req -new -nodes -sha256 -out rui.csr -keyout rui-orig.key -config req.cfg' % line.strip(),\r\n                shell=True)\r\n\r\n\r\ndef main():\r\n    create_dirs()\r\n    copy_config_files()\r\n    generate_certs()\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()","sub_path":"replace_esxi_certs/create_prereqs.py","file_name":"create_prereqs.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"175318109","text":"# -*- coding=utf-8 -*-\r\nimport requests\r\nimport re\r\nimport time\r\nfrom bs4 import BeautifulSoup\r\ntime1 = time.time()\r\nexist_url = []\r\ng_writercount = 0\r\ndef scrappy(url, depth = 1):\r\n    global g_writercount\r\n    try:\r\n        headers = {'User-Agent': 'Mozilla/5.0 (Windows;U;Windows NT 6.1;en-US;rv:1.9.1.6) Gecko/20091201 Firefox/3.5.6'}\r\n        r = requests.get(url, headers=headers)\r\n        #html = r.content.decode('utf-8')\r\n        html = r.text.encode('utf-8').decode(requests.utils.get_encodings_from_content(r.text)[0])\r\n        # print(html)\r\n    except Exception as e:\r\n        print(e)\r\n        print('下载失败:' + url)\r\n        return None\r\n    finally:\r\n        exist_url.append(url)\r\n    link_list = re.findall('.*?<a target=_blank href=\"/item/([^:#=<>]*?)\".*?</a>', html)\r\n    unique_list = list(set(link_list) - set(exist_url))\r\n\r\n    for eachone in unique_list:\r\n        g_writercount += 1\r\n        output = \"No.\" + str(g_writercount) + \"\\t Depth:\" + str(depth) + \"\\t\" + url + ' - > ' + eachone + \"\\n\"\r\n        print(output)\r\n        with open('url.txt', \"a+\") as f:\r\n            f.write(output)\r\n            f.close()\r\n        if depth < 2:\r\n            scrappy(\"https://baike.baidu.com/item/\"+eachone, depth+1)\r\n\r\nscrappy(\"https://baike.baidu.com/item/百度百科\")\r\ntime2 = time.time()\r\nprint(\"时间:\", time2-time1)","sub_path":"baidubaike/aa.py","file_name":"aa.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"17115865","text":"import pygame\n\nimport random\n\nimport sys\n\n\n\n\ncount3 = 0\np1Score = 0\nAiScore = 0\n\n\ngame_over = True\n\nclass Paddle(pygame.sprite.Sprite):\n\n def __init__(self, image, pongPaddlex,pongPaddley):\n\n  pygame.sprite.Sprite.__init__(self)\n\n\n  self.rect = image.get_rect()\n\n  self.rect.x =  pongPaddlex\n  self.rect.y = pongPaddley\n\n\n\n\n\n\n\n\n\n\n def is_collided_with(self, sprite):\n     return self.rect.colliderect(sprite.rect)\n\n\nclass Ball(pygame.sprite.Sprite):\n    def __init__(self,image,ballxPosition1,ballyPosition1):\n        pygame.sprite.Sprite.__init__(self)\n\n        self.rect = image.get_rect()\n\n        self.rect.x = ballxPosition1\n        self.rect.y = ballyPosition1\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\npygame.init()\n\n\nsize = (width,height) = (720,600)\nbackground_color = (255,255,255)\nscreen = pygame.display.set_mode(size)\nscreen.fill(background_color)\npygame.display.flip()\ndest = (0,300)\nhitBottomWall = False\nhitTopWall = False\nnotleftWall = False\nhitLeftWall = False\nhitRightWall = False\nxPositionForPaddle1 = 175\nxPositionForPaddle2 = 175\nrandomNumber13 = 5\nballGoingDiagonallyLeftUp = False\nballGoingDiagonallyLeftDown = False\ncount7 = 0\ngameMode = \"None\"\n\n\n\n\nRecoverAi1Left = False\nRecoverAi1Right = False\n\nRecoverAiUp = False\nRecoverAiDown = False\n\n\n\n\nrandomNumber = 5\n\nxForTopRightCornerOfRect = 110\nyForTopRightCornerOfRect = 360\n\nxForLowRightCornerOfRect = 110\nyForLowRightCornerOfRect = 560\n\nxyForTopRightCornerOfRect = (xForTopRightCornerOfRect, yForTopRightCornerOfRect)\nxyForLowRightCornerOfRect = (xForLowRightCornerOfRect, yForLowRightCornerOfRect)\n\nkeydownHeld = True\n\nyPosition = dest[1]\nxyPosition = (60,yPosition)\n\n\n\nxPositionForUpperLeftOfAi2Paddle = 545\nxPositionForUpperRightOfAi2Paddle = 585\nxPositionForLowerRightOfAi2Paddle = 585\nxPositionForLowerLeftOfAi2Paddle = 545\n\n\nxyPositionForUpperLeftOfAi2Paddle = (xPositionForUpperLeftOfAi2Paddle, 560)\nxyPositionForUpperRightOfAi2Paddle = (xPositionForUpperRightOfAi2Paddle, 560)\nxyPositionForLowerRightOfAi2Paddle = (xPositionForLowerRightOfAi2Paddle, 590)\nxyPositionForLowerLeftOfAi2Paddle = (xPositionForLowerLeftOfAi2Paddle,590)\n\n\npaddles = pygame.sprite.Group()\n\n\nyPositionForUpperLeftCornerOfAiPaddle = 300\nyPositionForUpperRightCornerOfAiPaddle = 300\nyPositionForLowerLeftCornerOfAiPaddle = 350\nyPositionForLowerRightCornerOfAiPaddle = 350\nxyPositionForUpperLeftCornerOfAiPaddle = (700,yPositionForUpperLeftCornerOfAiPaddle)\nxyPositionForUpperRightCornerOfAiPaddle = (720,yPositionForUpperRightCornerOfAiPaddle)\nxyPositionForLowerLeftCornerOfAiPaddle = (700,yPositionForLowerLeftCornerOfAiPaddle)\nxyPositionForLowerRightCornerOfAiPaddle = (720,yPositionForLowerRightCornerOfAiPaddle)\n\n\n\n\n\n\n\n\n\n\n\nmovingUp = False\n\nhitBall = False\n\n\n\n\nnet = pygame.image.load(\"images/net.gif\")\nnet = pygame.transform.scale(net,(60,600))\npongPaddle = pygame.image.load(\"images/download.png\")\nAiPongPaddle = pygame.image.load(\"images/download.png\")\npongPaddle1 = pygame.image.load(\"images/pongPaddle.png\")\n\npongPaddle2 = pygame.image.load(\"images/pongPaddle.png\")\n\nAiPongPaddle1 = pygame.image.load(\"images/pongPaddle.png\")\nAiPongPaddle2 = pygame.image.load(\"images/pongPaddle.png\")\n\n\n\n\n\npaddles.add_internal(pongPaddle)\npaddles.add_internal(pongPaddle1)\npaddles.add_internal(pongPaddle2)\n\nxPositionForAiPongPaddle1 = 540\nxPositionForAIPongPaddle2 = 540\n\n\npongPaddle1 = pygame.transform.scale(pongPaddle1 , (50,60))\npongPaddle2 = pygame.transform.scale(pongPaddle2, (50, 60)).convert()\n\nAiPongPaddle = pygame.transform.scale(AiPongPaddle,(20,50))\n\nball = pygame.image.load(\"images/ball.png\")\n\n\npongPaddle = pygame.transform.scale(pongPaddle, (20,50))\nball = pygame.transform.scale(ball,(50,50))\n\n\n\nyForAiPongPaddleLocation = 300\n\nAiPongPaddleLocation = (700,300)\n\n\n\nAiPongPaddle1 = pygame.transform.scale(AiPongPaddle1,(50,60))\nAiPongPaddle2 = pygame.transform.scale(AiPongPaddle2,(50,60))\nscreen.blit(AiPongPaddle1,(xPositionForAiPongPaddle1, -15))\nscreen.blit(AiPongPaddle2,(xPositionForAIPongPaddle2, 550))\nscreen.blit(pongPaddle1,(175,-15))\nscreen.blit(pongPaddle2, (175,550))\nscreen.blit(net,(350,0))\n\nscreen.blit(AiPongPaddle,AiPongPaddleLocation)\nscreen.blit(pongPaddle,dest)\nscreen.blit(ball,(360, 300))\ncount1 = 0\ncount2 = 0\ncount4 = 0\ncount5 = 0\nrandomNumber2 = 5\nrandomNumbe3 = 5\ncount6 = 0\nrandomNumber4 = 5\nrandomNumber5 = 5\n\n\ncount3 = 0\nballxPosition = 360\nballyPosition = 300\nrandomNumber1 = 5\nleftPaddle = False\nleftPaddle1 = False\nAiXPaddleLocation = 660\nAiYPaddleLocation = 360\n\nreachedTop = False\nyPositionForAiDot1 = 360\nyPositionForAiDot2 = 560\nyPositionForAiDot3 = 400\nyPositionForAiDot4 = 500\nxyForAiDot1 = (670,360)\nxyForAiDot2 = (670,560)\nxyForAiDot3 = (670,400)\nxyForAiDot4 = (670,500)\nhitTopRegionOfAiPaddle = False\nhitMiddleREgionOfAiPaddle = False\nhitBottomRegionOfAiPaddle = False\nbouncedOffTopDiagonallyP1 = False\nbouncedOffBottomDiagonallyP1 = False\nbouncedOffTopDiagonallyAI = False\nbouncedOffBottomDiagonallyAI = False\nbouncedDirectlyTowardsLeftWallDiagonallyDown = False\nbouncedDirectlyTowardsLeftWAllDiagonallyUp = False\nxCoordinateForLeftBoundaryOfPaddle1 = 175\nxCoordinateForRightBoundaryOfPAddle1 = 222\nxyForLeftBoundaryPongPaddle1 = (xCoordinateForLeftBoundaryOfPaddle1,30)\nxyForRightBoundaryPongPaddle1 = (xCoordinateForRightBoundaryOfPAddle1, 30)\n\nyCoordinateForTopBoundaryOfPaddle = 300\nyCoordinateForBottomBoundaryOfPaddle = 350\nxyForTopBoundaryOfPaddle = (20,yCoordinateForTopBoundaryOfPaddle)\nxyForBottomBoundaryOfPaddle = (20,yCoordinateForBottomBoundaryOfPaddle)\nxPositionForBottomLeftBoundaryOfPaddle1 = 175\nxPositionForBottomRightBoundaryOfPaddle1 = 225\nxyPositionForBottomRightBoundaryOfPaddle1 = (xPositionForBottomRightBoundaryOfPaddle1,5)\nxyPositionForBottomLeftBoundaryOfPaddle1 = (xPositionForBottomLeftBoundaryOfPaddle1,5)\nyPositionForBottomTopOfPaddle = 300\nyPositionForBottomBottomOfPaddle = 350\nxyPositionForBottomTopOfPaddle = (0,yPositionForBottomTopOfPaddle)\nxyPositionForBottomBottomOfPaddle = (0,yPositionForBottomBottomOfPaddle)\n\nxPositionForTopLeftOfPaddle2 = 175\nxPositionForBottomLeftOfPaddle2 = 175\nxPositionForTopRightOfPaddle2 = 225\nxPositionForBottomRightOfPaddle2 = 225\nxyTopLeftBoundaryForPaddle2 = (xPositionForTopLeftOfPaddle2, 570)\nxyBottomLeftBoundaryForPaddle2 = (xPositionForBottomLeftOfPaddle2, 595)\nxyTopRightBoundaryForPaddle2 = (xPositionForTopRightOfPaddle2, 570)\nxyBottomRightBoundaryForPaddle2 = (xPositionForBottomRightOfPaddle2,595)\n\npongPaddle1Rect = pongPaddle1.get_rect()\nballRect = ball.get_rect()\ncollided = pongPaddle1Rect.colliderect(ballRect)\nPongPaddleRect = pongPaddle.get_rect()\ncount1 = 0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncount = 0\nrandomNumber12 = 5\nballGoingDiagonallyUp = False\nballGoingDiagonallyDown = False\nDeflectBallLeftDiagonallyDown = False\nDeflectBallLeftDiagonallyUp = False\nDeflectBallRightDiagonallyUp = False\nDeflectBallRightDiagonallyDown = False\nPongPaddleHitBall = False\nAiPaddle2HitBall = False\nstartRecoveryToMiddle = False\nAiPaddleHitBall = False\nballGoingDiagonallyRightDown = False\nballGoingDiagonallyRightUp = False\ncollidedWithPaddle = False\ncollidedWithPaddle1 = False\ncollidedWithPaddle2 = False\ncollideWithAiPaddle = False\ncollideWithAiPaddle1 = False\ncollideWithAiPaddle2 = False\nplayingUpTo = 0\n\n\n\n\ndef GameOver():\n  global playingUpTo\n  global count1\n  global game_over\n  global gameMode\n  while game_over:\n\n\n\n    print(\"In here of game over\")\n\n    if(count1 == 0):\n\n       screen.fill((255,255,255))\n    pygame.font.init()\n    myfont = pygame.font.SysFont('Comic Sans MS', 50)\n    textsurface = myfont.render('Pong-game AI no walls', False, (0,0,0))\n\n\n    textsurface1 = myfont.render('Level:', False, (0,0,0))\n\n    textsurface2 = myfont.render('Easy', False, (0, 0, 0))\n\n    textsurface3 = myfont.render('Medium', False, (0, 0, 0))\n\n    textsurface4 = myfont.render('Hard', False, (0, 0, 0))\n    textsurface5 = myfont.render('Play Up to:', False, (0, 0, 0))\n    textsurface6 = myfont.render('10', False, (0, 0, 0))\n    textsurface7 = myfont.render('15', False, (0,0,0))\n    textsurface8 = myfont.render('20', False, (0, 0, 0))\n    textsurface9 = myfont.render(\"Play Now!\", False, (0,0,0))\n    spRect9 = textsurface9.get_rect()\n    spRect2 = textsurface2.get_rect()\n    spRect2.centerx = 150\n    spRect2.centery = 50\n    spRect3 = textsurface3.get_rect()\n    spRect3.centerx = 300\n    spRect3.centery = 50\n    spRect4 = textsurface4.get_rect()\n    spRect4.centerx = 450\n    spRect4.centery = 50\n    spRect6 = textsurface6.get_rect()\n    spRect6.centerx = 200\n    spRect6.centery = 80\n    spRect7 = textsurface7.get_rect()\n    spRect7.centerx = 300\n    spRect7.centery = 80\n    spRect8 = textsurface8.get_rect()\n    spRect8.centerx = 400\n    spRect8.centery = 80\n    spRect9.centerx = 400\n    spRect9.centery = 200\n\n\n\n\n\n\n    if(count1 == 0):\n\n\n\n\n\n\n     \n     screen.blit(textsurface9,spRect9)\n\n     screen.blit(textsurface5, (0, 60))\n     screen.blit(textsurface6,spRect6)\n     screen.blit(textsurface7,(spRect7))\n     screen.blit(textsurface8,spRect8)\n     screen.blit(textsurface, (0, 0))\n\n\n    mousPos = pygame.mouse.get_pos()\n\n    for event in pygame.event.get():\n        print(event)\n        if event.type == pygame.QUIT:\n            pygame.quit()\n            quit()\n        if event.type == pygame.MOUSEBUTTONDOWN:\n            if event.button == 1:\n\n                if spRect9.collidepoint(mousPos):\n                    if playingUpTo != 0:\n                        screen.fill((255,255,255))\n                        game_over = False\n\n\n\n                if spRect6.collidepoint(mousPos):\n                    print(\"In here\")\n\n                    playingUpTo = 10\n\n\n                    textsurface6 = myfont.render('10', False, (0, 0, 255))\n                    textsurface7 = myfont.render('15', False, (0, 0, 0))\n                    textsurface8 = myfont.render('20', False, (0, 0, 0))\n\n\n                    screen.blit(textsurface5, (0, 60))\n                    screen.blit(textsurface6, spRect6)\n                    screen.blit(textsurface7, spRect7)\n                    screen.blit(textsurface8, spRect8)\n                    screen.blit(textsurface, (0, 0))\n\n                    screen.blit(textsurface9, spRect9)\n                    pygame.display.update()\n\n                if spRect7.collidepoint(mousPos):\n                    print(\"In here\")\n\n                    playingUpTo = 15\n\n\n\n\n\n                    textsurface6 = myfont.render('10', False, (0, 0, 0))\n                    textsurface7 = myfont.render('15', False, (0, 0, 255))\n                    textsurface8 = myfont.render('20', False, (0, 0, 0))\n\n                    screen.blit(textsurface5, (0, 60))\n                    screen.blit(textsurface6, spRect6)\n                    screen.blit(textsurface7, spRect7)\n                    screen.blit(textsurface8, spRect8)\n                    screen.blit(textsurface, (0, 0))\n\n                    screen.blit(textsurface9, spRect9)\n                    pygame.display.update()\n\n                if spRect8.collidepoint(mousPos):\n\n\n                    playingUpTo = 20\n\n\n\n\n\n\n                    textsurface6 = myfont.render('10', False, (0, 0, 0))\n                    textsurface7 = myfont.render('15', False, (0, 0, 0))\n                    textsurface8 = myfont.render('20', False, (0, 0, 255))\n\n\n                    screen.blit(textsurface5, (0, 60))\n                    screen.blit(textsurface6, spRect6)\n                    screen.blit(textsurface7, spRect7)\n                    screen.blit(textsurface8, spRect8)\n                    screen.blit(textsurface9, spRect9)\n                    screen.blit(textsurface, (0, 0))\n\n                    pygame.display.update()\n\n    if count1 == 0 :\n     pygame.display.update()\n     pygame.display.flip()\n\n\n    count1 = count1 + 1\n\n\n\nGameOver()\n\n\n\n\n\nwhile True:\n\n\n\n\n\n\n\n\n while game_over != True:\n    pygame.font.init()\n    pygame.mixer.init()\n    sounda = pygame.mixer.Sound(\"Sounds/hitPongBall.wav\")\n\n\n\n\n    myfont = pygame.font.SysFont('Comic Sans MS', 50)\n    textsurface19 = myfont.render(str(p1Score), False, (0, 0, 0))\n    textsurface20 = myfont.render(str(AiScore), False, (0,0,0))\n    tpRect19 = textsurface19.get_rect()\n    tpRect20 = textsurface20.get_rect()\n\n\n\n\n    tpRect19.centerx = 30\n    tpRect19.centery = 540\n\n\n    tpRect20.centerx = 540\n    tpRect20.centery = 540\n\n\n    screen.blit(textsurface19,tpRect19)\n    screen.blit(textsurface20,tpRect20)\n\n\n\n    pygame.display.update()\n\n\n\n\n\n\n\n\n    print(\"xPsotiionForTopLEftOfPaddle2\")\n    print(xPositionForTopLeftOfPaddle2)\n    print(\"xPositionForTopRightOfPaddle2\")\n    print(xPositionForTopRightOfPaddle2)\n    print(\"xcoordfortpbndryofPaddle\")\n    print(yCoordinateForTopBoundaryOfPaddle)\n    print(\"yCoordforbtmboundaryofPaddle\")\n    print(yCoordinateForBottomBoundaryOfPaddle)\n\n    print(\"ballxPosition\")\n    print(ballxPosition)\n    print(\"xpositionForUpperLEftOfAi2Paddle\")\n    print(xPositionForUpperLeftOfAi2Paddle)\n    print(\"xPositionForUpperRightOfAi2Paddle\")\n    print(xPositionForUpperRightOfAi2Paddle)\n    print(\"ballyPosition\")\n    print(ballyPosition)\n\n\n    print(\"yPostiion for lowerr left corner of ai paddle\")\n    print(yPositionForLowerLeftCornerOfAiPaddle)\n    print(\"YPosition for upper left corner of ai paddle\")\n    print(yPositionForUpperLeftCornerOfAiPaddle)\n\n    paddle12 = Paddle(pongPaddle, 0, yPosition)\n    paddle13 = Paddle(pongPaddle1, xPositionForPaddle1,-15)\n    paddle14 = Paddle(pongPaddle2,xPositionForPaddle2,550)\n    AiPaddle = Paddle(AiPongPaddle,700,yForAiPongPaddleLocation)\n    AiPaddle1 = Paddle(AiPongPaddle1,xPositionForAiPongPaddle1,-15)\n    AiPaddle2= Paddle(AiPongPaddle2,xPositionForAIPongPaddle2,550)\n\n\n    ball12 = Ball(ball, ballxPosition, ballyPosition)\n\n\n    if paddle13.is_collided_with(ball12) and ballGoingDiagonallyLeftUp:\n        ballGoingDiagonallyLeftDown = True\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightDown = False\n        ballGoingDiagonallyRightUp = False\n        collidedWithPaddle = False\n        collidedWithPaddle1 = True\n        collidedWithPaddle2 = False\n        collideWithAiPaddle = False;\n        collideWithAiPaddle1 = False\n        collideWithAiPaddle2 = False\n        sounda.play()\n\n    if paddle13.is_collided_with(ball12) and ballGoingDiagonallyRightUp:\n        ballGoingDiagonallyLeftDown = False\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightDown = True\n        ballGoingDiagonallyRightUp = False\n        collidedWithPaddle = False\n        collidedWithPaddle1 = True\n        collidedWithPaddle2 = False\n        collideWithAiPaddle = False;\n        collideWithAiPaddle1 = False\n        collideWithAiPaddle2 = False\n        sounda.play()\n\n    print(\"paddle13 collided with ball 12\")\n    print(paddle14.is_collided_with(ball12))\n\n\n    if paddle14.is_collided_with(ball12) and  ballGoingDiagonallyRightDown:\n        ballGoingDiagonallyRightUp = True\n        ballGoingDiagonallyLeftDown = False\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightDown = False\n        collidedWithPaddle = False\n        collidedWithPaddle1 = False\n        collidedWithPaddle2 = True\n        collideWithAiPaddle = False;\n        collideWithAiPaddle1 = False\n        collideWithAiPaddle2 = False\n        sounda.play()\n\n    if paddle14.is_collided_with(ball12) and ballGoingDiagonallyLeftDown:\n        ballGoingDiagonallyRightUp = False\n        ballGoingDiagonallyLeftDown = False\n        ballGoingDiagonallyLeftUp = True\n        ballGoingDiagonallyRightDown = False\n        collidedWithPaddle = False\n        collidedWithPaddle1 = False\n        collidedWithPaddle2 = True\n        collideWithAiPaddle = False;\n        collideWithAiPaddle1 = False\n        collideWithAiPaddle2 = False\n        sounda.play()\n\n    if paddle12.is_collided_with(ball12) and ballGoingDiagonallyLeftUp:\n        ballGoingDiagonallyRightUp = True\n        ballGoingDiagonallyLeftDown = False\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightDown = False\n        collidedWithPaddle = True\n        collidedWithPaddle1 = False\n        collidedWithPaddle2 = False\n        collideWithAiPaddle = False;\n        collideWithAiPaddle1 = False\n        collideWithAiPaddle2 = False\n        sounda.play()\n\n    if paddle12.is_collided_with(ball12) and ballGoingDiagonallyLeftDown:\n        ballGoingDiagonallyRightUp = False\n        ballGoingDiagonallyLeftDown = False\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightDown = True\n        collidedWithPaddle = True\n        collidedWithPaddle1 = False\n        collidedWithPaddle2 = False\n        collideWithAiPaddle = False\n\n        collideWithAiPaddle1 = False\n        collideWithAiPaddle2 = False\n        sounda.play()\n\n\n\n    if AiPaddle.is_collided_with(ball12) and ballGoingDiagonallyRightDown:\n        ballGoingDiagonallyRightUp = False\n        ballGoingDiagonallyLeftDown = True\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightDown = False\n        collidedWithPaddle = False\n        collidedWithPaddle1 = False\n        collidedWithPaddle2 = False\n        collideWithAiPaddle = True\n        collideWithAiPaddle1 = False\n        collideWithAiPaddle2 = False\n        sounda.play()\n\n    if AiPaddle.is_collided_with(ball12) and ballGoingDiagonallyRightUp:\n            ballGoingDiagonallyRightUp = False\n            ballGoingDiagonallyLeftDown = False\n            ballGoingDiagonallyLeftUp = True\n            ballGoingDiagonallyRightDown = False\n            collidedWithPaddle = False\n            collidedWithPaddle1 = False\n            collidedWithPaddle2 = False\n            collideWithAiPaddle = True\n            collideWithAiPaddle1 = False\n            collideWithAiPaddle2 = False\n            sounda.play()\n\n\n    if AiPaddle1.is_collided_with(ball12) and ballGoingDiagonallyRightUp:\n        ballGoingDiagonallyRightUp = False\n        ballGoingDiagonallyLeftDown = False\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightDown = True\n        collidedWithPaddle = False\n        collidedWithPaddle1 = False\n        collidedWithPaddle2 = False\n        collideWithAiPaddle = False\n        collideWithAiPaddle1 = True\n        collideWithAiPaddle2 = False\n        sounda.play()\n\n\n\n\n\n\n\n\n\n    if AiPaddle1.is_collided_with(ball12) and ballGoingDiagonallyLeftUp:\n        ballGoingDiagonallyRightUp = False\n        ballGoingDiagonallyLeftDown = True\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightDown = False\n        collidedWithPaddle = False\n        collidedWithPaddle1 = False\n        collidedWithPaddle2 = False\n        collideWithAiPaddle = False\n        collideWithAiPaddle1 = True\n        collideWithAiPaddle2 = False\n        sounda.play()\n\n    if AiPaddle2.is_collided_with(ball12) and ballGoingDiagonallyRightDown:\n            ballGoingDiagonallyRightUp = False\n            ballGoingDiagonallyLeftDown = False\n            ballGoingDiagonallyLeftUp = True\n            ballGoingDiagonallyRightDown = False\n            collidedWithPaddle = False\n            collidedWithPaddle1 = False\n            collidedWithPaddle2 = False\n            collideWithAiPaddle = False\n            collideWithAiPaddle1 = False\n            collideWithAiPaddle2 = True\n            sounda.play()\n\n\n    if AiPaddle2.is_collided_with(ball12) and ballGoingDiagonallyLeftDown:\n            ballGoingDiagonallyRightUp = True\n            ballGoingDiagonallyLeftDown = False\n            ballGoingDiagonallyLeftUp = False\n            ballGoingDiagonallyRightDown = False\n            collidedWithPaddle = False\n            collidedWithPaddle1 = False\n            collidedWithPaddle2 = False\n            collideWithAiPaddle = False\n            collideWithAiPaddle1 = False\n            collideWithAiPaddle2 = True\n            sounda.play()\n\n\n\n\n\n    if(p1Score == playingUpTo or AiScore == playingUpTo):\n        playingUpTo = 0\n        gameMode = \"None\"\n        ballGoingDiagonallyRightUp = False\n        ballGoingDiagonallyLeftDown = False\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightDown = False\n        collidedWithPaddle = False\n        collidedWithPaddle1 = False\n        collidedWithPaddle2 = False\n        collideWithAiPaddle = False\n        collideWithAiPaddle1 = False\n        collideWithAiPaddle2 = False\n        screen.fill((255,255,255))\n        game_over = True\n        count = 0\n        count1 = 0\n        p1Score = 0\n        AiScore = 0\n        GameOver()\n\n\n\n\n\n    print(\"ballgonigdiagonallyrightdown\")\n    print(ballGoingDiagonallyRightDown)\n    print(\"ai paddle 1\")\n    print(collideWithAiPaddle1)\n\n\n\n    if(((collidedWithPaddle and ballGoingDiagonallyRightUp) or  (collidedWithPaddle2 and ballGoingDiagonallyRightUp) or (collideWithAiPaddle2 and ballGoingDiagonallyRightUp) or ballGoingDiagonallyRightDown or ballGoingDiagonallyRightUp)and yForAiPongPaddleLocation != ballyPosition):\n\n\n\n        if (ballyPosition > yForAiPongPaddleLocation and yForAiPongPaddleLocation !=  540):\n            yForAiPongPaddleLocation = yForAiPongPaddleLocation + 1\n\n        if (ballyPosition < yForAiPongPaddleLocation and yForAiPongPaddleLocation != 60):\n            yForAiPongPaddleLocation = yForAiPongPaddleLocation - 1\n\n\n\n\n\n    if((collidedWithPaddle and ballGoingDiagonallyRightDown) or (collidedWithPaddle1 and ballGoingDiagonallyRightDown) or (collideWithAiPaddle1 and ballGoingDiagonallyRightDown)) and yForAiPongPaddleLocation != ballyPosition:\n        print(\"Here i am in the zone\")\n        if (ballyPosition > yForAiPongPaddleLocation and yForAiPongPaddleLocation != 540):\n            yForAiPongPaddleLocation = yForAiPongPaddleLocation + 1\n\n        if (ballyPosition < yForAiPongPaddleLocation and yForAiPongPaddleLocation != 60):\n            yForAiPongPaddleLocation = yForAiPongPaddleLocation - 1\n\n\n\n    if((collidedWithPaddle and ballGoingDiagonallyRightUp) or (collidedWithPaddle and ballGoingDiagonallyRightDown) or (collidedWithPaddle1 and ballGoingDiagonallyRightDown) or (collidedWithPaddle2 and ballGoingDiagonallyRightUp) or (collideWithAiPaddle and ballGoingDiagonallyLeftUp ) or (collideWithAiPaddle2 and ballGoingDiagonallyRightUp) or (collideWithAiPaddle2 and ballGoingDiagonallyLeftUp)) and ballxPosition != xPositionForAiPongPaddle1:\n        print(\" In ai paddle 1 movement\")\n\n        if ballxPosition > xPositionForAiPongPaddle1 and xPositionForAiPongPaddle1 != 720:\n            xPositionForAiPongPaddle1 = xPositionForAiPongPaddle1 + 1;\n\n        if ballxPosition < xPositionForAiPongPaddle1 and xPositionForAiPongPaddle1 != 380:\n            xPositionForAiPongPaddle1 = xPositionForAiPongPaddle1 -1;\n\n\n\n    if((collidedWithPaddle and ballGoingDiagonallyRightDown) or (collidedWithPaddle1 and ballGoingDiagonallyRightDown) or (collideWithAiPaddle and ballGoingDiagonallyLeftDown) or (collideWithAiPaddle1 and ballGoingDiagonallyLeftDown) or (collideWithAiPaddle1 and ballGoingDiagonallyRightDown) or (collideWithAiPaddle and ballGoingDiagonallyLeftDown) ):\n\n        if ballxPosition > xPositionForAIPongPaddle2 and xPositionForAIPongPaddle2 != 720:\n            xPositionForAIPongPaddle2 = xPositionForAIPongPaddle2 + 1\n\n        if ballxPosition < xPositionForAIPongPaddle2 and xPositionForAIPongPaddle2 != 380:\n            xPositionForAIPongPaddle2= xPositionForAIPongPaddle2 -1\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    if paddle12.is_collided_with(ball12):\n        print(\"hit the ball\")\n\n\n\n    if(count7 == 0):\n        randomNumber13 = random.randrange(0,4)\n        count7 = count7 + 1\n\n\n        if(randomNumber13 == 0):\n          ballGoingDiagonallyLeftUp = True\n\n\n        if(randomNumber13 == 1):\n          ballGoingDiagonallyLeftDown = True\n\n        if(randomNumber13 == 2):\n          ballGoingDiagonallyRightUp = True\n\n        if(randomNumber13 == 3):\n            ballGoingDiagonallyRightDown = True\n\n\n\n\n\n\n\n\n\n    if ballGoingDiagonallyRightDown:\n        ballxPosition = ballxPosition +1\n        ballxPosition = ballxPosition +1\n\n\n    if ballGoingDiagonallyLeftUp == True:\n\n        ballxPosition = ballxPosition -1\n        ballyPosition = ballyPosition -1\n\n\n    if ballGoingDiagonallyLeftDown == True:\n\n        ballxPosition = ballxPosition -1\n        ballyPosition = ballyPosition + 1\n\n\n    if ballGoingDiagonallyRightUp == True:\n        ballxPosition = ballxPosition +1\n        ballyPosition = ballyPosition -1\n\n    if ballGoingDiagonallyRightDown == True:\n        ballxPosition = ballxPosition +1\n        ballyPosition = ballyPosition + 1\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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    if((collidedWithPaddle2 == False and (ballyPosition == 586 and ballxPosition < 350)) or (collidedWithPaddle1 == False and (ballyPosition ==-35 and ballxPosition < 350 )) or (collidedWithPaddle == False and ballxPosition == -24)  ):\n        AiScore = AiScore + 1\n        ballxPosition = 360\n        ballyPosition = 300\n        collideWithAiPaddle = False\n        collideWithAiPaddle1 = False\n        collideWithAiPaddle2 = False\n        ballGoingDiagonallyLeftDown = False\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightUp = False\n        ballGoingDiagonallyRightDown = False\n        count7 = 0\n\n    if ((collideWithAiPaddle2 == False and (ballxPosition > 350 and ballyPosition == 586)) or (\n            collideWithAiPaddle1 == False and (ballyPosition == -35 and ballxPosition > 350)) or (\n            collideWithAiPaddle == False and ballxPosition > 720)):\n        p1Score = p1Score + 1\n        ballxPosition = 360\n        ballyPosition = 300\n        collideWithAiPaddle = False\n        collideWithAiPaddle1 = False\n        collideWithAiPaddle2 = False\n        ballGoingDiagonallyLeftDown = False\n        ballGoingDiagonallyLeftUp = False\n        ballGoingDiagonallyRightUp = False\n        ballGoingDiagonallyRightDown = False\n        count7 = 0\n\n\n\n\n\n\n\n\n\n    screen.fill((255, 255, 255))\n    screen.blit(pongPaddle1, (xPositionForPaddle1, -15))\n\n\n\n    screen.blit(pongPaddle, dest)\n    screen.blit(net, (350, 0))\n    screen.blit(AiPongPaddle, (700,yForAiPongPaddleLocation))\n    screen.blit(ball, (ballxPosition, ballyPosition))\n    screen.blit(pongPaddle2, (xPositionForPaddle2, 550))\n    screen.blit(AiPongPaddle1, (xPositionForAiPongPaddle1, -15))\n    screen.blit(AiPongPaddle2, (xPositionForAIPongPaddle2, 550))\n    keys = pygame.key.get_pressed()\n\n\n    if keys[pygame.K_q] and xPositionForPaddle1 > 0:\n        xPositionForPaddle1 = xPositionForPaddle1 -1\n        xCoordinateForRightBoundaryOfPAddle1 = xCoordinateForRightBoundaryOfPAddle1 -1\n        xCoordinateForLeftBoundaryOfPaddle1 = xCoordinateForLeftBoundaryOfPaddle1 -1\n        xyForLeftBoundaryPongPaddle1 = (xCoordinateForLeftBoundaryOfPaddle1,30)\n        xyForRightBoundaryPongPaddle1 = (xCoordinateForRightBoundaryOfPAddle1,30)\n        xPositionForBottomLeftBoundaryOfPaddle1 = xPositionForBottomLeftBoundaryOfPaddle1 -1\n        xPositionForBottomRightBoundaryOfPaddle1 = xPositionForBottomRightBoundaryOfPaddle1-1\n        xyPositionForBottomRightBoundaryOfPaddle1 = (xPositionForBottomRightBoundaryOfPaddle1, 5)\n        xyPositionForBottomLeftBoundaryOfPaddle1 = (xPositionForBottomLeftBoundaryOfPaddle1, 5)\n\n\n        screen.fill((255,255,255))\n\n        screen.blit(pongPaddle1,(xPositionForPaddle1,-15))\n\n        screen.blit(pongPaddle, dest)\n        screen.blit(net, (350, 0))\n        screen.blit(AiPongPaddle, (700, yForAiPongPaddleLocation))\n        screen.blit(ball, (ballxPosition, ballyPosition))\n        screen.blit(pongPaddle2,(xPositionForPaddle2,550))\n        screen.blit(AiPongPaddle1,(xPositionForAiPongPaddle1,-15))\n        screen.blit(AiPongPaddle2, (xPositionForAIPongPaddle2, 550))\n\n\n\n    if keys[pygame.K_w] and xPositionForPaddle1 < 320:\n            print(\"In herez\")\n            xPositionForPaddle1 = xPositionForPaddle1 + 1\n            xCoordinateForLeftBoundaryOfPaddle1 = xCoordinateForLeftBoundaryOfPaddle1 +1\n            xCoordinateForRightBoundaryOfPAddle1 = xCoordinateForRightBoundaryOfPAddle1 + 1\n            xyForLeftBoundaryPongPaddle1 = (xCoordinateForLeftBoundaryOfPaddle1, 30)\n            xyForRightBoundaryPongPaddle1 = (xCoordinateForRightBoundaryOfPAddle1, 30)\n            xPositionForBottomLeftBoundaryOfPaddle1 = xPositionForBottomLeftBoundaryOfPaddle1 +1\n            xPositionForBottomRightBoundaryOfPaddle1 = xPositionForBottomRightBoundaryOfPaddle1 + 1\n            xyPositionForBottomRightBoundaryOfPaddle1 = (xPositionForBottomRightBoundaryOfPaddle1, 5)\n            xyPositionForBottomLeftBoundaryOfPaddle1 = (xPositionForBottomLeftBoundaryOfPaddle1, 5)\n\n            screen.fill((255, 255, 255))\n\n            screen.blit(pongPaddle1, (xPositionForPaddle1, -15))\n\n            screen.blit(pongPaddle, dest)\n            screen.blit(net, (350, 0))\n            screen.blit(AiPongPaddle, (700, yForAiPongPaddleLocation))\n            screen.blit(ball, (ballxPosition, ballyPosition))\n            screen.blit(AiPongPaddle1, (xPositionForAiPongPaddle1, -15))\n            screen.blit(AiPongPaddle2, (xPositionForAIPongPaddle2, 550))\n            screen.blit(pongPaddle2, (xPositionForPaddle2, 550))\n\n\n\n\n    if keys[pygame.K_z] and xPositionForPaddle2 > 0:\n            xPositionForPaddle2 = xPositionForPaddle2 - 1\n            xPositionForTopRightOfPaddle2 = xPositionForTopRightOfPaddle2 -1\n            xPositionForBottomRightOfPaddle2 = xPositionForBottomRightOfPaddle2-1\n            xPositionForTopLeftOfPaddle2 = xPositionForTopLeftOfPaddle2 -1\n            xPositionForBottomLeftOfPaddle2 = xPositionForBottomLeftOfPaddle2-1\n\n\n            xyTopLeftBoundaryForPaddle2 = (xPositionForTopLeftOfPaddle2 , 570)\n            xyBottomLeftBoundaryForPaddle2 =( xPositionForBottomLeftOfPaddle2, 595)\n            xyTopRightBoundaryForPaddle2 = (xPositionForTopRightOfPaddle2, 570)\n            xyBottomRightBoundaryForPaddle2 = (xPositionForBottomRightOfPaddle2, 595)\n\n\n            screen.fill((255, 255, 255))\n\n            screen.blit(pongPaddle2, (xPositionForPaddle2, 550))\n\n            screen.blit(pongPaddle, dest)\n            screen.blit(net, (350, 0))\n            screen.blit(AiPongPaddle, (700, yForAiPongPaddleLocation))\n            screen.blit(AiPongPaddle1, (xPositionForAiPongPaddle1, -15))\n            screen.blit(AiPongPaddle2, (xPositionForAIPongPaddle2, 550))\n            screen.blit(ball, (ballxPosition, ballyPosition))\n            screen.blit(pongPaddle1, (xPositionForPaddle1, -15))\n\n\n\n    if keys[pygame.K_x] and xPositionForPaddle2  < 320:\n                xPositionForPaddle2 = xPositionForPaddle2 + 1\n\n                screen.fill((255, 255, 255))\n\n                screen.blit(pongPaddle2, (xPositionForPaddle2, 550))\n\n                screen.blit(pongPaddle, dest)\n                screen.blit(net, (350, 0))\n                screen.blit(AiPongPaddle, (700, yForAiPongPaddleLocation))\n                screen.blit(ball, (ballxPosition, ballyPosition))\n                screen.blit(pongPaddle1, (xPositionForPaddle1, -15))\n                screen.blit(AiPongPaddle1, (xPositionForAiPongPaddle1, -15))\n                screen.blit(AiPongPaddle2, (xPositionForAIPongPaddle2, 550))\n\n                xPositionForTopRightOfPaddle2 = xPositionForTopRightOfPaddle2 +1\n                xPositionForBottomRightOfPaddle2 = xPositionForBottomRightOfPaddle2 +1\n                xPositionForTopLeftOfPaddle2 = xPositionForTopLeftOfPaddle2 + 1\n                xPositionForBottomLeftOfPaddle2 = xPositionForBottomLeftOfPaddle2 + 1\n\n                xyTopLeftBoundaryForPaddle2 = (xPositionForTopLeftOfPaddle2, 570)\n                xyBottomLeftBoundaryForPaddle2 = (xPositionForBottomLeftOfPaddle2, 595)\n                xyTopRightBoundaryForPaddle2 = (xPositionForTopRightOfPaddle2, 570)\n                xyBottomRightBoundaryForPaddle2 = (xPositionForBottomRightOfPaddle2, 595)\n\n\n\n    if keys[pygame.K_UP] and yPosition > 60:\n\n\n\n\n\n\n\n        yPosition = yPosition - 1\n        yCoordinateForTopBoundaryOfPaddle = yCoordinateForTopBoundaryOfPaddle -1\n        yCoordinateForBottomBoundaryOfPaddle = yCoordinateForBottomBoundaryOfPaddle-1\n        xyForTopBoundaryOfPaddle = (20,yCoordinateForTopBoundaryOfPaddle)\n        xyForBottomBoundaryOfPaddle = (20, yCoordinateForBottomBoundaryOfPaddle)\n        print(\"yPosition\")\n        print(yPosition)\n        yPositionForBottomTopOfPaddle = yPositionForBottomTopOfPaddle - 1\n        yPositionForBottomBottomOfPaddle = yPositionForBottomBottomOfPaddle - 1\n        xyPositionForBottomTopOfPaddle = (0, yPositionForBottomTopOfPaddle)\n        xyPositionForBottomBottomOfPaddle = (0, yPositionForBottomBottomOfPaddle)\n\n        yForTopRightCornerOfRect = yForTopRightCornerOfRect -1\n        yForLowRightCornerOfRect = yForLowRightCornerOfRect -1\n        xyForTopRightCornerOfRect = (xForTopRightCornerOfRect,yForTopRightCornerOfRect)\n        xyForLowRightCornerOfRect = (xForLowRightCornerOfRect,yForLowRightCornerOfRect)\n        dest = (0, yPosition)\n\n        screen.fill((255, 255, 255))\n\n        print(yPosition)\n        screen.blit(pongPaddle, dest)\n        screen.blit(net,(350,0))\n        screen.blit(AiPongPaddle, (700, yForAiPongPaddleLocation))\n        screen.blit(ball,(ballxPosition,ballyPosition))\n        screen.blit(pongPaddle1,(xPositionForPaddle1,-15))\n        screen.blit(pongPaddle2,(xPositionForPaddle2, 550))\n        screen.blit(AiPongPaddle1, (xPositionForAiPongPaddle1, -15))\n        screen.blit(AiPongPaddle2, (xPositionForAIPongPaddle2, 550))\n\n\n\n\n\n    if keys[pygame.K_DOWN] and yPosition < 500 :\n        print(\"ball y Position\")\n        print(ballyPosition)\n        print(\"yForTopRightCornerOfRect\")\n        print(yForTopRightCornerOfRect)\n        print(\"ball x Position\")\n        print(ballxPosition)\n        print(\"xForLowRightCornerOfRect\")\n        print(yForLowRightCornerOfRect)\n        print(\"ball y Position\")\n        print(ballyPosition)\n\n\n\n        yPosition = yPosition + 1\n        yCoordinateForTopBoundaryOfPaddle = yCoordinateForTopBoundaryOfPaddle + 1\n        yCoordinateForBottomBoundaryOfPaddle = yCoordinateForBottomBoundaryOfPaddle + 1\n        xyForTopBoundaryOfPaddle = (20, yCoordinateForTopBoundaryOfPaddle)\n        xyForBottomBoundaryOfPaddle = (20, yCoordinateForBottomBoundaryOfPaddle)\n        print(\"this is the yz position\")\n        print(yPosition)\n        yForTopRightCornerOfRect = yForTopRightCornerOfRect + 1\n        yForLowRightCornerOfRect = yForLowRightCornerOfRect + 1\n        xyForTopRightCornerOfRect = (xForTopRightCornerOfRect, yForTopRightCornerOfRect)\n        xyForLowRightCornerOfRect = (xForLowRightCornerOfRect, yForLowRightCornerOfRect)\n        yPositionForBottomTopOfPaddle = yPositionForBottomTopOfPaddle + 1\n        yPositionForBottomBottomOfPaddle = yPositionForBottomBottomOfPaddle + 1\n        xyPositionForBottomTopOfPaddle = (0, yPositionForBottomTopOfPaddle)\n        xyPositionForBottomBottomOfPaddle = (0, yPositionForBottomBottomOfPaddle)\n\n        dest = (0,yPosition)\n        screen.fill((255,255,255))\n        screen.blit(net,(350,0))\n        screen.blit(pongPaddle,dest)\n        screen.blit(pongPaddle1, (xPositionForPaddle1, -15))\n        screen.blit(pongPaddle2, (xPositionForPaddle2, 550))\n        screen.blit(AiPongPaddle, (700, yForAiPongPaddleLocation))\n        screen.blit(ball,(ballxPosition,ballyPosition))\n        screen.blit(AiPongPaddle1, (xPositionForAiPongPaddle1, -15))\n        screen.blit(AiPongPaddle2, (xPositionForAIPongPaddle2, 550))\n\n\n    for event in pygame.event.get():\n        print(event)\n        if event.type == pygame.QUIT:\n            pygame.quit()\n            quit()\n\n    pygame.draw.circle(screen, (0, 0, 255), xyForLeftBoundaryPongPaddle1, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), xyForRightBoundaryPongPaddle1, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), xyPositionForBottomLeftBoundaryOfPaddle1, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), xyPositionForBottomRightBoundaryOfPaddle1, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), xyPositionForBottomBottomOfPaddle, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), xyPositionForBottomTopOfPaddle, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), xyTopLeftBoundaryForPaddle2, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), xyBottomLeftBoundaryForPaddle2, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), xyTopRightBoundaryForPaddle2, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), xyBottomRightBoundaryForPaddle2, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255),xyPositionForUpperLeftOfAi2Paddle, 2, 2)# circle 1\n    pygame.draw.circle(screen, (0, 0, 255), xyPositionForUpperRightOfAi2Paddle, 2, 2)#circle 2\n    pygame.draw.circle(screen, (0, 0, 255), xyPositionForLowerRightOfAi2Paddle, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), xyPositionForLowerLeftOfAi2Paddle, 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), (700,300), 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), (720, 300), 2, 2)\n    pygame.draw.circle(screen,(0,0,255),(700,350),2,2)\n    pygame.draw.circle(screen, (0, 0, 255), (720, 350), 2, 2)\n    pygame.draw.circle(screen, (0, 0, 255), (360, 300), 2, 2)\n\n\n\n    pygame.display.update()\n\n\n    pygame.display.flip()\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":"pongGame.py","file_name":"pongGame.py","file_ext":"py","file_size_in_byte":36878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"502034663","text":"import argparse\nfrom .. import GoodConf\n\n\ndef argparser_add_argument(parser: argparse.ArgumentParser, config: GoodConf):\n    \"\"\"Adds argument for config to existing argparser\"\"\"\n    help = \"Config file.\"\n    if config.file_env_var:\n        help += (\" Can also be configured via the \"\n                 \"environment variable: {}\".format(config.file_env_var))\n    if config.default_files:\n        help += (\" Defaults to the first file that exists from \"\n                 \"[{}].\".format(', '.join(config.default_files)))\n    parser.add_argument('-C', '--config', metavar='FILE', help=help)\n","sub_path":"goodconf/contrib/argparse.py","file_name":"argparse.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"372716844","text":"import os\nimport sys\nimport io\nimport unittest\nfrom mock import patch, Mock\nimport tempfile\nimport shutil\n\nimport rosdoc.epyenator\nfrom rosdoc.rdcore import BUILDER_KEY\n\nclass EpyenatorTest(unittest.TestCase):\n\n    def test_prepare_command_empty(self):\n        with patch('rosdoc.rdcore.RosdocContext') as ctx:\n            ctx.rd_configs = {}\n            result = rosdoc.epyenator._prepare_command(ctx, 'foo')\n            self.assertEqual((None, None), result)\n\n\n    def test_prepare_command_simple(self):\n        with patch('rosdoc.rdcore.RosdocContext') as ctx:\n            ctx.rd_configs = {'foo': [{BUILDER_KEY: rosdoc.epyenator.PLUGIN_ID}]}\n            ctx.docdir = 'foo_dir'\n            result = rosdoc.epyenator._prepare_command(ctx, 'foo')\n            self.assertEqual(('foo_dir/foo/html', ['epydoc',\n                                                   '--html',\n                                                   'foo',\n                                                   '-o',\n                                                   'foo_dir/foo/html',\n                                                   '--inheritance', 'included',\n                                                   '--no-private']), result)\n\n    def test_prepare_command_exclude(self):\n        with patch('rosdoc.rdcore.RosdocContext') as ctx:\n            ctx.rd_configs = {'foo': [{BUILDER_KEY: rosdoc.epyenator.PLUGIN_ID, 'exclude': ['bar']}]}\n            ctx.docdir = 'foo_dir'\n            result = rosdoc.epyenator._prepare_command(ctx, 'foo')\n            self.assertEqual(('foo_dir/foo/html', ['epydoc',\n                                                   '--html',\n                                                   'foo',\n                                                   '-o',\n                                                   'foo_dir/foo/html',\n                                                   '--exclude',\n                                                   'bar',\n                                                   '--inheritance', 'included',\n                                                   '--no-private']), result)\n\n    def test_prepare_command_config(self):\n        with patch('rosdoc.rdcore.RosdocContext') as ctx:\n            ctx.rd_configs = {'rosdoc': [{BUILDER_KEY: rosdoc.epyenator.PLUGIN_ID, 'config': 'bum'}]}\n            ctx.docdir = 'foo_dir'\n            result = rosdoc.epyenator._prepare_command(ctx, 'rosdoc')\n            self.assertEqual(('foo_dir/rosdoc/html',\n                              ['epydoc',\n                               '--html',\n                               'rosdoc',\n                               '-o',\n                               'foo_dir/rosdoc/html',\n                               '--config',\n                               '/home/kruset/work/fuerte/documentation/rosdoc/bum']),\n                             result)\n","sub_path":"rosdoc/test/test_epyenator.py","file_name":"test_epyenator.py","file_ext":"py","file_size_in_byte":2849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"311231725","text":"# Given the root of a binary tree, return the inorder traversal of its nodes' values.\n\n \n\n# Example 1:\n# Input: root = [1,null,2,3]\n# Output: [1,3,2]\n\n# Example 2:\n# Input: root = []\n# Output: []\n\n# Example 3:\n# Input: root = [1]\n# Output: [1]\n \n\n# Constraints:\n\n# The number of nodes in the tree is in the range [0, 100].\n# -100 <= Node.val <= 100\n \n# Follow up: Recursive solution is trivial, could you do it iteratively?\n\n# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\n\n# DFS, Recursion:\nclass Solution(object):\n    def inorderTraversal(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: List[int]\n        \"\"\"\n        if not root:\n            return []\n        return self.inorderTraversal(root.left) + [root.val] + self.inorderTraversal(root.right)\n# Runtime: 16 ms, faster than 78.91% of Python online submissions for Binary Tree Inorder Traversal.\n# Memory Usage: 13.3 MB, less than 95.08% of Python online submissions for Binary Tree Inorder Traversal.\n\n\n# Iterative method:\nclass Solution(object):\n    def inorderTraversal(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: List[int]\n        \"\"\"\n        res = []\n        stack = [root]\n        visted = {root}\n        while stack:\n            curr = stack.pop()\n            while curr.left:\n                stack.append(curr.left)\n        return res\n\n\nclass Solution:\n    def inorderTraversal(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: List[int]\n        \"\"\"\n        res, stack = [], []\n        cur = root\n        while cur or stack:\n            while cur: # travel to each node's left child, till reach the left leaf\n                stack.append(cur)\n                cur = cur.left\n            cur = stack.pop() # this node has no left child\n            res.append(cur.val) # so let's append the node value \n            cur = cur.right # visit its right child --> if it has left child ? append left and left.val, otherwise append node.val, then visit right child again... cur = node.right\n        return res\n        \n# Runtime: 20 ms, faster than 54.36% of Python online submissions for Binary Tree Inorder Traversal.\n# Memory Usage: 13.5 MB, less than 50.76% of Python online submissions for Binary Tree Inorder Traversal.","sub_path":"94. Binary Tree Inorder Traversal.py","file_name":"94. Binary Tree Inorder Traversal.py","file_ext":"py","file_size_in_byte":2399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"561927247","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nimport json\nimport sys\nimport requests\nimport doco.client\nimport pandas\nimport codecs\nimport csv\n\n# Create your views here.\nREPLY_ENDPOINT = 'https://api.line.me/v2/bot/message/reply'\nACCESS_TOKEN = 'Enter your LINE ACCESS TOKEN'\nFILE_ID = \"Enter the Google Drive ID of the photo\"\n\n#test\ndef index(request):\n    return HttpResponse(\"This is bot api.\")\n\n#request受信\ndef callback(request):\n    reply = \"\"\n    request_json = json.loads(request.body.decode('utf-8')) # requestの情報をdict形式で取得\n    for e in request_json['events']:\n        reply_token = e['replyToken']  # 返信先トークンの取得\n        message_type = e['message']['type']   # typeの取得\n\n        if message_type == 'text' or message_type == 'image' or message_type == 'sticker':\n            reply += reply_img(reply_token)   # LINEにセリフを送信する関数\n    return HttpResponse(reply) #views.pyはこれをreturnしないといけないらしい\n\ndef reply_img(reply_token):\n    image_url = \"https://drive.google.com/uc?export=view&id=\" + FILE_ID\n    header = {\n        \"Content-Type\": \"application/json\",\n        \"Authorization\": \"Bearer \" + ACCESS_TOKEN\n    }\n    payload = {\n          \"replyToken\":reply_token,\n          \"messages\":[\n                {\n                    \"type\":\"image\",\n                    \"originalContentUrl\":image_url,\n                    \"previewImageUrl\":image_url\n                }\n            ]\n    }\n    requests.post(REPLY_ENDPOINT, headers=header, data=json.dumps(payload))\n    return \"ok\"\n","sub_path":"NAFT_viewbot/linebot/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"576664040","text":"\n\nfrom xai.brain.wordbase.nouns._shaver import _SHAVER\n\n#calss header\nclass _SHAVERS(_SHAVER, ):\n\tdef __init__(self,): \n\t\t_SHAVER.__init__(self)\n\t\tself.name = \"SHAVERS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"shaver\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_shavers.py","file_name":"_shavers.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"307084360","text":"from scipy.special import expit\nimport numpy as np\nimport itertools\n##########-----------This CODE IS VERY UNSUCCESSFUL----------------------#############\n\n#feed forward network to attempt to learn tic tac toe\n\n#class for neural net\nclass nn:\n    def __init__(self, player_tag):\n        self.in_nodes = 9 #9 in nodes for each spot in a tic tac toe board\n        self.out_nodes = 9 #9 outputs for 9 spots on a board\n        self.hnodes = 50 #random number for nodes, lower number actually worked better\n        self.iteration = 0 #tracks number of times this class was trained\n        self.iterations = 100000 # used to slowly reduce learning rate\n        self.lr = 1. #lstarting learning rate\n        self.player_tag = player_tag #player tag given from class declaration\n        \n        #weights generated randomly with normal distribution\n        self.wih = np.random.normal(0.0, pow(self.hnodes, -.5), (self.hnodes, self.in_nodes))\n        self.who = np.random.normal(0.0, pow(self.out_nodes, -.5), (self.out_nodes, self.hnodes))\n        \n    def test(self, board): #test function\n        board = np.array(list(itertools.chain(*board))) #flattens board to a 1d array\n        inputs = np.array(board, ndmin=2).T \n        hout = expit(np.dot(self.wih, inputs)) # sigmoid of dot product between weights and inputs\n        final_out = np.array((expit(np.dot(self.who, hout)).T).tolist()) #output array converted to list\n        \n        for_guess = (final_out - board).tolist()[0] # because outputs are between 0-1, subtracting the board, the tags would \n        #give position on the board a negative guess which would keep the nn from trying to make an illegal move\n        player_turn = for_guess.index(max(for_guess)) # uses the max in output list for player turn\n        board[player_turn] = self.player_tag #places the player tag in the position it guessed on the board\n        board_return = [] #creates a list for a board to return\n        for i in range(3):\n            row = []\n            for j in range(3):\n                row.append(board[j + i*3])\n            board_return.append(row) #converts 1d board back to a 2d board\n        \n        return np.array(board_return), board, int(player_turn / 3), (player_turn % 3) #returns 1d and 2d of board and the row and column index of the move\n        \n    def train(self, game_moves, winner):\n    #have 2 sub functions in train, one with the training and another to run backwards through each move in the game and train the model based on each board it saw throughout the game\n        \n        #train function starts at line 65\n        def train_for_realzies(self,board, move): #actrual training fucntion\n            inputs = np.array(board, ndmin=2).T #converts input and target to easier format for calculating\n            targets = np.array(move, ndmin=2).T\n        \n            h_out = expit(np.dot(self.wih, inputs)) #sigmoid of dot between input and weights\n            final_out = expit(np.dot(self.who, h_out))\n        \n            out_error = targets- final_out #calculate error\n            h_error = np.dot(self.who.T, out_error) #get error of hidden nodes\n            \n            self.who += self.lr * np.dot((out_error * final_out * (1.0-final_out)), h_out.T) #update weights\n            self.wih += self.lr * np.dot((h_error * h_out * (1.0-h_out)), inputs.T)\n            \n            self.lr *= (1. - self.iteration / self.iterations) #update learning rate\n            self.iteration += 1 #adds to iteration\n        \n        if self.player_tag == winner: #doesn't train when the computer wins\n            pass\n        elif winner != 0: #runs the train function when computer losses, based on moves of the winner\n            game_moves =  game_moves[::-1] #flips list of game moves\n            for i in range(len(game_moves) - 1):\n                if i % 2 == 0: #winner would have the last move so this would give each move of the winner\n                    move = game_moves[i]\n                    last_turn = game_moves[i+1] # gives the game board of the last move\n                    index =  [ 1 if (move[i]!=last_turn[i]) else 0 for i in range(len(move)) ] # gives a 1 for last move for target data\n                    train_for_realzies(self, last_turn, index) #calls actual training function\n                    \n        else: #wasn't sure how to train this for ties\n            pass\n            \"\"\"\n            game_moves =  game_moves[::-1]\n            for i in range(len(game_moves) - 1):\n                move = game_moves[i]\n                last_turn = game_moves[i+1]\n                index =  [ 1 if (move[i]!=last_turn[i]) else 0 for i in range(len(move)) ]\n                train_for_realzies(self, last_turn, index)\n             \"\"\"\n        \n        \n        \n","sub_path":"nn_ttt.py","file_name":"nn_ttt.py","file_ext":"py","file_size_in_byte":4744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"117726151","text":"from tensorflow_serving.apis import predict_pb2\nfrom tensorflow.python.framework import tensor_util\nimport tensorflow as tf\n\nimport numpy as np\nimport cv2\n\ndef find_face_bounding_box(boxes, scores):\n  min_score_thresh = 0.2\n  boxes_list = []\n  for i in range(0, boxes.shape[0]):\n    if scores[i] > min_score_thresh:\n      # return tuple(boxes[i].tolist())\n      boxes_list.append(tuple(boxes[i].tolist()))\n  return boxes_list\n\nclass FaceDetector:\n\n  # initialize static variable here\n  @staticmethod\n  def Setup():\n    pass\n\n  # convert predict_pb2.PredictRequest()'s content to data_dict\n  # input: request[\"image\"] = image\n  #        request[\"meta\"] = meta\n  # output: data_dict[\"image\"] = image\n  #         data_dict[\"meta\"] = meta\n  def GetDataDict(self, request, grpc_flag):\n    data_dict = dict()\n\n    # do the conversion for each key in predict_pb2.PredictRequest()\n    if (grpc_flag):\n      raw_image = tensor_util.MakeNdarray(request.inputs[\"client_input\"])\n    else:\n      raw_image = request[\"client_input\"]\n\n    image_np = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB)\n    image_np_expanded = np.expand_dims(image_np, axis=0)\n    frame_height, frame_width = raw_image.shape[:2]\n\n    data_dict[\"raw_image\"] = raw_image\n    data_dict[\"image_np_expanded\"] = image_np_expanded\n    data_dict[\"frame_height\"] = frame_height\n    data_dict[\"frame_width\"] = frame_width\n\n    return data_dict\n\n  # for an array of requests from a batch, convert them to a dict,\n  # where each key has a lit of values\n  # input: data_array = [{\"image\": image1, \"meta\": meta1}, {\"image\": image2, \"meta\": meta2}]\n  # output: batched_data_dict = {\"image\": [image1, image2], \"meta\": [meta1, meta2]}\n  def GetBatchedDataDict(self, data_array, batch_size):\n    if (len(data_array) != batch_size):\n      print(\"[Error] GetBatchedDataDict() batch size not matched...\")\n      return None\n    else:\n      batched_data_dict = dict()\n\n      # for each key in data_array[0], convert it to batched_data_dict[key][]\n      batched_data_dict[\"image_np_expanded\"] = data_array[0][\"image_np_expanded\"]\n      for data in data_array[1:]:\n        batched_data_dict[\"image_np_expanded\"] = np.append(batched_data_dict[\"image_np_expanded\"], data[\"image_np_expanded\"], axis = 0)\n\n      batched_data_dict[\"raw_image\"] = []\n      for data in data_array:\n        batched_data_dict[\"raw_image\"].append(data[\"raw_image\"])\n\n      batched_data_dict[\"frame_height\"] = []\n      for data in data_array:\n        batched_data_dict[\"frame_height\"].append(data[\"frame_height\"])\n\n      batched_data_dict[\"frame_width\"] = []\n      for data in data_array:\n        batched_data_dict[\"frame_width\"].append(data[\"frame_width\"])\n\n      return batched_data_dict\n\n  # input: batched_data_dict = {\"image\": [image1, image2], \"meta\": [meta1, meta2]}\n  # output: batched_result_dict = {\"bounding_boxes\": [[bb1_in_image1, bb2_in_image1], [bb1_in_image2]]}\n  def Apply(self, batched_data_dict, batch_size, istub):\n    if (batch_size != len(batched_data_dict[batched_data_dict.keys()[0]])):\n      print(\"[Error] Apply() batch size not matched...\")\n      return None\n    else:\n      batched_result_dict = dict()\n\n      request = predict_pb2.PredictRequest()\n      request.model_spec.name = 'face_detector'\n      request.model_spec.signature_name = 'predict_output'\n      request.inputs['image_tensor'].CopyFrom(\n        tf.contrib.util.make_tensor_proto(batched_data_dict[\"image_np_expanded\"], shape = batched_data_dict[\"image_np_expanded\"].shape))\n\n      result = istub.Predict(request, 10.0)\n\n      boxes = tensor_util.MakeNdarray(result.outputs['boxes'])\n      scores = tensor_util.MakeNdarray(result.outputs['scores'])\n      classes = tensor_util.MakeNdarray(result.outputs['classes'])\n      num_detections = tensor_util.MakeNdarray(result.outputs['num_detections'])\n\n      face_detector_output = []\n\n      for i in range(len(boxes)):\n        new_boxes_list = []\n        boxes_list = find_face_bounding_box(boxes[i], scores[i])\n        # print(\"len(boxes_list = %d)\" % len(boxes_list))\n\n        for j in range(len(boxes_list)):\n          box = boxes_list[j]\n\n        # # debug only...\n        # for i in range(4):\n        #   box = boxes_list[0]\n\n          ymin, xmin, ymax, xmax = box\n          (left, right, top, bottom) = (xmin * batched_data_dict[\"frame_width\"][0], xmax * batched_data_dict[\"frame_width\"][0], ymin * batched_data_dict[\"frame_height\"][0], ymax * batched_data_dict[\"frame_height\"][0])\n          normalized_box = \"%s-%s-%s-%s\" % (left, right, top, bottom)\n\n          new_boxes_list.append(normalized_box)\n\n        face_detector_output.append(new_boxes_list)\n\n      batched_result_dict[\"face_detector_output\"] = face_detector_output\n      batched_result_dict[\"raw_image\"] = batched_data_dict[\"raw_image\"]\n\n      return batched_result_dict\n\n  # input: batched_result_dict = {\"bounding_boxes\": [[bb1_in_image1, bb2_in_image1], [bb1_in_image2]]}\n  # output: batched_result_array = [{\"bounding_boxes\": [bb1_in_image1, bb2_in_image1]}, {\"bounding_boxes\": [bb1_in_image2]}]\n  def GetBatchedResultArray(self, batched_result_dict, batch_size):\n    if (batch_size != len(batched_result_dict[batched_result_dict.keys()[0]])):\n      print(\"[Error] GetBatchedResultArray() batch size not matched...\")\n      return None\n    else:\n      batched_result_array = []\n\n      for i in range(batch_size):\n        my_dict = dict()\n        my_dict[\"raw_image\"] = batched_result_dict[\"raw_image\"][i]\n        my_dict[\"face_detector_output\"] = batched_result_dict[\"face_detector_output\"][i]\n        my_dict[\"output_flag\"] = [1] + [0] * (len(batched_result_dict[\"face_detector_output\"][i]) - 1)\n        batched_result_array.append(my_dict)\n\n      return batched_result_array\n\n  # input: result_dict = {\"bounding_boxes\": [bb1_in_image1, bb2_in_image1]}\n  # output: result_list = [{\"bounding_boxes\": bb1_in_image1}, {\"bounding_boxes\": bb2_in_image1}]\n  def GetResultList(self, result_dict):\n    result_list = []\n\n    # for bb and dr, need to handle differently.\n\n    # # bb\n    # for i in range(len(result_dict[\"face_detector_output\"])):\n    #   if (result_dict[\"face_detector_output\"][i] is not None):\n    #     result_list.append({\"raw_image\": result_dict[\"raw_image\"][i], \"face_detector_output\": result_dict[\"face_detector_output\"][i]})\n\n    # # dr\n    # if (result_dict[\"face_detector_output\"] is not None):\n    #   result_list.append({\"raw_image\": result_dict[\"raw_image\"], \"face_detector_output\": result_dict[\"face_detector_output\"]})\n    for i in range(len(result_dict[\"face_detector_output\"])):\n      result_list.append({\"raw_image\": result_dict[\"raw_image\"], \"face_detector_output\": result_dict[\"face_detector_output\"][i], \"output_flag\": result_dict[\"output_flag\"][i]})\n\n    return result_list\n\n  # input: result = {\"bounding_boxes\": bb1_in_image1}\n  # output: next_request[\"boudning_boxes\"] = bb1_in_image1\n  def GetNextRequest(self, result, grpc_flag):\n    if (grpc_flag):\n      next_request = predict_pb2.PredictRequest()\n      next_request.inputs[\"raw_image\"].CopyFrom(\n        tf.make_tensor_proto(result[\"raw_image\"]))\n      next_request.inputs[\"face_detector_output\"].CopyFrom(\n        tf.make_tensor_proto(result[\"face_detector_output\"]))\n      next_request.inputs[\"output_flag\"].CopyFrom(\n        tf.make_tensor_proto(result[\"output_flag\"]))\n    else:\n      next_request = dict()\n      next_request[\"raw_image\"] = result[\"raw_image\"]\n      next_request[\"face_detector_output\"] = result[\"face_detector_output\"]\n      next_request[\"output_flag\"] = result[\"output_flag\"]\n    return next_request\n","sub_path":"modules_avatar/face_detector_d2_v2.py","file_name":"face_detector_d2_v2.py","file_ext":"py","file_size_in_byte":7526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"636045897","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom terminaltables import AsciiTable\n\n\ndef output_result_algorithm(result):\n    for i in enumerate(result):\n        print('Machine ', i[0] + 1, i[1])\n\n\ndef output_result_values(use, overfulfillment, deficit):\n    machine = []\n    for i in range (len(deficit)):\n        machine.append(i + 1)\n    resultTable = list(zip( ['Machine'] + machine, ['Sum'] + use, ['R(i)'] + overfulfillment, ['Delta Δ(i)'] +deficit))\n    table = AsciiTable(resultTable)\n    print(table.table)\n\n\n\n\ndef output_ideal(normvalue, k, C):\n    print('------------------')\n    print('C: ', C, '|')\n    print('------------------')\n    machine = []\n    for i in range(len(k)):\n        machine.append(i+1)\n    int_c = []\n    e = []\n    for i in normvalue:\n        int_c.append(i //1)\n        e.append(i % 1)\n\n    print('------------------')\n    print(' Delta (Σe): ', sum(e))\n    print('------------------')\n\n    resultTable = list(zip(['i'] + machine, ['Ki'] + k, ['Ci*'] + normvalue, ['[Ci*]'] + int_c, ['e']+ e))\n    table = AsciiTable(resultTable)\n    print(table.table)\n\ndef result_of_opt1(T, k, x, e):\n\n    xe = [0] * len(k)\n    kxe = [0] * len(k)\n    Tq = [0] * len(k)\n    for i in range(len(k)):\n        xe[i] = x[i] - e[i]\n        kxe[i] = k[i] * xe[i]\n        Tq[i] = T[i] + x[i]*k[i]\n    print('max(k(x-e)) = {} ' .format(max(kxe)))\n    resultTable = list(zip(['T'] + T, ['Ki'] + k, ['x'] + x,  ['e'] + e, ['x - e'] +xe , ['k(x-e)'] + kxe, ['T*'] + Tq))\n    table = AsciiTable(resultTable)\n    print(table.table)\n\ndef result_of_opt2(T, k, x, e):\n\n    xe = [0] * len(k)\n    kxe = [0] * len(k)\n    Tq = [0] * len(k)\n    for i in range(len(k)):\n        xe[i] = e[i] - x[i]\n        kxe[i] = k[i] * xe[i]\n        Tq[i] = T[i] + k[i]*x[i]\n    print('max| (k(e - x)) | = {} ' .format(max(list(map(abs, kxe)))))\n    resultTable = list(zip(['T'] + T, ['Ki'] + k, ['x'] + x,  ['e'] + e, ['e - x'] +xe , ['k(e-x)'] + kxe, ['T*'] + Tq))\n    table = AsciiTable(resultTable)\n    print(table.table)\n\n\n\ndef output_input_data(n, p, m, k):\n    print(\"n = {0}\\np = {1}\\nm = {2}\\nk = {3}\\n\".format(n, p, m, k))\n\n\ndef make_plot(data):\n    value = []\n    for i in data:\n        value.append(list(i.values()))\n\n    key = []\n    for i in data:\n        key.append(list(i.keys()))\n\n    data = pd.DataFrame(value)  # , index=title)\n    key = pd.DataFrame(key)\n\n    data = data.fillna(0)\n    key = key.fillna(0)\n\n    npdf = data.T.as_matrix()\n    npkey = key.T.as_matrix()\n\n    ind = np.arange(data.shape[0])\n\n    title = []\n    for j in range(data.shape[0] + 1):\n        numer = ('Machine {}'.format(j))\n        title.append(numer)\n\n    # print('npdf',npdf)\n\n\n\n\n\n\n    width = 1\n    bottom_size = [0] * data.shape[0]\n    fig, ax = plt.subplots()\n\n    for i in range(data.shape[1]):\n        for j in range(data.shape[0]):\n            p1 = plt.bar(ind[j], npdf[i, j], width, bottom=bottom_size[j], edgecolor='black')  # ,label=npkey[i,j])\n            # red_patch = mpatches.Patch(label=str(npkey[i, j]))\n            # plt.legend(handles=[red_patch])\n        bottom_size += npdf[i]\n    # ax.set_xticklabels(title)\n\n\n\n    # plt.legend(p1, npkey)\n    # red_patch = mpatches.Patch(label=npkey)\n    # plt.legend(handles=[red_patch])\n    plt.show()\n","sub_path":"outputData.py","file_name":"outputData.py","file_ext":"py","file_size_in_byte":3263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"51362873","text":"import torch\nimport torchvision.transforms as transforms\nfrom dataloaders.elec import Elec, ElecSampler\n\n\n# OPTS FOR COMPCARS\nBATCH_SIZE = 64\nTEST_BATCH_SIZE = 100\n\nEPOCHS= 15\nSTEP=5\nLR=0.001\nDECAY=0.000001\nMOMENTUM=0.9\nBANDWIDTH=0.5\n\n\nDATES=['2009','2010','2011','2012','2013','2014']\nVIEWS=['1','2','3','4','5']\n\nCLASSES = 2\n\nDOMAINS = [[x for x in range(30)]]\nACTUAL_TARGET_DOMAIN = 29\nSOURCE_DOMAINS = [x for x in range(29)]\nNUM_META = 1\n\nDATALIST='../../data/Elec2/'\n\n\ndef domain_converter(meta):\n    year = meta\n    if isinstance(year,tuple):\n        year = year[0]\n    # year = int(year)-int(DATES[0])\n    # viewpoint = int(viewpoint)-int(VIEWS[0])\n    # print(year)\n    return year #viewpoint*len(DATES)+year\n\n\ndef init_loader(bs, domains=[], shuffle=False, auxiliar= False, size=224, std=[0.229, 0.224, 0.225]):\n    data_transform=transforms.Compose([\n            transforms.Resize((size,size)),\n                      transforms.ToTensor(),\n            transforms.Normalize([0.485, 0.456, 0.406], std)\n         ])\n\n    dataset = Elec(DATALIST,domains=domains)\n    print(\"Dataloader with %d samples\"%len(dataset))\n\n    if not auxiliar:\n        return torch.utils.data.DataLoader(dataset, batch_size=bs, drop_last=False,num_workers=4, shuffle=shuffle)\n\n    else:\n        return torch.utils.data.DataLoader(dataset, batch_size=bs, drop_last=False,num_workers=4, sampler=ElecSampler(dataset,bs))\n\n\ndef compute_edge(x,dt,idx, self_connection = 1.):\n    x = x/30\n    dt = dt/30\n    edge_w=torch.exp(-torch.pow(torch.norm(x.view(1,-1)-dt,dim=1),2)/(2.*BANDWIDTH))\n    edge_w[idx]=edge_w[idx]*self_connection\n    return edge_w/edge_w.sum()\n\n\ndef get_meta_vector(meta):\n    angle = meta\n    return torch.FloatTensor(angle)\n","sub_path":"src/adagraph/configs/config_elec.py","file_name":"config_elec.py","file_ext":"py","file_size_in_byte":1723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"65572708","text":"\r\nfrom common import config\r\nimport sqlite3\r\n\r\ndbfile = config.getdbfile()\r\n\r\ndef opendb():\r\n    conn = sqlite3.connect(dbfile)\r\n    c = conn.cursor()\r\n    return conn, c\r\n\r\ndef quote_fix(string):\r\n    new_string = \"\"\r\n    for i in range(len(string)):\r\n        if string[i] == \"'\":\r\n            new_string += \"'\"\r\n        new_string += string[i]\r\n    return new_string\r\n\r\ndef getQuizQuestion(playerid, competition_name):\r\n    con, cur = opendb()\r\n    \r\n    strSql = \"SELECT QId, Quesdesc, Choice1,Choice2,Choice3,Choice4,ValidTill,Points,NegativePoints FROM QuestionBank WHERE CompetitionName = 'Lockdown-2020 Quiz' AND datetime(ValidTill) > datetime('now') AND QId not in (select QId from PlayerResponse WHERE PlayerResponse.QId = QuestionBank.QId AND PlayerResponse.PlayerId = '\" + playerid + \"') ORDER BY datetime(ValidTill) ASC LIMIT 1\"\r\n    \r\n    #print (strSql)\r\n\r\n    cur.execute(strSql)\r\n\r\n    rows = cur.fetchall()\r\n    \r\n    cur.close()\r\n    con.close()\r\n\r\n    qid = \"\"\r\n    qdesc = \"\"\r\n    ch1 = \"\"\r\n    ch2 = \"\"\r\n    ch3 = \"\"\r\n    ch4 = \"\"\r\n    validtill = \"\"\r\n    points = \"\"\r\n    negativepoints = \"\"\r\n\r\n    rowDict = {}\r\n    \r\n    for item in rows:\r\n        qid = item[0]\r\n        qdesc = item[1]\r\n        ch1 = item[2]\r\n        ch2 = item[3]\r\n        ch3 = item[4]\r\n        ch4 = item[5]\r\n        validtill = item[6]\r\n        points = item[7]\r\n        negativepoints = item[8]\r\n    \r\n        rowDict = {\"qid\": qid,\r\n                    \"qdesc\": qdesc,\r\n                    \"ch1\": ch1,\r\n                    \"ch2\": ch2,\r\n                    \"ch3\": ch3,\r\n                    \"ch4\": ch4,\r\n                    \"validtill\": validtill,\r\n                    \"points\": points,\r\n                    \"negativepoints\": negativepoints\r\n                    }\r\n    \r\n    return rowDict\r\n\r\ndef createQuestion(qno, ques_desc, choice1, choice2, choice3, choice4, choice_right, valid_till):\r\n    conn, cur = opendb()\r\n    \r\n    sql = \"INSERT INTO QuestionBank VALUES (\" + str(qno) + \",'\" + quote_fix(str(choice1)) + \"','\" + quote_fix(str(choice2)) + \"','\" + quote_fix(str(choice3)) + \"','\" + quote_fix(str(choice4)) + \"','\" + quote_fix(str(choice_right)) + \"','\" + quote_fix(str(ques_desc)) + \"', datetime('\" + str(valid_till) + \"'))\"\r\n    #print(sql)\r\n\r\n    cur.execute(str(sql))\r\n    \r\n    conn.commit()\r\n\r\n    cur.close()\r\n    conn.close()\r\n\r\ndef CheckLogin(loginid):\r\n    sql = \"SELECT name, competitionname, password FROM Players WHERE mobile = \" + str(loginid)\r\n    \r\n    conn, cur = opendb()\r\n\r\n    cur.execute(sql)\r\n    rows = cur.fetchall()\r\n\r\n    cur.close()\r\n    conn.close()\r\n\r\n    if len(rows) > 0:\r\n        playername = rows[0][0]\r\n        competition = rows[0][1]\r\n        playerpwd = rows[0][2]\r\n    else:\r\n        playername = \"\"\r\n        competition = \"\"\r\n        playerpwd = \"\"\r\n    \r\n    return playername, competition, playerpwd\r\n\r\ndef registerPlayer(name, email, passwd, mobileNo, competitionname):\r\n    isRegistrationOK = False\r\n    try:\r\n        conn, cur = opendb()\r\n    \r\n        sql = \"INSERT INTO Players VALUES ('\" + str(name) + \"','\" + str(email) + \"','\" + str(passwd) + \"', \" + mobileNo + \", '\" + str(competitionname) + \"')\"\r\n        #print(sql)\r\n\r\n        cur.execute(str(sql))\r\n\r\n        conn.commit()\r\n\r\n        cur.close()\r\n        conn.close()\r\n\r\n        isRegistrationOK = True\r\n    except Exception as ex:\r\n        print(ex)\r\n        #pass\r\n    \r\n    return isRegistrationOK\r\n\r\ndef getCompetitions():\r\n    sql = \"SELECT name FROM competition WHERE datetime('now') < datetime(EndingOn)\"\r\n    \r\n    conn, cur = opendb()\r\n\r\n    cur.execute(sql)\r\n    dbrows = cur.fetchall()\r\n\r\n    cur.close()\r\n    conn.close()\r\n\r\n    return dbrows\r\n\r\ndef isResponded(player_id, qid):\r\n    conn, cur = opendb()\r\n    \r\n    sql = \"SELECT * FROM PlayerResponse WHERE PlayerId = \" + player_id + \" AND QId = \" + qid\r\n\r\n    cur.execute(sql)\r\n    rows = cur.fetchall()\r\n    \r\n    conn.commit()\r\n\r\n    cur.close()\r\n    conn.close()\r\n\r\n    isResponded = False\r\n\r\n    if len(rows) > 0:\r\n        isResponded = True\r\n    \r\n    return isResponded\r\n\r\ndef registerResponse(player_id, competition_name, qid, qdesc, ans, resptime, points):\r\n    isRegisterOK = True\r\n    try:\r\n        conn, cur = opendb()\r\n        \r\n        sql = \"INSERT INTO PlayerResponse VALUES(\"\r\n        sql += player_id + \", \"\r\n        sql += \"'\" + competition_name + \"', \"\r\n        sql += qid + \", \"\r\n        sql += \"'\" + qdesc + \"', \"\r\n        sql += \"'\" + ans + \"', \"\r\n        sql += \"'\" + str(resptime) + \"', \"\r\n        sql += points + \")\"\r\n\r\n        cur.execute(sql)\r\n        rows = cur.fetchall()\r\n        \r\n        conn.commit()\r\n\r\n        cur.close()\r\n        conn.close()\r\n    except Exception as ex:\r\n            isRegisterOK = False\r\n    \r\n    return isRegisterOK\r\n\r\ndef getScore(playerid):\r\n    conn,  cur = opendb()\r\n    sql = \"SELECT name, pr.CompetitionName, pr.Question, Points FROM PlayerResponse pr, Players p WHERE p.mobile = \" + playerid + \" AND pr.PlayerId = p.mobile ORDER BY Points DESC\"\r\n    cur.execute(sql)\r\n    rows = cur.fetchall()\r\n    cur.close()\r\n    conn.close()\r\n    return rows\r\n\r\ndef getRanks():\r\n    conn,  cur = opendb()\r\n    sql = \"SELECT name, pr.competitionname as competionname, sum(points) as totalpoints, \\\r\n        RANK () OVER ( \\\r\n        ORDER BY sum(points) DESC \\\r\n        ) pointrank \\\r\n        FROM PlayerResponse pr, Players p \\\r\n        WHERE pr.PlayerId = p.mobile \\\r\n        GROUP BY name, pr.competitionname \\\r\n        ORDER BY sum(Points) DESC\"\r\n\r\n    cur.execute(sql)\r\n    rows = cur.fetchall()\r\n    cur.close()\r\n    conn.close()\r\n    return rows\r\n\r\ndef getAccount(playerid):\r\n    conn,  cur = opendb()\r\n    sql = \"SELECT name, email, mobile, competitionname FROM Players WHERE mobile = \" + str(playerid)\r\n    cur.execute(sql)\r\n    rows = cur.fetchall()\r\n    cur.close()\r\n    conn.close()\r\n\r\n    rowDict = {}\r\n\r\n    if len(rows) > 0:\r\n        rowDict = {\"playername\": rows[0][0],\r\n            \"email\": rows[0][1],\r\n            \"playerid\": rows[0][2],\r\n            \"competitionname\": rows[0][3]\r\n            }\r\n    \r\n    return rowDict\r\n\r\ndef updateProfile(mobilNo, playername, email, competition):\r\n    isUpdateOK = True\r\n\r\n    try:\r\n        sql = \"UPDATE Players SET \\\r\n                name = '\" + playername + \"', \\\r\n                email = '\" + email + \"', \\\r\n                competitionname = '\" + competition + \"' \\\r\n            WHERE mobile = \" + str(mobilNo)\r\n        conn,  cur = opendb()\r\n        cur.execute(sql)\r\n        rows = cur.fetchall()\r\n        \r\n        conn.commit()\r\n\r\n        cur.close()\r\n        conn.close()\r\n    except Exception as ex:\r\n        isUpdateOK = False\r\n\r\n    return isUpdateOK\r\n\r\n\r\ndef UpdatePassword(mobileNo, npasswd1):\r\n    isUpdateOK = True\r\n\r\n    try:\r\n        sql = \"UPDATE Players SET \\\r\n                password = '\" + npasswd1 + \"' \\\r\n            WHERE mobile = \" + str(mobileNo)\r\n\r\n        conn,  cur = opendb()\r\n        cur.execute(sql)\r\n        rows = cur.fetchall()\r\n        \r\n        conn.commit()\r\n\r\n        cur.close()\r\n        conn.close()\r\n    except Exception as ex:\r\n        print (ex)\r\n        isUpdateOK = False\r\n\r\n    return isUpdateOK\r\n\r\ndef getAnswer(competition_name, qid):\r\n    conn,  cur = opendb()\r\n    sql = \"SELECT ChoiceAnswer FROM QuestionBank WHERE Qid = \" + str(qid) + \" AND CompetitionName = '\" + competition_name + \"'\"\r\n    cur.execute(sql)\r\n    rows = cur.fetchall()\r\n    cur.close()\r\n    conn.close()\r\n\r\n    rowDict = {}\r\n    answer = \"\"\r\n\r\n    if len(rows) > 0:\r\n        answer = rows[0][0]\r\n    \r\n    return answer\r\n","sub_path":"quiz/repositories/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":7522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"194252186","text":"# Script is tested on OS X 10.12\n# YOUR MILEAGE MAY VARY\n\nimport sys\nimport shutil\nimport subprocess\nfrom pathlib import Path\n\nsource_paths = [\n    Path(\"CMSIS/Core/Include\"),\n    Path(\"CMSIS/DSP/Include\"),\n    Path(\"CMSIS/DSP/Source\")\n]\n\n# clone the repository\nif not Path(\"CMSIS_5\").exists():\n    subprocess.run(\"git clone https://github.com/arm-software/CMSIS_5.git\", shell=True)\n\n# remove the sources in this repo\nif Path(\"CMSIS\").exists():\n    shutil.rmtree(\"CMSIS\")\n\nfor path in source_paths:\n    # create path and parents\n    path.parent.mkdir(parents=True, exist_ok=True)\n    shutil.copytree(str(\"CMSIS_5\" / path), str(path))\n","sub_path":"update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"612945676","text":"'''\n多态： 一种事务的多种形态\n\n\n'''\n\nfrom animal import Animal\nfrom person import Person\n\n\ndef main ( ) :\n\ttom = Animal ( \"Tom\" )\n\tjerry = Animal ( \"jerry\" )\n\t# tom.eat ( )\n\t# jerry.eat ( )\n\tper = Person ( )\n\tper.feedAnimal ( tom )\n\tper.feedAnimal ( jerry )\n\n\nif __name__ == '__main__' :\n\tmain ( )\n","sub_path":"OPP/多态/多态.py","file_name":"多态.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"167765820","text":"# coding: utf-8\n\nfrom __future__ import absolute_import, print_function, unicode_literals\n\nfrom pip._vendor.distlib.util import parse_requirement\n\nfrom medikit.events import subscribe\nfrom . import Feature, SUPPORT_PRIORITY\n\n\nclass SphinxFeature(Feature):\n    class Config(Feature.Config):\n        def __init__(self):\n            self._theme = None\n\n        def set_theme(self, theme):\n            self._theme = parse_requirement(theme)\n\n        def get_theme(self):\n            return self._theme\n\n    @subscribe('medikit.feature.python.on_generate')\n    def on_python_generate(self, event):\n        event.config['python'].add_requirements(dev=[\n            'sphinx >=1.6,<2.0',\n        ])\n\n        theme = event.config['sphinx'].get_theme()\n        if theme:\n            event.config['python'].add_requirements(dev=[theme.requirement])\n\n    @subscribe('medikit.feature.make.on_generate', priority=SUPPORT_PRIORITY)\n    def on_make_generate(self, event):\n        makefile = event.makefile\n\n        makefile['SPHINX_BUILD'] = '$(PYTHON_DIRNAME)/sphinx-build'\n        makefile['SPHINX_OPTIONS'] = ''\n        makefile['SPHINX_SOURCEDIR'] = 'docs'\n        makefile['SPHINX_BUILDDIR'] = '$(SPHINX_SOURCEDIR)/_build'\n\n        makefile.add_target(\n            '$(SPHINX_SOURCEDIR)',\n            '''\n            $(SPHINX_BUILD) -b html -D latex_paper_size=a4 $(SPHINX_OPTIONS) $(SPHINX_SOURCEDIR) $(SPHINX_BUILDDIR)/html\n        ''',\n            deps=('install-dev', ),\n            phony=True\n        )\n\n\n__feature__ = SphinxFeature\n","sub_path":"medikit/feature/sphinx.py","file_name":"sphinx.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"73485724","text":"\"\"\"Access functions to read spectra from data files.\n\nThe following structure is expected:\n    - Reading functions for each data file type defined in specific modules\n    - Modules named as {datatype}.py, e.g. swan.py\n    - Functions named as read_{dataname}, e.g. read_swan\n\nAll functions defined with these conventions will be dynamically\nimported at the module level\n\n\"\"\"\nimport xarray as xr\n\nfrom wavespectra.core.attributes import attrs\n\n\nto_keep = {\n    attrs.SPECNAME,\n    attrs.WSPDNAME,\n    attrs.WDIRNAME,\n    attrs.DEPNAME,\n    attrs.LONNAME,\n    attrs.LATNAME,\n}\n\n\ndef chunks_dict(chunks, mapping):\n    \"\"\"Get the chunks dict either from keys or values in mapping.\n\n    Handy function to allow specifying chunks using dim names from either original\n        dataset or from wavespectra.\n\n    \"\"\"\n    if not mapping:\n        mapping = {key: key for key in chunks.keys()}\n    _chunks = {}\n    for key, val in chunks.items():\n        if key in mapping.keys():\n            dim = key\n        elif key in mapping.values():\n            dim = list(mapping.keys())[list(mapping.values()).index(key)]\n        else:\n            raise KeyError(\n                f\"Dim '{key}' not in chunks, supported dims are: \"\n                f\" {list(mapping.keys()) + list(mapping.values())}\"\n            )\n        _chunks.update({dim: val})\n    return _chunks\n\n\ndef open_netcdf_or_zarr(filename_or_fileglob, file_format, mapping={}, chunks={}):\n    \"\"\"Read spectra dataset in either netcdf or zarr format.\n\n    Args:\n        - filename_or_fileglob (str): filename or fileglob specifying multiple\n          files to read.\n        - file_format (str): format of file to open, one of `netcdf` or `zarr`.\n        - mapping (dict): coordinates mapping from original dataset to wavespectra.\n        - chunks (dict): chunk sizes for dimensions in dataset. By default\n          dataset is loaded using single chunk for all dimensions (see\n          xr.open_mfdataset documentation).\n\n    Returns:\n        - dset (Dataset): spectra dataset object read from ww3 file.\n\n    \"\"\"\n    # Allow chunking using wavespctra names\n    _chunks = chunks_dict(chunks, mapping)\n    if file_format == \"netcdf\":\n        dset = xr.open_mfdataset(\n            filename_or_fileglob, chunks=_chunks, combine=\"by_coords\"\n        )\n    elif file_format == \"zarr\":\n        dset = xr.open_zarr(filename_or_fileglob, consolidated=True, chunks=_chunks)\n    else:\n        raise ValueError(\"file_format must be one of ('netcdf', 'zarr')\")\n    return dset\n","sub_path":"wavespectra/input/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"248397934","text":"import logging\nfrom collections import namedtuple\nfrom datetime import datetime\n\nfrom recordclass import recordclass\n\nfrom AgentExternals import *\nfrom BaseAgent import BaseAgent\nfrom captureAgents import CaptureAgent\nfrom game_code.capture import SIGHT_RANGE\n\ntimestamp='{:%m-%d_%H.%M.%S}'.format(datetime.now())\nlogger = logging.getLogger(\"base\")\nlogger.setLevel(logging.DEBUG)\nlogger.addHandler(logging.FileHandler(\"logs/all_\"+timestamp+\".txt\"))\n\n#currently not in use, will use later if needed\n# off_log = logging.getLogger(\"base.offense\")\n# offense_handler=logging.FileHandler(\"logs/offense_\"+timestamp+\".txt\")\n# off_log.addHandler(offense_handler)\n#\n# def_log = logging.getLogger(\"base.defense\")\n# defense_handler=logging.FileHandler(\"logs/defense_\"+timestamp+\".txt\")\n# def_log.addHandler(defense_handler)\n\n\ndef createTeam(firstIndex, secondIndex, isRed,\n               first='UtilAgent', second='UtilAgent'):\n    \"\"\"\n    This function should return a list of two agents that will form the\n    team, initialized using firstIndex and secondIndex as their agent\n    index numbers.  isRed is True if the red team is being created, and\n    will be False if the blue team is being created.\n    \"\"\"\n\n    # The following line is an example only; feel free to change it.\n    return [eval(first)(firstIndex), eval(second)(secondIndex)]\n\n\nclass HLA:\n    #due to dependency issues, set the members of HLA inside HardwiredAgent\n    pass\n    # set each of the HLAs to the method in HardwiredAgent that defines the behavior in that case\n    # calling each of these should just require passing in the current object as the self parameter\n\n\nclass UtilAgent(object, BaseAgent):\n    def registerInitialState(self, gameState):\n        super(UtilAgent).registerInitialState(gameState)\n        self.offensive = self.data.getOffensive()\n        if self.offensive:\n            self.logger = logger\n        else:\n            self.logger = logger\n\n\n        # set up distribution list that will hold belief distributions for agents\n\n    def chooseAction(self, gameState):\n        self.logger.info(\"Beginning new move\")\n        self.knownEnemies = {}  # enemy position, key is enemy index\n        self.data.logFood(gameState)\n        self.updatePosDist(gameState)\n        if self.getScaredMovesRemaining(gameState) > 2:\n            self.offensive = True\n        elif self.getScaredMovesRemaining(gameState) == 1:\n            self.offensive = self.data.getOffensive()\n        # print \"infer time: \", time()-startTime\n        self.displayDistributionsOverPositions(self.data.mDistribs)\n        action = self.action_search(gameState)\n        new_pos = game.Actions.getSuccessor(self.getMyPos(gameState), action)\n        if new_pos in self.knownEnemies.values():\n            for e, p in self.knownEnemies.items():\n                if new_pos==p:\n                    self._capturedAgent(e)\n        return action\n\n    def action_search(self, gamestate):\n        #how much to discount future utility by, so if a state is 3 in the future, its utility is UTIL_DISCOUNT**3 * calculated_utility\n        UTIL_DISCOUNT = .8\n\n        #maximum depth of search\n        MAX_SEARCH_DEPTH = 3\n        #queue that holds future states to visit\n        toVisit = util.Queue()\n        #each item on toVisit is a tuple (starting action, gamestate, beliefs, depth, prev_action)\n        #beliefs is the belief distribution of enemy positions at that point\n        #prev action is the action taken to get to this state, to avoid checking the immediately previous position again\n        State = namedtuple(\"State\", [\"starting_action\", \"gamestate\", \"beliefs\", \"depth\", \"prev_action\"])\n\n\n        #dictionary to track the utility of each state spawned off each starting action\n        #action_utils[action] is a list of the utilities of all the states generated from this starting action\n        action_utils = {}\n\n        #create initial states\n        legal_a=gamestate.getLegalActions(self.index)\n        for action in legal_a:\n            # if action == game.Directions.STOP and len(legal_a)>2:\n            #     continue #only consider stop if there is only 1 alternative\n            newgs = gamestate.generateSuccessor(self.index, action)\n            toVisit.push(State(action, newgs, self.data.mDistribs, 0, action))\n            action_utils[action]=[] #initialize action_utils for this action\n\n        while not toVisit.isEmpty(): #while toVisit still has stuff in it\n            #new \"State\" tuple\n            ns = toVisit.pop()\n            #calculate the utility of this state and add it to action utils\n            self.logger.debug(\"\\nconsidering position %s\", self.getMyPos(ns.gamestate))\n            nsu = self.getUtility(ns.gamestate, ns.beliefs)\n            action_utils[ns.starting_action].append(nsu * UTIL_DISCOUNT**ns.depth)\n\n            #only explore further if doesn't excede max search depth\n            if ns.depth<MAX_SEARCH_DEPTH:\n                successor_actions = ns.gamestate.getLegalActions(self.index)\n                if len(successor_actions)>1:#if there are several subsequent actions, remove the 'double back' option\n                    successor_actions.remove(game.Actions.reverseDirection(ns.prev_action))\n                #the above code should, in the case that we went north to get here, and we can go either north or south\n                #remove the option \"south\" from legal actions, so we do not needlessly double back\n                #if however we entered a dead end, and we can thus only double back, it allows that to occur\n                nsb = self.genNewBeliefs(ns.beliefs, ns.gamestate)  # generate the new beliefs for this state\n                for action in successor_actions: #add all legal successors\n                    newgs = ns.gamestate.generateSuccessor(self.index, action)\n\n                    toVisit.push(State(ns.starting_action, newgs, nsb, ns.depth+1, action))\n\n        #TODO: pick action based on highest average or highest sum?\n        avg_action_util = [(u, sum(action_utils[u])/len(action_utils[u])) for u in action_utils.keys()]\n        #TODO: figure out what to do here\n        #trim all lists to be the same length\n        #otherwise the action with the fewest possibilities will often win, as it's highest utility course will\n        #pull the average more than that of the other courses\n        min_length = min(len(x) for x in action_utils.values())\n\n        new_pairs = {a : sorted(v, reverse=True)[:min_length] for a, v in action_utils.items()}\n        avg_new_pair = [(u, sum(new_pairs[u])/len(new_pairs[u])) for u in new_pairs.keys()]\n        best_action = max(new_pairs.keys(), key = lambda x:  sum(new_pairs[x])/len(new_pairs[x]))\n        self.logger.info(\"Action: %s \\n\", best_action)\n        return best_action\n\n\n    #######################################\n    #        Belief manipulation?\n    #######################################\n\n    def genNewBeliefs(self, oldBeliefs, gamestate):\n        ### Limitations\n        #Doesn't account for other players on this team moving\n        #very basic move logic\n        #doesn't update position in gamestate\n        #TODO: Idea for updating pos in gs: have it move to a position with the assumed probability it goes there\n            # TODO: (see position distribution, if goes .8 it goes to a place, it moves there w/ prob .8)\n        ####\n        nb = [None]*len(oldBeliefs)\n        for i in self.getOpponents(gamestate):\n            nb[i]=self.positionMoveInfer(i, gamestate, oldBeliefs[i])\n\n        return nb\n    #######################################\n    #        Features/Weights\n    #######################################\n\n    # <editor-fold desc=\"Features/Weights stuff\">\n    Features = recordclass(\"Features\", [\"e_ghost_dist\", \"e_pac_dist\", \"food_dist\", \"capsule_dist\", \"score\",\n                                       \"my_scared_moves\", \"enemy_scared_moves\", \"my_food\", \"home_dist\", \"enemy_food\",\n                                       \"safe_path_to_home\", \"e_dist_to_food\"])\n\n    #TODO: add the following features:\n        #enemy distance to food\n        #enemy distance to capsule\n\n    def getUtility(self, gamestate, belief_distrib):\n\n        weights = self.getWeights(gamestate)\n        features = self.getFeatures(gamestate, belief_distrib)\n        util = 0\n        for w, f, n in zip(weights, features, weights._fields):\n            delta = w(gamestate, f)\n            util+= delta\n            self.logger.debug(\"Feature %s with value %s changed utility %f to new total %f\", n, f, delta, util)\n        return util\n\n    #called weights, but really each entry is a function that calculates the utility from the given feature\n    def getWeights(self, gamestate):\n        #fill with dummy values\n        weights = UtilAgent.Features(*range(len(UtilAgent.Features._fields)))\n\n        def eghostutil(gs, ghost_dists):\n            if gamestate.getAgentState(self.index).isPacman and ghost_dists:\n                return min(-1./d for d in ghost_dists if d>0)\n            else:\n                return 0\n\n\n        def epacutil(gs, pac_dists):\n            u=0\n            for d in pac_dists:\n                if d>0:\n                    u+=1./d\n                else:\n                    u+=1.3\n            if len(pac_dists)==0:\n                u+=1.3\n            if self.offensive:\n                return .5*u\n            else:\n                return u\n\n        def efoodutil(gs, efl):\n            return sum(-.8*ef for ef in efl)\n\n        def home_dist(gs, d):\n            mfood =  self.getFoodEatenBySelf(gamestate)\n            if mfood>0:\n                return .5*mfood*.8/d\n            else:\n                return 0\n\n        def score_util(gs, s):\n            if s!=0:\n                return 1.8*s\n            else:\n                return -.5\n\n        def food_dist_u(gs, d):\n            if d>0:\n                if self.getScore(gs)<=0:\n                    return 1.7/d\n                else:\n                    return 1.3/d\n            else:\n                return 0\n\n        weights.e_ghost_dist = eghostutil\n        weights.e_pac_dist = epacutil\n        weights.food_dist = food_dist_u\n        weights.capsule_dist = lambda gs, d : .8/d if d>0 else 0\n        weights.score = score_util\n        weights.my_scared_moves = lambda gs, m : 0\n        weights.enemy_scared_moves = lambda gs, m: .2*m\n        weights.my_food = lambda gs, food: 1.2*food\n        weights.home_dist = home_dist\n        weights.enemy_food = efoodutil\n        weights.safe_path_to_home = lambda gs, b: 1 if b else -1\n\n        return weights\n\n    #features of the current position\n    def getFeatures(self, gamestate, belief_distrib):\n        #flesh with dummy values\n        feat = UtilAgent.Features(*range(len(UtilAgent.Features._fields)))\n\n        e_ghost_dists=[]\n        e_pac_dists=[]\n        e_food=[]\n        #do calculations that occur for each enemy\n        for enemy in self.getOpponents(gamestate):\n            # for each enemy, calculate the distance to them, then add that distance to the list corresponding to their mode\n            dist = self.getMyDistanceToEnemy(gamestate, enemy, belief_distrib)\n            if gamestate.getAgentState(enemy).isPacman:\n                e_pac_dists.append(dist)\n            else:\n                e_ghost_dists.append(dist)\n\n            e_food.append(gamestate.getAgentState(enemy).numCarrying)\n\n\n\n        feat.e_ghost_dist = e_ghost_dists\n        feat.e_pac_dist = e_pac_dists\n\n        feat.food_dist = self.calcFoodDist(gamestate)\n\n        feat.capsule_dist = self.getDistToNearestCapsule(gamestate)\n        feat.score = self.getScore(gamestate)\n        feat.my_scared_moves = self.getScaredMovesRemaining(gamestate)\n        feat.enemy_scared_moves = self.getScaredMovesRemaining(gamestate) #TODO: factor this out into above loop?\n        feat.my_food = self.getFoodEatenBySelf(gamestate)\n        feat.enemy_food = e_food\n\n        if self.onMySide(self.getMyPos(gamestate)):\n            feat.safe_path_to_home=True\n            feat.home_dist=0\n        else:\n            path_home = self.pathToHome(gamestate, belief_distrib)\n            if path_home:\n                feat.safe_path_to_home = True\n                feat.home_dist = len(path_home)\n            else:\n                #no safe path home\n                feat.safe_path_to_home = False\n                feat.home_dist = -1 #set to -1 to indicate bad value\n        return feat\n    # </editor-fold>\n\n\n    #######################################\n    #        Pathfinding Functions\n    #######################################\n\n    # <editor-fold desc=\"Pathfinding Stuff\">\n\n\n\n        # called when the agent should procede home\n\n    def pathToHome(self, gamestate, beliefs):\n\n        # find shortest path to home\n        # generate exclusion zones around the enemies, find shortest path that doesn't go through an exclusion zone\n\n        ez = self.genExclusionZones(gamestate, beliefs)\n\n        goHomeProb = PacmanPosSearch(self.getMyPos(gamestate), self.data.borderPositions, gamestate, ez)\n\n        def heuristic(state, problem):\n            if state in self.data.borderDistances:\n                return self.data.borderDistances[state]\n            else:\n                return 0\n\n        path, _ = search.astar(goHomeProb, heuristic)\n        if not path:\n            # # relax the exclusion zones to only be where the enemy is\n            # ghp2 = PacmanPosSearch(self.getMyPos(gamestate), self.data.borderPositions, gamestate,\n            #                        list(self.knownEnemies.values()))\n            # path, _ = search.astar(ghp2, heuristic)\n            # if not path:\n            #     # still no path home, probably screwed, just stop and pray\n            #     path = [game.Directions.STOP]  # just wait, because can't go anywhere\n            # # hollup\n            pass\n        return path # return the first action in the path\n\n    # </editor-fold>\n\n\n\n\n\n\n","sub_path":"UtilTeam.py","file_name":"UtilTeam.py","file_ext":"py","file_size_in_byte":13839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"218908046","text":"import sys\nimport socket\nimport time\nimport hashlib\nimport select\n\n# Get arguments from command line\nserverIpAddress = sys.argv[1]\nserverUDPPort = int(sys.argv[2])\nserverTCPPort = int(sys.argv[3])\nsenderUDPPort = int(sys.argv[4])\nsenderTCPPort = int(sys.argv[5])\n\n# Create TCP Socket\nTCPSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n# Bind to sender TCP Port\nTCPSocket.bind(('', senderTCPPort))\n\n# Connect to server TCP Port\nTCPSocket.connect((serverIpAddress, serverTCPPort))\n\n# File path of file which will be sent by TCP\nTCPPath = \"./transfer_file_TCP.txt\"\n\n# Open File\nTCPFile = open(TCPPath, 'r')\n\n# Firstly read 970 bytes from File\nTCPText = TCPFile.read(970)\n\n# Do until the end of file\nwhile TCPText != \"\":\n\n    # Starting time before send packet to server\n    start = time.time()\n\n    # Send time information and 970 bytes from TCP File to server\n    TCPSocket.send((f'{start:<30}' + TCPText).encode())\n\n    # Again read 970 bytes from File\n    TCPText = TCPFile.read(970)\n\n# Close TCP File\nTCPFile.close()\n\n# Close TCP Socket\nTCPSocket.close()\n\n\n\"\"\"UDP PART\"\"\"\n# Create UDP Socket\nUDPSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\n# Bind to sender UDP Port\nUDPSocket.bind(('', senderUDPPort))\n\n# Connect to server UDP Port\nUDPSocket.connect((serverIpAddress, serverUDPPort))\n\n# File path of file which will be sent by UDP\nUDPPath = \"./transfer_file_UDP.txt\"\n\n# Open File\nUDPFile = open(UDPPath, 'r')\n\n# Variable for keeping number of packets\ni = 0\n\n# Array for keeping packets\npaketler = []\n\n# Creating packets\nwhile True:\n    # Read 928 character each time and put them to packet and packets array\n    text = UDPFile.read(928)\n    if not text:\n        break\n    package = f'{i:<10}' + text\n    i += 1\n    paketler.append(package)\n\n# Total Count of packets\ntotalPacketCount = len(paketler)\n\n# Next packet which will be sent\nnextPacket = 0\n\n# Starting of the window which shows the first element of Window\nstartingOfWindow = 0\n\n# Windows size normally fixed to 5, but as implementation based it can be decreased\nwindowSize = min(totalPacketCount - startingOfWindow, 5)\n\n# Variable for count how many packets resend\nresend = 0\n\n# Do sending till end of the packets array\nwhile startingOfWindow < totalPacketCount:\n    # variable for calculating is timeout happened or not\n    timeout = time.time()\n\n    # If the packet which wanted to be sent to server is can be locate in window, so send it\n    while nextPacket < startingOfWindow + windowSize:\n        start = time.time()\n        msg = f'{start:<30}' + paketler[nextPacket]\n        md5 = hashlib.md5(msg.encode()).hexdigest()\n        UDPSocket.send((f'{md5:<32}' + msg).encode())\n        nextPacket += 1\n\n    # I used select module for getting input from server with timeout feature\n    UDPSocket.setblocking(0)\n\n    # If the timeout happened while waiting for message from server,\n    # or the message is received from server,\n    # do necessary calculations\n    Halo = select.select([UDPSocket], [], [], 1)\n    if Halo[0]:\n        response = UDPSocket.recv(20)\n        num = int(response[4:])\n        # if acknowledged packet is in window, increase starting of window\n        if num >= startingOfWindow and time.time() - timeout < 1:\n            startingOfWindow = num + 1\n            windowSize = min(totalPacketCount - startingOfWindow, 5)\n    # Timeout happened resend packets\n    if time.time() - timeout >= 1:\n        resend += windowSize\n        nextPacket = startingOfWindow\n# Send message for informing it is over with file\nUDPSocket.send(''.encode())\n# Send total count of resended packets\nUDPSocket.send(str(resend).encode())\n\n# Close socket and File\nUDPSocket.close()\nUDPFile.close()\n","sub_path":"CENG435 - Data Communications and Networking/THE2/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"23664012","text":"# extract_pagelist.py\n\n# This opens a zipfile and reads all the pages;\n# it returns a list of pages, where each page is\n# a list of lines.\n\n# To specify a zipfile, the function extract()\n# requires a \"suffix\": this is the part of the\n# volume id after the first period.\n\nfrom zipfile import ZipFile\nimport os, sys\n\ndef extract(suffix, startpage, stoppage):\n\t''' Reads pages in the zipfile named by suffix.\n\tFirst it sorts the pages to put them in integer order.\n\tIt only reads pages after startpage, and stops at\n\tstoppage.\n\t'''\n\n\tdatadir = '/media/secure_volume/clm/'\n\tzippath = datadir + suffix + '.zip'\n\n\tpages = []\n\n\twith ZipFile(zippath) as z:\n\t\tpageinfo = z.infolist()\n\n\t\tpagetuples = []\n\n\t\tfor p in pageinfo:\n\t\t\tsequence = p.filename.split('/')[-1].replace('.txt', '')\n\t\t\tif '_' in sequence:\n\t\t\t\tsequence = sequence.split('_')[-1]\n\t\t\ttry:\n\t\t\t\tsequence = int(sequence)\n\t\t\texcept:\n\t\t\t\tprint('non-integer filename: ', sequence)\n\t\t\tpagetuples.append((sequence, p.filename))\n\n\t\tpagetuples.sort()\n\n\t\tfor sequence, filename in pagetuples:\n\t\t\tif sequence < startpage:\n\t\t\t\tcontinue\n\t\t\telif sequence >= stoppage:\n\t\t\t\tcontinue\n\n\t\t\tpg = str(z.read(filename), 'utf-8')\n\t\t\tlines = pg.split('\\n')\n\t\t\tpages.append(lines)\n\n\treturn pages\n\nif __name__ == \"__main__\":\n\tsuffix = sys.argv[1]\n\tpages = extract(suffix)\n\n\n\n","sub_path":"littlemags/extract_pagelist.py","file_name":"extract_pagelist.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"157529806","text":"import socket\n\nfrom tkinter import *\n\nhote = \"127.0.0.1\"\nport = 28961\n\nserv = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserv.connect((hote, port))\nprint(\"Conexion done on port {}\".format(port))\n\ncmd = \"\"\nwhile cmd != \"fin\":\n\tcmd = raw_input(\"> \")\n\tserv.send(cmd)\n\trecv = serv.recv(1024)\n\tprint(recv)\n\nprint(\"closed\")\nserv.close()","sub_path":"Client.py","file_name":"Client.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"316580460","text":"'''\nDescripttion: \nversion: \nAuthor: Jim Huang\nDate: 2021-05-09 22:30:47\nLastEditors: CoderXZ\nLastEditTime: 2021-05-15 21:56:59\n'''\nsource = {'a':{'b':1,'c':2},'d':{'e':3,'f':{'g':4}}}\ntarget = {}\n\ndef flatmap(src, prefix = \"\"):\n    for k,v in src.items():\n        if isinstance(v,(tuple,list,dict)):\n            flatmap(v,prefix=prefix+k+\".\")\n        else:\n            target[prefix+k] = v\n    return target\nflatmap(source)\nprint(target)\n\n","sub_path":"MagePython/Chapter4/homework-1.py","file_name":"homework-1.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"562853086","text":"diasMes = (31,28,31,30,31,30,31,31,30,31,30)\r\n\r\nnombre = input(\"¿Cómo te llamas? \")\r\nprint(\"Hola \", nombre)\r\n\r\nstrEdad = input(\"¿Cuántos años tienes? \")\r\nstrAnno = input(\"¿En qué año estamos? \")\r\nstrMes = input(\"¿En qué mes estamos? \")\r\nstrDia = input(\"¿En qué día estamos? \")\r\n\r\nedad = int(strEdad)\r\nanno = int(strAnno)\r\nmes = int(strMes)\r\ndia = int(strDia)\r\n\r\ntranscurridos = 0\r\nindice = 0\r\n\r\nwhile indice < mes - 1:\r\n    transcurridos = transcurridos + diasMes[indice]\r\n    indice = indice + 1\r\n    \r\ntranscurridos = transcurridos + dia\r\n\r\nprob = transcurridos / 365 * 100\r\nnacimiento = anno - edad\r\n\r\nprint(nombre, \"naciste en\", nacimiento, \"con una probabilidad de\", prob)\r\nprint(\" o en \", nacimiento - 1, \"con una probabilidad de\", 100 - prob)\r\n","sub_path":"python3/p04.py","file_name":"p04.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"62589519","text":"import numpy\nimport pylab\nimport analysis\nfrom route import *\n\nsim = 'L0025N0376'\nvoid_type = 'MD'\navg = '50'\novl = '40'\nrmin = '0970'\n\nDPM_REF = 'DP_VC_REF' + sim + '_' + void_type + '_avg'\\\n+ avg + '_rmin' + rmin + '_ovl' + ovl + '.dat' \n\nDPM_DM = 'DP_VC_DM' + sim + '_' + void_type + '_avg'\\\n+ avg + '_rmin' + rmin + '_ovl' + ovl + '.dat'\n\npath = '/Users/epaillas/Eagle/out/twopoint/'\n\ntitle = void_type + ' - avg ' + avg + ' - ovl ' + ovl\n\nREF = numpy.genfromtxt(path + DPM_REF) \nDMO = numpy.genfromtxt(path + DPM_DM) \nbins = numpy.genfromtxt(path + 'bins_L0025N0376.dat')\n\nanalysis.density_profile(REF=REF, DMO=DMO, bins=bins, title=title)\n\noutfile = 'DPM' + sim + '_' + void_type + '_avg' + avg\\\n          + '_ovl' + ovl + '.pdf'\n\npylab.tight_layout()\n\nfor plot_dir in route.plots:\n    pylab.savefig(route.home + plot_dir +  outfile)\n\npylab.show()\n","sub_path":"plot_xc.py","file_name":"plot_xc.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"58491495","text":"# -*- coding: utf-8 -*-\nclass MatrizQuad(object):\n    def __init__(self, dim):\n        self.dim = dim\n        self.matriz = []\n        for i in range(1, dim+1):\n            linhas = []\n            for j in range(1, dim+1):\n                linhas.append(int(input(\"Digite o elemento a%d%d da matriz: \"%(dim, dim))))\n            self.matriz.append(linhas)\n            \n    def diagonalDominante(self):\n        cont = 0\n        for i in range(0, self.dim):\n            if self.matriz[i][i] > (sum(self.matriz[i]) - self.matriz[i][i]):\n                cont += 1\n                \n        if cont == self.dim:\n            print(\"SIM\")\n            \n        else:\n            print(\"NAO\")\n            \n#################################### - PROGRAMA PRINCIPAL - ####################################\nn = int(input(\"Digite a ordem da matriz: \"))\nA = MatrizQuad(n)\nA.diagonalDominante()\n            \n        \n","sub_path":"moodledata/vpl_data/337/usersdata/292/99257/submittedfiles/diagonaldominante.py","file_name":"diagonaldominante.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"294547737","text":"#from semanthicanalyzer.states import StatesChain\n#from semanthicanalyzer.states import ExpressionChain\nfrom semanthicanalyzer import states\n\nclass AttributionChain(states.StatesChain):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, state=self.__begin, **kwargs)\n        self.entry = None\n\n    def __begin(self, token):\n        self.entry = self.scope_manager.search_identifier(token[0])\n\n        self.state = self.__attribution\n    \n    def __attribution(self, token):\n        if token[0] == ':=': \n            return\n        else:\n            self.__call_expression_chain()\n\n    def __call_expression_chain(self):\n        exp_chain = states.ExpressionChain(self.scope_manager, \n                                   self.token_list,\n                                   self.index)\n        self.entry.value = exp_chain.exec()\n        if self.entry.type == 'boolean' and self.entry.value != 0:\n            self.entry.value = 1\n        self._finalize()\n            ","sub_path":"semanthicanalyzer/states/AttributionChain.py","file_name":"AttributionChain.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"500965448","text":"\"\"\"\n# used to process the raw data and bring it into a format the neural network can use for training/predictions\n# script is based on kaggle tutorial https://www.kaggle.com/kmader/mri-heart-processing \nIs closer to the original version than data2input_formatting_own.py (use of dbag which seems \nto be quite a bit faster than my implemented for loop; however, it does not save the \npatients that cannot be processed.)\n\"\"\"\n\n#%% import section\nimport numpy as np\nimport os\nimport sys\nimport psutil\nfrom matplotlib import image\nimport matplotlib.pyplot as plt\n\n# own import section\nsys.path.insert(1, 'utils')\n# print(sys.path)\nfrom classes import DatasetSAX\nfrom functions import rezoom, read_and_process\n\n#%%\nprint(DatasetSAX.__doc__) # print documentation of loaded module\n\n#%%\n# number of bins to use in histogram for gaussian regression\nNUM_BINS = 100\n# number of standard deviatons past which we will consider a pixel an outlier\nSTD_MULTIPLIER = 2\n# number of points of our interpolated dataset to consider when searching for\n# a threshold value; the function by default is interpolated over 1000 points,\n# so 250 will look at the half of the points that is centered around the known\n# myocardium pixel\nTHRESHOLD_AREA = 250\n# number of pixels on the line within which to search for a connected component\n# in a thresholded image, increase this to look for components further away\nCOMPONENT_INDEX_TOLERANCE = 20\n# number of angles to search when looking for the correct orientation\nANGLE_SLICES = 36\nALL_DATA_DIR =  os.path.join('C:\\\\Users\\\\Znerp\\\\Documents\\\\GitHub\\\\kaggle-dsb2-keras\\\\data', 'train', 'train')\n#X_DIM, Y_DIM = 64, 64\nX_DIM, Y_DIM = 128, 128\nT_DIM = 30\n# how many patients are processed (maximally 500)\nN_PATIENTS = 3\n\n\n#print(N_PATIENTS)\nCD = os.getcwd()\nif N_PATIENTS == 500:\n    SAVENAME = os.path.join(CD, 'train_mri_{}_{}.h5'.format(X_DIM, Y_DIM))\nelse:\n    SAVENAME = os.path.join(CD, 'train_mri_{}_{}_sub{}.h5'.format(X_DIM, Y_DIM, N_PATIENTS))\nprint('Savename is ' + SAVENAME + ('.\\nProcessing {} patients.'.format(N_PATIENTS)))\n\n\n#%% define functions etc\n\n# # scale images to previously specified size (t,x,y)\n# from scipy.ndimage import zoom\n# # print(T_DIM, X_DIM, Y_DIM)\n# rezoom = lambda in_data: zoom(in_data.images, [1, \n#                                                T_DIM/in_data.images.shape[1], \n#                                                X_DIM/in_data.images.shape[2], \n#                                                Y_DIM/in_data.images.shape[3]])\n\n# continue processing\nfrom glob import glob\nbase_path = os.path.join(ALL_DATA_DIR, '*')\nall_series = glob(base_path) # get all paths in the folder (only directories?)\n\n# from warnings import warn\n# def read_and_process(in_path): # reads data in with pre-defined function and scales it to 128x128\n#     try:\n#         cur_data = DatasetSAX(in_path,\n#                            os.path.basename(in_path)) # gets the name of the lowest folder\n#         cur_data.load()\n#         if cur_data.time is not None: # when would that ever be none? only if no sax data was found? but then there would be a problem anyway!?\n#             zoom_time = zoom(cur_data.time, [T_DIM/len(cur_data.time)]) \n#         else:\n#             zoom_time = range(T_DIM)\n#         return [in_path, zoom_time, cur_data.area_multiplier, rezoom(cur_data)] # scale images\n#     except Exception as e: # catches exceptions without letting them stop the code\n#         warn('\\nWarning: {}\\nPatient: {}'.format(e, os.path.basename(in_path)), RuntimeWarning)\n#         failedPatients.append()\n#         return None\n\n# read and process one example\na,d,b,c = read_and_process(all_series[-100])\nprint(c.shape)\n\n## Processing of MULTIPLE examples\nimport dask\nimport dask.diagnostics as diag\nfrom bokeh.io import output_notebook\nfrom bokeh.resources import CDN\nfrom dask import bag as dbag\nfrom multiprocessing.pool import ThreadPool\n\npath_bag = dbag.from_sequence(np.random.choice(all_series, N_PATIENTS)) # randomly selects N patients\nimage_bag = path_bag.map(read_and_process) # maps the function to all elements of the sequence\n\n#%% computation\nwith diag.ProgressBar(), diag.Profiler() as prof, diag.ResourceProfiler(0.5) as rprof, dask.config.set(pool = ThreadPool(4)):\n    all_img_data = image_bag.compute()\n\n#%% \nprint(len(all_img_data))\n\n#%%\nim_stack = np.concatenate([x[-1] for x in all_img_data if x is not None],0)\nprint(im_stack.shape)\n\n#%%\n# area multiplier stack\nam_stack = np.concatenate([ [x[2]]*x[-1].shape[0] for x in all_img_data if x is not None],0)\nprint(am_stack.shape)\n\n#%%\n# id stack\npath_stack = np.concatenate([ [os.path.basename(x[0])]*x[-1].shape[0] for x in all_img_data if x is not None],0)\nprint(path_stack.shape)\n\n#%%\ntime_stack = np.concatenate([ [x[1]]*x[-1].shape[0] for x in all_img_data if x is not None],0)\nprint(time_stack.shape)\n\n#%%\nimport pandas as pd\ntrain_file = 'C:\\\\Users\\\\Znerp\\\\Documents\\\\GitHub\\\\kaggle-dsb2-keras\\\\data\\\\train.csv'\ntrain_targets = {int(k['Id']): k for k in pd.read_csv(train_file).T.to_dict().values()}\n\n#%% \nos.getcwd()\n#%% save\nimport h5py\nwith h5py.File(SAVENAME, 'w') as w:\n    w.create_dataset('image', data = im_stack, compression = 9)\n    w.create_dataset('systole', data = [train_targets[int(c_id)]['Systole'] for c_id in path_stack])\n    w.create_dataset('diastole', data = [train_targets[int(c_id)]['Diastole'] for c_id in path_stack])\n    w.create_dataset('id', data = [int(c_id) for c_id in path_stack])\n    w.create_dataset('area_multiplier', data = am_stack)\n    w.create_dataset('time', data = time_stack)","sub_path":"tutorial_kaggle/data2input_formatting.py","file_name":"data2input_formatting.py","file_ext":"py","file_size_in_byte":5544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"108144823","text":"import sqlite3\nimport pathlib\nimport contextlib\n\n\nBASE_DIR = pathlib.Path(__file__).resolve().parent\n\n\n@contextlib.contextmanager\ndef get_db():\n    db = sqlite3.connect(\n        BASE_DIR / 'tkpy-credential.sqlite',\n        detect_types=sqlite3.PARSE_DECLTYPES\n    )\n    db.row_factory = sqlite3.Row\n    try:\n        yield db\n    finally:\n        db.close()\n\n\ndef init_db():\n    if not (BASE_DIR / 'tkpy-credential.sqlite').is_file():\n        with get_db() as db:\n            with open(BASE_DIR / 'schema.sql', 'r') as f:\n                db.executescript(f.read())\n","sub_path":"tkpy/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"281169384","text":"#!/usr/bin/python3\n\"\"\"This module contain a function that divides all elements of a matrix\"\"\"\n\n\ndef matrix_divided(matrix, div):\n    \"\"\"matrix_divided function divides all elements of a matrix\n\n    Args:\n    matrix (list of lists): two dimension matrix\n    div (int, float): divisor\n\n    Returns:\n        list of lists: new matrix with all elements divided\"\"\"\n\n    if type(matrix) != list or matrix == []:\n        raise TypeError(\"matrix must be a matrix\\\n (list of lists) of integers/floats\")\n\n    # capture the first row len\n    len_row = len(matrix[0])\n\n    # validate div conditions\n    if type(div) != int and type(div) != float:\n        raise TypeError(\"div must be a number\")\n    elif div == 0:\n        raise ZeroDivisionError(\"division by zero\")\n\n    # validate rows len in matrix and values inside each list\n    for i in matrix:\n        if type(i) != list or i == []:\n            raise TypeError(\"matrix must be a matrix\\\n (list of lists) of integers/floats\")\n        if len(i) == len_row:\n            for j in i:\n                if type(j) != int and type(j) != float:\n                    raise TypeError(\"matrix must be a matrix\\\n (list of lists) of integers/floats\")\n        else:\n            raise TypeError(\"Each row of the matrix must have the same size\")\n    return list(map(lambda x: list(map(lambda y: round(y/div, 2), x)), matrix))\n","sub_path":"0x07-python-test_driven_development/2-matrix_divided.py","file_name":"2-matrix_divided.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"179142972","text":"def shellsort(inl, order=True):\n    '''\n        Shellsort uses Marcin Ciura's gap sequence,\n        with an inner insertion sort\n        - Time complexity (avg) = N*log N\n        - Space complexity = const\n    '''\n    ciura = [701, 301, 132, 57, 23, 10, 4, 1]\n    outl = inl[:]\n    outl_len = len(outl)\n\n    for gap in ciura:\n        # pass gaps\n        if gap >= outl_len:\n            continue\n        # sort subgroups\n        for g in range(gap):\n            i = g\n            while i < outl_len:\n                j = i - gap\n                target = outl[i]\n                while j >= 0 and (target <= outl[j]) is order:\n                    outl[j + gap] = outl[j]\n                    j -= gap\n                outl[j + gap] = target\n                i += gap\n    return outl\n","sub_path":"Sorting/shellsort.py","file_name":"shellsort.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"323740116","text":"import bpy\nimport bgl\nimport gpu\nimport mathutils\nfrom gpu_extras.batch import batch_for_shader\n\n\ndef QWPlaneDrawHandler(wPlane, context):\n    bgl.glLineWidth(2)\n    bgl.glEnable(bgl.GL_BLEND)\n    bgl.glBlendFunc(bgl.GL_SRC_ALPHA, bgl.GL_ONE_MINUS_SRC_ALPHA)\n    coords = []\n\n    for x in range(wPlane.numSteps * 2 + 1):\n        coordX = wPlane.bRectMinMax[0] + x * wPlane.gridstep\n        posX1 = mathutils.Vector((coordX, wPlane.bRectMinMax[0], 0)) \n        posX2 = mathutils.Vector((coordX, wPlane.bRectMinMax[1], 0))\n        posY1 = mathutils.Vector((wPlane.bRectMinMax[0], coordX, 0)) \n        posY2 = mathutils.Vector((wPlane.bRectMinMax[1], coordX, 0))\n        posX1 = wPlane.matrix @ posX1\n        posX2 = wPlane.matrix @ posX2\n        posY1 = wPlane.matrix @ posY1\n        posY2 = wPlane.matrix @ posY2\n        coords.extend([posX1,posX2, posY1, posY2])\n\n    shader = gpu.shader.from_builtin('3D_UNIFORM_COLOR')\n    batch = batch_for_shader(shader, 'LINES', {\"pos\": coords})\n\n    shader.bind()\n    shader.uniform_float(\"color\", (0.655, 0.655, 0.655, 0.2))\n    batch.draw(shader)\n\nclass WorkingPlane:\n    Instance = None\n    matrix = mathutils.Matrix()\n    gridstep = 1.0\n    origFloor = True\n    isActive = False\n    wasActive = False\n\n    numSteps = 5\n    bRectMinMax = (0, 0)\n\n    def __init__(self, _context, _matrix):\n        self.matrix = _matrix.copy()\n        self.gridstep = _context.space_data.overlay.grid_scale_unit \n        edgeDist = self.numSteps * self.gridstep\n        self.bRectMinMax = (-edgeDist, edgeDist)\n        print(\"QWPlane created!\")\n\n    def SetMatrix(self, _matrix):\n        self.matrix = _matrix.copy()\n\n    def SetActive(self, _context, _state):\n        if _state:\n            self.gridstep = _context.space_data.overlay.grid_scale_unit \n            edgeDist = self.numSteps * self.gridstep\n            self.bRectMinMax = (-edgeDist, edgeDist)\n            args = (self, _context)\n            self._handle = bpy.types.SpaceView3D.draw_handler_add(QWPlaneDrawHandler, args, 'WINDOW', 'POST_VIEW')\n            self.origFloor = _context.space_data.overlay.show_floor\n            _context.space_data.overlay.show_floor = False\n            self.isActive = True\n        else:\n            bpy.types.SpaceView3D.draw_handler_remove(self._handle, 'WINDOW')\n            _context.space_data.overlay.show_floor = self.origFloor\n            self.isActive = False\n\n\n    def ChangeStepDist(self, _distp):\n        x = _distp[0]\n        y = _distp[1]\n        if x > y:\n            self.gridstep = x\n        else:\n            self.gridstep = y\n        edgeDist = self.numSteps * self.gridstep\n        self.bRectMinMax = ( -edgeDist, edgeDist)\n\n","sub_path":"All_In_One/addons/QBlocker/qwplane.py","file_name":"qwplane.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"25170219","text":"import os\r\nimport pyttsx3 as tlk\r\nimport time\r\nimport calendar\r\n\r\n\r\ntlk.speak('hello, i am sims, How can i help?')\r\nwhile True:\r\n\tprint('You:', end='')\r\n\tx = input()\r\n\tif ('run' in x) and ('notepad' in x): #notepad\r\n\t\ttlk.speak(\"Launching Notepad....\")\r\n\t\tprint(\"sism:Launched Notepad....\")\r\n\t\tos.system('notepad')\t\r\n\r\n\telif (('run' in x) and ('chrome' in x)): #chrome\r\n\t\ttlk.speak(\"Launching chrome....\")\r\n\t\tprint(\"sism:Launched Chrome....\")\r\n\t\tos.system('chrome')\r\n\r\n\telif (('run' in x) and ('paint' in x)): #paint\r\n\t\ttlk.speak(\"Launching paint....\")\r\n\t\tprint(\"sism:Launched Paint....\")\r\n\t\tos.system('mspaint')\r\n\r\n\telif (('dir' in x) or ('list' in x)): #Dir to show all files in current directory\r\n\t\tos.system('dir')\r\n\r\n\telif (('run' in x) and ('cmd' in x)): #cmd\r\n\t\ttlk.speak(\"Launching command prompt....\")\r\n\t\tprint(\"sism:Launched Cmd....\")\r\n\t\tos.system('cmd')\r\n\r\n\telif (('run' in x) and (('virtual box' in x) or ('virtualbox' in x))): #virtualbox\r\n\t\ttlk.speak(\"Launching virtaul box....\")\r\n\t\tprint(\"sism:Launched VirtualBox....\")\r\n\t\tos.system('VirtualBox')\r\n\r\n\telif (('run' in x) and ('telegram' in x) ): #Telegram\r\n\t\ttlk.speak(\"Launching telegram....\")\r\n\t\tprint(\"sism:Launched Telegram....\")\r\n\t\tos.system('telegram')\r\n\r\n\telif (('run' in x) and ('android studio' in x) ): #android studio\r\n\t\ttlk.speak(\"Launching android studio....\")\r\n\t\tprint(\"sism:Launched Aandroid Studio....\")\r\n\t\tos.system('studio64')\t\t\r\n\r\n\telif (('time' in x) or ('date' in x)): #date\r\n\t\tlocaltime = time.asctime( time.localtime(time.time()) )\r\n\t\tprint (\"Local current time :\",localtime)\r\n\r\n\telif ('calendar' in x):  #calendar\r\n\t\ttlk.speak(\"which year's calendar you want to see, enter year ....\")\r\n\t\tyr = input(\"sism:Enter year-\")\r\n\t\ttlk.speak(\"which month you want to see in \"+yr+\", enter month ....\")\r\n\t\tmn = input(\"sism:Enter month-\")\r\n\t\tcal = calendar.month(int(yr),int(mn))\r\n\t\tprint (\"sism:Here is the calendar of \"+mn+\"th month from \"+yr+\" year:\\n\",cal)\r\n\t\ttlk.speak(\"Here is the calendar of \"+mn+\"th month from \"+yr+\" year ....\")\t\r\n\r\n\telif (('say' in x) or ('talk' in x) ): #Telegram\r\n\t\ttlk.speak(\"What you want to hear from me? Enter here\")\r\n\t\tspk = input(\"sism:Enter here-\")\t\t\r\n\t\ttlk.speak(spk)\r\n\r\n\telif (('exit' in x) or ('quit' in x) or ('end' in x) or ('bye' in x)):  #exit\r\n\t\ttlk.speak(\"Bye Bye! It was great experince with you, come soon\")\r\n\t\tbreak\r\n\t\r\n\telse:\r\n\t\ttlk.speak(\"You entered, \"+x+\", i can't to anything for this\")\r\n","sub_path":"sims.py","file_name":"sims.py","file_ext":"py","file_size_in_byte":2416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"365861502","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Feb 20 09:51:24 2021\n\n@author: ryanar\n\"\"\"\n\nimport os\nimport random\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom pprint import pprint\nfrom pylab import plt, mpl\n\nplt.style.use('seaborn')\n\nfrom keras.preprocessing.sequence import TimeseriesGenerator\nfrom keras.models import Sequential\nfrom keras.layers import SimpleRNN, LSTM, Dense \n\ndef set_seeds(seed=100):\n    random.seed(seed)\n    np.random.seed(seed)\n    tf.random.set_seed(seed)   \nset_seeds()\n\ndef transform(x):\n    y = 0.05 * x ** 2 + 0.2 * x + np.sin(x) + 5\n    y += np.random.standard_normal(len(x)) * 0.2\n    return y\n\n# Create sample data\nx = np.linspace(-2 * np.pi, 2 * np.pi, 500)\na = transform(x)\n\nplt.figure(figsize=(10,6))\nplt.plot(x,a)\n\n# Change a array to a two column vector\na = a.reshape((len(a), -1))\n\nlags = 5\n\n# Create batches of lagged sequential data.\ng = TimeseriesGenerator(a, a, length=lags, batch_size=5)\n\n# The sequential mode is designed to work with exactly one input tensor and one output tensor.\nmodel = Sequential()\n\nmodel.add(SimpleRNN(500, activation='relu', input_shape = (lags, 1)))\n\nmodel.add(Dense(1, activation='linear'))\n\nmodel.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n\nmodel.summary()\n\nmodel.fit(g, epochs=500, steps_per_epoch=10, verbose=False)\n\nx = np.linspace(-6 * np.pi, 6 * np.pi, 1000)\nd = transform(x)\n\ng_ = TimeseriesGenerator(d, d, length=lags, batch_size=len(d))\n\nf = list(g_)[0][0].reshape((len(d) - lags, lags, 1))\ny = model.predict(f, verbose=False)\n\nplt.figure(figsize=(10,6))\nplt.plot(x[lags:], d[lags:], label='data', alpha=0.75)\nplt.plot(x[lags:], y, 'r.', label='pred', ms=3)\nplt.axvline(-2*np.pi, c='g' , ls='--')\nplt.axvline(2*np.pi, c='g' ,ls = '--')\nplt.text(-15, 22, 'out-of-sample') \nplt.text(-2, 22, 'in-sample') \nplt.text(10, 22, 'out-of-sample') \nplt.legend()","sub_path":"stock_analysis/stock_neuralnetwork.py","file_name":"stock_neuralnetwork.py","file_ext":"py","file_size_in_byte":1864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"21187547","text":"\"\"\"\nbyceps.services.shop.order.actions.revoke_tickets\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n:Copyright: 2006-2021 Jochen Kupperschmidt\n:License: Revised BSD (see `LICENSE` file for details)\n\"\"\"\n\nfrom typing import Sequence\n\nfrom .....typing import UserID\n\nfrom ....ticketing.dbmodels.ticket import Ticket\nfrom ....ticketing import ticket_revocation_service, ticket_service\n\nfrom ...article.transfer.models import ArticleNumber\n\nfrom .. import event_service\nfrom ..dbmodels.order_action import Parameters\nfrom ..transfer.models import Order, OrderID\n\n\ndef revoke_tickets(\n    order: Order,\n    article_number: ArticleNumber,\n    quantity: int,\n    initiator_id: UserID,\n    parameters: Parameters,\n) -> None:\n    \"\"\"Revoke all tickets in this order.\"\"\"\n    tickets = ticket_service.find_tickets_created_by_order(order.order_number)\n\n    ticket_ids = {t.id for t in tickets}\n    ticket_revocation_service.revoke_tickets(ticket_ids, initiator_id)\n\n    _create_order_events(order.id, tickets, initiator_id)\n\n\ndef _create_order_events(\n    order_id: OrderID, tickets: Sequence[Ticket], initiator_id: UserID\n) -> None:\n    event_type = 'ticket-revoked'\n\n    datas = [\n        {\n            'ticket_id': str(ticket.id),\n            'ticket_code': ticket.code,\n            'initiator_id': str(initiator_id),\n        }\n        for ticket in tickets\n    ]\n\n    event_service.create_events(event_type, order_id, datas)\n","sub_path":"byceps/services/shop/order/actions/revoke_tickets.py","file_name":"revoke_tickets.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"605729441","text":"#! /usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n# @File  : ReadLog.py\r\n# @Author: ZhouGui\r\n# @Date  : 2018/11/15\r\n# @Description :\r\nimport os\r\nimport time\r\nfrom django.test import TestCase\r\n\r\nseek_file = \"/tmp/log_seek.log\"\r\n\r\n\r\ndef getFilePos():\r\n    if not os.path.exists(seek_file):\r\n        os.mknod(seek_file)\r\n        FILE_POS = 0\r\n    else:\r\n        fd = open(seek_file, 'r')\r\n        FILE_POS = int(fd.read())\r\n    return FILE_POS\r\n\r\n\r\ndef writeFilePos(FILE_POS):\r\n    with open(seek_file, 'w+')  as fd:\r\n        fd.write(str(FILE_POS))\r\n        fd.close()\r\n\r\n\r\ndef readFile(LOGFILE, FILE_POS):\r\n    fd = open(LOGFILE, 'r')\r\n    fd.seek(FILE_POS)\r\n    for line in fd:\r\n        print(line, end='')\r\n        FILE_POS += len(line)\r\n        time.sleep(1)\r\n        writeFilePos(FILE_POS)\r\n    fd.close()\r\n\r\n\r\ndef main():\r\n    BASE_DIR = os.path.dirname(os.path.abspath(__file__))\r\n    LOGFILE = os.path.join(BASE_DIR, \"access.log\")\r\n    FILE_POS = getFilePos()\r\n    readFile(LOGFILE, FILE_POS)\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","sub_path":"lesson12/zhougui/ReadLog.py","file_name":"ReadLog.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"334934326","text":"# encoding=utf-8\nfrom datetime import datetime\nimport requests\nfrom config import Config\n\n\nclass AccessToken(object):\n    access_token = ''\n    expired_in = 0\n    update_time = datetime.now()\n\n    def __init__(self):\n        config = Config()\n        self.AppId = config.WeiXin_AppId\n        self.AppSecret = config.WeiXin_AppSecret\n        self.expires_in, self.access_token = self.__fetch_access_token_from_weixin()\n\n    def __fetch_access_token_from_weixin(self):\n        url = 'https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid=%s&secret=%s'\n        r = requests.get(\n            url % (self.AppId, self.AppSecret)\n        )\n        \n\n    def get_access_token(self):\n        pass\n\n\naccess_token = AccessToken()\n","sub_path":"app/access_token.py","file_name":"access_token.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"256022164","text":"# -*- coding: utf-8 -*-\n######################################################\n###jubiGuiMain.py\n###Author::Wang Zonghao\n###657824033@qq.com\n###Last modified::2012-04-23\n###note::Save this file as uft-8\n#####################################################\nimport wx\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom matplotlib.dates import AutoDateLocator, DateFormatter\nimport matplotlib.dates as dt\nfrom datetime import datetime\nfrom datetime import timedelta\nimport time\n# matplotlib采用WXAgg为后台,将matplotlib嵌入wxPython中\nmatplotlib.use(\"WXAgg\")\n\nfrom matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas\nfrom matplotlib.backends.backend_wxagg import NavigationToolbar2WxAgg as NavigationToolbar\nfrom matplotlib.ticker import MultipleLocator, FuncFormatter\n\n\nfrom matplotlib import pyplot as plt\n\nimport jubiMain\n\n\n# events = [\n#     'resize_event',\n#     'draw_event',\n#     'key_press_event',\n#     'key_release_event',\n#     'button_press_event',\n#     'button_release_event',\n#     'scroll_event',\n#     'motion_notify_event',\n#     'pick_event',\n#     'idle_event',\n#     'figure_enter_event',\n#     'figure_leave_event',\n#     'axes_enter_event',\n#     'axes_leave_event',\n#     'close_event'\n# ]\nclass MyNavigationToolbar2WxAgg(NavigationToolbar):\n    def __init__(self, canvas):\n        NavigationToolbar.__init__(self, canvas=canvas)\n        self.zoomedFun = None\n    def setZoomedFun(self, fun):\n        self.zoomedFun = fun\n    def release_zoom(self, event):\n        NavigationToolbar.release_zoom(self, event)\n        if self.zoomedFun:\n            self.zoomedFun(self.canvas.figure.get_axes()[0].get_xlim())\n    def home(self, *args):\n        NavigationToolbar.home(self, *args)\n        if self.zoomedFun:\n            self.zoomedFun(self.canvas.figure.get_axes()[0].get_xlim())\n\n\n\n\n\n######################################################################################\nclass MPL_Panel_base(wx.Panel):\n    ''' #MPL_Panel_base面板,可以继承或者创建实例'''\n\n    def __init__(self, parent):\n        wx.Panel.__init__(self, parent=parent, id=-1)\n\n        self.date_length = 3600 * 4\n\n        self.Figure = matplotlib.figure.Figure(figsize=(4, 3))\n        self.axes = self.Figure.add_axes([0.1, 0.1, 0.8, 0.8])\n        self.FigureCanvas = FigureCanvas(self, -1, self.Figure)\n\n        # 继承鼠标移动显示鼠标处坐标的事件\n        self.FigureCanvas.mpl_connect('motion_notify_event', self.MPLOnMouseMove)\n        # self.FigureCanvas.mpl_connect('')\n\n        self.NavigationToolbar = MyNavigationToolbar2WxAgg(self.FigureCanvas)\n\n        self.StaticText = wx.StaticText(self, -1, label='Show Help String')\n\n        self.SubBoxSizer = wx.BoxSizer(wx.HORIZONTAL)\n        self.SubBoxSizer.Add(self.NavigationToolbar, proportion=0, border=2, flag=wx.ALL | wx.EXPAND)\n        self.SubBoxSizer.Add(self.StaticText, proportion=-1, border=2, flag=wx.ALL | wx.EXPAND)\n\n        self.TopBoxSizer = wx.BoxSizer(wx.VERTICAL)\n        self.TopBoxSizer.Add(self.SubBoxSizer, proportion=-1, border=2, flag=wx.ALL | wx.EXPAND)\n        self.TopBoxSizer.Add(self.FigureCanvas, proportion=-10, border=2, flag=wx.ALL | wx.EXPAND)\n\n        self.SetSizer(self.TopBoxSizer)\n        self.resolution = None\n        self.statusBarFun = None\n        self.data = jubiMain.all_data\n        self.selectedItem = ''\n        self.selectedData = None\n        self.tickerWidth = 1\n\n\n\n    # def adapterDataMap(self, data):\n    #     self.dataMap = data\n\n    def setBarResolution(self, resolution):\n        self.barResolution = resolution\n\n    def MPLOnMouseMove(self, event):\n        ex = event.xdata  # 这个数据类型是numpy.float64\n        ey = event.ydata  # 这个数据类型是numpy.float64\n        if ex and ey:\n            # 可以将numpy.float64类型转化为float类型,否则格式字符串可能会出错\n            self.StaticText.SetLabel('%10.5f,%10.5f' % (float(ex), float(ey)))\n            # 也可以这样\n            # self.StaticText.SetLabel('%s,%s' % (ex,ey))\n            # self.statusbar.SetStatusText('%10.5f,%10.5f' % (float(ex), float(ey)), 0)\n            if self.statusBarFun:\n                self.statusBarFun(ex, ey)\n\n    def setStatusBarFun(self, s):\n        self.statusBarFun = s\n\n    # 设置放大缩小的回调函数\n    def setZoomedFun(self, fun):\n        self.NavigationToolbar.setZoomedFun(fun)\n\n    def UpdatePlot(self):\n        '''#修改图形的任何属性后都必须使用self.UpdatePlot()更新GUI界面 '''\n        self.FigureCanvas.draw()\n\n    def plot(self, *args, **kwargs):\n        '''#最常用的绘图命令plot '''\n        self.axes.plot(*args, **kwargs)\n        self.UpdatePlot()\n\n    def semilogx(self, *args, **kwargs):\n        ''' #对数坐标绘图命令 '''\n        self.axes.semilogx(*args, **kwargs)\n        self.UpdatePlot()\n\n    def semilogy(self, *args, **kwargs):\n        ''' #对数坐标绘图命令 '''\n        self.axes.semilogy(*args, **kwargs)\n        self.UpdatePlot()\n\n    def loglog(self, *args, **kwargs):\n        ''' #对数坐标绘图命令 '''\n        self.axes.loglog(*args, **kwargs)\n        self.UpdatePlot()\n\n    def grid(self, flag=True):\n        ''' ##显示网格  '''\n        if flag:\n            self.axes.grid()\n        else:\n            self.axes.grid(False)\n\n    def title_MPL(self, TitleString=\"wxMPL Example\"):\n        ''' # 给图像添加一个标题   '''\n        self.axes.set_title(TitleString)\n\n    def xlabel(self, XabelString=\"X\"):\n        ''' # Add xlabel to the plotting    '''\n        self.axes.set_xlabel(XabelString)\n\n    def ylabel(self, YabelString=\"Y\"):\n        ''' # Add ylabel to the plotting '''\n        self.axes.set_ylabel(YabelString)\n\n    def xticker(self, major_ticker=1.0, minor_ticker=0.1):\n        ''' # 设置X轴的刻度大小 '''\n        self.axes.xaxis.set_major_locator(MultipleLocator(major_ticker))\n        self.axes.xaxis.set_minor_locator(MultipleLocator(minor_ticker))\n\n    def yticker(self, major_ticker=1.0, minor_ticker=0.1):\n        ''' # 设置Y轴的刻度大小 '''\n        self.axes.yaxis.set_major_locator(MultipleLocator(major_ticker))\n        self.axes.yaxis.set_minor_locator(MultipleLocator(minor_ticker))\n\n    def legend(self, *args, **kwargs):\n        ''' #图例legend for the plotting  '''\n        self.axes.legend(*args, **kwargs)\n\n    def xlim(self, x_min, x_max):\n        ''' # 设置x轴的显示范围  '''\n        self.axes.set_xlim(x_min, x_max)\n\n    def ylim(self, y_min, y_max):\n        ''' # 设置y轴的显示范围   '''\n        self.axes.set_ylim(y_min, y_max)\n\n    def savefig(self, *args, **kwargs):\n        ''' #保存图形到文件 '''\n        self.Figure.savefig(*args, **kwargs)\n\n    def cla(self):\n        ''' # 再次画图前,必须调用该命令清空原来的图形  '''\n        self.axes.clear()\n        self.Figure.set_canvas(self.FigureCanvas)\n        self.UpdatePlot()\n\n\n################################################################\n\nclass KLinePanel(MPL_Panel_base):\n    def __init__(self, parent):\n\n        MPL_Panel_base.__init__(self, parent=parent)\n\n\n\n    def plot(self, *args, **kwargs):\n        self.axes.clear()\n        self.selectedData = self.data[self.selectedItem].tickers[:, 0 : self.data[self.selectedItem].tickers_index]\n        self.axes.set_ylim((np.min(self.selectedData[1, :])/ 1.1, np.max(self.selectedData[1, :]) * 1.1))\n        timeFmt = DateFormatter('%H:%M:%S')\n        self.axes.xaxis.set_major_formatter(timeFmt)\n        self.axes.plot_date(dt.num2date(self.selectedData[0, :] / 3600 / 24 + 719163 + 0.3333334), self.selectedData[1, :], 'o--')\n        self.UpdatePlot()\n        # MPL_Panel_base.plot(self, *args, **kwargs)\n    def MPLOnMouseMove(self, event):\n        ex = event.xdata  # 这个数据类型是numpy.float64\n        ey = event.ydata  # 这个数据类型是numpy.float64\n        if ex and ey:\n            # 可以将numpy.float64类型转化为float类型,否则格式字符串可能会出错\n            self.StaticText.SetLabel('时间:' + dt.num2date(ex).strftime(\"%D %X\") + \"   价格:\" + str(ey))\n            # 也可以这样\n            # self.StaticText.SetLabel('%s,%s' % (ex,ey))\n            # self.statusbar.SetStatusText('%10.5f,%10.5f' % (float(ex), float(ey)), 0)\n            if self.statusBarFun:\n                self.statusBarFun(ex, ey)\n\nclass QuantityPanel(MPL_Panel_base):\n    def __init__(self, parent):\n        MPL_Panel_base.__init__(self, parent=parent)\n        self.start_timestrmp = 0\n        self.timedelta = 1\n        self.buy_line = None\n        self.sell_line = None\n        self.buy_data = [[],[],[],[]]\n        self.sell_data = [[], [], [], []]\n        self.FigureCanvas.mpl_connect('pick_event', self.onPick)\n\n    def onPick(self, event):\n        N = event.ind\n        figi = plt.figure()\n        print(N)\n        if len(N) == 0:\n            return True\n        n = N[int(len(N) / 2)]\n        if event.artist == self.buy_line:\n            # index = [i for i in range(len(self.buy_data[1])) if self.buy_data[1][i] == self.buy_data[1][n]]\n            ndate = self.buy_data[1][n]\n        elif event.artist == self.sell_line:\n            # index = [i for i in range(len(self.sell_data[1])) if self.sell_data[1][i] == self.sell_data[1][n]]\n            ndate = self.sell_data[1][n]\n        else:\n            return\n        # 画图，在panel上frame上画图\n        # bs = wx.BoxSizer(wx.VERTICAL)\n        # p = wx.Frame(parent=None, title='pickers')\n        # f_pickers = matplotlib.figure.Figure()\n        # f_pickers_axes = f_pickers.add_axes([0.1, 0.1, 0.8, 0.8])\n        # f_pickers_canvas = FigureCanvas(p, -1, f_pickers)\n        # navigation_bar = MyNavigationToolbar2WxAgg(f_pickers_canvas)\n        # bs.Add(self.NavigationToolbar, proportion=2, border=2, flag=wx.ALL | wx.EXPAND)\n        # bs.Add(f_pickers_canvas, proportion=2, border=2, flag=wx.ALL | wx.EXPAND)\n        # p.SetSizer(bs)\n\n\n        fig = plt.Figure()\n        delta = 0.95\n\n        # 找到buydata里分段日期与ndate相同的点\n        try:\n            start = self.buy_data[1].index(ndate)\n            index = [self.buy_data[3][start] / 2]\n            width = [self.buy_data[3][start] * delta]\n            heigth = [self.buy_data[2][start]]\n            end = start + 1\n            while end < len(self.buy_data[1]):\n                if (self.buy_data[1][end] == ndate):\n                    index.append((self.buy_data[3][end - 1] + self.buy_data[3][end]) / 2)\n                    width.append((self.buy_data[3][end] - self.buy_data[3][end - 1]) * delta)\n                    heigth.append(self.buy_data[2][end])\n                    end += 1\n                else:\n                    break\n            plt.bar(index, heigth, width, color = 'r')\n\n        except:\n            bdata = None\n        # sell 图\n        try:\n            start = self.sell_data[1].index(ndate)\n            index = [-self.sell_data[3][start] / 2]\n            width = [-self.sell_data[3][start] * delta]\n            heigth = [-self.sell_data[2][start]]\n            end = start + 1\n            while end < len(self.sell_data[1]):\n                if (self.sell_data[1][end] == ndate):\n                    index.append(-(self.sell_data[3][end - 1] + self.sell_data[3][end]) / 2)\n                    width.append((self.sell_data[3][end - 1] - self.sell_data[3][end]) * delta)\n                    heigth.append(-self.sell_data[2][end])\n                    end += 1\n                else:\n                    break\n            plt.bar(index, heigth, width, color='b')\n        except:\n            sdata = None\n\n        plt.show(block=False)\n\n\n\n\n\n\n    def plot(self, *args, **kwargs):\n        self.axes.clear()\n        index = self.data[self.selectedItem].orders_index\n        ddata = self.data[self.selectedItem].orders[:, 0 : index]\n        cdata = ddata.copy()\n        last_buy = last_sell = 0\n        last_time = 0\n        cdata[1,:] = (np.round((cdata[1,:] - self.start_timestrmp) / self.timedelta) + 0.5) * self.timedelta / 3600 / 24 + 719163 + 0.3333334\n        for i in range(index):\n            if cdata[1, i] != last_time:\n                last_sell = last_buy = 0\n\n            if cdata[4, i] == 1:\n                cdata[3, i] = last_buy + cdata[3, i]\n                last_buy = cdata[3, i]\n            else:\n                cdata[3, i] = last_sell - cdata[3, i]\n                last_sell = cdata[3, i]\n        min_time = cdata[1,0]\n        max_time = cdata[1, index - 1]\n        da = (max_time - min_time) / 20\n        timeFmt = DateFormatter('%H:%M:%S')\n        self.axes.xaxis.set_major_formatter(timeFmt)\n        self.axes.plot_date(dt.num2date([min_time - da, max_time + da]), [0, 0], 'g-', linewidth=3)\n        self.order_line, = self.axes.plot_date(dt.num2date(cdata[1,:]), cdata[3,:], 'ro', picker=3,\n                                             markersize=5)\n        maxAmount = np.max(np.abs(cdata[3,:]))\n\n\n\n\n        # last_buy = last_sell = 0\n        # for data in self.data[self.selectedItem].orders:\n        #     if data[4] == 'buy':\n        #         self.buy_data[0].append(data[0])\n        #         self.buy_data[1].append((int((data[1] - self.start_timestrmp) / self.timedelta) + 0.5) * self.timedelta / 3600 / 24 + 719163 + 0.33333333334)\n        #         self.buy_data[2].append(data[2])\n        #         if data[1] != last_buy:\n        #             self.buy_data[3].append(data[3])\n        #             last_buy = data[1]\n        #         else:\n        #             self.buy_data[3].append(self.buy_data[3][-1] + data[3])\n        #     elif data[4] == 'sell':\n        #         self.sell_data[0].append(data[0])\n        #         self.sell_data[1].append((int(\n        #             (data[1] - self.start_timestrmp) / self.timedelta) + 0.5) * self.timedelta / 3600 / 24 + 719163 + 0.33333333334)\n        #         self.sell_data[2].append(data[2])\n        #         if data[1] != last_sell:\n        #             self.sell_data[3].append(-data[3])\n        #             last_sell = data[1]\n        #         else:\n        #             self.sell_data[3].append(self.sell_data[3][-1] - data[3])\n        # ltz = None\n        # min_time = min(min(self.buy_data[1]), min(self.sell_data[1]));\n        # max_time = max(max(self.buy_data[1]), max(self.sell_data[1]))\n        # da = (max_time - min_time) / 20\n        # timeFmt = DateFormatter('%H:%M:%S')\n        # self.axes.xaxis.set_major_formatter(timeFmt)\n        # self.axes.plot_date(dt.num2date([ min_time - da,  max_time + da]), [0,0], 'g-', linewidth = 3)\n        # self.buy_line, = self.axes.plot_date(dt.num2date(self.buy_data[1], tz = ltz), self.buy_data[3], 'ro', picker = 3, markersize = 5)\n        # self.sell_line, = self.axes.plot_date(dt.num2date(self.sell_data[1], tz = ltz), self.sell_data[3], 'bo', picker=3, markersize = 5)\n\n\n\n        self.axes.set_ylim([-maxAmount * 1.5, maxAmount * 1.5])\n        self.UpdatePlot()\n\n    def MPLOnMouseMove(self, event):\n        ex = event.xdata  # 这个数据类型是numpy.float64\n        ey = event.ydata  # 这个数据类型是numpy.float64\n        if ex and ey:\n            # 可以将numpy.float64类型转化为float类型,否则格式字符串可能会出错\n            if ey > 0:\n                self.StaticText.SetLabel('时间:' + dt.num2date(ex).strftime(\"%D %X\") + \"   当前区间段累计买量:\" + str(ey))\n            else:\n                self.StaticText.SetLabel('时间:' + dt.num2date(ex).strftime(\"%D %X\") + \"   当前区间段累计卖量:\" + str(-ey))\n            # self.StaticText.SetLabel('时间:' + dt.num2date(ex).strftime(\"%D %X\") + \"   价格:\" + str(ey))\n            # 也可以这样\n            # self.StaticText.SetLabel('%s,%s' % (ex,ey))\n            # self.statusbar.SetStatusText('%10.5f,%10.5f' % (float(ex), float(ey)), 0)\n            if self.statusBarFun:\n                self.statusBarFun(ex, ey)\n\n\n","sub_path":"guis/panelBase2.py","file_name":"panelBase2.py","file_ext":"py","file_size_in_byte":15883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"132117170","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nfile = \"Resources/budget_data.csv\"\n\ndf = pd.read_csv(file)\ndf.head()\n\n\n# In[4]:\n\n\nunique_date = df[\"Date\"].unique()\n\n\n# In[5]:\n\n\ndf.count()\n\n\n# In[6]:\n\n\ndf.dtypes\n\n\n# In[7]:\n\n\ndf['Date'].unique()\n\n\n# In[8]:\n\n\ndf['Date'] = pd.to_datetime(df['Date'])   \n\n\n# In[9]:\n\n\ntotal_months = df['Date'].nunique()\ntotal_months\n\n\n# In[19]:\n\n\ntotal = df['Profit/Losses'].sum()\ntotal\n\n\n# In[47]:\n\n\n\n\n\n# In[11]:\n\n\ndf['Change'] = df['Profit/Losses'].diff()#.fillna(df['Profit/Losses'])\n\n\n# In[12]:\n\n\ndf['Change']\n\n\n# In[13]:\n\n\ngreatest_increase = df['Change'].max()\ngreatest_increase\n\n\n# In[45]:\n\n\n\n\n        \n\n\n# In[15]:\n\n\ngreatest_decrease = df['Change'].min()\ngreatest_decrease\n\n\n# In[17]:\n\n\naverage_change = df['Change'].mean()\naverage_change\n\n\n# In[20]:\n\n\n#Financial_Analysis_Output \nprint('Financial Analysis')\nprint(\"--------------------------------\")\nprint(f\"Total Months: \" + str(total_months))\nprint(f\"Total: \" + str(total))\nprint(f\"Average Change: \" + str(average_change))\nprint(f\"Greatest Increase in Profits: \" + str(greatest_increase))\nprint(f\"Greatest Decrease in Profits: \" + str(greatest_decrease))\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"PyBank/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"606100383","text":"import boto3\nimport json\n\nclient = boto3.client('route53')\n\ndef lambda_handler(event, context):\n    \n    response = client.change_resource_record_sets(\n    #Hosted zone is static for all domains under awsproject.club\n    HostedZoneId='ZC99NSLWE3IJB',\n    ChangeBatch={\n        'Comment': 'DOGE',\n        'Changes': [\n            {\n                'Action': 'CREATE',\n                'ResourceRecordSet': {\n                    'Name': event['subdomain']+'.awsproject.club',\n                    'Type': 'A',\n                    'AliasTarget': {\n                        'HostedZoneId': event['HostedZone'],\n                        'DNSName': 'dualstack.' + event['DNSName'],\n                        'EvaluateTargetHealth': False\n                    }\n                }\n            }\n        ]\n    }\n    )\n\n    return { \"Id\" : response['ChangeInfo']['Id'], \"Status\" : response['ChangeInfo']['Status']};\n","sub_path":"lambda/createSubdomainRecord.py","file_name":"createSubdomainRecord.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"613372418","text":"class TreeNode(object):\n    def __init__(self, val):\n        self.children = {}\n        self.val = val\n        self.is_word = False\n\nclass Trie(object):\n    def __init__(self):\n        self.root = TreeNode(None)\n\n    def add_word(self, word):\n        node = self.root\n        for ch in word:\n            if ch not in node.children:\n                node.children[ch] = TreeNode(ch)\n            node = node.children[ch]\n        node.is_word = True\n\n    def get_shortest_match(self, word):\n        node = self.root\n        for idx, ch in enumerate(word):\n            if ch in node.children:\n                node = node.children[ch]\n                if node.is_word:\n                    return word[: idx + 1]\n            else:\n                break\n        return None\n\n\nclass Solution(object):\n    def replaceWords(self, dict, sentence):\n        \"\"\"\n        :type dict: List[str]\n        :type sentence: str\n        :rtype: str\n        \"\"\"\n        words = sentence.split()\n        trie = Trie()\n        for word in dict:\n            trie.add_word(word)\n        ret = []\n        for word in words:\n            sm = trie.get_shortest_match(word)\n            if sm is not None:\n                ret.append(sm)\n            else:\n                ret.append(word)\n        return ' '.join(ret)\n\n","sub_path":"replace-words/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"387073106","text":"import os, datetime\nfrom loader import *\n\ninfo = {\n\t'name': 'Momento',\n\t'version': 1.0,\n\t'description': \"\"\"Hooks into the data provided by Momento's\n(http://momentoapp.com/) \\\"Export to Text Files\\\" function. To use,\nexport one file for each day (it's an option in the menu) and dump the contents\nof the zip that it gives you to this module's folder. You should have an Attachments \nfolder and all of the entries.\"\"\"\n}\n\n\"\"\"Checks to see if the folder specified by path has proper Momento data in it.\nReturns true or false.\"\"\"\ndef check(path):\n\tfiles = os.listdir(path)\n\n\tfor file in files:\n\n\t\t# See if we can parse it\n\t\ttry:\n\t\t\tDay.fromFile(path + '\\\\' + file)\n\t\t\treturn True\n\t\texcept:\n\t\t\tcontinue\n\treturn False\n\n\n\"\"\"Gets all the data. Returns it as a tuple of (datetime.datetime,dictionary)\nfor each data point.\"\"\"\ndef parse(path):\n\tfiles = os.listdir(path)\n\n\tdays = []\n\tfor file in files:\n\t\ttry:\n\t\t\tdays.append(Day.fromFile(path + '\\\\' + file))\n\t\texcept:\n\t\t\tcontinue\n\n\tentries = []\n\tfor day in days:\n\t\tdayTime = day.date\n\t\tfor entry in day.entries:\n\t\t\tentryTime = entry['time']\n\t\t\ttimeStamp = datetime.datetime(dayTime.year,\n\t\t\t\t\t\t\t\t\t\t  dayTime.month,\n\t\t\t\t\t\t\t\t\t\t  dayTime.day,\n\t\t\t\t\t\t\t\t\t\t  entryTime.hour,\n\t\t\t\t\t\t\t\t\t\t  entryTime.minute,\n\t\t\t\t\t\t\t\t\t\t  entryTime.second)\n\t\t\tdel entry['time']\n\t\t\tentries.append((timeStamp,entry))\n\n\treturn entries","sub_path":"quotidian/modules/momento/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"110716825","text":"class MyGraph:\n    \n    def __init__(self,g ={}):\n        self.graph = g\n    \n    def get_nodes(self):\n        return list(self.graph.keys())\n    \n    def get_edges(self):\n        edges = []\n        for v in self.graph.keys():\n            for d in self.graph[v]:\n                edges.append((v,d))\n        return edges\n    \n    def size(self):\n        return len(self.get_nodes()) , len(self.get_edges())\n    \n    def print_graph(self):\n        for v in self.graph.keys():\n            print(v,\"->\", self.graph[v])\n    \n    def add_vertex(self,v):\n        if v not in self.graph.keys():\n            self.graph[v] = []\n    \n    #Adding edge, o: origin node, d:destination node\n    def add_edge(self,o,d):\n        if o not in self.graph.keys():\n            self.add_vertex(o)\n        if d not in self.graph.keys():\n            self.add_vertex(d)\n        # if the edge doesn't already exist, add the new edge\n        if d not in self.graph[o]:\n            self.graph[o].append(d)\n    \n    # for an ordered pair (s,v) in a graph\n    # s is the predecessor of v, v is the successor\n    def get_successors(self,v):\n        return list(self.graph[v])\n    \n    def get_predecessors(self,v):\n        result = []\n        for k in self.graph.keys():\n            if v in self.graph[k]:\n                result.append(k)\n        return result\n    \n    # Two nodes are adjacent if one is successor of another\n    def get_adjacents(self,v):\n        successors = self.get_successors(v)\n        predessors = self.get_predecessors(v)\n        result = predessors\n        for s in successors:\n            if s not in result:\n                result.append(s)\n        \n        return result\n    \n    def out_degree(self,v):\n        return len(self.graph[v])\n    \n    def in_degree(self,v):\n        return len(self.get_predecessors(v))\n    \n    def degree(self,v):\n        return (len(self.get_adjacents(v)))\n    \n    # Compute degree of all the nodes\n    # deg_type can be \"in\", \"out\" or \"inout\"\n    # Returns dictionary node ---> degree\n    def all_degrees(self,deg_type= 'inout'):\n        degrees = {}\n        # for eavery node out degree is equal to number of \n        # connection it has to other node 3 -> [2, 4]\n        # len(list) for each node \n        for v in self.graph.keys():\n            if deg_type == \"out\" or deg_type == \"inout\":\n                degrees[v] = len(self.graph[v])\n            else:\n                degrees[v] = 0\n        if deg_type == \"in\" or deg_type == \"inout\":\n            for v in self.graph.keys():\n                for d in self.graph[v]:\n                    if deg_type == \"in\" or v not in self.graph[d]:\n                        degrees[d] = degrees[d]+1\n        return degrees\n    \n    \"\"\"BFS start by the source nodem then visit all it successors,\n    folloed by thier sucessors , until all possible nodes are visited\n    \"\"\"\n    \n    def breadth_first_search(self,v):\n        visited = [v]\n        res = []\n        while len(visited) > 0:\n            node = visited.pop(0)\n            if node != v:\n                res.append(node)\n            for element in self.graph[node]:\n                if element not in res and element not in visited:\n                    visited.append(element)\n        return res\n    \n    \"\"\"DFS: start at teh source node, explore first the successors\n    tehen its first successor unitl no further exploration is possible\n    then back trake to explore further alternatives\"\"\"\n    \n    def depth_first_search(self,v):\n        visited = [v]\n        res = []\n        while len(visited)>0:\n            node = visited.pop(0)\n            if node != v:\n                res.append(node)\n            s = 0\n            for elem in self.graph[node]:\n                if elem not in res and elem not in visited:\n                    visited.insert(s,elem)\n                    s += 1\n        return res\n    \n    # Finding the distance between the source and destination node\n    def distance(self,s,d):\n        if s == d:\n            return 0\n        l = [(s,0)]\n        visited = [s]\n        while len(l) > 0:\n            node , dist = l.pop(0)\n            for element in self.graph[node]:\n                if element == d:\n                    return dist+1\n                elif element not in visited:\n                    l.append((element, dist+1))\n                    visited.append(element)\n        return None\n    \n    \n    # Find shortest path\n    def shortest_path(self,s,d):\n        if s == d:\n            return 0\n        l = [(s,[])]\n        visited = [s]\n        while len(l) > 0:\n            node,preds = l.pop(0)\n            for element in self.graph[node]:\n                if element == d:\n                    return preds+[node,element]\n                elif element not in visited:\n                    l.append((element,preds+[node]))\n                    visited.append(element)\n                    \n        return None\n    \n    def is_in_tuple_list(self,tl,value):\n        res = False\n        for (x,y) in tl:\n            if x == value:\n                return True\n        return res\n    \n    def reachable_with_distance(self,s):\n        res = []\n        l = [(s,0)]\n        while len(l) > 0:\n            node , dist = l.pop(0)\n            if node != s:\n                res.append((node,dist))\n            for element in self.graph[node]:\n                if not self.is_in_tuple_list(l,element) and not self.is_in_tuple_list(res,element):\n                    l.append((element,dist+1))\n        return res\n    \n    \n    def node_has_cycle(self,v):\n        l = [v]\n        res = False\n        visited = [v]\n        while len(l)>0:\n            node = l.pop()\n            for element in self.graph[node]:\n                if element == v:\n                    return True\n                elif element not in visited:\n                    l.append(element)\n                    visited.append(element)\n        return res\n    \n    def has_cycle(self):\n        res = False\n        for v in self.graph.keys():\n            if self.node_has_cycle(v):\n                return True\n        return res\n    \n    def is_connected(self):\n        total = len(self.graph.keys())\n        for v in self.graph.keys():\n            reachable_node = self.breadth_first_search(v)\n            if (len(reachable_node) < total):\n                return False\n        return True\n\n\ndef test_graph():\n    gr = MyGraph()\n    gr.add_vertex(1)\n    gr.add_vertex(2)\n    gr.add_vertex(3)\n    gr.add_vertex(4)\n    gr.add_edge(1,2)\n    gr.add_edge(2,3)\n    gr.add_edge(3,2)\n    gr.add_edge(3,4)\n    gr.add_edge(4,2)\n    gr.print_graph()\n    print(\"Successors of node 2: \",gr.get_successors(2))\n    print(\"Predecessors of node 2: \",gr.get_predecessors(2))\n    print(\"Adjacents of node 2: \",gr.get_adjacents(2))\n    print(\"In degree of 2 : \", gr.in_degree(2))\n    print(\"Out degree of 2 : \", gr.out_degree(2))\n    print(\"Degree of 2: \", gr.degree(2))\n    gr2 = MyGraph({1:[2,3,4],2:[5,6],3:[6,8],4:[8],5:[7],6:[],7:[],8:[]})\n    print (gr2.breadth_first_search(1))\n    print (gr2.depth_first_search(1))\n    print (gr2.distance(1,7))\n    print (gr2.shortest_path(1,7))\n    print (gr2.distance(1,8))\n    print (gr2.shortest_path(1,8))\n    print (gr2.distance(6,1))\n    print (gr2.shortest_path(6,1))\n    gr2.print_graph()\n    print(gr2.reachable_with_distance(2))\n    print(gr.has_cycle())\n    print(gr2.has_cycle())\n\ntest_graph()\n\ndef graph_distance_tests():\n    gr2 = MyGraph({1:[2,3,4],2:[5,6],3:[6,8],4:[8],5:[7],6:[],7:[],8:[]})\n    gr2.print_graph()\n    print(\"BFS: \",gr2.breadth_first_search(1))\n    print(\"DFS: \",gr2.depth_first_search(1))\n    print(\"Distance b/w 1 and 7: \", gr2.distance(1,7))\n    print(\"Shortest Path b/w 1 and 7\", gr2.shortest_path(1,7))\n    print(\"Reachable with distance fron 1: \", gr2.reachable_with_distance(1))\n    print(\"Has cycle ?: \", gr2.has_cycle())\n    print(\"Is connected ? :\", gr2.is_connected())\n    print(\"Adjecents of 2: \", gr2.get_adjacents(2))\n    \n\ngraph_distance_tests()","sub_path":"GRAPHS/Graph.py","file_name":"Graph.py","file_ext":"py","file_size_in_byte":7917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"462527418","text":"import sqlite3\nimport sys\n\nscript = sys.argv[0]\nother = sys.argv[1:]\n\ndb = sqlite3.connect('data/dictionary.db')\nc = db.cursor()\n\nfile_path = other[0]\ndata = open(file_path, 'r')\n\nfor row in data:\n\tproject = row.split(',')\n\tinsert = \"INSERT INTO projects VALUES ('\"+project[0]+\"','\"+project[1]+\"','\"+project[2]+\"')\"\n\tprint(insert)\n\tc.execute(insert)\n\ndata.close()\ndb.commit()\ndb.close()\n","sub_path":"scripts/db_maintenence/add_project.py","file_name":"add_project.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"361839863","text":"import os\nimport sys\nsys.path.append(\"..\") \nfrom utils import utils\nimport pandas as pd \nfrom matplotlib import colors\n\n\ndef create_folder(pathname):\n    if not os.path.exists(pathname):\n        os.makedirs(pathname)\n    return pathname\n\n################################################################\n########                        folders                 ########\n################################################################\n\ndef getResultDir():\n    pathname = os.path.join(os.path.expanduser('~'), 'gfc_wrapper_results') + '/'\n    return create_folder(pathname)\n\ndef getDwnDir():\n    pathname = os.path.join(getResultDir(), 'downloads') + '/'\n    return create_folder(pathname) \n\ndef getRootDir():\n    pathname = os.path.join(os.path.expanduser('~'), 'gfc_wrapper_python') + '/'\n    return create_folder(pathname) \n\ndef getscriptDir():\n    pathname = os.path.join(getRootDir(), 'scripts') + '/'\n    return create_folder(pathname)\n\ndef getDataDir():\n    pathname = os.path.join(getRootDir(), 'data') + '/'\n    return create_folder(pathname)\n\ndef getTmpDir():\n    pathname = os.path.join(getResultDir(), 'tmp') + '/'\n    return create_folder(pathname)\n\ndef getGfcDir():\n    pathname = os.path.join(getResultDir(), 'gfc') + '/'\n    return create_folder(pathname)\n\ndef getAoiDir():\n    pathname = os.path.join(getRootDir(), 'aoi') + '/'\n    return create_folder(pathname)\n\ndef getStatDir():\n    pathname = os.path.join(getResultDir(), 'stat') + '/'\n    return create_folder(pathname)\n\ndef getMspaDir():\n    pathname = os.path.join(getRootDir(), 'mspa') + '/'\n    return create_folder(pathname)\n\ndef getTmpMspaDir():\n    pathname = os.path.join(getResultDir(), 'tmp/mspa') + '/'\n    return create_folder(pathname)\n\ndef getUtilsDir():\n    pathname = os.path.join(getRootDir(), 'utils') + '/'\n    return create_folder(pathname)\n\n########################################################\n##########              hard-coded params          #####\n########################################################\ndef getMaxYear():\n    return 19\n\ndef getDataset():\n    return 'UMD/hansen/global_forest_change_2019_v1_7'\n\n##########################################################\n#########                     legend                ######\n##########################################################\nmspa_colors = {\n    'background': [220, 220, 220, 255],\n    'branch': [255, 140, 0, 255],\n    'perforation': [0, 0, 255, 255],\n    'islet': [160, 60, 0, 255], \n    'core': [0, 220, 0, 255], \n    'bridge': [255, 0, 0, 255], \n    'loop': [255, 255, 0, 255],\n    'edge': [0, 0, 0, 255],\n    'no-data': [255, 255, 255, 255],\n}\n\ndef get_gfc_colors():\n    \n    gfc_colors = {}\n    for i in range(1, getMaxYear()+1):\n        gfc_colors['{} loss'.format(2000+i)] = colors.to_rgba(utils.colorFader(i))\n    gfc_colors['non forest'] = colors.to_rgba('lightgrey')\n    gfc_colors['forest'] = colors.to_rgba('darkgreen')\n    gfc_colors['gains'] = colors.to_rgba('lightgreen')\n    gfc_colors['gain + loss'] = colors.to_rgba('purple')\n    \n    return gfc_colors\n\ndef getMyClasses():\n    \n    years = [i for i in range (1, getMaxYear()+1)]\n    pos = 1\n    my_classes = [0,30,40,50,51]\n    my_classes[pos:pos] = years\n\n    return my_classes\n\ndef getMyLabel():\n    \n    years = ['loss_{}'.format(str(2000+i)) for i in range (1, getMaxYear()+1)]\n    labels = years + [\"non forest\",\"forest\",\"gains\",\"gain+loss\"]\n    \n    return labels\n\ndef getCodes():\n    codes = [i for i in range(1, getMaxYear() + 1)] + [30, 40, 50, 51]\n    codes = [str(i) for i in codes]\n    \n    return codes\n\ndef getSldStyle():\n    #Define an SLD style of discrete intervals to apply to the image.\n    color_map_entry = '\\n<ColorMapEntry color=\"{0}\" quantity=\"{1}\" label=\"{2}\"/>' \n    \n    # TODO can do it programatically\n    sld_intervals = '<RasterSymbolizer>' \n    sld_intervals += '\\n<ColorMap type=\"intervals\" extended=\"false\" >' \n    \n    sld_intervals += color_map_entry.format(colors.to_hex('black').upper(), 0, 'no data')\n    for i in range(1, getMaxYear()+1):\n        sld_intervals += color_map_entry.format(colors.to_hex(utils.colorFader(i)).upper(), i, 'loss-' + str(2000+i))\n    sld_intervals += color_map_entry.format(colors.to_hex('lightgrey').upper(), 30, 'non forest')\n    sld_intervals += color_map_entry.format(colors.to_hex('darkgreen').upper(), 40, 'stable forest')\n    sld_intervals += color_map_entry.format(colors.to_hex('lightgreen').upper(), 50, 'gain')\n    sld_intervals += color_map_entry.format(colors.to_hex('purple').upper(), 51, 'gain + loss')\n                                            \n    sld_intervals += '\\n</ColorMap>'\n    sld_intervals += '\\n</RasterSymbolizer>'    \n    \n    return sld_intervals\n\ndef getColorTable():\n        \n    length = len(getMyClasses())\n    color_r = {}\n    color_g = {}\n    color_b = {}\n        \n    color_r[0], color_g[0], color_b[0] = colors.to_rgb('black') #no data\n    for i in range(1, getMaxYear()+1):\n        color_r[i], color_g[i], color_b[i] = colors.to_rgb(utils.colorFader(i))\n    color_r[30], color_g[30], color_b[30] = colors.to_rgb('lightgrey') #non forest\n    color_r[40], color_g[40], color_b[40] = colors.to_rgb('darkgreen') #forest\n    color_r[50], color_g[50], color_b[50] = colors.to_rgb('lightgreen') #gains\n    color_r[51], color_g[51], color_b[51] = colors.to_rgb('purple') #gain + loss\n        \n    color_table = { k:(int(color_r[k]*255), int(color_g[k]*255), int(color_b[k]*255), 255) for k in color_r.keys()}\n    \n    return color_table\n\ndef getColorPalette():\n    hex_palette = []\n    \n    #hex_palette.append(colors.to_hex('black')) #no data\n    for i in range(1,getMaxYear()+1):\n        hex_palette.append(colors.to_hex(utils.colorFader(i))) #year loss\n    hex_palette.append(colors.to_hex('lightgrey')) #non forest\n    hex_palette.append(colors.to_hex('darkgreen')) #forest\n    hex_palette.append(colors.to_hex('lightgreen')) #gains\n    hex_palette.append(colors.to_hex('purple')) #gain + loss \n    \n    return hex_palette","sub_path":"utils/parameters.py","file_name":"parameters.py","file_ext":"py","file_size_in_byte":5956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"288952452","text":"#!/usr/bin/python\n\nfrom twiggy import quick_setup, log as l\n\nfilename=\"ddata1.txt\"\nGROUPS=1\nDATA=[]\nfor g in range(GROUPS): DATA.append([])\n\ndef getData(cases, lines):\n    global DATA\n\n    i=0\n    f=open(\"oout1.txt\", \"a+\")\n    lg=l.name(\"getData\")\n    for ncase in range(cases):\n        data=(ncase+1, process(int(lines[ncase])))\n        lg.fields(ncase=data[0], number=int(lines[ncase]), process=data[1]).info(\"process\")\n        f.write(\"case #%d: %s\\n\" % data)\n    f.close()\n\n\ndef checkDigits(digits, n):\n\n    sn=\"%d\" % n\n    for digit in list(sn):\n        if not digit in digits:\n            digits+=digit\n    if len(\"\".join(digits))==10:\n        return digits, n\n    else:\n        return digits, -1\n\ndef process(number):\n\n    digits=[]\n    for n in range(1, 1000000):\n        digits, r=checkDigits(digits, n*number)\n        if r >0:\n            return r\n    return \"INSOMNIA\"\n\ndef main():\n\n    quick_setup()\n    lines=open(filename, \"r\").readlines()\n    cases=int(lines[0].strip())\n    lg=l.name(\"main\")\n    lg.fields(cases=cases).info(\"init\")\n    getData(cases, lines[1:])\n\nif __name__ == \"__main__\":\n    main()","sub_path":"codes/CodeJamCrawler/16_0_1_neat/16_0_1_Javitux_test0.py","file_name":"16_0_1_Javitux_test0.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"481834012","text":"import hashlib\nimport json\nimport os\nfrom logging import getLogger\n# noinspection PyPep8Naming\nimport keras.backend as K\n\nfrom keras.engine.topology import Input\nfrom keras.engine.training import Model\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.core import Activation, Dense, Flatten\nfrom keras.layers.merge import Add\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.losses import mean_squared_error\nfrom keras.regularizers import l2\n\nfrom gomoku_zero.config import Config\n\nimport keras\n\nlogger = getLogger(__name__)\n\n\nclass GomokuModel:\n    def __init__(self, config: Config):\n        '''\n        Manages the model saving and loading\n        :param config: configuration settings\n        '''\n        self.config = config\n        self.model = None  # type: Model\n        self.digest = None\n\n    def build(self, res_layers):\n        '''\n        Constructs the ResNet model based on number of layers\n        :param res_layers: number of layers in the model\n        '''\n        mc = self.config.model\n        in_x = x = Input((2, 8, 5))  # [own(8x8), enemy(8x8)]\n\n        # (batch, channels, height, width)\n        x = Conv2D(filters=mc.cnn_filter_num, kernel_size=mc.cnn_filter_size, padding=\"same\",\n                   data_format=\"channels_first\", kernel_regularizer=l2(mc.l2_reg))(x)\n        x = BatchNormalization(axis=1)(x)\n        x = Activation(\"relu\")(x)\n\n        logger.debug(f\"Build Model with %d Res Blocks\" % res_layers)\n\n        #for _ in range(mc.res_layer_num):\n        # build with number of res blocks\n        for _ in range(res_layers):\n            x = self._build_residual_block(x)\n\n        res_out = x\n        # for policy output\n        x = Conv2D(filters=2, kernel_size=1, data_format=\"channels_first\", kernel_regularizer=l2(mc.l2_reg))(res_out)\n        x = BatchNormalization(axis=1)(x)\n        x = Activation(\"relu\")(x)\n        x = Flatten()(x)\n        # no output for 'pass'\n        policy_out = Dense(self.config.n_labels, kernel_regularizer=l2(mc.l2_reg), activation=\"softmax\",\n                           name=\"policy_out\")(x)\n\n        # for value output\n        x = Conv2D(filters=1, kernel_size=1, data_format=\"channels_first\", kernel_regularizer=l2(mc.l2_reg))(res_out)\n        x = BatchNormalization(axis=1)(x)\n        x = Activation(\"relu\")(x)\n        x = Flatten()(x)\n        x = Dense(mc.value_fc_size, kernel_regularizer=l2(mc.l2_reg), activation=\"relu\")(x)\n        value_out = Dense(1, kernel_regularizer=l2(mc.l2_reg), activation=\"tanh\", name=\"value_out\")(x)\n\n        self.model = Model(in_x, [policy_out, value_out], name=\"connect4_model\")\n\n    def _build_residual_block(self, x):\n        ''' Build a single residual block '''\n        mc = self.config.model\n        in_x = x\n        x = Conv2D(filters=mc.cnn_filter_num, kernel_size=mc.cnn_filter_size, padding=\"same\",\n                   data_format=\"channels_first\", kernel_regularizer=l2(mc.l2_reg))(x)\n        x = BatchNormalization(axis=1)(x)\n        x = Activation(\"relu\")(x)\n        x = Conv2D(filters=mc.cnn_filter_num, kernel_size=mc.cnn_filter_size, padding=\"same\",\n                   data_format=\"channels_first\", kernel_regularizer=l2(mc.l2_reg))(x)\n        x = BatchNormalization(axis=1)(x)\n        x = Add()([in_x, x])\n        x = Activation(\"relu\")(x)\n        return x\n\n    @staticmethod\n    def fetch_digest(weight_path):\n        if os.path.exists(weight_path):\n            m = hashlib.sha256()\n            with open(weight_path, \"rb\") as f:\n                m.update(f.read())\n            return m.hexdigest()\n\n    def load(self, config_path, weight_path):\n        ''' Load model with existing weights '''\n\n        if os.path.exists(config_path) and os.path.exists(weight_path):\n            logger.debug(f\"loading model from {weight_path}\")\n            with open(config_path, \"rt\") as f:\n                self.model = Model.from_config(json.load(f))\n            self.model.load_weights(weight_path)\n            self.digest = self.fetch_digest(weight_path)\n            logger.debug(f\"loaded model digest = {self.digest}\")\n            return True\n        else:\n            logger.debug(f\"model files does not exist at {config_path} and {weight_path}\")\n            return False\n\n    def save(self, config_path, weight_path):\n        '''\n        Save the model into specified path\n        :param config_path: config path to save at\n        :param weight_path: weight file path to save at\n        '''\n\n        logger.debug(f\"save model to {weight_path}\")\n        with open(config_path, \"wt\") as f:\n            json.dump(self.model.get_config(), f)\n            self.model.save_weights(weight_path)\n        #self.model.save(weight_path)\n        self.digest = self.fetch_digest(weight_path)\n        logger.debug(f\"saved model digest {self.digest}\")\n\n    def update_model(self, target):\n        '''\n        Update the model with the target model weights\n        :param target: the target model to copy from\n        '''\n        copy_layers = target.get_num_res_layers() * 7 + 4\n        model_layers = len(self.model.layers)\n        target_layers = len(target.model.layers)\n\n        # set the weights of the previous layers\n        for i in range(copy_layers):\n            weight = target.model.layers[i].get_weights()\n            self.model.layers[i].set_weights(weight)\n\n        # set the weights of the layers after the res block\n        for i in range(5 * 2 + 1):\n            weight = target.model.layers[target_layers-i-1].get_weights()\n            self.model.layers[model_layers-i-1].set_weights(weight)\n\n    def get_num_res_layers(self):\n        ''' Computes the number of layers in the architecture'''\n        print('model layers %f' % len(self.model.layers))\n        return int((len(self.model.layers) - 4 - 5 * 2 - 1) / 7)\n\ndef objective_function_for_policy(y_true, y_pred):\n    # can use categorical_crossentropy??\n    return K.sum(-y_true * K.log(y_pred + K.epsilon()), axis=-1)\n\ndef objective_function_for_value(y_true, y_pred):\n    return mean_squared_error(y_true, y_pred)\n\n\n","sub_path":"src/gomoku_zero/agent/model_gomoku.py","file_name":"model_gomoku.py","file_ext":"py","file_size_in_byte":6020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"606243491","text":"import pygame\n\n#Creation d'une fenetre de 1080x720\nwindow = pygame.display.set_mode((1080, 720))\n\n#importation de l'image de fond et creation de son rectangle\nbackground = pygame.image.load(\"decor/map.png\")\nmap_rect = background.get_rect()\nx_map, y_map = 0, 0\n\ngame = True\n\nwhile game:\n    #Efface tout l'ecran (noir)\n    window.fill((0, 0, 0))\n\n    #Quand touche espace appuyé ferme la fenetre\n    for event in pygame.event.get():\n        if event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_ESCAPE:\n                game = False\n\n    #Bouge le fond en fonction de la touche appuyé\n    key_move  = pygame.key.get_pressed()\n    if key_move[pygame.K_LEFT] and x_map < 0:\n        x_map += 10\n    elif key_move[pygame.K_RIGHT] and x_map > -map_rect[2]+1080:\n        x_map -= 10\n    if key_move[pygame.K_UP] and y_map < 0:\n        y_map += 10\n    elif key_move[pygame.K_DOWN] and y_map > -map_rect[3]+720:\n        y_map -= 10\n\n    #Affiche l'image de fond\n    window.blit(background, (x_map, y_map))\n    pygame.display.flip()\n\npygame.quit()\nexit()","sub_path":"map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"271960086","text":"import os\nimport json\nfrom association_finder import  freqDist, compute_words_cooccurence_frequency_from_file, compute_associations_strengths_coefficients\n\nrootDir = 'data/preprocessed_plwiki3'\ninput_data_tag = 'wiki'\nwindow_width = 12\nstimus_words = ['karabin', 'kula', 'wojsko', 'żołnierz']\nmin_number_of_occurences = 10\nnumber_of_printed_associations = 30\n\nalpha = 0.66\nbeta = 0.00002\ngamma = 0.00002\n\nocurrences = {}\ncooccurences = {}\n\nprint('Compute occurences')\nfor dirName, subdirList, fileList in os.walk(rootDir):\n\tprint(dirName)\n\tfor fname in fileList:\n\t\tinput_filename = os.path.join(dirName, fname)\n\t\tfreqDist(ocurrences, input_filename)\n\nprint('Compute coocurences')\nfor dirName, subdirList, fileList in os.walk(rootDir):\n\tprint(dirName)\n\tfor fname in fileList:\n\t\tinput_filename = os.path.join(dirName, fname)\n\t\tcompute_words_cooccurence_frequency_from_file(cooccurences, input_filename, ocurrences, stimus_words, window_width, min_number_of_occurences)\n\n\nprint('Compute associations')\nassociations = compute_associations_strengths_coefficients(\n\t\t\t\t\tcooccurences,\n\t\t\t\t\tocurrences,\n\t\t\t\t\talpha,\n\t\t\t\t\tbeta,\n\t\t\t\t\tgamma,\n\t\t\t\t\tsum(ocurrences.values()))\n\nfor stimus_word in stimus_words:\n\toutput_filename = 'data/' + input_data_tag + '_' + stimus_word + '.json'\n\twith open(output_filename, 'w') as fp:\n\t\tjson.dump(associations[stimus_word], fp, ensure_ascii=False)\n","sub_path":"find_associations_in_batch.py","file_name":"find_associations_in_batch.py","file_ext":"py","file_size_in_byte":1374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"380736460","text":"class config_Globals:\n\n       welcome_text = {\n           \"text\": \"LEMP Manager!\",\n           \"font\": \"slant\"\n       }\n\n       work_dir = {\n           \"name\": \"temp-dir\",\n           \"path\": \"/home/ubuntu\"\n       }\n\n       binaries_url = {\n           \"pig\": \"https://www-eu.apache.org/dist/pig/pig-0.17.0/pig-0.17.0.tar.gz\"\n\n       }\n\n       install_path = {\n           \"pig\": \"/usr/local/pig-0.17.0\"\n       }\n\n\n       load = {\n           \"text\": \"Loading\",\n           \"spinner\": \"dots\"\n       }\n\n       logs = {\n           \"nginx-access\": \"/var/log/nginx/access.log\",\n           \"nginx-error\": \"/var/log/nginx/access.log\",\n           \"mysql-error\": \"/var/log/mysql/error.log\",\n           \"php\": \"not configured yet\"\n       }\n\n","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"63026266","text":"import abc\nfrom random import randint\nimport mailbox\nimport re\n\n\nclass Detector(object):\n    __metaclass__ = abc.ABCMeta\n\n    @abc.abstractmethod\n    def create_sender_profile(self):\n        \"\"\"Creates sender to profile map.\n           Ex: sender_profile = {}\n               sender_profile[\"jenna\"] = set([\"text/plain\", \"multipart/mixed\"])\n           return: sender_profile.\n        \"\"\"\n        return\n\n    @abc.abstractmethod\n    def classify(self, phish):\n        \"\"\"Determine if phish is detected as a phishing email by checking if\n           it is in the sender's profile.\n           input: type(phish) = mailbox.Message\n           return: boolean value. \"\"\"\n        return\n\n    @abc.abstractmethod\n    def check_header(self, msg):\n        \"\"\"Checks if msg contains the desired email header field. ex: x-mailer.\n           input: type(msg) = mailbox.Message\n           return: boolean value. \"\"\"\n        return\n    \n    @abc.abstractmethod\n    def modify_phish(self, phish, msg):\n        \"\"\"Adds the desired email header field from msg to phish.\n           input: type(msg) = mailbox.Message\n           input: type(phish) = mailbox.Message\n           return: phish. \"\"\"\n        return\n                          \n    def length(self):\n        return len(self.inbox)\n   \n    def extract_from(self, msg):\n        from_header = msg[\"From\"]\n        if not from_header:\n            return None\n        from_header = from_header.lower()\n        r = re.compile(\" *<.*> *\")\n        from_header = r.sub(\"\", from_header)\n        r = re.compile(\"^ +\")\n        from_header = r.sub(\"\", from_header)\n        return from_header\n\n    def make_phish(self):\n        has_sender = None\n        random_msg = None\n        random_from = None\n        validEmail = None\n        while not has_sender or not validEmail:\n            random_msg = self.inbox[randint(0, len(self.inbox)-1)]\n            random_from = self.inbox[randint(0, len(self.inbox)-1)]\n            has_sender = random_from[\"From\"]\n            validEmail = not (\"List-Unsubscribe\" in random_from.keys() or \"nexusapoorvacus19@gmail.com\" in random_from[\"From\"])\n            \n        phish = mailbox.mboxMessage()\n        phish['From'] = random_from['From']\n        phish['To'] = random_msg['To']\n        phish['Subject'] = random_msg['Subject'] \n        phish = self.modify_phish(phish, random_msg)\n        phish.set_payload(\"This is the body for a generated phishing email.\\n\")\n        return phish\n    \n    def run_trials(self, num_trials=1000):\n        self.detected = 0\n        for i in range(num_trials):\n            phish = self.make_phish()\n            if self.classify(phish):\n                self.detected += 1\n        return self.detected*1.0 / num_trials * 100.0\n\n\n\n\n        \n\n\n\n","sub_path":"experimentation/apoorva/detector.py","file_name":"detector.py","file_ext":"py","file_size_in_byte":2732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"605094026","text":"import cv2\nfrom path_names import PathNamesSegmentation as pns\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import ndimage as ndi\n\nfrom skimage import morphology\n\nOUTPUT_FOLDER_MASK = pns.SEGMENTED + \"otsu\\\\mask\\\\image2\"\nOUTPUT_FOLDER_SEGMENTED = pns.SEGMENTED + \"otsu\\\\segmented\\\\image2\"\n\n\ndef do_Otsu(file_path, output_path):\n    image_color = cv2.imread(pns.DEFAULT_IMAGE)\n    image_color = cv2.cvtColor(image_color,cv2.COLOR_BGR2RGB)\n    image = cv2.imread(file_path, 0)\n    ret1, thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n    thresh = cv2.morphologyEx(thresh,cv2.MORPH_ERODE,np.ones((8,8)))\n    thresh = cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,np.ones((3,3)))\n\n    thresh = ndi.binary_fill_holes(thresh,np.ones((5,5)))\n    thresh = morphology.remove_small_objects(thresh, 950,connectivity=8)\n    thresh = thresh.astype(np.uint8)\n\n    mask = np.zeros((image.shape[0], image.shape[1], 3), dtype=np.uint8)\n    mask[thresh == 0] = [255, 255, 255]\n    mask = cv2.bitwise_not(mask)\n    edged = cv2.Canny(mask, 100, 210)\n\n    contours, hierarchy = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n    cv2.drawContours(mask, contours, -1, (255, 0, 0), 1)\n\n    segmented = cv2.bitwise_and(image_color, mask)\n\n    plt.imsave(OUTPUT_FOLDER_SEGMENTED + output_path, segmented)\n    plt.imsave(OUTPUT_FOLDER_MASK + output_path, mask)\n\n\ndo_Otsu(pns.DEFAULT_IMAGE,\"\\\\default_image2.png\")\n#\ndo_Otsu(pns.GAUSSIAN_LOW, \"\\\\gaussian\\\\gaussian_low.png\")\ndo_Otsu(pns.GAUSSIAN_MODERATE, \"\\\\gaussian\\\\gaussian_moderate.png\")\ndo_Otsu(pns.GAUSSIAN_HIGH, \"\\\\gaussian\\\\gaussian_high.png\")\n\ndo_Otsu(pns.LAPLACIAN_LOW, \"\\\\laplacian\\\\laplacian_low.png\")\ndo_Otsu(pns.LAPLACIAN_MODERATE, \"\\\\laplacian\\\\laplacian_moderate.png\")\ndo_Otsu(pns.LAPLACIAN_HIGH, \"\\\\laplacian\\\\laplacian_high.png\")\n\ndo_Otsu(pns.POISSON_LOW, \"\\\\poisson\\\\poisson_low.png\")\ndo_Otsu(pns.POISSON_MODERATE, \"\\\\poisson\\\\poisson_moderate.png\")\ndo_Otsu(pns.POISSON_HIGH, \"\\\\poisson\\\\poisson_high.png\")\n\ndo_Otsu(pns.SPECKLE_LOW, \"\\\\speckle\\\\speckle_low.png\")\ndo_Otsu(pns.SPECKLE_MODERATE, \"\\\\speckle\\\\speckle_moderate.png\")\ndo_Otsu(pns.SPECKLE_HIGH, \"\\\\speckle\\\\speckle_high.png\")\n\ndo_Otsu(pns.UNIFORM_LOW, \"\\\\uniform\\\\uniform_low.png\")\ndo_Otsu(pns.UNIFORM_MODERATE, \"\\\\uniform\\\\uniform_moderate.png\")\ndo_Otsu(pns.UNIFORM_HIGH, \"\\\\uniform\\\\uniform_high.png\")\n\ndo_Otsu(pns.PEPPER_LOW, \"\\\\pepper\\\\pepper_low.png\")\ndo_Otsu(pns.PEPPER_MODERATE, \"\\\\pepper\\\\pepper_moderate.png\")\ndo_Otsu(pns.PEPPER_HIGH, \"\\\\pepper\\\\pepper_high.png\")\n\ndo_Otsu(pns.SALT_LOW, \"\\\\salt\\\\saltr_low.png\")\ndo_Otsu(pns.SALT_MODERATE, \"\\\\salt\\\\salt_moderate.png\")\ndo_Otsu(pns.SALT_HIGH, \"\\\\salt\\\\saltr_high.png\")\n\ndo_Otsu(pns.SP_LOW, \"\\\\salt&pepper\\\\salt&pepper_low.png\")\ndo_Otsu(pns.SP_MODERATE, \"\\\\salt&pepper\\\\salt&pepper_moderate.png\")\ndo_Otsu(pns.SP_HIGH, \"\\\\salt&pepper\\\\salt&pepper_high.png\")\n\n\n\n\n\n\n\n# image = cv2.imread(pns.GAUSSIAN_HIGH,0)\n# image_color = cv2.imread(pns.GAUSSIAN_HIGH)\n# image_color = cv2.cvtColor(image_color,cv2.COLOR_BGR2RGB)\n# new_image = np.zeros((image.shape[0],image.shape[1], 3), dtype=np.uint8)\n# ret1, thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n# thresh = ndi.binary_fill_holes(thresh)\n# thresh = thresh.astype(np.uint8)\n# new_image[thresh==1] = [255,255,255]\n#\n# result = cv2.bitwise_and(image_color,new_image)\n# plt.imshow(result)\n# plt.show()\n\n\n# POMARANCZE\n# image = cv2.imread(pns.ORIGINAL2,0)\n# ret1, segmented = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n# segmented = cv2.morphologyEx(segmented,cv2.MORPH_OPEN,np.ones((7,7)))\n\n# NOWOTWÓR\n# image = cv2.imread(pns.ORIGINAL1)\n# image = image[:,:,0]\n# ret1, segmented = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n# segmented = cv2.morphologyEx(segmented,cv2.MORPH_OPEN,np.ones((4,4)))\n\n# def imshow_components(labels):\n#     # Map component labels to hue val\n#     label_hue = np.uint8(179*labels/np.max(labels))\n#     blank_ch = 255*np.ones_like(label_hue)\n#     labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])\n#\n#     # cvt to BGR for display\n#     labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)\n#\n#     # set bg label to black\n#     labeled_img[label_hue==0] = 0\n#\n#     cv2.imshow('labeled.png', labeled_img)\n#     cv2.waitKey()\n","sub_path":"files/segmentation/otsu/OtsuImage2.py","file_name":"OtsuImage2.py","file_ext":"py","file_size_in_byte":4295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"69591906","text":"from __future__ import print_function\r\nimport sys\r\nfrom datetime import datetime\r\nfrom flask import render_template, flash, redirect, url_for, request\r\nfrom flask_sqlalchemy import sqlalchemy\r\n\r\nfrom app import app, db\r\n\r\nfrom app.forms import PostForm, SortForm\r\nfrom app.models import Post, Tag, postTags\r\n\r\n\r\n@app.before_first_request\r\ndef initDB(*args, **kwargs):\r\n    db.create_all()\r\n    if Tag.query.count() == 0:\r\n        tags = ['funny', 'inspiring', 'true-story', 'heartwarming', 'friendship']\r\n        for t in tags:\r\n            db.session.add(Tag(name=t))\r\n        db.session.commit()\r\n\r\n\r\n@app.route('/', methods=['GET','POST'])\r\n@app.route('/index', methods=['GET','POST'])\r\ndef index():\r\n    number_of_smiles = Post.query.count()\r\n    sortform = SortForm()\r\n    if sortform.validate_on_submit():\r\n        sort_id = int(sortform.sort_by.data)\r\n        if sort_id == 4: # date\r\n            posts = Post.query.order_by(Post.timestamp.desc())\r\n        elif sort_id == 3: # title\r\n            posts = Post.query.order_by(Post.title.asc())\r\n        elif sort_id == 2: # num likes\r\n            posts = Post.query.order_by(Post.likes.desc())\r\n        else: # happiness level\r\n            posts = Post.query.order_by(Post.happiness_level.desc())\r\n        return render_template('index.html', title=\"Smile Portal\", posts=posts.all(), number_of_smiles=number_of_smiles, sort=sortform)\r\n    \r\n    posts = Post.query.order_by(Post.timestamp.desc())\r\n    return render_template('index.html', title=\"Smile Portal\", posts=posts.all(), number_of_smiles=number_of_smiles, sort=sortform)\r\n\r\n\r\n@app.route('/postsmile', methods=['GET', 'POST'])\r\ndef postsmile():\r\n    form = PostForm(csrf_enabled=True)\r\n    if request.method == 'POST':\r\n        if form.validate_on_submit():\r\n            p = Post(title=request.form['title'], body=request.form['body'],\r\n                     happiness_level=request.form['happiness_level'])\r\n            for tag in form.tag.data:  # Appends all tags to post form.\r\n                p.tags.append(tag)\r\n            db.session.add(p)\r\n            db.session.commit()\r\n            flash('Posted!')\r\n            return redirect(url_for('index'))\r\n        else:\r\n            flash('All fields were not entered', 'error')\r\n    return render_template('create.html', form=form)\r\n\r\n\r\n@app.route('/likepost/<post_id>', methods=['GET'])\r\ndef like_post(post_id):\r\n    post = Post.query.filter_by(id=post_id).first()\r\n    post.likes += 1\r\n    db.session.commit()\r\n    return redirect(url_for('index'))\r\n","sub_path":"blogPostSite/getty-warmup/app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":2518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"96701982","text":"#!/usr/bin/python\n# Original Author: Christopher Nyland\n# Original Creation Date: 2012/09/14\n\"\"\"\nThis __init__ file dynamically compiles all the UI files in its directory,\ninterrogates them for there class names. And then imports those class names\ninto this module.\n\"\"\"\nimport os\nimport sys\nimport ast\nimport py_compile\nfrom PyQt4 import uic\n\nfile_names = []\n\n\nclass ClassParser(ast.NodeVisitor):\n\n    \"\"\" This class is used with the AST model to collect all the class names\n    in a file\n\n    \"\"\"\n\n    def __init__(self):\n        self.class_names = []\n\n    def parse(self, code):\n        \"\"\" parser function\n\n        This function is the actual parser of the code. Its input is the code\n        to be parsed and is the one that calls the visitors for each part.\n        It returns the class names\n        \"\"\"\n        self.class_names = []\n        tree = ast.parse(code)\n        self.visit(tree)\n        return self.class_names\n\n    def visit_ClassDef(self, stmt):\n        \"\"\" Visitor for class definitions \"\"\"\n        self.class_names.append(stmt.name)\n        super(ClassParser, self).generic_visit(stmt)\n\n\ndef modname(ui_dir, ui_file):\n    \"\"\" This is a rename mapping function for compileUiDir \"\"\"\n    out_dir = ui_dir\n    out_file = ui_file.replace('.py', '_Ui.py')\n#     out_file = '{0}'.format(ui_file)\n    out_path = os.path.join(out_dir, out_file)\n    file_names.append(out_path)\n    return (out_dir, out_file)\n    \nif not hasattr(sys, '_MEIPASS'):\n    parser = ClassParser()\n    file_classes = {}\n\n    # Create py files from ui files\n    # file_names is passed around here as it is outside of each functions scope\n    pth = os.path.split(__file__)[0]\n    if not pth:\n        pth = './'\n    if [k for k in os.listdir(pth) if '.ui' in k]:\n        uic.compileUiDir(pth, map=modname, execute=True)\n\n    # Parse those py_ui files for the class names\n    for File in file_names:\n        with open(File, 'rb') as fid:\n            code = fid.read()\n        file_classes[File] = parser.parse(code)\n\n    # Import the Classes\n    # First split the file names to get the import modules then append those to the\n    # package name in dot format. I the from list include the class names from the\n    # AST parse above. Make the import into the local variable \"_\". Then move the\n    # classes we want into the local name space with getattr.\n    for File, k_list in file_classes.items():\n        mod = os.path.splitext(os.path.split(File)[1])[0]\n        import_stmt = \"{}.{}\".format(__package__, mod)\n        _ = __import__(import_stmt, fromlist=k_list)\n        for klass in k_list:\n            locals()[klass] = getattr(_, klass)\n","sub_path":"UIFiles/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"128049013","text":"import math\n\ndef fatorial(n):\n  fact=1\n  for i in range(1,n+1):\n    fact=fact*i\n  return fact\n\ndef seno(x,j):\n  soma=0\n  for n in range(0,j+1):\n    soma=soma+(math.pow(-1,n)*math.pow(x,2*n+1)/fatorial(2*n+1))\n  return soma\n\ndef cosseno(x,j):\n  soma=0\n  for n in range(0,j+1):\n    soma=soma+(math.pow(-1,n)*math.pow(x,2*n)/fatorial(2*n))\n  return soma\n  \n\nprint(\"Sin serie=> {0}, math=> {1}\".format(seno(0.4,1),math.sin(0.4)))\n\nerro_absoluto=math.fabs(seno(0.4,1)-math.sin(0.4))/math.sin(0.4)\n\nprint(\"Error=>\",erro_absoluto)\n","sub_path":"serie_seno_cosseno.py","file_name":"serie_seno_cosseno.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"467183058","text":"\n# Louis Philippe B. Facun\n# DogeClick Bot Channel from dogeclick.com\n# Auto joiner (/join)\n\nimport asyncio\nimport logging\nimport re\nimport time\nimport os\nimport sys\n\nlogging.basicConfig(level=logging.ERROR)\n\nfrom telethon import TelegramClient, events\nfrom telethon.tl.functions.channels import JoinChannelRequest\nfrom telethon.tl.functions.messages import GetBotCallbackAnswerRequest\nfrom datetime import datetime\nfrom colorama import Fore, init as color_ama\ncolor_ama(autoreset=True)\n\nos.system('cls' if os.name=='nt' else 'clear')\n\n# Get your own values from my.telegram.org\napi_id = 800812\napi_hash = 'db55ad67a98df35667ca788b97f771f5'\n\n''' DogeClick Bot Channel from dogeclick.com\nOptions:\n1. Dogecoin_click_bot\n2. Litecoin_click_bot\n3. BCH_click_bot\n4. Zcash_click_bot\n5. Bitcoinclick_bot\n# '''\ndogeclick_channel = 'Dogecoin_click_bot'\n\ndef print_msg_time(message):\n\tprint('[' + Fore.CYAN + f'{datetime.now().strftime(\"%H:%M:%S\")}' + Fore.RESET + f'] {message}')\n\nasync def main():\n\tprint(Fore.MAGENTA + '__      _____ _    ___ ___  __  __ ___ ')\n\tprint(Fore.MAGENTA + '\\ \\    / / __| |  / __/ _ \\|  \\/  | __|')\n\tprint(Fore.MAGENTA + ' \\ \\/\\/ /| _|| |_| (_| (_) | |\\/| | _| ')\n\tprint(Fore.MAGENTA + '  \\_/\\_/ |___|____\\___\\___/|_|  |_|___|\\n' + Fore.RESET)\n\tprint(Fore.GREEN + '    -   BY LOUIS PHILIPPE FACUN   -   \\n' + Fore.RESET)\n                                \n\t# Check if phone number is not specified\n\tif len(sys.argv) < 2:\n\t\tprint('Usage: python start.py phone_number')\n\t\tprint('-> Input number in international format (example: +639162995600)\\n')\n\t\te = input('Press any key to exit...')\n\t\texit(1)\n\t\t\n\tphone_number = sys.argv[1]\n\t\n\tif not os.path.exists(\"session\"):\n\t\tos.mkdir(\"session\")\n   \n\t# Connect to client\n\tclient = TelegramClient('session/' + phone_number, api_id, api_hash)\n\tawait client.start(phone_number)\n\tme = await client.get_me()\n\t\n\tprint(f'Current account: {me.first_name}({me.username})\\n')\n\tprint_msg_time('Sending /bots command')\n\t\n\t# Start command /bots\n\tawait client.send_message(dogeclick_channel, '/bots')\n\t\n\t# Message the bot\n\t@client.on(events.NewMessage(chats=dogeclick_channel, incoming=True))\n\tasync def join_start(event):\n\t\tmessage = event.raw_text\n\t\tif 'Forward a message to' in message:\t\n\t\t\tchannel_msg = event.original_update.message.reply_markup.rows[0].buttons[0].url\n\t\t\tprint_msg_time(f'URL @{channel_msg}')\n\t\t\t\n\t\t\tif '?' in channel_msg:\n\t\t\t\tchannel_name = re.search(r't.me\\/(.*?)\\?', channel_msg).group(1)\n\t\t\telif '?' not in channel_msg:\n\t\t\t\tchannel_name = re.search(r't.me\\/(.*?)', channel_msg).group(1)\n\t\t\t\n\t\t\tprint_msg_time(f'Messaging @{channel_name}...')\n\t\t\tawait client.send_message(channel_name, '/start')\n\t\t\t\n\t\t\t# Forward the bot message\n\t\t\t@client.on(events.NewMessage(chats=channel_name, incoming=True))\n\t\t\tasync def earned_amount(event):\n\t\t\t\tmsg_id = event.message.id,\n\t\t\t\tawait client.forward_messages(dogeclick_channel, msg_id, channel_name)\n\t\n\t# Print earned amount\n\t@client.on(events.NewMessage(chats=dogeclick_channel, incoming=True))\n\tasync def earned_amount(event):\n\t\tmessage = event.raw_text\n\t\tif 'You earned' in message:\t\n\t\t\tprint_msg_time(Fore.GREEN + event.raw_text + '\\n' + Fore.RESET)\n\t\t\t\n\t# No more ads\n\t@client.on(events.NewMessage(chats=dogeclick_channel, incoming=True))\n\tasync def no_ads(event):\n\t\tmessage = event.raw_text\n\t\tif 'no new ads available' in message:\t\n\t\t\tprint_msg_time(Fore.RED + 'Sorry, there are no new ads available\\n' + Fore.RESET)\n\t\t\te = input('Press any key to exit...')\n\t\t\texit(1)\n\t\t\t\n\tawait client.run_until_disconnected()\n\t\nasyncio.get_event_loop().run_until_complete(main())\n","sub_path":"msg.py","file_name":"msg.py","file_ext":"py","file_size_in_byte":3579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"22935680","text":"# Copyright (c) 2012 Web Notes Technologies Pvt Ltd (http://erpnext.com)\n# \n# MIT License (MIT)\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 IMPLIED, \n# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A \n# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT \n# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF \n# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE \n# OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n# \n\nverbose = False\nimport os\t\t\t\t\n\ndef build():\n\t\"\"\"concat / minify js files\"\"\"\n\tfrom py.build.bundle import Bundle\n\tbundle = Bundle()\n\tbundle.make('build.json')\n\tincrement_version()\n\ndef get_version():\n\t\"\"\"get from version.num file and increment it\"\"\"\n\tif os.path.exists('version.num'):\n\t\twith open('version.num', 'r') as vfile:\n\t\t\tversion = int(vfile.read()) + 1\n\telse:\n\t\tversion = 1\n\n\treturn version\n\ndef increment_version():\n\t\"\"\"incremenet version by 1\"\"\"\n\tversion = get_version()\n\twith open('version.num', 'w') as vfile:\n\t\tvfile.write(str(version))\n\t\t\n\treturn version","sub_path":"py/build/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"58467841","text":"#!/usr/bin/python\nimport random\n\nmaxNum = 1000000\n# The array that is to be sorted\narr = [int(maxNum * random.random()) for i in range(10000)]\n\nfor i in range(1, len(arr)):\n    x = arr[i]\n    j = i - 1\n    while j >= 0 and arr[j] > x:\n        arr[j + 1] = arr[j]\n        j -= 1\n    arr[j + 1] = x\nprint(arr)\n","sub_path":"Algorithms/Sequences/Insertion Sort/InsertionSort.py","file_name":"InsertionSort.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"498426029","text":"# post.forms for lionlist\n# by Ian Zapolsky\n#\n# django imports\nimport traceback\n \nfrom django import forms\n\n# app imports\nfrom .models import PostModel, BookModel\n# from .models import TicketModel\n\n\nTYPE_CHOICES = (\n    ('forsale', 'for sale'),\n    ('wanted', 'wanted'),\n)\n\nclass PostForm(forms.ModelForm):\n\n    class Meta:\n        model = PostModel\n        fields = ['title', 'description', 'image', 'forsale', 'price']\n        widgets = {\n            'forsale': forms.Select(choices=TYPE_CHOICES),\n            'description': forms.Textarea(),\n        }\n\n    def __init__(self, *args, **kwargs):\n        super(PostForm, self).__init__(*args, **kwargs)\n        self.fields['title'].label = 'post title'\n        self.fields['title'].help_text = '(required)'\n        self.fields['description'].label = 'description'\n        self.fields['description'].help_text = '(optional)'\n        self.fields['image'].label = 'image'\n        self.fields['image'].help_text = '(optional)'\n        self.fields['forsale'].label = 'for sale or wanted' \n        self.fields['forsale'].help_text = '(required)'\n        self.fields['price'].label = 'price'\n        self.fields['price'].help_text = '(required) -- e.g. 15, 15.0 or 15.00'\n\n    def clean(self):\n        cleaned_data = super(PostForm, self).clean()\n        title = cleaned_data.get(\"title\")\n        price = cleaned_data.get(\"price\")\n\n        try:\n            existing = PostModel.objects.get(title=title)\n        except:\n            existing = None\n        if existing and existing != self.instance:\n            msg = ('please choose a different title, this title is already taken')\n            self._errors['title'] = self.error_class([msg])\n            del cleaned_data['title']\n        return cleaned_data\n\n\nclass BookForm(forms.ModelForm):\n\n    def __init__(self, *args, **kwargs):\n        super(BookForm, self).__init__(*args, **kwargs)\n        self.fields['title'].label = 'post title'\n        self.fields['title'].help_text = '(required)'\n        self.fields['description'].label = 'description'\n        self.fields['description'].help_text = '(optional)'\n        self.fields['image'].label = 'image'\n        self.fields['image'].help_text = '(optional)'\n        self.fields['forsale'].label = 'for sale or wanted' \n        self.fields['forsale'].help_text = '(required)'\n        self.fields['price'].label = 'price'\n        self.fields['price'].help_text = '(required) -- e.g. 15, 15.0, 15.00'\n        self.fields['author'].label = 'author'\n        self.fields['author'].help_text = '(required)'\n        self.fields['edition'].label = 'edition'\n        self.fields['edition'].help_text = '(optional) -- e.g. 7'\n        self.fields['isbn'].label = 'isbn'\n        self.fields['isbn'].help_text = '(optional) -- please do not use dashes'\n        self.fields['course'].label = 'department/course'\n        self.fields['course'].help_text = '(required) -- if not affiliated with a course please select the department/course NOT AFFILIATED WITH COURSE'\n    \n    class Meta:\n        model = BookModel\n        fields = ['title', 'author', 'edition', 'isbn', \n                  'course', 'description', 'image', 'forsale', 'price'] \n        widgets = {\n            'forsale': forms.Select(choices=TYPE_CHOICES),\n            'course': forms.Select(),\n            'description': forms.Textarea(),\n        }\n\n    def clean(self):\n        cleaned_data = super(BookForm, self).clean()\n        title = cleaned_data.get(\"title\")\n\n        try:\n            existing = BookModel.objects.get(title=title)\n        except:\n            existing = None\n        if existing and existing != self.instance:\n            msg = ('please choose a different title, this title is already taken')\n            self._errors['title'] = self.error_class([msg])\n            del cleaned_data['title']\n        return cleaned_data\n","sub_path":"lionlist/post/post/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"483655910","text":"from django.contrib.contenttypes.models import ContentType\nimport pyexcel as pe\nfrom django.core.management.base import BaseCommand, CommandError\nfrom django.core.files import File\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.conf import settings\nfrom django.shortcuts import get_list_or_404\nfrom company_db.models import CompanyMaster\nimport datetime\nimport os\nfrom student_db.models import StudentUser\n\n\nclass Command(BaseCommand):\n\n\tdef add_arguments(self, parser):\n\t\tparser.add_argument('which_ps', nargs='+')\n\n\tdef handle(self, **options):\n\t\tprint(options['which_ps'])\n\t\tusers= get_list_or_404(StudentUser, user__is_active=True,which_ps=options['which_ps'][0])\n\t\tstart_dur=users[0].duration_start\n\t\tend_dur=users[0].duration_end\n\t\tduration = str(start_dur)+'-'+str(end_dur)\n\t\tcompany_master = CompanyMaster.objects.all()\n\t\tsheet = pe.Sheet()\n\t\tsheet.row += [\n\t\t\t'Company Code',\n\t\t\t'Company Name',\n\t\t\t'Broad Location',\n\t\t\t'Address',\n\t\t\t'Description',\n\t\t\t'Phone Number',\n\t\t\t'Website',\n\t\t\t]\n\n\t\tfor company in company_master:\n\t\t\ttemp = [\n\t\t\tcompany.company_code,\n\t\t\tcompany.name,\n\t\t\tcompany.broad_location.location_name,\n\t\t\tcompany.address,\n\t\t\tcompany.description,\n\t\t\tcompany.phone_no,\n\t\t\tcompany.website,\n\t\t\t]\n\t\t\tsheet.row += temp\n\n\t\troot = settings.MEDIA_ROOT\n\t\tdirectory = duration\n\t\tif options['which_ps'][0] == 'ps1':\n\t\t\tdire = os.path.join(root, 'Archived_Data', directory, 'PS-I')\n\t\telif options['which_ps'][0] == 'ps2':\n\t\t\tdire = os.path.join(root, 'Archived_Data', directory, 'PS-II')\n\t\telse:\n\t\t\tdire = os.path.join(root, 'Archived_Data', directory, 'PS-III')\n\t\tif not os.path.exists(dire):\n\t\t\tos.makedirs(dire)\n\t\tfilename = dire + '/Company_Master.xlsx'\n\t\tsheet.save_as(filename)\n","sub_path":"company_db/management/commands/export_companymaster.py","file_name":"export_companymaster.py","file_ext":"py","file_size_in_byte":1717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"86102903","text":"from importlib import import_module\nimport argparse\nfrom termcolor import colored\nfrom . import constants as const\n\n\ndef _show_logo():\n    print(colored(const.ascii_logo, 'cyan'))\n    print()\n\n\ndef cli():\n    _show_logo()\n\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('command', type=str, help='command to execute')\n    parser.add_argument('--backend', type=str,\n                        help='hypergraph backend (leveldb, null)',\n                        default='leveldb')\n    parser.add_argument('--hg', type=str,\n                        help='hypergraph name', default='gb.hg')\n    parser.add_argument('--infile', type=str, help='input file', default=None)\n    parser.add_argument('--outfile', type=str,\n                        help='output file', default=None)\n    parser.add_argument('--fields', type=str, help='field names', default=None)\n    parser.add_argument('--show_namespaces',\n                        help='show namespaces', action='store_true')\n    parser.add_argument('--lang', type=str, help='language', default='en')\n\n    args = parser.parse_args()\n\n    params = {\n        'backend': args.backend,\n        'hg': args.hg,\n        'infile': args.infile,\n        'outfile': args.outfile,\n        'log': args.log,\n        'fields': args.fields,\n        'model_type': args.model_type,\n        'model_file': args.model_file,\n        'show_namespaces': args.show_namespaces,\n        'lang': args.lang\n    }\n\n    command = args.command\n\n    try:\n        cmd_module = import_module('graphbrain.commands.%s' % command)\n        cmd_module.run(params)\n    except ImportError:\n        print('unkown command: %s' % command)\n\n\nif __name__ == '__main__':\n    cli()\n","sub_path":"graphbrain/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"230607435","text":"class Pessoa:\n\n    def __init__(self, nome, cpf, email, endereco, data_nasc, telefone):\n        self.nome = nome\n        self.data_nasc = data_nasc\n        self.cpf = cpf\n        self.email = email\n        self.telefone = telefone\n        self.endereco = endereco\n\n\n    def __str__(self):\n        return f\"{self.nome}_{self.data_nasc}_{self.cpf}_{self.email}_{self.telefone}_{self.endereco}\"\n\n\n","sub_path":"Exercicios/Aulas02/Aula05/pessoas.py","file_name":"pessoas.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"194277464","text":"from __future__ import print_function, division, absolute_import\nimport os, h5py, glob\n# Use scikit-learn to grid search the batch size and epochs\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nn=0\nfactor=1000.\n\ndata_path = \"/pylon5/as5phnp/tbilling/sandbox/hyper_param_optimiz/ml_paper1/opt_versions/wedgefilter_v9/\"\n\nresults = glob.glob(data_path+\"bestmodel_pred_results_*.npy\")\nfold = [\"Fold 1\",\"Fold 2\",\"Fold 3\",\"Fold 4\",\"Fold 5\",\"Fold 6\",\"Fold 7\",\"Fold 8\",\"Fold 9\",\"Fold 10\"]\n\n\nfig, axes = plt.subplots(figsize=(10,8),nrows=2, ncols=1, sharex=False, sharey=False)\nfor r, res in enumerate(results):\n    #load reaults\n    result=np.load(res)\n\n    axes[0].scatter(result[\"truth\"][:,n]/factor, result[\"prediction\"][:,n]/factor,\n    s=6, lw=0, alpha=0.9, label=fold[r])\n    x = np.linspace(0.95*np.min(result[\"truth\"][:,n]/factor),\n                    1.05*np.max(result[\"truth\"][:,n]/factor), 1000)\n    axes[0].plot(x, x, 'k--',lw=5,alpha=0.2)\n\n\n    axes[1].plot(x-x, 'k--',lw=5,alpha=0.2)\n    relative_error = (result[\"prediction\"][:,n]/factor-result[\"truth\"][:,n]/factor)/result[\"truth\"][:,n]\n    axes[1].scatter(result[\"truth\"][:,n]/factor, relative_error,s=6, lw=0, alpha=0.9, label=fold[r])\n\naxes[1].set_xlabel('True')\naxes[0].set_xlim(0.028,0.1)\naxes[1].set_xlim(0.028,0.1)\naxes[0].set_xscale('log')\naxes[1].set_xscale('linear')\n\naxes[0].set_ylabel('Predicted')\naxes[0].set_ylim(0.028,0.1)\naxes[1].set_ylabel('Residual Relative Difference')\naxes[0].set_yscale('log')\naxes[1].set_yscale('linear')\n\naxes[0].legend(markerscale=2.5)\nplt.savefig(data_path+\"bestmodel_residual_linearScale.png\")\n#axes[1].legend(markerscale=2.5)\n","sub_path":"ML/ML_Paper1_Scripts/plot_predictions_relativediff.py","file_name":"plot_predictions_relativediff.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"436961093","text":"import numpy as np\nimport random\nimport torch\nfrom collections import deque, namedtuple\n\nclass ReplayBuffer:\n    \"\"\"Fixed-size buffer to store experience tuples.\"\"\"\n\n    def __init__(self, buffer_size, batch_size, device):\n        \"\"\"Initialize a ReplayBuffer object.\n        Params\n        ======\n            buffer_size (int): maximum size of buffer\n            batch_size (int): size of each training batch\n            seed (int): random seed\n        \"\"\"\n        self.device = device\n        self.memory = deque(maxlen=buffer_size)  \n        self.batch_size = batch_size\n        self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n    \n    def add(self, state, action, reward, next_state, done):\n        \"\"\"Add a new experience to memory.\"\"\"\n        e = self.experience(state, action, reward, next_state, done)\n        self.memory.append(e)\n    \n    def sample(self):\n        \"\"\"Randomly sample a batch of experiences from memory.\"\"\"\n        experiences = random.sample(self.memory, k=self.batch_size)\n\n        tmp = [e.state for e in experiences if e is not None and len(e.state)!=2]\n        print(tmp)\n        states = torch.from_numpy(np.stack([e.state for e in experiences if e is not None and len(e.state)!=2])).float().to(self.device)\n        if states.shape != (self.batch_size, 4):\n            states = torch.cat((states, torch.zeros((self.batch_size -states.shape[0] ), 4)), dim=0)\n        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(self.device)\n        if actions.shape != (self.batch_size, 1):\n            actions = torch.cat((actions, torch.zeros(self.batch_size -states.shape[0], 1)), dim=0)\n        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(self.device)\n        if rewards.shape != (self.batch_size, 1):\n            rewards = torch.cat((rewards, torch.zeros(self.batch_size -states.shape[0], 1)), dim=0)\n        next_states = torch.from_numpy(np.stack([e.next_state for e in experiences if e is not None and len(e.state)!=2])).float().to(self.device)\n        if next_states.shape != (self.batch_size, 4):\n            next_states = torch.cat((next_states, torch.zeros(self.batch_size -next_states.shape[0], 4)), dim=0)\n        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(self.device)\n        if dones.shape != (self.batch_size, 1):\n            dones = torch.cat((dones, torch.zeros(self.batch_size -states.shape[0], 1)), dim=0)\n  \n        return (states, actions, rewards, next_states, dones)\n\n    def __len__(self):\n        \"\"\"Return the current size of internal memory.\"\"\"\n        return len(self.memory)\n","sub_path":"CQL/buffer.py","file_name":"buffer.py","file_ext":"py","file_size_in_byte":2759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"56905686","text":"###############################################################\n\n# enedis_sensor.py\n# Données mensuelles des kwh de chez Enedis/Linky\n# https://github.com/KaoruKanon\n\n###############################################################\n\nfrom requests import get\nfrom datetime import datetime\nimport os\n\nabspath = os.path.abspath(__file__)\ndname = os.path.dirname(abspath)\nos.chdir(dname)\n\nd = datetime.now()\n\n# restful HASS\nurl = \"http://[REDACTED IP:PORT]/api/states/sensor.myenedis\"\nheaders = {\n    \"Authorization\": \"Bearer [REDACTED API]\",\n    \"content-type\": \"application/json\",\n}\n\nresponse = get(url, headers=headers).json()\n\n# get kwh value from json dict restful HASS\nkwh_current_month = int(round(response['attributes']['current_month'] /1000))\nkwh_last_month = int(round(response['attributes']['last_month'] / 1000))\n\n#open data \nwith open('data.kwh') as f:\n    data = f.readline()\n\n# split data with comma sepator\ndata = data.split(\",\")\n\n# get date and months \ndate = d.strftime(\"%F\")\ncurrent_month = int(d.strftime(\"%m\"))\nlast_month = int(d.strftime(\"%m\")) - 1\ndate_last_month = data[last_month - 1 ].split(' ')[1]\n\n\n# write kwh to data\ndata[current_month - 1] = str(current_month) + ' ' +  date + ' ' + str(kwh_current_month)\ndata[last_month - 1] = \"{0:0=2d}\".format(last_month) + ' ' +  date_last_month + ' ' + str(kwh_last_month)\n\n\n#recalculate total and create data_string for output\ntotal = 0\ndata_string = ''\nfor element in data[:-1]:\n\tif int(element.split(' ')[1].split('-')[0]) == int(d.strftime(\"%Y\")):\n\t\ttotal = total + int(element.split(' ')[2])\n\ndata[12]= 'total=' + str(total) + '\\n'\n\n\n# output data\nwith open('data.kwh', 'w') as f:\n    f.write(','.join(data))","sub_path":"python/enedis_sensor.py","file_name":"enedis_sensor.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"534849750","text":"# -*- coding: utf-8 -*-\n\"\"\"\nScript to preprocess images and arugment from fish dataset\n\"\"\"\n\n\nimport os\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\n\n\ndef cropping(image_path,x1,y1, height, width, xvar ):\n    \"\"\" \n    Function to crop images based on top corner coordinates and image width and\n    height\n    \"\"\"\n    \n    \n    try:\n        img = cv2.imread(image_path)\n    except:\n        print(\"Unexpected error\")          \n    crop_img = img[y1:(y1+height), x1:(x1+width)]\n    crop_img1 = img[y1:(y1+height), x1+xvar:(x1+width+xvar)]\n    crop_img2 = img[y1:(y1+height), x1-xvar:(x1+width-xvar)]\n        \n    new_path=image_path.replace(\"raw-data\", \"cropped-data\")\n    try:\n        cv2.imwrite(new_path,crop_img)\n        new_path=new_path.replace(\"Image\", \"Image_shift1\")\n        cv2.imwrite(new_path,crop_img1)  \n        new_path=new_path.replace(\"Image_shift1\", \"Image_shift2\")\n        cv2.imwrite(new_path,crop_img2)\n        \n    except:\n        print(\"Unexpected error\")\n        \n\ndef crop_all(x1,width, y1, height, path, folders, xvar):\n    \"\"\" \n    script to crop all JPG images and saved in cropped-data\n    \"\"\"\n    #Cropping all images in folders and saving in database folder cropped-data\\\n    for folder in folders:\n        data_path=os.path.join(path, folder)\n        images=os.listdir(data_path)\n        print('Cropping', folder)\n        \n        for image in images:\n            if (image.endswith(\".JPG\") or image.endswith(\".jpg\")): #Checking for JPG extension\n                image_path=os.path.join(data_path, image) \n                cropping(image_path,x1, y1, width, height, xvar)\n                \n                \ndef flip_all(path, input_folder, output_folder):\n    \"\"\" \n    script to flip images in path/input_folder to path/output_folder\n    \"\"\"\n\n\n    input_path=os.path.join(path, input_folder)\n    images=os.listdir(input_path)\n    print('Flipping', input_folder, 'to', output_folder)\n        \n    for image in images:\n        if (image.endswith(\".JPG\") or image.endswith(\".jpg\")): #Checking for JPG extension\n            image_path=os.path.join(input_path, image) \n            image=cv2.imread(image_path)\n            image=flip_axis(image)\n            out_path=image_path.replace(input_folder, output_folder)\n            out_path=out_path.replace('Image', 'Image_flip_')    \n            cv2.imwrite(out_path,image)\n            \n\n\n\ndef max_normalize_image(images):\n    \"\"\"\n    Normalize image based on maxrange [0,255] to [-1, 1]\n    \"\"\"\n    pixel_depth = 255.0 \n    images = (images - 0.5*pixel_depth)/(0.5*pixel_depth)\n    return images.astype(np.float32)\n\n\ndef flip_axis(x, axis=1):\n    \"\"\"\n    Flip image on y axis\n    \"\"\"\n    x = np.asarray(x).swapaxes(axis, 0)\n    x = x[::-1, ...]\n    x = x.swapaxes(0, axis)\n    return x\n\n\n\n\n\ndef images_to_array(data_path, height, width): \n    \"\"\"\n    Function to read all images, resize, rescale and return numpy array\n    \"\"\"\n    images=os.listdir(data_path) #getting list of all files in folder\n    #Tensor to save formatted images with 3 channels\n    np_images=np.zeros(( len(images), height, width,3)).astype(np.float32)\n\n       \n    #Loop over number of images\n    for i in range(len(images)):\n\n        if (images[i].endswith(\".JPG\") or images[i].endswith(\".jpg\")) == False: #Checking for JPG extension\n            print('Skipping ', images[i], ' in ', data_path)\n            continue\n        \n        image_path=os.path.join(data_path, images[i]) \n        image=cv2.imread(image_path) #reading image\n        image= cv2.resize(image, (width, height)) #Resize image                     \n        image=max_normalize_image(image) #Max scale normalization\n        np_images[i, :, :, :] =image.astype(np.float32) #save all images to array \n           \n    return np_images\n\n\n\ndef save_np_arrays(cropped_path, array_path, height, width, folders):\n    \"\"\"\n    Function to save images from all folders as numpy arrays\n    \"\"\"\n    for folder in folders:\n        data_path=os.path.join(cropped_path, folder)\n        np_array= images_to_array(data_path, height, width) #getting formatted images as array\n        \n        save_path=os.path.join(array_path, folder)\n        np.save(save_path, np_array)\n\n\ndef move_test_images(cropped_path, folders, fraction=0.2):\n    \"\"\" \n    script to crop select random portion of images and save in test-data\n    \"\"\"\n    #Cropping all images in folders and saving in database folder cropped-data\\\n    for folder in folders:\n        data_path=os.path.join(cropped_path, folder)\n        images=os.listdir(data_path)\n        print('Moving random test images from', folder)\n        random.shuffle(images)\n        \n        for i in range(0, int(np.floor(fraction*len(images)))):\n            if (images[i].endswith(\".JPG\") or images[i].endswith(\".jpg\")): #Checking for JPG extension\n                image_path=os.path.join(data_path, images[i]) \n                os.rename(image_path, image_path.replace('cropped-data', 'test-data'))    \n\n        \n\n    \ndef main():\n    \n    \"\"\"main function to call preprocessing fuctions\"\"\"\n    \n    \n    #Pixel size for image crop\n    x1= 265\n    ydelta= 155\n    y1=0\n    xdelta= 480\n    xvar=10 #cropping according to 0,+xvar and -xvar\n    #Folders to crop images from\n    folders = [ 'Felvand', 'Makrill-OK', 'Sill-OK', 'Tomfack']\n        \n    #Path to raw database\n    raw_path=os.path.join('data', 'raw-data')\n    \n    \n    \"\"\"\"\"\"\"\"\"\"\"\"\"FLYTTA TEST BILDER INNAN CROP OCH ARGUMENTATION\"\"\"\"\"\"\"\"\"\n\n    #Crop images and save to \\cropped-data\n    #crop_all(x1,xdelta, y1, ydelta, raw_path, folders, xvar)\n    \n    #Path to cropped database\n    cropped_path= os.path.join('data', 'cropped-data')\n    \n    #Save cropped images to Test folder before argumenting\n    move_test_images(cropped_path, folders)\n    \n    \n    \n    #Flip all images in Makrill-OK and save in Felvand\n    #flip_all(cropped_path, 'Makrill-OK', 'Felvand')\n    \n    #Path to array database\n    array_path= os.path.join('data', 'image-arrays')\n\n    #resize, normalize and save images as np arrays\n  #  save_np_arrays(cropped_path, array_path, height, width, folders)\n    \n    \n   \n\n    \n    \n\n\nif __name__ == \"__main__\": main()\n\n","sub_path":"Image_preprocessing_argumentation.py","file_name":"Image_preprocessing_argumentation.py","file_ext":"py","file_size_in_byte":6154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"110283077","text":"import os\nimport socket\nimport subprocess\n\nfrom charms.reactive import when, when_not, set_state\nfrom charmhelpers.core.hookenv import (\n    config, log, ERROR, service_name)\nfrom charmhelpers.core.host import service_restart\nfrom charmhelpers.contrib.network.ip import (\n    get_address_in_network\n)\nimport jinja2\n\nfrom charms.apt import queue_install\n\nTEMPLATES_DIR = 'templates'\n\n\ndef render_template(template_name, context, template_dir=TEMPLATES_DIR):\n    templates = jinja2.Environment(\n        loader=jinja2.FileSystemLoader(template_dir))\n    template = templates.get_template(template_name)\n    return template.render(context)\n\n\n@when_not('apt.installed.ceph-mds')\ndef install_cephfs():\n    queue_install(['ceph-mds'])\n\n\n@when('cephfs.configured')\n@when('ceph-mds.pools.available')\n@when_not('cephfs.started')\ndef setup_mds(relation):\n    try:\n        service_restart('ceph-mds')\n        set_state('cephfs.started')\n    except subprocess.CalledProcessError as err:\n        log(message='Error: {}'.format(err), level=ERROR)\n\n\n@when('ceph-mds.available')\ndef config_changed(ceph_client):\n    charm_ceph_conf = os.path.join(os.sep,\n                                   'etc',\n                                   'ceph',\n                                   'ceph.conf')\n    key_path = os.path.join(os.sep,\n                            'var',\n                            'lib',\n                            'ceph',\n                            'mds',\n                            'ceph-{}'.format(socket.gethostname())\n                            )\n    if not os.path.exists(key_path):\n        os.makedirs(key_path)\n    cephx_key = os.path.join(key_path,\n                             'keyring')\n    ceph_context = {\n        'fsid': ceph_client.fsid(),\n        'auth_supported': ceph_client.auth(),\n        'use_syslog': str(config('use-syslog')).lower(),\n        'mon_hosts': ' '.join(ceph_client.mon_hosts()),\n        'loglevel': config('loglevel'),\n        'hostname': socket.gethostname(),\n        'mds_name': socket.gethostname(),\n    }\n\n    try:\n        with open(charm_ceph_conf, 'w') as ceph_conf:\n            ceph_conf.write(render_template('ceph.conf', ceph_context))\n    except IOError as err:\n        log(\"IOError writing ceph.conf: {}\".format(err))\n\n    try:\n        with open(cephx_key, 'w') as key_file:\n            key_file.write(\"[mds.{}]\\n\\tkey = {}\\n\".format(\n                socket.gethostname(),\n                ceph_client.mds_key()\n                # ceph_client.mds_bootstrap_key()\n            ))\n    except IOError as err:\n\n        log(\"IOError writing mds-a.keyring: {}\".format(err))\n    set_state('cephfs.configured')\n\n\ndef get_networks(config_opt='ceph-public-network'):\n    \"\"\"Get all configured networks from provided config option.\n\n    If public network(s) are provided, go through them and return those for\n    which we have an address configured.\n    \"\"\"\n    networks = config(config_opt)\n    if networks:\n        networks = networks.split()\n        return [n for n in networks if get_address_in_network(n)]\n\n    return []\n","sub_path":"src/reactive/ceph_fs.py","file_name":"ceph_fs.py","file_ext":"py","file_size_in_byte":3051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"632030521","text":"from flask import Flask\nfrom flask import render_template\nfrom flask import request\nfrom flask import redirect, url_for\n\nfrom flask_wtf import FlaskForm\nfrom flask_wtf.file import FileField, FileRequired\n\nimport os\nimport datetime\nimport time\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = \"secret\"\n\nclass FileForm(FlaskForm):\n\tfiles = FileField(validators=[FileRequired('업로드할 파일을 넣어주세요')])\n\ndef stamp2real(stamp):\n\treturn datetime.datetime.fromtimestamp(stamp)\n\ndef info(filename):\n\tctime = os.path.getctime(filename) # 만든시간\n\tmtime = os.path.getmtime(filename) # 수정시간\n\tatime = os.path.getatime(filename) # 마지막 엑세스시간\n\tsize = os.path.getsize(filename) # 파일크기 (단위: bytes)\n\treturn ctime, mtime, atime, size\n\n@app.route('/', methods = ['GET', 'POST'])\ndef upload_page():\n    form = FileForm()\n    if form.validate_on_submit():\n        f = form.files.data\n        f.save('./uploads/' + f.filename)\n        fileinfo = {}\n        ctime, mtime, atime, size = info('./uploads/' + f.filename)\n        fileinfo[\"create\"] = stamp2real(ctime)\n        fileinfo[\"modify\"] = stamp2real(mtime)\n        fileinfo[\"access\"] = stamp2real(atime)\n        if(size <= 1000000):\n            fileinfo[\"size\"] = \"%.2f KB\" % (size / 1024)\n        else:\n            fileinfo[\"size\"] = \"%.2f MB\" % (size / (1024.0 * 1024.0))\n\n        return render_template('check.html', fileinfo = fileinfo)\n    return render_template('upload.html', form=form)\n\nif __name__ == '__main__':\n\t#서버 실행\n\tapp.run()","sub_path":"step3/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"530405437","text":"#!/usr/bin/python\nimport argparse\nimport json\nimport os\nimport subprocess\nimport types\n\n# Converts a dict into a dynamic object\nclass DictObject(dict):\n\n  def __init__(self, d):\n    self.update(d)\n\n  def __getattr__(self, name):\n    if not self.has_key(name):\n      return None\n    return self[name]\n\ndef as_object(d):\n  if type(d) is types.DictType:\n    d = DictObject(d)\n    for key in d.iterkeys():\n      d[key] = as_object(d[key])\n  elif type(d) is types.ListType or type(d) is types.TupleType:\n    for idx,elem in enumerate(d):\n      d[idx] = as_object(d[idx])\n\n  return d\n\n\ndef read_config():\n  with open(\".modules.config\", 'r') as f:\n    return as_object(json.load(f))\n\ndef read_json(filename):\n  with open(filename, 'r') as f:\n    return as_object(json.load(f))\n\ndef git_pull(root, module):\n  module_dir = os.path.join(root, module.folder)\n\n  if not os.path.exists(module_dir):\n    print(\"Cloning sub-module: %s\" %module.folder)\n\n    parent_dir, name = os.path.split(module_dir)\n\n    if not os.path.exists(parent_dir):\n      print(\"Creating folder: %s\" %parent_dir)\n      os.makedirs(parent_dir)\n\n    cmd = [\"git\", \"clone\", \"-b\", module.branch, module.repository, name]\n    p = subprocess.Popen(cmd, cwd=parent_dir)\n    p.communicate()\n\n  else:\n    cmd = [\"git\", \"pull\", \"origin\", module.branch]\n    print(\"Pulling sub-module: %s, (%s)\" %(module.folder, \" \".join(cmd)))\n    p = subprocess.Popen(cmd, cwd=module_dir)\n    p.communicate()\n\ndef git_push(root, module):\n  module_dir = os.path.join(root, module.folder)\n\n  print(\"Pushing sub-module: %s\" %module.folder)\n  cmd = [\"git\", \"push\", \"origin\", module.branch]\n  p = subprocess.Popen(cmd, cwd=module_dir)\n  p.communicate()\n\ndef git_checkout(root, module, args):\n  module_dir = os.path.join(root, module.folder)\n\n  branch = module.branch\n  if args['checkout'] != True:\n    branch = args['checkout']\n\n  print(\"git checkout\", branch)\n  cmd = [\"git\", \"checkout\", branch]\n  p = subprocess.Popen(cmd, cwd=module_dir)\n  p.communicate()\n\ndef git_command(root, module, args):\n  module_dir = os.path.join(root, module.folder)\n\n  cmd = [\"git\"] + args[\"git\"]\n  print(\"git command: \", cmd)\n  p = subprocess.Popen(cmd, cwd=module_dir)\n  p.communicate()\n\ndef npm_publish(root, module, args):\n  module_dir = os.path.join(root, module.folder)\n\n  package_json = os.path.join(module_dir, \"package.json\")\n  if os.path.exists(package_json):\n    package = read_json(package_json)\n    if not package.private:\n      print(\"Publishing sub-module: %s\" %module.folder)\n      cmd = [\"npm\", \"publish\"]\n      if (args['force'] == True):\n        cmd.append(\"--force\")\n      p = subprocess.Popen(cmd, cwd=module_dir)\n      p.communicate()\n\ndef git_status(root, module, porcelain=True):\n  cmd = [\"git\", \"status\"]\n  if porcelain:\n    cmd.append(\"--porcelain\")\n  p = subprocess.Popen(cmd, stdout=subprocess.PIPE, cwd=os.path.join(root, module.folder))\n  out, err = p.communicate()\n\n  name = os.path.basename(module.folder)\n  if name == \".\":\n    name = \"Mama Mia!\"\n\n  if len(out) > 0:\n\n    print(\"%s\" %name)\n    print(\"--------\\n\")\n    print(out)\n\ndef create_symlink(root, source, link):\n  source_path = os.path.join(root, source)\n  link_path = os.path.join(root, link)\n\n  if not os.path.exists(link_path):\n    parent_dir = os.path.dirname(link_path)\n\n    if not os.path.exists(parent_dir):\n      print(\"Creating parent dir(s).\")\n      os.makedirs(parent_dir)\n\n    # recreate broken links\n    if os.path.islink(link_path):\n      os.remove(link_path)\n\n    print(\"Creating symlink: %s -> %s\"%(link_path, source_path))\n    os.symlink(source_path, link_path)\n\ndef npm_build(root, module):\n  module_dir = os.path.join(root, module.folder)\n  if os.path.exists(os.path.join(module_dir, \"package.json\")):\n    print(\"Building node-module: %s\" %module.folder)\n    cmd = [\"npm\", \"install\"]\n    p = subprocess.Popen(cmd, cwd=module_dir)\n    p.communicate();\n\ndef status(root, config, args=None):\n  for m in config.modules:\n    git_status(root_dir, m)\n\ndef symlinks_old(root, config, args=None):\n  for symlink in config.symlinks:\n    if not symlink.links == None:\n      for l in symlink.links:\n        create_symlink(root, symlink.source, l)\n    else:\n      create_symlink(root, symlink.source, symlink.link)\n\ndef npm_symlinks(root, module):\n  module_dir = os.path.join(root, module.folder)\n  module_config_file = os.path.join(module_dir, \"package.json\");\n  if os.path.exists(module_config_file):\n    module_config = read_json(module_config_file)\n\n    deps = []\n\n    if (\"dependencies\" in module_config):\n      deps += [dep for dep in module_config.dependencies if dep.startswith(\"substance\")]\n\n    if (\"devDependencies\" in module_config):\n      deps += [dep for dep in module_config.devDependencies if dep.startswith(\"substance\")]\n\n    for dep in deps:\n      create_symlink(root, os.path.join(\"node_modules\", dep), os.path.join(module_dir, \"node_modules\", dep))\n\ndef symlinks(root, config, args=None):\n  for m in config.modules:\n    npm_symlinks(root, m)\n\ndef pull(root, config, args=None):\n  for m in config.modules:\n    git_pull(root_dir, m)\n\ndef push(root, config, args=None):\n  for m in config.modules:\n    git_push(root_dir, m)\n\ndef checkout(root, config, args=None):\n  for m in config.modules:\n    git_checkout(root_dir, m, args)\n\ndef git(root, config, args=None):\n  for m in config.modules:\n    git_command(root_dir, m, args)\n\ndef publish(root, config, args=None):\n  for m in config.modules:\n    npm_publish(root_dir, m, args)\n\ndef build(root, config, args=None):\n  for m in config.modules:\n    npm_build(root_dir, m)\n\ndef update(root, config, args=None):\n  pull(root, config, args)\n  symlinks(root, config, args)\n  build(root, config, args)\n\n\nactions = {\n \"status\": status,\n \"symlinks\": symlinks,\n \"push\": push,\n \"pull\": pull,\n \"checkout\": checkout,\n \"publish\": publish,\n \"git\": git,\n \"build\": build,\n \"update\": update\n}\n\n# Command line arguments\n# ========\n\nparser = argparse.ArgumentParser(description='Update the mothership.')\n\nparser.add_argument('--pull', '-u', action='store_const', dest=\"action\", const=\"pull\", help='Pull all sub-modules.')\nparser.add_argument('--push', '-p', action='store_const', dest=\"action\", const=\"push\", help='Push all sub-modules.')\nparser.add_argument('--status', '-s', action='store_const', dest=\"action\", const=\"status\", help='Git status for all sub-modules.')\nparser.add_argument('--symlinks', action='store_const', dest=\"action\", const=\"symlinks\", help='Create symbolic links.')\nparser.add_argument('--build', action='store_const', dest=\"action\", const=\"build\", help='Build node-modules.')\nparser.add_argument('--checkout', nargs='?', const=True, default=False, help='Checkout a given branch or the one specified in .modules.config')\nparser.add_argument('--git', nargs='+', default=False, help='Execute a git command on all modules.')\nparser.add_argument('--publish', action='store_const', dest=\"action\", const=\"publish\", help='Publish node-modules.')\nparser.add_argument('--force', action='store_const', dest=\"force\", const=True, default=False, help='Force.')\n\n# Main\n# ========\n\nargs = vars(parser.parse_args())\n\naction = args['action']\nif args['checkout']:\n  action = \"checkout\"\nelif args['git']:\n  action = \"git\"\nelif action == None:\n  action = \"update\"\nconfig = read_config()\nroot_dir = os.path.realpath(os.path.dirname(__file__))\n\nactions[action](root_dir, config, args)\n","sub_path":"update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":7334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"83467957","text":"# coding=utf-8\nimport time\nimport pygame\nimport mp3play\n\n# f='C:Users/lambda/Music/Taylor Swift - Love Story.mp3' # windows test\n\nf='Taylor Swift - Love Story.mp3' # windows test\n\nclip=mp3play.load(f)\nclip.play()\ntime.sleep(20)\nclip.stop()\n\npygame.mixer.init()\nprint('播放泰勒')\ntrack=pygame.mixer.music.load(f)\n\npygame.mixer.music.play() # 播放\ntime.sleep(10)\npygame.mixer.music.stop() # 停止\n\n","sub_path":"playMusic/playmp3.py","file_name":"playmp3.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"311136435","text":"import pytest\n\nfrom app import tasks\n\npytestmark = [\n    pytest.mark.django_db,\n    pytest.mark.usefixtures('member_is_always_new'),\n]\n\n\ndef test_task(subscribe, user):\n    tasks.subscribe_to_mailchimp.delay(user.pk)\n\n    subscribe.assert_called_once_with(user)\n\n\ndef test_task_does_not_do_things_if_there_is_no_mailchimp_contact_list_id(subscribe, user, settings):\n    settings.MAILCHIMP_CONTACT_LIST_ID = None\n\n    tasks.subscribe_to_mailchimp.delay(user.pk)\n\n    subscribe.assert_not_called()\n","sub_path":"src/app/tests/mailchimp/tests_mailchimp_subscribe_task.py","file_name":"tests_mailchimp_subscribe_task.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"177512210","text":"# -*- coding: utf-8 -*-\n\nfrom django.db import models\nfrom django.conf import settings\nfrom django.contrib.sites.models import Site\nfrom django.template.defaultfilters import slugify, striptags\nfrom datetime import datetime\nfrom time import time\nfrom ckeditor.fields import RichTextField\nfrom core.model.basemodel import BaseModel\nfrom core.model.prefs import Preferences\nfrom core.model.language import LanguageModel\n\nclass MenuManager(models.Manager):\n\n    def optfilter(self, options = dict()):\n        kwargs = dict()\n        exclude = dict()\n        if options.has_key('active'):\n            if int(options['active']) == 1:\n                kwargs['active__id'] = options['activesite']\n            elif int(options['active']) == 2:\n                exclude['active__id'] = options['activesite']\n            else:\n                pass\n\n        if not settings.ADMINALLSITES:\n            kwargs['sites__id'] = settings.SITE_ID\n\n        if options.has_key('site'):\n            if options['site'] != None:\n                kwargs['sites__id'] = int(options['site'])\n\n        if options.has_key('temp'):\n            kwargs['temp'] = options['temp']\n\n        if options.has_key('extra'):\n            kwargs.update(options['extra'])\n\n        return self.filter(**kwargs).exclude(**exclude)\n\n\nclass MegaMenuItemLanguageModel(models.Model):\n\n    name = models.CharField(max_length=255, verbose_name=u'Nazwa', blank=True, null=True)\n    temp = models.BooleanField(u'Tymczasowy?', default = False)\n    slug = models.SlugField(max_length=900, blank=True, null=True)\n    info = RichTextField(verbose_name=u'Opis', null=True, blank=True)\n    meta = models.CharField(max_length=255, verbose_name=u'Opis meta', blank=True, null=True)\n    objects = MenuManager()\n\n    class Meta:\n        db_table = 'megamenu_lang'\n        abstract = True\n\n    def __unicode__(self):\n        return \"%s\" % (self.name)\n\n    def get_language(self,language_id):\n        self.language = self.languages.get(language=language_id)\n\n    def save(self, *args, **kwargs):\n        if self.slug is not None:\n            if len(self.slug) == 0:\n                strtime = \"\".join(str(time()).split(\".\"))\n                string = \"%s-%s\" % (strtime[5:], self.name)\n                self.slug = slugify(string)\n        else:\n            self.slug = ''\n\n        if self.meta is not None:\n            if len(self.meta) == 0:\n                if self.info is not None:\n                    if len(self.info) == 0:\n                        self.meta = striptags(self.name[:160])\n                    else:\n                        self.meta = striptags(self.info[:160])\n                        metastring = self.meta.split()\n                        self.meta = \" \".join(metastring[:-1])\n                else:\n                    self.info = ''\n        self.meta = ''\n        super(MegaMenuItemLanguageModel, self).save(*args, **kwargs)\n\n\nclass MegaMenuItemModel(BaseModel, Preferences):\n    path = str(datetime.now().year) + '/' + str(datetime.now().month)\n    fullpath = 'images/megamenu/' + path\n    #parent = models.ForeignKey('self', blank=True, null=True, related_name='child')\n    image = models.ImageField(upload_to=fullpath, null=True, blank=True)\n    name = None\n    noedit = models.BooleanField(default=False)\n\n    objects = MenuManager()\n\n    def __init__(self, *args, **kwargs):\n        super(MegaMenuItemModel, self).__init__(*args, **kwargs)\n\n    #def get_parent(self):\n    #    return self.parent\n\n    def get_language(self,language_id):\n        self.language = self.languages.get(language=language_id)\n\n    def __unicode__(self):\n        return \"%s\" % (self.name)\n\n    class Meta:\n        db_table = 'megamenu'\n        abstract = True\n","sub_path":"core/model/megamenu.py","file_name":"megamenu.py","file_ext":"py","file_size_in_byte":3693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"321867668","text":"# -*- coding: utf-8 -*-\n# Part of Odoo. See LICENSE file for full copyright and licensing details.\n\nfrom odoo import api, fields, models, _\nfrom odoo.exceptions import UserError\nfrom odoo.tools.float_utils import float_round\n\nclass StockPicking(models.Model):\n    _inherit = 'stock.picking'\n\n    return_id = fields.Many2one('stock.return', string='Pickings Return')\n\nclass StockPicking(models.Model):\n    _inherit = 'stock.move.line'\n\n    return_line_id = fields.Many2one('stock.return.line', string='Return Line')\n    return_quantity = fields.Float('Qty', default=0.0)\n\nclass Return(models.Model):\n    _name = 'stock.return'\n    _description = 'Bulk Return'\n    _rec_name = 'partner_id'\n    _inherit = ['mail.thread', 'mail.activity.mixin']\n\n    partner_id = fields.Many2one('res.partner', string='Customer')\n    product_id = fields.Many2one('product.product', string='Product')\n    \n    tracking = fields.Selection([\n        ('serial', 'By Unique Serial Number'),\n        ('lot', 'By Lots'),\n        ('none', 'No Tracking')], related='product_id.tracking', string=\"Tracking\", default='none')\n\n    lot_id = fields.Char('Lot/Serial')\n    quantity = fields.Float(string='Qty', default=1.0)\n    return_type = fields.Selection([('sale','Sale'), ('purchase','Purchase')], default='sale')\n    \n    line_ids = fields.One2many('stock.return.line', 'return_id', string='Item Scanned for Return')\n    line_tmp_ids = fields.One2many('stock.return.line', 'return_tmp_id', string='Item Scanned for Return')\n\n    company_id = fields.Many2one('res.company', 'Company', default=lambda self: self.env.user.company_id)\n    state = fields.Selection([('draft','Draft'),('cancel', 'Cancel'), ('done', 'Done')], string='Status', index=True, \n        readonly=True, default='draft', track_visibility='onchange', copy=False)\n    reason_id = fields.Many2one('return.reason', 'Return Reason')\n\n    def _prepare_return_lines(self):\n        return_lines = []\n        domain = [('qty_done','>','stock_move_line.return_quantity')]\n        domain = []\n        transfer_type = 'outgoing'\n\n        if self.return_type == 'purchase':\n            transfer_type = 'incoming'\n\n        domain += [('picking_id.picking_type_id.code', '=', transfer_type), ('picking_id.partner_id', '=', self.partner_id.id)]\n\n        if self.product_id and self.lot_id:\n            lot = self.env['stock.production.lot'].search([('product_id', '=', self.product_id.id), \n                ('name', 'in', [self.lot_id, self.lot_id.swapcase()])], limit=1)\n            if lot:\n                domain += [('product_id', '=', self.product_id.id), ('lot_id', '=', lot.id)]\n            else:\n                raise UserError(_('Lot or serial not found for this product !'))\n        elif self.product_id and not self.lot_id:\n            domain += [('product_id', '=', self.product_id.id)]\n        elif self.lot_id and not self.product_id:\n            lot = self.env['stock.production.lot'].search([('name', 'in', [self.lot_id, self.lot_id.swapcase()])])\n            if lot:\n                domain += [('lot_id', '=', lot.id)]\n            else:\n                raise UserError(_('Lot or serial not found !'))\n        \n        move_lines = self.env['stock.move.line'].search(domain, order='id desc')\n        if not move_lines:\n            raise UserError(_('Picking not found for selected lot/serial or product'))\n        for line in move_lines.filtered(lambda x: x.return_quantity < x.qty_done):\n            return_lines.append({\n                'product_tmpl_id': line.product_id and line.product_id.product_tmpl_id and line.product_id.product_tmpl_id.id,\n                'product_id': line.product_id and line.product_id.id,\n                'lot_id': line.lot_id and line.lot_id.id or False,\n                'picking_id': line.picking_id and line.picking_id.id,\n                'move_id': line.move_id and line.move_id.id or False,\n                'quantity': 1.0 if line.product_id.tracking == 'serial' else 0.0,\n                'order_line_id': line.move_id.sale_line_id.id,\n                'return_quantity': line.return_quantity\n            })\n        return return_lines\n\n    @api.onchange('lot_id', 'quantity')\n    def onchange_lot_id(self):\n        if self.product_id and self.product_id.tracking == 'serial' and self.lot_id and self.quantity:\n            if self.line_tmp_ids.filtered(lambda x: x.lot_id.product_id.tracking == 'serial' and x.lot_id.name in [self.lot_id, self.lot_id.swapcase()]):\n                raise UserError(_('Serial %s is already added in return lines' % self.lot_id))\n        if self.partner_id and self.product_id and self.quantity:\n            return_lines = self._prepare_return_lines()\n            new_lines = []\n            if self.lot_id and self.product_id.tracking == 'serial':\n                for line in self.line_tmp_ids:\n                    new_lines.append((0, 0, line._convert_to_write(line._cache)))\n\n            for line in return_lines:\n                new_lines.append((0, 0, line))\n\n            self.line_tmp_ids = new_lines\n            self.lot_id = False\n\n    def confirm_lines(self):\n        self.ensure_one()\n        lines = self.env['stock.return.line']\n        self.line_tmp_ids.filtered(lambda x: x.quantity > 0).write({'return_id': self.id, 'return_tmp_id': False})\n        self.line_tmp_ids.filtered(lambda x: x.quantity <= 0).unlink()\n        self.product_id = False\n\n    def action_sale_return(self):\n        picking_ids = []\n        for picking in self.env['stock.return.line'].read_group([('return_id','=',self.id)], ['picking_id'], ['picking_id']):\n            picking_id = picking['picking_id'][0]\n            return_wizard = self.env['stock.return.picking'].with_context(active_id=picking_id).create({})\n            product_return_moves = [(2, line_id.id, False) for line_id in return_wizard.product_return_moves]\n            for picking_line in self.env['stock.return.line'].read_group(picking.get('__domain'), ['move_id', 'product_id', 'quantity'], ['move_id', 'product_id']):\n                move = self.env['stock.move'].browse(picking_line.get('move_id')[0])\n                vals = {\n                    'wizard_id': return_wizard.id, \n                    'product_id': move.product_id.id, \n                    'quantity': picking_line.get('quantity', 0), \n                    'move_id': move.id, \n                    'to_refund': True, \n                    'uom_id': move.product_id.uom_id.id, \n                }\n                product_return_moves.append((0, 0, vals))\n            return_wizard.write({'product_return_moves': product_return_moves, 'location_id': self.reason_id.location_dest_id.id if self.reason_id and self.reason_id.location_dest_id else return_wizard.location_id.id\n                })\n            new_picking_id, pick_type_id = return_wizard._create_returns()\n            picking_ids.append(new_picking_id)\n\n        pickings = self.env['stock.picking'].browse(picking_ids) \n        pickings.write({'return_id': self.id})\n\n        for picking in pickings:\n            serial_id = []\n            for line in picking.move_line_ids:\n                new_line = self.env['stock.return.line'].search([('return_id','=', self.id), ('move_id','=',line.move_id.origin_returned_move_id.id),\n                    ('picking_id','=',line.move_id.origin_returned_move_id.picking_id.id), ('product_id','=', line.product_id.id), ('lot_id','not in', serial_id)], limit=1)\n\n                old_line = self.env['stock.move.line'].search([('move_id','=',line.move_id.origin_returned_move_id.id),\n                    ('picking_id','=',line.move_id.origin_returned_move_id.picking_id.id), ('product_id','=', line.product_id.id), ('lot_id','not in', serial_id)], limit=1)\n\n                line.lot_id = new_line.lot_id.id\n                serial_id += [new_line.lot_id.id]\n                line.qty_done = new_line.quantity\n                line.return_line_id = new_line.id\n                old_line.return_quantity += new_line.quantity\n\n        self.write({'state': 'done'})\n\n    @api.multi\n    def action_cancel(self):\n        for rec in self:\n            rec.state = 'cancel'\n\n    @api.multi\n    def action_set_to_draft(self):\n        for rec in self:\n            rec.state = 'draft'\n\n    @api.multi\n    def action_view_delivery(self):\n        action = self.env.ref('stock.action_picking_tree_all').read()[0]\n        pickings = self.env['stock.picking'].search([('return_id','=', self.id)])\n        if pickings:\n            if len(pickings) > 1:\n                action['domain'] = [('id', 'in', pickings.ids)]\n            elif pickings:\n                action['views'] = [(self.env.ref('stock.view_picking_form').id, 'form')]\n                action['res_id'] = pickings.id\n            return action\n\n    @api.multi\n    def action_view_orders(self):\n        sale_orders = self.env['sale.order']\n        action = self.env.ref('sale.action_orders').read()[0]\n        pickings = self.env['stock.picking'].search([('return_id','=', self.id)])\n        if pickings:\n            sale_orders = pickings.mapped('sale_id')\n        if sale_orders:\n            if len(sale_orders) > 1:\n                action['domain'] = [('id', 'in', sale_orders.ids)]\n            elif sale_orders:\n                action['views'] = [(self.env.ref('sale.view_order_form').id, 'form')]\n                action['res_id'] = sale_orders.id\n            return action\n\n    @api.multi\n    def action_view_refunds(self):\n        invoice_ids = self.env['account.invoice']\n        action = self.env.ref('account.action_invoice_tree').read()[0]\n        pickings = self.env['stock.picking'].search([('return_id','=', self.id)])\n        if pickings:\n            sale_orders = pickings.mapped('sale_id')\n            if sale_orders:\n                invoice_ids = sale_orders.mapped('invoice_ids').filtered(lambda x: x.type == 'out_refund')\n            if invoice_ids:\n                if len(invoice_ids) > 1:\n                    action['domain'] = [('id', 'in', invoice_ids.ids)]\n                elif invoice_ids:\n                    action['views'] = [(self.env.ref('account.invoice_form').id, 'form')]\n                    action['res_id'] = invoice_ids.id\n                return action\n\n\nclass ReturnLines(models.Model):\n    _name = 'stock.return.line'\n    _description = 'Return Line'\n\n    return_id = fields.Many2one('stock.return')\n    return_tmp_id = fields.Many2one('stock.return')\n\n    product_tmpl_id = fields.Many2one('product.template', string='Product', readonly=True)\n    product_id = fields.Many2one('product.product', string='Product Variant', readonly=True)\n    lot_id = fields.Many2one('stock.production.lot', 'Lot/Serial', readonly=True)\n    quantity = fields.Float('Qty')\n    picking_id = fields.Many2one('stock.picking', string='Pickings', readonly=True)\n    move_id = fields.Many2one('stock.move', 'Return Move', readonly=True)\n\n    return_quantity = fields.Float('Returned Qty', readonly=True)\n\n    order_line_id = fields.Many2one('sale.order.line', readonly=True)\n    price_unit = fields.Float(related='order_line_id.price_unit', readonly=True)\n    product_uom_qty = fields.Float(related='order_line_id.product_uom_qty', readonly=True)\n    qty_delivered = fields.Float(related='order_line_id.qty_delivered', readonly=True)\n\n    @api.onchange('quantity')\n    def onchange_qty(self):\n        if self.quantity and self.quantity > self.product_uom_qty - self.return_quantity:\n            raise UserError(_('You can not return more then available qty !'))\n\n\n","sub_path":"stock_return/models/sale_purchase_return.py","file_name":"sale_purchase_return.py","file_ext":"py","file_size_in_byte":11434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"59603425","text":"# -*- coding: utf-8 -*-\nimport requests\nimport re\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\nif __name__ == '__main__':\n    url = 'http://photostock.china.com.cn/Web_CHN/SpecialTopicPhoto.aspx?Id=296'\n    html = requests.get(url)\n    img_src = re.findall('<img alt=.*?src=\"..(.*?)\".*?/>', html.text, re.S)\n    imgUrl = []\n    for each_src in img_src:\n        imgUrl.append(\"http://photostock.china.com.cn\" + each_src)\n    picName = 100\n    for each in imgUrl:\n        imgContext = requests.get(each).content\n        with open(\"lovelyAnimals/\" + str(picName) + \".jpg\", \"wb\") as code:\n            code.write(imgContext)\n        picName += 1\n\n'''\n下载文件的3种方法\n(1): 使用urllib.urlretrieve方法，可在callbackfunc函数中显示下载进度\ndef callbackfunc(blocknum, blocksize, totalsize):\n    # 回调函数\n    # @blocknum:\n    #     已经下载的数据块\n\n    # @blocksize:\n    #     数据块的大小\n\n    # @totalsize:\n    #     远程文件的大小\n    percent = 100.0 * blocknum * blocksize / totalsize\n    if percent > 100:\n        percent = 100\n    print \"%.2f%%\"% percent\nurl = 'http://www.sina.com.cn'\nlocal = 'lovelyAnimals/sina.html'\nurllib.urlretrieve(url, local, callbackfunc)\n\n(2):使用urllib2.urlopen\nimport urllib2\nurl = 'http://www.sina.com.cn'\nf = urllib2.urlopen(url)\ndata = f.read()\nwith open(\"lovelyAnimals/sina.html\", \"wb\") as code:\n\tcode.write(data)\n\n(3):使用requests模块\nimport requests\nurl = 'http://www.sina.com.cn'\nhtml = requests.get(url)\nwith open(\"lovelyAnimals/sina.html\", \"wb\") as code:\n\tcode.write(html.content)\n'''\n","sub_path":"examples/lovelyAnimals.py","file_name":"lovelyAnimals.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"264481701","text":"a, b = map(float, input().split())\n\n\ndef score(a, b):\n    if a>=4.0 and b >= 4.0:\n        return \"A\"\n    elif a>=3.0 and b >= 3.0:\n        return \"B\"\n    else :\n        return \"C\"\n\nmy_score = score(a, b)\nprint(my_score)\n","sub_path":"532.py","file_name":"532.py","file_ext":"py","file_size_in_byte":220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"57636102","text":"#!python3\n\"\"\"\nPrint the list named \"people\"\nAsk the user to enter a word from the list\nAsk the user to enter another word\nReplace the first word with the second word.\n\ninputs:\nstring\nstring\n\noutputs:\nlist\n\nexample:\n['Alain', 'Brian', 'Chris', 'Justin', 'Angela', 'Rick']\nChoose a person from the list to replace:Rick\nEnter the replacement:Dan\n['Alain', 'Brian', 'Chris', 'Justin', 'Angela', 'Dan']\n\n\"\"\"\nList = ['Alain', 'Brian', 'Chris', 'Justin', 'Angela', 'Rick']\nprint(List)\nword1=input(\"Choose a person from the list to replace:\").strip()\nnumber=List.index(word1)\nList.pop(int(number))\nword2=input(\"Enter the replacement:\").strip()\nList.insert(int(number),word2)\nprint(List)\n","sub_path":"problem1.py","file_name":"problem1.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"88400834","text":"import os\nimport arrow\nimport logging\nimport pickle\nimport math\nfrom collections import defaultdict\nfrom requests_futures.sessions import FuturesSession\n\n\ndef worker_task(sess, resp):\n    \"\"\"Process json in background\"\"\"\n    resp.data = resp.json()\n\n\nclass Hegemony():\n\n    def __init__(self, start, end, originasns=None, asns=None, af=4, session=None,\n                 cache=True, cache_dir=\"cache/\",\n                 url='https://ihr.iijlab.net/ihr/api/hegemony/',\n                 nb_threads=2):\n        \"\"\"\n        :originasn: Origin ASN of interest. It can be a list of ASNs or a single\n        int value. Set to 0 for global hegemony.\n        :start: Start date/time.\n        :end: End date/time.\n        :asn: Return dependency only to the given ASNs. By default return all\n        dependencies.\n        :af: Adress family, default is IPv4\n        :session: Requests session to use\n        :page: Page number for paginated results.\n        :cache: Set to False to ignore cache\n        :cache_dir: Directory used for cached results.\n        :url: API root url\n        :nb_threads: Maximum number of parallel downloads\n        Notes: By default results are cached on disk.\n        \"\"\"\n\n        if isinstance(originasns, int):\n            originasns = [originasns]\n        elif originasns is None:\n            originasns = [None]\n\n        if isinstance(asns, int):\n            asns = [asns]\n        elif asns is None:\n            asns = [None]\n\n        self.originasns = set(originasns)\n        self.asns = set(asns)\n        self.start = start\n        self.end = end\n        self.af = af\n        self.session = session\n        self.cache = cache\n        if session is None:\n            self.session = FuturesSession(max_workers=nb_threads)\n        else:\n            self.session = session\n\n        self.url = url\n        self.cache_dir = cache_dir\n        if not os.path.exists(cache_dir):\n            os.mkdir(cache_dir)\n        self.params = {}\n        self.queries = defaultdict(list)\n\n    def query_api(self, originasn, asn, page):\n        \"\"\"Single API query. Don't call this method, use get_results instead.\"\"\"\n\n        params = dict(\n            timebin__gte=arrow.get(self.start),\n            timebin__lte=arrow.get(self.end),\n            af=self.af,\n            page=page,\n            format=\"json\"\n        )\n\n        if asn is not None:\n            params[\"asn\"] = asn\n\n        if originasn is not None:\n            params[\"originasn\"] = originasn\n\n        if originasn is None and asn is None:\n            logging.error(\"You should give at least a origin ASN or an ASN.\")\n            return None\n        self.params = params\n        logging.info(\"query results for {}, page={}\".format((originasn, asn), page))\n        return self.session.get(\n            url=self.url, params=params,\n            background_callback=worker_task\n        )\n\n    def get_results(self):\n        \"\"\"Fetch AS dependencies (aka AS hegemony) results.\n        Return AS dependencies for the given origin AS between the start and\n        end dates.\n        :returns: Dictionary of AS dependencies.\n        \"\"\"\n\n        # Main loop\n        queries = {}\n\n        # Query the API\n        for originasn in self.originasns:\n            for asn in self.asns:\n                # Skip the query if we have the corresponding cache\n                cache_fname = \"{}/hege_originasn{}_start{}_end{}_asn{}_af{}.pickle\".format(\n                    self.cache_dir, originasn, self.start, self.end, asn, self.af)\n                if self.cache and os.path.exists(cache_fname):\n                    continue\n\n                queries[(originasn, asn)] = self.query_api(originasn, asn, 1)\n\n        # Fetch the results\n        for originasn in self.originasns:\n            for asn in self.asns:\n                cache_fname = \"{}/hege_originasn{}_start{}_end{}_asn{}_af{}.pickle\".format(\n                    self.cache_dir, originasn, self.start, self.end, asn, self.af)\n\n                if self.cache and os.path.exists(cache_fname):\n                    #  get results from cache\n                    logging.info(\"Get results from cache\")\n                    for res in pickle.load(open(cache_fname, \"rb\")):\n                        yield res\n\n                else:\n                    # fetch results\n                    all_results = []\n                    resp = queries[(originasn, asn)].result()\n                    logging.info(\"got results for {}\".format((originasn, asn)))\n                    if resp.ok and \"results\" in resp.data and len(resp.data[\"results\"]) > 0:\n                        yield resp.data[\"results\"]\n                        all_results.append(resp.data[\"results\"])\n                    else:\n                        logging.warning(\"No hegemony results for  {}\".format(self.params))\n\n                    # if results are incomplete get the other pages\n                    if resp.data.get(\"next\"):\n                        nb_pages = math.ceil(resp.data[\"count\"] / len(resp.data[\"results\"]))\n                        pages_queries = []\n                        logging.info(\"{} more pages to query\".format(nb_pages))\n                        for p in range(2, int(nb_pages) + 1):\n                            pages_queries.append(self.query_api(originasn, asn, p))\n\n                        for i, page_resp in enumerate(pages_queries):\n                            resp = page_resp.result()\n                            if resp.ok and \"results\" in resp.data and len(resp.data[\"results\"]) > 0:\n                                yield resp.data[\"results\"]\n                                all_results.append(resp.data[\"results\"])\n                            else:\n                                logging.warning(\"No hegemony results for {}, page={}\".format(self.params, i + 2))\n\n                    if self.cache and len(all_results) > 0 and len(all_results[0]):\n                        logging.info(\"caching results to disk\")\n                        pickle.dump(all_results, open(cache_fname, \"wb\"))\n\n\nif __name__ == \"__main__\":\n    FORMAT = '%(asctime)s %(processName)s %(message)s'\n    logging.basicConfig(format=FORMAT, filename=\"hegemony.log\", level=logging.INFO,\n                        datefmt='%Y-%m-%d %H:%M:%S')\n    res = Hegemony(\n        originasns=[2907, 7922], start=\"2018-09-15\", end=\"2018-10-16\"\n    ).get_results()\n\n    for r in res:\n        print(r)","sub_path":"src/hegemony.py","file_name":"hegemony.py","file_ext":"py","file_size_in_byte":6343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"414019697","text":"from unittest import TestCase\nimport data_manager as dm\nimport torch\n\n\n\nclass TestData_loader(TestCase):\n    cifar_path = '../data'\n\n    def test_should_return_single_image(self):\n        # given\n        batch_size = 4\n\n        data_loader = dm.data_loader(dm.classes.FROG, batch_size, True, self.cifar_path)\n        data_iter = iter(data_loader)\n        image, isTrue = data_iter.next()\n\n        self.assertEqual(image.shape, torch.Size([batch_size, 3, 32,32]))\n\n    def test_iterator_should_return_2_images(self):\n        # given\n        batch_size = 2\n        data_loader = dm.data_loader(dm.classes.FROG, batch_size, True, self.cifar_path)\n\n        returned_batch_size = []\n        # when\n        for batch, labels in iter(data_loader):\n            returned_batch_size = batch.size()\n            break\n\n\n        # then\n        self.assertEqual(returned_batch_size, torch.Size([2,3,32,32]))\n\n    def test_iterator_should_return_2_labels(self):\n        # given\n        batch_size = 2\n        data_loader = dm.data_loader(dm.classes.FROG, batch_size, True, self.cifar_paths)\n\n        label_size = []\n        # when\n        for batch, labels in iter(data_loader):\n            label_size = len(labels)\n            break\n\n        # then\n        self.assertEqual(label_size, 2)\n\n    def test_data_iterator_should_not_throw_exception(self):\n        # given\n        batch_size = 15\n        incorrect_per_correct = 2\n        data_loader = dm.data_loader(dm.classes.FROG, batch_size, True, self.cifar_path)\n        data_iterator = data_loader._data_iterator(data_loader._trainerloader,\n                                                   data_loader._batch_size,\n                                                   dm.classes.FROG,\n                                                   incorrect_per_correct)\n\n        # when\n        for imgs, labels in data_iterator:\n            continue\n\n        # then\n        self.assertTrue(True)\n\n    def test_data_iterator_should_have_correct_data_ammount(self):\n        # given\n        batch_size = 15\n        images_in_cifar_count = 5000\n        incorrect_per_correct = 3\n        data_loader = dm.data_loader(dm.classes.FROG, batch_size, True, self.cifar_path)\n\n        # when\n        data_iterator = data_loader._data_iterator(data_loader._trainerloader,\n                                                   data_loader._batch_size,\n                                                   dm.classes.FROG,\n                                                   incorrect_per_correct,\n                                                   )\n\n        # then\n        self.assertEqual(len(data_iterator.data), images_in_cifar_count * (incorrect_per_correct + 1))\n\n    def test_data_iterator_should_remove_data(self):\n        # given\n        batch_size = 15\n        incorrect_per_correct = 2\n        data_loader = dm.data_loader(dm.classes.FROG, batch_size, True, self.cifar_path)\n        data_iterator = data_loader._data_iterator(data_loader._trainerloader,\n                                                   data_loader._batch_size,\n                                                   dm.classes.FROG,\n                                                   incorrect_per_correct)\n\n        # when\n        for imgs, labels in data_iterator:\n            continue\n\n        # then\n        self.assertTrue(len(data_iterator.data) < batch_size)\n\n    def test_data_iterator_should_return_correct_labels(self):\n        # given\n        batch_size = 15\n        incorrect_per_correct = 2\n        expected_labels = [0,1,1] * 2\n        data_loader = dm.data_loader(dm.classes.FROG, batch_size, True, self.cifar_path)\n        data_iterator = data_loader._data_iterator(data_loader._trainerloader,\n                                                   data_loader._batch_size,\n                                                   dm.classes.FROG,\n                                                   incorrect_per_correct)\n\n        # when\n        imgs, labels = data_iterator.next()\n\n        # then\n        self.assertEqual(labels[:6].tolist(), expected_labels)\n\n\n\n\n","sub_path":"Tests/test_data_loader.py","file_name":"test_data_loader.py","file_ext":"py","file_size_in_byte":4053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"269584552","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as mcolors\nimport six\n\nnp.random.seed(12345)\n\ndf = pd.DataFrame([np.random.normal(33500,150000,3650),\n                   np.random.normal(41000,90000,3650),\n                   np.random.normal(41000,120000,3650),\n                   np.random.normal(48000,55000,3650)],\n                  index=[1992,1993,1994,1995])\n\n\ndef get_colormap(color_start, color_mid, color_end):\n    color_start_rgb = mcolors.hex2color(color_start)\n    color_end_rgb = mcolors.hex2color(color_end)\n    midpoint_rgb = mcolors.hex2color(color_mid)\n\n    color_dict = {'red': ((0, color_start_rgb[0], color_start_rgb[0]), (0.5, midpoint_rgb[0], midpoint_rgb[0]),\n                          (1, color_end_rgb[0], color_end_rgb[0])),\n                  'green': ((0, color_start_rgb[1], color_start_rgb[1]), (0.5, midpoint_rgb[1], midpoint_rgb[1]),\n                            (1, color_end_rgb[1], color_end_rgb[1])),\n                  'blue': ((0, color_start_rgb[2], color_start_rgb[2]), (0.5, midpoint_rgb[2], midpoint_rgb[2]),\n                           (1, color_end_rgb[2], color_end_rgb[2]))}\n\n    return mcolors.LinearSegmentedColormap('colors', color_dict, COL_CUTS)\n\n\ndef confidence_mean(x_normal):\n    mean = x_normal.mean()\n    distance = 1.96 * x_normal.std() / x_normal.size ** 0.5\n    return mean - distance, mean, mean + distance\n\n\ndef normalize(x, lower, upper):\n    return min((max(lower, x) - lower)/((upper - lower) * 1.0), 1)\n\n\ndef plot(y_value):\n\n    # transform data\n    df_plot = pd.DataFrame([val for val in zip(*df.apply(confidence_mean, axis=1))],\n                           index=['5%', 'mean', '95%'], columns=df.index).transpose()\n\n    # make a figure ready\n    color_bar = plt.contourf([[0, 0], [0, 0]], [1.0*val / COL_CUTS for val in range(0, COL_CUTS + 1, 1)], cmap=COLORS)\n\n    # plot\n    plt.clf()\n    plt.colorbar(color_bar, orientation='horizontal')\n    bars = plt.bar(df_plot.index, df_plot['mean'],\n                   width=0.8,\n                   tick_label=df.index,\n                   align='center',\n                   alpha=1)\n\n    # remove ticks\n    plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')\n\n    # remove box edges\n    for spine in plt.gca().spines.values():\n        spine.set_visible(False)\n\n    # set background color\n    plt.gca().set_axis_bgcolor('lightgrey')\n\n    # change layout to look better\n    plt.tight_layout()\n\n    # add horizontal line\n    plt.plot([df_plot.index.min() - 0.5, df_plot.index.max() + 0.5], [y_value] * 2, '-',\n             color='black', linewidth=2, alpha=0.8)\n    plt.xlim([df_plot.index.min() - 0.5, df_plot.index.max() + 0.5])\n\n    # add confidence intevals\n    for year in df_plot.index:\n        plt.plot([year]*2, df_plot.loc[year, ['5%', '95%']], '-', color='black', linewidth=8, alpha=0.8)\n\n    # change color of bars\n    for bar in bars:\n        col_idx = COL_CUTS - int(normalize(y_value,\n                                           *df_plot.loc[int(bar.get_x()) + 1, ['5%', '95%']].tolist()) * COL_CUTS)\n        rgb = COLORS(col_idx)\n        bar.set_color(rgb)\n        bar.set_edgecolor('black')\n\n    plt.show()\n\n\ndef onclick(event):\n    plot(event.ydata)\n\n# set up some definitions\nCOL_CUTS = 20\nBLUE = '#000080'\nWHITE = '#ffffff'\nRED = '#8b0000'\nCOLORS = get_colormap(BLUE, WHITE, RED)\n\n# plot\nplt.figure()\nplot(38000)\n\n# add interactivity\nplt.gcf().canvas.mpl_connect('button_press_event', onclick)\n","sub_path":"applied_data_science_with_python/applied_plotting_charting_and_data_representation_in_python/assignments/assignment3.py","file_name":"assignment3.py","file_ext":"py","file_size_in_byte":3509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"306287411","text":"import os\nimport pandas as pd\nfrom elasticsearch import Elasticsearch\nfrom elasticsearch.client import IndicesClient\nimport mysql.connector as sql\n\nes = Elasticsearch([{'host':'localhost','port':9200}])\nes_idx = IndicesClient(es)\nes_idx.delete(index='_all')\na = os.popen('''\ncurl -X PUT 'localhost:9200/main_idx' -d '\n{\n  \"mappings\": {\n    \"docs\": {\n      \"properties\": {\n        \"pure_text\": {\n          \"type\": \"text\",\n          \"analyzer\": \"ik_smart\",\n          \"search_analyzer\": \"ik_smart\"\n        }\n      }\n    }\n  }\n}'\n''').read()\nfor line in a.split('\\n'):\n    print(line)\n    \ndb_connection = sql.connect(host='rm-2zet5lw17as40fty28o.mysql.rds.aliyuncs.com',\n                            database='medo_master', \n                            user='snowball', \n                            password='MEDOsnow$%^&')\nnsfc_data = pd.read_sql('SELECT summaryCh FROM nsfc_data', con = db_connection)\nnsfc_data.dropna(axis=0, inplace=True)\n\ndoc_list = []\nfor idx,row in nsfc_data.iterrows():\n    doc = {\"pure_text\": row['summaryCh']}\n    doc_list.append(doc)\n\nfor doc in doc_list:\n    temp = os.popen('''\n    curl -X POST 'localhost:9200/main_idx/docs' -d '\n    {}\n    '\n    '''.format(str(doc).replace(\"\\\"\", \"\\'\").replace(\"\\'\", \"\\\"\"))).read()\n    for line in temp.split('\\n'):\n        print(line)","sub_path":"nsfc_index_build.py","file_name":"nsfc_index_build.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"350221520","text":"# TODO edit problem towards mnist usage\n\nimport numpy as np\n#from scipy.stats import weibull_min\nimport scipy.stats as scst\nfrom sklearn.metrics import confusion_matrix, accuracy_score, f1_score\nimport tensorflow as tf\nimport os\nimport six\nfrom six.moves import cPickle as pickle\nimport matplotlib.pyplot as plt\n\nimport operators\nimport arguments\nimport mutate_methods as mut\nimport mate_methods as mate\n\n# constants\ngeneration_limit = 19\nscore_min = 0.00 # terminate immediately when 100% accuracy is achieved\n\n\n# Helper to load in CIFAR-10\ndef load_CIFAR_batch(filename):\n    \"\"\" load single batch of cifar \"\"\"\n    with open(filename, 'rb') as f:\n        if six.PY2:\n            datadict = pickle.load(f)\n        elif six.PY3:\n            datadict = pickle.load(f, encoding='latin1')\n        X = datadict['data']\n        Y = datadict['labels']\n        X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype(\"float\")\n        Y = np.array(Y)\n        return X, Y\n\ndef load_CIFAR10(ROOT):\n    \"\"\" load all of cifar \"\"\"\n    xs = []\n    ys = []\n    for b in range(1,6):\n        f = os.path.join(ROOT, 'data_batch_%d' % (b, ))\n        X, Y = load_CIFAR_batch(f)\n        xs.append(X)\n        ys.append(Y)\n    Xtr = np.concatenate(xs)\n    Ytr = np.concatenate(ys)\n    del X, Y\n    Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\n    return Xtr, Ytr, Xte, Yte\n\n\ndef get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n    \"\"\"\n    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n    it for the two-layer neural net classifier.\n    \"\"\"\n    # Load the raw CIFAR-10 data\n    cifar10_dir = './cifar-10-batches-py'\n    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n\n    # Subsample the data\n    mask = range(num_training, num_training + num_validation)\n    X_val = X_train[mask]\n    y_val = y_train[mask]\n    mask = range(num_training)\n    X_train = X_train[mask]\n    y_train = y_train[mask]\n    mask = range(num_test)\n    X_test = X_test[mask]\n    y_test = y_test[mask]\n\n    # Normalize the data: subtract the mean image\n    mean_image = np.mean(X_train, axis=0)\n\n    # X_train -= mean_image\n    # X_val -= mean_image\n    # X_test -= mean_image\n\n    # Reshape data to rows\n    # X_train = X_train.reshape(num_training, -1)\n    # X_val = X_val.reshape(num_validation, -1)\n    # X_test = X_test.reshape(num_test, -1)\n\n    return X_train/255.0, y_train, X_val/255.0, y_val, X_test/255.0, y_test\n\ndef get_cifar10_batch(filename):\n    with open(filename, 'rb') as f:\n        if six.PY2:\n            datadict = pickle.load(f)\n        elif six.PY3:\n            datadict = pickle.load(f, encoding='latin1')\n        X = datadict['data']\n        Y = datadict['labels']\n        X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype(\"float\")\n        Y = np.array(Y)\n        return X, Y\n\n\ndef get_dummy_data(data_dimensions):\n    # function that returns dummy datapoints and label so that evaluate\n    # can properly build the tensorflow graph with knowledge of dimensions\n    # adding [1] for #block inputs\n    return (np.zeros([1] + data_dimensions), np.zeros(data_dimensions[0]))\n\n# Invoke the above function to get our data.\nx_train, y_train, x_val, y_val, x_test, y_test = get_CIFAR10_data()\n# x_train = x_train.reshape(-1, 28, 28, 1)\n# x_test = x_test.reshape(-1, 28, 28, 1)\nTRAIN_SIZE = 1000\n# TRAIN_SIZE = x_train.shape[0]\n\nx_train = np.array([x_train[:TRAIN_SIZE]])\ny_train = np.array(y_train[:TRAIN_SIZE])\n\nx_test = np.array([x_test])\n\nprint('Train data shape: ', x_train.shape)\nprint('Train labels shape: ', y_train.shape)\nprint('Validation data shape: ', x_val.shape)\nprint('Validation labels shape: ', y_val.shape)\nprint('Test data shape: ', x_test.shape)\nprint('Test labels shape: ', y_test.shape)\n\n# we make dummy data that is dimensionally representative of the data we will\n# feed in to the individual so that the evaluate method can accurately construct\n# the tensorflow graph\nx_train, y_train = get_dummy_data([1, 32, 32, 3])\n\ndef scoreFunction(predict, actual):\n    try:\n        acc_score = accuracy_score(actual, predict)\n        avg_f1_score = f1_score(actual, predict, average='macro')\n        return 1 - acc_score, 1 - avg_f1_score\n    except ValueError:\n        print('Malformed predictions passed in. Setting worst fitness')\n        return 1, 1 # 0 acc_score and avg f1_score b/c we want this indiv ignored\n\n# play with difference sizes, and different distribution\n\n# mnist = tf.keras.datasets.mnist\n# (x_train, y_train),(x_test, y_test) = mnist.load_data()\n# x_train, x_test = x_train / 255.0, x_test / 255.0\n\n\n# print(x_train.shape[0])\n# # val_size = int(0.1 * x_train.shape[0]) # percentage of training data\n# val_size = 2000 # exact value done so that x_train has a size multiple of batch_size\n# print(val_size)\n# val_ind = np.random.choice(a=np.arange(x_train.shape[0]), size=val_size, \\\n#     replace=False)\n# val_mask = np.zeros(x_train.shape[0], dtype=bool)\n# val_mask[val_ind] = True\n\n# x_train = x_train.reshape(-1, 28, 28, 1)\n# x_test = x_test.reshape(-1, 28, 28, 1)\n\n# x_val = x_train[val_mask]\n# y_val = y_train[val_mask]\n\n# x_train = x_train[~val_mask]\n# y_train = y_train[~val_mask]\n\n# x_train = [x_train]\n# x_test = [x_test]\n\n\n\n# print('Train: X: {} y: {}'.format(x_train[0].shape, y_train.shape))\n# print('Validation: X: {} y: {}'.format(x_val.shape, y_val.shape))\n# print('Test: X: {} y: {}'.format(x_test[0].shape, y_test.shape))\n\n# print('Loaded MNIST dataset. x_train: {} y_train: {} x_test: {} y_test: {}'\n#     .format(x_train.shape, y_train.shape, x_test.shape, y_test.shape))\n\n# NOTE: a lot of this is hastily developed and I do hope to improve the 'initialization'\n#structure of the genome; please note your own ideas and we'll make that a 'project' on github soon\n\n\nskeleton_block = { #this skeleton defines a SINGLE BLOCK of a genome\n    'tensorblock_flag': True,\n    'batch_size': 128,\n    'n_epochs': 1, #the number of epochs to run for while training\n    # 'large_dataset': None,\n    'large_dataset': (['cifar-10-batches-py/data_batch_1', \\\n        'cifar-10-batches-py/data_batch_2', \\\n        'cifar-10-batches-py/data_batch_3'], get_cifar10_batch),\n    'nickname': 'tensor_mnist_block',\n    'setup_dict_ftn': {\n        #declare which primitives are available to the genome,\n        #and assign a 'prob' so that you can control how likely a primitive will be used;\n        #prob: float btwn 0 and 1 -> assigns that prob to that primitive...the sum can't be more than 1\n        #prob: 1 -> equally distribute the remaining probability amoung all those remaining (hard to explain, sorry)\n        #operators.input_layer: {'prob': 1},\n        #operators.add_tensors: {'prob': 1},\n        #operators.sub_tensors: {'prob': 1},\n        #operators.mult_tensors: {'prob': 1},\n        operators.dense_layer: {'prob': 1},\n        # operators.conv_layer: {'prob': 1},\n        # operators.max_pool_layer: {'prob': 1},\n        # operators.avg_pool_layer: {'prob': 1},\n        # operators.concat_func: {'prob': 1},\n        # operators.sum_func: {'prob': 1},\n        # operators.conv_block: {'prob': 1},\n        # operators.res_block: {'prob': 1},\n        #operators.sqeeze_excitation_block: {'prob': 1},\n        #operators.identity_block: {'prob': 1}, # TODO replace this with info from operator_dict?\n    },\n    'setup_dict_arg': {\n        #if you have an 'arguments genome', declare which argument-datatypes should fill the argument genome\n        #not used for now...arguments genome still needs to be tested\n        # arguments.argInt: {'prob': 1}}, #arg count set to 0 though\n        arguments.argPow2: {'prob': 1},\n        arguments.argFilterSize: {'prob': 1}},\n    'setup_dict_mate': {\n        #declare which mating methods are available to genomes\n        mate.Mate.dont_mate: {'prob': 1, 'args': []}},\n    'setup_dict_mut': {\n        #declare which mutation methods are available to the genomes\n        mut.Mutate.mutate_singleInput: {'prob': 1, 'args': []},\n        mut.Mutate.mutate_singleArgValue: {'prob': 1, 'args': []},\n        mut.Mutate.mutate_singleArgIndex: {'prob': 1, 'args': []},\n        mut.Mutate.mutate_singleFtn: {'prob': 1, 'args': []},\n    },\n    'operator_dict': operators.operDict, #further defines what datatypes what arguments are required for each primitive\n    'block_input_dtypes': [tf.Tensor], #placeholder datatypes so that the genome can be built off datatypes instead of real data\n    'block_outputs_dtypes': [tf.Tensor],\n    'block_main_count': 4, #10 genes\n    'block_arg_count': 20, #not used...no primitives require arguments\n    'block_mut_prob': 1, #mutate genome with probability 1...always\n    'block_mate_prob': 0 #mate with probability 0...never\n}\n\nskeleton_genome = { # this defines the WHOLE GENOME\n    'input': [np.ndarray], # we don't pass in the labels since the labels are only used at evaluation and scoring time\n    'output': [np.ndarray],\n    1: skeleton_block\n}\n","sub_path":"problem.py","file_name":"problem.py","file_ext":"py","file_size_in_byte":8922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"253771392","text":"import torch\nimport torchvision\nimport torchvision.transforms as transforms\nfrom torchvision.datasets import CIFAR10\nfrom torchvision.datasets import CIFAR100\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.dataloader import default_collate\n\nfrom functools import partial\n\nimport numpy as np\n\ndef train_transforms():\n    transforms_train = transforms.Compose([transforms.RandomCrop(32, padding=4),\n                                            transforms.RandomHorizontalFlip(),\n                                            transforms.ToTensor(),\n                                            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n                                            ])\n\n    return transforms_train\n\n\ndef rotate_img(img, rot):\n    if rot == 0: # 0 degrees rotation\n        return img\n    elif rot == 90: # 90 degrees rotation\n        return np.flipud(np.transpose(img, (1,0,2)))\n    elif rot == 180: # 90 degrees rotation\n        return np.fliplr(np.flipud(img))\n    elif rot == 270: # 270 degrees rotation / or -90\n        return np.transpose(np.flipud(img), (1,0,2))\n    else:\n        raise ValueError('rotation should be 0, 90, 180, or 270 degrees')\n\nclass data_loader(object):\n    def __init__(self, hps):\n        self.hps = hps\n\n    def train_loader(self):\n        if not self.hps.s4l_mode:\n            if self.hps.num_classes == 10:\n                transforms_train = train_transforms()\n                dataset_train = CIFAR10(root='../data', train=True, \n                                        download=True, transform=transforms_train)\n                dataset_supervised, _ = torch.utils.data.random_split(dataset_train,\n                                                                    [self.hps.num_labeled, \n                                                                    len(dataset_train) - self.hps.num_labeled])\n                loader = DataLoader(dataset_supervised, batch_size=self.hps.batch_size, shuffle=True)\n                return loader\n            \n            elif self.hps.num_classes == 100:\n                transforms_train = train_transforms()\n                dataset_train = CIFAR100(root='../data', train=True, \n                                        download=True, transform=transforms_train)\n                loader = DataLoader(dataset_train, batch_size=self.hps.batch_size, shuffle=True)\n                return loader\n            \n            else:\n                raise NotImplementedError\n\n        else:\n            class s4l_dataset(Dataset):\n                def __init__(self, hps):\n                    self.dataset = CIFAR10(root='../data', train=True, download=True)\n                    self.hps = hps\n                    self.dataset_supervised, \\\n                        self.dataset_unsupervised = torch.utils.data.random_split(\n                            self.dataset, [self.hps.num_labeled, len(self.dataset) - self.hps.num_labeled])\n                    self.transform = train_transforms()\n\n                def __getitem__(self, idx):\n                    def _load_function(idx, supervised_mode=False):\n                        idx = idx % len(self.dataset)\n                        img0, labels = self.dataset[idx]\n                        rotated_imgs = [\n                            self.transform(img0),\n                            self.transform(img0.rotate(90)),\n                            self.transform(img0.rotate(180)),\n                            self.transform(img0.rotate(270))\n                        ]\n                        class_labels = torch.LongTensor([labels] * 4)\n                        rotation_labels = torch.LongTensor([0, 1, 2, 3])\n\n                        if supervised_mode:\n                            return torch.stack(rotated_imgs, dim=0), class_labels, rotation_labels\n                        else:\n                            # fake lables\n                            class_labels = torch.LongTensor([self.hps.num_classes] * 4)\n                            return torch.stack(rotated_imgs, dim=0), class_labels, rotation_labels\n\n                    def unified_load_fun(idx):\n                        idx = idx % (len(self.dataset_unsupervised) + len(self.dataset_supervised))\n                        if idx in [i for i in range(len(self.dataset_supervised))]:\n                            return _load_function(idx, True)\n                        else:\n                            return _load_function(idx - len(self.dataset_supervised), False)\n\n                    return unified_load_fun(idx)\n                \n                def __len__(self):\n                    return len(self.dataset_supervised) + len(self.dataset_unsupervised)\n            \n            def _collate_fun(batch):\n                batch = default_collate(batch)\n                assert(len(batch)==3)\n                batch_size, rotations, channels, height, width = batch[0].size()\n                batch[0] = batch[0].view([batch_size*rotations, channels, height, width])\n                batch[1] = batch[1].view([batch_size*rotations])\n                batch[2] = batch[2].view([batch_size*rotations])\n                return batch\n\n            return DataLoader(s4l_dataset(self.hps), batch_size=self.hps.batch_size, \n                              collate_fn=_collate_fun, shuffle=True)\n        \n\n    def test_transforms(self):\n        transforms_test = transforms.Compose([transforms.ToTensor(),\n                                              transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n                                              ])\n\n        return transforms_test\n\n    \n    def test_loader(self):\n        if self.hps.num_classes == 10:\n            transforms_test = self.test_transforms()\n            dataset_test = CIFAR10(root='../data', train=False,\n                                download=True, transform=transforms_test)\n            loader = DataLoader(dataset_test, batch_size=self.hps.batch_size, shuffle=False)\n\n            return loader\n        \n        elif self.hps.num_classes == 100:\n            transforms_test = self.test_transforms()\n            dataset_test = CIFAR100(root='../data', train=False,\n                                download=True, transform=transforms_test)\n            loader = DataLoader(dataset_test, batch_size=self.hps.batch_size, shuffle=False)\n\n            return loader\n        \n        else:\n            raise NotImplementedError","sub_path":"ssl_torch/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":6449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"219631364","text":"#================================================\r\n#=== Anna Carolina de Oliveira Vale Mendes - 1B PROF: MASSANORI - 2020 ====\r\n#================================================\r\n\r\n'''' Terceira lista de exercicios/ lembrando que o Desafio se encontra nessa lista - Desafio são númerdos de 6 à 10, contando que essa lista, está numera de 1 à 5, a sequência 6=1 e assim, segue a sequência'''\r\n\r\nz=\"s\"\r\nwhile z==\"s\":\r\n    r=int(input(\"Selecione os exercícios da 'Lista III e LISTA III(Desafio (Desafio númerados de 6 à 10, considerando que essa sequência é como se fosse de 1 à 5, como representado em lista.))- De Python - Professor Massanori - FATEC - SJC',selecione o exercício que deseja ver, de (1 a 10): \"))\r\n    print (\"Digite s, quando quiser sair\")\r\n    print (\"Eu sou feliz, gosto de programar. Anna Carolina de Oliveira Vale Mendes - 30/08/2019.\")\r\n\r\n    if r == 1:\r\n        print('1. Faça um programa que peça uma nota, entre zero e dez. Mostre uma mensagem caso o valor seja inválido e continue pedindo até que o usuário informe um valor válido.')\r\n        print(\"Resolução abaixo:\")\r\n\r\n        nota=float(input(\"Digite Nota entre (0 a 10): \"))\r\n\r\n        while nota <0 or nota >10:\r\n            print(\"Nota entre (0 a 10): \")\r\n            nota=float(input(\"Digite Nota entre (0 a 10): \"))\r\n\r\n    elif r == 2:\r\n        print(\"2. Faça um programa que leia um nome de usuário e a sua senha e não aceite a senha igual ao nome do usuário, mostrando uma mensagem de erro e voltando a pedir as informações.\")\r\n        print(\" Resolução abaixo:\")\r\n\r\n        nome=input(\"Digite Usuário: \")\r\n        senha=input(\"Digite senha do Usuário: \")\r\n\r\n        while nome == senha:\r\n            print(\"Senha igual ao Usuário, digite senha diferente de Usuário\")\r\n            nome=input(\"Digite Usuário: \")\r\n            senha=input(\"Digite senha do Usuário: \")\r\n\r\n    elif r == 3:\r\n        print(\"3. Supondo que a população de um país A seja da ordem de 80000 habitantes com uma taxa anual de crescimento de 3% e que a população de B seja 200000 habitantes com uma taxa de crescimento de 1.5%. Faça um programa que calcule e escreva o número de anos necessários para que a população do país A ultrapasse ou iguale a população do país B, mantidas as taxas de crescimento\")\r\n        print(\"Resolução abaixo:\")\r\n\r\n        a = 80000\r\n        b = 200000\r\n        anos = 0\r\n\r\n        while a <= b:\r\n            a = a + a * 0.03\r\n            b = b + b * 0.015\r\n            anos = anos + 1\r\n        print(anos)\r\n\r\n    elif r == 4:\r\n        print(\"4. A seqüência de Fibonacci é a seguinte: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, ... Sua regra de formação é simples: os dois primeiros elementos são 1; a partir de então, cada elemento é a soma dos dois anteriores. Faça um algoritmo que leia um número inteiro calcule o seu número de Fibonacci. F1 = 1, F2 = 1, F3 = 2, etc.\")\r\n        print(\"Resolução abaixo\")\r\n\r\n        n = int(input('Digite o valor de n: '))\r\n\r\n        a, b = 1, 1\r\n        k = 1\r\n\r\n        while k <= n - 2:\r\n            a, b = b, a + b\r\n            k = k + 1\r\n\r\n        print(b)\r\n\r\n    elif r == 5:\r\n        print(\"5. Dados dois números inteiros positivos, determinar o máximo divisor comum entre eles usando o algoritmo de Euclides. \")\r\n        a = int(input('Valor de a: '))\r\n        b = int(input('Valor de b: '))\r\n\r\n        while a % b != 0:\r\n            a, b = b, a % b\r\n        print('mdc = %d' % b)\r\n\r\n    elif r==6:\r\n        print (\"6. Dizemos que um número natural é triangular se ele é produto de três números naturais consecutivos. Exemplo: 120 é triangular, pois 4.5.6 = 120. Dado um inteiro não-negativo n, verificar se n é triangular. \")\r\n\r\n        n = int(input('N: '))\r\n        k = 0\r\n        while k * (k + 1) * (k + 2) < n:\r\n            k = k + 1\r\n\r\n        print (k * (k + 1) * (k + 2) == n)\r\n\r\n    elif r==7:\r\n        print (\"7. Indique como um troco deve ser dado utilizando-se um número mínimo de notas. Seu algoritmo deve ler o valor da conta a ser paga e o valor do pagamento efetuado desprezando os centavos. Suponha que as notas para troco sejam as de 50, 20, 10, 5, 2 e 1 reais, e que nenhuma delas esteja em falta no caixa.\")\r\n        conta = int(input('Conta: '))\r\n        pgto = int(input('Pagamento: '))\r\n        troco = pgto - conta\r\n        notas = [50, 20, 10, 5, 2, 1]\r\n        for nota in notas:\r\n            while troco >= nota:\r\n                print ('Uma nota de %d' %nota)\r\n                troco -= nota\r\n    \r\n    elif r==8:\r\n        print (\"8.Verifique se um inteiro positivo n é primo.\")\r\n        num=int(input(\"Numero:\"))\r\n        if num%2==1:\r\n            print(\"primo\")\r\n        else:\r\n            print (\"não é primo\")\r\n\r\n    elif r==9:\r\n        print (\"9.Dado um número inteiro positivo, determine a sua decomposição em fatores primos calculando também a multiplicidade de cada fator.\")\r\n        n = int(input('Número: '))\r\n        for k in range(2, n):\r\n          while n % k == 0:\r\n            print (k)\r\n            n = n / k\r\n\r\n\r\n    elif r==10:\r\n        print (\"10. Faça um programa que peça um inteiro positivo e o mostre invertido. Ex.: 1234 gera 4321\")\r\n        n = int(input('Número: '))\r\n        inv = 0\r\n        while n > 0:\r\n            inv *= 10\r\n            inv += n % 10\r\n            n //= 10\r\n            print(inv)\r\n\r\n\r\n    else:\r\n        r !=\"s\"\r\n        break\r\n\r\n        \r\n\r\n\r\n\r\n\r\n","sub_path":"Anna Carolina Mendes_L3_Com_Desafio_incluso.py","file_name":"Anna Carolina Mendes_L3_Com_Desafio_incluso.py","file_ext":"py","file_size_in_byte":5437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"541481351","text":"from django.shortcuts import render, get_object_or_404\nfrom django.http import HttpResponseRedirect, Http404\nfrom django.core.urlresolvers import reverse\nfrom django.forms import ModelForm\nfrom participant.models import Participant, ParticipantForm, FindForm\n\n# Create your views here.\n\ndef register(request):\n    if request.method == 'POST':\n        form = ParticipantForm(request.POST)\n        if form.is_valid():\n            participant_object = form.save()\n            return HttpResponseRedirect(reverse('participant:status', args=(participant_object.id,)))\n    else:\n        form = ParticipantForm()\n    return render(\n        request, \n        'participant/register.html', \n        {'form': form})\n\ndef find(request):\n    if request.method == 'POST':\n        form = FindForm(request.POST)\n        if form.is_valid():\n            participant_object = get_object_or_404(Participant, mobileNo= form.cleaned_data['participant_mobile'])\n            return HttpResponseRedirect(reverse('participant:status', args=(participant_object.id,)))\n    else:\n        form = FindForm()\n    return render(\n        request,\n        'participant/find.html',\n        {'form': form})\n\ndef status(request, id):\n    participant_object = get_object_or_404(Participant, id=id)\n    return render(\n        request, \n        'participant/status.html',\n        { 'content': 'Your participant ID is %d' %(participant_object.id)})\n","sub_path":"participant/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"623251779","text":"#!/usr/bin/env python\nfrom flask import Flask, jsonify, send_from_directory, request\nfrom services.image_mask import  HSV\nimport base64\nimport  cv2\napp = Flask(__name__)\n\nhsv_object=None\n\n@app.route('/')\ndef index():\n    return send_from_directory(\"templates\", \"index.html\")\n\n@app.route('/inputImage', methods=['POST'])\ndef inputImage():\n    content = dict(request.json)\n    print(content)\n    hsv_object= HSV(content['inputImage'])\n    global hsv_object\n    return \"Uploaded\"\n\n\n\n\n@app.route('/sendHSV', methods=['POST'])\ndef sendHSV():\n    content = dict(request.json)\n    print(content)\n    global hsv_object\n    result=hsv_object.select_color(content)\n    _, buffer = cv2.imencode('.jpg', result)\n    jpg_as_text = base64.b64encode(buffer)\n\n\n    return jpg_as_text\n\n\n\nif __name__ == '__main__':\n    app.debug = True\n    app.run()\nprint('Started')","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"606027328","text":"#!/usr/bin/env python\n'''Curve-Fitting functions'''\nimport numpy as np\n\ndef sum_squares(func,data):\n    '''Returns D = Sum[Delta^2] = Sum[(Y - y)^2].  Fufnc and data should be array-likes'''\n    func = np.array(func)\n    data = np.array(data)\n    delta = func - data\n    return np.sum(delta*delta)\n    \ndef chi_squared(func,data,sigma_y):\n    '''Returns D = Sum[Delta^2] = Sum[(Y - y)^2].  func, data, and sigmay should be array-likes'''\n    func = np.array(func)\n    data = np.array(data)\n    sigma_y = np.array(sigma_y)\n    delta = func - data\n    return np.sum( (delta/sigma_y)**2 )\n    \ndef lin_reg(x,y,sigma_y):\n    '''Returns parameters a1,a2 of the linear fit (and their associated errors), and the chi^2 value of the fit'''\n    sig_sq = sigma_y*sigma_y\n    s      = np.sum(1./sig_sq)\n    sx     = np.sum(x/sig_sq)\n    sy     = np.sum(y/sig_sq)\n    sxx    = np.sum(x*x/sig_sq)\n    sxy    = np.sum(x*y/sig_sq)\n    \n    a1 = (sy*sxx - sx*sxy)/(s*sxx - sx*sx)\n    a2 = (s*sxy - sy*sx)/(s*sxx - sx*sx)\n    \n    da1 = np.sqrt(sxx/(s*sxx - sx*sx))\n    da2 = np.sqrt(s/(s*sxx - sx*sx))\n    std = np.sqrt((1./(len(y)-2))*np.sum((y-(a1+a2*x))**2))\n    \n    chi2 = chi_squared( a1 + a2*x, y, sigma_y)\n    return [(a1,da1),(a2,da2),chi2]\n\ndef iterative_method(x,y,sx,sy,beta0,eps):\n    '''Iteratively determines a linear regression with errors in x,y'''\n    n=0\n    tol=1\n    d_beta=0\n    d_alpha=0\n    while tol > eps:\n        (alpha_new, d_alpha_new), (beta_new, d_beta_new), chi2 = \\\n        lin_reg(x,y,np.sqrt(sx**2 + (beta0**2)*sy**2))\n        #\n        tol = np.abs((beta0-beta_new)/beta0)\n        beta0=beta_new\n        d_beta = d_beta_new\n        alpha=alpha_new\n        d_alpha = d_alpha_new\n        n=n+1\n    return (alpha,d_alpha), (beta0,d_beta), n\n    \n","sub_path":"ws5/curvefit.py","file_name":"curvefit.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"484645528","text":"\n\nfrom xai.brain.wordbase.nouns._aptitude import _APTITUDE\n\n#calss header\nclass _APTITUDES(_APTITUDE, ):\n\tdef __init__(self,): \n\t\t_APTITUDE.__init__(self)\n\t\tself.name = \"APTITUDES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"aptitude\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_aptitudes.py","file_name":"_aptitudes.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"392694596","text":"from keras.layers import GlobalAveragePooling2D ,Dense\nfrom keras.models import Model\nfrom keras.optimizers import SGD\nimport tensorflow as tf\nimport keras\nimport math\nimport random\nimport numpy as np\nimport glob\nimport scipy.io\nimport cv2\nfrom imgaug import augmenters as iaa\nimport os\nimport keras.layers as layers\nimport keras.models as models\nimport keras.backend as backend\n\n\ndef identity_block(input_tensor, kernel_size, filters, stage, block):\n    \"\"\"The identity block is the block that has no conv layer at shortcut.\n\n    # Arguments\n        input_tensor: input tensor\n        kernel_size: default 3, the kernel size of\n            middle conv layer at main path\n        filters: list of integers, the filters of 3 conv layer at main path\n        stage: integer, current stage label, used for generating layer names\n        block: 'a','b'..., current block label, used for generating layer names\n\n    # Returns\n        Output tensor for the block.\n    \"\"\"\n    filters1, filters2, filters3 = filters\n    if backend.image_data_format() == 'channels_last':\n        bn_axis = 3\n    else:\n        bn_axis = 1\n    conv_name_base = 'res' + str(stage) + block + '_branch'\n    bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n    x = layers.Conv2D(filters1, (1, 1),\n                      name=conv_name_base + '2a')(input_tensor)\n    x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n    x = layers.Activation('relu')(x)\n\n    x = layers.Conv2D(filters2, kernel_size,\n                      padding='same', name=conv_name_base + '2b')(x)\n    x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n    x = layers.Activation('relu')(x)\n\n    x = layers.Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)\n    x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n\n    x = layers.add([x, input_tensor])\n    x = layers.Activation('relu')(x)\n    return x\n\n\ndef conv_block(input_tensor,\n               kernel_size,\n               filters,\n               stage,\n               block,\n               strides=(2, 2)):\n    \"\"\"A block that has a conv layer at shortcut.\n\n    # Arguments\n        input_tensor: input tensor\n        kernel_size: default 3, the kernel size of\n            middle conv layer at main path\n        filters: list of integers, the filters of 3 conv layer at main path\n        stage: integer, current stage label, used for generating layer names\n        block: 'a','b'..., current block label, used for generating layer names\n        strides: Strides for the first conv layer in the block.\n\n    # Returns\n        Output tensor for the block.\n\n    Note that from stage 3,\n    the first conv layer at main path is with strides=(2, 2)\n    And the shortcut should have strides=(2, 2) as well\n    \"\"\"\n    filters1, filters2, filters3 = filters\n    if backend.image_data_format() == 'channels_last':\n        bn_axis = 3\n    else:\n        bn_axis = 1\n    conv_name_base = 'res' + str(stage) + block + '_branch'\n    bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n    x = layers.Conv2D(filters1, (1, 1), strides=strides,\n                      name=conv_name_base + '2a')(input_tensor)\n    x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n    x = layers.Activation('relu')(x)\n\n    x = layers.Conv2D(filters2, kernel_size, padding='same',\n                      name=conv_name_base + '2b')(x)\n    x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n    x = layers.Activation('relu')(x)\n\n    x = layers.Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)\n    x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n\n    shortcut = layers.Conv2D(filters3, (1, 1), strides=strides,\n                             name=conv_name_base + '1')(input_tensor)\n    shortcut = layers.BatchNormalization(\n        axis=bn_axis, name=bn_name_base + '1')(shortcut)\n\n    x = layers.add([x, shortcut])\n    x = layers.Activation('relu')(x)\n    return x\n\n\ndef ResNet50_224(input_shape=(224,224,3),classes=2):\n    \"\"\"Instantiates the ResNet50 architecture.\n\n    Optionally loads weights pre-trained on ImageNet.\n    Note that the data format convention used by the model is\n    the one specified in your Keras config at `~/.keras/keras.json`.\n\n    # Arguments\n        include_top: whether to include the fully-connected\n            layer at the top of the network.\n        weights: one of `None` (random initialization),\n              'imagenet' (pre-training on ImageNet),\n              or the path to the weights file to be loaded.\n        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)\n            to use as image input for the model.\n        input_shape: optional shape tuple, only to be specified\n            if `include_top` is False (otherwise the input shape\n            has to be `(224, 224, 3)` (with `channels_last` data format)\n            or `(3, 224, 224)` (with `channels_first` data format).\n            It should have exactly 3 inputs channels,\n            and width and height should be no smaller than 197.\n            E.g. `(200, 200, 3)` would be one valid value.\n        pooling: Optional pooling mode for feature extraction\n            when `include_top` is `False`.\n            - `None` means that the output of the model will be\n                the 4D tensor output of the\n                last convolutional layer.\n            - `avg` means that global average pooling\n                will be applied to the output of the\n                last convolutional layer, and thus\n                the output of the model will be a 2D tensor.\n            - `max` means that global max pooling will\n                be applied.\n        classes: optional number of classes to classify images\n            into, only to be specified if `include_top` is True, and\n            if no `weights` argument is specified.\n\n    # Returns\n        A Keras model instance.\n\n    # Raises\n        ValueError: in case of invalid argument for `weights`,\n            or invalid input shape.\n    \"\"\"\n\n    img_input = layers.Input(shape=input_shape)\n\n    bn_axis = 3\n\n\n    y = layers.Conv2D(32, (224, 1), padding='same',name='conv1_RF_0')(img_input)\n    y = layers.BatchNormalization(axis=bn_axis, name='bn_conv1_RF_0')(y)\n    y = layers.Activation('relu')(y)\n\n\n\n    y = layers.Conv2D(64, (224, 1), padding='valid',name='conv1_RF_1')(y)\n    y = layers.BatchNormalization(axis=bn_axis, name='bn_conv1_RF_1')(y)\n    y = layers.Activation('relu')(y)\n\n    y = layers.Conv2D(1, (1, 1), padding='valid',name='conv1_RF_2')(y)\n    y = layers.BatchNormalization(axis=bn_axis, name='bn_conv1_RF_2')(y)\n    y = layers.Activation('relu')(y)\n\n    y = layers.Flatten()(y)\n\n    x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input)\n    x = layers.Conv2D(64, (7, 7),\n                      strides=(2, 2),\n                      padding='valid',\n                      name='conv1')(x)\n    x = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')(x)\n    x = layers.Activation('relu')(x)\n    x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)\n\n    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))\n    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')\n    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')\n\n    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')\n    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')\n    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')\n    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')\n\n    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')\n    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')\n    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')\n    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')\n    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')\n    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')\n\n    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')\n    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')\n    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')\n\n    # x = layers.AveragePooling2D((7, 7), name='avg_pool')(x)\n    # x = layers.Flatten()(x)\n    # x = layers.Dense(classes, activation='softmax', name='fc1000')(x)\n    x = layers.GlobalAveragePooling2D()(x)\n    x = layers.Dense(512, activation='relu')(x)\n\n    x = layers.concatenate([x, y], axis=1)\n    predict = layers.Dense(2, activation='softmax')(x)\n    inputs = img_input\n    # Create model.\n    model = models.Model(inputs, predict, name='resnet50')\n\n\n    return model\n\n\nImg_3c =np.zeros([224,224,3])\nImg_3c_val =np.zeros([224,224,3])\n\ndef augmentNumpy(imgs):\n    sometimes = lambda aug: iaa.Sometimes(0.5, aug)\n    seq_imgs = iaa.Sequential(\n        [iaa.Fliplr(0.5),  # horizontally flip 50% of all images\n         iaa.Flipud(0.2),\n         iaa.Affine(\n             scale={'x': (0.8, 1.2), 'y': (0.8, 1.2)},\n             # rotate=(-15, 15),\n             shear=(-8, 8)\n         )\n\n         ], random_order=True)\n    seq_imgs_deterministic = seq_imgs.to_deterministic()\n\n    imgs_aug = seq_imgs_deterministic.augment_image(imgs)\n\n    return imgs_aug\n\ndef fuc(x, i):\n    img_part_path = os.path.join(str(i), x)\n    return (img_part_path)\n\n\ndef read_data(train_set, data_path):\n    glob.glob(data_path)\n    img_list = []\n    for i in train_set:\n        train_path = os.path.join(data_path, str(i))\n        part_list = list(map(fuc, os.listdir(train_path), [i] * len(os.listdir(train_path))))\n        img_list += part_list\n    return img_list\n\n\ndef data_precess(img_path_ill,imgList_ill,array='ill_Array'):\n\n    img_name = random.choice(imgList_ill)\n    img_dir = os.path.join(img_path_ill, img_name)\n    mat_list = os.listdir(img_dir)\n    mat_name = random.choice(mat_list)\n    img_dir = os.path.join(img_dir, mat_name)\n    data = scipy.io.loadmat(img_dir)\n    data = data[array]\n\n    data = (data - data.min()) / (data.max() - data.min())\n    w = data.shape[1]\n    h = data.shape[0]\n    expend = math.ceil(224 / w)\n    data = np.tile(data, (1, expend))\n    data_resize = data[:, 0:224]\n    Img = cv2.resize(data_resize, (224, 224))\n    # Img = augmentNumpy(Img)\n    return Img\ndef batch_img(img_path_ill,imgList_ill ,img_path_normal,imgList_normal,batch_size=8):\n    batch_images = []\n    batch_labels = []\n    Img_3c = np.zeros([batch_size, 224, 224, 3])\n\n    for i in range(batch_size):\n        seed_random = random.randint(0,1)\n        if seed_random == 0:\n            Img = data_precess(img_path_ill, imgList_ill, array='ill_Array')\n\n            Label_one_hot_train = np.array([0,1])\n        else:\n            Img = data_precess(img_path_normal, imgList_normal, array='normal_Array')\n\n            Label_one_hot_train = np.array([1,0])\n\n        batch_images.append(Img)\n        batch_labels.append(Label_one_hot_train)\n    batch_images = np.array(batch_images)\n    batch_labels = np.array(batch_labels)\n    Img_3c[:, :, :, 0] = batch_images\n    Img_3c[:, :, :, 1] = batch_images\n    Img_3c[:, :, :, 2] = batch_images\n    return Img_3c,batch_labels\n\nmodel = ResNet50_224(input_shape=(224,224,3),classes=2)\n\nmodel.compile(optimizer=SGD(lr=0.0001), loss='categorical_crossentropy')\nepochs = 200\nbatchs = 1000\nimg_path_ill = '/mnt/data/VGG_ultrasound/RF/spectrogram/RF4/data/ill'\nimg_path_normal = '/mnt/data/VGG_ultrasound/RF/spectrogram/RF4/data/normal'\n\nprint(model.summary())\n\nimgList_normal = read_data(train_set=[1, 2, 3, 5],\n                           data_path=img_path_normal)\nimgList_ill = read_data(train_set=[1, 2, 3, 5],\n                        data_path=img_path_ill)\n\nval_imgList_normal = read_data(train_set=[4],\n                           data_path=img_path_normal)\nval_imgList_ill = read_data(train_set=[4],\n                        data_path=img_path_ill)\n\nlogdir_train = '/mnt/data/VGG_ultrasound/RF/spectrogram/RF3/resnet/record/train'\nlogdir_test = '/mnt/data/VGG_ultrasound/RF/spectrogram/RF3/resnet/record/test'\n\n# writer_train = tf.summary.FileWriter(logdir_train)\n# train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,width_shift_range=0.2,height_shift_range=0.2)\n# train_generator = train_datagen.flow_from_directory(directory='',target_size=(224,224),batch_size=64)\n# with tf.Session() as sess:\ndef acc(predict,label):\n    corret_pred = np.equal(np.argmax(predict, 1), np.argmax(label, 1))\n    accuracy = np.mean(corret_pred)\n    return accuracy\nall_acc = 0\nall_acc_val = 0\nwriter = tf.summary.FileWriter(logdir_train)\nwriter_val = tf.summary.FileWriter(logdir_test)\nn = 0\n# model.load_weights('/mnt/data/VGG_ultrasound/RF/spectrogram/RF3/resnet/model/incept_24501.h5')\nfor epoch in range(epochs):\n    for i in range(batchs):\n        n += 1\n        seed_random = random.randint(0,1)\n######################## dataread\n        Img_3c_final, Label_one_hot_train = batch_img(img_path_ill=img_path_ill,\n                                            imgList_ill=imgList_ill,\n                                            img_path_normal=img_path_normal,\n                                            imgList_normal=imgList_normal,\n                                            batch_size=32,\n                                            )\n\n        Img_3c_final_val, Label_one_hot_train_val = batch_img(img_path_ill=img_path_ill,\n                                            imgList_ill=val_imgList_ill,\n                                            img_path_normal=img_path_normal,\n                                            imgList_normal=val_imgList_normal,\n                                            batch_size=32,\n                                            )\n\n\n        result = model.train_on_batch(Img_3c_final,Label_one_hot_train)\n        predict = model.predict_on_batch(Img_3c_final)\n        loss_val = model.test_on_batch(Img_3c_final_val,Label_one_hot_train_val)\n        predict_val = model.predict_on_batch(Img_3c_final_val)\n\n        accuracy = acc(predict,Label_one_hot_train)\n        accuracy_val = acc(predict_val,Label_one_hot_train_val)\n\n        print('the epochs is {},the times is {}'.format(epoch,i))\n        print('the label is {}, the predict is {},the accuracy is {}'.format(Label_one_hot_train,predict,accuracy))\n        all_acc += accuracy\n        all_acc_val += accuracy_val\n\n        if i % 500 == 0:\n            mean_acc = all_acc/500\n            mean_acc_val = all_acc_val/500\n\n            print('the mean acc is {}'.format(mean_acc))\n            summary = tf.Summary(value=[tf.Summary.Value(tag='acc', simple_value=mean_acc),\n                                        tf.Summary.Value(tag='loss',simple_value=result),\n                                        tf.Summary.Value(tag='lr', simple_value=keras.backend.get_value(model.optimizer.lr)),])\n            summary_val = tf.Summary(value=[tf.Summary.Value(tag='acc', simple_value=mean_acc_val),\n                                            tf.Summary.Value(tag='loss', simple_value=loss_val),\n                                            tf.Summary.Value(tag='lr', simple_value=keras.backend.get_value(model.optimizer.lr))])\n\n\n            writer.add_summary(summary,n)\n            writer_val.add_summary(summary_val,n)\n\n            model.save_weights('/mnt/data/VGG_ultrasound/RF/spectrogram/RF3/resnet/model/incept_{}.h5'.format(n))\n\n            all_acc = 0.0\n            all_acc_val = 0.0\n        # if i % 999 == 0:\n        #     lr = keras.backend.get_value(model.optimizer.lr)\n        #     keras.backend.set_value(model.optimizer.lr, 0.95 * lr)\n","sub_path":"resnet/Nx1_conv/224x1_conv_resnet.py","file_name":"224x1_conv_resnet.py","file_ext":"py","file_size_in_byte":15503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"25362481","text":"\"\"\"Comp120-Contract4\nDavid Brown\nLast Updated: 21/10/2019.\n\"\"\"\n\n\"\"\"Pygame and sys are imported for further use in the program.\"\"\"\n\nimport sys\nimport pygame\n\n\"\"\"Sets the size of the screen.\"\"\"\nscreen = pygame.display.set_mode((400, 400))\n\"\"\"Sets the tag on the top bar of the window to say Contract-4-Program\"\"\"\npygame.display.set_caption(\"Contract-4-Program\")\n\n\"\"\"This loads in the original image and ensures that it is casted onto the screen.\"\"\"\n\n\ndef add_image():\n    entity = pygame.image.load(\"Ground.png\").convert()\n    screen.blit(entity, (0, 0))\n\n\n\"\"\"This function changes the colour of the image depending on the values on line 33.\nhttps://youtu.be/XE47muJAnXs?t=1 used to help.\n\"\"\"\n\n\ndef entity_colour_changer(surface=pygame.Surface((1, 1))):\n    for x in range(surface.get_width()):\n        for y in range(surface.get_height()):\n            pixel = surface.get_at((x, y))\n            surface.set_at(\n                (x, y),\n                pygame.Color(int(pixel.r * 2), int(pixel.g * 0.1), int(pixel.b))\n            )\n\n\n\"\"\"Main game loop, continues until the user presses the exit button.\"\"\"\n\nwhile True:\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            sys.exit()\n\n    \"\"\"This calls the functions needed to change the colour of the asset and saves the picture.\"\"\"\n\n    screen.fill((255, 255, 255))\n    add_image()\n    entity_colour_changer(screen)\n    pygame.display.flip()\n    pygame.image.save(screen, 'newImage.png')\n\npygame.quit()\n","sub_path":"Contract 4/Comp120-Contract4.py","file_name":"Comp120-Contract4.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"288092313","text":"import functools\nfrom datetime import datetime, time\nimport math\nimport random\n\n\nclass Solution:\n    def lastRemaining(self, n: int) -> int:\n        leftToRight = True\n        start, end, u = 1, n, 1\n        while start < end:\n            n = (end - start) // u + 1\n            if n % 2 == 1:\n                start += u\n                end -= u\n            elif leftToRight:\n                start += u\n            else:\n                end -= u\n            u *= 2\n            leftToRight = not leftToRight\n        return start\n\n\ns = Solution()\nstartTime = datetime.now()\nprint(s.lastRemaining(100))\nprint(datetime.now() - startTime)\n","sub_path":"leetcode/2020/lastRemaining.py","file_name":"lastRemaining.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"451093926","text":"#!/usr/bin/env python3\n\nimport importlib\nimport argparse\nfrom epics_db import EPICS_db, print_res_file\n#from tango_db import Tango_db\nimport config\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description=\"Generate a ressource file \" \\\n            + \"to create Tango devices from epics db file.\")\n    parser.add_argument(type=str, help=\"IOC name\", dest=\"ioc_name\")\n    parser.add_argument(type=str, help=\"EPICS db filename\", dest=\"db_filename\")\n    parser.add_argument(type=str, help=\"Scope of the PV \" \\\n            + \"(can be global, horizontal or vertical)\", dest=\"current_scope\")\n\n    args = parser.parse_args()\n    ioc_name = args.ioc_name\n    db_filename = args.db_filename\n    current_scope = args.current_scope\n\n    T2E_rules = importlib.import_module('T2E_' + ioc_name + '_rules')\n    tango_dev_name = getattr(config, ioc_name + '_dev_name')\n    tango_instance_name = getattr(config, ioc_name + '_instance_name')\n    \n    dev_name = tango_dev_name[current_scope]\n    instance_name = tango_instance_name[current_scope]\n\n    # Make dico_tango_make from db file\n    db = EPICS_db(db_filename, config.vars_users)\n    dico_tango_make = db.build_tango_dico(T2E_rules.pv_dot_PROC, \n            T2E_rules.pv_dot_SCAN, T2E_rules.e2t_exceptions)\n\n    # Add scope key, and keep only desired scope\n    T2E_rules.add_scope_field(dico_tango_make)\n    T2E_rules.keep_one_scope(dico_tango_make, current_scope)\n\n    #dico_tango = {}\n    #dico_tango_make.update(Tango_db(dev_name, scope=current_scope))\n\n    # Print for ressource file\n    print_res_file(dico_tango_make, instance_name, dev_name)\n","sub_path":"tango/tools/T2E_make_res.py","file_name":"T2E_make_res.py","file_ext":"py","file_size_in_byte":1608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"2700852","text":"import csv\nimport Util\nimport time\nimport MultiSave\n\nreadpath = 'D:/data/www2011 twitter credibility/information_credibility_twitter_labels/trendid-tweetid.csv'\ntweetList = []\ndic = {}\n  \nwith open(readpath) as csvfile:\n    spamreader = csv.reader(csvfile, delimiter='\\t')\n    for tweet in spamreader:\n        eventid = tweet[0]\n        tweetid = tweet[1]\n        tweetList.append(tweetid)\n        dic[tweetid] = eventid\n        \ntotal = len(tweetList)\nend = 0\n\nfor i in range(732,total):\n    \n    if i+100 <= total:\n        end = i+100\n    else:\n        end = total\n    tws = tweetList[i:i+100]\n    accPath = 'D:/data/twitter/auth/acc'\n    api = Util.twitterAuth(accPath+'1.txt')\n    r = api.request('statuses/lookup', {'id': tws,'map':'true'})\n    \n    MultiSave.saveLookup(r,dic)\n    \n    i = end        \n#===============================================================================\n# tweetId = '15002103673,15010577475,15013306605,15003918806,15026824346'\n# \n# \n# # SAVE YOUR APPLICATION CREDENTIALS IN TwitterAPI/credentials.txt.\n# o = TwitterOAuth.read_file()\n# \n# # Using OAuth1...\n# api = TwitterAPI(\n#     o.consumer_key,\n#     o.consumer_secret,\n#     o.access_token_key,\n#     o.access_token_secret)\n# \n# r = api.request('statuses/lookup', {'id': tweetId})\n# \n# save_json('test', r)\n# \n# for tweet in r:\n#     name = tweet['id_str']\n#     save_json(name, tweet)\n#===============================================================================\n    #===========================================================================\n    # fw = open('earthquake2/earthquake_'+tweet['id_str']+'.txt','w')\n    # tks = tweet.keys()\n    # for tk in tks:\n    #     fw.write(str(tk)+':')\n    #     print str(tk)\n    #     blank = 1;\n    #     curContent = tweet[tk]\n    #     if isinstance(curContent, dict):\n    #         fw.write('\\n')\n    #         ttks = curContent.keys()\n    #         for ttk in ttks:\n    #             fw.write( '  '*blank +str(ttk))\n    #             try:\n    #                 fw.write( '  '*blank+str(curContent[ttk])+'\\n')\n    #             except Exception:\n    #                 fw.write( '  '*blank+'exception'+'\\n') \n    #             \n    #     else:\n    #         try:\n    #             print str(tweet[tk])\n    #             fw.write(str(tweet[tk])+'\\n')\n    #         except Exception:\n    #             print tweet[tk]\n    #             print tweet[tk].encode('utf-8')\n    #             fw.write(tweet[tk].encode('utf-8')+'\\n')\n    #         \n    #     print tweet[tk]\n    # fw.close()\n    #===========================================================================\n","sub_path":"Examples/lookup_tweetid.py","file_name":"lookup_tweetid.py","file_ext":"py","file_size_in_byte":2602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"507092367","text":"\n\"\"\"This module holds classes for image loading and manipulation.\"\"\"\nimport warnings\n\nfrom dicom.errors import InvalidDicomError\nimport numpy as np\nfrom PIL import Image as pImage\nimport dicom\nfrom scipy import ndimage\nfrom scipy.misc import imresize\nimport matplotlib.pyplot as plt\n\nfrom pylinac.core.decorators import type_accept\nfrom pylinac.core.geometry import Point\nfrom pylinac.core.io import get_filepath_UI, get_filenames_UI\nfrom pylinac.core.utilities import array2logical, typed_property\n\n\nDICOM = 'DICOM'\nIMAGE = 'Image'\nARRAY = 'Array'\nMM_per_INCH = 25.4\n\n\nclass Image:\n    \"\"\"A class that holds an image as a numpy array, relevant image metadata (dpi, SID, etc),\n    and methods for image (array) manipulation (resize, rotate, etc).\n\n    There are 3 types of Images: DICOM, Image, and Array. For DICOM and Image types,\n    relevant metadata (see Attributes) is extracted where possible. Metadata can also be set directly.\n\n    * A \"DICOM\" image is just what it sounds like. It could be an EPID capture, or a scanned film/CR cassette.\n    * An \"Image\" image is what you think normally of an image. It could be a JPEG, BMP, TIF, etc.\n    * An \"Array\" image is one created directly from an existing array.\n\n    Attributes\n    ----------\n    array : numpy.ndarray\n        The actual image pixel array.\n    dpi : int, float\n        The Dots-per-inch of the image.\n    dpmm : int, float\n        The Dots-per-mm of the image, defined at isocenter. E.g. if an EPID image is taken at 150cm SID,\n        the dpmm will scale back to 100cm.\n    SID : int, float\n        The Source-to-Image distance in cm.\n    im_type : {'DICOM', 'Image', 'Array'}\n        Image type.\n    center : geometry.Point\n        The center pixel of the image as a Point.\n\n    Examples\n    --------\n    Load an image from a file::\n\n        >>> my_image = \"C:/QA/image.tif\"\n        >>> img = Image(my_image)\n        >>> img.median_filter(5)\n\n    Additionally, load from a UI dialog box::\n\n        >>> img = Image.from_UI()\n\n    Or, load from an existing array::\n\n        >>> arr = np.arange(36).reshape(6,6)\n        >>> img = Image(arr)\n    \"\"\"\n    # SID = typed_property('SID', (int, float, np.number))\n    im_type = typed_property('im_type', str)\n    array = typed_property('array', np.ndarray)\n\n    def __init__(self, filename=None):\n        \"\"\"\n        Parameters\n        ----------\n        filename : str\n            Path to the image file.\n        \"\"\"\n        if filename is not None:\n            try:\n                self._load_file(filename)\n            except (IOError, AttributeError):\n                raise TypeError(\"Image input type not understood\")\n\n    @classmethod\n    def from_array(cls, array):\n        obj = cls()\n        obj.array = array\n        obj.im_type = ARRAY\n        return obj\n\n    @property\n    def dpi(self):\n        return getattr(self, '_dpi', None)\n\n    @dpi.setter\n    @type_accept(dpi=(int, float, np.number))\n    def dpi(self, dpi):\n        self._dpi = dpi\n        # update the DPmm attr if need be\n        updated_dpmm = dpi / MM_per_INCH\n        if self.dpmm != updated_dpmm:\n            self.dpmm = updated_dpmm\n\n    @property\n    def dpmm(self):\n        return getattr(self, '_dpmm', None)\n\n    @dpmm.setter\n    @type_accept(dpmm=(int, float, np.number))\n    def dpmm(self, dpmm):\n        self._dpmm = dpmm\n        # update the DPI attr if need be\n        updated_dpi = dpmm * MM_per_INCH\n        if self.dpi != updated_dpi:\n            self.dpi = updated_dpi\n\n    @property\n    def center(self):\n        \"\"\"Return the center position of the image array as a Point.\"\"\"\n        x_center = self.shape[1] / 2\n        y_center = self.shape[0] / 2\n        return Point(x_center, y_center)\n\n    @property\n    def SID(self):\n        \"\"\"Return the SID.\"\"\"\n        return getattr(self, '_SID', None)\n\n    @SID.setter\n    def SID(self, value):\n        \"\"\"Set the SID.\"\"\"\n        if not isinstance(value, (int, float, np.number)):\n            raise ValueError(\"SID must be a number\")\n        self._SID = value\n        # scale the dpmm/dpi by the SID\n        self.dpmm = self.dpmm * self.SID / 100\n\n    def check_inversion(self):\n        \"\"\"Check the image for inversion by sampling the 4 image corners.\n        If the average value of the four corners is above the average pixel value, then it is very likely inverted.\n        \"\"\"\n        outer_edge = 10\n        inner_edge = 30\n        TL_corner = self.array[outer_edge:inner_edge, outer_edge:inner_edge]\n        BL_corner = self.array[-inner_edge:-outer_edge, -inner_edge:-outer_edge]\n        TR_corner = self.array[outer_edge:inner_edge, outer_edge:inner_edge]\n        BR_corner = self.array[-inner_edge:-outer_edge, -inner_edge:-outer_edge]\n        corner_avg = np.mean((TL_corner, BL_corner, TR_corner, BR_corner))\n        if corner_avg > np.mean(self.array.flatten()):\n            self.invert()\n\n    @classmethod\n    def from_UI(cls, caption='', to_gray=True):\n        \"\"\"Load an image using a UI dialog.\"\"\"\n        file_path = get_filepath_UI()\n        if file_path:\n            obj = cls(file_path, to_gray)\n            return obj\n\n    @classmethod\n    def from_multiple_UI(cls, caption='', to_gray=True):\n        \"\"\"Load multiple images using a UI dialog.\n\n        .. versionadded:: 0.5.1\n\n        All files must be images, and must be the same size and shape.\n        Image metadata, e.g. DPI, is all based on the first image selected.\n        \"\"\"\n        file_list = get_filenames_UI()\n        if file_list:\n            obj = cls.from_multiples(file_list)\n            return obj\n\n    def _load_file(self, file_path):\n        \"\"\"Load a file.\"\"\"\n        try:\n            self._construct_dicom(file_path)\n        except InvalidDicomError:\n            try:\n                self._construct_image(file_path)\n            except OSError:\n                raise IOError(\"Image type not supported\")\n\n    def _construct_image(self, file_path):\n        \"\"\"Construct an object from an image file (TIF, JPEG, etc).\"\"\"\n        try:\n            file_path.seek(0)\n        except AttributeError:\n            pass\n        img = pImage.open(file_path)\n\n        # convert to gray if need be\n        if img.mode == 'RGB' or img.mode == 'HSV':\n            img = img.convert('F')\n\n        self.array = np.array(img)\n        self.im_type = IMAGE\n\n        # explicitly set dpi, which also sets dpmm\n        if 'dpi' in img.info:\n            if len(img.info['dpi']) > 1:\n                if img.info['dpi'][0] != img.info['dpi'][1]:\n                    raise TypeError(\"Input image has different DPI values for horizontal and vertical\")\n                self.dpi = img.info['dpi'][0]\n            else:\n                self.dpi = img.info['dpi']\n        else:\n            warnings.warn(\"Pixel distance information was not available. You can set the attribute\"\n                          \"explicitly if you know it.\")\n\n    def _construct_dicom(self, file_path):\n        \"\"\"Construct an object from a DICOM file (.dcm).\"\"\"\n        try:\n            file_path.seek(0)\n        except AttributeError:\n            pass\n        dcm = dicom.read_file(file_path)\n        self.array = dcm.pixel_array\n        self.im_type = DICOM\n        # try to set the pixel spacing\n        try:\n            # most dicom files have this tag\n            dpmm = 1/dcm.PixelSpacing[0]\n        except AttributeError:\n            # EPID images sometimes have this tag\n            dpmm = 1/dcm.ImagePlanePixelSpacing[0]\n        except:\n            warnings.warn(\"Pixel distance information for the DICOM file was not determined. You can set the \"\n                          \"attribute (.dpi or .dpmm) explicitly if you know it.\")\n        finally:\n            self.dpmm = float(dpmm)\n\n        # try to set the SID\n        try:\n            sid = float(dcm.RTImageSID) / 10\n            self.SID = sid\n        except (AttributeError, ValueError):\n            pass  # just don't set the SID\n\n    def plot(self):\n        \"\"\"Plot the image.\"\"\"\n        plt.clf()\n        plt.imshow(self.array, cmap=plt.cm.Greys)\n        plt.show()\n\n    def median_filter(self, size=3, mode='reflect'):\n        \"\"\"Apply a median filter to the image.\n\n        Wrapper for scipy's `median <http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.filters.median_filter.html>`_ filter function:\n        \"\"\"\n        if isinstance(size, float):\n            if size < 1:\n                size = max(int(self.array.shape[0]*size), 1)\n            else:\n                raise ValueError(\"If size is a float, it must be <1.0\")\n        self.array = ndimage.median_filter(self.array, size=size, mode=mode)\n\n    @type_accept(pixels=int)\n    def remove_edges(self, pixels=15):\n        \"\"\"Removes pixels on all edges of the image.\n\n        Parameters\n        ----------\n        pixels : int\n            Number of pixels to cut off all sides of the image.\n        \"\"\"\n        self.array = self.array[pixels - 1:-pixels-1, pixels - 1:-pixels-1]\n\n    def invert(self):\n        \"\"\"Invert (imcomplement) the image.\"\"\"\n        orig_array = self.array\n        self.array = -orig_array + orig_array.max() + orig_array.min()\n\n    # def rotate(self, angle, order=3):\n    #     raise NotImplementedError()\n        # self.array = ndimage.interpolation.rotate(self.array, angle, order=order, mode='wrap', reshape=False)\n\n    def rot90(self, n=1):\n        \"\"\"Wrapper for numpy.rot90.\"\"\"\n        self.array = np.rot90(self.array, n)\n\n    def resize(self, size, interp='bilinear'):\n        \"\"\"Resize/scale the image.\n\n        Wrapper for scipy's `imresize <http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.misc.imresize.html>`_:\n        \"\"\"\n        self.array = imresize(self.array, size=size, interp=interp, mode='F')\n\n    def threshold(self, threshold):\n        \"\"\"Convert the pixel array to a black & white array based on the threshold.\n\n        Parameters\n        ----------\n        threshold : int\n            If the value is less than the threshold it is set to 0, otherwise to 1.\n\n        Returns\n        -------\n        A numpy array the same size as the original image.\n        \"\"\"\n        arr = array2logical(self.array, threshold)\n        return Image.from_array(arr)\n\n    @type_accept(point=(Point, tuple))\n    def dist2edge_min(self, point):\n        \"\"\"Calculates minimum distance from given point to image edges.\n\n        Parameters\n        ----------\n        point : geometry.Point, tuple\n\n        Returns\n        -------\n        float\n        \"\"\"\n        if isinstance(point, tuple):\n            point = Point(point)\n        rows = self.shape[0]\n        cols = self.shape[1]\n        disttoedge = np.zeros(4)\n        disttoedge[0] = rows - point.y\n        disttoedge[1] = cols - point.x\n        disttoedge[2] = point.y\n        disttoedge[3] = point.x\n        return min(disttoedge)\n\n    def ground(self):\n        \"\"\"Ground the profile such that the lowest value is 0.\n\n        .. note::\n            This will also \"ground\" profiles that are negative or partially-negative.\n            For such profiles, be careful that this is the behavior you desire.\n        \"\"\"\n        min_val = self.array.min()\n        self.array -= min_val\n        return min_val\n\n    @classmethod\n    def from_multiples(cls, image_file_list):\n        \"\"\"Combine multiple image files into one superimposed image.\n\n        .. versionadded:: 0.5.1\n        \"\"\"\n        # open first one to get initial settings\n        init_obj = cls(image_file_list[0])\n        concat_arr = init_obj.array\n        initial_shape = init_obj.shape\n        for img_file in image_file_list[1:]:\n            obj = cls(img_file)\n            if obj.shape != initial_shape:\n                 raise AttributeError(\"Images must be the same size when combining.\")\n            concat_arr = np.dstack((concat_arr, obj.array))\n\n        # create new mean array\n        combined_arr = np.mean(concat_arr, axis=2)\n        # use the initial Image object and replace its array (thus keeping all the other properties)\n        init_obj.array = combined_arr\n        return init_obj\n\n    def __getattr__(self, item):\n        \"\"\"Set the Attribute getter to grab from the array if possible (for things like .shape, .size, etc).\"\"\"\n        return getattr(self.array, item)\n\nif __name__ == '__main__':\n    img = Image.from_multiple_UI()\n\n","sub_path":"pylinac/core/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":12247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"204848710","text":"import dash_html_components as html\nfrom collections import namedtuple\n\nfrom .app import app\nfrom .utils import DashRouter, DashNavBar\nfrom .pages import character_counter, page2, page3\nfrom .components import fa\n\n\n# Ordered iterable of routes: tuples of (route, layout), where 'route' is a\n# string corresponding to path of the route (will be prefixed with Dash's\n# 'routes_pathname_prefix' and 'layout' is a Dash Component.\nurls = (\n    (\"\", character_counter.get_layout),\n    (\"character-counter\", character_counter.get_layout),\n    (\"page2\", page2.layout),\n    (\"page3\", page3.layout),\n    (\"section2/page1\", page2.layout),\n    (\"section2/page2\", page3.layout),\n)\n\n# Ordered iterable of navbar items: tuples of `(route, display)`, where `route`\n# is a string or a list of tuples corresponding to path of the route (will be prefixed with\n# 'routes_pathname_prefix') and 'display' is a valid value for the `children`\n# keyword argument for a Dash component (ie a Dash Component or a string).\nnav_item = namedtuple(\"nav_item\", \"route display\")\nnav_items = (\n    nav_item(route=\"character-counter\", display=html.Div([fa(\"fas fa-keyboard\"), \"Character Counter\"])),\n    nav_item(route=\"page2\", display=html.Div([fa(\"fas fa-chart-area\"), \"Page 2\"])),\n    nav_item(route=\"page3\", display=html.Div([fa(\"fas fa-chart-line\"), \"Page 3\"])),\n    (\n        [\n            nav_item(route=\"section2/page1\", display=\"S2 P1\"),\n            nav_item(route=\"section2/page2\", display=\"S2 P2\"),\n        ],\n        \"Section 2\",\n    ),\n    \n)\n\nrouter = DashRouter(app, urls)\nnavbar = DashNavBar(app, nav_items)\n","sub_path":"{{cookiecutter.project_slug}}/src/{{cookiecutter.project_slug}}/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"72765404","text":"import copy\nimport json\nimport os\nimport requests\n\n\ndef main():\n    API_KEY = os.environ['YOUTUBE_bookmarks']\n    BASE_URL = \"https://www.googleapis.com/youtube/v3/videos?part=snippet&id=%s&key=\" + API_KEY\n\n    with open('_data/youtube-raw.json') as f:\n        youtube_raw = json.load(f)\n    with open('_data/youtube-generated.json') as f:\n        youtube_gen = json.load(f)\n\n    gen_by_id = {video['vid']: video for video in youtube_gen}\n    youtube_new = []\n\n    for video in youtube_raw:\n        vid = video['vid']\n        if vid in gen_by_id:\n            result = gen_by_id[vid]\n        else:\n            # scrape from api\n            print('scraping ' + video['title'])\n            url = BASE_URL % video['vid']\n            resp = requests.get(url)\n            data = resp.json()['items'][0]\n\n            result = copy.deepcopy(video)\n            result['meta'] = data['snippet']\n        youtube_new.append(result)\n\n    youtube_sorted = sorted(\n        youtube_new,\n        key=lambda video: video['meta']['publishedAt'],\n        reverse=True,\n    )\n\n    with open('_data/youtube-generated.json', 'w') as f:\n        json.dump(\n            youtube_sorted, f,\n            ensure_ascii=False,\n            indent=2,\n            separators=(',', ': '),\n            sort_keys=True,\n        )\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"_py/youtube_metadata.py","file_name":"youtube_metadata.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"294741426","text":"import os\n\nimport dj_database_url\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nPROJECT_PATH = os.path.dirname(os.path.abspath(__file__))\n\nSECRET_KEY = os.environ['DJANGO_SECRET_KEY']\n\nDEBUG = False\n\nALLOWED_HOSTS = [\n    'localhost',\n    '127.0.0.1',\n    'pramodk.herokuapp.com',\n    'www.pramodk.net'\n]\n\nINSTALLED_APPS = [\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'home.apps.HomeConfig',\n    'resume.apps.ResumeConfig',\n    'portfolio.apps.PortfolioConfig',\n    'compressor',\n    'ckeditor',\n]\n\nMIDDLEWARE_CLASSES = [\n    'django.middleware.gzip.GZipMiddleware',\n    'htmlmin.middleware.HtmlMinifyMiddleware',\n    'htmlmin.middleware.MarkRequestMiddleware',\n    'django.middleware.security.SecurityMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'pramodk_net.urls'\n\nTEMPLATES = [\n    {\n        'BACKEND': 'django.template.backends.django.DjangoTemplates',\n        'DIRS': [],\n        'APP_DIRS': True,\n        'OPTIONS': {\n            'context_processors': [\n                'django.template.context_processors.debug',\n                'django.template.context_processors.request',\n                'django.contrib.auth.context_processors.auth',\n                'django.contrib.messages.context_processors.messages',\n            ],\n        },\n    },\n]\n\nWSGI_APPLICATION = 'pramodk_net.wsgi.application'\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.sqlite3',\n        'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),\n    }\n}\n\ndb_from_env = dj_database_url.config(conn_max_age=500)\nDATABASES['default'].update(db_from_env)\n\nAUTH_PASSWORD_VALIDATORS = [\n    {\n        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n    },\n]\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n# STATIC FILES\nSTATIC_URL = '/static/'\nSTATIC_ROOT = os.path.join(PROJECT_PATH, 'staticfiles')\nSTATICFILES_FINDERS = (\n    'django.contrib.staticfiles.finders.FileSystemFinder',\n    'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n    'compressor.finders.CompressorFinder',\n)\nSTATICFILES_STORAGE = 'whitenoise.django.GzipManifestStaticFilesStorage'\n\n# MEDIA FILES\nMEDIA_ROOT = os.path.join(BASE_DIR, 'files')\nMEDIA_URL = '/files/'\n\n# COMPRESS SETTINGS\nCOMPRESS_PRECOMPILERS = (\n    ('text/x-scss', 'django_libsass.SassCompiler'),\n)\nCOMPRESS_URL = STATIC_URL\nCOMPRESS_ENABLED = os.environ.get('COMPRESS_ENABLED', False)\nCOMPRESS_CSS_FILTERS = ['compressor.filters.css_default.CssAbsoluteFilter', 'compressor.filters.cssmin.CSSMinFilter']\n\n# CKEDITOR SETTINGS\nCKEDITOR_JQUERY_URL = '//ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js'\n\n# HTML MINIFY SETTINGS\nHTML_MINIFY = True\n\nCKEDITOR_CONFIGS = {\n    'default': {\n        'toolbar': [\n            ['Cut', 'Copy', 'Paste', 'PasteText', 'PasteFromWord', '-', 'Undo', 'Redo'],\n            ['Find', 'Replace', '-', 'SelectAll', '-', 'SpellChecker', 'Scayt'],\n            ['Form', 'Checkbox', 'Radio', 'TextField', 'Textarea', 'Select', 'Button', 'ImageButton',\n             'HiddenField'],\n            ['Bold', 'Italic', 'Underline', 'Strike', 'Subscript', 'Superscript', '-', 'RemoveFormat'],\n            ['NumberedList', 'BulletedList', '-', 'Outdent', 'Indent', '-', 'Blockquote', 'CreateDiv',\n             '-', 'JustifyLeft', 'JustifyCenter', 'JustifyRight', 'JustifyBlock', '-', 'BidiLtr', 'BidiRtl'],\n            ['Link', 'Unlink', 'Anchor'],\n            ['Image', 'Flash', 'Table', 'HorizontalRule', 'Smiley', 'SpecialChar', 'PageBreak', 'Iframe'],\n            ['Styles', 'Format', 'Font', 'FontSize'],\n            ['TextColor', 'BGColor'],\n            ['Maximize', 'ShowBlocks', '-', 'About'],\n            ['RemoveFormat', 'Source', 'CodeSnippet'],\n        ],\n    }\n}\n","sub_path":"pramodk_net/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":4573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"378027124","text":"import numpy as np\nfrom hypothesis import given\n\nfrom stylo.domain import UnitSquare, RealDomain\nfrom stylo.testing.strategies import dimension\nfrom stylo.utils import get_parameters\n\n\nclass BaseShapeTest:\n    \"\"\"A base class for testing :code:`Shape` implementations.\n\n    When writing your test case for a new :code:`Shape` implementation you need to\n    declare it as follows.\n\n    .. code-block:: python\n\n       from unittest import TestCase\n       from stylo.testing.shape import BaseShapeTest\n\n       class TestMyShape(TestCase, BaseShapeTest)L\n           ...\n\n    .. note::\n\n       The order in which you write the classes is *very* important.\n\n    You also need to define the :code:`setUp` method to set the :code:`shape` attribute\n    equal to an instance of your shape implementation.\n\n    .. code-block:: python\n\n       def setUp(self):\n           self.shape = MyShape()\n\n    Then in addition to any tests you write, your :code:`Shape` implementation will be\n    automatically tested to see if it passes the checks defined below.\n    \"\"\"\n\n    @given(width=dimension, height=dimension)\n    def test_render_with_domain(self, width, height):\n        \"\"\"Ensure that a shape can be called with a domain and integers representing the\n        width and height in pixels of an image. The result must:\n\n        - Be numpy array\n        - With shape :code:`(height, width)`\n        - With elements of type bool.\n        \"\"\"\n        domain = UnitSquare()\n        mask = self.shape(domain, width, height)\n\n        self.assertTrue(isinstance(mask, (np.ndarray,)), \"Expected numpy array.\")\n        self.assertEqual((height, width), mask.shape)\n        self.assertTrue(mask.dtype == np.bool, \"Expected boolean array\")\n\n    @given(width=dimension, height=dimension)\n    def test_render_with_args(self, width, height):\n        \"\"\"Ensure that a shape can be called with keyword arguments. Each of the\n        arguments should be numpy arrays of the same shape with names corresponding to\n        each of the coordinate names the shape is defined with. The result must:\n\n        - Be a numpy array\n        - With shape :code:`(height, width)`\n        - With elements of type bool.\n        \"\"\"\n        domain = UnitSquare()\n        parameters = self.shape.parameters\n\n        values = domain[parameters](width, height)\n        params = {k: v for k, v in zip(parameters, values)}\n\n        mask = self.shape(**params)\n\n        self.assertTrue(isinstance(mask, (np.ndarray,)), \"Expected numpy array.\")\n        self.assertEqual((height, width), mask.shape)\n        self.assertTrue(mask.dtype == np.bool, \"Expected boolean array.\")\n\n    def test_parameters_property(self):\n        \"\"\"Ensure that the :code:`parameters` property returns the coordinates that the\n        shape is defined with. The parameters must\n\n        - Be iterable\n        - Be unique\n        - Be declared on the :code:`RealDomain` interface.\n        \"\"\"\n\n        coordinates = self.shape.parameters\n        unique = set(coordinates)\n\n        self.assertEqual(len(unique), len(coordinates))\n\n        for c in coordinates:\n            if c not in RealDomain._parameters:\n                message = \"{} is not a valid coordinate variable.\"\n                raise ValueError(message.format(c))\n\n    def test_draw_method(self):\n        \"\"\"Ensure that the :code:`draw` method returns a valid shape function. A shape\n        function must\n\n        - Be callable\n        - Only take coordinate arguments defined in :code:`RealDomain`\n        - These arguments must match those defined in the :code:`parameters` property.\n        \"\"\"\n\n        method = self.shape.draw()\n\n        self.assertTrue(callable(method), \"Expected callable.\")\n        parameters = get_parameters(method)\n\n        self.assertEqual(parameters, self.shape.parameters)\n","sub_path":"stylo/testing/shape.py","file_name":"shape.py","file_ext":"py","file_size_in_byte":3787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"318127373","text":"import functools\n\ndef memoize(obj):\n    \"\"\"Memoizer implementation from\n    https://wiki.python.org/moin/PythonDecoratorLibrary#Memoize\n    \"\"\"\n    cache = obj.cache = {}\n\n    @functools.wraps(obj)\n    def wrapper(*args, **kwargs):\n        if args not in cache:\n            cache[args] = obj(*args, **kwargs)\n        return cache[args]\n    return wrapper\n\n@memoize\ndef fibo(n):\n    \"\"\"Compute the nth Fibonacci number.\n    Raises if n is negative\n    :param n: the index of the Fibonacci number we want to compute\n    :type n: int\n    :returns: int\n    :raises: ValueError\n    \"\"\"\n    if n < 0:\n        raise ValueError(\"n must be positive.\")\n    result = 0\n    if n <= 2 :\n        result = n\n    else:\n        result = fibo(n-1) + fibo(n-2)\n    return result\n","sub_path":"git-bisect-sample/fibo.py","file_name":"fibo.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"290490969","text":"__author__ = 'wangcl'\n\nimport logging\nfrom logging.handlers import RotatingFileHandler\nimport os\n\n\ndef getLogger(name, level=logging.DEBUG):\n    logger = logging.getLogger(name)\n    path = os.path.join(os.path.dirname(__file__), \"../logs/\" + name + \".log\")\n    hdlr = RotatingFileHandler(path, maxBytes=5 * 1024 * 1024, backupCount=2)\n    formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')\n    hdlr.setFormatter(formatter)\n    logger.addHandler(hdlr)\n    logger.setLevel(logging.DEBUG)\n    return logger\n","sub_path":"log_ext/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"654135564","text":"print(\"Ця програма рахує Факторіал\")\r\nn = int(input(\"n = \"))\r\nfactorial = n\r\nif n == 1 :\r\n    print(\"n! = 1\")\r\nelse:\r\n    \r\n    while n > 1:\r\n       \r\n        factorial = factorial * (n-1)\r\n        \r\n        n = n-1        \r\n    print(\"n! = \", factorial)","sub_path":"task1 Week2.py","file_name":"task1 Week2.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"71944002","text":"import smtplib\nimport pymysql\nfrom multiprocessing.dummy import Pool as ThreadPool\nfrom random import choice\nimport socket\nimport os\nimport sys\nimport time\nimport re\n\nhostname = '127.0.0.1'\nusername = 'lincogixdb1'\npassword = 'lincogixdbgptools2017'\ndatabase = 'lincogixdb1'\nwords = ['a', 'b', 'c', 'd', 'e', 'f']\ntwotests = ['yahoo.com.br', 'bb.com.br']\n\ntry:\n    import DNS\n    ServerError = DNS.ServerError\n    DNS.DiscoverNameServers()\nexcept (ImportError, AttributeError):\n    DNS = None\n\n    class ServerError(Exception):\n        pass\nMX_DNS_CACHE = {}\n\n\ndef check_email_format(email):\n    match = re.match(\n        r\"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$)\", email)\n    if match == None:\n        return False\n    return True\n\n\ndef get_mx_ip(hostname):\n    if hostname not in MX_DNS_CACHE:\n        try:\n            MX_DNS_CACHE[hostname] = DNS.mxlookup(hostname)\n        except ServerError as e:\n            # NXDOMAIN (Non-Existent Domain) or SERVFAIL\n            if e.rcode == 3 or e.rcode == 2:\n                MX_DNS_CACHE[hostname] = False\n            else:\n                raise\n\n    return MX_DNS_CACHE[hostname]\n\n\ndef updateData(email, resul):\n    conn = pymysql.connect(host=hostname, user=username,\n                           passwd=password, db=database)\n    cur = conn.cursor()\n    qry = cur.execute(\"UPDATE checados_novo SET status = %s WHERE email = %s;\", (resul, email))\n    conn.commit()\n    conn.close()\n\n\ndef doQuery():\n    conn = pymysql.connect(host=hostname, user=username,\n                           passwd=password, db=database)\n    cur = conn.cursor()\n    qry = cur.execute(\n        \"SELECT email from checados_novo where status='check' limit 30000;\")\n    resul = cur.fetchall()\n    conn.close()\n\n    arry = []\n    for i in resul:\n        arry.append(i[0])\n\n    return arry\n\n\ndef smtp(email):\n    email = email.strip()\n    email = email.lower()\n    if check_email_format(email):\n        smtp = smtplib.SMTP()\n        smtp.set_debuglevel(0)\n        domain = email.split('@')\n        host = get_mx_ip(domain[1])\n        if host:\n            for mx in host:\n                try:\n                    smtp.connect(mx[1])\n                    status, _ = smtp.helo()\n                    if status != 250:\n                        smtp.quit()\n                        continue\n                    word = choice(words)\n                    smtp.mail(word+'@gmail.com')\n                    resul, _ = smtp.rcpt(email)\n                    if domain[1] in twotests:\n                        data = 0\n                        spam = ''\n                        erro = False\n                        try:\n                            data, spam = smtp.data('')\n                        except:\n                            pass\n                            erro = True\n                        if not erro:\n                            if resul == 250 and data == 250:\n                                resul = 'valid'\n                                print(email + ' - ' + resul)\n                                #updateData(email, resul)\n                            else:\n                                resul = 'invalid'\n                                print(email + ' - ' + resul)\n                                #updateData(email, resul)\n                        else:\n                            resul = 'invalid'\n                            print(email + ' - ' + resul)\n                            #updateData(email, resul)\n                    else:\n                        if resul == 250:\n                            resul = 'valid'\n                            print(email + ' - ' + resul)\n                            #updateData(email, resul)\n                        else:\n                            resul = 'invalid'\n                            print(email + ' - ' + resul)\n                            #updateData(email, resul)\n                    break\n                except Exception as e:\n                    print(e)\n        else:\n            resul = 'invalid'\n            print(email + ' - ' + resul)\n            #updateData(email, resul)\n    else:\n        resul = 'invalid'\n        print(email + ' - ' + resul)\n        #updateData(email, resul)\n\n\nsystem = sys.version_info\nif system[0] > 2:\n\n    one = input('Only one?  >> yN ')\nelse:\n    one = raw_input('Only one?  >> yN ')\nif one.lower() == 'y':\n    if system[0] > 2:\n        email = input('Enter email >> ')\n    else:\n        email = raw_input('Enter email >> ')\n\n    smtp(email)\n    quit()\n\nelse:\n    while True:\n        emails = doQuery()\n\n        if not emails:\n            break\n\n        pool = ThreadPool(3000)\n        try:\n            results = pool.map(smtp, emails)\n        except:\n            pass\n        pool.close()\n        pool.join()\n        time.sleep(1)\n","sub_path":"email_validation/validate_emails.py","file_name":"validate_emails.py","file_ext":"py","file_size_in_byte":4752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"221833735","text":"import math\ndef isHappy(n: int) -> bool:\n    sum=0\n    b=[]\n    while(sum!=1):\n        sum=0\n        while(n>0):\n            a=n%10\n            sum+=a*a\n            n=math.floor(n/10)\n        n=sum\n        if(sum in b):\n            return False\n        else:\n            b.append(sum)\n    return True\n\nn=2\nresult=isHappy(n)\nprint(result)  ","sub_path":"HappyNumber.py","file_name":"HappyNumber.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"585959649","text":"import requests\nfrom xml.etree import ElementTree\n\nclass Spamtitan(object):\n  def __init__(self, host):\n    self.base_url = 'http://%s' % host\n  \n  \n  def list_whitelist(self, user=None):\n    \"\"\" List Whitelisted Addresses and Domains\n    param:user: Optional. The email address or domain name of the user/domain for which \n                to view the whitelist\n    \"\"\"\n    url = '%s/whitelist/list' % (self.base_url)\n\n    if user:\n      url += '?user=%s' % user\n\n    r = requests.get(url)\n    if r.status_code == 200:\n     Exception('Error %s:%s' % (url, r.text))\n    \n    return r\n\n  def add_whitelist(self, sender, user=None):\n    \"\"\" Whitelist a sender\n    param:sender: Required. The email address or domain to add to the whitelist\n    param:user: Optional. The email address or domain name of the whitelist entry to add. \n                If no user is specified, then the entry will be added to the global whitelist.\n    \"\"\"\n    url = '%s/whitelist/add?sender=%s' % (self.base_url, sender)\n\n    if user:\n      url += '&user=%s' % user\n\n    r = requests.get(url)\n    if r.status_code == 200:\n     Exception('Error %s:%s' % (url, r.text))\n\n    return r\n  \n  def remove_whitelist(self, sender, user=None):\n    \"\"\" Remove Whitelist sender\n    param:sender: Required. The email address or domain to add to the whitelist\n    param:user: Optional. The email address or domain name of the whitelist entry to add. \n                If no user is specified, then the entry will be added to the global whitelist.\n    \"\"\"\n    url = '%s/whitelist/delete?sender=%s' % (self.base_url, sender)\n\n    if user:\n      url += '&user=%s' % user\n\n    r = requests.get(url)\n    if r.status_code == 200:\n     Exception('Error %s:%s' % (url, r.text))\n\n    return r\n\n  def edit_policy(self, user, **kwargs):\n    \"\"\" Edit user/domain policy\n    param:user: Username or Domain to edit policy.\n    param:virus_lover: Specifies if virus infected files should be passed for this user (Y/N). Default N.\n    param:spam_lover: Specifies if messages exceeding the spam threshold should be passed for this\n                        user (Y/N). Default N.\n    param:banned_files_lover: Specifies if messages containing banned attachment should be passed\n                              for this user (Y/N). Default N.\n    param:bad_header_lover: Specifies if messages containing banned attachment should be passed\n                            for this user (Y/N). Default N.\n    param:bypass_virus_checks: Specifies if virus checking should be disabled for this user (Y/N).\n                               Default N.\n    param:bypass_spam_checks: Specifies if spam checking should be disabled for this user (Y/N).\n                              Default N.\n    param:bypass_banned_checks: Specifies if banned attachment checking should be disabled for this\n                                user (Y/N). Default N.\n    param:bypass_header_checks: Specifies if header checks are bypassed for this user (Y/N). Default\n                                N.\n    param:spam_modifies_subj: Specifies if the mail subject is changed when spam is detected for this\n                              u ser (Y/N). Default N.\n    param:spam_tag_level; Add spam score headers to mail when score is greater than or\n                          equal. Default: 999. Type: float\n    param:spam_tag2_level; Specifies the threshold over which messages will be considered spam.\n                            Default: 5. Type: float\n    param:spam_kill_level; Quarantine or discard spam when score is greater than or equal to.\n                            Default: 5. Type: float\n    param:report_kill_level; Specifies the threshold over which mail will be included in reports. Default:\n                              999. Type: float\n    param:spam_dsn_cutoff_level; Spam score at which not to generate delivery status notifications..\n                                 Default: 0. Type: float\n    param:spam_quarantine_cutoff_level; Score at which not to quarantine. Default: 999. Type: float\n    param:spam_quarantine_to: Specifies how to deal with spam mail. (Default) spam-quarantine to\n                                quarantine mail, set to **nothing** to reject. Set to *nothing* and set spam_lover to \n                                Y to Pass and Tag mail.\n    param:virus_quarantine_to: Specifies how to deal with virus mail. (Default) virus-quarantine to\n                                quarantine mail, set to *nothing* to reject. Set to *nothing* and set virus_lover \n                                to Y to Pass and Tag mail.\n    param:banned_quarantine_to: Specifies how to deal with banned attachment mail. (Default)\n                                banned-quarantine to quarantine mail, set to  to reject. Set to  and set\n                                banned_files_lover to Y to Pass and Tag mail.\n    param:locked: Specifies if the policy is locked. If a policy is locked, changes to the domain policy will\n                  not be inherited by the locked user policy (Y/N). Default: N.\n    param:digest: Specifies if this user should receive a quarantine report. N=Never, D=Daily,\n                    WD=Week Days, M=Monthly. Default N.\n    param:report_type: Specifies the type of quarantine report to send the user. Possible values are N\n                        (New items since last report only) , A (all quarantine messages), X (All quarantined msgs,\n                        except viruses), Y (New items since last report, except viruses). Default N.\n    param:digest_language: Specifies the language that the report should be generated in\n                            (cs_CZ/da_DK/de_DE/en_US/fr_FR/nl_NL/ja_JP/it_IT/pl_PL/es_ES). Default: en_US\n    \"\"\"\n    url = '%s/policy/edit?user=%s' % (self.base_url, user)\n    for attribute, value in kwargs.iteritems():\n      aurl = '&attribute=%s&value=%s' % (attribute, value)\n      r = requests.get(url+aurl)\n      if r.status_code == 200:\n        Exception('Error %s:%s' % (url, r.text))\n    \n    return r\n\n  def edit_domain(self, domain_name, **kwargs):                  \n    ''' Modify settings for a particular domain.\n    :param domain_name: Required. The name of the Domain to add\n    :param server: Optional. The new destination server IP address or FQDN for this domain\n    :param rv: Optional. The recipient verification method to use for this server. Possible values are:\n    \"none\", \"dynamic\", \"ldap\", \"list\"\n    :param dyn_server: Optional. The recipient verification server to use if the recipient verification is\n    \"dynamic\". This setting will have no effect if the recipient verification (rv) setting is not set to\n    \"dynamic\"\n    :param ldap_server: Required.\n    :param ldap_port: Required.\n    :param ldap_search_dn: Optional.\n    :param ldap_password: Optional.\n    :param ldap_filter: Optional.\n    :param ldap_searchbase: Optional.\n    :param ldap_result_attribute: Optional. Defaults to mail.\n    :param email: Optional. If list based recipient verification is been used, then use this setting to add\n    email addresses to the list. The API call needs to be called once for each email address\n    added.\n    :param email / delemail: add or remove entry from list based recipient verification. View page 15 for\n    more information.\n    :param domaingroup: Domain Group to add domain to.\n    '''\n    url = '%s/domain/edit?name=%s' % (self.base_url, domain_name)\n    for attribute, value in kwargs.iteritems():\n      url += '&%s=%s' % (attribute, value)\n    r = requests.get(url)\n    if r.status_code == 200:\n     Exception('Error %s:%s' % (url, r.text))\n\n    return r\n\n  def delete_domain(self, domain_name):\n    ''' Deletes a Spamtitan domain_name\n    :param domain_name:str The domain you want to delete.\n    '''\n    url = '%s/domain/delete?name=%s' % (self.base_url, domain_name)\n     \n    r = requests.get(url)\n    if r.status_code == 200:\n     Exception('Error %s:%s' % (url, r.text))\n\n    return r\n\n  def create_domain(self, domain_name, relay):\n    ''' create a domain on spamtitan\n    :param domain_name:str the domain name you want to create.\n    :param ralay:str server: Required. The destination mail server for this domain. \n          To specify a non-default port (25) append \":8025\".\n          o To specify a fallback server enter a list of IP addresses/FQDNs separated by spaces.\n          SpamTitan will attempt delivery in the order listed. E.g.: server=1.2.3.4 5.6.2.3\n          o Specify a comma (,) separated list of IP addresses and/or FQDNs. In this\n          case SpamTitan will attempt delivery in a round-robin fashion between the\n          listed servers. E.g.:server=1.2.3.4,1.2.3.5 \n    '''\n    url = '%s/domain/add?name=%s&server=%s' % (self.base_url, domain_name, relay)\n     \n    r = requests.get(url)\n    if r.status_code == 200:\n     Exception('Error %s:%s' % (url, r.text))\n\n    return r\n \n  def domain_exist(self, domain_name):\n    ''' check if a domain exist\n    :param domain_name:str the domain name you want to validate if exist or not.\n    '''\n    url = '%s/domain/exists?name=%s' % (self.base_url, domain_name) \n    r = requests.get(url)\n    if r.status_code != 200:\n      Exception('Error connecting the server %s' % url)\n    \n    try:\n      tree = ElementTree.fromstring(r.content)\n      if tree[0].tag == 'success':\n        return True\n      else:\n        return False\n    except e:\n      Exception('Error Parsing Server Response: %s' % e)","sub_path":"spamtitan/spamtitan.py","file_name":"spamtitan.py","file_ext":"py","file_size_in_byte":9399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"502911432","text":"import os\nimport sys\nimport time\nimport logging\n\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\n\nfrom config_factory import *\n\nfrom matplotlib.colors import to_hex\nimport plotly.graph_objects as go\n\n\n\ndef enduro_plot_factory(root='/home/allenchen/Moana_proj/neural_skill_search/log/other/final_20M_top3/Enduro',\n                           trace_groups=['ori', 'ev', 'dqn/top3'],\n                           colors=['green', 'blue', 'red'],\n                           legends=['Primitive', 'Evolution', 'Ours'],\n                           title='Enduro',\n                           filename='outputs/Enduro/enduro_plot.png',\n                           scale=4):\n\n    template = get_template()\n\n    template['filename'] = filename\n    #template['width'] = width\n    #template['height'] = height\n    template['scale'] = scale\n    template['subplots']['layout']['title']['text'] = title\n    template['subplots']['layout']['xaxis']['range'] = [0, 10000000]\n    template['subplots']['layout']['yaxis']['dtick'] = 200\n\n    content = new_content(title=title)\n\n    add_content(template, content)\n\n    # for each group, grab all folders under this group, then add them to sources\n    for idx, group in enumerate(trace_groups):\n        group_root = os.path.join(root, group)\n        sources = grab_folders(group_root)\n        '''\n        add_data(config=template,\n                 root=group_root,\n                 sources=sources, \n                 name=group,\n                 process='process.load_results', \n                 inputs={\n                     'max_steps': 10000000,\n                     'smooth_window': 200\n                 })\n        '''\n        process = new_process(process_name='process.load_results',\n                                  inputs={\n                                      'root': group_root,\n                                      'sources': sources,\n                                      'max_steps': 10000000,\n                                      'smooth_window': 200\n                                  })\n\n        data = new_data(name=group)\n\n        add_process(data, process)\n        add_data(template, data)\n\n        color = 'id'\n        if colors is not None:\n            color = colors[idx]\n\n        legend = '{}-{}'.format(title, group)\n        if legends is not None:\n            legend = legends[idx]\n\n        trace = new_trace(group, legend_name=legend, color=color, fillopacity=0.2)\n        \n        add_trace(content, trace)\n    \n    return template\n        \n\n\n\ndef enduro_plot_factory_v2(root='/home/allenchen/Moana_proj/neural_skill_search/log/other/20M_top3_life/Seaquest/dqn/a2c',\n                           trace_groups=['ori', 'ev', 'top3'],\n                           colors=['green', 'blue', 'red'],\n                           legends=['Primitive', 'Evolution', 'Ours'],\n                           title='Enduro',\n                           filename='outputs/Enduro/v2/enduro_plot.png',\n                           max_steps=10000000,\n                           scale=4,\n                           **kwargs):\n    '''\n    kwargs:\n        process: (list) process number, 1, 2, or 3, which refer to 'load_results', 'load_period_eval' and 'load_option_critic', respectively\n        dash: (list) dash style name, please refer to plotly documentation for more information.\n            Sets the dash style of lines. Set to a dash type string (\"solid\", \"dot\", \"dash\", \"longdash\", \"dashdot\", or \"longdashdot\") or a dash length list in px (eg \"5px,10px,2px,2px\").\n    '''\n\n    template = get_template(**kwargs)\n\n    template['filename'] = filename\n    #template['width'] = width\n    #template['height'] = height\n    template['scale'] = scale\n    template['subplots']['layout']['title']['text'] = title\n    template['subplots']['layout']['xaxis']['range'] = [0, max_steps]\n    template['subplots']['layout']['yaxis']['dtick'] = 200\n    template['subplots']['layout']['yaxis']['rangemode'] = 'tozero'\n\n    content = new_content(title=title)\n\n    add_content(template, content)\n\n    # for each group, grab all folders under this group, then add them to sources\n    for idx, group in enumerate(trace_groups):\n        group_root = os.path.join(root, group)\n        \n\n        if 'process' in kwargs:\n            if isinstance(kwargs['process'], (list, tuple)):\n                process_number = kwargs['process'][idx]\n            else:\n                process_number = kwargs['process']\n        else:\n            process_number = 2\n            \n\n        if process_number == 1:\n            sources = grab_folders(group_root)\n        elif process_number == 2:\n            sources = grab_folders(group_root, target_folder='**/period_eval')\n        elif process_number == 3:\n            files = grab_folders(group_root, target_folder='**/training_results_ep_life.csv')\n            sources = [grab_name(f, pattern='(.+)/training_results_ep_life.csv') for f in files]\n\n        process = get_process(process=process_number, max_steps=max_steps, sources=sources, root=group_root)\n\n        process = new_process(**process)\n\n        data = new_data(name=group)\n\n        add_process(data, process)\n        add_data(template, data)\n\n        color = 'id'\n        if colors is not None:\n            color = colors[idx]\n\n        legend = '{}-{}'.format(title, group)\n        if legends is not None:\n            legend = legends[idx]\n\n        # line\n        line = {}\n        if 'dash' in kwargs:\n            dash = kwargs['dash']\n\n            if not isinstance(data, (tuple, list)):\n                line['dash'] = dash\n            else:\n                line['dash'] = dash[idx]\n\n        trace = new_trace(group, legend_name=legend, color=color, fillopacity=0.2, line=line)\n        \n        add_trace(content, trace)\n    \n    return template     \n\n\n\n\n'''\nbeamrider_plot_config = {\n    'subplots': {\n        'settings': {\n            'rows': 1,\n            'cols': 1,\n            'shared_xaxes': False,\n            'shared_yaxes': False,\n            'start_cell': 'top-left',\n            'print_grid': True,\n            'horizontal_spacing': None,\n            'vertical_spacing': None,\n            #'subplot_titles': ['BeamRider'],\n            'column_widths': None,\n            'row_heights': None,\n            'specs': None,\n            'insets': None,\n            'column_titles': None,\n            'row_titles': None,\n            #'x_title': 'Timesteps',\n            #'y_title': 'Rewards'\n        },\n        'layout': {\n            'title': {\n                'text': 'BeamRider',\n                'font.size': 32,\n                'xref': 'paper',\n                'x': 0.5,\n            },\n            'template': 'plotly_white',\n            'font.size': 22,\n            'legend': {\n                'font.size': 22,\n                'x': 1.01,\n                'y': 1.0,\n                'tracegroupgap': 0\n            },\n            'margin': {\n                'l': 80,\n                'r': 10,\n                't': 80,\n                'b': 100\n            },\n            'xaxis': {\n                'tick0': 0,\n                'tickformat': 's',\n                \n            },\n            'yaxis': {\n                'tick0': 0,\n                'tickformat': 's',\n            },\n            'annotations': [\n                go.layout.Annotation(\n                    x=0.5,\n                    y=-0.10,\n                    showarrow=False,\n                    text='Timesteps',\n                    xref='paper',\n                    yref='paper',\n                    font=dict(size=22)\n                ),\n                go.layout.Annotation(\n                    x=-0.05,\n                    y=0.5,\n                    showarrow=False,\n                    textangle=-90,\n                    text='Rewards',\n                    xref='paper',\n                    yref='paper',\n                    font=dict(size=22)\n                ),\n                \n            ],\n        },\n        'contents': [\n            {\n                'title': 'BeamRider',\n                'type': 'confidence',\n                'traces': [\n                    {\n                        'data': 'BeamRider-ori',\n                        'legend_name': 'BeamRider-ori',\n                        'color': to_hex('xkcd:blue'),\n                        'x_column': 'timesteps',\n                        'y_column': 'rewards'\n                    },\n                    {\n                        'data': 'BeamRider-top',\n                        'legend_name': 'BeamRider-top',\n                        'color': to_hex('xkcd:red'),\n                        'x_column': 'timesteps',\n                        'y_column': 'rewards'\n                    }\n                ]\n            },\n        ]\n    },\n\n    'data': {\n        'BeamRider-top': {\n            'process': [\n                (\n                    'process.load_results',\n                    {\n                        'inputs': {\n                            'root': 'data/BeamRider/dqn/top1/BeamRiderNoFrameskip-v4',\n                            'sources': [\n                                '[[0, 1, 1], [3, 2]]/monitor',\n                                '[[0, 1, 1], [3, 2]]_0/monitor',\n                                '[[0, 1, 1], [3, 2]]_1/monitor'\n                            ],\n                            'combine_sources': True,\n                            'to_dataframe': True,\n                            'max_steps': 10000000,\n                        }\n                    }\n                )\n            ]\n        },\n        'BeamRider-ori': {\n            'process': [\n                (\n                    'process.load_results',\n                    {\n                        'inputs': {\n                            'root': 'data/BeamRider/dqn/ori/BeamRiderNoFrameskip-v4',\n                            'sources': [\n                                '[]/monitor',\n                                '[]_0/monitor',\n                                '[]_1/monitor'\n                            ],\n                            'combine_sources': True,\n                            'to_dataframe': True,\n                            'max_steps': 10000000,\n                        }\n                    }\n                )\n            ]\n        },\n    },\n\n    'offline': True,\n    'filename': 'beamrider_plot.png',\n    'width': 1600,\n    'height': 800\n}\n\n'''","sub_path":"lib/plot_lib/plot/configs/enduro_plot.py","file_name":"enduro_plot.py","file_ext":"py","file_size_in_byte":10288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"410870695","text":"\"\"\"\nSimple graph implementation\n\"\"\"\nfrom util import Stack, Queue  # These may come in handy\n\nclass Graph:\n\n    \"\"\"Represent a graph as a dictionary of vertices mapping labels to edges.\"\"\"\n    def __init__(self):\n        self.vertices = {}\n\n    def add_vertex(self, vertex_id):\n       self.vertices[vertex_id] = set()\n\n    def add_edge(self, v1, v2):\n        if v2 in self.vertices and v2 in self.vertices:\n            self.vertices[v1].add(v2)\n        else:\n            raise IndexError(\"nonexistent vertex\")\n\n    def get_neighbors(self, vertex_id):\n        return self.vertices[vertex_id]\n\n    def bft(self, starting_vertex):\n        q = Queue()\n        visited = set()\n        # ancestor problem:\n        # need list of earlier nodes\n        ancestors = []\n        # initialize\n        q.enqueue(starting_vertex)\n\n        while q.size() > 0:\n            v= q.dequeue()\n\n            if v not in visited:\n                # print(v) # original purpose\n                ancestors.append(v) # ancestor purpose\n                visited.add(v)\n\n                for neighbor in self.get_neighbors(v):\n                    q.enqueue(neighbor)\n        # print(ancestors)\n        return ancestors\n\n    def dft(self, starting_vertex):\n        s = Stack()\n        visited = set()\n        # initialize\n        s.push(starting_vertex)\n\n        while s.size() > 0:\n            v= s.pop()\n\n            if v not in visited:\n                print(v)\n                visited.add(v)\n\n                for neighbor in self.get_neighbors(v):\n                    s.push(neighbor)\n\n    def dft_recursive(self, starting_vertex, visited=None):\n        \"\"\"\n        Print each vertex in depth-first order\n        beginning from starting_vertex.\n        This should be done using recursion.\n        \"\"\"\n        if not visited:\n            visited = set()\n        # add to visited\n        visited.add(starting_vertex)\n        # print this value\n        print(starting_vertex)\n        # for each vertice\n        for found_vertex in self.vertices[starting_vertex]:\n            # call dft_recursive on this vertice, with this visited\n            if found_vertex not in visited:\n                self.dft_recursive(found_vertex, visited)\n\n\n    def bfs(self, starting_vertex, destination_vertex):\n        \"\"\"\n        Return a list containing the shortest path from\n        starting_vertex to destination_vertex in\n        breath-first order.\n        \"\"\"\n        q = Queue()\n        visited = set()\n\n        # initialize\n        q.enqueue([starting_vertex])\n\n        while q.size() > 0:\n            # Dequeue the first path\n            path = q.dequeue()\n            # save last element from the path\n            v = path[-1]\n            # check that vertex has not been visited\n            if v not in visited:\n                # check if vertex is destination\n                if v == destination_vertex:\n                    # if so, return path\n                    return path\n                # set as visited\n                visited.add(v)\n                # Then add path to its neighbors to the back of the queue\n                for neighbor in self.get_neighbors(v):\n                    # make a copy of the path\n                    path_copy = path.copy()\n                    # append neighbor to the end of queue\n                    path_copy.append(neighbor)\n                    q.enqueue(path_copy)\n        return None\n\n    def dfs(self, starting_vertex, destination_vertex):\n        \"\"\"\n        Return a list containing a path from\n        starting_vertex to destination_vertex in\n        depth-first order.\n        \"\"\"\n        s = Stack()\n        visited = set()\n\n        # initialize\n        s.push([starting_vertex])\n\n        while s.size() > 0:\n           # pop off the first path\n            path = s.pop()\n            # save last element from the path\n            v = path[-1]\n            # check that vertex has not been visited\n            if v not in visited:\n                # check if vertex is destination\n                if v == destination_vertex:\n                    # if so, return path\n                    return path\n                # set as visited\n                visited.add(v)\n                # Then add path to its neighbors to the back of the queue\n                for neighbor in self.get_neighbors(v):\n                    # make a copy of the path\n                    path_copy = path.copy()\n                    # append neighbor to the end of queue\n                    path_copy.append(neighbor)\n                    s.push(path_copy)\n        return None\n\n    def dfs_recursive(self, starting_vertex, destination_vertex, visited=None, path=None):\n        \"\"\"\n        Return a list containing a path from\n        starting_vertex to destination_vertex in\n        depth-first order.\n\n        This should be done using recursion.\n        \"\"\"\n        if not visited:\n            visited = set()\n\n        if not path:\n            path = [starting_vertex]\n            \n        visited.add(starting_vertex)\n\n        for neighbor in self.get_neighbors(starting_vertex):\n            if neighbor not in visited:\n\n                path_copy = path.copy()\n                path_copy.append(neighbor)\n\n                if neighbor == destination_vertex:\n                    return path_copy\n\n                dfs_path = self.dfs_recursive(neighbor, destination_vertex, visited, path_copy)\n                if dfs_path is not None:\n                    return dfs_path\n        return None\n\nif __name__ == '__main__':\n    graph = Graph()  # Instantiate your graph\n    # https://github.com/LambdaSchool/Graphs/blob/master/objectives/breadth-first-search/img/bfs-visit-order.png\n    graph.add_vertex(1)\n    graph.add_vertex(2)\n    graph.add_vertex(3)\n    graph.add_vertex(4)\n    graph.add_vertex(5)\n    graph.add_vertex(6)\n    graph.add_vertex(7)\n    graph.add_edge(5, 3)\n    graph.add_edge(6, 3)\n    graph.add_edge(7, 1)\n    graph.add_edge(4, 7)\n    graph.add_edge(1, 2)\n    graph.add_edge(7, 6)\n    graph.add_edge(2, 4)\n    graph.add_edge(3, 5)\n    graph.add_edge(2, 3)\n    graph.add_edge(4, 6)\n\n    '''\n    Should print:\n        {1: {2}, 2: {3, 4}, 3: {5}, 4: {6, 7}, 5: {3}, 6: {3}, 7: {1, 6}}\n    '''\n    print(graph.vertices)\n    print(\"______\\nBFT\")\n    '''\n    Valid BFT paths:\n        1, 2, 3, 4, 5, 6, 7\n        1, 2, 3, 4, 5, 7, 6\n        1, 2, 3, 4, 6, 7, 5\n        1, 2, 3, 4, 6, 5, 7\n        1, 2, 3, 4, 7, 6, 5\n        1, 2, 3, 4, 7, 5, 6\n        1, 2, 4, 3, 5, 6, 7\n        1, 2, 4, 3, 5, 7, 6\n        1, 2, 4, 3, 6, 7, 5\n        1, 2, 4, 3, 6, 5, 7\n        1, 2, 4, 3, 7, 6, 5\n        1, 2, 4, 3, 7, 5, 6\n    '''\n    graph.bft(1)\n    print(\"______\\nDFT\")\n\n    '''\n    Valid DFT paths:\n        1, 2, 3, 5, 4, 6, 7\n        1, 2, 3, 5, 4, 7, 6\n        1, 2, 4, 7, 6, 3, 5\n        1, 2, 4, 6, 3, 5, 7\n    '''\n    graph.dft(1)\n    print(\"______\\nDFT-R\")\n    graph.dft_recursive(1)\n    print(\"______\\nBFS\")\n\n    '''\n    Valid BFS path:\n        [1, 2, 4, 6]\n    '''\n    print(graph.bfs(1, 6))\n    print(\"______\\nDFS\")\n\n    '''\n    Valid DFS paths:\n        [1, 2, 4, 6]\n        [1, 2, 4, 7, 6]\n    '''\n    print(graph.dfs(1, 6))\n    print(\"______\\nDFS-R\")\n    print(graph.dfs_recursive(1, 6))\n","sub_path":"projects/ancestor/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":7174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"245363009","text":"# stepik_sequence.py\nimport argparse\nimport sys\n\n# Grab arguments\n# parser = argparse.ArgumentParser(description='Print sequence of integers')\n# parser.add_argument('length', type=int, default=7,\n# \t\t\t\t\thelp='Length of sequence to print')\n# args = parser.parse_args()\n\ndef _Generate_seq(n):\n    '''\n    Return sequence by only appending to end of list\n    '''\n    len_counter = 0\n    cur_counter = 0\n    seq = []\n\n    while len_counter < n:\n        cur_counter += 1\n        for i in range(cur_counter):\n            seq.append(cur_counter)\n            len_counter += 1\n            if len_counter >= n: break\n    return seq\n\nif __name__ == \"__main__\":\n\t#n = args.length\n\t#print(sys.argv)\n\tif len(sys.argv) > 1:\n\t\tn = sys.argv[-1]\n\n\t\tseq = _Generate_seq(int(n))\n\n\t\tfor entry in seq:\n\t\t\tprint(entry, end=' ')\n\t\tprint()\n","sub_path":"sequence.py","file_name":"sequence.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"25911506","text":"\nfrom nltk.tokenize import word_tokenize\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.corpus import stopwords\n\nfrom pattern.es import pprint\nfrom pattern.es import parse, split\nfrom pattern.es import singularize\nfrom pattern.es import parsetree\nfrom pattern.es import attributive, predicative\nfrom pattern.es import FEMALE, PLURAL\n\nimport codecs\nimport unicodedata\n\n\nblacklist  = ['ley','inciso','articulos','diputados','senadores','proyecto',\n'articulo', 'mocion','parlamentaria','decreto','transitorio','bis','numeral']\npunctuations = [',', '.', ':', ';']\n\n\ndef replace_accents(data):\n    return unicodedata.normalize('NFKD', data).encode('ASCII', 'ignore')\n\ndef is_number(s):\n    try:\n        float(s)\n        return True\n    except ValueError:\n        pass\n\n    try:\n        import unicodedata\n        unicodedata.numeric(s)\n        return True\n    except (TypeError, ValueError):\n        pass\n\n    return False\n\ndef contains_digit(s):\n    d = [\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"]\n    contains = False\n    for dd in d:\n        if dd in s:\n            contains = True\n\n    return contains\n    \n    \ndef get_words(path, encod):\n    with codecs.open(path,'r',encoding=encod) as f:\n        text = f.read()\n    contenedor = []\n    st = parsetree(\n        replace_accents(unicode(text.lower())),relations=True, lemmata=True)\n    for sentence in st:\n        for chunk in sentence.chunks:\n            for word in chunk.words:\n                if word.string not in stopwords.words('spanish') and \\\n                word.string not in blacklist and \\\n                word.string not in punctuations and \\\n                word.string.isdigit()==False and \\\n                \"http\" not in word.string and \\\n                \".pdf\" not in word.string and \\\n                \"%\" not in word.string and \\\n                \":\" not in word.string and \\\n                \"www\" not in word.string and \\\n                \"@\" not in word.string and \\\n                \"/\" not in word.string and \\\n                is_number(word.string) == False and \\\n                contains_digit(word.string) == False and \\\n                len(word.string) > 2 :\n                    contenedor.append(word.string)\n    return contenedor\n\n\n","sub_path":"preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"496025935","text":"# Copyright © 2019 Province of British Columbia\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\"\"\"Meta information about the service.\n\nCurrently this only provides API versioning information\n\"\"\"\n\nimport datetime\n\nfrom flask import current_app\nfrom colin_api.utils import convert_to_json_date\n\nfrom colin_api.exceptions import FilingNotFoundException, InvalidFilingTypeException\nfrom colin_api.models import Business\nfrom colin_api.resources.db import db\n\n\nclass Filing():\n    \"\"\"Class to contain all model-like functions such as getting and setting from database.\"\"\"\n\n    # dicts containing data\n    business = None\n    header = None\n    body = None\n    filing_type = None\n\n    def __init__(self):\n        pass\n\n    def get_corp_num(self):\n        \"\"\"Get corporation num, aka identifier.\"\"\"\n        return self.business.business['identifier']\n\n    def get_last_name(self):\n        \"\"\"Get last name; currently is whole name.\"\"\"\n        return self.body['certifiedBy']\n\n    def get_email(self):\n        \"\"\"Get email address.\"\"\"\n        return self.body['email']\n\n    def as_dict(self):\n        \"\"\"Return dict of object that can be json serialized and fits schema requirements.\"\"\"\n        return {\n            \"filing\": {\n                \"header\": self.header,\n                self.filing_type: self.body,\n                \"business\": self.business.business\n            }\n        }\n\n    @classmethod\n    def find_filing(cls, business: Business = None, filing_type: str = None, year: int = None):\n        \"\"\"Return a Filing.\"\"\"\n        if not business or not filing_type:\n            return None\n\n        try:\n            identifier = business.get_corp_num()\n\n            # set filing type code from filing_type (string)\n            if filing_type == 'annualReport':\n                filing_type_code = 'OTANN'\n                filing_obj = cls.find_ar(business, identifier, year)\n\n            elif filing_type == 'changeOfAddress':\n                filing_obj = cls.find_change_of_addr(business, identifier, year)\n            else:\n                # default value\n                filing_type_code = 'FILE'\n                raise InvalidFilingTypeException\n\n            return filing_obj\n\n        except FilingNotFoundException as err:\n            # pass through exception to caller\n            raise err\n\n        except Exception as err:\n            # general catch-all exception\n            current_app.logger.error(err.with_traceback(None))\n\n            # pass through exception to caller\n            raise err\n\n    @classmethod\n    def add_filing(cls, filing):\n        \"\"\"Add new filing to COLIN tables.\n\n        :param filing: Filing dict.\n        :returns (int): the filing ID of the new filing.\n        \"\"\"\n        try:\n            corp_num = filing.get_corp_num()\n\n            # get db connection and start a session, in case we need to roll back\n            con = db.connection\n            con.begin()\n            cursor = con.cursor()\n\n            # create new event record, return event ID\n            event_id = cls._get_event_id(cursor, corp_num, 'FILE')\n\n            # create new filing user\n            cls._add_filing_user(cursor, event_id, filing)\n\n            if filing.filing_type == 'annualReport':\n                date = filing.body['annualGeneralMeetingDate']\n                filing_type_cd = 'OTANN'\n\n                # create new filing\n                cls._add_filing(cursor, event_id, filing, date, filing_type_cd)\n\n                # update corporation record\n                cls._update_corporation(cursor, corp_num, date)\n\n                # update corp_state TO ACT (active) if it is in good standing. From CRUD:\n                # - the current corp_state != 'ACT' and,\n                # - they just filed the last outstanding ARs\n                if filing.business.business['corpState'] != 'ACT':\n                    agm_year = int(date[:4])\n                    last_year = datetime.datetime.now().year - 1\n                    if agm_year >= last_year:\n                        cls._update_corp_state(cursor, event_id, corp_num, state='ACT')\n\n            elif filing.filing_type == 'changeOfAddress':\n\n                # set date to last agm date + 1\n                last_agm_date = filing.business.business['lastAgmDate']\n                dd = int(last_agm_date[-2:]) + 1\n                try:\n                    date = str(datetime.datetime.strptime(last_agm_date[:-2] + ('0' + str(dd))[1:], \"%Y-%m-%d\"))[:10]\n                except ValueError as err:\n                    try:\n                        dd = '-01'\n                        mm = int(last_agm_date[5:7]) + 1\n                        date = str(datetime.datetime.strptime(last_agm_date[:5] + ('0' + str(mm))[1:] + dd, \"%Y-%m-%d\"))[:10]\n                    except ValueError as err:\n                        mm_dd = '-01-01'\n                        yyyy = int(last_agm_date[:4]) + 1\n                        date = str(datetime.datetime.strptime(str(yyyy) + mm_dd, \"%Y-%m-%d\"))[:10]\n                filing_type_cd = 'OTADD'\n\n                # create new filing\n                cls._add_filing(cursor, event_id, filing, date, filing_type_cd)\n\n                # create new addresses for delivery + mailing, return address ids\n                delivery_addr_id = cls._get_address_id(cursor, filing.body['deliveryAddress'])\n                mailing_addr_id = cls._get_address_id(cursor, filing.body['mailingAddress'])\n\n                # update office table to include new addresses\n                cls._update_office(cursor, event_id, corp_num, delivery_addr_id, mailing_addr_id, 'RG')\n\n                # update corporation record\n                cls._update_corporation(cursor, corp_num, None)\n\n                # create new ledger text for address change\n                cls._add_ledger_text(cursor, event_id, 'Change to the Registered Office, effective on {} as filed with'\n                                                       ' {} Annual Report'.format(date, date[:4]))\n\n            else:\n                raise InvalidFilingTypeException(identifier=filing.business.business['identifier'],\n                                                 filing_type=filing.filing_type)\n\n            # success! commit the db changes\n            con.commit()\n\n        except Exception as err:\n            # something went wrong, roll it all back\n            current_app.logger.error(err.with_traceback(None))\n            if con:\n                con.rollback()\n\n            raise err\n\n    @classmethod\n    def _get_event_id(cls, cursor, corp_num, event_type='FILE'):\n        \"\"\"Get next event ID for filing.\n\n        :param cursor: oracle cursor\n        :return: (int) event ID\n        \"\"\"\n        cursor.execute(\"\"\"select noncorp_event_seq.NEXTVAL from dual\"\"\")\n        row = cursor.fetchone()\n        event_id = int(row[0])\n        try:\n            cursor.execute(\"\"\"\n            INSERT INTO event (event_id, corp_num, event_typ_cd, event_timestmp, trigger_dts)\n              VALUES (:event_id, :corp_num, :event_type, sysdate, NULL)\n            \"\"\",\n                           event_id=event_id,\n                           corp_num=corp_num,\n                           event_type=event_type\n                           )\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n\n        return event_id\n\n    @classmethod\n    def _add_filing(cls, cursor, event_id, filing, date, filing_type_code='FILE'):\n        \"\"\"Add record to FILING.\n\n        Note: Period End Date and AGM Date are both the AGM Date value for Co-ops.\n\n        :param cursor: oracle cursor\n        :param event_id: (int) event_id for all events for this transaction\n        :param filing: (obj) Filing data object\n        :param date: (str) period_end_date\n        :param filing_type_code: (str) filing type code\n        \"\"\"\n        if not filing_type_code:\n            raise FilingNotFoundException(filing.business.business['identifier'], filing.filing_type)\n        try:\n            if filing_type_code is 'OTANN':\n                cursor.execute(\"\"\"\n                INSERT INTO filing (event_id, filing_typ_cd, effective_dt, period_end_dt, agm_date)\n                  VALUES (:event_id, :filing_type_code, sysdate, TO_DATE(:period_end_date, 'YYYY-mm-dd'),\n                  TO_DATE(:agm_date, 'YYYY-mm-dd'))\n                \"\"\",\n                               event_id=event_id,\n                               filing_type_code=filing_type_code,\n                               period_end_date=date,\n                               agm_date=date\n                               )\n            elif filing_type_code is 'OTADD':\n                cursor.execute(\"\"\"\n                INSERT INTO filing (event_id, filing_typ_cd, effective_dt, period_end_dt)\n                  VALUES (:event_id, :filing_type_code, sysdate, TO_DATE(:period_end_date, 'YYYY-mm-dd'))\n                \"\"\",\n                               event_id=event_id,\n                               filing_type_code=filing_type_code,\n                               period_end_date=date,\n                               )\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n\n    @classmethod\n    def _add_filing_user(cls, cursor, event_id, filing):\n        \"\"\"Add to the FILING_USER table.\n\n        :param cursor: oracle cursor\n        :param event_id: (int) event_id for all events for this transaction\n        :param filing: (obj) Filing data object\n        \"\"\"\n        try:\n            cursor.execute(\"\"\"\n            INSERT INTO filing_user (event_id, user_id, last_nme, first_nme, middle_nme, email_addr, party_typ_cd,\n            role_typ_cd)\n              VALUES (:event_id, NULL, :last_name, NULL, NULL, :email_address, NULL, NULL)\n            \"\"\",\n                           event_id=event_id,\n                           last_name=filing.get_last_name(),\n                           email_address=filing.get_email()\n                           )\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n\n    @classmethod\n    def _update_corporation(cls, cursor, corp_num, date):\n        \"\"\"Update corporation record.\n\n        :param cursor: oracle cursor\n        :param corp_num: (str) corporation number\n        \"\"\"\n        try:\n            if date:\n                cursor.execute(\"\"\"\n                UPDATE corporation\n                SET\n                    LAST_AR_FILED_DT = sysdate,\n                    LAST_AGM_DATE = TO_DATE(:agm_date, 'YYYY-mm-dd'),\n                    LAST_LEDGER_DT = sysdate\n                WHERE corp_num = :corp_num\n                \"\"\",\n                               agm_date=date,\n                               corp_num=corp_num\n                               )\n\n            else:\n                cursor.execute(\"\"\"\n                                UPDATE corporation\n                                SET\n                                    LAST_LEDGER_DT = sysdate\n                                WHERE corp_num = :corp_num\n                                \"\"\",\n                               corp_num=corp_num\n                               )\n\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n\n    @classmethod\n    def _update_corp_state(cls, cursor, event_id, corp_num, state='ACT'):\n        \"\"\"Update corporation state.\n\n        End previous corp_state record (end event id) and and create new corp_state record.\n\n        :param cursor: oracle cursor\n        :param filing: (obj) Filing data object\n        \"\"\"\n        try:\n            cursor.execute(\"\"\"\n            UPDATE corp_state\n            SET end_event_id = :event_id\n            WHERE corp_num = :corp_num and end_event_id is NULL\n            \"\"\",\n                           event_id=event_id,\n                           corp_num=corp_num\n                           )\n\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n        try:\n            cursor.execute(\"\"\"\n            INSERT INTO corp_state (corp_num, start_event_id, state_typ_cd)\n              VALUES (:corp_num, :event_id, :state\n              )\n            \"\"\",\n                           event_id=event_id,\n                           corp_num=corp_num,\n                           state=state\n                           )\n\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n\n    @classmethod\n    def _add_ledger_text(cls, cursor, event_id, text):\n        try:\n            cursor.execute(\"\"\"\n            INSERT INTO ledger_text (event_id, ledger_text_dts, notation, dd_event_id)\n              VALUES (:event_id, sysdate, :notation, :dd_event_id)\n            \"\"\",\n                           event_id=event_id,\n                           notation=text,\n                           dd_event_id=event_id\n                           )\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n\n    @classmethod\n    def _get_address_id(cls, cursor, address_info):\n\n        cursor.execute(\"\"\"select noncorp_address_seq.NEXTVAL from dual\"\"\")\n        row = cursor.fetchone()\n        addr_id = int(row[0])\n        try:\n            cursor.execute(\"\"\"\n                    select country_typ_cd from country_type where full_desc=:country\n                  \"\"\",\n                           country=address_info['addressCountry'].upper()\n                           )\n            country_typ_cd = (cursor.fetchone())[0]\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n\n        try:\n            cursor.execute(\"\"\"\n                    INSERT INTO address (addr_id, province, country_typ_cd, postal_cd, addr_line_1, addr_line_2, city,\n                        delivery_instructions)\n                    VALUES (:addr_id, :province, :country_typ_cd, :postal_cd, :addr_line_1, :addr_line_2, :city,\n                        :delivery_instructions)\n                    \"\"\",\n                           addr_id=addr_id,\n                           province=address_info['addressRegion'].upper(),\n                           country_typ_cd=country_typ_cd,\n                           postal_cd=address_info['postalCode'].upper(),\n                           addr_line_1=address_info['streetAddress'].upper(),\n                           addr_line_2=address_info['streetAddressAdditional'].upper()\n                           if 'streetAddressAdditional' in address_info.keys() else '',\n                           city=address_info['addressCity'].upper(),\n                           delivery_instructions=address_info['deliveryInstructions'].upper()\n                           if 'deliveryInstructions' in address_info.keys() else ''\n                           )\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n\n        return addr_id\n\n    @classmethod\n    def _update_office(cls, cursor, event_id, corp_num, delivery_addr_id, mailing_addr_id, office_typ_cd):\n\n        try:\n            cursor.execute(\"\"\"\n                    UPDATE office\n                    SET end_event_id = :event_id\n                    WHERE corp_num = :corp_num and office_typ_cd = :office_typ_cd and end_event_id is null\n                    \"\"\",\n                           event_id=event_id,\n                           corp_num=corp_num,\n                           office_typ_cd=office_typ_cd\n                           )\n\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n\n        try:\n            cursor.execute(\"\"\"\n                    INSERT INTO office (corp_num, office_typ_cd, start_event_id, end_event_id, mailing_addr_id,\n                     delivery_addr_id)\n                    VALUES (:corp_num, :office_typ_cd, :start_event_id, null, :mailing_addr_id, :delivery_addr_id)\n                    \"\"\",\n                           corp_num=corp_num,\n                           office_typ_cd=office_typ_cd,\n                           start_event_id=event_id,\n                           mailing_addr_id=mailing_addr_id,\n                           delivery_addr_id=delivery_addr_id\n                           )\n\n        except Exception as err:\n            current_app.logger.error(err.with_traceback(None))\n            raise err\n\n    @classmethod\n    def find_ar(cls, business: Business = None, identifier: str = None, year: int = None):\n\n        # build base querystring\n        querystring = (\n            \"\"\"\n            select event.EVENT_TIMESTMP, EFFECTIVE_DT, AGM_DATE, PERIOD_END_DT, NOTATION,\n            FIRST_NME, LAST_NME, MIDDLE_NME, EMAIL_ADDR\n            from EVENT\n            join FILING on EVENT.EVENT_ID = FILING.EVENT_ID \n            left join FILING_USER on EVENT.EVENT_ID = FILING_USER.EVENT_ID \n            left join LEDGER_TEXT on EVENT.EVENT_ID = LEDGER_TEXT.EVENT_ID \n            where CORP_NUM=:identifier and FILING_TYP_CD=:filing_typ_cd  \n            \"\"\"\n        )\n\n        # condition by year on period end date - for coops, this is same as AGM date; for corps, this is financial\n        # year end date.\n        if year:\n            querystring += ' AND extract(year from PERIOD_END_DT) = {}'.format(year)\n\n        querystring += ' order by EVENT_TIMESTMP desc '\n\n        # get record\n        cursor = db.connection.cursor()\n        cursor.execute(querystring, identifier=identifier, filing_typ_cd='OTANN')\n        filing = cursor.fetchone()\n\n        if not filing:\n            raise FilingNotFoundException(identifier=identifier, filing_type='annualReport')\n\n        # add column names to resultset to build out correct json structure and make manipulation below more robust\n        # (better than column numbers)\n        filing = dict(zip([x[0].lower() for x in cursor.description], filing))\n\n        # if there is no AGM date in period_end_dt, check agm_date and effective date\n        try:\n            agm_date = next(item for item in [\n                filing['period_end_dt'], filing['agm_date'], filing['effective_dt']\n            ] if item is not None)\n        except StopIteration:\n            agm_date = None\n\n        # build filing user name from first, middle, last name\n        filing_user_name = ' '.join(filter(None, [filing['first_nme'], filing['middle_nme'], filing['last_nme']]))\n        if not filing_user_name:\n            filing_user_name = 'Unavailable'\n\n        # if email is blank, set as empty tring\n        if not filing['email_addr']:\n            filing['email_addr'] = 'missing@missing.com'\n\n        # convert dates and date-times to correct json format\n        filing['event_timestmp'] = convert_to_json_date(filing['event_timestmp'])\n        agm_date = convert_to_json_date(agm_date)\n\n        filing_obj = Filing()\n        filing_obj.business = business\n        filing_obj.header = {\n            'date': filing['event_timestmp'],\n            'name': 'annualReport'\n        }\n        filing_obj.body = {\n            'annualGeneralMeetingDate': agm_date,\n            'certifiedBy': filing_user_name,\n            'email': filing['email_addr']\n        }\n        filing_obj.filing_type = 'annualReport'\n\n        return filing_obj\n\n    @classmethod\n    def find_change_of_addr(cls, business: Business = None, identifier: str = None, year: int = None):\n        # build base querystring\n        # todo: check full_desc in country_type table matches with canada post api for foreign countries\n        querystring = (\n            \"\"\"\n            select ADDR_LINE_1, ADDR_LINE_2, ADDR_LINE_3, CITY, PROVINCE, COUNTRY_TYPE.FULL_DESC, POSTAL_CD,\n            DELIVERY_INSTRUCTIONS, EVENT.EVENT_TIMESTMP, FILING_USER.FIRST_NME, FILING_USER.LAST_NME,\n            FILING_USER.MIDDLE_NME, FILING_USER.EMAIL_ADDR\n            from ADDRESS\n            join OFFICE on ADDRESS.ADDR_ID = OFFICE.{addr_id_typ}\n            join COUNTRY_TYPE on ADDRESS.COUNTRY_TYP_CD = COUNTRY_TYPE.COUNTRY_TYP_CD\n            join EVENT on OFFICE.START_EVENT_ID = EVENT.EVENT_ID\n            left join FILING_USER on EVENT.EVENT_ID = FILING_USER.EVENT_ID\n            where OFFICE.END_EVENT_ID IS NULL and OFFICE.CORP_NUM=:corp_num and OFFICE.OFFICE_TYP_CD=:office_typ_cd\n            \"\"\"\n        )\n        querystring_delivery = (\n            querystring.format(addr_id_typ='DELIVERY_ADDR_ID')\n        )\n        querystring_mailing = (\n            querystring.format(addr_id_typ='MAILING_ADDR_ID')\n        )\n\n        # get record\n        cursor = db.connection.cursor()\n        cursor.execute(querystring_delivery, corp_num=identifier, office_typ_cd='RG')\n        delivery_address = cursor.fetchone()\n        test = cursor.fetchone()\n        if test:\n            current_app.logger.error('More than 1 delivery address returned - update oracle sql in find_reg_off_addr')\n\n        cursor.execute(querystring_mailing, corp_num=identifier, office_typ_cd='RG')\n        mailing_address = cursor.fetchone()\n        test = cursor.fetchone()\n        if test:\n            current_app.logger.error('More than 1 mailing address returned - update oracle sql in find_reg_off_addr')\n\n        if not delivery_address:\n            raise FilingNotFoundException(identifier=identifier, filing_type='change_of_address')\n\n        # add column names to result set to build out correct json structure and make manipulation below more robust\n        # (better than column numbers)\n        delivery_address = dict(zip([x[0].lower() for x in cursor.description], delivery_address))\n        mailing_address = dict(zip([x[0].lower() for x in cursor.description], mailing_address))\n\n        # build filing user name from first, middle, last name\n        filing_user_name = ' '.join(filter(None, [delivery_address['first_nme'], delivery_address['middle_nme'],\n                                                  delivery_address['last_nme']]))\n        if not filing_user_name:\n            filing_user_name = 'N/A'\n\n        # if email is blank, set as N/A\n        if not delivery_address['email_addr']:\n            delivery_address['email_addr'] = 'N/A'\n\n        # todo: check all fields for data - may be different for data outside of coops\n        # expecting data-fix for all bad data in address table for coops: this will catch if anything was missed\n        if delivery_address['addr_line_1'] and delivery_address['addr_line_2'] and delivery_address['addr_line_3']:\n            current_app.logger.error('Expected 2, but got 3 delivery address lines for: {}'.format(identifier))\n        if not delivery_address['addr_line_1'] and not delivery_address['addr_line_2'] and not delivery_address['addr_line_3']:\n            current_app.logger.error('Expected at least 1 delivery addr_line, but got 0 for: {}'.format(identifier))\n        if not delivery_address['city'] or not delivery_address['province'] or not delivery_address['full_desc'] \\\n                or not delivery_address['postal_cd']:\n            current_app.logger.error('Missing field in delivery address for: {}'.format(identifier))\n\n        if mailing_address['addr_line_1'] and mailing_address['addr_line_2'] and mailing_address['addr_line_3']:\n            current_app.logger.error('Expected 2, but 3 mailing address lines returned for: {}'.format(identifier))\n        if not mailing_address['city'] or not mailing_address['province'] or not mailing_address['full_desc'] \\\n                or not delivery_address['postal_cd']:\n            current_app.logger.error('Missing field in mailing address for: {}'.format(identifier))\n\n        # for cases where delivery addresses were input out of order - shift them to lines 1 and 2\n        if not delivery_address['addr_line_1']:\n            if delivery_address['addr_line_2']:\n                delivery_address['addr_line_1'] = delivery_address['addr_line_2']\n                delivery_address['addr_line_2'] = None\n        if not delivery_address['addr_line_2']:\n            if delivery_address['addr_line_3']:\n                delivery_address['addr_line_2'] = delivery_address['addr_line_3']\n                delivery_address['addr_line_3'] = None\n\n        delivery_address['country'] = delivery_address['full_desc']\n        mailing_address['country'] = mailing_address['full_desc']\n\n        delivery_address['event_timestmp'] = convert_to_json_date(delivery_address['event_timestmp'])\n        mailing_address['event_timestmp'] = convert_to_json_date(mailing_address['event_timestmp'])\n\n        filing_obj = Filing()\n        filing_obj.business = business\n        filing_obj.header = {\n            'date': delivery_address['event_timestmp'],\n            'name': 'changeOfAddress'\n        }\n        filing_obj.body = {\n            \"certifiedBy\": filing_user_name,\n            \"email\": delivery_address['email_addr'],\n            \"deliveryAddress\": {\n                \"streetAddress\": delivery_address['addr_line_1'],\n                \"streetAddressAdditional\": delivery_address['addr_line_2'] if delivery_address['addr_line_2'] else '',\n                \"addressCity\": delivery_address['city'],\n                \"addressRegion\": delivery_address['province'],\n                \"addressCountry\": delivery_address['country'],\n                \"postalCode\": delivery_address['postal_cd'],\n                \"deliveryInstructions\": delivery_address['delivery_instructions']\n                if delivery_address['delivery_instructions'] else ''\n            },\n            \"mailingAddress\": {\n                \"streetAddress\": mailing_address['addr_line_1'],\n                \"streetAddressAdditional\": mailing_address['addr_line_2'] if mailing_address['addr_line_2'] else '',\n                \"addressCity\": mailing_address['city'],\n                \"addressRegion\": mailing_address['province'],\n                \"addressCountry\": mailing_address['country'],\n                \"postalCode\": mailing_address['postal_cd'],\n                \"deliveryInstructions\": mailing_address['delivery_instructions']\n                if mailing_address['delivery_instructions'] else ''\n            }\n        }\n        filing_obj.filing_type = 'changeOfAddress'\n\n        return filing_obj\n","sub_path":"colin-api/colin_api/models/filing.py","file_name":"filing.py","file_ext":"py","file_size_in_byte":26683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"584958483","text":"def _minimize_bfgs(fun, x0, args=(), jac=None, callback=None, gtol=1e-05, norm=Inf, eps=_epsilon, maxiter=None, disp=False, return_all=False, **unknown_options):\n    '\\n    Minimization of scalar function of one or more variables using the\\n    BFGS algorithm.\\n\\n    Options\\n    -------\\n    disp : bool\\n        Set to True to print convergence messages.\\n    maxiter : int\\n        Maximum number of iterations to perform.\\n    gtol : float\\n        Gradient norm must be less than `gtol` before successful\\n        termination.\\n    norm : float\\n        Order of norm (Inf is max, -Inf is min).\\n    eps : float or ndarray\\n        If `jac` is approximated, use this value for the step size.\\n\\n    '\n    _check_unknown_options(unknown_options)\n    f = fun\n    fprime = jac\n    epsilon = eps\n    retall = return_all\n    x0 = asarray(x0).flatten()\n    if (x0.ndim == 0):\n        x0.shape = (1,)\n    if (maxiter is None):\n        maxiter = (len(x0) * 200)\n    (func_calls, f) = wrap_function(f, args)\n    if (fprime is None):\n        (grad_calls, myfprime) = wrap_function(approx_fprime, (f, epsilon))\n    else:\n        (grad_calls, myfprime) = wrap_function(fprime, args)\n    gfk = myfprime(x0)\n    k = 0\n    N = len(x0)\n    I = numpy.eye(N, dtype=int)\n    Hk = I\n    old_fval = f(x0)\n    old_old_fval = (old_fval + (np.linalg.norm(gfk) / 2))\n    xk = x0\n    if retall:\n        allvecs = [x0]\n    warnflag = 0\n    gnorm = vecnorm(gfk, ord=norm)\n    while ((gnorm > gtol) and (k < maxiter)):\n        pk = (- numpy.dot(Hk, gfk))\n        try:\n            (alpha_k, fc, gc, old_fval, old_old_fval, gfkp1) = _line_search_wolfe12(f, myfprime, xk, pk, gfk, old_fval, old_old_fval, amin=1e-100, amax=1e+100)\n        except _LineSearchError:\n            warnflag = 2\n            break\n        xkp1 = (xk + (alpha_k * pk))\n        if retall:\n            allvecs.append(xkp1)\n        sk = (xkp1 - xk)\n        xk = xkp1\n        if (gfkp1 is None):\n            gfkp1 = myfprime(xkp1)\n        yk = (gfkp1 - gfk)\n        gfk = gfkp1\n        if (callback is not None):\n            callback(xk)\n        k += 1\n        gnorm = vecnorm(gfk, ord=norm)\n        if (gnorm <= gtol):\n            break\n        if (not numpy.isfinite(old_fval)):\n            warnflag = 2\n            break\n        try:\n            rhok = (1.0 / numpy.dot(yk, sk))\n        except ZeroDivisionError:\n            rhok = 1000.0\n            if disp:\n                print('Divide-by-zero encountered: rhok assumed large')\n        if isinf(rhok):\n            rhok = 1000.0\n            if disp:\n                print('Divide-by-zero encountered: rhok assumed large')\n        A1 = (I - ((sk[:, numpy.newaxis] * yk[numpy.newaxis, :]) * rhok))\n        A2 = (I - ((yk[:, numpy.newaxis] * sk[numpy.newaxis, :]) * rhok))\n        Hk = (numpy.dot(A1, numpy.dot(Hk, A2)) + ((rhok * sk[:, numpy.newaxis]) * sk[numpy.newaxis, :]))\n    fval = old_fval\n    if np.isnan(fval):\n        warnflag = 2\n    if (warnflag == 2):\n        msg = _status_message['pr_loss']\n    elif (k >= maxiter):\n        warnflag = 1\n        msg = _status_message['maxiter']\n    else:\n        msg = _status_message['success']\n    if disp:\n        print(('%s%s' % (('Warning: ' if (warnflag != 0) else ''), msg)))\n        print(('         Current function value: %f' % fval))\n        print(('         Iterations: %d' % k))\n        print(('         Function evaluations: %d' % func_calls[0]))\n        print(('         Gradient evaluations: %d' % grad_calls[0]))\n    result = OptimizeResult(fun=fval, jac=gfk, hess_inv=Hk, nfev=func_calls[0], njev=grad_calls[0], status=warnflag, success=(warnflag == 0), message=msg, x=xk, nit=k)\n    if retall:\n        result['allvecs'] = allvecs\n    return result","sub_path":"Data Set/bug-fixing-3/b592902ab3b9c0f254bda87faeeb5ca3cb652458-<_minimize_bfgs>-fix.py","file_name":"b592902ab3b9c0f254bda87faeeb5ca3cb652458-<_minimize_bfgs>-fix.py","file_ext":"py","file_size_in_byte":3713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"579213012","text":"\"\"\"@author: LU\"\"\"\nimport csv\nfrom pathlib import Path\nimport torch\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\nfrom dataset_test import ImageDataset\nCUDA_DEVICES = 0\nDATASET_ROOT = '../dataset/test'\nCLASS_ = '../dataset/train'\nPATH_TO_WEIGHTS = './model-1.00-best_train_acc.pth'\n\ndef test():\n    \"\"\"test function\"\"\"\n    data_transform = transforms.Compose([\n        transforms.Resize((224, 224)),\n        transforms.ToTensor(),\n        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n    ])\n    test_set = ImageDataset(Path(DATASET_ROOT), data_transform)\n    data_loader = DataLoader(\n        dataset=test_set, batch_size=1, shuffle=False, num_workers=1)\n    classes = [_dir.name for _dir in Path(CLASS_).glob('*')]\n    model = torch.load(PATH_TO_WEIGHTS)\n    model = model.cuda(CUDA_DEVICES)\n    model.eval()\n    table = [['id', 'label']]\n    i = 0\n    with torch.no_grad():\n        for inputs, labels, filename in data_loader:\n            i += 1\n            print('i:', i)\n            inputs = Variable(inputs.cuda(CUDA_DEVICES))\n            labels = Variable(labels.cuda(CUDA_DEVICES))\n            outputs = model(inputs)\n            predicted = torch.max(outputs.data, 1)[1]\n            pre = predicted.item()\n            table.append([str(filename)[2:12], classes[pre]])\n\n    with open('test.csv', 'w', newline='') as csvfile:\n        writer = csv.writer(csvfile)\n        table.sort()\n        writer.writerows(table)\n\n\nif __name__ == '__main__':\n    test()\n    ","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"377458041","text":"#!/usr/bin/python3\n\"\"\"\nThe spreadsheets module calculates formulas in .csv files\n\"\"\"\nimport os\nimport sys\nimport csv\nimport re\nimport math\nfrom importlib.machinery import SourceFileLoader\n\nDEBUG = False\nMATH_MODULE_NS = []\nCUSTOM_MODULE_NS = []\nADDRESS_REPLACEMENT = {}\n\n\ndef custom_elt_handler(matrix, i, j):\n    \"\"\"\n    Handler for elements which contains custom module functions\n    :param matrix: table\n    :param i: row number\n    :param j: column number\n    :return: result of calculation\n    \"\"\"\n    elt = matrix[i][j][1:]\n    for name in CUSTOM_MODULE_NS:\n        elt = elt.replace(name, 'CUSTOM_MODULE.' + name)\n    return eval(elt, globals(), ADDRESS_REPLACEMENT)\n\n\ndef math_elt_handler(matrix, i, j):\n    \"\"\"\n    Handler for elements which contains math module functions\n    :param matrix: table\n    :param i: row number\n    :param j: column number\n    :return: result of calculation\n    \"\"\"\n    elt = matrix[i][j][1:]\n    for name in MATH_MODULE_NS:\n        elt = elt.replace(name, 'math.' + name)\n    return eval(elt, globals(), ADDRESS_REPLACEMENT)\n\n\ndef get_elt_handler(elt):\n    \"\"\"\n    Determines type of matrix element and returns handler for it\n    :param elt: element of the matrix\n    :return: handler for this type of element\n    \"\"\"\n    contains_math = False\n    for name in MATH_MODULE_NS:\n        contains_math = contains_math or name in elt\n    contains_custom = False\n    for name in CUSTOM_MODULE_NS:\n        contains_custom = contains_custom or name in elt\n    if contains_custom:\n        return custom_elt_handler\n    elif contains_math:\n        return math_elt_handler\n    else:\n        return lambda matrix, i, j: eval(matrix[i][j][1:], globals(),\n                                         ADDRESS_REPLACEMENT)\n\n\ndef unreduced(matrix):\n    \"\"\"\n    Checks if matrix has been already calculated\n    :param matrix: table\n    :return: whether the matrix is unreduced or not\n    \"\"\"\n    for row in matrix:\n        for elt in row:\n            if str(elt)[0] == '=':\n                return True\n    return False\n\n\ndef reduce_matrix(matrix):\n    \"\"\"\n    Calculates the matrix\n    :param matrix: table\n    \"\"\"\n    while unreduced(matrix):\n        for i in range(0, len(matrix)):\n            row = matrix[i]\n            for j in range(0, len(row)):\n                elt = row[j]\n                if str(elt)[0] == '=':\n                    handler = get_elt_handler(elt)\n                    matrix[i][j] = handler(matrix, i, j)\n\n\ndef get_elt_value(matrix, address):\n    \"\"\"\n    Returns the value for the address\n    :param matrix: table\n    :param address: item address (e.g. A1, B2, Z256, ...)\n    :return: element value\n    \"\"\"\n    i = int(address[1:]) - 1\n    j = ord(address[0]) - ord('A')\n    elt = matrix[i][j]\n    if '.' in elt and elt.replace('.', '').isdigit():\n        return float(elt)\n    elif elt.isdigit():\n        return int(elt)\n    else:\n        return str(elt)\n\n\ndef add_address(matrix, i, j):\n    \"\"\"\n    Adds address to the ADDRESS_REPLACEMENT dict\n    :param matrix: table\n    :param i: row number\n    :param j: column number\n    \"\"\"\n    elt = matrix[i][j][1:]\n    found = re.search(r'([A-Z])(\\d+)', elt)\n    while found:\n        ADDRESS_REPLACEMENT[found.group()] = get_elt_value(matrix,\n                                                           found.group())\n        if DEBUG:\n            print(found.group(), '->', ADDRESS_REPLACEMENT[found.group()])\n        elt = elt.replace(found.group(),\n                          str(ADDRESS_REPLACEMENT[found.group()]))\n        found = re.search(r'([A-Z])(\\d+)', elt)\n\n\ndef fill_address_replacement(matrix):\n    \"\"\"\n    Fills the ADDRESS_REPLACEMENT dict for the matrix\n    :param matrix: table\n    \"\"\"\n    for i in range(0, len(matrix)):\n        row = matrix[i]\n        for j in range(0, len(row)):\n            elt = row[j]\n            if elt[0] == '=':\n                add_address(matrix, i, j)\n\n\ndef handle_csv():\n    \"\"\"\n    Read input csv file, reduce it and write it to storage\n    \"\"\"\n    input_matrix = []\n    with open(INPUT_FILE_NAME, 'r') as input_file:\n        csv_reader = csv.reader(input_file)\n        for matrix_row in csv_reader:\n            input_matrix.append(matrix_row)\n    if DEBUG:\n        print(input_matrix)\n    fill_address_replacement(input_matrix)\n    reduce_matrix(input_matrix)\n    if DEBUG:\n        print(input_matrix)\n    with open(OUTPUT_FILE_NAME, 'w') as output_file:\n        csv_writer = csv.writer(output_file, delimiter=',')\n        for matrix_row in input_matrix:\n            csv_writer.writerow(matrix_row)\n\n\nif __name__ == '__main__':\n    PATH = os.getcwd() + '/'\n    INPUT_FILE_NAME = PATH + sys.argv[1]\n    OUTPUT_FILE_NAME = PATH + sys.argv[2]\n\n    if len(sys.argv) == 4:\n        CUSTOM_MODULE = SourceFileLoader(\n            'CUSTOM_MODULE',\n            PATH + sys.argv[3]).load_module()\n        CUSTOM_MODULE_NS = dir(CUSTOM_MODULE)\n\n    if DEBUG:\n        print(CUSTOM_MODULE_NS)\n\n    MATH_MODULE_NS = dir(math)\n\n    handle_csv()\n","sub_path":"hometask-01/spreadsheets.py","file_name":"spreadsheets.py","file_ext":"py","file_size_in_byte":4959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"187785938","text":"import numpy as np\nimport sys\nimport os.path\nimport argparse\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom subprocess import call\n#from fitFunctions import *\nfrom fittingFuncts import *\n\n\nif __name__ == '__main__':\n\n  # Import command line arguments\n  argP = argparse.ArgumentParser()\n  argP.add_argument('--QperPix', type=float, default=0,\n      help=\"Q density per pixel\")\n\n  args = argP.parse_args()\n\n  molecule = \"nitroBenzene\"\n  NdataBins = 50\n  scaleSimBins = 1\n\n  if args.QperPix:\n    QperPix = args.QperPix\n  else:\n    QperPix = 0.0223\n\n  Iebeam     = 5.\n  elEnergy   = 3.7e6\n  screenDist = 4.\n  beamCut    = 4\n\n  simFolder = \"/reg/ued/ana/scratch/\"\\\n      + molecule + \"/simulations/\"\n  simExeFile = \"/reg/neh/home/khegazy/analysis/\"\\\n      + molecule + \"/simulation/diffractionPattern/simulateRefPatterns.exe\"\n  mergeFolder = \"/reg/ued/ana/scratch/\"\\\n      + molecule + \"/mergeScans/\"\n\n  dataList = []\n  runs = [\"20180627_1551\", \"20180629_1630\", \"20180630_1925\", \"20180701_0746\"]\n\n  for rname in runs:\n    fileName = mergeFolder + \"data-\" + rname + \"-referenceAzm[555].dat\"\n    if os.path.isfile(fileName):\n      dataList.append(np.fromfile(fileName, dtype = np.double) + 0.03)\n    else:\n      print(\"ERROR: Cannot find dataFile: \" + fileName)\n      sys.exit(0)\n\n  plt.plot(dataList[0])\n  plt.savefig(\"testing.png\")\n\n  ###  Get size of vector  ###\n  size = dataList[0].shape[0]\n  maxQ = size*QperPix\n\n\n  #####  Import simulation  #####\n\n  ###  Find zero crossings  ###\n  atmDiffFile, molDiffFile = getSimNames(\n                                simFolder, \n                                \"nitrobenzene\", #molecule, \n                                size, \n                                maxQ, \n                                Iebeam, \n                                screenDist, \n                                elEnergy)\n\n  print(atmDiffFile, molDiffFile)\n  print(\"maxQ\", maxQ)\n  if not os.path.isfile(atmDiffFile):\n    call([simExeFile,\n          #\"-mol\", molecule,\n          \"-maxQ\", str(maxQ),\n          #\"-QperPix\", str(QperPix),\n          \"-Odir\", simFolder,\n          \"-Nbins\", str(size)])\n\n  atmDiff = np.fromfile(atmDiffFile, dtype=np.double)\n  molDiff = np.fromfile(molDiffFile, dtype=np.double)\n  zeroX = getZeroXQ(np.arange(size)*maxQ/size, molDiff) \n\n\n \n  ################################\n  #####  Fitting background  #####\n  ################################\n\n  for idt,data in enumerate(dataList):\n    figT, axT = plt.subplots()\n    axT.plot(data)\n    figT.savefig(\"test_\"+str(idt)+\".png\")\n    Q = np.arange(size)*maxQ/size\n    diffXvals = np.zeros(zeroX.shape[0])\n    res = np.zeros(zeroX.shape[0])\n    for i,q in enumerate(zeroX):\n      ind = np.argmin(np.abs(Q - q))\n      diffXvals[i] = atmDiff[ind]\n      res[i] = data[ind]\n      \"\"\"\n      if abs(Q[ind-1] - q) <= abs(Q[min(ind+1, Q.shape[0]-2)] - q):\n        diffXvals[i] = atmDiff[ind-1] +\\\n                        (atmDiff[ind] - atmDiff[ind-1])/(Q[ind] - Q[ind-1])*(q - Q[ind-1])\n        res[i]       = data[ind-1] +\\\n                        (data[ind] - data[ind-1])/(Q[ind] - Q[ind-1])*(q - Q[ind-1])\n      else:\n        diffXvals[i] = atmDiff[ind] +\\\n                        (atmDiff[ind+1] - atmDiff[ind])/(Q[ind+1] - Q[ind])*(q - Q[ind])\n        res[i]       = data[ind] +\\\n                        (data[ind+1] - data[ind])/(Q[ind+1] - Q[ind])*(q - Q[ind])\n      \"\"\"\n   \n    print(\"zeroX\",zeroX)\n    print(\"res\", res)\n    fig, ax = plt.subplots()\n    ax.plot(Q, molDiff)\n    for q in zeroX:\n      circ = plt.Circle((q, 0.0), 0.05, color='k')\n      ax.add_artist(circ)\n    ax.set_ylim([-0.25,0.3])\n\n    fig.savefig(\"plots/zeroCrossings-\" + runs[idt] + \".png\") \n    print(diffXvals)\n\n    \n\n\n    nullst = zeroX\n    null = res\n    print(\"start fit\")\n    print(null, nullst, Q)\n    expfit = fit_scatt_bkg(null, nullst, Q)\n    print(\"end fit\")\n\n    plt.figure()\n    plt.semilogy(Q,data,linewidth=2,label='Experimental Diffraction Pattern')\n    plt.semilogy(Q,expfit,linewidth=2,label='Exponential Fit')\n    plt.semilogy(nullst,null,'o',color='red',markersize=10)\n    #plt.ylim(0,600)\n    plt.xlabel('Q [$\\AA^{-1}$]')\n    plt.ylabel('Diffraction Intensity / a.u.')\n    plt.xlim(0,12)\n    plt.legend(loc='best',fontsize=25)\n    plt.tight_layout()\n    plt.savefig(\"plots/expfit-\" + runs[idt] + \".png\")\n\n\n    sMolexp  = (data-expfit)*Q/atmDiff\n    sMolsim  = molDiff*Q/atmDiff\n    sMolsim  *= np.min(sMolexp[size*0.25:size*0.5])/np.min(sMolsim[size*0.25:size*0.5]) \n    for j,k in enumerate(Q/atmDiff):\n      print(j,k)\n\n    plt.figure()\n    plt.plot(Q,sMolexp,linewidth=2,label='Experimental Molecular Diffraction')\n    plt.plot(Q,sMolsim,linewidth=2,label='Simulated Molecular Diffraction')\n\n    plt.plot(Q,np.zeros_like(Q),color='black')\n    #plt.ylim(-1250,2000)\n    plt.ylim(-30,60)\n    if runs[idt] is \"20161102_LongScan1\":\n      plt.ylim(-0.5e7,0.5e7)\n    plt.xlabel('Q [$\\AA^{-1}$]')\n    plt.ylabel('Molecular Diffraction Intensity / a.u.')\n    #plt.xlim(0,10.2)\n    plt.xlim(0,maxQ)\n    plt.legend(loc='best',fontsize=20)\n    plt.tight_layout()\n    plt.grid()\n    plt.xticks(range(0, int(maxQ), 1))\n    plt.savefig(\"plots/compareFit-\" + runs[idt] + \".png\")\n\n    sMolexp.tofile(\"./results/data-\" + runs[idt] + \"_staticSMS[\" + str(size) + \"].dat\");\n    sMolsim.tofile(\"./results/sim_staticSMS[\" + str(size) + \"].dat\");\n\n","sub_path":"UED/staticDiffraction/bkgFit.py","file_name":"bkgFit.py","file_ext":"py","file_size_in_byte":5335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"426440988","text":"from tkinter import *\r\nfrom PIL import ImageTk, Image\r\n\r\nclass kutuphane:\r\n\r\n    def __init__(self,anasayfa):\r\n        global pano, message\r\n        self.anasayfa = anasayfa\r\n        anasayfa.title = (\"Kütüphane Kataloğu\")\r\n        anasayfa.configure(background=\"black\")\r\n        root.wm_iconbitmap(\"katalogsimge.ico\")\r\n\r\n        self.fotograf = Image.open(\"hacettepe_logo_.png\")\r\n        self.tkimage = ImageTk.PhotoImage(self.fotograf)\r\n        self.hacettepelogo = Label(anasayfa, image=self.tkimage)\r\n        self.hacettepelogo.grid(column=1)\r\n        Label(anasayfa, bg=\"red\").grid(column=1, row=1)\r\n\r\n        self.kitap_ekle = Button(anasayfa, text=\"Kitap Ekle\", bg=\"black\",fg=\"red\",font = \"bold\", command = self.kitap_ekle)\r\n        self.kitap_ekle.grid(column=1, row=1)\r\n\r\n        self.kitapları_listele = Button(anasayfa, text=\"Kitapları Listele\", bg=\"black\", fg=\"red\", font=\"bold\", command= self.listele)\r\n        self.kitapları_listele.grid(column=1,row=2)\r\n\r\n        self.cikis = Button(anasayfa, text=\"Kapat\", command=exit, fg=\"red\", bg=\"black\", font=\"bold\")\r\n        self.cikis.grid(column=1, row=\"6\")\r\n\r\n    def kitap_ekle(self):\r\n        global kitap_adi, yazar_adi, tur, isbn, yayinevi, yayin_yili, kitapbak #global yazmazsak çalışmıyor.\r\n        eklemepencere = Tk()\r\n        eklemepencere.wm_iconbitmap(\"katalogsimge.ico\")\r\n        eklemepencere.geometry(\"250x220\")\r\n        pencerebaslik = eklemepencere.title(\"Kitap Ekle\")\r\n        eklemepencere.configure(background=\"black\")\r\n        kitap_adi = Entry(eklemepencere)\r\n        yazar_adi = Entry(eklemepencere)\r\n        tur = Entry(eklemepencere)\r\n        isbn = Entry(eklemepencere)\r\n        yayinevi = Entry(eklemepencere)\r\n        yayin_yili = Entry(eklemepencere)\r\n\r\n        kitap_adi.grid(column=2, row=2)\r\n        yazar_adi.grid(column=2, row=3)\r\n        tur.grid(column=2,row=4)\r\n        isbn.grid(column=2,row=5)\r\n        yayinevi.grid(column=2,row=6)\r\n        yayin_yili.grid(column=2,row=7)\r\n\r\n        Label(eklemepencere, font = \"bold\", bg=\"black\", text='Kitap Ekle', fg= \"red\").grid(column=2, row=1)\r\n        Label(eklemepencere, font = \"bold\", bg=\"black\", text='Kitap Adı :', fg= \"red\").grid(column=1, row=2,)\r\n        Label(eklemepencere, font = \"bold\", bg=\"black\", text='Yazar Adı :', fg= \"red\").grid(column=1, row=3,)\r\n        Label(eklemepencere, font = \"bold\", bg=\"black\", text='Kitabın Türü :', fg= \"red\").grid(column=1, row=4)\r\n        Label(eklemepencere, font = \"bold\", bg=\"black\", text='ISBN :', fg=\"red\").grid(column=1, row=5)\r\n        Label(eklemepencere, font = \"bold\", bg=\"black\", text='Yayınevi :', fg=\"red\").grid(column=1, row=6)\r\n        Label(eklemepencere, font = \"bold\", bg=\"black\", text='Yayın Yılı :', fg=\"red\").grid(column=1, row=7)\r\n\r\n        self.exit = Button(eklemepencere, bg=\"black\", text=\"Kapat\", font=\"bold\", fg=\"red\", command=eklemepencere.destroy)\r\n        self.exit.grid(column=1, row=8)\r\n        self.kaydet = Button(eklemepencere, bg=\"black\", text=\"Kaydet\", font=\"bold\", fg=\"red\", command=self.kaydet)\r\n        self.kaydet.grid(column=2, row=8)\r\n\r\n    def kaydet(self):\r\n        kaydedilecekbilgiler = [\"****  \" + \"Kitap Adı: \"+ kitap_adi.get()+ \" | \"+ \"Yazarı: \" + yazar_adi.get()+ \" | \"+ \"Türü: \" + tur.get()+ \" | \"+ \"ISBN: \" + isbn.get()+ \" | \"+ \"Yayınevi: \" + yayinevi.get()+ \" | \"+ \"Yayın Yılı: \" + yayin_yili.get()+ \"  ****\"+ \"\\n\"]\r\n        f = open(\"kitaplistesi.txt\", 'a')\r\n        for i in kaydedilecekbilgiler:\r\n            f.write(i)\r\n        f.close()\r\n\r\n    def listele(self):\r\n        liste = Tk()\r\n\r\n        file = open(\"kitaplistesi.txt\")\r\n        data = file.read()\r\n        file.close()\r\n        kitap_liste = Label(liste, text=data, bg=\"black\", fg =\"red\")\r\n        kitap_liste.grid(row=1, column=2)\r\n        self.exit = Button(liste, bg=\"black\", text=\"Kapat\", font=\"bold\", fg=\"red\",  command=liste.destroy)\r\n        self.exit.grid(column=2, row=3)\r\n\r\n        pencerebaslik = liste.title(\"Kitap Listesi\")\r\n        liste.configure(background=\"black\")\r\n\r\n\r\nroot = Tk()\r\nkutuphanem = kutuphane(root)\r\nroot.mainloop()\r\n","sub_path":"uygulama08.py","file_name":"uygulama08.py","file_ext":"py","file_size_in_byte":4079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"281451784","text":"from django.conf.urls import url\nfrom . import views\n\n\nurlpatterns = [\n    url(r'^$', views.todo_list, name='todos'),\n    url(r'^login/$', views.login, name='login'),\n    url(r'^addtodo/$', views.add_todo, name='add'),\n    url(r'^todofinish/(?P<id>\\d+)/$', views.finish_todo, name='finish'),\n    url(r'^todobackout/(?P<id>\\d+)/$', views.todoback,  name='backout'),\n    url(r'^updatetodo/(?P<id>\\d+)/$', views.updatetodo, name='update'),\n    url(r'^tododelete/(?P<id>\\d+)/$', views.tododelete, name='delete'),\n]\n","sub_path":"todo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"122786927","text":"#!/usr/bin/python3\n\"\"\"\nThis script makes a tgz archive from the contents of web_static\nfolder of the cloned repo using do_pack\nIt will make a directory named versions if not exists\nThen it will create, zip files, verbose the files to be zipped.\nTehn we return the parth of the file. If fails rerutn NONE\n\"\"\"\n\nfrom fabric.api import *\nfrom time import strftime\n\n\ndef do_pack():\n    \"\"\" function that does all the the stuff above \"\"\"\n    time = strftime(\"%Y%m%d%H%M%S\")\n    try:\n        local(\"sudo mkdir -p versions\")\n        local(\"sudo tar -cvzf versions/web_static_{}.tgz web_static/\"\n              .format(time))\n    except:\n        return None\n    return (\"versions/web_static_{}.tgz\".format(time))\n","sub_path":"1-pack_web_static.py","file_name":"1-pack_web_static.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"17387642","text":"import time\nfrom tkinter import *\ntk = Tk()\nplotno = Canvas(tk, width=400, height=400)\nplotno.pack()\nplotno.create_polygon(10,10,10,60,50,35)\nfor x in range(0,60):\n\tplotno.move(1,5,0)\n\ttk.update()\n\ttime.sleep(0.05)\n","sub_path":"osla_laczka/animacja_trojkat.py","file_name":"animacja_trojkat.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"532467816","text":"import scrapy\nfrom scrapy_splash import SplashRequest\nimport json\nfrom scrapy.selector import Selector\n#from scrapy.linkextractors.sgml import SgmlLinkExtractor\nfrom scrapy.http import Request\nimport time\nimport sys\nfrom ..items import IndeedItem\nfrom urllib.parse import urlencode, quote_plus\n\n\nclass CollawJobSpider(scrapy.Spider):\n    name = \"collawjob\"\n    allowed_domains = [\"collaw.com\"]\n    start_urls = [\"https://jobs.collaw.com/job/13884/paralegal/\", ]\n    \n    def start_requests(self): \n        for url in self.start_urls: \n            yield SplashRequest(url, self.parse, \n                endpoint='render.html', \n                args={'wait': 0.5}, \n           ) \n\n    def parse_next_site(self, response):\n\n        item = response.request.meta['item']\n        item['source_url'] = response.url\n        item['source_page_body'] = response.body\n        item['crawl_timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S')\n        return item\n\n\n    def parse(self, response):\n\n        self.log('\\n Crawling  %s\\n' % response.url)\n    \n        item = {}\n        item['job_title'] = str(response.css(\"h1.details-header__title::text\").get())\n        item['job_type'] = str(response.css(\"span.job-type__value::text\").get())\n        item['date_posted'] = str(response.css(\"li.listing-item__info--item-date::text\").get()).strip()\n        item['company'] = str(response.css(\"div.details-body__content:nth-child(4)::text\").get()).strip()\n        item['requirements'] = str(response.css(\"div.details-body__content > ul::text\").get())\n        item['email'] = str(response.css(\"div.pull-left details-body__left > div:nth-child(5)::text\").get())\n\n        with open(f'job.json', 'w', encoding='utf8', newline='') as output_file:\n            json.dump(item, output_file, indent=4)\n\n        return\n\n","sub_path":"scrapy/jobscraper/spiders/CollawJobSpider.py","file_name":"CollawJobSpider.py","file_ext":"py","file_size_in_byte":1786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"400560194","text":"# -*- coding: utf-8 -*-\n'''\nCreated on 2014-9-15\n\n@author: cheng.li\n'''\n\nfrom CAL.DateUtilities.Dates import Date\nfrom CAL.DateUtilities.Utilities import CheckDate\nimport CAL.CAL as CAL\nfrom CAL.Config.Conf import PickalableSWIG\n\n\nclass ExerciseProxy(PickalableSWIG):\n    def __init__(self, calExercise):\n        self.__impl__ = calExercise\n\n    def exerciseType(self):\n        '''\n        行权类型\n        '''\n        return self.__impl__.exerciseType()\n\n    def dates(self):\n        '''\n        可以行权的日期序列\n        '''\n        calDates = self.__impl__.dates()\n        res = [Date(d.year(), d.month(), d.dayOfMonth()) for d in calDates]\n        return res\n\n\nclass Exercise(ExerciseProxy):\n    '''\n    行权类型\n    '''\n    American = CAL.Exercise.American\n    European = CAL.Exercise.European\n    Bermudation = CAL.Exercise.Bermudan\n\n\nclass EuropeanExercise(Exercise):\n    def __init__(self,\n                 exerciseDate):\n        '''\n        欧式行权：\n        \n        Input:\n        exerciseDate -- 行权日\n        '''\n\n        exerciseDate = CheckDate(exerciseDate)\n        self._args = (exerciseDate,)\n\n        self.__impl__ = CAL.EuropeanExercise(exerciseDate.__impl__)\n\n\nclass AmericanExercise(Exercise):\n    def __init__(self,\n                 earliestDate,\n                 latestDate,\n                 payOffAtExpiry=False):\n        '''\n        美式行权:\n        \n        Input:\n        earliestDate   -- 行权起始日\n        latestDate     -- 行权终止日\n        payOffAtExpiry -- 是否在到期日偿付\n        '''\n\n        earliestDate = CheckDate(earliestDate)\n        latestDate = CheckDate(latestDate)\n\n        self._args = (earliestDate,\n                      latestDate,\n                      payOffAtExpiry)\n\n        self.__impl__ = CAL.AmericanExercise(earliestDate.__impl__,\n                                             latestDate.__impl__,\n                                             payOffAtExpiry)\n\n\nclass BermudanExercise(Exercise):\n    def __init__(self,\n                 exerciseDates,\n                 payOffAtExpiry=False):\n        '''\n        百慕大式行权\n        \n        Input:\n        exerciseDates  -- 行权日序列\n        payOffAtExpiry -- 是否在到期日偿付\n        \n        '''\n        self._args = (exerciseDates,\n                      payOffAtExpiry)\n\n        dates = [CheckDate(d).__impl__ for d in exerciseDates]\n\n        self.__impl__ = CAL.BermudanExercise(dates,\n                                             payOffAtExpiry)\n\n\nclass CallabilityPrice(PickalableSWIG):\n    Dirty = CAL.CallabilityPrice.Dirty\n    Clean = CAL.CallabilityPrice.Clean\n\n    def __init__(self,\n                 amount,\n                 priceType=Clean):\n        '''\n        可赎回或回购债券价格条款\n        \n        Input:\n        amount      -- 行权量\n        priceType   -- 价格类型， 例如CallabilityPrice.Clean\n        '''\n\n        self._args = (amount,\n                      priceType)\n\n        self.__impl__ = CAL.CallabilityPrice(amount, priceType)\n\n    def amount(self):\n        '''\n        行权量\n        '''\n        return self.__impl__.amount()\n\n    def type(self):\n        '''\n        价格类型\n        '''\n        return self.__impl__.type()\n\n\nclass CallabilityProxy(PickalableSWIG):\n    def __init__(self, calCallability):\n        self.__impl_ = calCallability\n\n\nclass Callability(CallabilityProxy):\n    Call = CAL.Callability.Call\n    Put = CAL.Callability.Put\n\n    def __init__(self,\n                 callabilityPrice,\n                 exerciseType,\n                 date):\n        '''\n         可赎回或回购债券条款\n         \n         Input:\n         callabilityPrice -- 可赎回或回购债券价格条款\n         exerciseType     -- 行权类型，例如Callability.Call\n         date             -- 行权日\n        '''\n        date = CheckDate(date)\n        self._args = (callabilityPrice,\n                      exerciseType,\n                      date)\n\n        if not isinstance(callabilityPrice, CallabilityPrice):\n            raise ValueError('#callabilityPrice should be in type as CallabilityPrice')\n\n        self.__impl__ = CAL.Callability(callabilityPrice.__impl__,\n                                        exerciseType,\n                                        date.__impl__)\n\n    def type(self):\n        '''\n        行权类型\n        '''\n        return self.__impl__.type()\n\n    def date(self):\n        '''\n        行权日\n        '''\n        res = self.__impl__.date()\n        return Date(res.year(), res.month(), res.dayOfMonth())\n\n\nclass SoftCallability(Callability):\n    def __init__(self,\n                 callabilityPrice,\n                 date,\n                 trigger):\n        '''\n        可有持有者选择是否行权的软性行权条件\n        \n        Input:\n        callabilityPrice -- 可赎回或回购债券价格条款\n        date             -- 行权日\n        trigger          -- 行权出发条件\n        '''\n        date = CheckDate(date)\n        self._args = (callabilityPrice,\n                      date,\n                      trigger)\n\n        if not isinstance(callabilityPrice):\n            raise ValueError('#callabilityPrice should be in type as CallabilityPrice')\n\n        self.__impl__ = CAL.SoftCallability(callabilityPrice.__impl__,\n                                            date.__impl__,\n                                            trigger)\n","sub_path":"quantlib/CAL-SWIG/Python/CAL/Instruments/Exercises.py","file_name":"Exercises.py","file_ext":"py","file_size_in_byte":5440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"605438552","text":"\"\"\"\nInput:  merged.csv (hard coded)\n        file in arg[1]\nOutput: updated.csv (hard coded)\n\nRead the CSV file merged from the individual page .doc files by\nthe tool chain [*.doc] --trans2html--> [*.html] --html2csv --> [merged.csv]\nand create a dictionary using the Publication, Date, and Page fields as the\nkey, checking for duplicates.\n\nRead the CSV file (arg[1]) created from the manually edited doc file and create\na similar dictionary (called \"edited\").\n\nCompare each entry in the merged .csv file with the corresponding entry in the\nedited .csv file.  Display the mismatches of the 'Title' fields.\nThere will be many of these as this is what has been corrected. Also check for\nany missing records (in either direction).\n\n\"\"\"\nimport argparse\nimport collections\nimport copy\nimport csv\nimport os.path\nimport sys\nimport time\n\nTRACE_ON = True\n# RESULTSDIR = os.path.join('/', 'Users', 'mlg', 'Documents', 'hrm', 'results')\n# RESULTSDIR = os.path.join('/', 'Users', 'mlg', 'pyprj', 'hrm', 'results')\nRESULTSDIR = os.path.join('.', 'results')\nCSVDIR = os.path.join(RESULTSDIR, 'csv')\n'''\nThe following file was produced by html2csv.py (see the description above).\n'''\nMERGEDPATH = os.path.join(CSVDIR, 'merged.csv')\n'''\nThe following file is the corrected output.\n'''\nUPDATEDPATH = os.path.join(CSVDIR, 'updated.csv')\n\"\"\"\nThe following two files will hold paired records whose Title field mismatch.\nThese can be diff-ed to see what was changed.\n\"\"\"\nFINALCORRPATH = os.path.join(CSVDIR, 'finalcorr.csv')\nMERGEDCORRPATH = os.path.join(CSVDIR, 'mergedcorr.csv')\n\nHEADING = ['Title', 'Periodical', 'Date', 'Page', 'File']\n\n\nRowtuple = collections.namedtuple('Rowtuple', ('Seq',) + tuple(HEADING))\n\n\ndef trace(template, *args):\n    if TRACE_ON:\n        print(template.format(*args))\n\n\ndef opencsvreader(csvpath):\n    csvfile = open(csvpath)\n    incsv = csv.reader(csvfile, delimiter='|')\n    trace('Input: {}', csvpath)\n    return incsv\n\n\ndef opencsvwriter(csvpath):\n    csvfile = open(csvpath, 'w', newline='')\n    outcsv = csv.writer(csvfile, delimiter='|')\n    trace('Output: {}', csvpath)\n    return outcsv\n\n\ndef build_dict(name, path):\n    seq = 0\n    reader = opencsvreader(path)\n    csvdict = collections.OrderedDict()\n    for row in reader:\n        seq += 1\n        nrow = Rowtuple(seq, *row)\n        key = (nrow.Periodical, nrow.Date, nrow.Page)\n        if key in csvdict:\n            old = csvdict[key]\n            print('------------\\nDuplicate: {} seq {}, {}'.\n                  format(name, old.Seq, seq))\n            print(old)\n            print('----')\n            print(nrow)\n        else:\n            csvdict[key] = nrow\n    trace('Merged: {} rows processed. Dict len: {}', seq, len(csvdict))\n    return csvdict\n\n\ndef compare_dicts(fromgoeff, original):\n    corrections = 0\n    errors = 0\n    corrected = copy.copy(fromgoeff)\n    mcorr_writer = opencsvwriter(MERGEDCORRPATH)\n    fcorr_writer = opencsvwriter(FINALCORRPATH)\n    updated_writer = opencsvwriter(UPDATEDPATH)\n\n    for key, row in original.items():\n        if key in corrected:\n            if row.Title != corrected[key].Title:\n                # strip leading row # and trailing filename\n                mcorr_writer.writerow(row[1:-1])\n                fcorr_writer.writerow(fromgoeff[key][:-1])\n                row = list(row)\n                row[1] = fromgoeff[key][1]\n                corrections += 1\n            del corrected[key]\n            updated_writer.writerow(row[1:])  # strip off leading row number\n        else:\n            print('---- original not found:    ', row)\n            errors += 1\n    print('{} not found in original. {} extras in original:'.\n          format(errors, len(corrected)))\n    for key, row in corrected.items():\n        print('    extra: ', row)\n        errors += 1\n    return corrections, errors\n\n\ndef get_args():\n    parser = argparse.ArgumentParser(description='''\n    Apply the manually edited texts to the records created from the\n    individual CSV files obtained from the one-per-page DOC files.''')\n    parser.add_argument('-e', '--edited', help='''This file contains the\n    merged original records except some were manually updated by the\n    proofreader.  The file named here must be in directory {}'''.\n                        format(CSVDIR))\n    args = parser.parse_args()\n    return args\n\n\ndef main(editedpath):\n    starttime = time.time()\n    merged = build_dict('merged', MERGEDPATH)\n    edited = build_dict('edited', editedpath)\n    corrections, errors = compare_dicts(edited, merged)\n    print('End correct_text. Elapsed time: {:.2f} seconds.'.\n          format(time.time() - starttime))\n    print('{} corrections.'.format(corrections))\n    return 1 if errors else 0\n\nif __name__ == '__main__':\n    if sys.version_info.major < 3:\n        raise ImportError('requires Python 3')\n    _args = get_args()\n    _editedpath = os.path.join(CSVDIR, _args.edited)\n    sys.exit(main(_editedpath))\n","sub_path":"src/trans/correct_text.py","file_name":"correct_text.py","file_ext":"py","file_size_in_byte":4912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"68713674","text":"from __future__ import with_statement\n\nimport rospy\nfrom tr_hat_msgs.msg import MotorPayload\nfrom tr_hat_msgs.srv import (GetBattery, GetBatteryResponse,\n                             GetFirmwareVer, GetFirmwareVerResponse)\n\nimport struct\nfrom threading import Lock\n\nimport frame\nfrom serial_comm import SerialComm\n\n\nclass Bridge():\n    def __init__(self):\n\n        serial_device = rospy.get_param(\"~device\", \"/dev/ttyAMA0\")\n\n        self.comm = SerialComm(serial_device)\n        self.comm.connect()\n\n        self.srv_lock = Lock()\n\n        self.motor_sub = rospy.Subscriber(\n            \"~motors\",\n            MotorPayload,\n            self.set_motors\n        )\n\n        self.battery_srv = rospy.Service(\n            \"~get_battery\",\n            GetBattery,\n            self.get_battery\n        )\n\n        self.firmware_ver_srv = rospy.Service(\n            \"~get_firmware_ver\",\n            GetFirmwareVer,\n            self.get_firmware_ver\n        )\n\n    def set_motors(self, data):\n        f = frame.motors(data.payload)\n        self.comm.send(f)\n\n    def get_battery(self, data):\n        with self.srv_lock:\n            self.comm.send(frame.battery())\n            status = self.comm.serial.read(1)\n\n        if not status:\n            success = False\n            rospy.logerr(\"Could not get battery status\")\n            battery_status = 0\n        else:\n            success = True\n            battery_status = struct.unpack(\">B\", status)[0]\n\n        return GetBatteryResponse(success, battery_status)\n\n    def get_firmware_ver(self, data):\n        with self.srv_lock:\n            self.comm.send(frame.firmware_ver())\n            firmware_ver = self.comm.readline()\n\n        if not firmware_ver:\n            success = False\n            rospy.logerr(\"Could not get firmware version\")\n        else:\n            success = True\n\n        return GetFirmwareVerResponse(success, firmware_ver)\n","sub_path":"tr_hat_bridge/src/tr_hat_bridge/bridge.py","file_name":"bridge.py","file_ext":"py","file_size_in_byte":1884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"62841016","text":"from __future__ import print_function\nimport scrapy\nimport json\nfrom scrapy.http import Request\nfrom scrapy.selector import Selector\nfrom abc import ABCMeta, abstractmethod, abstractproperty\nfrom scrapy.loader import ItemLoader\nfrom ..items import WikiItem, WikiItemLoader\nfrom scrapy.crawler import CrawlerProcess\nfrom scrapy.utils.project import get_project_settings\nfrom GenericItems import GeoItem\nfrom GenericSpiders import GeoSpider\n\n\nclass WikiSpider(scrapy.Spider, GeoSpider.GeoSpider):\n    allowed_domains = ['wikipedia.org', 'wikimedia.org']\n    handle_httpstatus_list = [404]\n    export_fields = ['title', 'category', 'date_start', 'date_end', 'location', 'lon', 'lat','province', 'country','link', 'views', 'page_id']\n\n    def __init__(self, template = None, view_threshold = 0, selectors = {}):\n            self.view_threshold = view_threshold\n            self.template = template\n\n            self.name = 'WikiSpider' + '-' + template\n            self.start_urls = ['https://nl.wikipedia.org/w/api.php?action=query&list=embeddedin&eititle=Template:Infobox_{0}&eilimit=250&format=json'.format(self.template)]\n            #self.start_urls = ['https://nl.wikipedia.org/w/api.php?action=query&list=embeddedin&eititle=Template:Infobox_sluis&eilimit=250&format=json']\n            self.selectors = selectors;\n            self.geoLevel = \"street\"\n            self.focusNL = True\n    \n    def parse(self, response):\n        #getpageid\n        \n        jsonresponse = json.loads(response.body_as_unicode())\n        \n        for page in jsonresponse['query']['embeddedin']:\n        \n            if (page['ns'] == 0):\n                yield Request('https://nl.wikipedia.org/w/api.php?action=parse&pageid={0}&format=json'.format(page['pageid']), callback=self.parsePage)\n\n        if ('continue' in jsonresponse):\n           yield Request('https://nl.wikipedia.org/w/api.php?action=query&list=embeddedin&eititle=Template:Infobox_{0}&eilimit=50&eicontinue={1}&format=json'.format(self.template, jsonresponse['continue']['eicontinue']))\n      \n    def parsePage(self, response):\n\n        jsonresponse = json.loads(response.body)\n        html = jsonresponse['parse']['text']['*']\n       \n        item = WikiItemLoader(item=WikiItem())\n\n        for td in Selector(text=html).xpath('//td'):\n            if (len(td.xpath('.//text()').extract()) > 0):\n                for key, value in self.selectors.iteritems():\n                    for selector in value:\n                       if (td.xpath('.//text()').extract()[0].strip().lower() == selector):\n                            result = \"\"\n                            for text in td.xpath('./following-sibling::td').xpath('.//text()').extract():\n                                result += \" \" + text.strip()\n                            item.add_value(key, result.strip())\n                            item.add_value('formatted_' + key, result.strip())\n\n        request = Request('http://wikimedia.org/api/rest_v1/metrics/pageviews/per-article/nl.wikipedia/all-access/user/{0}/daily/2015101300/2015102700'.format(jsonresponse['parse']['title'].encode('utf-8')), callback=self.getViewStats)\n        item.add_value('title', jsonresponse['parse']['title'].encode('utf-8'))\n        item.add_value('page_id', str(jsonresponse['parse']['pageid']))\n        item.add_value('category', self.template)\n\n        request.meta['item'] = item\n        yield request\n\n    def getURL(self, response):\n        jsonresponse = json.loads(response.body_as_unicode())\n        item = response.meta['item']\n        item.add_value('link', jsonresponse['query']['pages'][str(item.get_collected_values('page_id')[0])]['fullurl'])\n        \n        item = item.load_item()\n\n        yield item\n    \n    def getViewStats(self, response):\n\n        item = response.meta['item']\n        totalViews = 0\n        self.log('response: ' + str(response.status))\n        if (response.status == 200):\n            data = json.loads(response.body_as_unicode())\n            \n            for day in data['items']:\n                totalViews += day['views']\n\n        if (totalViews >= self.view_threshold or self.view_threshold == 0):\n                item.add_value('views', totalViews)\n\n                request = Request('https://nl.wikipedia.org/w/api.php?action=query&prop=info&pageids={0}&inprop=url&format=json'.format(str(item.get_collected_values('page_id')[0])), callback=self.getURL)\n                request.meta['item'] = item\n                yield request\n        else: \n            return\n\n \n   ","sub_path":"NDCDataCrawler/NDCDataCrawler/Crawler/Projects/WikiKalender/spiders/WikiSpider.py","file_name":"WikiSpider.py","file_ext":"py","file_size_in_byte":4499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"472078296","text":"from pyspark import SparkContext, HiveContext, SQLContext\nfrom pyspark.sql.types import StructType, StructField, IntegerType, FloatType, StringType\nfrom pyspark.sql.functions import udf\nfrom pyspark.sql.functions import lit\nimport common as cm\nimport utils\nfrom pyspark.sql.functions import *\nimport time\nimport tareas_2 as t2\nimport tareas_1 as t1\n\n\n\ndef preproc_tipo_identificacion(hc, dicc):\n    ##########################Condicion#############################\n    if 'prepo' not in dicc:\n        return 'Los campos de preprocesamiento no se encuentran en el diccionario base'\n    if len(dicc['prepo']) != 1:\n        return 'La longitud del diccionario base debe ser igual a un elemento'\n    campo = dicc['prepo'][0]\n    ####################################Extraer dfcruzar###########################\n    sent = \"select * from stage_uiaf.adres_bduda\"\n    dfcruzar = hc.sql(sent).groupBy(campo).count()\n    ##############################Adaptar cruze con columnas iguales###############################\n    dfcruzar = dfcruzar.select(col(campo).alias(\"tipoidentificacion\"))\n    #dfcruzar.registerTempTable(\"dfcruzar\")\n    \n    return dfcruzar\n\n\ndef preproc_dane(hc, dicc):\n    ######################Condicion##################################\n    if 'prepo' not in dicc:\n        return 'Los campos de preprocesamiento no se encuentran en el diccionario base'\n    \n    if len(dicc['prepo']) == 2:\n        \"\"\"\n        Ayuda el codigo de departamento y de municipio que se escriban \n        correspondientemente en la lista de python.\n        \"\"\"\n        \n        #############Seleccion y ajuste de acuerdo a la tabla de stage_uiaf################\n        iddepartamento = dicc['prepo'][0]\n        idmunicipio = dicc['prepo'][1]\n        sent = \"select * from stage_uiaf.adres_bduda\"\n        dfcruzar = hc.sql(sent)\n        dfcruzar.registerTempTable(\"prueba\")\n        dfcruzar = hc.sql(\"select concat(if(length({0}) = 1, concat('0',{0}), {0}), \\\n                          right(concat('000', {1}), 3)) as iddane from prueba\" \\\n                            .format(iddepartamento,idmunicipio))\n        dfcruzar = dfcruzar.groupBy('iddane').count()\n        \n    if len(dicc['prepo']) == 1:\n        return 'Solo existe un campo para preprocesar'\n    \n    return dfcruzar\n\n\ndef preproc_persona(hc, dicc):\n    ############################Condicion################################\n    \n    sent = \"select * from stage_uiaf.adres_bduda\"\n    dfcruzar = hc.sql(sent)\n    dfcruzar.registerTempTable(\"prueba\")\n    dfcruzar = hc.sql(\"select *, concat(if(length(nivelsisben) = 1, \\\n                      concat('0',nivelsisben), nivelsisben), \\\n        right(concat('000', coddepafi), 3)) as iddanevive, \\\n        concat(priapeafi,' ', segapeafi, ' ', prinomafi) as nombresrazonsocial \\\n        from prueba\")\n    \n    #dfcruzar.show()\n    \n    dfcruzar.registerTempTable(\"prueba\")\n    \n    dfcruzar = hc.sql(\"\"\"select from_unixtime(unix_timestamp(t1.fecnacafi, 'dd/MM/yyyy'), 'yyyy-MM-dd') as fechanacimientocreacion, \n                      t1.tipdocafi as identificacion, \n                      t2.idtipoidentificacion, t1.iddanevive, t1.nombresrazonsocial, \n                      from_unixtime(unix_timestamp(t1.tipafi, 'dd/MM/yyyy'), 'yyyy-MM-dd') as fechatransaccion\n                      from prueba t1 inner join dwh_uiaf.dimtipoidentificacionsistema t2 \n                      on trim(t1.segnomafi) = trim(t2.tipoidentificacion)\"\"\")\n    \n    dfcruzar.registerTempTable(\"prueba\")\n    dfcruzar = hc.sql(\"select t1.*,t2.idfecha as idfechatransaccion from prueba t1 \\\n                      inner join dwh_uiaf.dimfecha t2 \\\n                      on trim(t1.fechatransaccion) = trim(t2.fecha)\")\n    dfcruzar = dfcruzar.drop('fechatransaccion')\n    \n    \n    return dfcruzar\n\n\n\n\n\n","sub_path":"Ingesta_spark/preprocesamiento.py","file_name":"preprocesamiento.py","file_ext":"py","file_size_in_byte":3765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"242775590","text":"import threading\nimport warnings\nimport socket\nimport selectors\nimport types\nimport asyncore\n\nfrom pythonosc import osc_message_builder\n\nfrom udp import UDPClient, OSCServer\n\nclass TCPClient(asyncore.dispatcher_with_send):\n\n\tdef __init__(self, addr, *args, **kwargs):\n\t\tsuper(TCPClient, self).__init__(*args, **kwargs)\n\t\tself._addr = addr\n\t\tself._data_handler = None\n\t\treturn\n\n\tdef print_helper(self, description, addr=None, data=None, nl=False):\n\t\tprint('') if nl else None\n\t\tprint(\"TCPClient %15s:%5d:\" % self._addr, end=' ')\n\t\tprint(\"%12s\" % description, end=' ')\n\t\tprint(\"%s:%d\" % addr, end='') if addr else None\n\t\tprint(\"%a\" % data, end='') if data else None\n\t\tprint(' ')\n\t\treturn\n\n\tdef set_data_handler(self, handler):\n\t\tself._data_handler = handler\n\t\treturn\n\n\tdef handle_accept(self):\n\t\tpair = self.accept()\n\t\tif pair is not None:\n\t\t\tsock, addr = pair\n\t\t\tself.print_helper(\"Accepted\", addr=addr)\n\t\t\treturn\n\n\tdef handle_read(self):\n\t\tdata = self.recv(1024)\n\t\t# self.print_helper(\"Data in:\", data=data, nl=True)\n\t\tif data and self._data_handler:\n\t\t\tself._data_handler(self, data)\n\t\telse:\n\t\t\tself.close()\n\t\treturn\n\n\tdef handle_close(self):\n\t\tself.print_helper(\"Disconnected\")\n\t\tself.close()\n\t\treturn\n\n\tdef hendle_error(self):\n\t\tself.print_helper(\"Error\")\n\t\tself.error()\n\t\treturn\n\n\tdef send(self, data):\n\t\tself.print_helper(\"Data out:\", data=data)\n\t\tsuper(TCPClient, self).send(data)\n\t\treturn\n\nclass TCPServer(asyncore.dispatcher):\n\n\tdef __init__(self, addr):\n\t\tasyncore.dispatcher.__init__(self)\n\t\tif addr[0] is None:\n\t\t\tself._addr = (socket.gethostbyname(socket.gethostname()), addr[1])\n\t\telse:\n\t\t\tself._addr = addr\n\t\tself._clients = {}\n\t\tself.create_socket(socket.AF_INET, socket.SOCK_STREAM)\n\t\tself.set_reuse_addr()\n\t\tself.bind(self._addr)\n\t\tself.print_helper(\"Created\")\n\t\treturn\n\n\tdef print_helper(self, description, addr=None):\n\t\tprint(\"TCPServer %15s:%5d:\" % self._addr, end=' ')\n\t\tprint(\"%12s\" % description, end=' ')\n\t\tprint(\"%s:%d\" % addr) if addr else print(' ')\n\t\treturn\n\n\tdef begin(self):\n\t\tself.listen(5)\n\t\tself.print_helper(\"Serving\")\n\t\treturn\n\n\tdef set_data_handler(self, handler):\n\t\tself._data_handler = handler\n\t\treturn\n\n\tdef handle_accept(self):\n\t\tpair = self.accept()\n\t\tif pair is not None:\n\t\t\tsock, addr = pair\n\t\t\tself.print_helper(\"Accepted\", addr)\n\t\t\tself._clients[addr[0]] = TCPClient(addr, sock)\n\t\t\tself._clients[addr[0]].set_data_handler(self._data_handler)\n\t\treturn\n\n\tdef handle_read(self):\n\t\tdata = self.recv(1024)\n\t\t# self.print_helper(\"Data in:\", (\"???\", 0))\n\t\treturn\n\n\tdef handle_close(self):\n\t\tself.print_helper(\"Closing\", self._addr)\n\t\tself.close()\n\t\treturn\n\n\tdef hendle_error(self):\n\t\tself.print_helper(\"Error\")\n\t\tself.error()\n\t\treturn\n\nif __name__ == \"__main__\":\n\n\t# UDP Server main handler (only listens for /tcp messages)\n\tdef handle_udp_to_tcp(addr, *args):\n\n\t\tif len(args) < 3:\n\t\t\tprint(\"Invalid /tcp message...\\n\")\n\t\t\tprint(\"\tUsage: /tcp [dest_addr] [dest_port] [/oscpath] [arg1] ... [argN]\\n\")\n\t\t\treturn\n\n\t\t# Get the destination TCP client\n\t\tdest_addr = args[0]\n\t\tdest_port = args[1]\n\t\t\n\t\t# Make an OSC message with the third argument as the path\n\t\tbuilder = osc_message_builder.OscMessageBuilder(args[2])\n\t\tif (len(args) > 3):\n\t\t\t[builder.add_arg(val) for val in args[3:]]\n\t\tmsg = builder.build()\n\n\t\t# Retrieve the TCP client with the specified IP and send the message\n\t\ttry:\n\t\t\ttcp_client = tcp_server._clients[dest_addr]\n\t\t\tprint(\"-- Routing OSC Message: (UDP) %s:%d\" % udp_server._addr, end=' ')\n\t\t\tprint(\"--> (TCP) %s:%d\" % tcp_client._addr)\n\t\t\ttcp_client.send(msg.dgram)\n\t\texcept:\n\t\t\tprint(\"\\nNo TCP Client %s:%d\" % tcp_client._addr)\n\t\treturn\n\t\n\n\tdef handle_tcp_to_udp(tcp_client, data):\n\t\t\n\t\t# eff = tcp_client._addr + (udp_client._addr, udp_client._port)\n\t\tprint(\"-- Routing OSC Message: (TCP) %s:%d\" % tcp_client._addr, end=' ')\n\t\tprint(\"--> (UDP) %s:%d\" % udp_client._addr)\n\t\tudp_client.send(data)\n\n\n\t# Ports\n\tiot_port = 7771\t\t\t\t# IoT Device Port\n\tudp_server_port = 9000\n\n\t# Local UDP Client (e.g Max/MSP) --> Local UDP server --> TCP Clients\n\tudp_server = OSCServer(('localhost', udp_server_port))\n\tudp_server.dispatch('/tcp', handle_udp_to_tcp)\n\n\t# TCP Clients --> TCP Server --> Local UDP Client\n\ttcp_server = TCPServer((None, iot_port))\n\ttcp_server.set_data_handler(handle_tcp_to_udp)\n\tudp_client = UDPClient(('localhost', iot_port))\n\n\t# Go\n\tprint('')\n\tudp_server.begin()\n\ttcp_server.begin()\n\tasyncore.loop()\n\n","sub_path":"StateMachine/Python/TcpSocket/src/util/tcp.py","file_name":"tcp.py","file_ext":"py","file_size_in_byte":4368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"281780338","text":"#!/usr/bin/env python\n\nfrom OpenGL import GL\nimport numpy as np\n\nfrom UI import DRAWOPT_BONES\n\n\nclass GLBones:\n\tdef __init__(self, vertices, edges):\n\t\tself.vertices, self.edges = np.array(vertices,dtype=np.float32,copy=True).reshape(-1,3), np.array(edges,dtype=np.int32,copy=True).reshape(-1,2)\n\t\tself.color = (1,1,1)\n\n\tdef __len__(self): return int(self.edges.shape[0])\n\n\tdef paintGL(self, p0=0, p1=None, drawOpts=DRAWOPT_BONES):\n\t\t'''\n\t\t:param drawOpts: OR combination of draw flags. default is :data:`UI.DRAWOPT_BONES`\n\t\t'''\n\t\tif p1 is None: p1 = len(self)\n\t\tif p0 == p1: return\n\t\tif drawOpts & DRAWOPT_BONES:\n\t\t\tGL.glDisable(GL.GL_TEXTURE_2D)\n\t\t\tGL.glShadeModel(GL.GL_SMOOTH)\n\t\t\tGL.glColor3f(*self.color)\n\t\t\tGL.glLineWidth(1)\n\t\t\tGL.glEnableClientState(GL.GL_VERTEX_ARRAY)\n\t\t\tGL.glVertexPointerf(self.vertices)\n\t\t\tGL.glDrawElementsui(GL.GL_LINES, self.edges[p0:p1])\n\t\t\tGL.glDisableClientState(GL.GL_VERTEX_ARRAY)\n","sub_path":"UI/GLBones.py","file_name":"GLBones.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"132388990","text":"import os\n\nfrom django.contrib import admin\nfrom django.contrib.sitemaps import GenericSitemap, Sitemap\nfrom django.contrib.sitemaps.views import sitemap, index\nfrom django.contrib.sites.models import Site\nfrom django.contrib.staticfiles.urls import static\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\nfrom django.core.exceptions import ImproperlyConfigured\nfrom django.urls import path, include\nfrom MoscowCallingInfo import settings\nfrom manga.models import Page\nfrom news.models import Post\n\nclass PageSitemapClass(Sitemap):\n    priority = 0.9\n    def items(self):\n        return Page.objects.order_by('-id')\n\n    def __get(self, name, obj, default=None):\n        try:\n            attr = getattr(self, name)\n        except AttributeError:\n            return default\n        if callable(attr):\n            return attr(obj)\n        return attr\n\n    def get_urls(self, page=1, site=None, protocol=None):\n        if self.protocol is not None:\n            protocol = self.protocol\n        if protocol is None:\n            protocol = 'http'\n\n        if site is None:\n            if Site._meta.installed:\n                try:\n                    site = Site.objects.get_current()\n                except Site.DoesNotExist:\n                    pass\n            if site is None:\n                raise ImproperlyConfigured(\"To use sitemaps, either enable the sites framework or pass a Site/RequestSite object in your view.\")\n        domain = site.domain\n\n        urls = []\n        for item in self.paginator.page(page).object_list:\n            loc = \"%s://%s%s\" % (protocol, domain, self.__get('location', item))\n            priority = self.__get('priority', item, None)\n            url_info = {\n                'item': item,\n                'location': loc,\n                'lastmod': self.__get('lastmod', item, None),\n                'changefreq': self.__get('changefreq', item, None),\n                'priority': str(priority is not None and priority or ''),\n                'image': \"%s://%s%s\" % (protocol, domain, os.path.splitext(item.image.url)[0]),\n            }\n            urls.append(url_info)\n        return urls\n\n\nsitemaps = {\n    'manga': PageSitemapClass,\n\n    'news': GenericSitemap({\n        'queryset': Post.objects.all(),\n        'date_field': 'modified',\n    }, priority=0.5),\n}\n\nurlpatterns = [\n    path('{}/sitemap.xml'.format(settings.LANGUAGE_CODE), index, {'sitemaps': sitemaps}),\n    path('{}/sitemap-<section>.xml'.format(settings.LANGUAGE_CODE), sitemap, {'sitemaps': sitemaps, 'template_name': 'sitemap-images.xml'},\n         name='django.contrib.sitemaps.views.sitemap'),\n    path('{}/admin/'.format(settings.LANGUAGE_CODE), admin.site.urls),\n    path('{}/'.format(settings.LANGUAGE_CODE), include('main.urls')),\n]\nurlpatterns += staticfiles_urlpatterns()\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","sub_path":"MoscowCallingInfo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"279075949","text":"\"\"\"\n    Log functions\n\"\"\"\n\nimport sys\n\nSUCCESS = 1\nFAILURE = 2\nINFORMATION = 3\n\n\ndef log(\n    content: str = \"\",\n    design: int = INFORMATION,\n    verbosity: int = 0,\n    current_verbosity: int = 0,\n) -> None:\n    \"\"\"Print strings to stdout.\"\"\"\n    start = \"\"\n    if design == 1:\n        start = Colors.success\n    elif design == 2:\n        start = Colors.failure\n    elif design == 3:\n        start = Colors.information\n\n    if current_verbosity >= verbosity:\n        sys.stdout.write(start + content + \"\\n\")\n        sys.stdout.flush()\n\n\nclass Colors:\n    \"\"\"Constants for coloring output.\"\"\"\n\n    red = \"\\033[1;31m\"\n    green = \"\\033[1;32m\"\n    blue = \"\\033[1;34m\"\n    end = \"\\033[0m\"\n\n    success = green + \"[+]\" + end + \" \"\n    failure = red + \"[-]\" + end + \" \"\n    information = blue + \"[*]\" + end + \" \"\n","sub_path":"src/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"420322295","text":"#!/usr/bin/env python\n\nimport rospy\nimport actionlib\nfrom move_base_msgs.msg import MoveBaseAction, MoveBaseGoal\nfrom nav_msgs.msg import OccupancyGrid\nimport os\nimport sys\n\n##########################################################################\n# Hash of lanes and coordinates of their centers\n\nstreets = {\n\t\"Jona_1\": [0.438493632075, 4.8203509714],\n\t\"Jona_2\": [1.76667652672, 5.30125786506],\n\n\t\"Michael_1\": [-3.32576613371,6.14693860017],\n\t\"Michael_2\": [-3.52723502947,7.26553881339],\n\n\t\"Leany_1\": [-5.84500899539, 2.96008483529],\n\t\"Leany_2\": [-7.04378511487, 2.65267740985],\n\n\t\"Justin_1\": [-1.81138228479, 1.60819786502],\n\t\"Justin_2\": [-1.42586501257, 0.536739572219],\n\n\t\"Moriah_1\": [-5.30648958102, -3.25888441615],\n\t\"Moriah_2\": [-4.06846034873, -2.77632264326],\n\n\t\"Philip_1\": [-8.29654260368, -1.53114081275],\n\t\"Philip_2\": [-8.52467054427, -0.372919296002],\n\n\t\"Geneva_1\": [-9.96934811685, -4.50815161255],\n\t\"Geneva_2\": [-11.3035781777, -4.81524136944],\n\n\t\"Isaac_1\": [-6.93885908468, -6.12442501801],\n\t\"Isaac_2\": [-6.70432830569, -7.20632064922]\n\t}\n\n##########################################################################\n# Call back function which gets called every time a map is published.\n# Ensures that navigation only occurs after the map has been updated.\n# Unfortunatly it does not check to make sure that the move_base node\n# has latched the updated map before sending it a new goal. A delay is\n# added to help remidy this, but the system still isn't synchronous. \n\ndef callback(msg):\n\tif(flag):\n\t\trospy.sleep(1.0)\n\t\tclient.send_goal(goal)\n\t\t\n##########################################################################\n# Given a destination lane, end, this function returns a move_base goal\n# to that lane.\n\ndef get_goal(end):\n\tgoal = MoveBaseGoal()\n\tgoal.target_pose.header.frame_id = 'map'\n\tgoal.target_pose.pose.position.x = streets[end][0]\n\tgoal.target_pose.pose.position.y = streets[end][1]\n\tgoal.target_pose.pose.position.z = 0.0\n\tgoal.target_pose.pose.orientation.x = 0.0\n\tgoal.target_pose.pose.orientation.y = 0.0\n\tgoal.target_pose.pose.orientation.z = 0.0\n\tgoal.target_pose.pose.orientation.w = 1.0\n\treturn goal\n\n##########################################################################\n# Main. Asks user for start and destination lanes, routes a lane path to\n# the destination and fires up a map server to host it. Waits for the map\n# server to publish before navigating to the destination.\n\nname = 'robot1'\nif(len(sys.argv) > 1):\n  name = sys.argv[1]\nprint(\"Working with: \" + name)\n\nflag = 0\ngoal = MoveBaseGoal()\n\t\nrospy.init_node(name+'_nav')\nclient = actionlib.SimpleActionClient(name + '/move_base',MoveBaseAction)\nclient.wait_for_server()\nsub = rospy.Subscriber(name + '/lanes/map',OccupancyGrid,callback,queue_size=None)\n\nwhile True:\n\tstart = raw_input(\"Start: \")\n\tif(start == \"quit\"):\n\t\tbreak\n\tend = raw_input(\"End: \")\n\tif(end == \"quit\"):\n\t\tbreak\n\tflag = 1\n\tos.system(\"./Serve \" + name + \" \" + start + \" \" + end)\n\tgoal = get_goal(end)\n","sub_path":"mapGen/nav.py","file_name":"nav.py","file_ext":"py","file_size_in_byte":2971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"650779385","text":"from bs4 import BeautifulSoup\nimport requests\n    \nwith open('sample.html') as html_file:\n    soup = BeautifulSoup(html_file, 'lxml')\n\n# print(soup.prettify()) #lines up code in terminal\n\n# match = soup.title.text\n# print(match)\n\nfor article in soup.find_all('div', class_='article'):\n    headline = article.h2.a.text\n    print(headline)\n\n    summary = article.p.text\n    print(summary)\n\n    print()","sub_path":"scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"441783552","text":"##Script to plot the rscores from the log files\n\nimport matplotlib.pyplot as plt\nheaderLines = 21\n\n#plot for lambda = 1\n\nlineNumber = 0\ntime = []\nRscore = []\nnLines = 6205\nwith open('lambda0/7769114.bw.log') as f:\n\n\t#Start reading the lines with time and Rscore values\n\tfor line in f:\n\t\tlineNumber = lineNumber + 1\n\t\tif lineNumber > headerLines and lineNumber < nLines:\n\t\t\tsplitLine = line.split()\n\t\t\ttime.append(int(splitLine[1][:-1]))\n\t\t\tRscore.append(float(splitLine[7]))\n\n\tplt.plot(time, Rscore)\n\tplt.title('lambda = 0')\n\tplt.ylabel('Rscore')\n\tplt.xlabel('Time')\n\tplt.savefig('lambda0/rscore.png', bbox_inches='tight')\n\tplt.close()\nprint('plot saved for lambda = 0')\n\n","sub_path":"HW6/plot_rscore.py","file_name":"plot_rscore.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"475394586","text":"\"\"\"Word Level Tokenizer.\"\"\"\nimport warnings\nfrom typing import Any, Dict\nfrom typing import List\nfrom typing import Optional\n\nimport requests\n\nfrom konoha import word_tokenizers\nfrom konoha.data.resource import Resource\nfrom konoha.data.token import Token\nfrom konoha.word_tokenizers.tokenizer import BaseTokenizer\n\n\nclass WordTokenizer:\n    \"\"\"Tokenizer takes a sentence into tokens.\"\"\"\n\n    def __init__(\n        self,\n        tokenizer: str = \"MeCab\",\n        with_postag: bool = False,\n        user_dictionary_path: Optional[str] = None,\n        system_dictionary_path: Optional[str] = None,\n        model_path: Optional[str] = None,\n        mode: Optional[str] = None,\n        dictionary_format: Optional[str] = None,\n        endpoint: Optional[str] = None,\n    ) -> None:\n        \"\"\"Create tokenizer.\n\n        Keyword Arguments:\n            tokenizer {str or None} -- specify the type of tokenizer (default: {None})  # NOQA\n            flags {str} -- option passing to tokenizer (default: {''})\n        \"\"\"\n        user_dictionary = Resource(user_dictionary_path)\n        system_dictionary = Resource(system_dictionary_path)\n        model = Resource(model_path)\n\n        self._tokenizer_name = tokenizer.lower()\n        self._with_postag = with_postag\n        self._user_dictionary_path = user_dictionary.path\n        self._system_dictionary_path = system_dictionary.path\n        self._model_path = model.path\n        self._mode = mode.lower() if mode is not None else None\n        self._dictionary_format = dictionary_format\n        self._tokenizer = None  # type: Any\n        self._endpoint = endpoint\n\n        if not isinstance(endpoint, str):\n            self._setup_tokenizer()\n\n    def _setup_tokenizer(self) -> None:\n        if self._tokenizer_name == \"character\":\n            self._tokenizer = word_tokenizers.CharacterTokenizer()\n\n        if self._tokenizer_name == \"whitespace\":\n            self._tokenizer = word_tokenizers.WhitespaceTokenizer()\n\n        if self._tokenizer_name == \"kytea\":\n            self._tokenizer = word_tokenizers.KyTeaTokenizer(\n                with_postag=self._with_postag, model_path=self._model_path,\n            )\n\n        if self._tokenizer_name == \"sentencepiece\":\n            if self._model_path is None:\n                raise ValueError(\"`model_path` must be specified for sentencepiece.\")\n\n            self._tokenizer = word_tokenizers.SentencepieceTokenizer(\n                model_path=self._model_path,\n            )\n\n        if self._tokenizer_name == \"mecab\":\n            self._tokenizer = word_tokenizers.MeCabTokenizer(\n                user_dictionary_path=self._user_dictionary_path,\n                system_dictionary_path=self._system_dictionary_path,\n                with_postag=self._with_postag,\n                dictionary_format=self._dictionary_format,\n            )\n\n        if self._tokenizer_name == \"janome\":\n            self._tokenizer = word_tokenizers.JanomeTokenizer(\n                user_dictionary_path=self._user_dictionary_path,\n                with_postag=self._with_postag,\n            )\n\n        if self._tokenizer_name == \"sudachi\":\n            if self._mode is None:\n                raise ValueError(\"`mode` must be specified for sudachi.\")\n\n            self._tokenizer = word_tokenizers.SudachiTokenizer(\n                mode=self._mode, with_postag=self._with_postag,\n            )\n\n        if self._tokenizer_name == \"nagisa\":\n            self._tokenizer = word_tokenizers.NagisaTokenizer(\n                with_postag=self._with_postag,\n            )\n\n    def tokenize(self, text: str) -> List[Token]:\n        \"\"\"Tokenize input text\"\"\"\n\n        if isinstance(self._endpoint, str):\n            endpoint = self._endpoint\n            if not endpoint.startswith(\"http\"):\n                endpoint = \"http://\" + endpoint\n\n            payload = {\n                \"tokenizer\": self._tokenizer_name,\n                \"model_path\": self._model_path,\n                \"mode\": self._mode,\n                \"text\": text,\n            }\n            headers = {\"Content-Type\": \"application/json\"}\n            token_params = self._tokenize_with_remote_host(\n                endpoint=endpoint,\n                payload=payload,\n                headers=headers,\n            )\n\n            tokens = []\n            for token_param in token_params:\n                token = Token(surface=token_param[\"surface\"], postag=token_param[\"part_of_speech\"])\n                tokens.append(token)\n\n            return tokens\n\n        else:\n            return self._tokenizer.tokenize(text)\n\n    @staticmethod\n    def _tokenize_with_remote_host(\n        endpoint: str,\n        payload: Dict,\n        headers: Dict,\n    ) -> List[Dict]:\n        return requests.post(\n            endpoint,\n            json=payload,\n            headers=headers,\n        ).json()[\"tokens\"][0]\n\n    @property\n    def tokenizer(self) -> BaseTokenizer:\n        warnings.warn(\"attribute `tokenizer` will be removed in v5.0.0.\")\n        return self._tokenizer\n\n    @property\n    def name(self) -> str:\n        return self._tokenizer.name\n","sub_path":"konoha/word_tokenizer.py","file_name":"word_tokenizer.py","file_ext":"py","file_size_in_byte":5058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"632442223","text":"#!/usr/bin/env python\n\n# @Authors: Lukas Burget, Mireia Diez, Federico Landini, Jan Profant\n# @Emails: burget@fit.vutbr.cz, mireia@fit.vutbr.cz, landini@fit.vutbr.cz, jan.profant@phonexia.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\n\n# The recipe consists in doing Agglomerative Hierachical Clustering on\n# x-vectors in a first step. Then, Variational Bayes HMM over x-vectors\n# is applied using the AHC output as args.initialization.\n#\n# A detailed analysis of this approach is presented in\n# M. Diez, L. Burget, F. Landini, S. Wang, J. \\v{C}ernock\\'{y}\n# Optimizing Bayesian HMM based x-vector clustering for the second DIHARD speech\n# diarization challenge, ICASSP 2020\n# A more thorough description and study of the VB-HMM with eigen-voice priors\n# approach for diarization is presented in\n# M. Diez, L. Burget, F. Landini, J. \\v{C}ernock\\'{y}\n# Analysis of Speaker Diarization based on Bayesian HMM with Eigenvoice Priors,\n# IEEE Transactions on Audio, Speech and Language Processing, 2019\n# \n# TODO: Add new paper\n\nimport argparse\nimport os\nimport itertools\n\nimport h5py\nimport kaldi_io\nimport numpy as np\nfrom scipy.special import softmax\nfrom scipy.linalg import eigh\n\nfrom diarization_lib import read_xvector_timing_dict, l2_norm, cos_similarity, twoGMMcalib_lin, AHC, \\\n    merge_adjacent_labels, mkdir_p\nfrom kaldi_utils import read_plda\nfrom VB_diarization import VB_diarization\n\n\ndef write_output(fp, out_labels, starts, ends):\n    for label, seg_start, seg_end in zip(out_labels, starts, ends):\n        fp.write(f'SPEAKER {file_name} 1 {seg_start:03f} {seg_end - seg_start:03f} '\n                 f'<NA> <NA> {label + 1 if isinstance(label, int) else label} <NA> <NA>{os.linesep}')\n\n\ndef cross_interval_len(inv1, inv2):\n    return max(0, min(inv1[1], inv2[1]) - max(inv1[0], inv2[0]))\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--file-name', required=True, type=str)\n    parser.add_argument('--init', required=True, type=str, choices=['AHC', 'AHC+VB'],\n                        help='AHC for using only AHC or AHC+VB for VB-HMM after AHC initilization', )\n    parser.add_argument('--out-rttm-dir', required=True, type=str, help='Directory to store output rttm files')\n    parser.add_argument('--xvec-ark-file', required=True, type=str,\n                        help='Kaldi ark file with x-vectors from one or more input recordings. '\n                             'Attention: all x-vectors from one recording must be in one ark file')\n    parser.add_argument('--segments-file', required=True, type=str,\n                        help='File with x-vector timing info (see diarization_lib.read_xvector_timing_dict)')\n    parser.add_argument('--xvec-transform', required=True, type=str,\n                        help='path to x-vector transformation h5 file')\n    parser.add_argument('--plda-file', required=True, type=str,\n                        help='File with PLDA model in Kaldi format used for AHC and VB-HMM x-vector clustering')\n    parser.add_argument('--threshold', required=True, type=float, help='args.threshold (bias) used for AHC')\n    parser.add_argument('--lda-dim', required=True, type=int,\n                        help='For VB-HMM, x-vectors are reduced to this dimensionality using LDA')\n    parser.add_argument('--Fa', required=True, type=float,\n                        help='Parameter of VB-HMM (see VB_diarization.VB_diarization)')\n    parser.add_argument('--Fb', required=True, type=float,\n                        help='Parameter of VB-HMM (see VB_diarization.VB_diarization)')\n    parser.add_argument('--loopP', required=True, type=float,\n                        help='Parameter of VB-HMM (see VB_diarization.VB_diarization)')\n    parser.add_argument('--init-smoothing', required=False, type=float, default=5.0,\n                        help='AHC produces hard assignments of x-vetors to speakers. These are \"smoothed\" to soft '\n                             'assignments as the args.initialization for VB-HMM. This parameter controls the amount of'\n                             ' smoothing. Not so important, high value (e.g. 10) is OK  => keeping hard assigment')\n    parser.add_argument('--output-2nd', required=False, type=bool, default=False,\n                        help='Output also second most likely speaker of VB-HMM')\n    parser.add_argument('--reg-seg-file', required=False, type=str, default=None,\n                        help='File with registered segments, including start, end time and speaker label')\n    parser.add_argument('--fusion-factor', required=False, type=float, default=0.0,\n                        help='The hyperparameter controlling fusion of xvecs')\n    parser.add_argument('--replace-label', required=False, type=bool, default=False)\n\n    args = parser.parse_args()\n    assert 0 <= args.loopP <= 1, f'Expecting loopP between 0 and 1, got {args.loopP} instead.'\n\n    # segments file with x-vector timing information\n    segs_dict_old = read_xvector_timing_dict(args.segments_file)\n    segs_dict = segs_dict_old[args.file_name]\n    seg_to_duration_dict = dict()\n    for seg, dur in zip(segs_dict[0], segs_dict[1]):\n        seg_to_duration_dict[seg] = dur\n\n    kaldi_plda = read_plda(args.plda_file)\n    plda_mu, plda_tr, plda_psi = kaldi_plda\n    W = np.linalg.inv(plda_tr.T.dot(plda_tr))\n    B = np.linalg.inv((plda_tr.T / plda_psi).dot(plda_tr))\n    acvar, wccn = eigh(B, W)\n    plda_psi = acvar[::-1]\n    plda_tr = wccn.T[::-1]\n\n    # read registered segments from .regseg file\n    if args.reg_seg_file:\n        reg_segs = np.loadtxt(args.reg_seg_file, dtype=str)\n        for i, rs in enumerate(reg_segs):\n            rs[1] = rs[1].astype(float) + rs[0].astype(float)\n    else:\n        print(\"Info: registered segments are not given\")\n\n    # Open ark file with x-vectors and in each iteration of the following for-loop\n    # read a batch of x-vectors corresponding to one recording\n    arkit = kaldi_io.read_vec_flt_ark(args.xvec_ark_file)\n    recit = itertools.groupby(arkit, lambda e: e[0].rsplit('_', 1)[0]) # group xvectors in ark by recording name\n    for file_name, segs in recit:\n        print(file_name)\n        seg_names, xvecs = zip(*segs)\n        x = np.array(xvecs)\n\n        if args.reg_seg_file:\n            reg_label = np.array([' '] * x.shape[0])\n            for i, reg_and_seg in enumerate(zip(reg_label, seg_names)):\n                cur_label_dur = seg_to_duration_dict[reg_and_seg[1]]\n                max_dur = 0\n                max_label = ' '\n                for given_reg in reg_segs:\n                    cross_dur = cross_interval_len(\n                        [given_reg[0].astype(float), given_reg[1].astype(float)],\n                        [cur_label_dur[0], cur_label_dur[1]]\n                    )\n                    if cross_dur > max_dur and cross_dur > 1e-3:\n                        max_dur = cross_dur\n                        max_label = given_reg[2]\n                reg_label[i] = max_label\n        else:\n            reg_label = None\n\n        with h5py.File(args.xvec_transform, 'r') as f:\n            mean1 = np.array(f['mean1'])\n            mean2 = np.array(f['mean2'])\n            lda = np.array(f['lda'])\n            x = l2_norm(lda.T.dot((l2_norm(x - mean1)).transpose()).transpose() - mean2)\n\n        if args.init == 'AHC' or args.init.endswith('VB'):\n            if args.init.startswith('AHC'):\n                # Kaldi-like AHC of x-vectors (scr_mx is matrix of pairwise\n                # similarities between all x-vectors)\n                scr_mx = cos_similarity(x)\n                # Figure out utterance specific args.threshold for AHC.\n                thr, junk = twoGMMcalib_lin(scr_mx.ravel())\n                # output \"labels\" is an integer vector of speaker (cluster) ids\n                labels1st = AHC(scr_mx, thr + args.threshold)\n            if args.init.endswith('VB'):\n                # Smooth the hard labels obtained from AHC to soft assignments\n                # of x-vectors to speakers\n                qinit = np.zeros((len(labels1st), np.max(labels1st) + 1))\n                qinit[range(len(labels1st)), labels1st] = 1.0\n                qinit = softmax(qinit * args.init_smoothing, axis=1)\n                fea = (x - plda_mu).dot(plda_tr.T)[:, :args.lda_dim]\n                # Use VB-HMM for x-vector clustering. Instead of i-vector extractor model, we use PLDA\n                # => GMM with only 1 component, V derived accross-class covariance,\n                # and iE is inverse within-class covariance (i.e. identity)\n                sm = np.zeros(args.lda_dim)\n                siE = np.ones(args.lda_dim)\n                sV = np.sqrt(plda_psi[:args.lda_dim])\n                q, sp, L = VB_diarization(\n                    fea, sm, np.diag(siE), np.diag(sV),\n                    pi=None, gamma=qinit, maxSpeakers=qinit.shape[1],\n                    maxIters=40, epsilon=1e-6, \n                    loopProb=args.loopP, Fa=args.Fa, Fb=args.Fb,\n                    label=reg_label, fusionFactor=args.fusion_factor, \n                    pldaPsi=plda_psi)\n\n                labels1st = np.argsort(-q, axis=1)[:, 0]\n                spk_to_clus_lab = dict()\n                final_spk_to_clus_lab = dict()\n                if reg_label is not None and args.replace_label:\n                    for i, spk_with_clus_lab in enumerate(zip(reg_label, labels1st)):\n                        if spk_with_clus_lab[0] == ' ':\n                            continue\n                        if spk_with_clus_lab[0] not in spk_to_clus_lab:\n                            spk_to_clus_lab[spk_with_clus_lab[0]] = {spk_with_clus_lab[1]: 1}\n                        else:\n                            if spk_with_clus_lab[1] not in spk_to_clus_lab[spk_with_clus_lab[0]]:\n                                spk_to_clus_lab[spk_with_clus_lab[0]][spk_with_clus_lab[1]] = 1\n                            else:\n                                spk_to_clus_lab[spk_with_clus_lab[0]][spk_with_clus_lab[1]] += 1\n                    for key, value in spk_to_clus_lab.items():\n                        max_spk = None\n                        max_spk_num = -1\n                        for k, v in spk_to_clus_lab[key].items():\n                            if max_spk is None:\n                                max_spk = k\n                                max_spk_num = v\n                            else:\n                                if v > max_spk_num:\n                                    max_spk = k\n                                    max_spk_num = v\n                        if max_spk not in final_spk_to_clus_lab:\n                            final_spk_to_clus_lab[max_spk] = key\n                        else:\n                            raise Exception(\"Naming conflict during alignment of speakers to clustering labels\")\n                    labels1st_str = [''] * len(labels1st)\n                    for i, l in enumerate(labels1st):\n                        if labels1st[i] in final_spk_to_clus_lab:\n                            labels1st_str[i] = final_spk_to_clus_lab[labels1st[i]]\n                        else:\n                            labels1st_str[i] = str(labels1st[i])\n                    labels1st_str = np.array(labels1st_str)\n                else:\n                    labels1st_str = labels1st\n                    \n                if q.shape[1] > 1:\n                    labels2nd = np.argsort(-q, axis=1)[:, 1]\n        else:\n            raise ValueError('Wrong option for args.initialization.')\n\n        assert(np.all(segs_dict_old[file_name][0] == np.array(seg_names)))\n        start, end = segs_dict_old[file_name][1].T\n\n        starts, ends, out_labels = merge_adjacent_labels(start, end, labels1st_str)\n        mkdir_p(args.out_rttm_dir)\n        with open(os.path.join(args.out_rttm_dir, f'{file_name}.rttm'), 'w') as fp:\n            write_output(fp, out_labels, starts, ends)\n\n        if args.output_2nd and args.init.endswith('VB') and q.shape[1] > 1:\n            starts, ends, out_labels2 = merge_adjacent_labels(start, end, labels2nd)\n            output_rttm_dir = f'{args.out_rttm_dir}2nd'\n            mkdir_p(output_rttm_dir)\n            with open(os.path.join(output_rttm_dir, f'{file_name}.rttm'), 'w') as fp:\n                write_output(fp, out_labels2, starts, ends)\n","sub_path":"VBx/vbhmm.py","file_name":"vbhmm.py","file_ext":"py","file_size_in_byte":12716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"82941677","text":"# 2:写函数，接收两个数字参数，返回比较大的那个数字。\ndef com(a, b):\n    if a > b:\n        c = a\n    elif b > a:\n        c = b\n    else:\n        print(\"两数相等\")\n    return c\ncom(3,5)\nprint(com(3,5))\n\n# 3:写一个函数完成登陆功能,如果三次账号密码输入错误退出程序。\ndef login():\n    count = 0  # 保存用户名密码错误次数\n    marks = True\n    while marks:\n        print('请输入用户名：', end='')\n        name = input()\n        count += 1\n        if name != 'admin' and count < 3:\n            print('用户名错误，请重新输入')\n            continue\n        elif name != 'admin' and count >= 3:\n            print('用户名错误不能超过三次，请稍后再试')\n            marks = False\n            break\n        else:\n            count = 0  #记录密码次数清0\n            break\n    while marks:\n        print('请输入密码：',end='')\n        pwd = input()\n        if pwd == '123456':\n            print('登录成功')\n            return marks\n            count = 0  # 登录成功后密码错误次数清零\n            break\n        else:\n            count += 1\n            # print('计数:',count)\n            if count == 3:\n                print('密码连续错误次数达到3次，请确认后再次使用')\n                marks=False\n                return marks\n                break\n            else:\n                print('密码错误，请重新输入')\n                continue\nlogin()\n# 4:写一个判断素数的函数\ndef prime(a):\n    if a > 2:\n        for i in range(2,a):\n            if a % i == 0:\n                print(\"该数不是素数\")\n                break\n        if i == a-1:\n            print('该数是素数')\n    elif a == 2:\n        print(\"是素数\")\n    else:\n        print(\"既不是素数也不是合数\")\nprime(11)\n# 5:编写一个函数接收输入一个字符，判断这个字符是数字，还是大写字母，还是小写字母，还是其它符号(ASCII码)\ndef jug():\n    my_input = input(\"请输入一个字符:\")\n    if '0' <= my_input <= '9':\n        print(\"这是一个数字！\")\n    elif 'A' <= my_input <= 'Z':\n        print(\"这是���个大写字母\")\n    elif 'a' <= my_input <= 'z':\n        print(\"这是一个小写字母\")\n    else:\n        print(\"其他字符\")\njug()","sub_path":"basic/funciton.py","file_name":"funciton.py","file_ext":"py","file_size_in_byte":2319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"61431128","text":"import discord\r\nfrom discord.ext import commands\r\nimport json\r\nimport os\r\nfrom os import system, name\r\nimport random\r\nimport requests\r\nimport subprocess\r\nimport sys\r\nfrom colorama import Fore\r\n\r\nbot = commands.Bot(command_prefix='', self_bot=True, fetch_offline_members=False)\r\nbot.remove_command('help')\r\n\r\n# ASCII Splash Screens\r\n@bot.event\r\nasync def on_ready():\r\n    def ascii():\r\n        i = random.randrange(12) + 1\r\n        if sys.platform == 'win32':\r\n            os.system(\"cls\")\r\n            print(open(\"./art/\" + str(i) + '.txt').read())\r\n            print()\r\n            print(Fore.WHITE + \"[+] {0}\".format(bot.user) + Fore.RED)\r\n        elif sys.platform == 'linux' or 'darwin':\r\n            os.system(\"clear\")\r\n            os.system(\"cat art/\" + str(i) + '.txt | lolcat')\r\n            print()\r\n            print(Fore.WHITE + \"[+] {0}\".format(bot.user) + Fore.RED)\r\n    ascii()\r\n\r\n# Ignore missing commands in console but raise all other errors\r\n@bot.event\r\nasync def on_command_error(ctx, error):\r\n    if isinstance(error, commands.CommandNotFound):\r\n        pass\r\n    else:\r\n        raise error\r\n\r\n# Retrieves bot user's Discord WebSocket latency.\r\n@bot.command()\r\nasync def PING(ctx):\r\n    await ctx.message.edit(content=f\":ping_pong:`Ping: {round(bot.latency * 1000)}ms`\")\r\n\r\n# Restarts script from within itself without spawning a new terminal window.\r\n@bot.command()\r\nasync def rsc(ctx):\r\n    await ctx.message.delete()\r\n    subprocess.signal.SIGINT\r\n    os.execl(sys.executable, sys.executable, * sys.argv)\r\n\r\n# Message clear command. Clearing with a set amount currently counts messages from all users and not the bot user.\r\n@bot.command()\r\nasync def cl(ctx, amount=None):\r\n    if not amount:\r\n        amount = 2000\r\n    async for ctx.message in ctx.channel.history(limit=int(amount) + 1):\r\n        if ctx.message.author == bot.user:\r\n            if ctx.message.is_system():\r\n                pass\r\n            else:\r\n                await ctx.message.delete()\r\n\r\n# Crypto price command. Uses cryptowatch API.\r\n@bot.command()\r\nasync def PRICE(ctx):\r\n    embed = discord.Embed(color=0xf2a900)\r\n    price = 'https://api.cryptowat.ch/markets/kraken/btcusd/price'\r\n    response = requests.get(price).json()\r\n    btc = str(response[\"result\"][\"price\"])\r\n    price = 'https://api.cryptowat.ch/markets/kraken/ethusd/price'\r\n    response = requests.get(price).json()\r\n    eth = str(response[\"result\"][\"price\"])\r\n    embed.add_field(name=\"BTC\", value=\"$\" + btc, inline=False)\r\n    embed.add_field(name=\"ETH\", value=\"$\" + eth, inline=False)\r\n    await ctx.send(embed=embed)\r\n\r\n# Retrieves avatar from any user from any mutual server. For more accurate results, its best to mention the user but just appending the username works fine.\r\n@bot.command()\r\nasync def av(ctx, user: discord.User = None):\r\n    if not user:\r\n        user = bot.user\r\n    avatar = user.avatar_url_as(static_format='png', size=1024)\r\n    await ctx.send(avatar)\r\n\r\n# Accesses token from json file to prevent token leaks when sending a signal interrupt or editing code while screensharing with people :)\r\ntoken = json.loads(open(\"token.json\").read())['token']\r\nbot.run(token, bot=False)\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"419311899","text":"import os\r\nimport email\r\nattachment_dir=r\"C:\\Users\\USER\\Desktop\\att\"\r\ndef get_attachments(msg):\r\n    for part in msg.walk():\r\n        if part.get_content_maintype()=='multipart':\r\n            continue\r\n        if part.get('Content-Disposition') is None:\r\n            continue\r\n        fileName = part.get_filename()\r\n\r\n        if bool(fileName):\r\n            filePath = os.path.join(attachment_dir, fileName)\r\n            with open(filePath,'wb') as f:\r\n                f.write(part.get_payload(decode=True))\r\n\r\nimport imaplib\r\n# Connect to inbox\r\nimap_server = imaplib.IMAP4_SSL(host='imap.gmail.com')\r\nimap_server.login('rmainak01@gmail.com', 'hdcf48a01')\r\nimap_server.select()  # Default is `INBOX`\r\n\r\n# Find all emails in inbox and print out the raw email data\r\n_, message_numbers_raw = imap_server.search(None, 'ALL')\r\nfor message_number in message_numbers_raw[0].split():\r\n    _, msg = imap_server.fetch(message_number, '(RFC822)')\r\n\r\n    raw = email.message_from_bytes(msg[0][1])\r\n    get_attachments(raw)","sub_path":"gmailatt.py","file_name":"gmailatt.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"599359807","text":"#!/usr/bin/env python3\n\n\nimport boto3 \n\n\nsession = boto3.Session(region_name='us-east-1')\necs_client = session.client('ecs')\n\n\ndef chunks(l, n):\n    # For item i in a range that is a length of l,\n    for i in range(0, len(l), n):\n        # Create an index range for l of n items:\n        yield l[i:i+n]\n\ndef discovery():\n        succesStatus = \"(service {}) has reached a steady state.\"\n        failedServices = []\n        clusterList = ecs_client.list_clusters()\n        for clusterArn in clusterList[\"clusterArns\"]:\n            clusterName = clusterArn.split('/',1)[1]\n            serviceList = [x.split('/',1)[1] for x in ecs_client.list_services(cluster=clusterName, maxResults=100)['serviceArns']]\n            for b in list(chunks(serviceList,10)):\n                for service in  ecs_client.describe_services(cluster=clusterName, services=b)['services']:\n                     lastEvent = service['events'][0]\n                     if lastEvent['message'] == succesStatus.format(service['serviceName']):\n                          continue\n                     else:\n                          failedServices.append(service['serviceName'])\n\n        return failedServices\n\nfor f in discovery():\n    print(f)\n\n","sub_path":"ecs_service_status.py","file_name":"ecs_service_status.py","file_ext":"py","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"522005137","text":"from unittest import mock\n\nimport pytest\nfrom click import style\n\nfrom vendoring.errors import VendoringError\nfrom vendoring.ui import _UserInterface\n\n\n# --------------------------------------------------------------------------------------\n# Fixtures\n# --------------------------------------------------------------------------------------\n@pytest.fixture\ndef ui():\n    retval = _UserInterface()\n    with mock.patch.object(retval, \"_log\"):\n        yield retval\n\n\n@pytest.fixture\ndef verbose(ui):\n    ui.verbose = True\n\n\ndef test_is_silent_by_default(ui):\n    assert ui.verbose is False\n\n\ndef test_fixture_is_verbose(ui, verbose):\n    assert ui.verbose is True\n\n\n# --------------------------------------------------------------------------------------\n# Actual Tests\n# --------------------------------------------------------------------------------------\n@pytest.mark.parametrize(\n    [\"nl\", \"erase\", \"result\"],\n    [\n        (False, True, mock.call(\"text\\b\\b\\b\\b\", nl=False)),\n        (True, True, AssertionError),\n        (None, True, AssertionError),\n        (False, False, mock.call(\"text\", nl=False)),\n        (True, False, mock.call(\"text\", nl=True)),\n        (None, False, mock.call(\"text\", nl=True)),\n        (False, None, mock.call(\"text\", nl=False)),\n        (True, None, mock.call(\"text\", nl=True)),\n        (None, None, mock.call(\"text\", nl=True)),\n    ],\n)\ndef test__log_correctly_calls_click_echo(mocker, nl, erase, result):\n    ui = _UserInterface()\n    echo = mocker.patch(\"click.echo\")\n\n    kwargs = {\"nl\": nl, \"erase\": erase}\n    # In this test, None means omit.\n    kwargs = {k: v for k, v in kwargs.items() if v is not None}\n\n    if result is AssertionError:\n        with pytest.raises(AssertionError):\n            ui._log(\"text\", **kwargs)\n    else:\n        ui._log(\"text\", **kwargs)\n        assert echo.call_args_list == [result]\n\n\ndef test_shows_basic_log(ui):\n    ui.log(\"blah\")\n    ui._log.assert_any_call(\"blah\")\n\n\ndef test_shows_basic_log_when_verbose(ui, verbose):\n    ui.log(\"blah\")\n    ui._log.assert_any_call(\"blah\")\n\n\n@pytest.mark.parametrize(\"verbosity\", [True, False])\ndef test_shows_basic_warn(ui, verbosity):\n    ui.verbose = verbosity\n    ui.warn(\"blah\")\n\n    ui._log.assert_any_call(style(\"WARN: blah\", fg=\"yellow\"))\n\n\ndef test_shows_task_log_shown_when_verbose(ui, verbose):\n    with ui.task(\"Task Name\"):\n        ui.log(\"blah\")\n\n    ui._log.assert_has_calls([mock.call(\"Task Name... \", nl=True), mock.call(\"  blah\")])\n\n\ndef test_shows_task_log_redacted_when_silent(ui):\n    with ui.task(\"Task Name\"):\n        ui.log(\"blah\")\n\n    ui._log.assert_any_call(\"Task Name... \", nl=False)\n\n    # Ensure no calls made to log with the given message, since the task succeeded.\n    assert mock.call(\"blah\") not in ui._log.call_args_list\n    assert mock.call(\"  blah\") not in ui._log.call_args_list\n\n\ndef test_works_when_no_ui(ui):\n    with ui.task(\"Task Name\"):\n        pass\n\n    ui._log.assert_any_call(\"Task Name... \", nl=False)\n\n\ndef test_task_shows_spinner_when_silent(ui):\n    with ui.task(\"Task Name\"):\n        ui.log(\"blah\")\n        ui.log(\"foo\")\n        ui.log(\"boo\")\n\n    assert ui._log.call_args_list == [\n        mock.call(\"Task Name... \", nl=False),\n        mock.call(ui._spinner_frames[0], nl=False, erase=True),\n        mock.call(ui._spinner_frames[1], nl=False, erase=True),\n        mock.call(ui._spinner_frames[2], nl=False, erase=True),\n        mock.call(style(\"Done!\", fg=\"green\")),\n    ]\n\n\ndef test_nested_tasks_not_allowed(ui):\n    with pytest.raises(Exception, match=\"Only 1 task at a time.\"):\n        with ui.task(\"First\"):\n            with ui.task(\"Second\"):\n                assert False, \"should not get here\"\n\n\ndef test_show_error(ui):\n    try:\n        raise Exception(\"Yay!\")\n    except Exception as e:\n        ui.show_error(e)\n\n    assert ui._log.call_args_list == [\n        mock.call(\"  \" + style(\"Yay!\", fg=\"red\")),\n    ]\n\n\ndef test_show_error_verbose(ui, verbose):\n    try:\n        raise Exception(\"Yay!\")\n    except Exception as e:\n        ui.show_error(e)\n\n    assert ui._log.call_args_list == [\n        # XXX: This contains the entire traceback. We should check this but I'm tired.\n        mock.call(mock.ANY),\n    ]\n\n\ndef test_task_failure(ui):\n    # A failing task should raise the original error\n    with pytest.raises(VendoringError, match=\"ABORT ABORT ABORT!\"):\n        with ui.task(\"Task Name\"):\n            ui.log(\"Houston, there's a problem!\")\n\n            raise VendoringError(\"ABORT ABORT ABORT!\")\n\n    # Make sure that the state of the UI isn't messed up (i.e. can enter a new task)\n    with ui.task(\"Another Task\"):\n        ui.log(\"Works as expected\")\n\n    assert ui._log.call_args_list == [\n        mock.call(\"Task Name... \", nl=False),\n        mock.call(\"◴\", nl=False, erase=True),\n        mock.call(\" \"),\n        mock.call(\"  Houston, there's a problem!\"),\n        mock.call(\"Another Task... \", nl=False),\n        mock.call(\"◴\", nl=False, erase=True),\n        mock.call(style(\"Done!\", fg=\"green\")),\n    ]\n\n\ndef test_task_failure_verbose(ui, verbose):\n    # A failing task should raise the original error\n    with pytest.raises(VendoringError, match=\"ABORT ABORT ABORT!\"):\n        with ui.task(\"Task Name\"):\n            ui.log(\"Houston, there's a problem!\")\n\n            raise VendoringError(\"ABORT ABORT ABORT!\")\n\n    # Make sure that the state of the UI isn't messed up (i.e. can enter a new task)\n    with ui.task(\"Another Task\"):\n        ui.log(\"Works as expected\")\n\n    assert ui._log.call_args_list == [\n        mock.call(\"Task Name... \", nl=True),\n        mock.call(\"  Houston, there's a problem!\"),\n        mock.call(\"Another Task... \", nl=True),\n        mock.call(\"  Works as expected\"),\n        mock.call(\"  \" + style(\"Done!\", fg=\"green\")),\n    ]\n","sub_path":"tests/test_ui.py","file_name":"test_ui.py","file_ext":"py","file_size_in_byte":5736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"585686295","text":"# sources: https://realpython.com/python-requests/\n# api: https://pokeapi.co/\n# importing requests library\n# use 'pip install requests' if library is unavailable\n\nfrom apirequests.GetRequest import GetRequest\nfrom apirequests.Item import Item\nfrom apirequests.Pokemon import Pokemon\n\ndef main():\n\n    # Creating pokemon and displaying details\n    pokemon = Pokemon('charizard')\n    pokemon.display_properties()\n\n    print('\\n*******************************************************\\n')\n\n    # Creating item and displaying details\n    item = Item('stick')\n    item.display_properties()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"apirequests/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"601566922","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\ndic={\t'浙江省':{\n\t\t\t'金华市':['金华1','金华2','金华3'],\n\t\t\t'杭州市':['杭州1','杭州2','杭州3'],\n\t\t\t'丽水市':['丽水1','丽水2','丽水3']},\n\t\t'河南省':{\n\t\t\t'郑州市':['郑州1','郑州2','郑州3'],\n\t\t\t'登封市':['登封1','登封2','登封3'],\n\t\t\t'新郑市':['新郑1','新郑2','新郑3']},\n\t\t'山东省':{\n\t\t\t'济南市':['郑州1','郑州2','郑州3'],\n\t\t\t'青岛市':['登封1','登封2','登封3']}\n\t\t}\ndic_key=dic.keys()\nprint('--------------------------------')\nprint(' exit  退出程序    0  返回上一层')\nprint('--------------------------------')\nfor aa in dic_key:\n\tprint(\" \"+aa)\nwhile True:\t\n\tname=input('请输入省份:')\n\tif name in dic.keys():\n\t\tname_values=dic[name]\n\t\tfor values_key in name_values.keys():\n\t\t\tprint(\" \"+values_key)\n\t\twhile True:\n\t\t\tname1=input('请输入市:')\n\t\t\tif name1 in name_values.keys():\n\t\t\t\tname1_values=(dic[name])[name1]\n\t\t\t\tfor name1_values_key in name1_values:\n\t\t\t\t\tprint(\" \"+name1_values_key)\n\t\t\t\tbreak\n\t\t\telif name1 == 'exit':\n\t\t\t\texit()\n\t\t\telif name1 == '0':\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tprint('输入市错误！')\n\t\tcontinue\n\telif name == 'exit' or name == '0':\n\t\texit()\n\telse:\n\t\tprint('输入省份错误！')\n\n\t\n\n","sub_path":"day1/menu/Level_3_menu.py","file_name":"Level_3_menu.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"382334843","text":"import http.client\nimport json\n\nconnection = http.client.HTTPSConnection('https://marknadssok.fi.se/publiceringsklient') #'api.github.com')\n\nheaders = {'Content-type': 'application/json'}\n\nfoo = {'text': 'Hello world github/linguist#1 **cool**, and #1!'}\njson_foo = json.dumps(foo)\n\nconnection.request('POST', '/markdown', json_foo, headers)\n\nresponse = connection.getresponse()\nprint(response.read().decode())","sub_path":"pythonCarl/OpenWebPage/testConnectionCarl.py","file_name":"testConnectionCarl.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"476022767","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nDEBUG_FLAG = True\n\nMAX_TASK_ID     = 65535\nDB_PATH         = \"../db/irrigating_system.db\"\nMYSQL_SQL_PATH  = \"../db/mysql_creater.sql\"\nSQL_PATH        = \"../db/sqlite_creater.sql\"\n\nd_db_conn_local = {\n    \"HOST\"      : \"127.0.0.1\",\n    \"USER\"      : \"johnchain\",\n    \"PASSWORD\"  : \"johnchain111\",\n    \"DATABASE\"  : \"test\",\n    }\n\nd_task_status = {\n    \"SUCCEED\"     : 0,\n    \"FAILED\"      : 1,\n    \"DEALING\"     : 2,\n    \"SUBMITTING\"  : 3,\n    }\n\nclass t_insert_data():\n    def __init__(self, func_id=-1, keyword=\"\", domain_id=-1):\n        self.func_id = func_id\n        self.keyword = keyword\n        self.domain_id = domain_id","sub_path":"Server/head.py","file_name":"head.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"219828775","text":"#!/usr/bin/python\r\nimport smbus\r\nimport math\r\nimport time\r\n \r\n# Register\r\npower_mgmt_1 = 0x6b\r\npower_mgmt_2 = 0x6c\r\n \r\ndef read_byte(reg):\r\n    return bus.read_byte_data(address, reg)\r\n \r\ndef read_word(reg):\r\n    h = bus.read_byte_data(address, reg)\r\n    l = bus.read_byte_data(address, reg+1)\r\n    value = (h << 8) + l\r\n    return value\r\n \r\ndef read_word_2c(reg):\r\n    val = read_word(reg)\r\n    if (val >= 0x8000):\r\n        return -((65535 - val) + 1)\r\n    else:\r\n        return val\r\n \r\ndef dist(a,b):\r\n    return math.sqrt((a*a)+(b*b))\r\n \r\ndef get_y_rotation(x,y,z):\r\n    radians = math.atan2(x, dist(y,z))\r\n    return -math.degrees(radians)\r\n \r\ndef get_x_rotation(x,y,z):\r\n    radians = math.atan2(y, dist(x,z))\r\n    return math.degrees(radians)\r\n \r\nbus = smbus.SMBus(1) # bus = smbus.SMBus(0) fuer Revision 1\r\naddress = 0x68       # via i2cdetect\r\n \r\n# Aktivieren, um das Modul ansprechen zu koennen\r\nbus.write_byte_data(address, power_mgmt_1, 0)\r\n \r\n\r\ndef readGyroValues():\r\n    # gyro_xout = read_word_2c(0x43)\r\n    # gyro_yout = read_word_2c(0x45)\r\n    # gyro_zout = read_word_2c(0x47)\r\n     \r\n    # print(\"gyro_xout: \", (\"%5d\" % gyro_xout), \" scaled: \", (gyro_xout / 131))\r\n    # print(\"gyro_yout: \", (\"%5d\" % gyro_yout), \" scaled: \", (gyro_yout / 131))\r\n    # print(\"gyro_zout: \", (\"%5d\" % gyro_zout), \" scaled: \", (gyro_zout / 131))\r\n    accel_xout = read_word_2c(0x3b)\r\n    accel_yout = read_word_2c(0x3d)\r\n    accel_zout = read_word_2c(0x3f)\r\n     \r\n    accel_xout_scaled = accel_xout / 16384.0\r\n    accel_yout_scaled = accel_yout / 16384.0\r\n    accel_zout_scaled = accel_zout / 16384.0\r\n\r\n    x_angle = get_x_rotation(accel_xout_scaled, accel_yout_scaled, accel_zout_scaled)\r\n    y_angle = get_y_rotation(accel_xout_scaled, accel_yout_scaled, accel_zout_scaled)\r\n     \r\n    return(x_angle,y_angle)","sub_path":"IMU.py","file_name":"IMU.py","file_ext":"py","file_size_in_byte":1810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"229346531","text":"import os\nimport matplotlib.pyplot as plt\nfrom tools.predict import predict\nfrom dataset_scripts.metrics_eval import evaluate_detections, extract_mAP, extract_AP, get_classes\nfrom dataset_scripts.utils.coco_tools import leave_boxes\nfrom tqdm import tqdm\nimport argparse\nimport mmcv\nimport csv\nimport json\n\n\ndef build_parser():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-cfg', '--config-file', required=True, type=str)\n    parser.add_argument('-set', '--set-of-data', type=str, choices=['train', 'val', 'test'], default='val')\n    parser.add_argument('-ch-fld', '--checkpoints-folder', type=str)\n    parser.add_argument('-rep-fld', '--report-folder', type=str)\n    parser.add_argument('-img-fld', '--images-folder', type=str)\n    parser.add_argument('-ann', '--annotations-file', type=str)\n    parser.add_argument('-area', '--area', nargs=2, type=str, default=['0**2', '1e5**2'])\n    parser.add_argument('-shape', '--shape', nargs=2, type=int, default=(None, None))\n    parser.add_argument('-add', '--add', action='store_true')\n    parser.add_argument('-dont-repredict', '--dont-repredict', dest='repredict', action='store_false')\n    parser.add_argument('-mmdet-fld', '--mmdetection-folder', type=str, default='')\n    parser.add_argument('-gpu', '--gpu', type=int, default=0)\n    return parser\n\n\ndef get_existing_information(report_folder):\n    existing_epochs, existing_metrics = list(), list()\n    with open(os.path.join(report_folder, 'metrics.csv'), 'r') as f:\n        existing_information = csv.reader(f, delimiter=' ')\n        for new_information in existing_information:\n            existing_epochs.append(int(new_information[0]))\n            existing_metrics.append(list(map(float, new_information[1:])))\n    return existing_epochs, existing_metrics\n\n\ndef create_folders(report_folder):\n    if not os.path.exists(report_folder):\n        os.mkdir(report_folder)\n    if not os.path.exists(os.path.join(report_folder, 'predictions')):\n        os.mkdir(os.path.join(report_folder, 'predictions'))\n\n\ndef get_checkpoint_files(checkpoints_folder, existing_epochs):\n    checkpoint_files = os.listdir(checkpoints_folder)\n    epochs = []\n    for checkpoint_file in checkpoint_files:\n        if checkpoint_file.startswith(\"epoch\"):\n            if not int(checkpoint_file[6:-4]) in existing_epochs:\n                epochs.append(int(checkpoint_file[6:-4]))\n    checkpoint_files = []\n    for epoch in epochs:\n        checkpoint_files.append(os.path.join(checkpoints_folder, \"epoch_{}.pth\".format(epoch)))\n    return checkpoint_files, epochs\n\n\ndef run_models(config_file, checkpoint_files, epochs, report_folder, images_folder, annotations_file, repredict=True,\n               gpu=0):\n    for checkpoint_file, epoch in tqdm(list(zip(checkpoint_files, epochs))):\n        out_file = os.path.join(report_folder, 'predictions', 'epoch_{}.json'.format(epoch))\n        if os.path.exists(out_file) and not repredict:\n            continue\n        predict(config_file, checkpoint_file, out_file, detections_only=True, images_folder=images_folder,\n                images_file=annotations_file, threshold=0.01, nms=0.45, max_dets=100, gpu=gpu)\n\n\ndef calculate_metrics(epochs, report_folder, annotations_file, area=(0**2, 1e5**2), shape=(None, None)):\n    metrics = list()\n    # kostil' #\n    indexes_to_correct = list()\n    ###########\n    with open(annotations_file, 'r') as f:\n        annotations_dict = json.load(f)\n    leave_boxes(annotations_dict, area=area, width=shape[0], height=shape[1])\n\n    for epoch in tqdm(epochs):\n        detections_file = os.path.join(report_folder, 'predictions/epoch_{}.json'.format(epoch))\n        with open(detections_file, 'r') as f:\n            detections_dict = json.load(f)\n        # kostil' #\n        if detections_dict == list():\n            metrics.append(None)\n            indexes_to_correct.append(len(metrics)-1)\n            continue\n        ###########\n        detections_dict_with_images = {'images': annotations_dict['images'], 'annotations': detections_dict}\n        leave_boxes(detections_dict_with_images, area=area, width=shape[0], height=shape[1])\n        detections_dict = detections_dict_with_images['annotations']\n\n        results = evaluate_detections(annotations_dict, detections_dict)\n        classes = get_classes(results)\n        metric = [extract_mAP(results)]\n        metric += extract_AP(results, classes)\n        metrics.append(metric)\n    # kostil' #\n    for index in indexes_to_correct:\n        metrics[index] = [0] * (len(classes)+1)\n    ###########\n    return metrics, classes\n\n\ndef save_metrics(epochs, metrics, classes, report_folder):\n    with open(os.path.join(report_folder, 'metrics.csv'), 'w') as f:\n        writer = csv.writer(f, delimiter=' ')\n        for epoch, metric in zip(epochs, metrics):\n            writer.writerow([epoch] + metric)\n    with open(os.path.join(report_folder, 'classes.txt'), 'w') as f:\n        for i, cl in enumerate(classes):\n            f.write(cl)\n            if i != len(classes)-1:\n                f.write(' ')\n    mAP = list()\n    APs = list()\n    for metric in metrics:\n        mAP.append(metric[0])\n        APs.append(metric[1:])\n    plt.plot(epochs, mAP)\n    plt.grid()\n    plt.savefig(os.path.join(report_folder, 'mAP.png'))\n    plt.close()\n    plt.plot(epochs, APs)\n    plt.grid()\n    plt.savefig(os.path.join(report_folder, 'APs.png'))\n    plt.close()\n\n\ndef complete_args(config_file, set_of_data, checkpoints_folder, report_folder, images_folder, annotations_file,\n                  mmdetection_folder):\n    cfg = mmcv.Config.fromfile(config_file)\n    if checkpoints_folder is None:\n        checkpoints_folder = os.path.join(mmdetection_folder, cfg.work_dir)\n    if report_folder is None:\n        report_folder = os.path.join(mmdetection_folder, cfg.work_dir, 'report')\n    if images_folder is None:\n        images_folder = os.path.join(mmdetection_folder, cfg.data[set_of_data].img_prefix)\n    if annotations_file is None:\n        annotations_file = os.path.join(mmdetection_folder, cfg.data[set_of_data].ann_file)\n    return checkpoints_folder, report_folder, images_folder, annotations_file\n\n\ndef report(config_file, checkpoints_folder=None, report_folder=None, images_folder=None, annotations_file=None,\n           set_of_data='val', area=(0**2, 1e5**2), shape=(None, None), add=False, repredict=True, gpu=0,\n           mmdetection_folder=''):\n    assert set_of_data in ['train', 'val', 'test']\n    checkpoints_folder, report_folder, images_folder, annotations_file = complete_args(config_file, set_of_data,\n                                checkpoints_folder, report_folder, images_folder, annotations_file, mmdetection_folder)\n    if area[1] == -1:\n        area = (area[0], 1e5 ** 2)\n    if add:\n        existing_epochs, existing_metrics = get_existing_information(report_folder)\n    else:\n        existing_epochs, existing_metrics = list(), list()\n    create_folders(report_folder)\n    checkpoint_files, epochs = get_checkpoint_files(checkpoints_folder, existing_epochs)\n    run_models(config_file, checkpoint_files, epochs, report_folder, images_folder, annotations_file, repredict, gpu)\n    metrics, classes = calculate_metrics(epochs, report_folder, annotations_file, area, shape)\n    epochs += existing_epochs\n    metrics += existing_metrics\n    epochs, metrics = zip(*sorted(zip(epochs, metrics)))\n    save_metrics(epochs, metrics, classes, report_folder)\n\n\nif __name__ == '__main__':\n    parser = build_parser()\n    args = parser.parse_args()\n    args.area = list(map(eval, args.area))\n    kwargs = vars(args)\n    report(**kwargs)\n","sub_path":"tools/report.py","file_name":"report.py","file_ext":"py","file_size_in_byte":7565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"416451544","text":"'''\nBy David Amison\n'''\n\nimport random\n\nfrom pathlib import Path\n\nfrom PIL import Image\nfrom PIL import ImageFont\nfrom PIL import ImageDraw\n\n\nclass Coin:\n\n    '''Class for implementing the python dice and producing images in Telegram'''\n    \n    def __init__(self, images_folder, font_path=None, mode='pic'):\n        self.images_path = Path(images_folder)\n        self.font_path = Path(font_path)\n        self.mode = mode\n\n    def flip_coin(self, input_string):\n        '''Rolls the dice and produces output as defined in mode'''\n        # Extract only the first number from the input string\n        input_args = input_string.split(\" \")\n        command = int(input_args[0])\n\n        # Flip the coins\n        coin_out = self.flip(command)\n        total_H = sum(coin_out)\n        total_T = command-sum(coin_out)\n\n        # Check the output the user wants and give it\n        if self.mode == 'pic':\n            # create and return the combined image\n            try:\n                out_img = self.mode_pic(coin_out, total_H, total_T)\n                final_img = self.images_path / ('final.png')\n                out_img.save(str(final_img))\n                return final_img\n            except FileNotFoundError:\n                return self.mode_txt(coin_out, total_H, total_T)\n        elif self.mode == 'icon':\n            return ''\n        else:\n            # convert the rolls into strings\n            return self.mode_txt(coin_out, total_H, total_T)\n\n    def flip(self, n):\n        '''Flip a coin n times'''\n        results = []\n        for i in range(0, n):\n            # Heads is equivalent to 1, tails to 0\n            results.append(random.randint(0, 1))\n        return results\n\n    def mode_pic(self, coin_out, heads, tails):\n        '''Generates an image of a dice with the printed number value. Returns the\n        image'''\n        H_img = self.images_path / 'H.png'\n        T_img = self.images_path / 'T.png'\n\n        # Calculate the required image size and make it\n        coins_flipped = heads + tails\n        if coins_flipped == 1:\n            out_img_size = [100, 150]\n        elif coins_flipped < 3:\n            out_img_size = [300, 150]\n        elif coins_flipped < 5:\n            # extra 50 for total\n            out_img_size = [100 * coins_flipped, 150]\n        else:\n            # extra 50 for total\n            out_img_size = [500, 100*int((coins_flipped-1)/5)+150]\n\n        # Create the image to be output\n        out_img = Image.new('RGBA', out_img_size)\n\n        x_offset = 0\n        y_offset = 0\n        for flip in coin_out:\n            output_img = Image\n            if flip:\n                output_img = Image.open(str(H_img))\n            else:\n                output_img = Image.open(str(T_img))\n            out_img.paste(output_img, (x_offset, y_offset))\n\n            x_offset += 100\n            if x_offset > 400:\n                x_offset = 0\n                y_offset += 100\n\n        # Add text giving the total roll to the bottom\n        msg = ''\n        if heads + tails > 1:\n            msg = 'Heads: ' + str(heads) + ' Tails: ' + str(tails)\n        elif heads == 1:\n            msg = 'Heads'\n        elif tails == 1:\n            msg = 'Tails'\n\n        draw = ImageDraw.Draw(out_img)\n        try:\n            font = ImageFont.truetype(str(self.font_path),36)\n        except AttributeError:\n            font = ImageFont.load_default()\n        \n        w, h = draw.textsize(msg, font=font)\n        draw.text(\n            ((out_img_size[0]-w)/2, out_img_size[1]-50),\n            msg,\n            (0, 0, 0),\n            font=font)\n\n        return out_img\n\n    def mode_txt(self, coin_out, heads, tails):\n        # convert the flips into a string\n        out_str = ''\n        for flip in coin_out:\n            if flip:\n                out_str = out_str + 'H '\n            else:\n                out_str = out_str + 'T '\n\n        out_str = out_str+'= [H:'+str(heads)+', T:'+str(tails)+']'\n        return out_str\n","sub_path":"python/dicebeard/image_coin.py","file_name":"image_coin.py","file_ext":"py","file_size_in_byte":3936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"190593444","text":"from lab_4.main import tokenize_by_sentence\nfrom lab_4.main import WordStorage\nfrom lab_4.main import encode_text\n\nif __name__ == '__main__':\n    text = 'He is leon. He is happy.'\n    corpus = tokenize_by_sentence(text)\n\n    word_storage = WordStorage()\n    word_storage.update(corpus)\n\n    encoded_text = encode_text(word_storage, corpus)\n\n    RESULT = \"('he','is', 'leon' '<END>', 'he', 'is', 'happy', '<END>')\"\n    print(RESULT)\n    assert RESULT == \"('he','is', 'leon' '<END>', 'he', 'is', 'happy', '<END>')\", 'Something went wrong'\n","sub_path":"lab_4/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"123056995","text":"from flask import Flask,make_response,request,render_template,session\n\napp=Flask(__name__)\napp.config['SECRET_KEY'] = \"aixieshaxiesha\"\n\n\n\n@app.route('/01-setcookie')\ndef setcookie():\n    # 通过make_response构建响应对象\n    resp = make_response(\"保存cookies成功\")\n    # 1.保存名称为uname值为Maria，存期为1年\n    resp.set_cookie('uname','Maria',60*60*24*365)\n    # 2.保存一个名为upwd值为123456的cookie，不指定时长\n    resp.set_cookie('upwd', '123456')\n\n    # 响应：保存cookies成功\n    return resp\n\n@app.route('/02-login',methods=['POST','GET'])\ndef login_view():\n    if request.method == 'POST':\n        uname = request.form['uname']\n        pswd = request.form['pswd']\n\n        if uname =='admin' and pswd == 'admin':\n            resp = make_response('登录成功')\n            if 'rem' in request.form:\n                resp.set_cookie('uname',uname,60*60*24*365)\n                resp.set_cookie('pswd',pswd,60*60*24*365)\n            return resp\n        else:\n            return '账号或密码错误'\n    if 'uname' in request.cookies and 'pswd' in request.cookies:\n        uname = request.form['uname']\n        pswd = request.form['pswd']\n        if uname == 'admin' and pswd == 'admin':\n            return '欢迎'+request.cookies['uname']+'登录本站'\n\n        render_template('02-login.html')\n    return render_template('02-login.html')\n\n@app.route('/03-getcookie')\ndef getcookie():\n    print(request.cookies)\n    if 'uname' in request.cookies:\n        print(request.cookies['uname'])\n    if 'pswd' in request.cookies:\n        print(request.cookies['pswd'])\n    return '获取cookie成功'\n\n\n\n@app.route('/04-setsession')\ndef setsession_views():\n    session['uname'] = '李彤阿里'\n    session['upwd'] = '123456'\n    return '设置session成功'\n\n@app.route('/05-getsession')\ndef getsession_views():\n    if 'uname' in session and 'upwd' in session:\n        return '用户名：%s，密码：%s'%(session['uname'],session['upwd'])\n    return '得bu到session'\n\n@app.route('/06-login',methods=['POST','GET'])\ndef loginSession():\n    if request.method == 'POST':\n        uname = request.form['uname']\n        pswd = request.form['pswd']\n        if uname == 'admin' and pswd =='admin':\n            session['uname']=uname\n            session['pswd'] = pswd\n            return '欢迎%s登录'%uname\n        else:\n            return '账号或密码错误'\n    if 'uname' in session and 'pswd' in session:\n        return '欢迎%s登录'%session['uname']\n\n    return render_template('06-login.html')\n\nif __name__ == '__main__':\n    app.run(debug=True)","sub_path":"07-flask/2019-3-14/FlaskDemo08/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"466991814","text":"from unittest import TestCase\nfrom lessweb.application import Application, interceptor\nfrom lessweb.context import Context\n\n\ndef add(ctx:Context, a, b):\n    ctx.status_code, ctx.reason = 204, 'No Content'\n    return a + b\n\n\ndef wrapper(ctx:Context):\n    return '[{}]'.format(ctx())\n\n\nclass TestWebapi(TestCase):\n    def test_200s(self):\n        app = Application()\n        app.add_mapping('/add', 'GET', add)\n        app.add_interceptor('/add', 'GET', add)\n        ret = app.request('/add?a=1&b=2')\n        self.assertEquals(ret.status, '204 No Content')","sub_path":"test/test_webapi.py","file_name":"test_webapi.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"391025781","text":"import sys\nimport os\n#logFC   logCPM  PValue  FDR\n#ENSRNOG00000060048      12.6683572494449        5.23937555192889        1.56401184633849e-154   2.09374265869333e-150\n\nif len(sys.argv) < 3:\n    print (\"[USAGE] edgeR_GeneID2GeneName.py [GeneID2GeneName ensembl] [edgeR output]\")\n    sys.exit(1)\nelse:\n    f_DB_name = sys.argv[1]\n    f_edgeR_name = sys.argv[2]\n    if f_DB_name not in os.listdir('.'):\n        print ('File not found!')\n        sys.exit(1)\n    else:\n        f_DB = open(f_DB_name,'r')\n        f_edgeR_out = open(f_edgeR_name,'r')\n\ngeneID2gene = dict()\nf_DB.readline()\nfor line in f_DB:\n    token = line.strip().split('\\t')\n    gene_nm = token[1]\n    geneID = token[0]\n    geneID2gene[geneID] = gene_nm\n    \nf_DB.close()\nf_total_out = open('15Mo34_vs_6Mo56.fastq.trimmed.RAT_ens98_longest_cDNA.BWA.SAMBestCount.edgeR.GeneSymbol.clean+cpm.txt','w')\n#f_clean_out = open('15Mo34_vs_6Mo56.fastq.trimmed.RAT_ens98_longest_cDNA.BWA.SAMBestCount.edgeR.GeneSymbol.edgeR.clean.txt','w')\nedgeR_ttl = f_edgeR_out.readline()\nf_total_out.write('GeneName\\t'+ edgeR_ttl)\n#f_clean_out.write('GeneName\\t'+ edgeR_ttl)\n\ngeneID_list = list(geneID2gene.keys())\nfor line in f_edgeR_out:\n    tokens = line.strip().split('\\t')\n    geneInfo = tokens[1:]\n    geneID = tokens[0]\n    if geneID in geneID_list:\n        gene_name = geneID2gene[geneID]\n    else:\n        gene_name = 'NA'\n    f_total_out.write('%s\\t%s\\n'%(gene_name,'\\t'.join(geneInfo)))\n#    logFC = float(tokens[1])\n#    FDR = float(tokens[-1])\n#    if abs(logFC) > 1 and FDR < 0.05:\n#        f_clean_out.write('%s\\t%s\\n'%(gene_name,'\\t'.join(geneInfo)))\n\nf_edgeR_out.close()\nf_total_out.close()\n#f_clean_out.close()\n","sub_path":"IronBrain/edgeR_ens_GeneID2GeneSymbol.py","file_name":"edgeR_ens_GeneID2GeneSymbol.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"178227126","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May  7 18:55:13 2014\n\n@author: wenzek\n\"\"\"\n\nfrom learn_tree import main\n\nopts = [(\"-d\", 100),\n        (\"-n\", 20),\n        (\"--test\", \"treatedData/brevets_test.csv\"),\n        (\"--cross\", \"treatedData/cross_20.csv\")\n        ]\nargs = [\"treatedData/brevets_train.csv\"]\n\nclf = main(opts, args)\n\n","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"384885522","text":"# Copyright 2017 TensorHub, Inc.\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\"\"\"Bootstraps env for guild.main.\n\nThe primary bootstrap task is to configure sys.path with the location\nof Guild's external dependencies. This module supports two modes:\ndistribution and dev.\n\nExternal dependencies in distribution mode are assumed to be located\nin a single `GUILD_PKG_HOME/external` directory where `GUILD_PKG_HOME`\nis the `guild` directory within the Guild distribution location.\n\nExternal dependencies in dev mode are assumed be in multiple\ndirectories, one for each dependency, under\n`SCRIPT_DIR/guild.runfiles` where `SCRIPT_DIR` is the directory\ncontaining `sys.argv[0]` (i.e. the Guild executable script).\n\nThis module confirms that it can find each of the modules listed in\nguild.__requires__ but does not load the modules. The module exits\nwith an error and a user facing message for any missing requirements.\n\nAs the bootstrap process is used for every Guild command, it must\nexecute as quickly as possible.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\n\nimport imp\nimport os\nimport sys\n\nrunfile_import_paths = [\n    \"org_click\",\n    \"org_psutil\",\n]\n\ndef main():\n    sys.path[:0] = _external_import_paths()\n    _check_requires()\n    import guild.main\n    guild.main.main()\n\ndef _external_import_paths():\n    paths = _external_distribution() or _external_dev()\n    assert paths, \"could not find external paths\"\n    return paths\n\ndef _external_distribution():\n    guild_pkg_dir = os.path.dirname(__file__)\n    external_dir = os.path.join(guild_pkg_dir, \"external\")\n    return [external_dir] if os.path.exists(external_dir) else None\n\ndef _external_dev():\n    script_dir = os.path.dirname(sys.argv[0])\n    runfiles_dir = os.path.join(script_dir, \"guild.runfiles\")\n    return [os.path.join(runfiles_dir, path) for path in runfile_import_paths]\n\ndef _check_requires():\n    import guild\n    for mod_name, req in _sort_reqs(guild.__requires__):\n        try:\n            imp.find_module(mod_name)\n        except ImportError:\n            _handle_missing_req(req)\n\ndef _sort_reqs(required):\n    # Make sure pip is listed first. pip is used to install other\n    # required packages and we want to check it first to direct\n    # the user to install it before checking other reqs.\n    return sorted(\n        required,\n        key=lambda spec: (\"\" if spec[0] == \"pip\"\n                          else spec[0].lower()))\n\ndef _handle_missing_req(req):\n    msg_parts = [\"guild: missing required package '%s'\\n\" % req]\n    if req.startswith(\"pip\"):\n        msg_parts.append(\n            \"Refer to https://pip.pypa.io/en/stable/installing \"\n            \"for help installing pip.\")\n    else:\n        msg_parts.append(\"Try 'pip install %s' to install the package.\" % req)\n    sys.stderr.write(\"\".join(msg_parts))\n    sys.stderr.write(\"\\n\")\n    sys.exit(1)\n","sub_path":"guild/main_bootstrap.py","file_name":"main_bootstrap.py","file_ext":"py","file_size_in_byte":3366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"493658249","text":"from __future__ import absolute_import, division, print_function\n\nfrom dxtbx.format import FormatEigerStream\nfrom cctbx.eltbx import attenuation_coefficient\nfrom dxtbx.model import ParallaxCorrectedPxMmStrategy\nfrom dxtbx.format.FormatPilatusHelpers import determine_eiger_mask\n\ntry:\n    import lz4\nexcept ImportError:\n    lz4 = None\n\ntry:\n    import bitshuffle\nexcept (ImportError, ValueError):\n    bitshuffle = None\n\ninjected_data = {}\n\n\ndef inject_data(data):\n    FormatEigerStream.injected_data = data\n\n\nclass FormatEigerStreamSSRL(FormatEigerStream.FormatEigerStream):\n    \"\"\"\n    A format class to understand an EIGER stream at SSRL\n    \"\"\"\n\n    @staticmethod\n    def understand(image_file):\n        return True\n\n    def _detector(self):\n        \"\"\" Create an Eiger detector profile (taken from FormatCBFMiniEiger) \"\"\"\n        configuration = self.header[\"configuration\"]\n        info = self.header[\"info\"]\n\n        distance = configuration[\"detector_distance\"]\n        wavelength = configuration[\"wavelength\"]\n        beam_x = configuration[\"beam_center_x\"]\n        beam_y = configuration[\"beam_center_y\"]\n\n        pixel_x = configuration[\"x_pixel_size\"]\n        pixel_y = configuration[\"y_pixel_size\"]\n\n        material = configuration[\"sensor_material\"]\n        thickness = configuration[\"sensor_thickness\"] * 1000\n\n        nx = configuration[\"x_pixels_in_detector\"]\n        ny = configuration[\"y_pixels_in_detector\"]\n\n        if \"count_rate_correction_count_cutoff\" in configuration:\n            overload = configuration[\"count_rate_correction_count_cutoff\"]\n        else:\n            # hard-code if missing from Eiger stream header\n            overload = 4001400\n        underload = -1\n\n        try:\n            identifier = configuration[\"description\"]\n        except KeyError:\n            identifier = \"Unknown Eiger\"\n\n        table = attenuation_coefficient.get_table(material)\n        mu = table.mu_at_angstrom(wavelength) / 10.0\n        t0 = thickness\n\n        detector = self._detector_factory.simple(\n            sensor=\"PAD\",\n            distance=distance * 1000.0,\n            beam_centre=(beam_x * pixel_x * 1000.0, beam_y * pixel_y * 1000.0),\n            fast_direction=\"+x\",\n            slow_direction=\"-y\",\n            pixel_size=(1000 * pixel_x, 1000 * pixel_y),\n            image_size=(nx, ny),\n            trusted_range=(underload, overload),\n            mask=[],\n            px_mm=ParallaxCorrectedPxMmStrategy(mu, t0),\n            mu=mu,\n        )\n\n        for f0, f1, s0, s1 in determine_eiger_mask(detector):\n            detector[0].add_mask(f0 - 1, s0 - 1, f1, s1)\n\n        for panel in detector:\n            panel.set_thickness(thickness)\n            panel.set_material(material)\n            panel.set_identifier(identifier)\n            panel.set_mu(mu)\n\n        return detector\n","sub_path":"src/interceptor/format/FormatEigerStreamSSRL.py","file_name":"FormatEigerStreamSSRL.py","file_ext":"py","file_size_in_byte":2812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"336092415","text":"# -*- coding: utf-8 -*-\n# pylint: disable=C0103\n\"\"\"\nNaive Bayes classification.\n\"\"\"\n# Author: bertrand-l\n# License: BSD\n\n\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport numpy as np\nfrom .classification import confusion\nfrom .predictor import BaseClassificationPredictor\nfrom .util import assert_binary, assert_Xy, strfmt, strtab\n\n\n__all__ = ('NaiveBayesGaussian', 'NaiveBayesBinomial')\n\n\ndef binomial_loglik(X):\n    r\"\"\"\n    Returns the log-likelihood function :math:`\\sum_i log(P(x_i|y))`\n    for each feature vector x.\n\n    X should only correspond to samples where y is of a given class.\n    \"\"\"\n    # Prob(x=1|y=cls) with Laplace smoothing\n    prob = ((X == 1).astype(np.int).sum(axis=0) + 1) / (len(X) + 2)\n    return lambda X: np.log(prob * (X == 1).astype(np.int) +\n                            (1. - prob) * (X == 0).astype(np.int)).sum(axis=1)\n\n\ndef gaussian_loglik(X):\n    r\"\"\"\n    Returns the log-likelihood function :math:`\\sum_i log(P(x_i|y))`\n    for each feature vector x.\n\n    X should only correspond to samples where y is of a given class.\n    \"\"\"\n    mean = X.mean(axis=0)\n    var = X.var(axis=0)\n    return lambda X: - ((X - mean) ** 2 / (2 * var) +\n                        np.log(2 * np.pi * var)).sum(axis=1)\n\n\nclass BaseNaiveBayes(BaseClassificationPredictor):\n    \"\"\"\n    Base class for Naive Bayes classification.\n    \"\"\"\n    def __init__(self):\n\n        BaseClassificationPredictor.__init__(self)\n        self._loglik = None\n        self._logprior = None\n\n    def __str__(self):\n\n        stats = self._training_stats\n        txt = BaseClassificationPredictor.__str__(self)\n        if self.classes is not None and 'prior' in stats:\n            table = ([\"class\"] + [str(c) for c in self.classes],\n                     [\"prior\"] + [strfmt(stats['prior'][c], '.4f')\n                                  for c in self.classes])\n            txt += (\"\\n\" + strtab(table, margin=8, ffmt='s', title=\"Classes\"))\n            table = [[\"\", \"precision\", \"recall\", \"F-score\"]]\n            table += [(str(c), stats[c][\"precision\"], stats[c][\"recall\"],\n                       stats[c][\"fscore\"]) for c in self.classes]\n            txt += (\"\\n\" + strtab(table, margin=8, ffmt='.4f',\n                                  title=\"Precision and recall\"))\n        return txt\n\n    def learn(self, X, y, event_model):\n        \"\"\"\n        Learn by computing the prior for each class and conditional\n        probabilities for each feature given the class.\n\n        Parameters\n        ----------\n        X : array_like\n            Input features, shape=(n_samples, n_features).\n        y : array_like\n            targets, shape=(n_samples,). Targets must take at least two\n            distinct values or classes, and can have any type.\n\n        Returns\n        -------\n        self : BaseNaiveBayes object\n\n        Raises\n        ------\n        TypeError, ValueError\n        \"\"\"\n        X, y = assert_Xy(X, y)\n        n_samples, n_features = X.shape\n        BaseClassificationPredictor.learn(self, X, y)\n        classes = self.classes\n\n        logprior = {}\n        loglik = {}\n        for c in classes:\n            indices = y == c\n            logprior[c] = np.log(indices.astype(np.int).sum() / n_samples)\n            loglik[c] = event_model(X[indices])\n\n        self._loglik = loglik\n        self._logprior = logprior\n\n        y_hat = self.predict(X)\n        stats = {c: confusion(y, y_hat, c=c) for c in classes}\n        stats['prior'] = {c: np.exp(logprior[c]) for c in classes}\n        stats['mean'] = {c: X[y == c].mean(axis=0) for c in classes}\n        stats['stddev'] = {c: X[y == c].std(axis=0) for c in classes}\n        self._training_stats.update(stats)\n\n        return self\n\n    def predict(self, X):\n        \"\"\"\n        Predict the class of the input features.\n\n        Parameters\n        ----------\n        X : array_like\n            Input features, shape=(n_samples, n_features).\n\n        Returns\n        -------\n        classes : array_like\n\n        Raises\n        ------\n        ValueError\n        \"\"\"\n        X = BaseClassificationPredictor.predict(self, X)\n        classes = self._classes\n        loglik = self._loglik\n        logprior = self._logprior\n        if classes is None:\n            raise ValueError(\"The predictor has not been trained.\")\n        # use the log to avoid floating point errors\n        return np.array([classes[np.argmax([logprior[c] +\n                                            loglik[c](x[np.newaxis, :])\n                                            for c in classes])]\n                         for x in X])\n\n\nclass NaiveBayesGaussian(BaseNaiveBayes):\n    \"\"\"\n    Naive Bayes classifier assuming a Gaussian likelihood for features.\n    \"\"\"\n    def learn(self, X, y):\n        \"\"\"\n        Learn by computing the prior for each class and conditional\n        probabilities for each feature given the class.\n\n        Parameters\n        ----------\n        X : array_like\n            Input features, shape=(n_samples, n_features).\n        y : array_like\n            targets, shape=(n_samples,). Targets must take at least two\n            distinct values or classes, and can have any type.\n\n        Returns\n        -------\n        self : NaiveBayesGaussian object\n\n        Raises\n        ------\n        TypeError, ValueError\n        \"\"\"\n        return BaseNaiveBayes.learn(self, X, y, gaussian_loglik)\n\n\nclass NaiveBayesBinomial(BaseNaiveBayes):\n    \"\"\"\n    Naive Bayes classifier assuming a binomial likelihood for features.\n    \"\"\"\n    def learn(self, X, y):\n        \"\"\"\n        Learn by computing the prior for each class and conditional\n        probabilities for each feature given the class.\n\n        Parameters\n        ----------\n        X : array_like\n            Input features, shape=(n_samples, n_features). Feature values\n            must be either 0 or 1.\n        y : array_like\n            targets, shape=(n_samples,). Targets must take at least two\n            distinct values or classes, and can have any type.\n\n        Returns\n        -------\n        self : NaiveBayesBinomial object\n\n        Raises\n        ------\n        TypeError, ValueError\n        \"\"\"\n        X = assert_binary(X, 'X')\n        return BaseNaiveBayes.learn(self, X, y, binomial_loglik)\n","sub_path":"learnml/naive_bayes_classification.py","file_name":"naive_bayes_classification.py","file_ext":"py","file_size_in_byte":6246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"57912418","text":"#!/usr/bin/python2\n#coding=utf-8\n\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium import webdriver\nfrom page import element\nimport unittest\nimport time\n\nelement1 = element.Basepage(webdriver.Firefox())\nclass Test(unittest.TestCase):\n\n    def test_1(self):\n        # element1 = element1.Basepage(webdriver.Firefox())\n        # profile_directory = r'C:\\Users\\Administrator\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\hc9mht9x.default'\n        # profile = webdriver.FirefoxProfile(profile_directory)\n        element1.driver.maximize_window()\n        element1.driver.get(\"https://test.exexm.com/SysHome\")\n        element1.driver.implicitly_wait(10)\n        #企业ID\n        element1.id_key(\"txtTenantId\",\"dev\")\n        #账号\n        element1.id_key(\"txtLoginName\",\"admin\")\n        #密码\n        element1.id_key(\"txtPassword\",\"123\")\n        #登录\n        element1.id_click(\"btnLogin\")\n        time.sleep(3)\n        #验证登录是否成功\n        element1.iframe_mainFrame()\n        text1 = element1.driver.find_element_by_link_text('首页').text\n        print(text1)\n        self.assertEqual(text1,\"首页\",\"登录失败\" )\n        element1.iframe_back()\n\n\n","sub_path":"testcase/exam.py","file_name":"exam.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"41563366","text":"#\n# @lc app=leetcode id=103 lang=python3\n#\n# [103] Binary Tree Zigzag Level Order Traversal\n#\n\n# @lc code=start\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n  '''\n  Accepted\n  33/33 cases passed (24 ms)\n  Your runtime beats 97.49 % of python3 submissions\n  Your memory usage beats 42.75 % of python3 submissions (14.5 MB)\n\n  Time complexity : O(n)\n  Space complexity : O(log n)\n  '''\n  def zigzagLevelOrder(self, root: TreeNode) -> List[List[int]]:\n    if root is None:\n      return []\n\n    retval = []\n    stackLeft, stackRight = [root], list()\n    while stackLeft or stackRight:\n      temp = []\n      while stackLeft:\n        curr = stackLeft.pop()\n        temp.append(curr.val)\n        if curr.left:\n          stackRight.append(curr.left)\n        if curr.right:\n          stackRight.append(curr.right)\n      if temp:\n        retval.append(temp)\n\n      temp = []\n      while stackRight:\n        curr = stackRight.pop()\n        temp.append(curr.val)\n        if curr.right:\n          stackLeft.append(curr.right)\n        if curr.left:\n          stackLeft.append(curr.left)\n      if temp:\n        retval.append(temp)\n    \n    return retval\n# @lc code=end\n\n","sub_path":"leetcode/python3/[103]_binary-tree-zigzag-level-order-traversal.py","file_name":"[103]_binary-tree-zigzag-level-order-traversal.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"594005754","text":"import os\nimport sys\nimport gym\nfrom gym import wrappers\nimport malware_rl\n\nfrom IPython import embed\n\nmodule_path = os.path.split(os.path.abspath(sys.modules[__name__].__file__))[0]\n\n\nclass RandomAgent(object):\n    \"\"\"The world's simplest agent!\"\"\"\n    def __init__(self, action_space):\n        self.action_space = action_space\n\n    def act(self, observation, reward, done):\n        return self.action_space.sample()\n\nenv = gym.make('ember-train-v0')\noutdir = os.path.join(module_path, 'data/logs/random-agent-results')\nenv = wrappers.Monitor(env, directory=outdir, force=True)\nenv.seed(0)\nagent = RandomAgent(env.action_space)\nepisode_count = 250\nreward = 0\nevasions = 0\ndone = False\n\nfor i in range(episode_count):\n    ob = env.reset()\n    while True:\n        action = agent.act(ob, reward, done)\n        ob, reward, done, _ = env.step(action)\n        if done and reward >= 10.0:\n            evasions +=1\n            break\n        elif done:\n            break\n\nevasion_rate = (evasions/episode_count)*100\nprint(f\"{evasions}% samples evaded model...\")\nembed()","sub_path":"random_agent.py","file_name":"random_agent.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"644691169","text":"from flask import Flask, render_template, Response, request, redirect, url_for, send_from_directory\n\nfrom onvif import ONVIFCamera\nimport time\nimport cv2\nfrom onvif_ptz import onvif_ptz\n\n\napp = Flask(__name__)\n\nptz_list = []\nfor i in range(0,8):\n    ptz_list.append(onvif_ptz(IP = \"192.168.0.21\"+str(i),PORT = 80, USER = \"admin\", PASS = \"intflow3121\", wsdl_dir = \"/works/Camera-Streaming-On-ThingsBoard/python-onvif-zeep/wsdl/\"))\n\nXMAX = 1\nXMIN = -1\nYMAX = 1\nYMIN = -1\nZMAX = 1.0\nZMIN = 0\npositionrequest = None\nptz = None\nactive = False\nhomerequest = None\nmedia_profile = None\n\ndef do_move(ptz, request):\n    # Start continuous move\n    global active\n    if active:\n        ptz.Stop({'ProfileToken': request.ProfileToken})\n    active = True\n    ptz.ContinuousMove(request)\n\ndef do_move_home(ptz, request):\n    # Start continuous move\n    global active\n    if active:\n        ptz.Stop({'ProfileToken': request.ProfileToken})\n    active = True\n    ptz.AbsoluteMove(request)\n    \ndef do_zoom(ptz, request):\n    global active\n    if active:\n        ptz.Stop({'ProfileToken': request.ProfileToken})\n    active = True\n    ptz.AbsoluteMove(request)\n\ndef move_up(ptz, request):\n    request.Velocity.PanTilt.x = 0\n    request.Velocity.PanTilt.y = YMAX\n    do_move(ptz, request)\n\ndef move_down(ptz, request):\n    request.Velocity.PanTilt.x = 0\n    request.Velocity.PanTilt.y = YMIN\n    do_move(ptz, request)\n\ndef move_right(ptz, request):\n    request.Velocity.PanTilt.x = XMAX\n    request.Velocity.PanTilt.y = 0\n    do_move(ptz, request)\n\ndef move_left(ptz, request):\n    request.Velocity.PanTilt.x = XMIN\n    request.Velocity.PanTilt.y = 0\n    do_move(ptz, request)\n    \n\ndef move_upleft(ptz, request):\n    request.Velocity.PanTilt.x = XMIN\n    request.Velocity.PanTilt.y = YMAX\n    do_move(ptz, request)\n    \ndef move_upright(ptz, request):\n    request.Velocity.PanTilt.x = XMAX\n    request.Velocity.PanTilt.y = YMAX\n    do_move(ptz, request)\n    \ndef move_downleft(ptz, request):\n    request.Velocity.PanTilt.x = XMIN\n    request.Velocity.PanTilt.y = YMIN\n    do_move(ptz, request)\n    \ndef move_downright(ptz, request):\n    request.Velocity.PanTilt.x = XMAX\n    request.Velocity.PanTilt.y = YMIN\n    do_move(ptz, request)\n\ndef move_home(ptz, request):\n    request.Position.PanTilt.x = 1\n    request.Position.PanTilt.y = 0\n    request.Position.Zoom = 0\n    do_move_home(ptz,request)\n\ndef Zoom_in(ptz,request):\n    request.Velocity.PanTilt.x = 0\n    request.Velocity.PanTilt.y = 0\n    request.Velocity.Zoom.x = ZMAX\n    do_move(ptz, request)\n\ndef Zoom_out(ptz,request):\n    request.Velocity.PanTilt.x = 0\n    request.Velocity.PanTilt.y = 0\n    request.Velocity.Zoom.x = ZMIN\n    do_move(ptz,request)\n\n\n@app.route('/')\ndef index():\n    return render_template('camera_control.html')\n\n\n@app.route('/up', methods=[\"POST\"])\ndef up():\n    ptz_list[0].move_up(ptz_list[0].ptz,ptz_list[0].positionrequest)\n    ptz_list[1].move_up(ptz_list[1].ptz,ptz_list[1].positionrequest)\n    move_up(ptz,positionrequest)\n    return 'OK'\n\n@app.route('/down', methods=[\"POST\"])\ndef down():\n    ptz_list[0].move_down(ptz_list[0].ptz,ptz_list[0].positionrequest)\n    ptz_list[1].move_down(ptz_list[1].ptz,ptz_list[1].positionrequest)\n    move_down(ptz,positionrequest)\n    return 'OK'\n\n@app.route('/left', methods=[\"POST\"])\ndef left():\n    ptz_list[0].move_left(ptz_list[0].ptz,ptz_list[0].positionrequest)\n    ptz_list[1].move_left(ptz_list[1].ptz,ptz_list[1].positionrequest)\n    move_left(ptz,positionrequest)\n    return 'OK'\n\n@app.route('/right', methods=[\"POST\"])\ndef right():\n    ptz_list[0].move_right(ptz_list[0].ptz,ptz_list[0].positionrequest)\n    ptz_list[1].move_right(ptz_list[1].ptz,ptz_list[1].positionrequest)\n    move_right(ptz,positionrequest)\n    return 'OK'\n\n@app.route('/left_up', methods=[\"POST\"])\ndef left_up():\n    ptz_list[0].move_upleft(ptz_list[0].ptz,ptz_list[0].positionrequest)\n    ptz_list[1].move_upleft(ptz_list[1].ptz,ptz_list[1].positionrequest)\n    move_upleft(ptz,positionrequest)\n    return 'OK'\n\n@app.route('/right_up', methods=[\"POST\"])\ndef right_up():\n    ptz_list[0].move_upright(ptz_list[0].ptz,ptz_list[0].positionrequest)\n    ptz_list[1].move_upright(ptz_list[1].ptz,ptz_list[1].positionrequest)\n    move_upright(ptz,positionrequest)\n    return 'OK'\n\n@app.route('/left_down', methods=[\"POST\"])\ndef left_down():\n    ptz_list[0].move_downleft(ptz_list[0].ptz,ptz_list[0].positionrequest)\n    ptz_list[1].move_downleft(ptz_list[1].ptz,ptz_list[1].positionrequest)\n    move_downleft(ptz,positionrequest)\n    return 'OK'\n\n@app.route('/right_down', methods=[\"POST\"])\ndef right_down():\n    ptz_list[0].move_downright(ptz_list[0].ptz,ptz_list[0].positionrequest)\n    ptz_list[1].move_downright(ptz_list[1].ptz,ptz_list[1].positionrequest)\n    move_downright(ptz,positionrequest)\n    return 'OK'\n\n@app.route('/zoom_in', methods=[\"POST\"])\ndef zoom_in():\n    ptz_list[0].Zoom_in(ptz_list[0].ptz,ptz_list[0].positionrequest)\n    ptz_list[1].Zoom_in(ptz_list[1].ptz,ptz_list[1].positionrequest)\n    Zoom_in(ptz,positionrequest)\n    return 'OK'\n\n@app.route('/zoom_out', methods=[\"POST\"]) \ndef zoom_out():\n    ptz_list[0].Zoom_out(ptz_list[0].ptz,ptz_list[0].positionrequest)\n    ptz_list[1].Zoom_out(ptz_list[1].ptz,ptz_list[1].positionrequest)\n    Zoom_out(ptz,positionrequest)\n    return 'OK'\n\n@app.route('/home', methods=[\"POST\"])\ndef home():\n    ptz_list[0].move_home(ptz_list[0].ptz,ptz_list[0].homerequest)\n    ptz_list[1].move_home(ptz_list[1].ptz,ptz_list[1].homerequest)\n    move_home(ptz,homerequest)\n    return 'OK'\n\n@app.route('/stop', methods=[\"POST\"])\ndef stop():\n    ptz.Stop({'ProfileToken': positionrequest.ProfileToken})\n    ptz_list[0].ptz.Stop({'ProfileToken': ptz_list[0].positionrequest.ProfileToken})\n    ptz_list[1].ptz.Stop({'ProfileToken': ptz_list[1].positionrequest.ProfileToken})\n    return 'OK'\n\n\ndef main():\n    ip = \"192.168.0.112\"\n    os.system(\"pkill webrtc-streamer\")\n    time.sleep(1)\n\n    ### Run webRTC streamer\n    os.system(\"./webrtc-streamer -H 0.0.0.0:9000 -a rtsp://{}:8554/main  &\".format(ip))  \n    time.sleep(1)\n\n    app.run(host='0.0.0.0', port=5000, debug=False)\n    \n\nif __name__ == \"__main__\":\n    main()","sub_path":"apps/deepstream-test_rtsp-io/main_ptz.py","file_name":"main_ptz.py","file_ext":"py","file_size_in_byte":6153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"158466657","text":"from flask import Blueprint\nfrom core import db\nfrom core.apis import decorators\nfrom core.apis.responses import APIResponse\nfrom core.models.assignments import Assignment\n\nfrom .schema import AssignmentSchema, AssignmentGradeSchema\n\nteacher_assignments_resources = Blueprint('teacher_assignments_resources',\n                                          __name__)\n\n\n@teacher_assignments_resources.route('/assignments',\n                                     methods=['GET'],\n                                     strict_slashes=False)\n@decorators.auth_principal\ndef list_assignments(p):\n    \"\"\"Returns the list all assignments submitted to this teacher\"\"\"\n    submitted_assignments = Assignment.get_assignments_submitted_to_teacher(p.teacher_id)\n    students_assignments_dump = AssignmentSchema().dump(submitted_assignments,\n                                                        many=True)\n    return APIResponse.respond(data=students_assignments_dump)\n\n\n@teacher_assignments_resources.route('/assignments/grade',\n                                     methods=['POST'],\n                                     strict_slashes=False)\n@decorators.accept_payload\n@decorators.auth_principal\ndef grade_assignment(p, incoming_payload):\n    \"\"\"Grade an assignment\"\"\"\n    grade_assignment_payload = AssignmentGradeSchema().load(incoming_payload)\n\n    graded_submitted = Assignment.grade_assignment(\n        _id=grade_assignment_payload.id,\n        grade=grade_assignment_payload.grade,\n        principal=p\n    )\n    db.session.commit()\n    graded_assignment_dump = AssignmentSchema().dump(graded_submitted)\n    return APIResponse.respond(data=graded_assignment_dump)","sub_path":"core/apis/assignments/teacher.py","file_name":"teacher.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"251272097","text":"from flask import current_app\n\nfrom manager_rest.config import instance\nfrom manager_rest.manager_exceptions import NotFoundError\nfrom manager_rest.storage import get_storage_manager\nfrom manager_rest.constants import CLOUDIFY_TENANT_HEADER, ADMIN_ROLE_NAME\n\nfrom .user_handler import unauthorized_user_handler\nfrom .security_models import user_datastore\n\n\nclass TenantAuthorization(object):\n    def authorize(self, user, request):\n        logger = current_app.logger\n\n        logger.debug('Tenant authorization for user {0}'.format(user))\n\n        admin_role = user_datastore.find_role(ADMIN_ROLE_NAME)\n        tenant_name = request.headers.get(\n            CLOUDIFY_TENANT_HEADER,\n            instance.default_tenant_name\n        )\n        logger.debug(\n            'User attempting to connect with tenant `{0}`'.format(tenant_name)\n        )\n        try:\n            tenant = get_storage_manager().get_tenant_by_name(tenant_name)\n        except NotFoundError:\n            raise unauthorized_user_handler(\n                'Provided tenant name unknown: {0}'.format(tenant_name)\n            )\n        if tenant not in user.get_all_tenants() \\\n                and admin_role not in user.roles:\n            raise unauthorized_user_handler(\n                'User {0} is not associated with tenant {1}'.format(\n                    user.username,\n                    tenant_name\n                )\n            )\n\n        current_app.config['tenant_id'] = tenant.id\n\ntenant_authorizer = TenantAuthorization()\n","sub_path":"rest-service/manager_rest/security/tenant_authorization.py","file_name":"tenant_authorization.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"6018705","text":"import csv\nimport math\n################################################################################################\ndef loadCsv(filename):\n\tlines = csv.reader(open(filename,\"rb\"))\n\tdataset = list(lines)\n\tfor i in range(len(dataset)):\n\t\tdataset[i] = [float(x) for x in dataset[i]]\n\treturn dataset\n# Test 1\n# filename = 'pima-indians-diabetes.data.csv'\n# dataset = loadCsv(filename)\n# print('Loaded data file {0} with {1} rows').format(filename,len(dataset))\n\n# Function to split the dataset\nimport random\ndef splitDataset(dataset, splitRatio):\n\ttrainSize = int(len(dataset) * splitRatio)\n\ttrainSet = []\n\tcopy = list(dataset)\n\twhile len(trainSet) < trainSize:\n\t\tindex = random.randrange(len(copy))\n\t\ttrainSet.append(copy.pop(index))\n\treturn [trainSet, copy]\n# dataset = [[1],[2],[3],[4],[5]]\n# splitRatio = 0.67\n# train, test = splitDataset(dataset,splitRatio)\n# print('Split {0} rows into train with {1} and test with {2}').format(len(dataset),train,test)\n\n# Seperate the training dataset instances by class value so we can calculate\n# statistics for each class.\ndef separateByClass(dataset):\n        separated = {}\n        for i in range(len(dataset)):\n                vector = dataset[i]\n                if(vector[-1] not in separated):\n                        separated[vector[-1]] = []\n                separated[vector[-1]].append(vector)\n        return separated\n\n# Testing the separateByClass function\n# dataset = [[1,20,1],[2,21,0],[3,22,1]]\n# separated = separateByClass(dataset)\n# print('Seperated instances: {0}').format(separated)\n\n# Calculating the mean\ndef mean(numbers):\n        return sum(numbers)/float(len(numbers))\n\ndef stdev(numbers):\n        avg = mean(numbers)\n        variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers)-1)\n        return math.sqrt(variance)\n# testing stdev()\n# numbers = [1,2,3,4,5]\n# print(\"Summary of {0}: mean={1}, stdev={2}\").format(numbers,mean(numbers),stdev(numbers))\n\n# Summarize Dataset\ndef summarize(dataset):\n        summaries = [(mean(attribute),stdev(attribute)) for attribute in zip(*dataset)]\n        del summaries[-1]\n        return summaries\n\n# Testing Summarize\n# dataset = [[1,20,0],[2,21,1],[3,22,0]]\n# summary = summarize(dataset)\n# print('Attribute summaries: {0}').format(summary)\n\n\n# Summarizing attributes by class\ndef summarizeByClass(dataset):\n        separated = separateByClass(dataset)\n        summaries = {}\n        for classValue, instances in separated.iteritems():\n                summaries[classValue] = summarize(instances)\n                return summaries\n\n# Testing\n# dataset = [[1,20,1],[2,21,0],[3,22,1],[4,22,0]]\n# summary = summarizeByClass(dataset)\n# print('Summary by class value: {0}').format(summary)\n\n# Make a Prediction\n# Calculating the probability that a given data instance belongs to each class, then\n# Selecting the class with the largest probability as the prediction\ndef calculateProbability(x,mean,stdev):\n        exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))\n        return (1/(stdev*math.sqrt(2*math.pi)))*exponent\n\n# Calculating class probabilities\n# Combine the probabilities of all the attribute values for a data instance and come up with\n# A probability of the entire data instance belonging to the class\ndef calculateClassProbabilities(summaries,inputVector):\n        probabilities={}\n        for classValue,classSummaries in summaries.iteritems():\n                probabilities[classValue] = 1\n                for i in range(len(classSummaries)):\n                        mean, stdev = classSummaries[i]\n                        x = inputVector[i]\n                        probabilities[classValue] *= calculateProbability(x, mean, stdev)\n        return probabilities\n# Make a Prediction\ndef predict(summaries, inputVector):\n        probabilities = calculateClassProbabilities(summaries, inputVector)\n        bestLabel, bestProb = None, -1\n        for classValue, probability in probabilities.iteritems():\n                if bestLabel is None or probability > bestProb:\n                        bestProb = probability\n                        bestLabel = classValue\n        return bestLabel\n\n# Getting predictions\ndef getPredictions(summaries,testSet):\n        predictions = []\n        for i in range(len(testSet)):\n                result = predict(summaries,testSet[i])\n                predictions .append(result)\n        return predictions\n\n# Getting accuracy\ndef getAccuracy(testSet,predictions):\n        correct = 0\n        for x in range(len(testSet)):\n                if testSet[x][-1] == predictions[x]:\n                        correct += 1\n        return (correct/float(len(testSet)))*100.0\n\n\n# Main function,  putting it all together\ndef main():\n        filename = 'pima-indians-diabetes.data.csv'\n        splitRatio = 0.67\n        dataset = loadCsv(filename)\n        trainingSet, testSet = splitDataset(dataset, splitRatio)\n        print('Split {0} rows into train = {1} and test = {2} rows').format(len(dataset), len(trainingSet), len(testSet))\n        # pprepare model\n        summaries = summarizeByClass(trainingSet)\n        # test model\n        predictions = getPredictions(summaries, testSet)\n        accuracy = getAccuracy(testSet,predictions)\n        print('Accuracy: {0}%').format(accuracy)\nmain()\n","sub_path":"mldir/naive_bayes.py","file_name":"naive_bayes.py","file_ext":"py","file_size_in_byte":5253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"385742387","text":"def sumOfPrimes(n):\n    primes = [2]\n    for i in range(3,n):\n        flag = True\n        j = 2\n        while j*j <= i:\n            if i % j == 0:\n                flag = False\n                break\n            j += 1\n        if flag:\n            primes.append(i)\n            i += 1\n    return sum(primes)\n\nprint(sumOfPrimes(2000000))\n","sub_path":"src/problem10.py","file_name":"problem10.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"484573323","text":"s = input()\n\nsize = len(s)\nif size % 2 != 0:\n    size += 1\n\nsize = size // 2\n\nans = 0\nfor i in range(size):\n    ans += 0 if s[i] == s[-(i+1)] else 1\n\nprint(ans)\n","sub_path":"abc147/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"599475844","text":"\n\nclass Solution:\n    def findAnagrams(self, s, p):\n        res = []\n        p_dict = {}\n        for char in p:\n            p_dict[char] = p_dict.get(char, 0) + 1\n        \n        len_s = len(s)\n        len_p = len(p)\n        left = 0\n        right = len_p\n\n        tmp_dict ={}\n\n        print(type(left))\n        print(type(right))\n        print(s[left:right])\n        for char in s[left:right]:\n            tmp_dict[char] = tmp_dict.get(char,0) + 1\n        if tmp_dict == p_dict:\n            res.append(left)\n            print('append1')\n        \n        tmp_dict[s[left]] -= 1\n        left  += 1\n        right += 1\n\n        while right <len_s:\n            tmp_dict[s[right]] +=1\n            print(tmp_dict)\n\n            if tmp_dict == p_dict:\n                res.append(left)\n                print('append2')\n            \n            tmp_dict[s[left]] -= 1\n            left += 1\n            right += 1\n            \n\n\n        return res\n\n\n\n\nif __name__==\"__main__\":\n\n    s_str = 'abab'\n    p_str = 'ab'\n\n    s = Solution()\n    res = s.findAnagrams(s_str,p_str)\n\n    print(res)\n\n\n","sub_path":"438-找到字符串中所有字母异位词/438.py","file_name":"438.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"338769679","text":"import threading\nimport time\nimport random\nimport sys\n\n\n# Runs as a thread in the blockchain server.\nclass PeriodicCommit(threading.Thread):\n    def __init__(self, bc):\n        super(PeriodicCommit, self).__init__()\n        self.blockchain = bc\n\n    # Thread continually tries to guess a hash for the block following a rule.\n    def run(self):\n        while True:\n            nonce = random.randint(0, sys.maxsize)\n            if self.blockchain.commit(nonce):\n                print(self.blockchain)\n            time.sleep(0.05)","sub_path":"PeriodicCommit.py","file_name":"PeriodicCommit.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"493360399","text":"import cv2\nimport numpy as np\nimport os\n\ndef extractFrame(pathIn, pathOut):\n    os.mkdir(pathOut)\n\n    captures = cv2.VideoCapture(pathIn)\n    count = 0\n\n    while(captures.isOpened()):\n        ret, frame = captures.read()\n\n        if ret == True:\n            print('Read %d frame : ' % count, ret)\n            cv2.imwrite(os.path.join(pathOut, \"frame{:d}.jpg\".format(count)), frame)\n            count += 1\n        else:\n            break\n    captures.release()\n    cv2.destroyAllWindows()\n\ndef main():\n    extractFrame('video.avi', 'data')\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Step-4/extractFrame.py","file_name":"extractFrame.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"447835029","text":"import _thread as td\nimport RandName\nimport os , datetime , hashlib\nclass Client:\n    def __init__(self,con,add,request):\n        # Request True for Upload, false for Download\n        # Connection\n        self.Cn = con\n        # Connected address\n        self.Address = add\n        self.Log(\"\")\n        # buffer for recv method\n        self.BUFF = 2**2\n        self.Prog = 0\n        self.Date = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")\n        self.Log(self.Address ,\"connected at\" ,self.Date,\" Request:\",\"upload\" if request else \"Download\")\n        if request:\n            #Read File Info\n            self.RFI()\n        else:\n            #Send File Info\n            td.start_new_thread(self.SFI,())\n\n    # Send File Info\n    def SFI(self):\n        self.BUFF = 2**22\n        fileNames = os.listdir(\"uploads/\")\n        fileNames.sort() \n        msg = \"\"\n        for i in range(len(fileNames)):\n            msg += fileNames[i][:-17]+\"\\n\"\n        if len(fileNames) == 0:\n            msg = \"No file\"\n        self.Cn.send(msg.encode(\"utf-8\"))\n        print(\"names sended.\")\n        try:\n            fileIndex = self.Cn.recv(64)\n        except ConnectionResetError:\n            self.Cn.close()\n            return\n        while len(fileIndex) == 0:\n            try:\n                fileIndex = self.Cn.recv(64)\n            except ConnectionResetError:\n                self.Cn.close()\n                return\n\n        \n        hata = False\n        try:\n            fileIndex = int(fileIndex.decode(\"utf-8\"))\n        except ValueError:\n            hata = True\n\n        if fileIndex < 0 or fileIndex >= len(fileNames):\n            hata = True\n        if hata:\n            self.Log(\"File Index Error\")\n            self.Cn.send(\"0\".encode(\"utf-8\"))\n            self.Cn.close()\n            return\n        self.Cn.send(\"1\".encode(\"utf-8\"))\n        path = \"uploads/\"+fileNames[fileIndex]\n        self.Path=path\n        fileSize = (os.stat(path).st_size)\n        MD5 = self.md5()\n        msg = (str(fileSize) +\"\\n\"+ fileNames[fileIndex] +\"\\n\"+MD5+\"\\n\"+str(self.BUFF))\n        self.Cn.send(msg.encode(\"utf-8\"))\n\n        prog = self.Cn.recv(64)\n\n        if prog.decode(\"utf-8\") != \"1\":\n            self.Log(\"File ınformation couldn't send.\")\n            self.Cn.send(\"Error\".encode(\"utf-8\"))\n            self.Cn.close()\n            return\n        td.start_new_thread(self.StartDownload,(path,fileSize,))\n    def StartDownload(self,path,size):\n        fl = open(path,\"rb\")\n        size = size//self.BUFF\n        self.Log(\"Progress started.\")\n        for i in range(size + 1):\n            data = fl.read(self.BUFF)\n            self.Cn.send(data)\n        resp = self.Cn.recv(64)\n        print(resp.decode(\"utf-8\"))\n        self.Log(\"Progress Completed.\")\n        \n    # Read File Info\n    def RFI(self):\n        try:\n            info = self.Cn.recv(1024)\n        except ConnectionResetError:\n            self.Log(\"Connection closed by :\",self.Address)\n            return\n        info = info.decode(\"utf-8\").split('\\n')\n        hata = False\n        try:\n            #File Size\n            self.Size = int(info[0])\n            self.BUFF = int(info[3])\n        except ValueError:\n            hata = True\n        except IndexError:\n            hata = True\n\n        if hata:\n            self.Log(\"Index or Value error\")\n            self.Cn.send(\"Error\".encode(\"utf-8\"))\n            self.Cn.close()\n            return\n        #File Name\n        self.Fname = info[1]\n\n        self.MD5 = info[2]\n\n        self.Cn.send(\"Information received.\".encode(\"utf-8\"))\n        td.start_new_thread(self.StartUpload,())\n    # Starting upload\n    def StartUpload(self):\n        # File path for saving\n        name = self.Fname.replace(\"/\",\"\")+RandName.RandomName.Create(17)\n        self.Path = \"uploads/\"+ name\n        self.Log(self.Address , \" uploading :\",name,\"\\tMD5:\",self.MD5)\n        buffCount = self.Size // self.BUFF\n        fl = open(self.Path,\"wb\")\n        i = 0\n        while fl.tell() < self.Size:\n            try:\n                data = self.Cn.recv(self.BUFF)\n            except ConnectionResetError:\n                self.Log(\"Connection closed by :\",self.Address)\n                fl.close()\n                return\n            fl.write(data)\n        fl.close()\n        md5p = self.md5()\n        self.Log(self.Address , \" uploaded :\",name,\"\\tMD5:\",md5p)\n        self.Cn.send(md5p.encode(\"utf-8\"))\n\n    def Progress(self):\n        while self.Prog < 100:\n            print(self.Address,\": \",format(self.Prog,\".2f\"),\"%\")\n        print(self.Address,\": \",format(self.Prog,\".2f\"),\"%\")\n        print(\"Completed.\")\n\n    def Log(self,*data):\n        msg = \"\"\n        for i in range(len(data)):\n            msg += str(data[i])\n            msg += \" \"\n        msg += \"\\n\"\n        if os.path.exists(\"logs/logs.txt\"):\n            fl = open(\"logs/logs.txt\",\"a\")\n            fl.write(msg)\n        else:\n            fl = open(\"logs/logs.txt\",\"w\")\n            fl.write(msg)\n        fl.close()\n\n    def md5(self):\n        hash_md5 = hashlib.md5()\n        with open(self.Path, \"rb\") as f:\n            for chunk in iter(lambda: f.read(4096), b\"\"):\n                hash_md5.update(chunk)\n        return hash_md5.hexdigest()\n\n","sub_path":"ServerSide/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":5184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"585047728","text":"#! usr/bin/python\n# -*- coding:utf-8 -*-\n# author : ouyi\n# create_time : 2021-01-09\nimport os\nimport signal\nimport subprocess\nimport time\nfrom functools import wraps\n\nimport allure\nfrom appium.webdriver.webdriver import WebDriver\n\nfrom app_frame.my_configs import config\nfrom app_frame.my_utils.get_time import get_now_time\n\n\ndef black_list_find(func):\n    \"\"\"\n    自定义装饰器：给元素定位方法增加黑名单功能\n    :param func:\n    :return:\n    \"\"\"\n    @wraps(func)\n    def wrapper(*args,**kwargs):\n        self = args[0]\n        try:\n            res = func(*args,**kwargs)\n        except Exception as e:\n            # 添加黑名单功能，当元素查找失败时。遍历黑名单\n            self._driver.implicitly_wait(1)\n            config.case_log.info(f'查找元素 {args[3]} 失败，开始遍历黑名单')\n            for black in self.black_list:\n                black_eles = self._find_elments(*black)\n                if len(black_eles) > 0:\n                    black_eles[0].click()\n                    config.case_log.info(f'查找黑名单元素 {black[2]} 成功')\n                    return func(*args,**kwargs)\n            config.case_log.error(f'遍历黑名单元素结束，未找到黑名单元素')\n            config.case_log.error(f'find element {args[3]} failed (method={args[1]},message={args[2]})')\n            raise e\n        return res\n    return wrapper\n\n\ndef black_list_wait(func):\n    \"\"\"\n    自定义装饰器：给显示等待方法增加黑名单功能\n    :param func:\n    :return:\n    \"\"\"\n    @wraps(func)\n    def wrapper(*args,**kwargs):\n        self = args[0]\n        try:\n            res = func(*args,**kwargs)\n        except Exception as e:\n            # 添加黑名单功能，当元素查找失败时。遍历黑名单\n            self._driver.implicitly_wait(1)\n            config.case_log.info(f'查找元素 {args[3]} 失败，开始遍历黑名单')\n            for black in self.black_list:\n                black_eles = self._find_elments(*black)\n                if len(black_eles) > 0:\n                    black_eles[0].click()\n                    config.case_log.info(f'查找黑名单元素 {black[2]} 成功')\n                    return func(*args,**kwargs)\n            config.case_log.error(f'遍历黑名单元素结束，未找到黑名单元素')\n            config.case_log.error(f'wait element {args[3]} to click failed (method={args[1]},message={args[2]})')\n            raise e\n        return res\n\n    return wrapper\n\n\ndef get_video(func):\n    @wraps(func)\n    def wrapper(*args,**kwargs):\n        path = os.path.join(config.BASE_DIR, 'video')\n        video_name = f'{func.__name__}-{get_now_time()}.mp4'\n        cmd = f'scrcpy --record {video_name}'\n        p = subprocess.Popen(cmd, shell=True, cwd=path)  # 注意，这里不能使用os.system来执行，因为os.system不能手动结束命令\n        time.sleep(2)\n        s = func(*args,**kwargs)\n        os.kill(p.pid, signal.CTRL_C_EVENT)  # 使用CTRL_C结束进程\n        time.sleep(2)\n        allure.attach.file(source=os.path.join(path,video_name),name='录屏',attachment_type=allure.attachment_type.MP4)\n        return s\n\n    return wrapper\n","sub_path":"app_frame/common_decorator.py","file_name":"common_decorator.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"6827951","text":"import re\n\nclass PigLatin():\n  def __init__(self, english):\n    self.english = english\n\n  def convert(self):\n    def _1(word):\n      lword = word.lower()\n      match = re.search(r'^([^aeiou]*)(.*)$', lword)\n      if not match:\n        raise Exception('error')\n      ay = 'way' if len(match.group(1)) == 0 else 'ay'\n      pword = match.group(2) + match.group(1) + ay\n      if re.search(r'^[A-Z]', word):\n        return pword[0].upper() + pword[1:]\n      else:\n        return pword\n    pig_latin_words = map(_1, self.english.split(' '))\n    return ' '.join(pig_latin_words)\n","sub_path":"lib/Pig/Latin.py3","file_name":"Latin.py3","file_ext":"py3","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"379641064","text":"# -*- coding: utf-8 -*-\nimport json\nimport requests\n\nurl = 'https://www.googleapis.com/oauth2/v3/tokeninfo'\n\n\ndef get_user_profile(access_token):\n    response = requests.get(url, {'id_token': access_token})\n    json_data = json.loads(response.text)\n    third_id = json_data['sub']\n    username = json_data['name']\n    avatar_url = json_data['picture']\n    return third_id, username, avatar_url","sub_path":"luckycommon/third/google_login.py","file_name":"google_login.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"140904636","text":"# -*- coding: utf-8 -*-\n'''setup for graphalchemy'''\n\nfrom __future__ import unicode_literals\n\nimport os\nimport sys\n\ntry:\n    from setuptools import setup\nexcept ImportError:\n    from distutils.core import setup\n\ninstall_requires = [\n        'spine', 'nanotime', 'stuf>=0.8.1', 'MarkupSafe', 'six>=1.1.0'\n        'zope.interface>=3.8.0',\n    ]\nif sys.version_info[0] == 2 and sys.version_info[1] < 7:\n    install_requires.extend(['importlib', 'ordereddict', 'unittest2'])\n\nsetup(\n    name='graphalchemy',\n    version='0.1.0',\n    url='http://bitbucket.com/lcrees/graphalchemy',\n    description='a graph database frontend',\n    long_description=open(os.path.join(os.getcwd(), 'README.rst'), 'r').read(),\n    author='L. C. Rees',\n    license='BSD',\n    author_email='lcrees@gmail.com',\n    packages=['graphalchemy', 'graphalchemy.tests'],\n    classifiers=[\n        'Development Status :: 3 - Alpha',\n        'License :: OSI Approved :: BSD License',\n        'Natural Language :: English',\n        'Operating System :: OS Independent',\n        'Programming Language :: Python :: 2.7',\n        'Programming Language :: Python :: Implementation :: CPython',\n        'Programming Language :: Python',\n        'Topic :: Database :: Front-Ends',\n        'Topic :: Database',\n    ],\n    requirements=install_requires,\n    zip_safe=False,\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"198402320","text":"from urllib.parse import quote_plus\nfrom pymongo import MongoClient, DESCENDING, errors, ASCENDING\n\nfrom app.libs.db.db import Db\n\n\nclass MongoDB(Db):\n    client = None\n    db_table = None\n\n    def __init__(self):\n        Db.__init__(self)\n        try:\n            client = MongoClient(host=\"mongodb://%s:%s@%s\" % (quote_plus(self.USER), quote_plus(self.PASSWORD), self.HOST))\n        except Exception as ex:\n            client = MongoClient(self.HOST, self.PORT)\n            # self.client.authenticate(name=self.USER, password=self.PASSWORD)\n        self.client = client[self.DB]\n\n    def find_one(self, where=None):\n        if self.db_table is not None and self.db_table != '':\n            return self.client[self.db_table].find_one(where)\n\n        return None\n\n    def find(self, where=None, page=0, limit=100, sort_field=\"_id\"):\n        if self.db_table is not None and self.db_table != '':\n            skip = page * limit\n            return self.client[self.db_table].find(where).skip(skip).limit(limit).sort(sort_field, ASCENDING)\n\n        return []\n\n    def insert(self, data):\n        raise NotImplementedError\n\n    def update(self, where, data):\n        raise NotImplementedError\n\n    def delete(self, where=None):\n        if self.db_table is not None and self.db_table != '':\n            return self.client[self.db_table].delete_many(where)\n\n        return None\n\n    def upsert(self, where, data):\n        try:\n            if self.db_table is not None and self.db_table != '':\n                result = self.client[self.db_table].find_and_modify(\n                    query=where,\n                    update={\n                        \"$set\": data\n                    },\n                    upsert=True,\n                    new=True,\n                    full_response=True\n                )\n                return result['lastErrorObject']['updatedExisting'], result['value']\n            return None\n        except BaseException as ex:\n            raise ex\n\n    def select_advanced(self, sql, *args):\n        raise NotImplementedError\n\n    def count(self, where=None):\n        if self.db_table is not None and self.db_table != '':\n            return self.client[self.db_table].count(where)\n\n        return 0\n\n    def aggregate(self, aggregation):\n        if self.db_table is not None and self.db_table != '':\n            return self.client[self.db_table].aggregate(aggregation)\n\n        return 0\n\n    def get_next_sequence(self, name, is_update=False):\n        collection = 'sys_counters'\n        if is_update:\n            result = self.client[collection].find_and_modify(\n                query={\"_id\": name},\n                update={\n                    \"$inc\": {\"seq\": 1}\n                },\n                new=True,\n            )\n        else:\n            result = self.client[collection].find_one({\"_id\": name})\n\n        if result is None:\n            self.client[collection].insert_one({\n                '_id': name,\n                'seq': 0\n            })\n            return 1\n        if is_update:\n            return result['seq']\n        else:\n            return result['seq'] + 1\n","sub_path":"app/repositories/db/mongo_db.py","file_name":"mongo_db.py","file_ext":"py","file_size_in_byte":3096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"250980801","text":"import os\nimport logging\n\nimport numpy as np\nfrom imageio import imread\nimport matplotlib\nfrom matplotlib import pyplot as plt\n\nimport morphsnakes as ms\n\nclass morphSnake:\n    def __init__(self, maskPred, center, dia):\n        self.maskPred = maskPred\n        self.center = center\n        self.dia = dia\n\n    def visual_callback_2d(background, fig=None):\n        \"\"\"\n        Returns a callback than can be passed as the argument `iter_callback`\n        of `morphological_geodesic_active_contour` and\n        `morphological_chan_vese` for visualizing the evolution\n        of the levelsets. Only works for 2D images.\n        \n        Parameters\n        ----------\n        background : (M, N) array\n            Image to be plotted as the background of the visual evolution.\n        fig : matplotlib.figure.Figure\n            Figure where results will be drawn. If not given, a new figure\n            will be created.\n        \n        Returns\n        -------\n        callback : Python function\n            A function that receives a levelset and updates the current plot\n            accordingly. This can be passed as the `iter_callback` argument of\n            `morphological_geodesic_active_contour` and\n            `morphological_chan_vese`.\n        \n        \"\"\"\n        \n        # Prepare the visual environment.\n        if fig is None:\n            fig = plt.figure()\n        fig.clf()\n        ax1 = fig.add_subplot(1, 2, 1)\n        ax1.imshow(background, cmap=plt.cm.gray)\n\n        ax2 = fig.add_subplot(1, 2, 2)\n        ax_u = ax2.imshow(np.zeros_like(background), vmin=0, vmax=1)\n        plt.pause(0.001)\n\n        def callback(levelset):\n            \n            if ax1.collections:\n                del ax1.collections[0]\n            ax1.contour(levelset, [0.5], colors='r')\n            ax_u.set_data(levelset)\n            fig.canvas.draw()\n            plt.pause(0.001)\n\n        return callback\n\n    def rgb2gray(img):\n        \"\"\"Convert a RGB image to gray scale.\"\"\"\n        return 0.2989 * img[..., 0] + 0.587 * img[..., 1] + 0.114 * img[..., 2]\n\n    def example_lakes():\n        logging.info('Running: example_lakes (MorphACWE)...')\n        \n        # Load the image.\n        img = self.maskPred\n        \n        # MorphACWE does not need g(I)\n        \n        # Initialization of the level-set.\n        init_ls = ms.circle_level_set(img.shape, (self.center[0], self.center[1]), (self.dia)/4)\n        \n        # Callback for visual plotting\n        callback = visual_callback_2d(img)\n\n        # Morphological Chan-Vese (or ACWE)\n        ms.morphological_chan_vese(img, iterations=200,\n                                   init_level_set=init_ls,\n                                   smoothing=3, lambda1=1, lambda2=1,\n                                   iter_callback=callback)\n\n\nif __name__ == '__USexp__':\n\tlogging.basicConfig(level=logging.DEBUG)\n\texample_lakes()\n\n\t# Uncomment the following line to see a 3D example\n\t# This is skipped by default since mplot3d is VERY slow plotting 3d meshes\n\t# example_confocal3d()\n\n\tlogging.info(\"Done.\")\n\tplt.show()\n","sub_path":"codes/USexp.py","file_name":"USexp.py","file_ext":"py","file_size_in_byte":3058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"513154153","text":"from django.urls import path\nfrom .views import home, delete_task, create_task, update_task, task_list\n\nurlpatterns = [\n    path('', home, name=\"home\"),\n    path('tasks/', task_list, name=\"task_list\"),\n    path('tasks/create/', create_task, name=\"create_task\"),\n    path('tasks/<int:id>/update/', update_task, name=\"update_task\"),\n    path('tasks/<int:id>/delete/', delete_task, name=\"delete_task\"),\n    \n]\n","sub_path":"core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"143841031","text":"#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements.  See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License.  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\nfrom __future__ import absolute_import\n\nimport unittest\n\nfrom mock import patch\n\nfrom apache_beam.examples.snippets.util import *\n\n\nclass UtilTest(unittest.TestCase):\n  def test_parse_example_empty(self):\n    # python path/to/snippets.py\n    argv = []\n    with self.assertRaises(SystemExit):\n      self.assertEqual(parse_example(argv), 'example()')\n\n  def test_parse_example_no_arguments(self):\n    # python path/to/snippets.py example\n    argv = ['example']\n    self.assertEqual(parse_example(argv), 'example()')\n\n  def test_parse_example_one_argument(self):\n    # python path/to/snippets.py example A\n    argv = ['example', 'A']\n    self.assertEqual(parse_example(argv), \"example('A')\")\n\n  def test_parse_example_multiple_arguments(self):\n    # python path/to/snippets.py example A B \"C's\"\n    argv = ['example', 'A', 'B', \"C's\"]\n    self.assertEqual(parse_example(argv), \"example('A', 'B', \\\"C's\\\")\")\n\n  @patch('subprocess.call', lambda cmd: None)\n  def test_run_shell_commands(self):\n    commands = [\n        '  # this is a comment  ',\n        '  !  echo   this   is   a   shell   command  ',\n        '  !echo {variable}  ',\n        '  echo \"quoted arguments work\"  # trailing comment  ',\n    ]\n    actual = list(run_shell_commands(commands, variable='hello world'))\n    expected = [\n        ['echo', 'this', 'is', 'a', 'shell', 'command'],\n        ['echo', 'hello', 'world'],\n        ['echo', 'quoted arguments work'],\n    ]\n    self.assertEqual(actual, expected)\n\n\nif __name__ == '__main__':\n  unittest.main()\n","sub_path":"sdks/python/apache_beam/examples/snippets/util_test.py","file_name":"util_test.py","file_ext":"py","file_size_in_byte":2301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"300635814","text":"# -*- coding:utf-8 -*-\n\"\"\"\nCopyright (c) 2017--2018 URUNDP All Rights Reserved.\n----------------------------------------------------------\nFile Name : registered.py\nDescription : 资源注册\nAuthor : Toby\nE-Mail : 996298929@qq.com\nChange Activity: 2018-7-3\n----------------------------------------------------------\n\"\"\"\n\nfrom flask import Flask\nfrom flask_restful import Api\n\nfrom entrance import Cfom, PortMon, AutoDeploy, MonAlarm\n\n\napp = Flask(__name__)\napi = Api(app)\n\n\n# 定义资源\nRESOURCE = (\n    (Cfom, 'cfom'),\n    (PortMon, 'portmon'),\n    (AutoDeploy, 'autodeploy'),\n    (MonAlarm, 'monalarm')\n)\n\n\n# 开始注册\nfor resource, url_prefix in RESOURCE:\n    url_unite = '/urundp/cloudapi/v1/%s' % (url_prefix)\n    api.add_resource(resource, url_unite)","sub_path":"pcmp/registered.py","file_name":"registered.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"253555826","text":"from flask import Flask, render_template\nimport os\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef index():\n    return \"Hello World!\"\n\n\n@app.route(\"/lista\")\ndef pagina_1():\n    lista_dir = os.listdir(\"static/files/\")\n    return render_template(\"lista.html\", lista_dir=lista_dir)\n\n\nif __name__ == \"__main__\":\n    app.run()\n","sub_path":"static/files/04_jinja.py","file_name":"04_jinja.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"541030742","text":"import pygame\nimport time\nfrom GamePyscard import getCard\npygame.init()\npygame.display.set_caption(\"Naexys\")\nscreen = pygame.display.set_mode((1540, 800))\n\n### G L O B A L ###\n\n#Ici on créer les variables gloabales\nfinish = False\nfps = 60\nx = 50\ny = 50\nsize = (400,400)\nvie = 0\ngravity = 10\nnotonground = False\nxi = x\nplay = 1\nwall = 1\nwall2 = 1\ncard = getCard()\n\n\n\nisjump = False\n# Force (v) up and mass m.\nv = 8\nm = 2\n\n\n### C O L O R ###\ncolor = (0,0,0)\nblack = (0,0,0)\nwhite = (255,255,255)\nred = (255,0,0)\nblue = (0,0,255)\ngreen = (0,255,0)\nyellow = (255,255,0)\npurple = (255,0,255)\ncyan = (0,255,255)\npink = (255,200,200)\n\n###\n\n### I N I T . P G ###\n\nscreen = pygame.display.set_mode(size)\n\n\n###\n\n### I M A G E ###\n\n###\n\n### C L A S S ###\n\n\n\n\nclass Player:\n    global vie\n    vie = 3\n\n    def __init__(self, c_couleur):\n        self.couleur = c_couleur\n###\n\n### F U N C T I O N ###\ndef ChooseColor():\n    global color\n    if (text == \"blue\") or ( card == \"3B8F8001804F0CA000000306030001000000006A\"):\n        color = blue\n    if (text == \"red\"):\n        color = red\n    if (text == \"green\"):\n        color = green\n    if (text == \"yellow\"):\n        color = yellow\n    if (text == \"cyan\"):\n        color = cyan\n    if (text == \"purple\"):\n        color = purple\n\n\ndef NFC():\n    verif = False\n    while verif == False:\n        if (card == 'no card inserted'):\n            pass\n        elif (card == \"3B8F8001804F0CA000000306030001000000006A\"):\n            color = cyan\n            verif = True\n\n\ndef text():\n    global text\n    input_box = pygame.Rect(100, 100, 140, 32)\n    font = pygame.font.Font(None, 32)\n    color_inactive = (255,255,255)\n    color_active =(0,0,255)\n    color_false = (255,0,0)\n    color_true = (0,255,0)\n    color = color_inactive\n    active = False\n    text = ''\n    done = False\n    p = False\n\n    while not done:\n\n        ##print(card)\n        if (p == True):\n\n\n\n\n\n            time.sleep(0.5)\n\n\n\n\n            done = True\n        for event in pygame.event.get():\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                # If the user clicked on the input_box rect.\n                if input_box.collidepoint(event.pos):\n                    # Toggle the active variable.\n                    active = not active\n                else:\n                    active = False\n                # Change the current color of the input box.\n                color = color_active if active else color_inactive\n            if event.type == pygame.KEYDOWN:\n                if active:\n                    if event.key == pygame.K_RETURN:\n                        if (text ==\"red\" or text ==\"blue\" or text ==\"yellow\" or text == \"green\" or text ==\"purple\" or text == \"cyan\"):\n                            color = color_true\n                            print(text)\n                            p = True\n                        else :\n                            text = ''\n                            color = color_false\n                            done = False\n                    elif event.key == pygame.K_BACKSPACE:\n                        text = text[:-1]\n                    else:\n                        text += event.unicode\n\n        screen.fill(black)\n        # Render the current text.\n        txt_surface = font.render(text, True, color)\n        # Resize the box if the text is too long.\n        width = max(200, txt_surface.get_width()+10)\n        input_box.w = width\n        # Blit the text.\n        screen.blit(txt_surface, (input_box.x+5, input_box.y+5))\n        # Blit the input_box rect.\n        pygame.draw.rect(screen, color, input_box, 2)\n        pygame.display.flip()\n\n\ndef jump():\n    global x,y,play,wall\n    time.sleep((0.01))\n    Draw()\n    pygame.display.flip()\n    y = y-5\n    if evt.type == pygame.key.get_pressed() and evt.key == pygame.K_RIGHT:\n        x = x + 2\n    if evt.type == pygame.key.get_pressed() and evt.key == pygame.K_LEFT:\n        x = x - 2\n\ndef Draw():\n    global play\n    global wall\n    global wall2\n    screen.fill(black)\n    play = pygame.draw.rect(screen, color, (x, y, 50, 50))\n    wall = pygame.draw.rect(screen, white, (xi-100, 300, 500, 50))\n    wall2 = pygame.draw.rect(screen, pink, (xi + 100, 200, 50, 100))\n\n###\n\n### F P S ###\n\nclock = pygame.time.Clock()\ndelta_ms = clock.tick(fps)\n\n###\n\n### I N I T ###\ntext()\npygame.key.set_repeat(1,20)\nh1 = Player(text)\nChooseColor()\n##NFC()\nscreen.fill(black)\n###\n\n### C O D E ###\nwhile not finish:\n    for evt in pygame.event.get():\n        if evt.type == pygame.QUIT:\n            finish = True\n        if evt.type == pygame.KEYDOWN and evt.key == pygame.K_BACKSPACE:\n            finish = True\n        if evt.type == pygame.KEYDOWN and evt.key == pygame.K_SPACE and notonground == True :\n            while  y > 0:\n                jump()\n        if evt.type == pygame.KEYDOWN and evt.key == pygame.K_RIGHT :\n            x = x+2\n        if evt.type == pygame.KEYDOWN and evt.key == pygame.K_LEFT :\n            x = x-2\n    Draw()\n    if not play.colliderect(wall):\n\n        y = (y + 2)\n        time.sleep(0.01)\n        notonground = False\n    else :\n        notonground = True\n\n    if play.colliderect(wall2):\n        x = 50\n        y = 50\n\n\n\n\n\n\n    pygame.display.flip()\n###","sub_path":"PyscardGame/Game.py","file_name":"Game.py","file_ext":"py","file_size_in_byte":5184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"399976992","text":"\"\"\"\n\tFunctions to process tweet meta data\n\texpand URLs, add counts of urls/hashtags/mentions, and list of hashtags/mentions.\n\n\"\"\"\n\nimport sys\nfrom datetime import datetime, timedelta\nimport time\nfrom email.utils import parsedate_tz\nimport simplejson\nimport re\nimport hashlib\nimport string\nfrom collections import defaultdict\nimport traceback\n\n\n# Parse Twitter created_at datestring and turn it into\ndef to_datetime(datestring):\n    time_tuple = parsedate_tz(datestring.strip())\n    dt = datetime(*time_tuple[:6])\n    return dt\n\ndef ck_coded_url(urlstring):\n\tcur.execute(\"\"\"select code, hashtag from tweets_sample_test where url = %s and hashtag in ('ows','occupyoakland','occupyseattle') and date(created_at) between '2011-10-19' and '2012-04-30' and spike is null\"\"\", urlstring.encode(\"utf-8\"))\n\tresult = cur.fetchone()\n\tif result:\n\t\treturn result\n\telse:\n\t\treturn None\n\ndef parse_url(url_entity):\n    url_code = None\n    if 'long-url' in url_entity and url_entity['long-url'] is not None:\n        url_code = ck_coded_url(url_entity['long-url'])\n    elif \"expanded_url\" in url_entity and url_entity['expanded_url'] is not None:\n        url_code = ck_coded_url(url_entity['expanded_url'])\n    elif \"url\" in url:\n        url_code = ck_coded_url(url_entity['url'])\n    if url_code:\n        url_entity['code'] = url_code[0]\n        url_entity['hashtag'] = url_code[1]\n        return url_entity['code']\n    \ndef process_tweet(line, track_list, expand_url=False):\n\n    tweet = simplejson.loads(line)\n\n    track_set = set(track_list)\n    # List of punct to remove from string for track keyword matching\n    punct = re.escape('!\"$%&\\'()*+,-./:;<=>?@[\\\\]^`{|}~')\n\n\n    if \"entities\" in tweet and \"created_at\" in tweet and \"created_at\" in tweet['user']:\n\n        tweet['hashtags'] = []\n        # hashtag_num = 0\n        if \"hashtags\" in tweet['entities']:         \n            # hashtag_num = len(tweet['entities']['hashtags'])\n            for index in range(len(tweet['entities']['hashtags'])):\n                tweet['hashtags'].append(tweet['entities']['hashtags'][index]['text'].lower())\n            \n        tweet['codes'] = []\n        urls_num = 0\n        if \"urls\" in tweet['entities']:\n            urls_num = len(tweet['entities']['urls'])\n            if expand_url:\n                for url in tweet['entities']['urls']:\n                    tweet['codes'] = tweet['codes'].append(parse_url(url))              \n            # coded_url_num = len(tweet['codes'])\n\n        tweet['mentions'] = []\n        # mentions_num = 0\n        if \"user_mentions\" in tweet['entities']:         \n            # mentions_num = len(tweet['entities']['user_mentions'])\n            for index in range(len(tweet['entities']['user_mentions'])):\n                if \"screen_name\" in tweet['entities']['user_mentions'][index]:\n                    tweet['mentions'].append(tweet['entities']['user_mentions'][index]['screen_name'].lower())\n                    \n        tweet['track_kw'] = {}\n        # Check to see if we have a retweet\n        if \"retweeted_status\" in tweet and \"entities\" in tweet['retweeted_status']:\n            # In case of the retweet losing part of text or entities contained in its original tweet\n            rt_hashtags = []\n            rt_mentions = []\n            \n            for index in range(len(tweet['retweeted_status']['entities']['hashtags'])):\n                rt_hashtags.append(tweet['retweeted_status']['entities']['hashtags'][index]['text'].lower())\n            for index in range(len(tweet['retweeted_status']['entities']['user_mentions'])):\n                rt_mentions.append(tweet['retweeted_status']['entities']['user_mentions'][index]['screen_name'].lower())\n                           \n            tweet['hashtags'] = list(set(tweet['hashtags']).union(set(rt_hashtags)))\n            tweet['mentions'] = list(set(tweet['mentions']).union(set(rt_mentions)))\n\n            if urls_num == 0:\n                urls_num = len(tweet['retweeted_status']['entities']['urls'])\n            else:\n                urls_num = len(set([url['url'] for url in tweet['entities']['urls']]).union(set([url['url'] for url in tweet['retweeted_status']['entities']['urls']])))\n            if expand_url:\n                for url in tweet['retweeted_status']['entities']['urls']:\n                    tweet['codes'] = tweet['codes'].append(parse_url(url))\n                tweet['codes'] = list(set(tweet['codes']))\n            \n            # Track rule matches in text\n            tweet_text = re.sub('[%s]' % punct, ' ', tweet['text'])\n            rt_text = re.sub('[%s]' % punct, ' ', tweet['retweeted_status']['text'])\n            tweet_text = tweet_text.lower().split()\n            rt_text = rt_text.lower().split()\n            union_text = set(rt_text).union(set(tweet_text))\n            tweet['track_kw']['text'] = list(union_text.intersection(track_set))\n        else:\n            # Track rule matches in text      \n            tweet_text = re.sub('[%s]' % punct, ' ', tweet['text'])\n            tweet_text = tweet_text.lower().split()\n            tweet['track_kw']['text'] = list(set(tweet_text).intersection(track_set))\n\n        # Track rule matches in hashtags and mentions\n        parsed_hashtag_list = []\n        for hashtag in tweet['hashtags']:\n            parsed_hashtag_list += hashtag.split('_')\n        tweet['track_kw']['hashtags'] = list(set(parsed_hashtag_list).intersection(track_set))\n        parsed_mention_list = []\n        for mention in tweet['mentions']:\n            parsed_mention_list += mention.split('_')\n        tweet['track_kw']['mentions'] = list(set(parsed_mention_list).intersection(track_set))\n     \n        tweet['hashtags'].sort()\n        tweet['codes'].sort()\n        tweet['mentions'].sort()\n\n        tweet['counts'] = {\n            'urls': urls_num,\n            'hashtags': len(tweet['hashtags']),\n            'user_mentions': len(tweet['mentions']),\n            'coded_urls': len(tweet['codes'])\n            };\n\n        tweet['text_hash'] = hashlib.md5(tweet['text'].encode(\"utf-8\")).hexdigest()\n        \n        # Convert dates 2012-09-22 00:10:46\n        # Note that we convert these to a datetime object and then convert back to string\n        # and update the tweet with the new string. We do this becuase we want to find\n        # and log any process issues here, not when we do an insert.\n        #\n        #tweet['created_ts'] = to_datetime(tweet['created_at'])\n        #tweet['user']['created_ts'] = to_datetime(tweet['user']['created_at'])\n        t = to_datetime(tweet['created_at'])\n        tweet['created_ts'] = t.strftime('%Y-%m-%d %H:%M:%S')\n\n        t = to_datetime(tweet['user']['created_at'])\n        tweet['user']['created_ts'] = t.strftime('%Y-%m-%d %H:%M:%S')\n\n        #print tweet['created_ts']\n\n    tweet_out_string = simplejson.dumps(tweet).encode('utf-8') + '\\n'\n\n    return tweet_out_string\n\n\n","sub_path":"tweetprocessing.py","file_name":"tweetprocessing.py","file_ext":"py","file_size_in_byte":6845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"27965241","text":"#\n# @lc app=leetcode.cn id=77 lang=python3\n#\n# [77] 组合\n#\n\n\n# @lc code=start\nclass Solution:\n    def combine(self, n: int, k: int) -> List[List[int]]:\n        #方法2：回溯+剪枝\n        def helper(n, k, start, track):\n            if len(track) == k:\n                res.append(track.copy())\n                return\n            for i in range(start, n - k + len(track) + 2):\n                track.append(i)\n                helper(n, k, i + 1, track)\n                track.pop()\n\n        res = []\n        if k <= 0 or n <= 0:\n            return res\n        helper(n, k, 1, [])\n        return res\n        '''\n        #方法1：回溯\n        def helper(n, k, start, track):\n            if len(track) == k:\n                #res.append(track.copy())\n                res.append(track[:])\n                return\n            for i in range(start, n + 1):\n                track.append(i)\n                helper(n, k, i + 1, track)\n                track.pop()\n                #helper(n, k, i+1, track+[i])\n\n        res = []\n        if k <= 0 or n <= 0:\n            return res\n        helper(n, k, 1, [])\n        return res\n        '''\n\n\n# @lc code=end\n","sub_path":"Week_03/77.组合.py","file_name":"77.组合.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"179542677","text":"\"\"\"\nCreate a player class.\n\nThe player class houses the stats for a single player.\n\"\"\"\n\n\nclass Player:\n    \"\"\"The Player class.\"\"\"\n\n    def __init__(self, name='', wins=0, losses=0, ratio=0.0, spaces=[]):\n        \"\"\"Initialize Player attributes.\"\"\"\n        self.name = name\n        self.wins = wins\n        self.losses = losses\n        self.ratio = ratio\n        self.spaces = spaces\n","sub_path":"Player.py","file_name":"Player.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"335817797","text":"#\n# V-Ray For Blender\n#\n# http://chaosgroup.com\n#\n# Author: Andrei Izrantcev\n# E-Mail: andrei.izrantcev@chaosgroup.com\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program.  If not, see <http://www.gnu.org/licenses/>.\n#\n# All Rights Reserved. V-Ray(R) is a registered trademark of Chaos Software.\n#\n\nimport bpy\nimport mathutils\n\nfrom vb30.lib import ExportUtils\n\n\nTYPE = 'BRDF'\nID   = 'BRDFDiffuse_forSSS'\nNAME = 'Diffuse For SSS'\nDESC = \"\"\n\nPluginParams = (\n    {\n        'attr' : 'color',\n        'desc' : \"\",\n        'type' : 'COLOR',\n        'default' : (1, 1, 1),\n    },\n    {\n        'attr' : 'color_tex',\n        'desc' : \"\",\n        'type' : 'TEXTURE',\n        'default' : (0.0, 0.0, 0.0),\n    },\n    {\n        'attr' : 'color_tex_mult',\n        'desc' : \"\",\n        'type' : 'FLOAT',\n        'default' : 1,\n    },\n    {\n        'attr' : 'transparency',\n        'desc' : \"\",\n        'type' : 'COLOR',\n        'default' : (0, 0, 0),\n    },\n    {\n        'attr' : 'transparency_tex',\n        'desc' : \"\",\n        'type' : 'TEXTURE',\n        'default' : (0.0, 0.0, 0.0),\n    },\n    {\n        'attr' : 'transparency_tex_mult',\n        'desc' : \"\",\n        'type' : 'FLOAT',\n        'default' : 1,\n    },\n    {\n        'attr' : 'back_color',\n        'desc' : \"Color on the back sides of the material\",\n        'type' : 'TEXTURE',\n        'default' : (1, 1, 1),\n    },\n    {\n        'attr' : 'roughness',\n        'desc' : \"\",\n        'type' : 'FLOAT_TEXTURE',\n        'default' : 1.0,\n    },\n    {\n        'attr' : 'use_irradiance_map',\n        'desc' : \"\",\n        'type' : 'BOOL',\n        'default' : True,\n    },\n)\n\nPluginWidget = \"\"\"\n{ \"widgets\": [\n\n    {   \"layout\" : \"ROW\",\n        \"attrs\" : [\n            { \"name\" : \"use_irradiance_map\" }\n        ]\n    }\n]}\n\"\"\"\n\n\ndef writeDatablock(bus, pluginModule, pluginName, propGroup, overrideParams):\n    overrideParams.update({\n        'color' : mathutils.Color((0.0, 0.0, 0.0)),\n        'color_tex_mult' : 1.0,\n        'transparency' : mathutils.Color((0.0, 0.0, 0.0)),\n        'transparency_tex_mult' : 1.0,\n    })\n\n    return ExportUtils.WritePluginCustom(bus, pluginModule, pluginName, propGroup, overrideParams)\n","sub_path":"plugins/brdf/BRDFDiffuse_forSSS.py","file_name":"BRDFDiffuse_forSSS.py","file_ext":"py","file_size_in_byte":2704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"79375018","text":"# -*- coding: utf-8 -*-\nn=(input('Digite a quantidade de valores que você quer : '))\nn=[]\nsomai=0\nfor i in range(0,n-1,1):\n    while n%2!=0:\n        somai=somai+n\n    print(somai)\n        \n        \n        \n        \n\n","sub_path":"moodledata/vpl_data/330/usersdata/281/93517/submittedfiles/lista1.py","file_name":"lista1.py","file_ext":"py","file_size_in_byte":218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"409974571","text":"###########################################\r\n# Author: klion26\r\n# Date: 2014/10/30\r\n# Problem: Next Prime Number\r\n###########################################\r\n\r\n# Test the input n is a prime number or not\r\ndef isPrime(n):\r\n    if n<2: # n is less than 2\r\n        return False\r\n    elif n == 2: # n equals to 2\r\n        return True\r\n    if n%2 == 0: # n is even number\r\n        return False\r\n    # test all odd numbers between 3 and sqrt(n) \r\n    for i in range(3, (int)(n ** 0.5) + 1, 2):\r\n        if n%i == 0: # if i divids n, then n isn't a prime number\r\n            return False\r\n    return True # n is a prime number\r\n\r\n# Generate the next prime begin with n(include)\r\ndef genNextPrime(n):\r\n    while True:\r\n        if isPrime(n):\r\n            break;\r\n        else:\r\n            n += 1\r\n    return n\r\n\r\n# Warp of main function\r\ndef main():\r\n    num = 2\r\n\r\n    while True:\r\n        # Get the user input\r\n        ans = raw_input(\"Would you like to see the next prime? (Y/N) \")\r\n\r\n        # Test the input\r\n        if ans.lower().startswith('y'):\r\n            print(num)\r\n            num = genNextPrime(num+1)\r\n        else:\r\n            break\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","sub_path":"Next_Prime_Number.py","file_name":"Next_Prime_Number.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"407216463","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport jsonfield.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('create', '0014_artwork_template'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='track',\n            name='scan',\n            field=jsonfield.fields.JSONCharField(default=[], max_length=3072),\n        ),\n    ]\n","sub_path":"apps/create/migrations/0015_track_scan.py","file_name":"0015_track_scan.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"72245452","text":"import numpy as np\nfrom math import pi\nfrom pymicro.crystal.lattice import *\nfrom pymicro.xray.laue import *\nfrom pymicro.xray.detectors import RegArrayDetector2d, Varian2520\nfrom pymicro.crystal.microstructure import Orientation\nfrom matplotlib import pyplot as plt, cm\nfrom matplotlib import image as mpimg\nfrom pymicro.view.vol_utils import alpha_cmap\nfrom scipy import ndimage\nfrom skimage.filters import threshold_yen, threshold_isodata\nfrom pymicro.xray.laue import gnomonic_projection\nfrom pymicro.crystal.texture import PoleFigure\nfrom pymicro.external.tifffile import TiffFile\nimport os\n\ndef binomial(n, k):\n    if 0 <= k <= n:\n        ntok = 1\n        ktok = 1\n        for t in xrange(1, min(k, n - k) + 1):\n            ntok *= n\n            ktok *= t\n            n -= 1\n        return ntok // ktok\n    else:\n        return 0\n\n#Create a text file for saving solutions matrices\nf = open('Optim_Results.txt','w')\nf.write('Script :\\n%s\\n\\n' % os.path.basename(__file__))\n\nolivine = Lattice.orthorhombic(1.022, 0.596, 0.481)  # nm Barret & Massalski convention\ndetector = RegArrayDetector2d(size=(512, 512), u_dir=[0, -1, 0], v_dir=[0, 0, -1]) \ndetector.pixel_size = 0.200  # mm, 0.1 mm with factor 2 binning\ndetector.ucen = 235\ndetector.vcen = 297\n\neuler = (191.9, 69.9, 138.9) # Roe Convention, /!\\ in Wenk's paper coordinate system\norientation_wenk_paper = Orientation.from_euler(euler, convention='Roe')\n# changing local frame to our XYZ: in Wenk's paper, X is -Y, Z is -X, Y is Z\nT = np.array([[ 0,  0, -1],\n              [-1,  0,  0],\n              [ 0,  1,  0]])\ng_wenk = np.dot(orientation_wenk_paper.orientation_matrix(), T.T)\norientation_wenk = Orientation(g_wenk)\n\nhkl_planes = [HklPlane(-1, 1, 0, olivine), HklPlane(-2, 1, 1, olivine),\n              HklPlane(-3, 1, 3, olivine), HklPlane(2, 0, -3, olivine)]\n\n\n# Create the data base\nZA_list = [HklDirection(1, 1, 1, olivine),\n           HklDirection(3, 3, 2, olivine)]\nall_hkl_planes = []\nmax_hkl = 4\nfor ZA in ZA_list:\n    ZA_plane = ZA.find_planes_in_zone(max_hkl)\n    all_hkl_planes.extend(ZA_plane)\nall_hkl_planes = HklObject.skip_higher_order(all_hkl_planes, keep_friedel_pair=True, verbose=False)\nprint('after skipping higher order function :', len(all_hkl_planes))\n\n\n# notebook copie\ndistance_list = [130.67539985930918, 130.73697038901238, 130.69254989609715, 130.76625535405333, 130.46140906761224,\n                 131.19937460642225, 130.926859144623, 130.91220903578116, 131.09642347174713, 130.93335081578115,\n                 130.86249479795805, 130.92647187959312, 130.92126864500258, 130.8980026173117, 131.45359941650526,\n                 131.71885508643655, 131.76550341502877, 131.01669264009706, 131.8237682899434, 133.4585236857289,\n                 132.47755273186442, 131.88984088619966, 132.1271140734323, 129.03046247233846, 133.3875262820381,\n                 133.56179901901237, 131.39975258319444, 132.1095540862288, 130.5572907249937, 132.0715863261055,\n                 132.28353614823246, 134.19855710717556, 131.71108116568794, 133.06044531934182, 131.94928830559937,\n                 132.12463437326738, 133.9097267714349, 132.3758823292933, 133.20072173635933, 133.66896067193713,\n                 132.43267465509595, 132.78338735309282, 132.63245840784475, 133.68436409854098, 133.3269975172332]\n\nmatrix_counter = 0\nerror_counter = 0\nerror_sets = []\nnb_solutions =[]\nfor i, pos in enumerate(distance_list):\n    #g = open('optim_files/Data_set_%d.txt' %i, 'w')\n    #f.write('(%d' %i)\n    detector.ref_pos = np.array([pos, 0., 0.]) + (detector.size[0] / 2 - detector.ucen) * detector.u_dir * detector.pixel_size +     \\\n                       (detector.size[1] / 2 - detector.vcen) * detector.v_dir * detector.pixel_size  # mm\n    #f.write('\\nDistance sample / detector = %d mm' % detector.ref_pos[0])\n\n    # compute the pattern with the new distance\n    compute_Laue_pattern(orientation_wenk, detector, hkl_planes, r_spot=0, show_direct_beam=False, color_field='constant')\n\n    # get the plane normals from the gnomonic projection\n    detector.data = (255 * (detector.data.astype(np.uint8) > 1)).astype(np.uint8) # I will never find that alone...\n    gnom_pymicro = gnomonic_projection(detector)\n    uv_g = np.argwhere(gnom_pymicro.data == 1)  # points on the gnomonic projection\n\n    # compute the hkl plane normals in the XYZ frame\n    OP = [gnom_pymicro.pixel_to_lab(uv_g[m, 0], uv_g[m, 1]).tolist() for m in range(len(uv_g))]\n    hkl_normals = [(n / np.linalg.norm(n)).tolist() for n in OP]  # normalized list of vectors\n\n    # perform indexing\n    solutions = index(hkl_normals, all_hkl_planes, tol_angle=1.0, tol_disorientation=3.0, crystal_structure='none')\n\n    try:\n        for j in range(len(solutions[0])):\n            matrix_counter = matrix_counter + 1\n            np.savetxt('optim_files/dataset_%d_%d' %(i, j), solutions[0][j])\n        nb_solutions.append(len(solutions[0]))\n    except(TypeError):\n        error_counter = error_counter + 1\n        error_sets.append(i)\n        np.savetxt('optim_files/dataset_%d_%d' %(i, i), np.zeros([3, 3]))\n        nb_solutions.append(0)\n\nnb_solutions = np.array(nb_solutions)\nnp.savetxt('optim_files/nb_solutions', nb_solutions)\n\nf.write('\\n\\n - Data base details - \\n\\n')\nf.write('\\n* Gnomonic projection normals = %d' % len(hkl_normals))\nf.write('\\n* hkl maximal value to find plane in zone = %d' % max_hkl)\nf.write('\\n* Number of zone axis used = %d' %len(ZA_list))\nf.write('\\n* hkl planes number = %d planes' % len(all_hkl_planes))\nf.write('\\n* Number of triplet = %s' % str(binomial(len(all_hkl_planes), 3)))\n\nf.write('\\n\\n - Results details - \\n\\n')\nf.write('\\n* %d data sets in trouble : %s' %(error_counter, error_sets))\nf.write('\\n* There are %d matrices in that file, keep smiling' % matrix_counter)\n\n\nimport sys\nsys.exit(0)\n\n#---------------------------------------------------------------------------------------------------------\n# THIS IS POST PROCESSING\n\no_indexed = Orientation(solutions[0][0])\nplans_simu = build_list(olivine, 7)\npattern_simu = compute_Laue_pattern(o_indexed, detector, plans_simu, r_spot=1, show_direct_beam=True)\n\nplt.figure(figsize=(12, 12))\nplt.plot(detector.ucen, detector.vcen, 'ws', markersize=10)\nplt.imshow(pattern_wenk.T, cmap=cm.gray)\nplt.imshow(pattern_simu.T, cmap=mycmap)\n# add the identified hkl planes\nfor i, hkl in enumerate(sel_hkl_planes):\n    K = diffracted_vector(hkl, o_indexed)\n    R = detector.project_along_direction(K, origin=[0., 0., 0.])\n    (u, v) = detector.lab_to_pixel(R)\n    plt.plot(u, v, 'o', \n             markersize=10, markeredgewidth=2, \n             markeredgecolor=hkl_colors[i], markerfacecolor='None', \n             label=hkl.miller_indices())\n# add the identified zone axes\nfor j, uvw in enumerate(uvw_list):\n    (u, v, w) = uvw.miller_indices()\n    print(u, v, w)\n    gt = o_indexed.orientation_matrix().T\n    print(np.dot(gt, uvw.direction()))\n    try:\n        ellipsis_mm = compute_ellipsis(o_indexed, detector, uvw, n=101, verbose=False)\n        ellipsis_px = np.empty((ellipsis_mm.shape[0], 2))\n        for i in range(ellipsis_mm.shape[0]):\n            ellipsis_px[i, :] = detector.lab_to_pixel([detector.ref_pos[0], ellipsis_mm[i, 0], ellipsis_mm[i, 1]])\n        plt.plot(ellipsis_px[:, 0], ellipsis_px[:, 1], color=uvw_colors[j], label='[%d%d%d]' % (u, v, w), linewidth=1.5)\n    except:\n        print('exception in computing [%d%d%d] ellipsis' % (u, v, w))\n        pass\nplt.clim(0, 1)\nplt.title('simulation with the indexed orientation')\nplt.axis('tight')\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., numpoints=1)\nplt.show()\n\n\n\npf = PoleFigure.plot(o_indexed, lattice=olivine, hkl='001')\n\n\n# Indexation by identification\n\n\n\n# select hkl planes two by two to find two different zone axes\nfor i in range(len(sel_hkl_planes)):\n    for j in range(i + 1, len(sel_hkl_planes)):\n        print('i = %d, j = %d' % (i, j))\n        OPi = sel_data_gp[i]\n        OPj = sel_data_gp[j]\n        #print(OPi, OPj)\n        hkl_normal_i = OPi / np.linalg.norm(OPi)\n        hkl_normal_j = OPi / np.linalg.norm(OPj)\n        g_id = transformation_matrix(sel_hkl_planes[i], sel_hkl_planes[j], hkl_normal_i, hkl_normal_j)\n        o_id = Orientation(g_id)\n        #print(g_id)\n        g_id_hkl_normal_i = np.dot(g_id.T, sel_hkl_planes[i].normal())\n        g_id_hkl_normal_j = np.dot(g_id.T, sel_hkl_planes[j].normal())\n        (hi, ki, li) = sel_hkl_planes[i].miller_indices()\n        print('XYZ coordinates of (%d%d%d) hkl normal (Wenk) = %s' % (hi, ki, li, g_id_hkl_normal_i))\n        (hj, kj, lj) = sel_hkl_planes[j].miller_indices()\n        print('XYZ coordinates of (%d%d%d) hkl normal (Wenk) = %s' % (hj, kj, lj, g_id_hkl_normal_j))\n\n'''\nThis do not work since every set of two planes share one zone axis so our orientation \nis defined around this direction. We should look in detail at the code in \"transformation_matrix\"\n'''\n\n# in Wenk's paper, X is -Y, Z is -X, Y is Z\nT = np.array([[ 0,  0, -1],\n              [-1,  0,  0],\n              [ 0,  1,  0]])\ng_wenk_paper = np.array([[ 0.38895738, -0.58984702,  0.70766705], \n                         [ 0.06567034,  0.78395283,  0.61733731], \n                         [-0.91891216, -0.19364517,  0.34365969]])\ng_wenk = np.dot(g_wenk_paper, T.T)\nprint('- Wenk, for reference check -')\nprint(g_wenk)\nfor i in range(len(sel_hkl_planes)):\n    xyz_hkl_normal_wenk = np.dot(g_wenk.T, sel_hkl_planes[i].normal())\n    (h, k, l) = sel_hkl_planes[i].miller_indices()\n    print('XYZ coordinates of (%d%d%d) hkl normal (Wenk) = %s' % (h, k, l, xyz_hkl_normal_wenk.view()))\n\n\n# Double check the Wenk Orientation\n# ---------------\n\n# In[16]:\n\n\nfrom pymicro.crystal.texture import PoleFigure\n\neuler = (191.9, 69.9, 138.9) # Roe Convention, /!\\ in Wenk's paper coordinate system\norientation_wenk_paper = Orientation.from_euler(euler, convention='Roe')\nprint(orientation_wenk_paper)\npf = PoleFigure.plot(orientation_wenk_paper, lattice=olivine, hkl='001')\n\n\n\n# changing local frame to our XYZ: in Wenk's paper, X is -Y, Z is -X, Y is Z\nT = np.array([[ 0,  0, -1],\n              [-1,  0,  0],\n              [ 0,  1,  0]])\ng_wenk = np.dot(orientation_wenk_paper.orientation_matrix(), T.T)\nprint('g_wenk:', g_wenk)\norientation_wenk = Orientation(g_wenk)\nprint(orientation_wenk)\n\n# FIXME moving to fundamental zone\nom_fz = Lattice.move_rotation_to_FZ(g_wenk, crystal_structure='orthorhombic')\nprint(om_fz)\n\nplans_simu = build_list(olivine, 20)\npattern_simu = compute_Laue_pattern(orientation_wenk, detector, plans_simu, r_spot=0, show_direct_beam=True)\n\nplt.figure(figsize=(12, 12))\nplt.plot(detector.ucen, detector.vcen, 'ws', markersize=10)\nplt.imshow(pattern_wenk.T, cmap=cm.gray)\nplt.imshow(pattern_simu.T, cmap=mycmap)\n\"\"\"\n# also plot the chosen planes\nfor i, hkl in enumerate(sel_hkl_planes):\n    K = diffracted_vector(hkl, orientation_wenk)\n    R = detector.project_along_direction(K, origin=[0., 0., 0.])\n    (u, v) = detector.lab_to_pixel(R)\n    plt.plot(u, v, 'o', \n             markersize=10, markeredgewidth=2, \n             markeredgecolor=hkl_colors[i], markerfacecolor='None', \n             label=hkl.miller_indices())\n\nfor j, uvw in enumerate(uvw_list):\n    (u, v, w) = uvw.miller_indices()\n    print(u, v, w)\n    gt = orientation_wenk.orientation_matrix().T\n    print(np.dot(gt, uvw.direction()))\n    try:\n        ellipsis_mm = compute_ellipsis(orientation_wenk, detector, uvw, n=101, verbose=False)\n        ellipsis_px = np.empty((ellipsis_mm.shape[0], 2))\n        for i in range(ellipsis_mm.shape[0]):\n            ellipsis_px[i, :] = detector.lab_to_pixel([detector.ref_pos[0], ellipsis_mm[i, 0], ellipsis_mm[i, 1]])\n        plt.plot(ellipsis_px[:, 0], ellipsis_px[:, 1], color=uvw_colors[j], label='[%d%d%d]' % (u, v, w), linewidth=1.5)\n    except:\n        print('exception in computing [%d%d%d] ellipsis' % (u, v, w))\n        pass\n\"\"\"\nplt.clim(0, 1)\nplt.title('simulation with the orientation found by Wenk and transformed to our local frame')\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., numpoints=1)\nplt.axis('tight')\nplt.show()\n\n\n# In[20]:\n\n\n# pole figure in our XYZ laboratory frame\npf = PoleFigure.plot(orientation_wenk, lattice=olivine, hkl='001')\n\n# compute the disorientation between this orientation matrix and the one obtained by the indexing\n(angle, axis, axis_xyz) = orientation_wenk.disorientation(Orientation(solutions[0][0]), crystal_structure='orthorhombic')\nprint('misorientation = %.1f deg' % (angle * 180 / np.pi))\nprint('misorientation axis (crystal frame) = {0}'.format(axis))\nprint('misorientation axis (lab frame) = {0}'.format(axis_xyz))\n\n\n# In[21]:\n\n\n# verify the angles between the plane normals given by the gnomonic projection\n# this show clearly that we have to pick the normals in the same half plane (ie considering the Friedel pair)\n\n#p1 = HklPlane(1, -1, 0, olivine)\n#p2 = HklPlane(2, -1, -1, olivine)\np1 = HklPlane(2, 0, -3, olivine)\np2 = HklPlane(3, -1, -3, olivine)\n\nangle = 180 / np.pi * np.arccos(np.dot(p1.normal(), p2.normal()))\nprint('angle between normals is %.1f deg' % angle)\nK1 = diffracted_vector(p1, orientation_wenk)\nK2 = diffracted_vector(p2, orientation_wenk)\nR1 = detector.project_along_direction(K1, origin=[0., 0., 0.])\nR2 = detector.project_along_direction(K2, origin=[0., 0., 0.])\nprint(R1)\nprint(R2)\nOP1 = gnomonic_projection_point(R1)[0]\nOP2 = gnomonic_projection_point(R2)[0]\nprint(OP1)\nprint(OP2)\nhkl_normal1 = OP1 / np.linalg.norm(OP1)\nhkl_normal2 = (OP2 / np.linalg.norm(OP2))\nangle_gp = 180 / np.pi * np.arccos(np.dot(hkl_normal1, hkl_normal2))\nprint('angle computed by the gnomonic projection is %.1f deg' % angle_gp)\n\n","sub_path":"Direct_Indexation/Wenk_article/Optimize_det_distance.py","file_name":"Optimize_det_distance.py","file_ext":"py","file_size_in_byte":13583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"339751693","text":"\n\ndef main():\n    argument_spec = openstack_full_argument_spec(server=dict(required=True), state=dict(default='present', choices=['absent', 'present']), network=dict(required=False, default=None), floating_ip_address=dict(required=False, default=None), reuse=dict(required=False, type='bool', default=False), fixed_address=dict(required=False, default=None), nat_destination=dict(required=False, default=None, aliases=['fixed_network', 'internal_network']), wait=dict(required=False, type='bool', default=False), timeout=dict(required=False, type='int', default=60), purge=dict(required=False, type='bool', default=False))\n    module_kwargs = openstack_module_kwargs()\n    module = AnsibleModule(argument_spec, **module_kwargs)\n    server_name_or_id = module.params['server']\n    state = module.params['state']\n    network = module.params['network']\n    floating_ip_address = module.params['floating_ip_address']\n    reuse = module.params['reuse']\n    fixed_address = module.params['fixed_address']\n    nat_destination = module.params['nat_destination']\n    wait = module.params['wait']\n    timeout = module.params['timeout']\n    purge = module.params['purge']\n    (sdk, cloud) = openstack_cloud_from_module(module)\n    try:\n        server = cloud.get_server(server_name_or_id)\n        if (server is None):\n            module.fail_json(msg='server {0} not found'.format(server_name_or_id))\n        if (state == 'present'):\n            public_ip = cloud.get_server_public_ip(server)\n            f_ip = (_get_floating_ip(cloud, public_ip) if public_ip else public_ip)\n            if f_ip:\n                if network:\n                    network_id = cloud.get_network(name_or_id=network)['id']\n                else:\n                    network_id = None\n                if nat_destination:\n                    nat_floating_addrs = [addr for addr in server.addresses.get(cloud.get_network(nat_destination)['name'], []) if ((addr['addr'] == public_ip) and (addr['OS-EXT-IPS:type'] == 'floating'))]\n                    if (len(nat_floating_addrs) == 0):\n                        module.fail_json(msg=\"server {server} already has a floating-ip on a different nat-destination than '{nat_destination}'\".format(server=server_name_or_id, nat_destination=nat_destination))\n                if all([fixed_address, (f_ip.fixed_ip_address == fixed_address), network, (f_ip.network != network_id)]):\n                    module.fail_json(msg=\"server {server} already has a floating-ip on requested interface but it doesn't match requested network {network}: {fip}\".format(server=server_name_or_id, network=network, fip=remove_values(f_ip, module.no_log_values)))\n                if ((not network) or (f_ip.network == network_id)):\n                    module.exit_json(changed=False, floating_ip=f_ip)\n            server = cloud.add_ips_to_server(server=server, ips=floating_ip_address, ip_pool=network, reuse=reuse, fixed_address=fixed_address, wait=wait, timeout=timeout, nat_destination=nat_destination)\n            fip_address = cloud.get_server_public_ip(server)\n            f_ip = _get_floating_ip(cloud, fip_address)\n            module.exit_json(changed=True, floating_ip=f_ip)\n        elif (state == 'absent'):\n            if (floating_ip_address is None):\n                if (not server_name_or_id):\n                    module.fail_json(msg='either server or floating_ip_address are required')\n                server = cloud.get_server(server_name_or_id)\n                floating_ip_address = cloud.get_server_public_ip(server)\n            f_ip = _get_floating_ip(cloud, floating_ip_address)\n            if (not f_ip):\n                module.exit_json(changed=False)\n            changed = False\n            if f_ip['fixed_ip_address']:\n                cloud.detach_ip_from_server(server_id=server['id'], floating_ip_id=f_ip['id'])\n                f_ip = cloud.get_floating_ip(id=f_ip['id'])\n                changed = True\n            if purge:\n                cloud.delete_floating_ip(f_ip['id'])\n                module.exit_json(changed=True)\n            module.exit_json(changed=changed, floating_ip=f_ip)\n    except sdk.exceptions.OpenStackCloudException as e:\n        module.fail_json(msg=str(e), extra_data=e.extra_data)\n","sub_path":"Data Set/bug-fixing-1/0924a8cf678909d04195a1b3dd5856145b03bace-<main>-fix.py","file_name":"0924a8cf678909d04195a1b3dd5856145b03bace-<main>-fix.py","file_ext":"py","file_size_in_byte":4216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"359242858","text":"#!/usr/bin/python\n\nimport random \nimport numpy as np\nimport pickle\nimport argparse\nimport os\nfrom os import system, remove\nfrom collections import Counter\n\ndef uniform_dist(n):\n    return np.matrix([1/float(n)] * n)\n\n# Generate a biased categorical distribution by generating n random numbers and then \ndef biased_dist(n):\n    rnum = [random.randrange(100) for i in range(0, n)]\n    return matrix(map(lambda x : x / float(sum(rnum)), rnum))\n\ndef to_file(data, fname):\n    out = open(fname, 'wb')\n    pickle.dump(data, out)\n    out.close()\n\ndef from_file(fname):\n    inp = open(fname, 'rb')\n    loaded = pickle.load(inp)\n    inp.close()\n    return loaded\n\n# Generate a list of lists of tuples containing indices at which specific sum\n# values can be found. e.g. if adding two die rolls, can get a sum of 2 at row\n# 1, col 1, sum of 3 at (1,2) or (2,1) and so on. Minimum possible sum is 2, but\n# list[0] will give indices for possible sums of 2, list[1] for 3, etc.\ndef generate_sum_indices(n):\n    sum_inds = [list() for _ in range(2*n - 1)]\n    for row in range(0,n):\n        for col in range(0,n):\n            _sum = row + col + 2 # compensate for the zero base\n            sum_inds[_sum - 2].append((row, col)) # reverse compensation\n\n    return sum_inds\n\n# Extract the sum probabilities for the CPT created from the player and table\n# distributions, and put them into a dict for easy access.\ndef compute_sum_probabilities_dict(cpt, sum_indices):\n    return {n+2: sum(map(lambda i : cpt[i[0], i[1]], inds)) for n,inds in enumerate(sum_indices)}\n\ndef compute_sum_probabilities_list(cpt, sum_indices):\n    # print([inds for inds in sum_inds])\n    # print([len(inds) for inds in sum_inds])\n    # print([map(lambda i : cpt[i[0], i[1]], inds) for inds in sum_inds])\n    return [sum(map(lambda i : cpt[i[0], i[1]], inds)) for n,inds in enumerate(sum_indices)]\n\ndef next_table_from_prev(prev):\n    # if prev = 1, 1-prev = 0, so go to 0 3/4 of the time\n    # if prev = 0, 1-prev = 1, so go to 1 3/4 of the time\n    return 1 - prev if random.random() > 0.25 else prev\n\ndef sample_roll_from_dist(dist):\n    cumulative = np.cumsum(dist).tolist()[0]\n    rn = random.random()\n    return next(i+1 for i,x in enumerate(cumulative) if x >= rn)\n\n\nclass PlayerData:\n    own_rolls = []\n    table_rolls = []\n    sums = []\n    table_refs = []\n    distribution = []\n\n    def __init__(self, own, table, _sums, refs, dist):\n        self.own_rolls = own\n        self.table_rolls = table\n        self.sums = _sums\n        self.table_refs = refs\n        self.dist = dist\n\n    def __str__(self):\n        o = \"Own rolls: \" + str(self.own_rolls) + \"\\n\"\n        t = \"Table rolls: \" + str(self.table_rolls) + \"\\n\"\n        r = \"Table refs: \" + str(self.table_refs) + \"\\n\"\n        s = \"Sums: \" + str(self.sums) + \"\\n\"\n        d = \"Dist: \" + str(self.dist) + \"\\n\"\n        return o + t + s + r + d\n\nclass TimestepData:\n    player_rolls = []\n    table_rolls = []\n    sums = []\n    table_refs = []\n\n    def __init__(self, player, table, _sums, refs):\n        self.player_rolls = player\n        self.table_rolls = table\n        self.sums = _sums\n        self.table_refs = refs\n\n    def __str__(self):\n        o = \"Player rolls: \" + str(self.player_rolls) + \"\\n\"\n        t = \"Table rolls: \" + str(self.table_rolls) + \"\\n\"\n        r = \"Table refs: \" + str(self.table_refs) + \"\\n\"\n        s = \"Sums: \" + str(self.sums) + \"\\n\"\n        return o + t + s + r\n\n# Construct playerdata objects from the lists constructed by generate_data\ndef player_data_from_timesteps(player_rolls, table_rolls, sums, table_refs, player_dists):\n    nsteps = len(player_rolls)\n    nplayers = len(player_rolls[0])\n    player_data = []\n    for player in range(0, nplayers):\n        own = [player_rolls[step][player] for step in range(0, nsteps)]\n        table = [table_rolls[step][player] for step in range(0, nsteps)]\n        _sums = [sums[step][player] for step in range(0, nsteps)]\n        refs = [table_refs[step][player] for step in range(0, nsteps)]\n        dist = player_dists[player]\n        player_data.append(PlayerData(own, table, _sums, refs, dist))\n\n    return player_data\n\ndef generate_data(faces, players, tables, output=\"\", table_bias=\"\", table_bias_row=None, player_bias=\"\"):\n    biased_low = np.matrix([0.37, 0.25, 0.15, 0.1, 0.08, 0.05]) # biased towards low rolls\n    biased_high = np.matrix([0.05, 0.08, 0.1, 0.15, 0.25, 0.37])# biased towards high rolls\n    if player_bias == \"low\":\n        print(\"Player biased low\")\n        player_dists = [biased_low for i in range(0, players)]\n    elif player_bias == \"high\":\n        print(\"Player biased high\")\n        player_dists = [biased_high for i in range(0, players)] # reverse the list to get bias to high rolls\n    else:\n        print(\"Player unbiased\")\n        player_dists = [uniform_dist(faces) for i in range(0, players)]\n\n    if table_bias == \"low\" and table_bias_row == 0:\n        print(\"Table row 0 biased low\")\n        table_dists = [[biased_low, uniform_dist(faces)] for i in range(0, tables)]\n    elif table_bias == \"low\" and table_bias_row == 1:\n        print(\"Table row 1 biased low\")\n        table_dists = [[uniform_dist(faces), biased_low] for i in range(0, tables)]\n    elif table_bias == \"high\" and table_bias_row == 0:\n        print(\"Table row 0 biased high\")\n        table_dists = [[biased_high, uniform_dist(faces)] for i in range(0, tables)]\n    elif table_bias == \"high\" and table_bias_row == 1:\n        print(\"Table row 1 biased high\")\n        table_dists = [[uniform_dist(faces), biased_high] for i in range(0, tables)]\n    else:\n        print(\"Tables unbiased\")\n        table_dists = [[uniform_dist(faces), uniform_dist(faces)] for i in range(0, tables)]\n    \n    # Indexing is done by the table index, not player index - each time step of\n    # the process is represented by a single list, as opposed to the results of\n    # each player being in a single list\n    player_rolls = [[] for i in range(0, tables)]\n    table_rolls = [[] for i in range(0, tables)]\n    table_refs = [[] for i in range(0, tables)] # T_k (0) or T'_k (1) for the given step\n    sums = [[] for i in range(0, tables)]\n\n    # first table is uniform probability\n    table_refs[0] = map(lambda x : 1 if x > 0.5 else 0, [random.random() for i in range(0, players)])\n    # rest of tables based on the previous table the player was at 1/4 to same row of tables, 3/4 to other\n    for i in range(1, tables):\n        table_refs[i] = [next_table_from_prev(table_refs[i-1][pl]) for pl in range(0, players)]\n\n    # Run the simulation, generating table rolls and player rolls\n    for i in range(0, tables):\n#        cpts = [np.transpose(table_dists[i][table_refs[i][pl]]) * player_dists[pl] for pl in range(0, players)]\n        player_rolls[i] = [sample_roll_from_dist(player_dists[pl]) for pl in range(0, players)]\n        table_rolls[i] = [sample_roll_from_dist(table_dists[i][table_refs[i][pl]]) for pl in range(0, players)]\n\n        sums[i] = [pl[0] + pl[1] for pl in zip(player_rolls[i], table_rolls[i])]\n        # print(\"table: \" + str(table_rolls[i]))\n        # print(\"player: \" + str(player_rolls[i]))\n        # print(\"sum: \" + str(sums[i]))\n\n    pdata = player_data_from_timesteps(player_rolls, table_rolls, sums, table_refs, player_dists)\n    timedata = TimestepData(player_rolls, table_rolls, sums, table_refs)\n\n#    analyse_data_player(faces, pdata)\n    analyse_data_time(faces, timedata, output + \"timedata\")\n\n    # if (output):\n    #     to_file(pdata, output + \"player.pkl\")\n    #     to_file(timedata, output + \"time.pkl\")\n\ndef analyse_data_player(faces, player_data):\n    print(\"Player data\")\n    # mean and standard deviation for the observations of each player\n    mu = [np.mean(player_data[pl].sums) for pl in range(0, len(player_data))]\n    stdv = [np.std(player_data[pl].sums) for pl in range(0, len(player_data))]\n    \n    # histogram of the observations for each player\n    hists = []\n    for pl in range(0, len(player_data)):\n        hist = [0] * (2 * faces + 1) # num possible sums (including impossible ones like 0 and 1)\n        # Extract counts with counter object,\n        # convert to dict, and then extract the counts from the dict and place\n        # them into the hist array at the correct location\n        counts = dict(Counter(player_data[pl].sums))\n        for sum_ in counts.keys():\n            hist[sum_] = counts[sum_]\n        hists.append(hist);\n\n    print(\"Player means: \" + str(mu))\n    print(\"Player stdv: \" + str(stdv))\n    print(\"Histograms:\")\n    for i in range(0, len(hists)):\n        print(hists[i])\n\n# Extract histogram for a specific timestep and table reference from the timestep data.\ndef hist_from_table(time_data, faces, step, table_ref):\n    # get indices of players at the requested table at this step\n    inds = [i for i,v in enumerate(time_data.table_refs[step]) if v == table_ref]\n\n    # extract the rolls for the players and count them\n    rolls = [time_data.sums[step][i] for i in inds]\n    counts = dict(Counter(rolls))\n\n    # num possible sums (including impossible ones like 0 and 1)\n    hist =  [0] * (2 * faces + 1)\n    \n    # extract from the dict and put into the histogram. \n    for sum_ in counts.keys():\n        hist[sum_] = counts[sum_]\n\n    return hist\n\n# Construct a gnuplot script to display the distribution generated by the time\n# analysis.\ndef gen_gnuplot_script_time(outfile, plotfile=\"timehist.plt\"):\n    f = open(plotfile, 'w')\n    f.write(\"set terminal pngcairo\\n\")\n    f.write(\"set output \\\"\" + os.path.splitext(outfile)[0] + \".png\\\"\\n\")\n    f.write(\"set title \\\"\" + os.path.splitext(outfile)[0] + \"\\\"\\n\")\n    f.write(\"plot \\\"\" + outfile + \"\\\" using 1:2 title \\\"T dist\\\" with boxes,\\\\\\n\")\n    f.write(\"\\t\\\"\"+ outfile + \"\\\" using 1:3 title \\\"T' dist\\\" with boxes\")\n    f.close\n\ndef analyse_data_time(faces, time_data, output=\"\"):\n    print(\"Timestep data\")\n    tables = len(time_data.table_refs)\n    # construct histograms for each timestep, separating out the tables, which\n    # should show differences between the distributions if there are any\n    \n    # accumulate all histograms in these lists - at the end we will take the average\n    hist_acc = [0] * (2 * faces + 1)\n    prime_acc = [0] * (2 * faces + 1)\n    for t in range(0, tables):\n        # print(\"Timestep: \" + str(t))\n        # extract indices for which players ended up at which table so that we\n        # can look at the different distributions\n        hist = hist_from_table(time_data, faces, t, 0)\n        prime_hist = hist_from_table(time_data, faces, t, 1)\n\n        hist_acc = map(lambda x: x[0] + x[1], zip(hist, hist_acc));\n        prime_acc = map(lambda x: x[0] + x[1], zip(prime_hist, prime_acc));\n        \n        # print(\"Roll histograms: \")\n        # print(\"T_\" + str(t) + \": \"+ str(hist))\n        # print(\"T'_\" + str(t) + \": \" + str(prime_hist))\n        \n        # print(\"Accumulated histograms:\")\n        # print(str(hist_acc))\n        # print(str(prime_acc))\n\n    hist_sum = float(sum(hist_acc)) # total rolls on the non-prime tables\n    prime_sum = float(sum(prime_acc)) # total rolls on the prime tables\n    \n    hist_dist = map(lambda x: x/hist_sum, hist_acc)\n    prime_dist = map(lambda x: x/prime_sum, prime_acc)\n\n    print(\"Sum distributions:\")\n    print(\"T: \" + str(hist_dist))\n    print(\"T': \" + str(prime_dist))\n    \n    # write the rolls and the corresponding probability from the distributions to a file\n    out = os.path.splitext(output)[0] + \"_time_analysis.txt\" if output else \"time_analysis.txt\"\n    f = open(out, 'w')\n    f.write(\"# roll p(roll|T) p(roll|T')\\n\")\n    for roll in range(2,13):\n        f.write(str(roll) + \" \" + str(hist_dist[roll]) + \" \" + str(prime_dist[roll]) + \"\\n\")\n\n    f.close()\n\n    # write a gnuplot file to use to plot the data, and then plot the data\n    plotfile = \"timehist.plt\"\n    gen_gnuplot_script_time(out, plotfile)\n    \n    system('gnuplot --persist ' + plotfile)\n    \ndef main():\n\n    # Define arguments for script input\n    parser = argparse.ArgumentParser(description=\"Generate data from players and tables with some distribution, or process generated data.\")\n    parser.add_argument('-r', metavar='datafile',\n                        dest='datafile',\n                        help='Read data from the specified file and get some information about it')\n\n    parser.add_argument('-o', metavar='outname',\n                        default=\"\",\n                        dest='outname',\n                        help='Prefix for output data files')\n\n    parser.add_argument('-p', metavar='nplayers',\n                        dest='players',\n                        type=int,\n                        help='Number of players')\n\n    parser.add_argument('-t', metavar='ntables',\n                        dest='tables',\n                        type=int,\n                        help='Number of tables in each row')\n\n    parser.add_argument('-d',\n                        choices=['player', 'time'],\n                        type=str,\n                        dest='dtype',\n                        help='Type of data to process. Two types are generated - player and timestep data.')\n\n    parser.add_argument('-b',\n                        action='append',\n                        choices=['playerlow', 'playerhigh', '0low', '0high', '1low', '1high'],\n                        type=str,\n                        dest='bias',\n                        help='Bias one or more of the distributions used to generate data. Can bias towards,\\\n                        either low or high rolls, and can bias the player or one,\\\n                        of the rows of tables. Pass this argument multiple times,\\\n                        to use multiple biased distributions. Cannot bias more than one row.')\n\n    ns=parser.parse_args()\n    print(ns)\n    faces = 6\n\n    # Choose what to do based on arguments received\n    print(\"Players: \" + str(ns.players))\n    print(\"Tables: \" + str(ns.tables))\n\n    if ns.datafile:\n        if ns.dtype == 'player':\n            outname = ns.outname if ns.outname else \"player_out\"\n            print(\"todo...\")\n        elif ns.dtype == 'time':\n            outname = ns.outname if ns.outname else \"time_out\"\n            analyse_data_time(faces, from_file(ns.datafile), outname)\n        else:\n            print(\"You must choose a data type to analyse\")\n            parser.print_help()\n    else:\n        pt_info = \"_\" + str(ns.players) + \"p_\" + str(ns.tables) + \"t_\" + (\"_\".join(ns.bias) + \"_\" if ns.bias else \"\")\n        outname = ns.outname + pt_info if ns.outname else \"gen_data\" + pt_info\n        table_bias=\"\"\n        bias_row=None\n        player_bias=\"\"\n        \n        if ns.bias:\n            if \"playerlow\" in ns.bias:\n                player_bias=\"low\"\n            elif \"playerhigh\" in ns.bias:\n                player_bias=\"high\"\n            elif \"0low\" in ns.bias:\n                table_bias=\"low\"\n                bias_row=0\n            elif \"1low\" in ns.bias:\n                table_bias=\"low\"\n                bias_row=1\n            elif \"0high\" in ns.bias:\n                table_bias=\"high\"\n                bias_row=0\n            elif \"1high\" in ns.bias:\n                table_bias=\"high\"\n                bias_row=1\n        \n        generate_data(faces, ns.players, ns.tables, outname, table_bias, bias_row, player_bias)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"1/lab1.py","file_name":"lab1.py","file_ext":"py","file_size_in_byte":15269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"431710692","text":"# print(\"Hello, World\")\nimport random\nfrom tkinter import *\nfrom PIL import ImageTk, Image\n\nwindow = Tk()\nwindow.title(\"my practice program\")\n\n\nmy_img = ImageTk.PhotoImage(Image.open(\"treble.png\"))\nmy_label = Label(image=my_img)\nmy_label.image = my_img\n\nmy_label.grid(row=0, column = 1)\n\nkeys = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\"]\n\nrandom_keyselected = random.choice(keys)\n\n\ndef randomkey():\n    keys = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\"]\n    key_selected = random.choice(keys)\n    return key_selected\n\n# # key_pressed = input(\"press the key shown:\" + key_selected)\n# # if key_pressed == key_selected :\n# #  print(\"good\")\n\n\n\n\nmytoplabel = Label(window, text=random_keyselected)\nmytoplabel.grid(row=0, column = 1)\n\ndef check_keypressed(buttonpressed):\n    key_button = buttonpressed\n    print(f\"You tried to click: {buttonpressed}\")\n    if buttonpressed == random_keyselected:\n        mylable = Label(window, text=\"Good!\")\n\n        mylable.grid(row = 5, column =1)\n\ndef buttonclick():\n    mylabel = Label(window, text=\"Hello World!\")\n    mylabel.pack()\n# switch()\n\nff_add = lambda a,b : a+ b\n\nresult = ff_add(3, 4)\n\n# (a, b) => a+b\n\nmyButton1 = Button(window, text= keys[0], command = lambda : check_keypressed(keys[0]))\nmyButton2 = Button(window, text= keys[1], command = lambda : check_keypressed(keys[1]))\nmyButton3 = Button(window, text= keys[2], command =lambda : check_keypressed(keys[2]))\nmyButton4 = Button(window, text= keys[3], command = lambda : check_keypressed(keys[3]))\nmyButton5 = Button(window, text= keys[4], command = lambda : check_keypressed(keys[4]))\nmyButton6 = Button(window, text= keys[5], command = lambda : check_keypressed(keys[5]))\nmyButton7 = Button(window, text= keys[6], command = lambda : check_keypressed(keys[6]))\nmyButton1.grid(row=1, column=0)\nmyButton2.grid(row=1, column=2)\nmyButton3.grid(row=2, column=0)\nmyButton4.grid(row=2, column=2)\nmyButton5.grid(row=3, column=0)\nmyButton6.grid(row=3, column=2)\nmyButton7.grid(row=4, column=0)\n\n\n# mylabel1 = Label(window, text=\"Hello World!\")\n# mylabel2 = Label(window, text=\"My name is prosun\")\n#\n# mylabel1.grid(row=0, column= 0)\n# mylabel2.grid(row=1, column=0)\n\n# mylabel.pack()\nwindow.mainloop()\n\n","sub_path":"Firstproject.py","file_name":"Firstproject.py","file_ext":"py","file_size_in_byte":2180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"423951194","text":"def fizzbuzz(l):\n    for item in l:\n        if item % 15 == 0:\n            print(\"fizzbuzz\")\n        elif item % 3 == 0:\n            print(\"fizz\")\n        elif item % 5 == 0:\n            print(\"buzz\")\n        else:\n            print(item)\n\ndef fb_adv(items, divisors):\n    for item in items:\n        output = \"\"\n        for divisor, val in divisors.items():\n            if item % divisor == 0:\n                output += val\n        if output != \"\":\n            print(output)\n        else:\n            print(item)\n\n# yeah not real solutions but funny to do:\n\ndef fb_unread(items, divisors):\n    for item in items:\n        out = \"\".join([d_val if item % divisor == 0 else \"\" for divisor, d_val in\n            divisors.items()])\n        print(out if len(out) > 0 else item)\n\ndef fb_oneline(items, divisors):\n     print(\"\\n\".join([\"\".join([d_val if item % divisor == 0 else \"\" for divisor, d_val in\n            divisors.items()]) if any([item % divisor == 0 for divisor in\n                divisors.keys()]) else str(item) for item in items]) )\n\n# ---\n\nif __name__ == '__main__':\n    divs = { 3: \"fizz\", 5: \"buzz\", }\n    fizzbuzz(range(1,101))\n    #fb_adv(range(1,101),divs)\n    #fb_unread(range(1,16),divs)\n    fb_oneline(range(1,16),divs)\n","sub_path":"examples/fizzbuzz.py","file_name":"fizzbuzz.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"310004721","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the sockMerchant function below.\ndef sockMerchant(n, ar):\n    ar_sort = ar\n    ar_sort.sort()\n\n    materials = sorted(set(ar))\n    m_itr = 0\n\n    pair_count = 0\n    count_pair_arr = []\n    tmp_count = 0\n    for i in range(n):\n        if ar_sort[i] == materials[m_itr]:\n            tmp_count += 1\n        else:\n            count_pair_arr.append(tmp_count)\n            m_itr += 1\n            if ar_sort[i] == materials[m_itr]:\n                print('!',materials[m_itr])\n                tmp_count = 1\n\n    count_pair_arr.append(tmp_count)\n\n    for i in count_pair_arr:\n        pair_count += int(i/2)\n    return pair_count\n\nif __name__ == '__main__':\n    # fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    # n = int(input())\n\n    # ar = list(map(int, input().rstrip().split()))\n\n    # result = sockMerchant(n, ar)\n\n    # fptr.write(str(result) + '\\n')\n\n    # fptr.close()\n\n    n = 10\n    # ar = [10, 20, 20, 10, 10, 30, 50, 10, 20]\n    input_str = \"1 1 3 1 2 1 3 3 3 3\"\n    ar = list(map(int, input_str.rstrip().split()))\n    print('ar',ar)\n\n\n    result = sockMerchant(n, ar)\n    print(result)\n","sub_path":"HackerRanker/sock_merchant.py","file_name":"sock_merchant.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"627158931","text":"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\nimport logging\n\nimport torch\nfrom ml.rl.parameters import DiscreteActionModelParameters\nfrom ml.rl.training.rl_trainer_pytorch import RLTrainer\nfrom ml.rl.training.training_data_page import TrainingDataPage\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass ImitatorTrainer(RLTrainer):\n    def __init__(\n        self, imitator, parameters: DiscreteActionModelParameters, use_gpu=False\n    ) -> None:\n        super().__init__(parameters, use_gpu=use_gpu)\n\n        self._set_optimizer(parameters.training.optimizer)\n        self.minibatch_size = parameters.training.minibatch_size\n        self.minibatches_per_step = parameters.training.minibatches_per_step or 1\n        self.imitator = imitator\n        self.imitator_optimizer = self.optimizer_func(\n            imitator.parameters(),\n            lr=parameters.training.learning_rate,\n            weight_decay=parameters.training.l2_decay,\n        )\n\n    def _imitator_accuracy(self, predictions, true_labels):\n        match_tensor = predictions == true_labels\n        matches = int(match_tensor.sum())\n        return round(matches / len(predictions), 3)\n\n    @torch.no_grad()  # type: ignore\n    def train(self, training_batch, train=True):\n        if isinstance(training_batch, TrainingDataPage):\n            training_batch = training_batch.as_discrete_maxq_training_batch()\n        learning_input = training_batch.training_input\n\n        with torch.enable_grad():\n            action_preds = self.imitator(learning_input.state.float_features)\n            # Classification label is index of action with value 1\n            pred_action_idxs = torch.max(action_preds, dim=1)[1]\n            actual_action_idxs = torch.max(learning_input.action, dim=1)[1]\n\n            if train:\n                imitator_loss = torch.nn.CrossEntropyLoss()\n                bcq_loss = imitator_loss(action_preds, actual_action_idxs)\n                bcq_loss.backward()\n                self._maybe_run_optimizer(\n                    self.imitator_optimizer, self.minibatches_per_step\n                )\n\n        return self._imitator_accuracy(pred_action_idxs, actual_action_idxs)\n\n\ndef get_valid_actions_from_imitator(imitator, input, drop_threshold):\n    \"\"\"Create mask for non-viable actions under the imitator.\"\"\"\n    if isinstance(imitator, torch.nn.Module):\n        # pytorch model\n        imitator_outputs = imitator(input.float_features)\n        on_policy_action_probs = torch.nn.functional.softmax(imitator_outputs, dim=1)\n    else:\n        # sci-kit learn model\n        on_policy_action_probs = torch.tensor(imitator(input.float_features.cpu()))\n\n    filter_values = (\n        on_policy_action_probs / on_policy_action_probs.max(keepdim=True, dim=1)[0]\n    )\n    return (filter_values >= drop_threshold).float()\n","sub_path":"ml/rl/training/imitator_training.py","file_name":"imitator_training.py","file_ext":"py","file_size_in_byte":2839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"524994271","text":"import os\nimport glob\nfrom typing import List\nimport pandas as pd\nimport numpy as np\n\nimport torch\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom torchvision.datasets.folder import default_loader\nfrom PIL import Image\n\n\ndef get_valid_videos(image_root):\n    valid_names = {}\n    for name in os.listdir(image_root):\n        sub_root = os.path.join(image_root, name)\n        if not os.path.isdir(sub_root):\n            continue\n        image_files = glob.glob(os.path.join(sub_root, \"*.jpg\"))\n        if len(image_files) != 10:\n            continue\n        image_files = sorted(\n            image_files,\n            key=lambda name: int(os.path.splitext(os.path.basename(name))[0]),\n        )\n        valid_names[name] = image_files\n    return valid_names\n\n\ndef read_and_merge_datasets(image_roots, label_files):\n    all_label_data = []\n    valid_videos = {}\n    for image_root, label_file in zip(image_roots, label_files):\n        all_label_data.append(pd.read_csv(label_file))\n        valid_videos.update(get_valid_videos(image_root))\n    df = pd.concat(all_label_data, axis=0, ignore_index=True)\n\n    # filter based on impr_cnt\n    impr_cnt = np.asarray(df[\"impr_cnt\"], dtype=int)\n    df = df[impr_cnt >= 10]\n\n    label_names = [\"click_ratio\", \"like_ratio\", \"play_finish_ratio\"]\n    labels = np.zeros([len(df), len(label_names)], dtype=int)\n    for i, name in enumerate(label_names):\n        threshold = np.median(df[name])\n        labels[:, i] = df[name] > threshold\n\n    # filter based on image\n    valid_labels = []\n    for vid, label in zip(df[\"vid\"], labels):\n        if vid in valid_videos:\n            valid_labels.append((vid, label))\n\n    # sort by vid\n    valid_labels = sorted(valid_labels, key=lambda item: item[0])\n\n    return valid_labels, valid_videos\n\n\nclass CoverDataset(Dataset):\n    def __init__(\n        self,\n        image_roots,\n        label_files,\n        subset=\"train\",\n        tranform=None,\n        image_loader=default_loader,\n        add_blank=0,\n        statistics=((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n    ):\n        self.transform = tranform\n        self.image_loader = image_loader\n        self.add_blank = add_blank\n        self.statistics = (torch.Tensor(statistics[0]), torch.Tensor(statistics[1]))\n\n        norm = transforms.Normalize(mean=statistics[0], std=statistics[1])\n        if self.transform is not None:\n            self.transform = transforms.Compose([self.transform, norm])\n        else:\n            self.transform = norm\n\n        labels, image_names = read_and_merge_datasets(image_roots, label_files)\n        self.image_names = image_names\n\n        split = int(len(labels) * 0.8)\n        np.random.seed(1234)\n        np.random.shuffle(labels)\n        if subset == \"train\":\n            self.labels = labels[0:split]  # [(vid, labels)]\n        else:\n            self.labels = labels[split:]\n\n        raw_labels = np.asarray([item[1] for item in self.labels])\n        pos_rate = np.mean(raw_labels > 0, axis=0)\n        print(\n            \"Collect {} videos for {}, positive rate for each target: {}\".format(\n                len(self.labels), subset, pos_rate\n            )\n        )\n\n    def __len__(self):\n        return len(self.labels) + self.add_blank\n\n    def __getitem__(self, i):\n        i -= self.add_blank\n\n        if i >= 0:\n            images, label = self.read_images(i)\n        else:\n            color = (255, 255, 255)\n            if i == -1:\n                color = (255, 255, 255)\n            elif i == -2:\n                color = (0, 0, 0)\n            else:\n                color = (np.random.randint(0, 255)) * 3\n            images, label = self.get_black_images(color)\n\n        images = torch.stack(images, 0)\n        return images, int(label[0])\n\n    def read_images(self, i):\n        vid, label = self.labels[i]\n        image_names = self.image_names[vid]\n\n        # read images\n        images = []  # type: List[torch.Tensor]\n        for fullname in image_names:\n            image = self.image_loader(fullname)\n            if self.transform:\n                image = self.transform(image)\n            images.append(image)\n        return images, label\n\n    def get_black_images(self, color=(255, 255, 255)):\n        image = np.zeros([100, 100, 3], dtype=np.uint8)\n        image[:, :] = color\n        image = Image.fromarray(image)\n        if self.transform:\n            image = self.transform(image)\n\n        label = np.asarray([0, 0, 0], dtype=int)\n        return [image] * 10, label\n","sub_path":"modules/cover_dataset.py","file_name":"cover_dataset.py","file_ext":"py","file_size_in_byte":4494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"442318675","text":"\"\"\"Simulated data used in this project\n\"\"\"\nimport numpy as np\nimport transforms\n\nfs = 1e3\nduration = 1.0\ntime = np.arange(duration * fs) / float(fs)\n\n# Stabbing\nmu_t = [duration / 2, duration / 4, duration / 4 * 3]\nmu_f = [-100, 0.0, 150.0]\nc = [0.0, 100.0, 200.0]\nsigma_t = [np.sqrt(2) * 10 / 3, np.sqrt(2) * 10 / 3, np.sqrt(2) * 10 / 3]\na = [1.0, 1.0, 1.5]\nsignal = np.array(a)[..., np.newaxis] * transforms.q_chirplet(time, mu_t, mu_f, c, sigma_t)\nsignal[1, int(1 * len(time) / 2):] = 0.0\nsignal[2, :int(1 * len(time) / 2)] = 0.0\nnoise1 = np.random.normal(scale=np.sqrt(0.5), size=time.shape)\nnoise2 = np.random.normal(scale=np.sqrt(0.5), size=time.shape)\nstabbing_signal = np.sum(signal, axis=0) + noise1 + 1j * noise2\n\n# Pickpocket\nmu_t = [duration / 2, duration / 4, duration / 4 * 3]\nmu_f = [-100, 0.0, -50.0]\nc = [0.0, 100.0, -200.0]\nsigma_t = [np.sqrt(2) * 10 / 3, np.sqrt(2) * 10 / 3, np.sqrt(2) * 10 / 3]\na = [1.0, 1.5, 1.5]\nsignal = np.array(a)[..., np.newaxis] * transforms.q_chirplet(time, mu_t, mu_f, c, sigma_t)\nsignal[1, int(1 * len(time) / 2):] = 0.0\nsignal[2, :int(1 * len(time) / 2)] = 0.0\nnoise1 = np.random.normal(scale=np.sqrt(0.5), size=time.shape)\nnoise2 = np.random.normal(scale=np.sqrt(0.5), size=time.shape)\npickpocket_signal = np.sum(signal, axis=0) + noise1 + 1j * noise2\n\n# Start Walking\nmu_t = [duration / 4, duration / 4 * 3]\nmu_f = [-80.0, -180.0]\nc = [-160.0, -0.0]\nsigma_t = [np.sqrt(2) * 2 / 3, np.sqrt(2) * 2 / 3]\na = [1.0, 1.0]\nsignal = np.array(a)[..., np.newaxis] * transforms.q_chirplet(time, mu_t, mu_f, c, sigma_t)\nsignal[0, int(1 * len(time) / 2):] = 0.0\nsignal[1, :int(1 * len(time) / 2)] = 0.0\nnoise1 = np.random.normal(scale=np.sqrt(1.0), size=time.shape)\nnoise2 = np.random.normal(scale=np.sqrt(1.0), size=time.shape)\nrunning_signal = np.sum(signal, axis=0) + noise1 + 1j * noise2\n\n# example chirplet\nmu_t = [duration / 2]\nmu_f = [70]\nc = [100.0]\nsigma_t = [0.3 * np.sqrt(2) / 3]\nsignal = transforms.q_chirplet(time, mu_t, mu_f, c, sigma_t)\nchirplet_signal = np.sum(signal, axis=0)\n\n# example warblet\nmu_t = [duration / 2]\nmu_f = [70]\nsigma_t = [0.3 * np.sqrt(2) / 3]\nsignal = transforms.warblet(time, mu_t, mu_f, 2.0, 40.0, np.pi / 2, sigma_t)\nwarblet_signal = np.sum(signal, axis=0)\n\n# example chirp\nmu_t = [duration / 2]\nmu_f = [100]\nc = [100.0]\nsigma_t = [0.3 * np.sqrt(2) / 3]\nsignal = transforms.q_chirp(time, mu_t, mu_f, c)\nchirp_signal = np.sum(signal, axis=0)\n\n# example warbling signal\nmu_t = [duration / 2]\nmu_f = [100]\nsigma_t = [0.3 * np.sqrt(2) / 3]\nsignal = transforms.warble(time, mu_t, mu_f, 2.0, 70.0, np.pi / 2)\nwarble_signal = np.sum(signal, axis=0)\n\n# lem test - 2 center\nmu_t = [duration * 3 / 8, duration * 4 / 5]\nmu_f = [-30, 80]\nc = [-70.0, 150.0]\nsigma_t = [0.2 * np.sqrt(2) / 3, 0.1 * np.sqrt(2) / 3]\nsignal = transforms.q_chirplet(time, mu_t, mu_f, c, sigma_t)\ntest_2center_signal = np.sum(signal, axis=0)\n\n# lem test - 3 center\nmu_t = [duration / 5, duration * 3 / 8, duration * 4 / 5]\nmu_f = [-40.0, 20.0, 50.0]\nc = [30.0, 0.0, -20]\nsigma_t = [0.1 * np.sqrt(2) / 3, 0.1 * np.sqrt(2) / 3, 0.2 * np.sqrt(2) / 3]\nsignal = transforms.q_chirplet(time, mu_t, mu_f, c, sigma_t)\ntest3_signal = np.sum(signal, axis=0)\n\n# warblet transform simulated data\nmu_t = [duration / 2, duration / 2]\nmu_f = [0, 0]\nfm = [2.0, 3.0]\nbm = [200.0, 300]\npm = [0.0, 0.0]\nsigma_t = [0.3 * np.sqrt(2) / 3]\nsignal = transforms.warble(time, mu_t, mu_f, fm, bm, pm)\nnoise1 = np.random.normal(scale=np.sqrt(0.5), size=time.shape)\nnoise2 = np.random.normal(scale=np.sqrt(0.5), size=time.shape)\nwaving_signal = np.sum(signal, axis=0) + noise1 + 1j * noise2\n\ndata = {}\ndata[\"Start Walking\"] = {\"signal\": running_signal, \"ncenter\": 3}\ndata[\"PickPocket\"] = {\"signal\": pickpocket_signal, \"ncenter\": 2}\ndata[\"Stabbing\"] = {\"signal\": stabbing_signal, \"ncenter\": 2}\ndata[\"Chirplet\"] = {\"signal\": chirplet_signal, \"ncenter\": 1}\ndata[\"Warblet\"] = {\"signal\": warblet_signal, \"ncenter\": 1}\ndata[\"Chirp\"] = {\"signal\": chirp_signal, \"ncenter\": 1}\ndata[\"Warble\"] = {\"signal\": warble_signal, \"ncenter\": 1}\ndata[\"Visualize LEM\"] = {\"signal\": test_2center_signal, \"ncenter\": 2}\ndata[\"Waving\"] = {\"signal\": waving_signal}","sub_path":"sim_radar_data.py","file_name":"sim_radar_data.py","file_ext":"py","file_size_in_byte":4148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"235839220","text":"import os\nimport sys\nimport random\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use('seaborn-white')\nimport seaborn as sns\nsns.set_style(\"white\")\n\nfrom functions import *\n\n# import cv2\nfrom sklearn.model_selection import train_test_split\n\nfrom tqdm import tqdm_notebook, tnrange\nfrom itertools import chain\nfrom skimage.io import imread, imshow, concatenate_images\nfrom skimage.transform import resize\nfrom skimage.morphology import label\n\nfrom keras.models import Model, load_model\nfrom keras.layers import Input,Dropout,BatchNormalization,Activation,Add\nfrom keras.layers.core import Lambda\nfrom keras.layers.convolutional import Conv2D, Conv2DTranspose\nfrom keras.layers.pooling import MaxPooling2D\nfrom keras.layers.merge import concatenate\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\nfrom keras import backend as K\n\nimport tensorflow as tf\n\nfrom keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img#,save_img\n\nPATH, IMAGE_PATH, TEST_PATH = set_paths_local()\n\n# Set some parameters\nimg_size_ori = 101\nimg_size_target = 101\nim_width = 101\nim_height = 101\nim_chan = 1\nbasicpath = '../input/'\npath_train = basicpath + 'train/'\npath_test = basicpath + 'test/'\n\npath_train_images = path_train + 'images/'\npath_train_masks = path_train + 'masks/'\npath_test_images = path_test + 'images/'\n\ndepth_csv = f'{PATH}/depths.csv'\nmask_csv = f'{PATH}/train.csv'\n\n# Create dataset for visualizations\ndepth_df = pd.read_csv(depth_csv, index_col = 'id')\nmask_df = pd.read_csv(mask_csv)\ntrain_df = mask_df.join(depth_df)\n\n# Load images and masks\ntrain_df[\"images\"] = [np.array(load_img(PATH + \"/train_images/{}.png\".format(idx), grayscale = True)) / 255 for idx in tqdm_notebook(train_df.id)]\ntrain_df[\"masks\"] = [np.array(load_img(PATH + \"/masks/{}.png\".format(idx), grayscale=True)) / 255 for idx in tqdm_notebook(train_df.id)]\n\n# Set image size\nimg_size = train_df.images.values[0].shape[0]\n\n# Count number of salt pixels and divide by image size\ntrain_df[\"coverage\"] = train_df.masks.map(np.sum) / (img_size**2)\ntrain_df[\"coverage_class\"] = train_df.coverage.map(cov_to_class)\nids_train, ids_valid, x_train, x_valid, y_train, y_valid, cov_train, cov_test, depth_train, depth_test = train_test_split(\n    train_df.index.values,\n    np.array(train_df.images.tolist()).reshape(-1, img_size_target, img_size_target, 1), \n    np.array(train_df.masks.tolist()).reshape(-1, img_size_target, img_size_target, 1), \n    train_df.coverage.values,\n    train_df.z.values,\n    test_size=0.2, stratify=train_df.coverage_class, random_state= 1234)\nACTIVATION = \"relu\"\n\ndef convolution_block(x, filters, size, strides=(1,1), padding='same', activation=True):\n    x = Conv2D(filters, size, strides=strides, padding=padding)(x)\n    x = BatchNormalization()(x)\n    if activation == True:\n        x = Activation(ACTIVATION)(x)\n    return x\n\ndef residual_block(blockInput, num_filters=16):\n    x = Activation(ACTIVATION)(blockInput)\n    x = BatchNormalization()(x)\n    x = convolution_block(x, num_filters, (3,3) )\n    x = convolution_block(x, num_filters, (3,3), activation=False)\n    x = Add()([x, blockInput])\n    return x\n\n# Build model\ndef build_model(input_layer, start_neurons, DropoutRatio = 0.5):\n    # 101 -> 50\n    conv1 = Conv2D(start_neurons * 1, (3, 3), activation=None, padding=\"same\")(input_layer)\n    conv1 = residual_block(conv1,start_neurons * 1)\n    conv1 = residual_block(conv1,start_neurons * 1)\n    conv1 = Activation(ACTIVATION)(conv1)\n    pool1 = MaxPooling2D((2, 2))(conv1)\n    pool1 = Dropout(DropoutRatio/2)(pool1)\n\n    # 50 -> 25\n    conv2 = Conv2D(start_neurons * 2, (3, 3), activation=None, padding=\"same\")(pool1)\n    conv2 = residual_block(conv2,start_neurons * 2)\n    conv2 = residual_block(conv2,start_neurons * 2)\n    conv2 = Activation(ACTIVATION)(conv2)\n    pool2 = MaxPooling2D((2, 2))(conv2)\n    pool2 = Dropout(DropoutRatio)(pool2)\n\n    # 25 -> 12\n    conv3 = Conv2D(start_neurons * 4, (3, 3), activation=None, padding=\"same\")(pool2)\n    conv3 = residual_block(conv3,start_neurons * 4)\n    conv3 = residual_block(conv3,start_neurons * 4)\n    conv3 = Activation(ACTIVATION)(conv3)\n    pool3 = MaxPooling2D((2, 2))(conv3)\n    pool3 = Dropout(DropoutRatio)(pool3)\n\n    # 12 -> 6\n    conv4 = Conv2D(start_neurons * 8, (3, 3), activation=None, padding=\"same\")(pool3)\n    conv4 = residual_block(conv4,start_neurons * 8)\n    conv4 = residual_block(conv4,start_neurons * 8)\n    conv4 = Activation(ACTIVATION)(conv4)\n    pool4 = MaxPooling2D((2, 2))(conv4)\n    pool4 = Dropout(DropoutRatio)(pool4)\n\n    # Middle\n    convm = Conv2D(start_neurons * 16, (3, 3), activation=None, padding=\"same\")(pool4)\n    convm = residual_block(convm,start_neurons * 16)\n    convm = residual_block(convm,start_neurons * 16)\n    convm = Activation(ACTIVATION)(convm)\n    \n    # 6 -> 12\n    deconv4 = Conv2DTranspose(start_neurons * 8, (3, 3), strides=(2, 2), padding=\"same\")(convm)\n    uconv4 = concatenate([deconv4, conv4])\n    uconv4 = Dropout(DropoutRatio)(uconv4)\n    \n    uconv4 = Conv2D(start_neurons * 8, (3, 3), activation=None, padding=\"same\")(uconv4)\n    uconv4 = residual_block(uconv4,start_neurons * 8)\n    uconv4 = residual_block(uconv4,start_neurons * 8)\n    uconv4 = Activation(ACTIVATION)(uconv4)\n    \n    # 12 -> 25\n    #deconv3 = Conv2DTranspose(start_neurons * 4, (3, 3), strides=(2, 2), padding=\"same\")(uconv4)\n    deconv3 = Conv2DTranspose(start_neurons * 4, (3, 3), strides=(2, 2), padding=\"valid\")(uconv4)\n    uconv3 = concatenate([deconv3, conv3])    \n    uconv3 = Dropout(DropoutRatio)(uconv3)\n    \n    uconv3 = Conv2D(start_neurons * 4, (3, 3), activation=None, padding=\"same\")(uconv3)\n    uconv3 = residual_block(uconv3,start_neurons * 4)\n    uconv3 = residual_block(uconv3,start_neurons * 4)\n    uconv3 = Activation(ACTIVATION)(uconv3)\n\n    # 25 -> 50\n    deconv2 = Conv2DTranspose(start_neurons * 2, (3, 3), strides=(2, 2), padding=\"same\")(uconv3)\n    uconv2 = concatenate([deconv2, conv2])\n        \n    uconv2 = Dropout(DropoutRatio)(uconv2)\n    uconv2 = Conv2D(start_neurons * 2, (3, 3), activation=None, padding=\"same\")(uconv2)\n    uconv2 = residual_block(uconv2,start_neurons * 2)\n    uconv2 = residual_block(uconv2,start_neurons * 2)\n    uconv2 = Activation(ACTIVATION)(uconv2)\n    \n    # 50 -> 101\n    #deconv1 = Conv2DTranspose(start_neurons * 1, (3, 3), strides=(2, 2), padding=\"same\")(uconv2)\n    deconv1 = Conv2DTranspose(start_neurons * 1, (3, 3), strides=(2, 2), padding=\"valid\")(uconv2)\n    uconv1 = concatenate([deconv1, conv1])\n    \n    uconv1 = Dropout(DropoutRatio)(uconv1)\n    uconv1 = Conv2D(start_neurons * 1, (3, 3), activation=None, padding=\"same\")(uconv1)\n    uconv1 = residual_block(uconv1,start_neurons * 1)\n    uconv1 = residual_block(uconv1,start_neurons * 1)\n    uconv1 = Activation(ACTIVATION)(uconv1)\n    \n    uconv1 = Dropout(DropoutRatio/2)(uconv1)\n    output_layer = Conv2D(1, (1,1), padding=\"same\", activation=\"sigmoid\")(uconv1)\n    \n    return output_layer\n\n\niou_thresholds = np.array([0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95])\n\ndef iou(img_true, img_pred):\n    i = np.sum((img_true*img_pred) >0)\n    u = np.sum((img_true + img_pred) >0)\n    if u == 0:\n        return u\n    return i/u\n\ndef iou_metric(imgs_true, imgs_pred):\n    num_images = len(imgs_true)\n    scores = np.zeros(num_images)\n    \n    for i in range(num_images):\n        if imgs_true[i].sum() == imgs_pred[i].sum() == 0:\n            scores[i] = 1\n        else:\n            scores[i] = (iou_thresholds <= iou(imgs_true[i], imgs_pred[i])).mean()\n            \n    return scores.mean()\n\ndef lovasz_grad(gt_sorted):\n    \"\"\"\n    Computes gradient of the Lovasz extension w.r.t sorted errors\n    See Alg. 1 in paper\n    \"\"\"\n    gts = tf.reduce_sum(gt_sorted)\n    intersection = gts - tf.cumsum(gt_sorted)\n    union = gts + tf.cumsum(1. - gt_sorted)\n    jaccard = 1. - intersection / union\n    jaccard = tf.concat((jaccard[0:1], jaccard[1:] - jaccard[:-1]), 0)\n    return jaccard\n\n\ndef lovasz_hinge(logits, labels, per_image=True, ignore=None):\n    \"\"\"\n    Binary Lovasz hinge loss\n      logits: [B, H, W] Variable, logits at each pixel (between -\\infty and +\\infty)\n      labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)\n      per_image: compute the loss per image instead of per batch\n      ignore: void class id\n    \"\"\"\n    if per_image:\n        def treat_image(log_lab):\n            log, lab = log_lab\n            log, lab = tf.expand_dims(log, 0), tf.expand_dims(lab, 0)\n            log, lab = flatten_binary_scores(log, lab, ignore)\n            return lovasz_hinge_flat(log, lab)\n        losses = tf.map_fn(treat_image, (logits, labels), dtype=tf.float32)\n        loss = tf.reduce_mean(losses)\n    else:\n        loss = lovasz_hinge_flat(*flatten_binary_scores(logits, labels, ignore))\n    return loss\n\n\ndef lovasz_hinge_flat(logits, labels):\n    \"\"\"\n    Binary Lovasz hinge loss\n      logits: [P] Variable, logits at each prediction (between -\\infty and +\\infty)\n      labels: [P] Tensor, binary ground truth labels (0 or 1)\n      ignore: label to ignore\n    \"\"\"\n\ndef compute_loss():\n    labelsf = tf.cast(labels, logits.dtype)\n    signs = 2. * labelsf - 1.\n    errors = 1. - logits * tf.stop_gradient(signs)\n    errors_sorted, perm = tf.nn.top_k(errors, k=tf.shape(errors)[0], name=\"descending_sort\")\n    gt_sorted = tf.gather(labelsf, perm)\n    grad = lovasz_grad(gt_sorted)\n    loss = tf.tensordot(tf.nn.relu(errors_sorted), tf.stop_gradient(grad), 1, name=\"loss_non_void\")\n    return loss\n\n# deal with the void prediction case (only void pixels)\nloss = tf.cond(tf.equal(tf.shape(logits)[0], 0),\n               lambda: tf.reduce_sum(logits) * 0.,\n               compute_loss,\n               strict=True,\n               name=\"loss\"\n               )\nreturn loss\n\n\ndef flatten_binary_scores(scores, labels, ignore=None):\n    \"\"\"\n    Flattens predictions in the batch (binary case)\n    Remove labels equal to 'ignore'\n    \"\"\"\n    scores = tf.reshape(scores, (-1,))\n    labels = tf.reshape(labels, (-1,))\n    if ignore is None:\n        return scores, labels\n    valid = tf.not_equal(labels, ignore)\n    vscores = tf.boolean_mask(scores, valid, name='valid_scores')\n    vlabels = tf.boolean_mask(labels, valid, name='valid_labels')\n    return vscores, vlabels\n\ndef lovasz_loss(y_true, y_pred):\n    y_true, y_pred = K.cast(K.squeeze(y_true, -1), 'int32'), K.cast(K.squeeze(y_pred, -1), 'float32')\n    #logits = K.log(y_pred / (1. - y_pred))\n    logits = y_pred #Jiaxin\n    loss = lovasz_hinge(logits, y_true, per_image = True, ignore = None)\n    return loss\n\ndef get_iou_vector(A, B):\n    batch_size = A.shape[0]\n    metric = []\n    for batch in range(batch_size):\n        t, p = A[batch]>0, B[batch]>0\n#         if np.count_nonzero(t) == 0 and np.count_nonzero(p) > 0:\n#             metric.append(0)\n#             continue\n#         if np.count_nonzero(t) >= 1 and np.count_nonzero(p) == 0:\n#             metric.append(0)\n#             continue\n#         if np.count_nonzero(t) == 0 and np.count_nonzero(p) == 0:\n#             metric.append(1)\n#             continue\n        \n        intersection = np.logical_and(t, p)\n        union = np.logical_or(t, p)\n        iou = (np.sum(intersection > 0) + 1e-10 )/ (np.sum(union > 0) + 1e-10)\n        thresholds = np.arange(0.5, 1, 0.05)\n        s = []\n        for thresh in thresholds:\n            s.append(iou > thresh)\n        metric.append(np.mean(s))\n\n    return np.mean(metric)\n\ndef my_iou_metric(label, pred):\n    return tf.py_func(get_iou_vector, [label, pred>0.5], tf.float64)\n\ndef my_iou_metric_2(label, pred):\n    return tf.py_func(get_iou_vector, [label, pred >0], tf.float64)\n\nx_train = np.append(x_train, [np.fliplr(x) for x in x_train], axis=0)\ny_train = np.append(y_train, [np.fliplr(x) for x in y_train], axis=0)\n\n# model\ninput_layer = Input((img_size_target, img_size_target, 1))\noutput_layer = build_model(input_layer, 16,0.5)\n\nmodel = Model(input_layer, output_layer)\nmodel.compile(loss=lovasz_loss, optimizer=\"adam\", metrics=[\"my_iou_metric\", \"my_iou_metric_2\"])\n\n\nearly_stopping = EarlyStopping(monitor='val_acc', mode = 'max',patience=20, verbose=1)\nmodel_checkpoint = ModelCheckpoint(\"./unet_best1.model\",monitor='val_acc', \n                                   mode = 'max', save_best_only=True, verbose=1)\nreduce_lr = ReduceLROnPlateau(monitor='val_acc', mode = 'max',factor=0.2, patience=5, min_lr=0.00001, verbose=1)\n#reduce_lr = ReduceLROnPlateau(factor=0.2, patience=5, min_lr=0.00001, verbose=1)\n\nepochs = 200\nbatch_size = 32\n\nhistory = model.fit(x_train, y_train,\n                    validation_data=[x_valid, y_valid], \n                    epochs=epochs,\n                    batch_size=batch_size,\n                    callbacks=[early_stopping, model_checkpoint, reduce_lr], \n                    verbose=1)\n\nimport matplotlib.pyplot as plt\n# summarize history for loss\nplt.plot(history.history['acc'][1:])\nplt.plot(history.history['val_acc'][1:])\nplt.title('model loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train','Validation'], loc='upper left')\nplt.show()\n\nfig, (ax_loss, ax_acc) = plt.subplots(1, 2, figsize=(15,5))\nax_loss.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\nax_loss.plot(history.epoch, history.history[\"val_loss\"], label=\"Validation loss\")\n\nmodel = load_model(\"./unet_best1.model\")\ndef predict_result(model,x_test,img_size_target): # predict both orginal and reflect x\n    preds_test = model.predict(x_test).reshape(-1, img_size_target, img_size_target)\n    preds_test += np.array([ np.fliplr(a) for a in model.predict(np.array([np.fliplr(x) for x in x_test])).reshape(-1, img_size_target, img_size_target)])\n    return preds_test/2.0\npreds_valid = predict_result(model,x_valid,img_size_target)\ndef filter_image(img):\n    if img.sum() < 100:\n        return np.zeros(img.shape)\n    else:\n        return img\n\n## Scoring for last model\nthresholds = np.linspace(0.3, 0.7, 31)\nious = np.array([iou_metric(y_valid.reshape((-1, img_size_target, img_size_target)), [filter_image(img) for img in preds_valid > threshold]) for threshold in tqdm_notebook(thresholds)])\nthreshold_best_index = np.argmax(ious) \niou_best = ious[threshold_best_index]\nthreshold_best = thresholds[threshold_best_index]\n\nplt.plot(thresholds, ious)\nplt.plot(threshold_best, iou_best, \"xr\", label=\"Best threshold\")\nplt.xlabel(\"Threshold\")\nplt.ylabel(\"IoU\")\nplt.title(\"Threshold vs IoU ({}, {})\".format(threshold_best, iou_best))\nplt.legend()\n\ndef rle_encode(im):\n    pixels = im.flatten(order = 'F')\n    pixels = np.concatenate([[0], pixels, [0]])\n    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n    runs[1::2] -= runs[::2]\n    return ' '.join(str(x) for x in runs)\nimport gc\n\ndel x_train, x_valid, y_train, y_valid, preds_valid\ngc.collect()\n132330\nx_test = np.array([(np.array(load_img(\"../input/test/images/{}.png\".format(idx), grayscale = True))) / 255 for idx in tqdm_notebook(test_df.index)]).reshape(-1, img_size_target, img_size_target, 1)\n\npreds_test = predict_result(model,x_test,img_size_target)\nimport time\nt1 = time.time()\npred_dict = {idx: rle_encode(filter_image(preds_test[i] > threshold_best)) for i, idx in enumerate(tqdm_notebook(test_df.index.values))}\nt2 = time.time()\n\nsub = pd.DataFrame.from_dict(pred_dict,orient='index')\nsub.index.names = ['id']\nsub.columns = ['rle_mask']\nsub.to_csv('submission_tf.csv')","sub_path":"scripts/tensorflow_unet.py","file_name":"tensorflow_unet.py","file_ext":"py","file_size_in_byte":15356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"392183927","text":"def wombat(state, time_left):\n    import random\n    \n    # command = random.choice(['turn', 'move', 'shoot'])\n    command = 'turn'\n    \n    if command == 'turn':\n        metadata = {\n            'direction': random.choice(['right', 'left', 'about-face'])\n            }\n    else:\n        metadata = {}\n                \n    # Note that the function name MUST be wombat\n    return {\n        'command': {\n            'action': command,\n            'metadata': metadata,\n        },\n        'state': {\n            'hello': 'world'\n        }\n    }\n","sub_path":"python-sample.py","file_name":"python-sample.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"601176544","text":"import pickle\nimport pytest\nfrom copy import copy\nimport numpy as np\n\nimport torch\nfrom torch import nn\n\nfrom src.models.neural_networks.ealstm import EALSTM, EALSTMCell, OrgEALSTMCell\nfrom src.models import EARecurrentNetwork\n\nfrom tests.utils import _make_dataset\n\n\nclass TestEARecurrentNetwork:\n\n    def test_save(self, tmp_path, monkeypatch):\n\n        features_per_month = 5\n        dense_features = [10]\n        input_dense_features = copy(dense_features)\n        hidden_size = 128\n        rnn_dropout = 0.25\n        include_latlons = True\n        include_pred_month = True\n        include_yearly_aggs = True\n        yearly_agg_size = 3\n\n        def mocktrain(self):\n            self.model = EALSTM(features_per_month, dense_features, hidden_size,\n                                rnn_dropout, include_latlons, include_pred_month,\n                                experiment='one_month_forecast', yearly_agg_size=yearly_agg_size)\n            self.features_per_month = features_per_month\n            self.yearly_agg_size = yearly_agg_size\n\n        monkeypatch.setattr(EARecurrentNetwork, 'train', mocktrain)\n\n        model = EARecurrentNetwork(hidden_size=hidden_size, dense_features=dense_features,\n                                   include_pred_month=include_pred_month,\n                                   include_latlons=include_latlons,\n                                   rnn_dropout=rnn_dropout, data_folder=tmp_path,\n                                   include_yearly_aggs=include_yearly_aggs)\n        model.train()\n        model.save_model()\n\n        assert (tmp_path / 'models/one_month_forecast/ealstm/model.pt').exists(), \\\n            f'Model not saved!'\n\n        model_dict = torch.load(model.model_dir / 'model.pt', map_location='cpu')\n\n        for key, val in model_dict['model']['state_dict'].items():\n            assert (model.model.state_dict()[key] == val).all()\n\n        assert model_dict['model']['features_per_month'] == features_per_month\n        assert model_dict['model']['yearly_agg_size'] == yearly_agg_size\n        assert model_dict['hidden_size'] == hidden_size\n        assert model_dict['rnn_dropout'] == rnn_dropout\n        assert model_dict['dense_features'] == input_dense_features\n        assert model_dict['include_pred_month'] == include_pred_month\n        assert model_dict['include_latlons'] == include_latlons\n        assert model_dict['include_yearly_aggs'] == include_yearly_aggs\n        assert model_dict['experiment'] == 'one_month_forecast'\n\n    @pytest.mark.parametrize('use_pred_months', [True, False])\n    def test_train(self, tmp_path, capsys, use_pred_months):\n        x, _, _ = _make_dataset(size=(5, 5), const=True)\n        y = x.isel(time=[-1])\n\n        test_features = tmp_path / 'features/one_month_forecast/train/hello'\n        test_features.mkdir(parents=True)\n\n        norm_dict = {'VHI': {'mean': 0, 'std': 1}}\n        with (tmp_path / 'features/one_month_forecast/normalizing_dict.pkl').open('wb') as f:\n            pickle.dump(norm_dict, f)\n\n        x.to_netcdf(test_features / 'x.nc')\n        y.to_netcdf(test_features / 'y.nc')\n\n        # static\n        x_static, _, _ = _make_dataset(size=(5, 5), add_times=False)\n        static_features = tmp_path / f'features/static'\n        static_features.mkdir(parents=True)\n        x_static.to_netcdf(static_features / 'data.nc')\n\n        static_norm_dict = {'VHI': {'mean': 0.0, 'std': 1.0}}\n        with (\n            tmp_path / f'features/static/normalizing_dict.pkl'\n        ).open('wb') as f:\n            pickle.dump(static_norm_dict, f)\n\n        dense_features = [10]\n        hidden_size = 128\n        rnn_dropout = 0.25\n\n        model = EARecurrentNetwork(hidden_size=hidden_size, dense_features=dense_features,\n                                   rnn_dropout=rnn_dropout, data_folder=tmp_path)\n        model.train()\n\n        captured = capsys.readouterr()\n        expected_stdout = 'Epoch 1, train smooth L1: 0.'\n        assert expected_stdout in captured.out\n\n        assert type(model.model) == EALSTM, \\\n            f'Model attribute not an EALSTM!'\n\n    @pytest.mark.parametrize('use_pred_months', [True, False])\n    def test_predict(self, tmp_path, use_pred_months):\n        x, _, _ = _make_dataset(size=(5, 5), const=True)\n        y = x.isel(time=[-1])\n\n        train_features = tmp_path / 'features/one_month_forecast/train/hello'\n        train_features.mkdir(parents=True)\n\n        test_features = tmp_path / 'features/one_month_forecast/test/hello'\n        test_features.mkdir(parents=True)\n\n        norm_dict = {'VHI': {'mean': 0.0, 'std': 1.0}}\n        with (tmp_path / 'features/one_month_forecast/normalizing_dict.pkl').open('wb') as f:\n            pickle.dump(norm_dict, f)\n\n        x.to_netcdf(test_features / 'x.nc')\n        y.to_netcdf(test_features / 'y.nc')\n\n        x.to_netcdf(train_features / 'x.nc')\n        y.to_netcdf(train_features / 'y.nc')\n\n        # static\n        x_static, _, _ = _make_dataset(size=(5, 5), add_times=False)\n        static_features = tmp_path / f'features/static'\n        static_features.mkdir(parents=True)\n        x_static.to_netcdf(static_features / 'data.nc')\n\n        static_norm_dict = {'VHI': {'mean': 0.0, 'std': 1.0}}\n        with (\n            tmp_path / f'features/static/normalizing_dict.pkl'\n        ).open('wb') as f:\n            pickle.dump(static_norm_dict, f)\n\n        dense_features = [10]\n        hidden_size = 128\n        rnn_dropout = 0.25\n\n        model = EARecurrentNetwork(hidden_size=hidden_size, dense_features=dense_features,\n                                   rnn_dropout=rnn_dropout, data_folder=tmp_path)\n        model.train()\n        test_arrays_dict, pred_dict = model.predict()\n\n        # the foldername \"hello\" is the only one which should be in the dictionaries\n        assert ('hello' in test_arrays_dict.keys()) and (len(test_arrays_dict) == 1)\n        assert ('hello' in pred_dict.keys()) and (len(pred_dict) == 1)\n\n        # _make_dataset with const=True returns all ones\n        assert (test_arrays_dict['hello']['y'] == 1).all()\n\n\nclass TestEALSTMCell:\n    @staticmethod\n    def test_ealstm(monkeypatch):\n        \"\"\"\n        We implement our own unrolled RNN, so that it can be explained with\n        shap. This test makes sure it roughly mirrors the behaviour of the pytorch\n        LSTM.\n        \"\"\"\n\n        batch_size, hidden_size, timesteps, dyn_input, static_input = 3, 5, 2, 6, 4\n\n        @staticmethod\n        def i_init(layer):\n            nn.init.constant_(layer.weight.data, val=1)\n        monkeypatch.setattr(EALSTMCell, '_reset_i', i_init)\n\n        def org_init(self):\n            \"\"\"Initialize all learnable parameters of the LSTM\"\"\"\n            nn.init.constant_(self.weight_ih.data, val=1)\n            nn.init.constant_(self.weight_sh, val=1)\n\n            weight_hh_data = torch.eye(self.hidden_size)\n            weight_hh_data = weight_hh_data.repeat(1, 3)\n            self.weight_hh.data = weight_hh_data\n\n            nn.init.constant_(self.bias.data, val=0)\n            nn.init.constant_(self.bias_s.data, val=0)\n        monkeypatch.setattr(OrgEALSTMCell, 'reset_parameters', org_init)\n\n        org_ealstm = OrgEALSTMCell(input_size_dyn=dyn_input,\n                                   input_size_stat=static_input,\n                                   hidden_size=hidden_size)\n\n        our_ealstm = EALSTMCell(input_size_dyn=dyn_input,\n                                input_size_stat=static_input,\n                                hidden_size=hidden_size)\n\n        static = torch.rand(batch_size, static_input)\n        dynamic = torch.rand(batch_size, timesteps, dyn_input)\n\n        with torch.no_grad():\n            org_hn, org_cn = org_ealstm(dynamic, static)\n            our_hn, our_cn = our_ealstm(dynamic, static)\n\n        assert np.isclose(org_hn.numpy(), our_hn.numpy(), 0.01).all(), \\\n            \"Difference in hidden state\"\n        assert np.isclose(org_cn.numpy(), our_cn.numpy(), 0.01).all(), \\\n            \"Difference in cell state\"\n","sub_path":"tests/models/neural_networks/test_ealstm.py","file_name":"test_ealstm.py","file_ext":"py","file_size_in_byte":7959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"272485141","text":"import os\nimport sys\n\nclass Mem():\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n\ndef change(obj, x):\n\n    if type(obj) == 'Mem':\n        obj.x = x\n    elif type(obj) == 'dict':\n        obj['x'] = x\n\nif __name__ == '__main__':\n   # m = Mem(3, 5)\n   m = {'x': 12}\n   change(m, 6)\n   print(m['x'])\n   print(\"--inital sys.path --\")\n   print(sys.path)\n   print(\"--inital PYTHONPATH--\")\n   print('exists' if 'PYTHONPATH' in os.environ else 'none')\n\n   \n   sys.path.append('/Users/yoshitaka.toyama/sample')\n   sys.path.append('/Users/yoshitaka.toyama/sample')\n   print(sys.path)\n   import tmppy\n   print(tmppy.AAA(2))\n","sub_path":"practice/learnpy/basic/01_type.py","file_name":"01_type.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"465939154","text":"import socket, sys, threading\n\n\nclass ThreadClient(threading.Thread):\n\n    def __init__(self, conn):\n        threading.Thread.__init__(self)\n        self.connexion = conn\n\n    def run(self):\n        nom = self.getName()\n        \n        while 1:\n            msgClient = self.connexion.recv(1024)\n            msgClient = msgClient.decode()\n\n            for cle in conn_client:\n                    if cle != nom:\n                        msgClient = msgClient.encode()\n                        conn_client[cle].send(msgClient)\n                \n  \n        \n\n\n\nHOST = socket.gethostbyname(socket.gethostname())\nPORT = 12345\nmySocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\nprint('IP :',HOST,'\\n')\n\nconn_client = {}\ninfos_client = {}\n\ntry:\n    mySocket.bind((HOST, PORT))\nexcept socket.error:\n    print (\"La liaison du socket à l'adresse choisie a échoué.\")\n    sys.exit()\nprint (\"Serveur en ligne, en attente de connexions ...\")\nmySocket.listen(5)\n\n\n\nwhile 1:\n    \n    connexion, adresse = mySocket.accept()\n    th = ThreadClient(connexion)\n    th.start()\n    it = th.getName()\n    conn_client[it] = connexion\n    infos_client[it] = adresse\n    print (\"Client (%s, %s) connecté.\" %adresse)\n    print('Clients connectés: ',str(len(conn_client)))\n\n    if len(conn_client) == 2:\n        first_player = 'joueur_1'\n        first_player = first_player.encode()\n\n        for cle in conn_client:\n                    if cle == it:\n                        conn_client[cle].send(first_player)\n        \n    \n                \n","sub_path":"SERVER morpion.py","file_name":"SERVER morpion.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"13233156","text":"#!/usr/bin/env python\nfrom app import create_app, db\nfrom app.models import Interval_pref, Flashcard, Deck\nimport argparse\n\n#My application structure was inspired by Miguel Grinberg's book on Flask.\n\nif __name__ == '__main__':\n    app = create_app('localserver')\n    with app.app_context():\n        db.create_all()\n    \n    parser = argparse.ArgumentParser(description='Run flashcard app')\n    #custom port\n    parser.add_argument('-p', type=int, default=5000)\n    args = parser.parse_args()\n    \n    app.run(port=args.p)\n\n","sub_path":"run_local.py","file_name":"run_local.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"625844483","text":"from itertools import combinations\nfrom sys import stdin\nfrom typing import TypeVar\n\nfrom ch7_copy.distance_matrix.DistanceMatrix import DistanceMatrix\nfrom helpers.InputUtils import str_to_list\n\nN = TypeVar('N')\n\n\ndef create_distance_matrix(m: list[list[float]]) -> DistanceMatrix:\n    d = {}\n    for i in range(len(m)):\n        for j in range(len(m)):\n            i1, i2 = sorted([i, j])\n            d[(f'v{i1}', f'v{i2}')] = float(m[i1][i2])\n    return DistanceMatrix(d)\n\n\n# MARKDOWN_QUARTET_TEST\ndef four_point_test(dm: DistanceMatrix[N], l0: N, l1: N, l2: N, l3: N) -> bool:\n    # Pairs of leaf node pairs\n    pair_combos = (\n        ((l0, l1), (l2, l3)),\n        ((l0, l2), (l1, l3)),\n        ((l0, l3), (l1, l2))\n    )\n    # Different orders to test pair_combos to see if they match conditions\n    test_orders = (\n        (0, 1, 2),\n        (0, 2, 1),\n        (1, 0, 2),\n        (1, 2, 0),\n        (2, 0, 1),\n        (2, 1, 0)\n    )\n    # Find at least one order of pair combos that passes the test\n    for p1_idx, p2_idx, p3_idx in test_orders:\n        p1_1, p1_2 = pair_combos[p1_idx]\n        p2_1, p2_2 = pair_combos[p2_idx]\n        p3_1, p3_2 = pair_combos[p3_idx]\n        s1 = dm[p1_1] + dm[p1_2]\n        s2 = dm[p2_1] + dm[p2_2]\n        s3 = dm[p3_1] + dm[p3_2]\n        if s1 <= s2 == s3:\n            return True\n    return False\n# MARKDOWN_QUARTET_TEST\n\n\n# MARKDOWN\ndef is_additive(dm: DistanceMatrix[N]) -> bool:\n    # Recall that an additive distance matrix of size <= 3 is guaranteed to be an additive distance\n    # matrix (try it and see -- any distances you use will always end up fitting a tree). Thats why\n    # you need at least 4 leaf nodes to test.\n    if dm.n < 4:\n        return True\n    leaves = dm.leaf_ids()\n    for quartet in combinations(leaves, r=4):\n        passed = four_point_test(dm, *quartet)\n        if not passed:\n            return False\n    return True\n# MARKDOWN\n\n\ndef main():\n    print(\"<div style=\\\"border:1px solid black;\\\">\", end=\"\\n\\n\")\n    print(\"`{bm-disable-all}`\", end=\"\\n\\n\")\n    try:\n        mat = []\n        for line in stdin:\n            row = [float(e) for e in str_to_list(line.strip(), 0)[0]]\n            mat.append(row)\n        dist_mat = create_distance_matrix(mat)\n        print('The distance matrix...')\n        print()\n        print('<table>')\n        print('<thead><tr>')\n        print('<th></th>')\n        for l in sorted(dist_mat.leaf_ids_it()):\n            print(f'<th>{l}</th>')\n        print('</tr></thead>')\n        print('<tbody>')\n        for l1 in sorted(dist_mat.leaf_ids_it()):\n            print('<tr>')\n            print(f'<td>{l1}</td>')\n            for l2 in sorted(dist_mat.leaf_ids_it()):\n                print(f'<td>{dist_mat[l1, l2]}</td>')\n            print('</tr>')\n        print('</tbody>')\n        print('</table>')\n        print()\n        if is_additive(dist_mat):\n            print('... is additive.')\n        else:\n            print('... is NOT additive.')\n        print()\n    finally:\n        print(\"</div>\", end=\"\\n\\n\")\n        print(\"`{bm-enable-all}`\", end=\"\\n\\n\")\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"docs/data/learn/Bioinformatics/input/ch8_code/src/ch7_copy/phylogeny/FourPointCondition.py","file_name":"FourPointCondition.py","file_ext":"py","file_size_in_byte":3099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"226431597","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, BooleanField, IntegerField, RadioField\nfrom wtforms.validators import DataRequired\n\nclass PC(FlaskForm):\n    abbrev = StringField('abbrev', validators=[DataRequired()])\n    name = StringField('name', validators=[DataRequired()])\n    pname = StringField('pname', validators=[DataRequired()])\n\nclass HP(FlaskForm):\n    source = StringField('source')\n    max = IntegerField('max')\n    current = IntegerField('current')\n    ablative_only = BooleanField('ablative_only')\n\nclass NewBuff(FlaskForm):\n    description = StringField('description')\n    duration_unit = RadioField('duration_unit', choices=['rd', 'mn', 'hr', 'dy'])\n    duration = IntegerField('duration')\n\n","sub_path":"app/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"455630988","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom .math import Hypothesis, Dataset\nfrom .perceptron import Perceptron\n\n\ndef plot_results(f: Hypothesis, d: Dataset, p: Perceptron):\n    \"\"\"Plot dataset with target function and fitted hypothesis\"\"\"\n    plt.figure(figsize=(15, 15))\n    d.plot()\n    f.plot(lw=3, color='green', label='target')\n    p.plot(lw=3, color='red', label='model')\n    plt.xticks(size=14)\n    plt.yticks(size=14)\n    plt.legend(prop={'size': 14})\n    plt.xlim(-1.1, +1.1)\n    plt.ylim(-1.1, +1.1)\n    plt.title('PLA results', size=20, style='italic')\n    plt.show()\n\n\ndef plot_hist(data: np.array, title: str = None):\n    \"\"\"Plot histogram of values with cutting off the outliers\"\"\"\n    plt.figure(figsize=(10, 4))\n    data = np.array(data)\n    minval = np.quantile(data, 0.05)\n    maxval = np.quantile(data, 0.95)\n    data[data < minval] = minval\n    data[data > maxval] = maxval\n    plt.hist(data, bins=30)\n    if title:\n        plt.title(title, size=16)\n    plt.xlim(minval, maxval)\n    plt.xticks(size=12)\n    plt.yticks(size=12)\n    plt.show()\n","sub_path":"src/lfd/hw1/plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"1496903","text":"from datetime import datetime\nimport numpy as np\nimport pandas as pd\n\nninja_trader_file = \"FDAX1840_NO_OPT_STRONG\"\n\ndata = pd.read_excel(\"../data/strats/\"+ninja_trader_file+\".xlsx\")\ndata = data.dropna()\n\n#Transform parenthesis values\ndata['Profit'] = (data['Profit'].replace( '[\\$,)]','', regex=True ).replace( '[(]','-',   regex=True ).astype(float))\ndata['Success'] = np.where(data['Profit']>0, 1,0)\n\n# source_file = \"ES1H.csv\"\n# source_file = \"FDAX1H.csv\"\nsource_file = \"EURUSD1H.csv\"\n\n#Load sources\nsource = pd.read_csv('../data/NT/' + source_file, sep=\";\", header = None)\nsource[1] = source[1].str.replace(' ', '')\n\ndef print_log(date,dif_close_open):\n    if i % 20 == 0:\n        print(\"----\")\n        print(date)\n        print(\"dif_close_open \" + bar + \" = \" + str(dif_close_open))\n\ndef clean_transform_value(value):\n    if isinstance(value, str):\n        value = value.replace(',', '')\n        value = value.replace(' ', '')\n        return float(value)\n    else :return float(value)\n\n\ndef get_column_name(column1,column2):return column1+\"_\"+column2\n\ndef format_utc(date):\n    dt64 = np.datetime64(date)\n    ts = (dt64 - np.datetime64('1970-01-01T00:00:00Z')) / np.timedelta64(1, 's')\n    return datetime.utcfromtimestamp(ts).strftime(\"%d-%b-%y\")\n\n\ndef get_bar_column(bar, i):\n\n    if not bar in data.columns: data[bar] = \"\"\n\n    dif_close_open = np.NaN\n\n    date = format_utc(data['Entry time'][i])\n\n    source_red = source[(source[0] == date) & (source[1] == bar)]\n\n    if not source_red.empty:\n\n        row = source_red.iloc[0]\n\n        value_open = clean_transform_value(row.iloc[2])\n        value_close = clean_transform_value(row.iloc[5])\n        value_volume = clean_transform_value(row.iloc[6])\n\n\n        dif_close_open = ((value_close-value_open) / value_open)\n        dif_close_open = round(dif_close_open,6)\n\n\n        print_log(date, dif_close_open)\n\n    data.loc[i, bar] = dif_close_open\n\n\nlist_columns = [\n                \"08:00:00\",\n                \"09:00:00\",\n                \"10:00:00\",\n                \"11:00:00\",\n                \"12:00:00\",\n                \"13:00:00\",\n                \"14:00:00\",\n                \"15:00:00\",\n                \"16:00:00\",\n                \"17:00:00\",\n                ]\n\n#Add all sources\nfor i in range(0,len(data)):\n\n    for bar in list_columns:\n        get_bar_column(bar, i)\n\nlist_columns.append(\"Profit\")\n\ndata = data[list_columns]\ndata = data.iloc[::-1]\n\nversion_ag = \"7_\" + source_file[0:2]\ndata.to_csv(\"../data/AG_\"+str(version_ag)+\"_\"+ninja_trader_file[9:]+\".csv\", sep=',',index=False)","sub_path":"data/LONG/data_aggregator_7.py","file_name":"data_aggregator_7.py","file_ext":"py","file_size_in_byte":2546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"217721102","text":"# Talk: <<Tim Jensen - Building Asynchronous Microservices with Tornado (PyTexas 2017)>> -- https://www.youtube.com/watch?v=cGgqtKmz2cI\n\n\n# What does it take to implement a web server in Tornado ?\n\n#   - Application\n#       - Defines routes\n#       - Configure initial state\n#   - HTTP server\n#       - Enables HTTP access to Application\n#       - Listens on port\n#   - IO Loop\n#       - Start the IO loop to make it run\n#   - Request handlers\n#       - Where work is done\n\n\n# Application\ndef make_app(config):\n  return tornado.web.Application([\n      (\"/api/v1/bart\", BartHandler),\n      (\"/api/v1/flanders\", FlandersHandler),\n      (\"/api/v1/healthcheck\", HealthcheckHandler),\n      (\"/api/v1/homers/(.*)\", HomersHandler),\n      (\"/api/v1/lisas/(.*)\", LisasHandler),\n      (\"/api/v1/maggie\", MaggieHandler),\n      (\"/api/v1/marges/(.*)\", MargesHandler)\n  ], **config)\n\napp = make_app({\"mongo_db\": mongo_db})\n\n\n# HTTP server\nserver = tornado.httpserver.HTTPServer(app)\nserver.listen(int(environ[\"SERVER_PORT\"]), \"localhost\")\n\n\n# IO Loop\nioloop = tornado.ioloop.IOLoop.current()\nioloop.start()\n\n\n# Request handlers\nclass FlandersHandler(tornado.web.RequestHandler):\n  def get(self):\n    self.finish(\"Hi-dilly-ho, neighborino!\")\n\n\n# MongoDB\n\n# Use `Motor` to asynchronously interface with MongoDB.\n#   https://motor.readthedocs.io\n# API like PyMongo but with `futures`\n#\n# SETUP\n# - Main creates MotorClient\nmotor_client = motor.motor_tornado.MotorClient(\n    environ[\"MONGODB_URI\"])\n# and gets default database\nmongo_db = motor_client.get_default_database()\n\n\nclass HomersHandler(tornado.web.RequestHandler):\n\n  # Reading\n  async def get(self, name):\n    homer = await self.settings[\"mongo_db\"].homers.find_one(\n      {\"name\": name})\n\n    if homer is None:\n      raise tornado.web.HTTPError(\n        404, f\"Missing homer: {name}\")\n\n    self.finish(homer[\"content\"])\n\n  # Writing\n  async def post(self, name):\n    await self.settings[\"mongo_db\"].homers.replace_one(\n      {\"name\": name},\n      {\n        \"name\": name,\n        \"content\": json.loads(self.request.body)\n      },\n      upsert=True)\n\n    self.set_status(204)\n    self.finish()\n\n\n# Redis\n\n# aioredis\n# asyncio Redis client library\n# https://aioredis.readthedocs.io\n\n# IO Loop\n# Configure Tornado to use asyncio event loop\ntornado.platform.asyncio.AsyncIOMainLoop().install()\n\n# Setup\nasync def connect_redis(environ):\n  return await aioredis.create_redis(\n    (\n      environ[\"REDIS_HOST\"], environ[\"REDIS_PORT\"]\n    ))\n\nredis = ioloop.run_sync(functools.partial(\n  connect_redis, environ))\n\n\n# Reading\nclass MergesHandler(tornado.web.RequestHandler):\n  async def get(self, name):\n    marge = await self.settings[\"redis\"].get(name)\n\n    if marge is None:\n      raise tornado.web.HTTPError(\n        404, f\"Missing marge: {name}\")\n\n    self.finish(marge)\n\n\n\n\n\n# HTTP Client\nclass MaggieHandler(tornado.web.RequestHandler):\n  async def get(self):\n    client = tornado.httpclient.AsyncHTTPClient()\n    response = await client.fetch(self.settings[\"weather_uri\"])\n\n    body = json.loads(response.body)\n    item = body[\"query\"][\"results\"][\"channel\"][\"item\"]\n    condition = item[\"condition\"]\n    temp = condition[\"temp\"]\n    text = condition[\"text\"]\n\n    self.finish(\n      f\"Currently {temp} degrees and {text} in Austin, TX\")\n\n","sub_path":"py/tornado/Building_Async_Microservices_with_Tornado.py","file_name":"Building_Async_Microservices_with_Tornado.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"614048651","text":"class Solution():\n    def diffWaysToCompute(self, input):\n        # 给一个计算式添加括号优先级，得出所有的计算结果\n        # 拆分\n        num_list = []\n        symbol_list = []\n        temp = ''\n        for char in input:\n            if char in ['+', '*', '-']:\n                num_list.append(int(temp))\n                temp = ''\n                symbol_list.append(char)\n            else:\n                temp += char\n        num_list.append(int(temp))\n\n        def posible_list(num_list, symbol_list):\n            posible_result = []\n            if not symbol_list:  # 如果只剩下一个数，那么只有一种情况\n                posible_result.append(num_list[0])\n            else:\n                for symbol_index in range(len(symbol_list)):  # 否则遍历所有的符号，找出所有的可能\n                    left_posible = posible_list(num_list[:symbol_index+1], symbol_list[:symbol_index])  # 这部分是符号左侧所有的可能\n                    right_posible = posible_list(num_list[symbol_index+1:], symbol_list[symbol_index+1:])  # 这部分是符号右侧所有的可能\n                    the_symbol = symbol_list[symbol_index]\n                    if the_symbol == '+':\n                        posible_result.extend(left+right for left in left_posible for right in right_posible)\n                    elif the_symbol == '*':\n                        posible_result.extend(left*right for left in left_posible for right in right_posible)\n                    else:\n                        posible_result.extend(left-right for left in left_posible for right in right_posible)\n            return posible_result\n\n\nif __name__ == '__main__':\n    solu = Solution()\n    pass\n","sub_path":"Origin/Leetcode/Solution/string.py","file_name":"string.py","file_ext":"py","file_size_in_byte":1723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"466655354","text":"from __future__ import division\n\nimport numpy as np\nimport MDAnalysis\n\nimport argparse\nfrom IPython import embed\nimport os, glob, pathlib\n\nimport math\n\nfrom mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\n\nimport numpy as np\n\nfrom scratch.sam.util import *\n\nbeta = 1 / (k*300)\nkappa = beta*0.386\n\ndef extract_pq(name):\n    splits = name.split('_')\n\n    return int(splits[1]), int(splits[3])\n\n\n## Collect all *new* uniform OH- surface free energies\n\nds_bulk = np.load(\"sam_pattern_bulk_pure.npz\")\nds_old = np.load(\"old_sam_pattern_pure.npz\")\n\ne_bulk = ds_bulk['energies']\npq = ds_bulk['pq']\ne_old = ds_old['energies'][1::2]\n\nassert np.array_equal(ds_old['pq'][1::2], pq)\n\ndnames = glob.glob('P_*/k_00/d_*/trial_0')\n\ne_new = np.zeros_like(e_old)\nerr_new = np.zeros_like(e_new)\n\nfor dname in dnames:\n    path = pathlib.Path(dname)\n    p,q = extract_pq(path.parts[0])\n\n    idx = np.where((pq[:,0] == p) & (pq[:,1] == q))[0].item()\n\n    test_p, test_q = pq[idx]\n    assert test_p == p\n    assert test_q == q\n\n    print(\"doing p : {} q : {}\".format(p,q))\n\n    pvn = np.loadtxt(\"{}/PvN.dat\".format(dname))\n\n    e_new[idx] = pvn[0,1]\n    err_new[idx] = pvn[0,2]\n\n\ntot_new_energies = np.zeros_like(ds_old['energies'])\ntot_new_energies[::2] = ds_old['energies'][::2]\ntot_new_energies[1::2] = e_new\n\ntot_new_errs = np.zeros_like(ds_old['err_energies'])\ntot_new_errs[::2] = ds_old['err_energies'][::2]\ntot_new_errs[1::2] = err_new\n\nnp.savez_compressed('sam_pattern_pure', energies=tot_new_energies, pq=ds_old['pq'], err_energies=tot_new_errs, \n                    dx=ds_old['dx'], dy=ds_old['dy'], dz=ds_old['dz'], base_energy=ds_old['base_energy'], base_err=ds_old['base_err'])\n\n\n","sub_path":"scratch/blah.py","file_name":"blah.py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"561906676","text":"#!/usr/bin/env python3\n\"\"\"\n    Dump the content of a plc to a file\n\"\"\"\n\nimport argparse\nimport pickle\nimport pylibmodbus\n\ndef dump(args):\n    plc_handle = pylibmodbus.ModbusTcp(ip=args.host, port=args.port)\n    plc_handle.connect()\n\n    data = []\n    for address in range(0, 0xFFFF, 121):\n        try:\n            raw = plc_handle.read_registers(address, 121)\n        except:\n            print(\"Can't read at addr %d\", address)\n            break\n        pyobj = list(raw)\n        data.append(pyobj)\n\n\n    with open(args.file, 'wb') as f:\n        print(data)\n        pickle.dump(data, f)\n\n\ndef main():\n    parser = argparse.ArgumentParser(description='Dump data into a pickled file.')\n    parser.add_argument('--port', default=502, type=int)\n    parser.add_argument('--host', default='127.0.0.1')\n    parser.add_argument('--file', required=True)\n\n    args = parser.parse_args()\n    dump(args)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"plc_dump.py","file_name":"plc_dump.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"337545961","text":"import os\nimport pickle\nimport numpy as np\nfrom scipy import misc\nimport tensorflow as tf\nfrom deepbelief.layers.data import Data\nfrom deepbelief.layers.rbm import RBM\nfrom deepbelief.layers.sbm_lower import SBM_Lower\nfrom deepbelief.util import init_logging\n\n# Import local flags.py module without specifying absolute path\nimport importlib.util\nmodule_spec = importlib.util.spec_from_file_location('flags', 'flags.py')\nmodule = importlib.util.module_from_spec(module_spec)\nmodule_spec.loader.exec_module(module)\nflags = module.flags\n\n# Note, this script should be run after the model is trained (run train.py first).\n# Basically, the generalisation experiment consist in trying to \"reconstruct\" the\n# test data as closely as possible. The output of this script will be two Numpy arrays,\n# one simply containing the test data and the other one the corresponding data generated\n# by the model.\n\ninit_logging(flags['test_log_file'])\n\ntest_data = Data(flags['test_data'],\n                 shuffle_first=False,\n                 batch_size=flags['test_generalisation_batch_size'],\n                 log_epochs=flags['data_log_epochs'],\n                 name='TestData')\n\ntest_data_batch = test_data.next_batch()\n\nconfig = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))\nsession = tf.Session(config=config)\n\nlayer1 = SBM_Lower(session=session,\n                   side=flags['img_shape'][0],\n                   side_overlap=flags['layer_1_side_overlap'],\n                   num_h=flags['layer_1_num_h'],\n                   name=flags['layer_1_name'])\n\nlayer1.restore(flags['layer_1_ckpt'])\n\nlayer2 = RBM(session=session,\n             num_v=flags['layer_1_num_h'],\n             num_h=flags['layer_2_num_h'],\n             bottom=layer1,\n             name=flags['layer_2_name'])\n\nlayer2.restore(flags['layer_2_ckpt'])\n\ntable_path = os.path.join(flags['test_generalisation_plots_dir'], 'table')\n\nlayer2.test_generalisation(test_data=test_data_batch,\n                           output_table_path=table_path,\n                           num_iterations=flags['test_generalisation_num_iterations'],\n                           num_runs=flags['test_generalisation_num_runs'],\n                           num_samples_to_average=flags['test_generalisation_num_samples_to_average'])\n\nwith open(table_path, 'rb') as f:\n    table = pickle.load(f)\n\nassert len(table) == test_data_batch.shape[0]\nmin_dists = []\nnum_pixels = flags['img_shape'][0] * flags['img_shape'][1]\n\ntest_data_arr = np.zeros(shape=(test_data_batch.shape[0], num_pixels))\nmodel_data_arr = np.zeros(shape=(test_data_batch.shape[0], num_pixels))\n\nfor (test_point_index, distance, test_point, model_point) in table:\n    min_dists.append(distance / num_pixels)\n\n    misc.imsave(\n        os.path.join(flags['test_generalisation_plots_dir'], 'test_point_' + str(test_point_index) + '.png'),\n        np.reshape(test_point, newshape=flags['img_shape']))\n    misc.imsave(\n        os.path.join(flags['test_generalisation_plots_dir'], 'model_point_' + str(test_point_index) + '.png'),\n        np.reshape(model_point, newshape=flags['img_shape']))\n\n    test_data_arr[test_point_index] = test_point\n    model_data_arr[test_point_index] = model_point\n\nnp.save(os.path.join(flags['test_generalisation_plots_dir'], 'test_data'), test_data_arr)\nnp.save(os.path.join(flags['test_generalisation_plots_dir'], 'model_data'), model_data_arr)\n\nprint('Scaled mininum distance average: {}'.format(np.average(min_dists)))\nprint('Scaled mininum distance std: {}'.format(np.std(min_dists)))\n\nsession.close()\n","sub_path":"examples/sbm_horses/test_generalisation.py","file_name":"test_generalisation.py","file_ext":"py","file_size_in_byte":3536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"113556292","text":"from django.shortcuts import render\nfrom djora.models import Question, Answer\nfrom django.db.models import Q\n\ndef search(request):\n    query = request.GET.get('q')\n    if query:\n        q_query_set = (Q(title__icontains=query))|(Q(body__icontains=query))\n        a_query_set = (Q(answer__icontains=query))\n        q_results = Question.objects.filter(q_query_set).distinct()\n        a_results = Answer.objects.filter(a_query_set).distinct()\n        context = {\n            'q_results': q_results,\n            'a_results': a_results\n        }\n    else:\n        context = {}\n    return render(request, 'search.html', context)","sub_path":"35__Project__search/code/search/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"476026126","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 19 15:50:52 2020\n\n@author: Mert Barbaros\n\"\"\"\n\nimport socket\n\n#connection point that is not connected\n\"\"\"\nHere we made a socket instance and passed it two parameters. \nThe first parameter is AF_INET and the second one is SOCK_STREAM. AF_INET \nrefers to the address family ipv4. The SOCK_STREAM means connection \noriented TCP protocol.\n\"\"\"\nmysock = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n#connection\nmysock.connect( ('data.pr4e.org', 80) )\n\n\"\"\"\n'data.pr4e.org' is the host\n80 is the port\n\nSocket:\n    \nsocket\nconnect\nsend\nrecv\n\"\"\"\n\n#first send then receive \ncmd = 'GET http://data.pr4e.org/romeo.txt HTTP/1.0\\r\\n\\r\\n'.encode()\nmysock.send(cmd)\n\nwhile True:\n    data = mysock.recv(512)\n    if(len(data) > 1):\n        break\n    print(data.decode())\n\nmysock.close()\n","sub_path":"Basics/socket.py","file_name":"socket.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"561578545","text":"# Copyright 2018 Telstra Open Source\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 copy\nimport json\nimport pprint\n\nfrom topologylistener import message_utils\nfrom topologylistener import messageclasses\nfrom topologylistener import model\nfrom topologylistener.tests import share\n\n\nclass AbstractSpeakerFlowCommand(model.Abstract):\n    clazz = model.Default(None)\n    dpid = model.Default(None)\n    cookie = model.Default(None)\n    port_in = model.Default(None)\n    port_out = model.Default(None)\n\n    @classmethod\n    def of_kafka_channel(cls, channel_data, **extra):\n        channel_to_fields = {\n            'switch_id': 'dpid',\n            'output_port': 'port_out',\n            'input_port': 'port_in',\n            'cookie': 'cookie'}\n        fields = model.grab_fields(channel_data, channel_to_fields)\n        fields.update(extra)\n        return cls(**fields)\n\n    def as_tuple(self):\n        return (\n            self.clazz,\n            self.dpid,\n            self.cookie,\n            self.port_in, self.port_out)\n\n    def _sort_key(self):\n        return self.as_tuple()\n\n\nclass AbstractSpeakerFlowInstallCommand(AbstractSpeakerFlowCommand):\n    vlan_transit = model.Default(None)\n\n    @classmethod\n    def of_kafka_channel(cls, channel_data, **extra):\n        fields = model.grab_fields(channel_data, {\n            'transit_vlan_id': 'vlan_transit'})\n        fields.update(extra)\n        return super(AbstractSpeakerFlowInstallCommand, cls).of_kafka_channel(\n            channel_data, **fields)\n\n    def as_tuple(self):\n        return super(AbstractSpeakerFlowInstallCommand, self).as_tuple() + (\n            self.vlan_transit,)\n\n\nclass SpeakerFlowIngressCommand(AbstractSpeakerFlowInstallCommand):\n    vlan_input = model.Default(None)\n\n    @classmethod\n    def of_kafka_channel(cls, channel_data, **extra):\n        fields = model.grab_fields(\n            channel_data, {'input_vlan_id': 'vlan_input'})\n        fields.update(extra)\n        return super(SpeakerFlowIngressCommand, cls).of_kafka_channel(\n            channel_data, **fields)\n\n    def __init__(self, **fields):\n        super(AbstractSpeakerFlowCommand, self).__init__(\n            clazz='org.openkilda.messaging.command.flow.InstallIngressFlow',\n            **fields)\n\n    def as_tuple(self):\n        return super(SpeakerFlowIngressCommand, self).as_tuple() + (\n            self.vlan_input, )\n\n\nclass SpeakerFlowTransitCommand(AbstractSpeakerFlowInstallCommand):\n    def __init__(self, **fields):\n        super(AbstractSpeakerFlowCommand, self).__init__(\n            clazz='org.openkilda.messaging.command.flow.InstallTransitFlow',\n            **fields)\n\n\nclass SpeakerFlowEgressCommand(AbstractSpeakerFlowInstallCommand):\n    vlan_output = model.Default(None)\n\n    @classmethod\n    def of_kafka_channel(cls, channel_data, **extra):\n        fields = model.grab_fields(\n            channel_data, {'output_vlan_id': 'vlan_output'})\n        fields.update(extra)\n        return super(SpeakerFlowEgressCommand, cls).of_kafka_channel(\n            channel_data, **fields)\n\n    def __init__(self, **fields):\n        super(AbstractSpeakerFlowCommand, self).__init__(\n            clazz='org.openkilda.messaging.command.flow.InstallEgressFlow',\n            **fields)\n\n    def as_tuple(self):\n        return super(SpeakerFlowEgressCommand, self).as_tuple() + (\n            self.vlan_output, )\n\n\nclass SpeakerFlowRemoveCommand(AbstractSpeakerFlowCommand):\n    vlan_input = model.Default(None)\n\n    @classmethod\n    def of_kafka_channel(cls, channel_data, **extra):\n        fields = model.grab_fields(channel_data['criteria'], {\n            'cookie': 'cookie',\n            'in_port': 'port_in',\n            'out_port': 'port_out',\n            'in_vlan': 'vlan_input'})\n        fields.update(extra)\n        return super(SpeakerFlowRemoveCommand, cls).of_kafka_channel(\n            channel_data, **fields)\n\n    def __init__(self, **fields):\n        # FIXME(surabujin): TE fill incorrect values in `port_out`\n        # into FlowRemove command\n        fields['port_out'] = None\n\n        super(AbstractSpeakerFlowCommand, self).__init__(\n            clazz='org.openkilda.messaging.command.flow.RemoveFlow',\n            **fields)\n\n    def as_tuple(self):\n        return super(SpeakerFlowRemoveCommand, self).as_tuple() + (\n            self.vlan_input, )\n\n\nclazz_to_command = {\n    'org.openkilda.messaging.command.flow.InstallIngressFlow':\n        SpeakerFlowIngressCommand,\n    'org.openkilda.messaging.command.flow.InstallTransitFlow':\n        SpeakerFlowTransitCommand,\n    'org.openkilda.messaging.command.flow.InstallEgressFlow':\n        SpeakerFlowEgressCommand,\n    'org.openkilda.messaging.command.flow.RemoveFlow': SpeakerFlowRemoveCommand}\n\n\nclass TestFlow(share.AbstractTest):\n    def setUp(self):\n        super(TestFlow, self).setUp()\n\n        share.env.reset_kafka_producer()\n\n        self.allow_all_features()\n\n    def test_crud(self):\n        request = self.load_flow_request('flow-create-request.json')\n\n        # create\n        request['correlation_id'] = self.make_correlation_id()\n        create_request = copy.deepcopy(request)\n        self.flow_create(create_request)\n\n        # update\n        request['correlation_id'] = self.make_correlation_id()\n        request['payload']['operation'] = 'UPDATE'\n        self.mangle_cookie(request, lambda cookie: cookie + 1)\n        self.mangle_dst_port(request, lambda port: port + 1)\n        update_request = copy.deepcopy(request)\n        self.flow_update(update_request, create_request)\n\n        # delete\n        request['correlation_id'] = self.make_correlation_id()\n        request['payload']['operation'] = 'DELETE'\n        delete_request = copy.deepcopy(request)\n        self.flow_delete(delete_request)\n\n    def flow_create(self, create_request):\n        flow_pair = create_request['payload']['payload']\n        self.assertFalse(self.is_flow_exist(flow_pair['forward']['flowid']))\n        self.assertTrue(share.feed_message(create_request))\n\n        self.validate_db_flow(create_request)\n        expected_messages = self.flow_create_expected_commands(create_request)\n        self.validate_produced_commands(\n            expected_messages, create_request['correlation_id'])\n\n    def flow_update(self, update_request, create_request):\n        flow_pair = update_request['payload']['payload']\n        self.assertTrue(self.is_flow_exist(flow_pair['forward']['flowid']))\n        self.assertTrue(share.feed_message(update_request))\n\n        self.validate_db_flow(update_request)\n        expected_commands = self.flow_create_expected_commands(update_request)\n\n        expected_commands.update(self.flow_delete_expected_commands(\n            create_request))\n        self.validate_produced_commands(\n            expected_commands, update_request['correlation_id'])\n\n    def flow_delete(self, delete_request):\n        flow_pair = delete_request['payload']['payload']\n        flow_id = flow_pair['forward']['flowid']\n\n        self.assertTrue(self.is_flow_exist(flow_id))\n        self.assertTrue(share.feed_message(delete_request))\n        self.assertFalse(self.is_flow_exist(flow_id))\n\n        expect_commands = self.flow_delete_expected_commands(delete_request)\n        self.validate_produced_commands(\n            expect_commands, delete_request['correlation_id'])\n\n    def validate_db_flow(self, request):\n        flow_pair = request['payload']['payload']\n        flow_forward = flow_pair['forward']\n\n        flow_id = flow_forward['flowid']\n        expected_endpoints = set(self.extract_flow_endpoints(flow_forward))\n        with self.open_neo4j_session() as tx:\n            for db_flow in self.fetch_db_flow(tx, flow_id):\n                actual_endpoints = set(self.extract_flow_endpoints(db_flow))\n                self.assertEqual(expected_endpoints, actual_endpoints)\n\n    def validate_produced_commands(self, expected_commands, correlation_id):\n        kafka_stream = self.kafka_backlog_stream()\n        kafka_stream = self.stream_filter_by_key(\n            kafka_stream, 'correlation_id', correlation_id)\n        kafka_stream = self.stream_filter_by_key(\n            kafka_stream, 'clazz', message_utils.MT_COMMAND)\n        kafka_stream = self.stream_map(kafka_stream, lambda x: x['payload'])\n\n        extra_commands = []\n        for payload in kafka_stream:\n            try:\n                klass = clazz_to_command[payload['clazz']]\n            except KeyError:\n                extra_commands.append(payload)\n                continue\n\n            command = klass.of_kafka_channel(payload).as_tuple()\n            if command in expected_commands:\n                expected_commands.discard(command)\n            else:\n                extra_commands.append(payload)\n\n        if extra_commands:\n            message = 'Extra command messages have been produced'\n            print(message)\n            for idx, payload in enumerate(extra_commands):\n                print('#{}: {}'.format(idx, pprint.pformat(payload)))\n            raise AssertionError(message)\n\n        if expected_commands:\n            message = 'Expected command messages have not been produced'\n            print(message)\n            for idx, payload in enumerate(sorted(expected_commands)):\n                print('#{}: {}'.format(idx, pprint.pformat(payload)))\n            raise AssertionError(message)\n\n    def is_flow_exist(self, flow_id):\n        with self.open_neo4j_session() as tx:\n            return bool(list(self.fetch_db_flow(tx, flow_id)))\n\n    @staticmethod\n    def fetch_db_flow(tx, flow_id):\n        q = (\n            'MATCH (:switch)-[f:flow]->(:switch)\\n'\n            'WHERE f.flowid=$flow_id\\n'\n            'RETURN f')\n        p = {'flow_id': flow_id}\n\n        cursor = tx.run(q, p)\n        return (dict(x['f']) for x in cursor)\n\n    @staticmethod\n    def flow_create_expected_commands(request):\n        flow_pair = request['payload']['payload']\n        forward = flow_pair['forward']\n        reverse = flow_pair['reverse']\n\n        path_forward = forward['flowpath']['path']\n        path_reverse = reverse['flowpath']['path']\n        common_args_forward = {\n            'cookie': forward['cookie'],\n            'vlan_transit': forward['transit_vlan']\n        }\n        common_args_reverse = {\n            'cookie': reverse['cookie'],\n            'vlan_transit': reverse['transit_vlan']\n        }\n\n        return {\n            # forward\n            SpeakerFlowIngressCommand(\n                dpid=forward['src_switch'],\n                port_in=forward['src_port'],\n                port_out=path_forward[0]['port_no'],\n                vlan_input=forward['src_vlan'],\n                **common_args_forward).as_tuple(),\n            SpeakerFlowTransitCommand(\n                dpid=path_forward[1]['switch_id'],\n                port_in=path_forward[1]['port_no'],\n                port_out=path_forward[2]['port_no'],\n                **common_args_forward).as_tuple(),\n            SpeakerFlowEgressCommand(\n                dpid=forward['dst_switch'],\n                port_in=path_forward[3]['port_no'],\n                port_out=forward['dst_port'],\n                vlan_output=forward['dst_vlan'],\n                **common_args_forward).as_tuple(),\n            # reverse\n            SpeakerFlowIngressCommand(\n                dpid=reverse['src_switch'],\n                port_in=reverse['src_port'],\n                port_out=path_reverse[0]['port_no'],\n                vlan_input=reverse['src_vlan'],\n                **common_args_reverse).as_tuple(),\n            SpeakerFlowTransitCommand(\n                dpid=path_reverse[1]['switch_id'],\n                port_in=path_reverse[1]['port_no'],\n                port_out=path_reverse[2]['port_no'],\n                **common_args_reverse).as_tuple(),\n            SpeakerFlowEgressCommand(\n                dpid=reverse['dst_switch'],\n                port_in=path_reverse[3]['port_no'],\n                port_out=reverse['dst_port'],\n                vlan_output=reverse['dst_vlan'],\n                **common_args_reverse).as_tuple()}\n\n    @staticmethod\n    def flow_delete_expected_commands(request):\n        flow_pairs = request['payload']['payload']\n        forward = flow_pairs['forward']\n        reverse = flow_pairs['reverse']\n\n        path_forward = forward['flowpath']['path']\n        path_reverse = reverse['flowpath']['path']\n\n        common_args_forward = {'cookie': forward['cookie']}\n        common_args_reverse = {'cookie': reverse['cookie']}\n        return {\n            # forward\n            SpeakerFlowRemoveCommand(\n                dpid=forward['src_switch'],\n                port_in=forward['src_port'],\n                port_out=path_forward[0]['port_no'],\n                vlan_input=forward['src_vlan'],\n                **common_args_forward).as_tuple(),\n            SpeakerFlowRemoveCommand(\n                dpid=path_forward[1]['switch_id'],\n                port_in=path_forward[1]['port_no'],\n                port_out=path_forward[2]['port_no'],\n                vlan_input=forward['transit_vlan'],\n                **common_args_forward).as_tuple(),\n            SpeakerFlowRemoveCommand(\n                dpid=forward['dst_switch'],\n                port_in=path_forward[3]['port_no'],\n                port_out=forward['dst_port'],\n                vlan_input=forward['transit_vlan'],\n                **common_args_forward).as_tuple(),\n            # reverse\n            SpeakerFlowRemoveCommand(\n                dpid=reverse['src_switch'],\n                port_in=reverse['src_port'],\n                port_out=path_reverse[0]['port_no'],\n                vlan_input=reverse['src_vlan'],\n                **common_args_reverse).as_tuple(),\n            SpeakerFlowRemoveCommand(\n                dpid=path_reverse[1]['switch_id'],\n                port_in=path_reverse[1]['port_no'],\n                port_out=path_reverse[2]['port_no'],\n                vlan_input=reverse['transit_vlan'],\n                **common_args_reverse).as_tuple(),\n            SpeakerFlowRemoveCommand(\n                dpid=reverse['dst_switch'],\n                port_in=path_reverse[3]['port_no'],\n                port_out=reverse['dst_port'],\n                vlan_input=reverse['transit_vlan'],\n                **common_args_reverse).as_tuple()}\n\n    @staticmethod\n    def mangle_cookie(request, mangle):\n        flow_pair = request['payload']['payload']\n        for thread in flow_pair['forward'], flow_pair['reverse']:\n            thread['cookie'] = mangle(thread['cookie'])\n\n    @staticmethod\n    def mangle_dst_port(request, mangle):\n        flow_pair = request['payload']['payload']\n        for thread in flow_pair['forward'], flow_pair['reverse']:\n            thread['dst_port'] = mangle(thread['dst_port'])\n\n    @staticmethod\n    def extract_flow_endpoints(flow):\n        return [\n            flow['src_switch'],\n            flow['dst_switch']]\n\n    @staticmethod\n    def stream_filter_by_key(stream, key, value):\n        return (x for x in stream if x.get(key) == value)\n\n    @staticmethod\n    def stream_map(stream, action):\n        return (action(x) for x in stream)\n\n    @staticmethod\n    def kafka_backlog_stream():\n        return (\n            json.loads(x.payload) for x in share.env.kafka_producer_backlog())\n","sub_path":"services/topology-engine/queue-engine/topologylistener/tests/test_flow.py","file_name":"test_flow.py","file_ext":"py","file_size_in_byte":15675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"90512167","text":"# Create synthetic X-ray spectra with CR-NEI_hydro DEMs and PyAtomDB\n# This script does NOT convolve the photon spectra with RMF and ARF files\n\n\n# coding: utf-8\n\n\nimport numpy as np\n#import matplotlib.pyplot as plt\nfrom astropy.table import Table, vstack, Column\n\nimport os\nimport astropy.io.fits as pyfits\nimport pyatomdb as pyat\n#import time\n#t0 = time.time()\n\n\n\n#####################################################################################################################################\n\n# IMPORTANT: READ THIS\n# Definition of filename as an optional argument to run the script multiple times in\n# the terminal with all the different values via a simple loop in a shell script:\n# python create_spectra_resp.py filename\n# where filename is any subdirectory in the present location\n# See https://docs.python.org/3/library/argparse.html\n\n\nimport argparse\npathw = os.getcwd() + '/'\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        'index', metavar='str', type=str, choices=[x for x in os.listdir(pathw) if 'fits' in x],\n         nargs='+', help='introduce any FITS file in the present location')\n\n    args = parser.parse_args()\n\n\nfilename = args.index[0] # File selected to create an apec_nei_line.fits file which can be read by PyAtomDB. It is retrieved as a list\n\n#####################################################################################################################################\n\n\npath_files = pathw #+ filename + '/'\nexpl_file = pyfits.open(path_files + filename, format='fits')\n\n\n\n\n# Load the AtomDB files and the responses\n\nlinefile = os.path.expandvars(\"$ATOMDB/apec_nei_line.fits\")\ncocofile = os.path.expandvars(\"$ATOMDB/apec_nei_comp.fits\")\n\n#myrmf  = os.path.expandvars('$ATOMDB/N103B_xi03.rmf')\n#myarf  = os.path.expandvars('$ATOMDB/N103B_xi03_1p5.arf')\n\n\n\n\n# Define energy range for the spectra\n\n\n# IMPORTANT: READ THIS\n# PyAtomDB calculates emissivities in [ct cm^3 s^-1 bin^-1]\n# for given energy bins, e.g., E = [1, 2, 3, 4, 5...] eV,\n# and then retrieves a photon spectrum whose length is\n# len(ebins) -1, corresponding to the mean energy for\n# each bin, in this case E = [1.5, 2.5, 3.5, 4.5...] eV\n# (see dummyfirst flag in make_ion_spectrum)\n\n# By default, crhydronei calculates thermal RS/FS photon\n# spectra in 10000 energy bins with a step of 1.2 eV, from \n# 0.95 keV to 12.0938 keV. The same array will be used here\n# for the DEM-RS/FS photon spectra. To summarize,\n# Input: 10000 energy bins\n# Output: 9999 mean energy values, 9999 spectral values\n\n\nEn_start = 9.50E-2\nEn_step = 1.20E-3\nEn_nelem = 10000\n# Input energies\nEn_th_bins = np.asarray([En_start + En_step*x for x in range(En_nelem)]) # Energy bin limits from thermal spectra\n# Ouput energies that will be saved in the final files\nEn_th = np.asarray([En_start + En_step/2. + En_step*x for x in range(En_nelem - 1)]) # Average values for each bin (\"Ebin\")\n\nebins = En_th_bins \nEbin = En_th\n\n\n\n\n# Define a broadening, in keV, for the lines\n#de = 0.01\n\n\n\n\n# Define the temperature at which to plot (keV)\nte = np.loadtxt(os.path.expandvars('$ATOMDB/Te_Adam.txt'))\n\n\n\n\n# find the indices which are closest to the various temperatures\nite = []\n\nfor s in range(len(te)):\n    ite.append(s)\n    ite[s] = pyat.spectrum.get_index( te[s], teunits='keV', logscale=False)\n    \nite = np.asarray(ite)\n\n\n\n\n#####################################################################################################################################\n# DEM(T)\n# Each file will have a variable number of columns depending on the corresponding ionization states\n# For example, each expl_file_DEM[i][j][2] will have 7 columns (CI, CII, CIII, CIV, CV, CVI, CVII), i.e., C+0+6 - 7 columns\n# DEMs for each element and ion\n\n\n# CR-NEI-hydro structure:\n#       19 elements( H, He, C, N, O, Ne, Mg, Si, S, Ar, Ca, Fe, Ni, Na, Al, P, Ti, Cr, Mn )\nmyelem = np.array([1, 2, 6, 7, 8, 10, 12, 14, 16, 18, 20, 26, 28, 11, 13, 15, 22, 24, 25])\nmymetal = myelem[2:] # In the end, I will not care about H/He\n\n\n\n\n##### CREATION OF THE SYNTHETIC SPECTRA #####\n\n\n\n\nlente = len(te)\nlenmetal = len(mymetal)\n\nSpectra_em = np.zeros((1, lente, lenmetal), dtype=object)\nSpectra_em_sumions = np.zeros((1, lente, lenmetal), dtype=object) \nSpectra_em_sumelem = np.zeros((1, lente), dtype=object)\nSpectra_em_sumTe = np.zeros(1, dtype=object)\nSpectra_ph = np.zeros(1, dtype=object)\n# Unless dtype=object is included, setting array element within a sequence error will raise\n# If...elif...else, NEVER USE If...if...else or Python will get stuck and just apply the final \"else\"\n\n    \nSpectra_em_sumTe[0] = [0.]*len(Ebin)#*len(ebins)\n\n\nfor j, it in enumerate(ite):\n    \n    Spectra_em_sumelem[0][j] = [0.]*len(Ebin)#*len(ebins)\n\n    \n    for k, el in enumerate(mymetal): # I do not care about H or He (see the [k+4] for expl_file)\n\n        Spectra_em[0][j][k] = np.zeros(el+1, dtype=object) # Note the dtype=object again\n            \n        # So make_ion_spectrum(ebins,ite,6,7) is fully ionized carbon\n        # And make_ion_spectrum(ebins,ite,6,1) is neutral carbon\n        # Whereas make_ion_spectrum(ebins,ite,6,0/8) are null arrays\n\n            \n        for ion in range(1, el+2): #ion is the ion charge plus one (e.g. 3 for C III)\n\n            Spectra_em[0][j][k][ion-1] = [0.]*len(ebins)\n\n            # Generate the spectra multiplying the emissivities and the differential emission measures:\n            # epsilon [ph cm+3 s-1] * DEM [cm-5 eV-1] = [ph cm-2 s-1 eV-1]\n            # See the expression for the DEMs in the commented-out lines before this loop\n\n            Spectra_em[0][j][k][ion-1] = expl_file[k+4].data[j][ion-1]*\\\n            pyat.spectrum.make_ion_spectrum(ebins, it, el, ion, linefile = linefile,\\\n             cocofile = cocofile)\n         \n                \n                \n                \n        # Now I start to stack all the spectra. Over the various ions...  \n        #Spectra_em_sumions[i][j][k] = [0.]*len(ebins)\n        Spectra_em_sumions[0][j][k] = np.sum(Spectra_em[0][j][k])\n        \n    \n        # ...plus the different elements...\n        Spectra_em_sumelem[0][j] += Spectra_em_sumions[0][j][k]\n    \n\n    # ...and finally, the electron temperatures (many layers of each explosion model)\n    Spectra_em_sumTe[0] += Spectra_em_sumelem[0][j]\n\n\n# Last step, APEC normalization: necessary to multiply the convolved spectra by 1E14\n# Also, to go from bin-1 to keV-1, a 1/En_step factor is necessary\nSpectra_ph[0] = 1.E14 / En_step * Spectra_em_sumTe[0]\n    \n\n\n\n#####################################################################################################################################\n# Finally, save a FITS table with the energy bins in the first column and the spectrum in the second one\n\nu = np.zeros((2), dtype='object')\nu[0] = Column(Ebin, name='Energy', unit='keV')\n\n#explmodel = path_files.split(os.sep)[-2][6:]\n#ISMrho = path_files.split(os.sep)[-3][-3:]\n\nfor el in filename.split(\"_\"): # Split string based on underscores. Pretty convenient for my purposes!\n\n    if \"yr\" in el:\n\n        #age = int(el.replace(\"yr\", \"\"))\n        age = el\n\n\n\n# CHANGE COLUMN NAME ACCORDINGLY. THOUGHT TO BE, E.G., 108yr\n# See https://astropy.readthedocs.io/en/v0.1/wcs/units.html for all the units (including counts and photons)\nu[1] = Column(Spectra_ph[0], name = age, unit='count+1cm-2s-1keV-1') \n    \n\n\n\n# Save table    \nTable_end_ph = Table([y for y in u])\nTable_end_ph.write('spectra_ph_Enth_%s' % (filename), format='fits', overwrite='True') ","sub_path":"create_single_spectrum_Enth.py","file_name":"create_single_spectrum_Enth.py","file_ext":"py","file_size_in_byte":7482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"186794775","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 29 11:13:00 2017\n\n@author: michele\n\"\"\"\n\nimport json\nimport json\nimport numpy as np\nimport pandas as pd\nfrom sklearn import preprocessing\nimport re\n\nimport random\nimport string\nimport time\nimport logging\n\nimport matplotlib.pyplot as plt\n#import requests\n#from bs4 import BeautifulSoup\n\n#all_ratings = pd.read_csv(\"ml-20m/ratings.csv\")\n# all_ratings.head()\n#all_movies = pd.read_csv(\"ml-20m/movies.csv\")\n\nlinks = pd.read_csv(\"ml-latest-small/links.csv\")\nmovies = pd.read_csv(\"ml-latest-small/movies.csv\")\nratings = pd.read_csv(\"ml-latest-small/ratings.csv\")\n\ndef getdataset(jsonfile):\n    data = [] # list of movies from json\n    with open(jsonfile, encoding='utf-8') as datafile:\n        data = json.load(datafile)\n    return data\n\n\ndataset1 = getdataset('finaldata1-withclusters.json') # movie features dataset  WITH CLUSTERS!!!\n\nuserstats = ratings.groupby('userId', as_index=False).agg({'rating':[np.size,np.mean,np.std,np.min,np.max]}) # statistics on users\nratingswmstd = pd.merge(ratings, userstats, on ='userId', how='inner')\n# get normalized ratings!!\nratingswmstd['normrating'] = (ratingswmstd['rating'] - ratingswmstd[('rating', 'mean')])/ratingswmstd[('rating', 'std')]\n# leave only the normalized rating, userid and movieid in the new dataset\nratingswmstd[ratingswmstd['normrating']>0].movieId\n# 6734 movies!! :\nlen(list(set(ratingswmstd[ratingswmstd['normrating']>0].movieId)))\n\nmoviesabovethreshold = pd.DataFrame(ratingswmstd[ratingswmstd['normrating']>0].movieId).drop_duplicates()\n# select imdbids corresponding to the movies there!\nlinks = pd.merge(links, moviesabovethreshold, on = 'movieId', how = 'inner')\nlinksimdb = list(links.imdbId) # the list of imdbid corresponding to movies rated above the normalized rating threshold\n\nrr = ratingswmstd['rating']\nrmin = ratingswmstd[('rating', 'amin')]\nrmax = ratingswmstd[('rating', 'amax')]\nratingswmstd['minmaxrating'] = (rr-rmin)/(rmax-rmin)\n\n\nuser1 = ratingswmstd[ratingswmstd['userId']==1]\nuser2 = ratingswmstd[ratingswmstd['userId']==2]\nuser3 = ratingswmstd[ratingswmstd['userId']==3]\n\nuser1.rating.plot()\nuser1.minmaxrating.plot()\nuser1.normrating.plot()\nratingswmstd.plot.hexbin(x='userId', y='minmaxrating', gridsize=25,cmap='Blues')\nuser2.rating.plot()\nuser2.minmaxrating.plot()\nuser2.normrating.plot()\nuser3.rating.plot()\nuser3.minmaxrating.plot()\nuser3.normrating.plot()\n\n\nmovieratings = pd.merge(ratingswmstd, movies, on ='movieId', how='inner')\n\nmoviegroups = movieratings.groupby('title')\nmoviestats = moviegroups.agg({'rating':[np.size,np.mean,np.std]}) # statistics on movies\nmoviestats[moviestats['rating']['size'] >= 50].sort_values([('rating', 'size')], ascending=False) \n\n\n\n\n\n#get movie distribution by users:\nuserdistr = movieratings.groupby('userId').agg({'rating':[np.size]}).sort_values([('rating', 'size')], ascending=False) \nuserdistr.plot.hist(bins=100) # freq of ratings per users\nmovieratings.rating.plot.hist()\nmovieratings.title.value_counts().sort_values(ascending = True)[:25]\n\n\n\nimdbratings = pd.merge(movieratings, links, on='movieId', how='inner')#.drop(['timestamp',('rating', 'size'), ('rating', 'mean'),  ('rating', 'std'),'title'],1)\nimdbratings['binrating'] = np.where(imdbratings['normrating']>=0, 1, -1) # binarized ratings\n\n\nselectedratings = imdbratings[imdbratings['binrating']==1]\nuserids = list(set(selectedratings.userId))\nuserXratings = []\nfor uid in userids:\n    # get all ratings for that userid and put them into a list\n    moviesforuid = selectedratings[selectedratings['userId']==uid]\n    uxr = {'userid' : uid,\n           'countmovies' : moviesforuid.userId.count(),\n           'moviesratings' : moviesforuid[['imdbId','title','rating','normrating','timestamp']].to_dict(orient = 'records'),\n           'imdbids': list(moviesforuid.imdbId)}\n    userXratings.append(uxr)\n    \n    \n    \n    \n#countmovies = [u['countmovies'] for u  in userXratings] # get the number of movies rated by each user,\n# it is used to divide the tfidf values and get the local frequency of the features, so that \n# power users do not dominate, since there are users with 5 movies and some with more than 1000.\n\n# get ratings for each user: still in try phase\n#normratingarray = np.array([u['normrating'] for u in userXratings])\n\n\ndef importMoviesfromJSON(JSONfile):\n    data = [] # list of movies from json\n    with open(JSONfile, encoding='utf-8') as datafile:\n        data = json.load(datafile)\n    return data\n\n#userXfeatures = importMoviesfromJSON('userXfeatures1') # user feature dataset\n# make a merge with the dataset features!!!\n\nfor i,ur in enumerate(userXratings):\n    curru = ur['userid']\n    for j,mr in enumerate(ur['moviesratings']):\n        for mf in dataset1:            \n            if(mr['imdbId'] == mf['imdbid']):\n                userXratings[i]['moviesratings'][j]['cluster'] = mf['movcluster']#[m for m in mf['features'] if '(release)_' not in m]\n                \n\nuserXfeatures = []\nfor i,uid in enumerate(userXratings):\n    for ff in userXratings[i]['moviesratings']: \n        if('cluster' in ff.keys()):\n            userXfeatures.append({'userid':uid['userid'], 'cluster': ff['cluster'], 'imdbid': ff['imdbId'],'rating':ff['rating']})\n\n\notherfeatures = userXratings.copy()\nuserXfeatures2 = []\nfor i,uid in enumerate(userXratings):\n    features = [ff['cluster'] for ff in userXratings[i]['moviesratings'] if('cluster' in ff.keys())]\n    uxf = {'userid' : str(uid['userid']),\n           'cluster' : features}\n    userXfeatures2.append(uxf)\n\n\n# make a dataframe with all features and userids\nuserXfeaturesdf2 = pd.DataFrame({'userid' : [u['userid'] for u in userXfeatures2],\n                                'imdbids' : [u['imdbids'] for u in userXratings],\n                                'clusters' : [u['cluster'] for u in userXfeatures2]})\n\n\n\n# make a dataframe with all features and userids\nuserXfeaturesdf = pd.DataFrame(userXfeatures)\n#from the movie clusters and the userXratings ratings, make a new dataset of users\n#based on cluster frequency\ntheusers = userXfeaturesdf.groupby(['cluster','userid'], as_index=False).agg({'rating':[np.size,np.sum,np.mean]})\narr = theusers.pivot(index = 'userid',columns = 'cluster',values = ('rating','size')).fillna(0)\nR = arr.as_matrix() # just cluster frequency for each user\nR = R/R.sum(axis=1)[:,None] # normalize by sum of all movie ratings per user to get relative freq\n\n#dist_normR = pdist(R,'cosine')\n\ntfidfr = TfidfVectorizer(tokenizer=lambda i:i, lowercase=False)\nRtfidf = tfidfr.fit_transform(R)\nXR = scipy.sparse.csc_matrix(UClust).toarray()\nUSr = svduc.fit_transform(XR) # scikit returns the U*S matrix\n\n\n\nfrom sklearn.feature_extraction.text import CountVectorizer\ncountclusterusers = TfidfVectorizer(tokenizer=lambda i:i, lowercase=False)\nUClust = countclusterusers.fit_transform(userXfeaturesdf2.clusters)\nimport scipy\nXuc = scipy.sparse.csc_matrix(UClust)\n\nfrom sklearn.decomposition import TruncatedSVD # scikit implementation\n# with scikit learn implementation\nsvduc = TruncatedSVD(n_components=20, n_iter=10, random_state=0)\nUSuc = svduc.fit_transform(Xuc.toarray()) # scikit returns the U*S matrix\ncumulative = np.cumsum(svduc.explained_variance_ratio_)\nplt.plot(cumulative, c='blue')\nplt.show()\n\n#from bhtsne import tsne\n#tsne_Um = tsne(USr)\n#plt.scatter(tsne_Um[:, 0], tsne_Um[:, 1], alpha=0.4)\n\n\n\nfrom scipy.spatial.distance import pdist,cosine,squareform\n# COSINE METRIC\ndist_outusers = pdist(Xuc.toarray(),'cosine')\nhclust(dist_outusers,'complete')\n\n\n\n    ","sub_path":"project files/userclustering.py","file_name":"userclustering.py","file_ext":"py","file_size_in_byte":7512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"259271755","text":"from socket import *\nimport argparse\nimport select\nimport logging\nfrom log import server_log_config\n\nlogger = logging.getLogger('server')\n\n\ndef create_parser():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('addr', nargs='?', default='localhost')\n    parser.add_argument('port', nargs='?', default=8888)\n    return parser\n\n\ndef read_requests(r_clients, all_clients):\n   responses = {}\n\n   for sock in r_clients:\n       try:\n           data = sock.recv(1024).decode('utf-8')\n           responses[sock] = data\n           logger.info('Получен запрос')\n       except:\n           print('Клиент {} {} отключился'.format(sock.fileno(), sock.getpeername()))\n           all_clients.remove(sock)\n\n   return responses\n\ndef write_responses(requests, w_clients, all_clients):\n    for sock in w_clients:\n        if sock in requests:\n            try:\n                resp = requests[sock].encode('utf-8')\n                for i in all_clients:\n                    i.send(resp)\n            except:\n                print('Клиент {} {} отключился'.format(sock.fileno(), sock.getpeername()))\n                sock.close()\n                all_clients.remove(sock)\n\n\ndef main():\n    clients = []\n    parser = create_parser()\n    namespace = parser.parse_args()\n    s = socket(AF_INET, SOCK_STREAM)\n    s.bind((namespace.addr, namespace.port))\n    s.listen(5)\n    s.settimeout(0.2)\n    while True:\n      try:\n        client, addr = s.accept()\n      except OSError as e:\n          pass\n      else:\n          logger.info(\"Получен запрос на соединение от %s\" % str(addr))\n          print(\"Получен запрос на соединение от %s\" % str(addr))\n          clients.append(client)\n      finally:\n          wait = 10\n          r = []\n          w = []\n          try:\n              r, w, e = select.select(clients, clients, [], wait)\n          except:\n              pass\n          requests = read_requests(r, clients)\n          if requests:\n              write_responses(requests, w, clients)\n\n\nif __name__ == \"__main__\":\n    main()\n\n\n","sub_path":"HW_7/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"306489491","text":"state_u_heights = input()\nu_state_heights = input()\n\nstate_u_ints = [ int(height) for height in state_u_heights.split() ]\nu_state_ints = [ int(height) for height in u_state_heights.split() ]\n\nstate_u_ints.sort()\nu_state_ints.sort()\n\ntaller = 0\nfor index in range(5):\n    if state_u_ints[index] > u_state_ints[index]:\n        taller += 1\n\nprint(taller)\n","sub_path":"ICPC/2019-Practice/icpcF.py","file_name":"icpcF.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"411975912","text":"# Copyright OTT-JAX\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.\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nimport pytest\nfrom ott.math import utils as mu\n\n\n@pytest.mark.fast()\nclass TestGeometryLse:\n\n  def test_lse(self, rng: jax.random.PRNGKeyArray):\n    \"\"\"Test consistency of custom lse's jvp.\"\"\"\n    n, m = 12, 8\n    rngs = jax.random.split(rng, 5)\n    mat = jax.random.normal(rngs[0], (n, m))\n    # picking potentially negative weights on purpose\n    b_0 = jax.random.normal(rngs[1], (m,))\n    b_1 = jax.random.normal(rngs[2], (n, 1))\n\n    def lse_(x, axis, b, return_sign):\n      out = mu.logsumexp(x, axis, False, b, return_sign)\n      return jnp.sum(out[0] if return_sign else out)\n\n    lse = jax.value_and_grad(lse_, argnums=(0, 2))\n    for axis in (0, 1):\n      _, g = lse(mat, axis, None, False)\n      delta_mat = jax.random.normal(rngs[3], (n, m))\n      eps = 1e-3\n      val_peps = lse(mat + eps * delta_mat, axis, None, False)[0]\n      val_meps = lse(mat - eps * delta_mat, axis, None, False)[0]\n      np.testing.assert_allclose((val_peps - val_meps) / (2 * eps),\n                                 jnp.sum(delta_mat * g[0]),\n                                 rtol=1e-03,\n                                 atol=1e-02)\n    for b, dim, axis in zip((b_0, b_1), (m, n), (1, 0)):\n      delta_b = jax.random.normal(rngs[4], (dim,)).reshape(b.shape)\n      _, g = lse(mat, axis, b, True)\n      eps = 1e-3\n      val_peps = lse(mat + eps * delta_mat, axis, b + eps * delta_b, True)[0]\n      val_meps = lse(mat - eps * delta_mat, axis, b - eps * delta_b, True)[0]\n      np.testing.assert_allclose((val_peps - val_meps) / (2 * eps),\n                                 jnp.sum(delta_mat * g[0]) +\n                                 jnp.sum(delta_b * g[1]),\n                                 rtol=1e-03,\n                                 atol=1e-02)\n","sub_path":"tests/math/lse_test.py","file_name":"lse_test.py","file_ext":"py","file_size_in_byte":2350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"560799150","text":"#\n# Copyright 2021 Ocean Protocol Foundation\n# SPDX-License-Identifier: Apache-2.0\n#\nimport uuid\nfrom unittest.mock import patch\n\nimport pytest\nfrom ocean_lib.common.ddo.ddo import DDO\nfrom ocean_lib.exceptions import AquariusError, ContractNotFound, InsufficientBalance\nfrom ocean_lib.web3_internal.constants import ZERO_ADDRESS\nfrom ocean_lib.web3_internal.currency import to_wei\nfrom tests.resources.ddo_helpers import get_resource_path\nfrom tests.resources.helper_functions import get_publisher_wallet\n\n\ndef test_InsufficientBalance(publisher_ocean_instance):\n    alice = get_publisher_wallet()\n    sample_ddo_path = get_resource_path(\"ddo\", \"ddo_sa_sample.json\")\n    asset = DDO(json_filename=sample_ddo_path)\n    asset.metadata[\"main\"][\"files\"][0][\"checksum\"] = str(uuid.uuid4())\n\n    token = publisher_ocean_instance.create_data_token(\n        \"DataToken1\", \"DT1\", from_wallet=alice, blob=\"foo_blob\"\n    )\n\n    with pytest.raises(InsufficientBalance):\n        publisher_ocean_instance.assets.pay_for_service(\n            publisher_ocean_instance.web3,\n            to_wei(\"12345678999999.9\"),\n            token.address,\n            asset.did,\n            0,\n            ZERO_ADDRESS,\n            alice,\n            alice.address,\n        )\n\n\ndef test_ContractNotFound(publisher_ocean_instance, metadata):\n    publisher = get_publisher_wallet()\n    metadata_copy = metadata.copy()\n\n    # used a random address from Etherscan\n    token_address = \"0xB3b8239719403E38de3bdF19B9AC147B48c72BF2\"\n    with patch(\"ocean_lib.models.dtfactory.DTFactory.verify_data_token\") as mock:\n        mock.return_value = False\n        with pytest.raises(ContractNotFound):\n            publisher_ocean_instance.assets.create(\n                metadata_copy, publisher, data_token_address=token_address\n            )\n\n\ndef test_AquariusError(publisher_ocean_instance, metadata):\n    metadata_copy = metadata.copy()\n    publisher = get_publisher_wallet()\n\n    ocn = publisher_ocean_instance\n    alice_wallet = get_publisher_wallet()\n    dt = ocn.create_data_token(\n        \"DataToken1\", \"DT1\", alice_wallet, blob=ocn.config.metadata_cache_uri\n    )\n\n    with patch(\"ocean_lib.common.aquarius.aquarius.Aquarius.ddo_exists\") as mock:\n        mock.return_value = True\n        with pytest.raises(AquariusError):\n            publisher_ocean_instance.assets.create(\n                metadata_copy, publisher, data_token_address=dt.address\n            )\n","sub_path":"ocean_lib/test/test_exceptions.py","file_name":"test_exceptions.py","file_ext":"py","file_size_in_byte":2426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"584511556","text":"class Multiplexer34970A(object):\n    \"\"\"With add-in card Agilent 34901A on channel 1.\"\"\"\n    \n    def __init__(self, device):\n        self.dev = device\n        self.dev.write_termination = \"\\n\"\n        self.dev.read_termination = \"\\n\"\n        self.dev.write('*RST')\n\n    def open(self, route: int):\n       \"\"\"Open the relay on a route.\"\"\"\n       self.dev.write(\":ROUTE:OPEN (@1{:02d})\".format(route))\n       \n    def close(self, route: int):\n       \"\"\"Close the relay on a route.\"\"\"\n       self.dev.write(\":ROUTE:CLOSE (@1{:02d})\".format(route))\n\n\nif __name__=='__main__':\n    from visa import ResourceManager\n    from time import sleep\n\n    rm = ResourceManager('@py')\n\n    dev = rm.open_resource('GPIB::03::INSTR')\n\n    mux = Multiplexer34970A(dev)\n    \n    for i in range(5):\n        mux.open(12)\n        sleep(1)\n        mux.close(12)\n        sleep(1)\n    \n","sub_path":"agilent/multiplexer34907A.py","file_name":"multiplexer34907A.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"205795375","text":"#!/usr/bin/env python3\n\noperators = [\"+\", \"-\", \"/\", \"*\", \"^\", \"mod\", \"log\", \"sin\", \"cos\", \"cot\", \"tan\",\n             \"sec\", \"csc\"]\nnumbers = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"8\", \"9\", \"0\", \".\"]\n\n\n# Takes standard mathematical expressions\n# and returns a list of terms and operators\ndef pre_process(compilable):\n    preProcessed = []\n    not_done = True\n    count = 0\n\n    while not_done:\n        one_character = compilable[count]\n        three_characters = compilable[count:(count+3)]\n\n        if checkIfOperator(one_character):\n            preProcessed.append(one_character)\n\n        elif checkIfOperator(three_characters):\n            preProcessed.append(three_characters)\n            count += 2\n\n        elif checkIfNumber(one_character):\n            shift = 1\n            not_done_two = True\n            while not_done_two:\n                if count+shift >= len(compilable):\n                    break\n                num = compilable[count+shift]\n                not_done_two = checkIfNumber(num)\n                if not_done_two:\n                    shift += 1\n            preProcessed.append(compilable[count:(count+shift)])\n            count += (shift - 1)\n\n        else:\n            if not(one_character == \" \"):\n                preProcessed.append(one_character)\n\n        count += 1\n        if count >= len(compilable):\n            not_done = False\n    return preProcessed\n\n\n# Checks if a string is a operator\ndef checkIfOperator(input):\n    isOp = False\n    for op in operators:\n        isOp = op == input\n        if isOp:\n            break\n\n    return isOp\n\n\n# Checks if a string is a part of the numbers list\ndef checkIfNumber(input):\n    isNumber = False\n    for num in numbers:\n        isNumber = num == input\n        if isNumber:\n            break\n    return isNumber\n","sub_path":"pre_process.py","file_name":"pre_process.py","file_ext":"py","file_size_in_byte":1780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"471419764","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.common.exceptions import NoSuchElementException\nimport os\nimport time\n\nphone = os.environ[\"phone\"]\npw = os.environ[\"pw\"]\n\nchrome_driver_path = \"C:\\\\Users\\chane\\OneDrive\\Documents\\Development\\chromedriver.exe\"\ndriver = webdriver.Chrome(executable_path=chrome_driver_path)\n\nlink = \"https://tinder.com/\"\n\ndriver.get(link)\ntime.sleep(1)\n\nsign_in = driver.find_element_by_xpath(\"//span[text()='Log in']\")\nsign_in.click()\ntime.sleep(1)\n\nlogin_fb = driver.find_element_by_xpath(\"//span[text()='Log in with Facebook']\")\nlogin_fb.click()\ntime.sleep(1)\n\nbase_window = driver.window_handles[0]\nfb_login_window = driver.window_handles[1]\ndriver.switch_to.window(fb_login_window)\ntime.sleep(5)\n# print(driver.title)\n\nlogin_email = driver.find_element_by_xpath('//*[@id=\"email\"]')\nlogin_email.send_keys(phone)\nlogin_pw = driver.find_element_by_xpath('//*[@id=\"pass\"]')\nlogin_pw.send_keys(pw)\nlogin_pw.send_keys(Keys.ENTER)\ntime.sleep(3)\n\ndriver.switch_to.window(base_window)\n\nlocation = driver.find_element_by_xpath('//*[@id=\"t-1222506740\"]/div/div/div/div/div[3]/button[1]')\nlocation.click()\ntime.sleep(1)\n\nnotif = driver.find_element_by_xpath('//*[@id=\"t-1222506740\"]/div/div/div/div/div[3]/button[2]')\nnotif.click()\ntime.sleep(1)\n\ncookies = driver.find_element_by_xpath('//*[@id=\"t--1032254752\"]/div/div[2]/div/div/div[1]/button')\ncookies.click()\ntime.sleep(1)\n\nfor n in range(15):\n    time.sleep(1)\n    try:\n        match = driver.find_element_by_xpath('//*[@id=\"t-619317887\"]/div/div/div[1]/div/div[4]/button')\n        match.click()\n    except NoSuchElementException:\n        try:\n            add_home = driver.find_element_by_xpath('//*[@id=\"t-1222506740\"]/div/div/div[2]/button[2]')\n            add_home.click()\n        except NoSuchElementException:\n            try:\n                super_like = driver.find_element_by_xpath('// *[ @ id = \"t-1222506740\"] / div / div / button[2]')\n                super_like.click()\n            except NoSuchElementException:\n                like = driver.find_element_by_xpath(\n                    '//*[@id=\"t--1032254752\"]/div/div[1]/div/main/div[1]/div/div/div[1]/div[1]/div[2]/div[4]/button')\n                like.click()\n\ndriver.quit()\n","sub_path":"Day 50 - TinderSwipingBot Selenium/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"82937285","text":"import time\nimport uuid\nfrom simple_print.functions import sprint_f\nfrom starlette.endpoints import WebSocketEndpoint\n\n\nCHANNEL_GROUPS = {}\n\n\nclass Channel:\n    def __init__(self, websocket, expires, encoding):\n        self.channel_uuid = str(uuid.uuid1())\n        self.websocket = websocket\n        self.expires = expires\n        self.encoding = encoding\n        self.created = time.time()\n\n    async def _send(self, payload):\n        websocket = self.websocket\n        if self.encoding == \"json\":\n            await websocket.send_json(payload)\n        elif self.encoding == \"text\":\n            await websocket.send_text(payload)\n        elif self.encoding == \"bytes\":\n            await websocket.send_bytes(payload)\n        else:\n            await websocket.send(payload)\n        self.created = time.time()\n\n    def _is_expired(self):\n        return self.expires + int(self.created) < time.time()\n\n    def __repr__(self):\n        return f\"{self.channel_uuid}\"\n\n\nclass ChannelEndpoint(WebSocketEndpoint):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.expires = 60 * 60 * 24  \n        self.encoding = \"json\"\n        self.groups = CHANNEL_GROUPS\n\n    async def on_connect(self, websocket, **kwargs):\n        await super().on_connect(websocket, **kwargs)\n        self.channel = Channel(websocket=websocket, expires=self.expires, encoding=self.encoding)\n\n    async def on_disconnect(self, websocket, close_code):\n        await super().on_disconnect(websocket, close_code)\n        await self._remove(self.channel)\n\n\n    async def _remove(self, channel):\n        for group in self.groups:\n            if channel in self.groups[group]:\n                del self.groups[group][channel]\n\n    async def _validate_name(self, name):\n        if name.isidentifier():\n            return True\n        raise TypeError(\"Group names must be valid python identifier only alphanumerics and underscores are accepted\")\n\n    async def _clean_expired(self):\n        for group in self.groups:\n            for channel in self.groups.get(group, {}):\n                if channel._is_expired():\n                    del self.groups[group][channel]\n\n    async def get_or_create(self, group):\n        assert await self._validate_name(group), \"Invalid group name\"\n        self.groups.setdefault(group, {})\n        self.groups[group][self.channel] = \"\"\n        self.group = group\n\n    async def group_send(self, payload):\n        await self._clean_expired()\n        for channel in self.groups.get(self.group, {}):\n            await channel._send(payload)\n\n\nasync def group_send(group, payload):\n    for channel_group in CHANNEL_GROUPS:\n        if channel_group == group:\n            for channel in CHANNEL_GROUPS.get(group, {}):\n                await channel._send(payload)\n\ndef groups_show():\n    for group in CHANNEL_GROUPS:\n        sprint_f(f\"\\n{group}\", \"green\")\n        for channel in CHANNEL_GROUPS.get(group, {}):\n            sprint_f(channel, \"cyan\")\n            if channel._is_expired():\n                sprint_f(\"expired\", \"red\")\n\ndef groups_flush():\n    CHANNEL_GROUPS = {}","sub_path":"channel_box/channel_box.py","file_name":"channel_box.py","file_ext":"py","file_size_in_byte":3098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"251208255","text":"# you can write to stdout for debugging purposes, e.g.\n# print(\"this is a debug message\")\n\ndef solution(A):\n    # write your code in Python 3.6\n    head = 0\n    tail = sum(A)\n    res = float('Inf')\n    for ixn in A[:-1]:\n        head += ixn\n        tail -= ixn\n        res = min(res, abs(head - tail))\n    return res","sub_path":"codility/python/lesson3_TapeEquilibrium.py","file_name":"lesson3_TapeEquilibrium.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"86557993","text":"import json\nimport os\nimport sys\n\n\ndef getCurrentFileList(target_file, file_type):\n    file_list = os.listdir('.')\n    file_list = [file_name for file_name in file_list if file_name.endswith(file_type)]\n    with open(target_file, 'w') as target:\n        json.dump(file_list, target)\n\n\nif __name__ == '__main__':\n    assert len(sys.argv) == 3, 'Invalid number of parameter!'\n    getCurrentFileList(sys.argv[1], sys.argv[2])\n","sub_path":"other/data_generator.py","file_name":"data_generator.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"161718363","text":"# This project was designed to practice how to use class\n# The purpose of the project is to read the coordinate \n# of a molecule and move to other coordinate.\n# Author: lewisbase\n# Date: 2019.05.09\n\nimport math\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nimport dfATOM\n\nclass Atom(object):\n    def __init__(self,X=0.0,Y=0.0,Z=0.0,NAME='Null',CHARGE=0.0):\n        '''Define a new atom with coordinate, LJ, charge, atom mass\n        and atom name, the units should be A and K'''\n        self.x = X\n        self.y = Y\n        self.z = Z\n        self.sig = dfATOM.dfAtoms.loc['sigma',NAME]\n        self.eps = dfATOM.dfAtoms.loc['epsilon',NAME]\n        self.mass = dfATOM.dfAtoms.loc['mass',NAME]\n        self.AtomName = NAME\n        self.cha = CHARGE\n\n    def MoveAtom(self,*args):\n        '''Move the atom along the vector input, 3 floats \n        or a 1-d vector with 3 element are allowed to input.'''\n        if len(args) == 3:\n            xm,ym,zm = args\n            self.x += xm\n            self.y += ym\n            self.z += zm\n        elif len(args) == 1:\n            xm,ym,zm = args[0][0],args[0][1],args[0][2]\n            self.x += xm\n            self.y += ym\n            self.z += zm\n        else:\n            raise Exception('The input parameter must be a 3-d vector or 3 floats!')\n\n    def PrintAtom(self,HEADER='n'):\n        '''Print the atom information, set Header as y/yes to print the and units'''\n        if HEADER.lower() == 'y' or HEADER.lower() == 'yes':\n            print('name    X(A)    Y(A)    Z(A)    sigma(A)    epsilon(K)    charge    mass(g/mol)')\n        print('%4s    %4.2f    %4.2f    %4.2f    %8.2f    %10.2f    %6.2f    %11.2f'\n            %(self.AtomName,self.x,self.y,self.z,self.sig,self.eps,self.cha,self.mass))\n\n\n\nclass Molecule(object):\n    # Main Construct\n    def __init__(self,X=[],Y=[],Z=[],ATOMSNAME=[],NAME='Null',CHARGE=[0.0]):\n        '''Define a new molecule from a series of coordinate\n        , atomsname and molecule name'''\n        if not(len(X) == len(Y) and len(X) == len(Z) and len(X) == len(ATOMSNAME)):\n            raise Exception('The elements in x, y, z and atomsname must be equal!')\n        self.AtomNumber = len(X)\n        self.x = X\n        self.y = Y\n        self.z = Z\n        self.MoleculeName = NAME\n        self.AtomsName = ATOMSNAME\n        self.sig = []\n        self.eps = []\n        self.mass = []\n        for name in self.AtomsName:\n            self.sig.append(dfATOM.dfAtoms.loc['sigma',name])\n            self.eps.append(dfATOM.dfAtoms.loc['epsilon',name])\n            self.mass.append(dfATOM.dfAtoms.loc['mass',name])\n        self.MoleculeMass = np.sum(self.mass)\n        if len(CHARGE) == 1:\n            self.cha = CHARGE*len(X)\n        elif len(CHARGE) == len(X):\n            self.cha = CHARGE\n        else:\n            raise Exception('The length of CHARGE list should be equal to other parameter list \\\n                or set to default zero!')\n\n    # Minor Construct\n    @classmethod\n    def MoleculeFromAtom(cls,ATOMS,Name='Null'):\n        '''Define a new molecule from a series of atoms'''\n        X = []\n        Y = []\n        Z = []\n        atomsname = []\n        charge = []\n        MoleculeName = Name\n        for atom in ATOMS:\n            X.append(atom.x)\n            Y.append(atom.y)\n            Z.append(atom.z)\n            atomsname.append(atom.AtomName)\n            charge.append(atom.cha)\n        return cls(X,Y,Z,atomsname,MoleculeName,charge)\n\n    \n    def MassCenter(self):\n        '''Calculate the mass center of molecule.\n        Return the coordinate of mass center.'''\n        CenterX = 0.0\n        CenterY = 0.0\n        CenterZ = 0.0\n        for num in range(self.AtomNumber):\n            CenterX += self.x[num] * self.mass[num] \n            CenterY += self.y[num] * self.mass[num] \n            CenterZ += self.z[num] * self.mass[num] \n        CenterX /= self.MoleculeMass\n        CenterY /= self.MoleculeMass\n        CenterZ /= self.MoleculeMass\n        return CenterX,CenterY,CenterZ\n\n    def MoveMolecule(self,*args):\n        '''Move the molecule along a vector.\n        Change the original coordiante.'''\n        if len(args) == 3:\n            xm,ym,zm = args\n            self.x += xm\n            self.y += ym\n            self.z += zm\n        elif len(args) == 1:\n            xm,ym,zm = args[0][0],args[0][1],args[0][2]\n            self.x += xm\n            self.y += ym\n            self.z += zm\n        else:\n            raise Exception('The input parameter must be a 3-d vector or 3 floats!')\n    \n    def MaxLength(self):\n        '''Calculate the max length in x, y, z dimension.\n        Return three length.'''\n        Max_x = np.max(self.x)-np.min(self.x)\n        Max_y = np.max(self.y)-np.min(self.y)\n        Max_z = np.max(self.z)-np.min(self.z)\n        return Max_x,Max_y,Max_z\n\n    def MassCenterDistance(self,SecondMolecule):\n        '''Calculate the distance between two molecules,\n        return total distance and distance in x, y and z'''\n        firstx,firsty,firstz = self.MassCenter()\n        secondx,secondy,secondz = SecondMolecule.MassCenter()\n        distance_x = abs(firstx-secondx)\n        distance_y = abs(firsty-secondy)\n        distance_z = abs(firstz-secondz)\n        distance = math.sqrt((distance_x)**2+(distance_y)**2+(distance_z)**2)\n        return distance,distance_x,distance_y,distance_z\n    \n    def ShowMolecule(self):\n        '''Show molecule in a 3D scatter figure'''\n        fig = plt.figure()\n        ax = Axes3D(fig)\n        for num in range(self.AtomNumber):\n            if num % 2 == 0:\n                ScatterColor = 'r'\n            else:\n                ScatterColor = 'b'\n            ax.scatter(xs=self.x[num],ys=self.y[num],zs=self.z[num], \\\n                s = 4500*dfATOM.dfAtoms.loc['sigma',self.AtomsName[num]], \\\n                c = ScatterColor)\n        ax.set_xlabel('X')\n        ax.set_ylabel('Y')\n        ax.set_xlim(np.min([np.min(self.x),np.min(self.y)])-25,np.max([np.max(self.x),np.max(self.y)])+25)\n        ax.set_ylim(np.min([np.min(self.x),np.min(self.y)])-25,np.max([np.max(self.x),np.max(self.y)])+25)\n        ax.set_zlabel('Z')\n        plt.show()\n    \n    def PrintMolecule(self):\n        '''Print the name and cordinate information of molecule'''\n        print('NO. AtomName sigma(A) epsilon(K) x(A) y(A) z(A)')\n        for num in range(self.AtomNumber):\n            print('%3d %8s %8.2f %10.2f %4.2f %4.2f %4.2f' \\\n                %(num+1,self.AtomsName[num], \\\n                dfATOM.dfAtoms.loc['sigma',self.AtomsName[num]], \\\n                dfATOM.dfAtoms.loc['epsilon',self.AtomsName[num]], \\\n                self.x[num],self.y[num],self.z[num]))\n\n\n\ndef main():\n    pass\n\nif __name__ == '__main__':\n    main()","sub_path":"CGbeads/CGbeads.py","file_name":"CGbeads.py","file_ext":"py","file_size_in_byte":6752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"318244935","text":"from dscribe.descriptors import CoulombMatrix\n\natomic_numbers = [1, 8]\nrcut = 6.0\nnmax = 8\nlmax = 6\n\n# Setting up the CM descriptor\ncm = CoulombMatrix(\n    n_atoms_max=6,\n)\n\n# Creating an atomic system as an ase.Atoms-object\nfrom ase.build import molecule\nmethanol = molecule(\"CH3OH\")\nprint(methanol)\n\n# Create CM output for the system\ncm_methanol = cm.create(methanol)\n\nprint(cm_methanol)\nprint(\"flattened\", cm_methanol.shape)\n\n# No flattening\ncm = CoulombMatrix(\n    n_atoms_max=6, flatten=False\n)\ncm_methanol = cm.create(methanol)\n\nprint(cm_methanol)\nprint(\"not flattened\", cm_methanol.shape)\n\n\n# Introduce zero-padding\ncm = CoulombMatrix(\n    n_atoms_max=10, flatten=False\n)\ncm_methanol = cm.create(methanol)\n\nprint(\"zero-padded\", cm_methanol)\nprint(cm_methanol.shape)\n\n# Not meant for periodic systems\nmethanol.set_pbc([True, True, True])\nmethanol.set_cell([[10.0, 0.0, 0.0],\n    [0.0, 10.0, 0.0],\n    [0.0, 0.0, 10.0],\n    ])\n\ncm = CoulombMatrix(\n    n_atoms_max=6, flatten=False\n)\n\ncm_methanol = cm.create(methanol)\nprint(\"with pbc\", cm_methanol)\n\n## Sparse output\n# no example\n\n# Translation, rotation and permutation\ncm = CoulombMatrix(\n    n_atoms_max=6, flatten=False\n)\nprint(\"invariance with respect to translation, rotation and permutation\")\n# Translate\nmethanol.translate((5, 7, 9))\ncm_methanol = cm.create(methanol)\nprint(cm_methanol)\n\n# Rotate\nmethanol.rotate(90, 'z', center=(0, 0, 0))\ncm_methanol = cm.create(methanol)\nprint(cm_methanol)\n\n# Permutate\nupside_down_methanol = methanol[::-1]\ncm_methanol = cm.create(upside_down_methanol)\nprint(cm_methanol)\n\n# Options for permutation\n\n# No Sorting\ncm = CoulombMatrix(\n    n_atoms_max=6, flatten=False,\n    permutation='none'\n)\n\ncm_methanol = cm.create(methanol)\nprint(methanol.get_chemical_symbols())\nprint(\"in order of appearance\", cm_methanol)\n\n# Sort by L2-norm from high to low\ncm = CoulombMatrix(\n    n_atoms_max=6, flatten=False,\n    permutation='sorted_l2'\n)\n\ncm_methanol = cm.create(methanol)\nprint(\"default: sorted by L2-norm\", cm_methanol)\n\n# Smoothening by randomly sorting\ncm = CoulombMatrix(\n    n_atoms_max=6, flatten=False,\n    permutation='random',\n    sigma=70,\n    seed=None\n)\n\ncm_methanol = cm.create(methanol)\nprint(\"randomly sorted\", cm_methanol)\n\n# Denser descriptor Eigenvector\ncm = CoulombMatrix(\n    n_atoms_max=6, flatten=False,\n    permutation='eigenspectrum'\n)\n\ncm_methanol = cm.create(methanol)\nprint(\"eigenvalues\", cm_methanol)\n\n\n# Creating multiple descriptors in parallel\nfrom dscribe.utils import batch_create\nwater = molecule(\"H2O\")\n\nmolecule_lst = [water, methanol]\n\ncm = CoulombMatrix(\n    n_atoms_max=6,\n)\nbatch = batch_create(cm, molecule_lst, n_proc=1)\n\nprint(batch.shape)\n","sub_path":"examples/coulombmatrix.py","file_name":"coulombmatrix.py","file_ext":"py","file_size_in_byte":2677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"342117095","text":"import unittest\n\nfrom problem_8.solution import Node, count_unival_subtrees\n\nclass TestProblem8(unittest.TestCase):\n\n    def test_case_1(self):\n        root = Node(0, Node(1, None, None), Node(0, Node(1, Node(1, None, None), Node(1, None, None)), Node(0, None, None)))\n        \n        self.assertEqual(5, count_unival_subtrees(root))\n\n    \nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"problem_8/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"427016000","text":"\"\"\"\n测试某页的50条\n\"\"\"\nfrom datetime import datetime\n\nfrom simulate.config.const import PAGE_SIZE, URL_DDBK_CHECK_BILLS\nfrom simulate.hack_bills.core import s\n\nif __name__ == '__main__':\n    date_start = int(datetime(2021, 4, 23, 0, 0).timestamp())\n    date_end = int(datetime(2021, 4, 24, 0, 0).timestamp())\n    page_number = 12\n    params = {\n        \"orderType\": 0,\n        \"afterSaleType\": 0,\n        \"remarkStatus\": -1,\n        \"urgeShippingStatus\": -1,\n        \"groupStartTime\": date_start,\n        \"groupEndTime\": date_end,\n        \"pageNumber\": page_number,\n        \"pageSize\": PAGE_SIZE\n    }\n    bills = s.post(URL_DDBK_CHECK_BILLS, json=params).json()\n    docs = bills['result']['pageItems']\n","sub_path":"simulate/hack_bills/test_one_page.py","file_name":"test_one_page.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"537206842","text":"from js_cc import js_parse\nfrom js_process_ast import *\nfrom js_ast import *\nfrom js_global import glob\nfrom js_typespace import *\nfrom js_parser_only_ast import *\n\nimport os, sys, struct, time, random, math, os.path\ndef handle_potential_class(result, ni, typespace):\n  c = result[ni]\n  op = c[0].gen_js(0).strip();\n  \n  name = c[1][0].gen_js(0).strip()\n\n  if op == \"inherit\":\n    parents = [c[1][1].gen_js(0).strip()];\n  else:\n    parents = []\n  \n  cls = ClassNode(name, parents)\n  constructor = None\n  \n  i = ni-1\n  while i >= 0:\n    n = result[i]\n    if type(n) == FunctionNode:\n      if n.name == name:\n        constructor = n\n        break\n        \n    i -= 1\n  \n  if constructor == None:\n    typespace.error(\"Could not find constructor for \" + name + \".\", result[ni])\n  \n  \n  members = {}\n  i = ni;\n  m1 = js_parse(\"obj.prototype.func = {}\");\n  m2 = js_parse(\"obj.func = {}\");\n  while i < len(result):\n    n = result[i];\n    bad = type(n) != AssignNode\n    bad |= type(n[1]) != FunctionNode\n    \n    if bad: \n      i += 1\n      continue\n    \n    root = n[0]\n    while type(root) == BinOpNode:\n      root = root[0]\n    \n    bad = root.gen_js(0).strip() != name\n    if bad:\n      i += 1\n      continue\n    \n    is_static = False\n    if root.parent[1].gen_js(0) == \"prototype\":\n      method_name = root.parent.parent[1].gen_js(0).strip()\n    else:\n      method_name = root.parent[1].gen_js(0).strip()\n      is_static = True\n    \n    f = n[1]\n    \n    print(method_name)\n    m = MethodNode(method_name)\n    m.add(f[0])\n    \n    if len(f) > 2:\n      s = StatementList()\n      for c in f.children[1:]:\n        s.add(c)\n    else:\n      m.add(f[1])\n    cls.add(m)\n    \n    i += 1\n  \n  print(name, parents, constructor.name)\n  \n  if name != \"ClassTest\":\n    sys.exit()\n  \ndef refactor(data):\n  result = js_parse(data)\n  if result == None: result = StatementList()\n  \n  \"\"\"\n  def set_smap(n, smap):\n    n.smap = smap\n    for c in n:\n      set_smap(c, smap)\n  smap = SourceMap()\n  set_smap(result, smap)\n  result.gen_js(0)\n  #\"\"\"\n  \n  typespace = JSTypeSpace()\n  flatten_statementlists(result, typespace)\n  \n  for c in result:\n    if type(c) == FuncCallNode and c[0].gen_js(0).strip() in [\"inherit\", \"create_prototype\"]:\n      handle_potential_class(result, result.index(c), typespace)\n      \n  print(len(result))\n  \n  \n  buf = data\n  \n  return buf, result\n  \n","sub_path":"tools/extjs_cc/js_refactor.py","file_name":"js_refactor.py","file_ext":"py","file_size_in_byte":2355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"546447144","text":"import random\nimport matplotlib.pyplot as plt\n\n\ndef find_smallest(lst):\n    s = lst[0]\n    s_index = 0\n    for i in range(1, len(lst)):\n        if lst[i] < s:\n            s = lst[i]\n            s_index = i\n    return s_index\n\n\ndef sorting(lst):\n    new_lst = []\n    N = len(lst)\n    for i in range(N):\n        smallest = find_smallest(lst)\n        new_lst.append(lst.pop(smallest))\n    return new_lst\n\n\nplt.plot(sorting([random.randint(1, 3000) for i in range(1000)]))\nplt.ylabel('Sorted numbers')\nplt.show()\n","sub_path":"Choise_Sorting.py","file_name":"Choise_Sorting.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"506300743","text":"#!/usr/bin/env python\n\nfrom pydpiper.application import AbstractApplication\nimport pydpiper.file_handling as fh\nimport atoms_and_modules.registration_functions as rf\nimport atoms_and_modules.registration_file_handling as rfh\nimport atoms_and_modules.minc_modules as mm\nimport atoms_and_modules.minc_atoms as ma\nimport atoms_and_modules.stats_tools as st\nfrom datetime import date\nfrom os.path import abspath, isdir\nimport logging\nimport sys\n\nlogger = logging.getLogger(__name__)\n\n\"\"\"NOTE: This application needs a significant overhaul and/or combining with the RegistrationChain\n         Application. Until this comment is removed, please consider this class DEPRECATED.\"\"\"\n\nclass PairwiseNonlinear(AbstractApplication):\n    def setup_options(self):\n        group = self.parser.add_argument_group(\"Pairwise non-linear options\", \n                              \"Options for pairwise non-linear registration of lsq6 or lsq12 aligned brains.\")\n        group.add_argument(\"--input-space\", dest=\"input_space\",\n                      type=str, default=\"lsq6\", \n                      help=\"Option to specify space of input-files. Can be lsq6 (default), lsq12 or native.\")\n        self.parser.add_argument_group(group)\n        \"\"\"Add option groups from specific modules\"\"\"\n        rf.addGenRegArgumentGroup(self.parser)\n        st.addStatsArguments(self.parser)\n        \n    def setup_appName(self):\n        appName = \"Pairwise-nonlinear\"\n        return appName\n\n    def run(self):\n        \n        \"\"\"Directory handling etc as in MBM\"\"\"\n        if not self.options.pipeline_name:\n            pipeName = str(date.today()) + \"_pipeline\"\n        else:\n            pipeName = self.options.pipeline_name\n        \n        processedDirectory = fh.createSubDir(self.outputDir, pipeName + \"_processed\")\n        \n        \"\"\"Check that correct registration method was specified\"\"\"\n        if self.options.reg_method != \"minctracc\" and self.options.reg_method != \"mincANTS\":\n            logger.error(\"Incorrect registration method specified: \" + self.options.reg_method)\n            sys.exit()\n        \n        \"\"\"Create file handling classes for each image\"\"\"\n        inputs = rf.initializeInputFiles(self.args, processedDirectory, self.options.mask_dir)\n        \n        \"\"\"Put blurs into array\"\"\"\n        blurs = []\n        for i in self.options.stats_kernels.split(\",\"):\n            blurs.append(float(i))\n        \n        \"\"\"Create file handler for nlin average from MBM\"\"\"\n        if self.options.nlin_avg:\n            nlinFH = rfh.RegistrationFHBase(abspath(self.options.nlin_avg), processedDirectory)\n        else:\n            nlinFH = None\n        if self.options.mbm_dir and not isdir(abspath(self.options.mbm_dir)):\n            logger.error(\"The --mbm-directory specified does not exist: \" + abspath(self.options.mbm_dir))\n            sys.exit()\n        \n        \"\"\"Get transforms from inputs to final nlin average and vice versa as well as lsq6 files\"\"\"\n        if self.options.nlin_avg and self.options.mbm_dir:\n            rf.getXfms(nlinFH, inputs, self.options.input_space, abspath(self.options.mbm_dir))\n        else:\n            logger.info(\"MBM directory and nlin_average not specified.\")\n            logger.info(\"Calculating pairwise nlin only without resampling to common space.\")\n        \n        \"\"\"Create a dictionary of statistics. Each subject gets an array of statistics\n           indexed by inputFile.\"\"\"\n        subjectStats = {}\n        \n        \"\"\"Register each image with every other image.\"\"\"\n        for inputFH in inputs:\n            subjectStats[inputFH] = {}\n            for targetFH in inputs:\n                if inputFH != targetFH:\n                # MF TODO: Make generalization of registration parameters easier. \n                    if self.options.reg_method == \"mincANTS\":\n                        register = mm.LSQ12ANTSNlin(inputFH, targetFH)\n                        self.pipeline.addPipeline(register.p)\n                    elif self.options.reg_method == \"minctracc\":\n                        hm = mm.HierarchicalMinctracc(inputFH, targetFH)\n                        self.pipeline.addPipeline(hm.p)\n                    if nlinFH:\n                        resample = ma.mincresample(inputFH, targetFH, likeFile=nlinFH)\n                    else:\n                        resample = ma.mincresample(inputFH, targetFH, likeFile=inputFH)\n                    self.pipeline.addStage(resample)\n                    inputFH.setLastBasevol(resample.outputFiles[0])\n                    \"\"\"Calculate statistics\"\"\"\n                    stats = st.CalcChainStats(inputFH, targetFH, blurs)\n                    stats.calcFullDisplacement()\n                    stats.calcDetAndLogDet(useFullDisp=True)\n                    self.pipeline.addPipeline(stats.p)\n                    subjectStats[inputFH][targetFH] = stats.statsGroup\n                    \"\"\"Resample to nlin space from previous build model run, if specified\"\"\"\n                    if self.options.nlin_avg and self.options.mbm_dir:\n                        xfmToNlin = inputFH.getLastXfm(nlinFH, groupIndex=0)\n                        res = mm.resampleToCommon(xfmToNlin, inputFH, subjectStats[inputFH][targetFH], blurs, nlinFH)\n                        self.pipeline.addPipeline(res)\n                    \"\"\"Reset last base volume to original input before continuing to next pair in loop.\"\"\"\n                    inputFH.setLastBasevol(setToOriginalInput=True)\n\nif __name__ == \"__main__\":\n    \n    application = PairwiseNonlinear()\n    application.start()\n","sub_path":"applications/pairwise_nlin.py","file_name":"pairwise_nlin.py","file_ext":"py","file_size_in_byte":5516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"4058647","text":"# -*- coding: utf-8 -*-\nfrom django.conf.urls.defaults import patterns, url\nfrom apps.essay.views import EssayListView, EssayDetailView, HealerChoseYearArchiveView, LatestEntriesFeed\n\n\nurlpatterns = patterns('',\n                       url(r'^(?P<year>\\d{4})/$', EssayListView.as_view(),\n                           name=\"index\"),\n                       url(r'^(?P<year>\\d{4})/(?P<pk>\\d+)/$', EssayDetailView.as_view(),\n                           name=\"item\"),\n#                       url(r'^mass_media/(?P<pk>\\d+)/$',\n#                           ChangesDetailView.as_view(),\n#                           name=\"changes_item\"),\n#                       url(r'^archive/(?P<year>\\d+)/$', HealerChoseYearArchiveView.as_view(\n#                           date_field='published_date',\n#                           make_object_list=True, ),\n#                           name=\"archive\"),\n                       url(r'^feed/$', LatestEntriesFeed(), name=\"feed\")\n)\n","sub_path":"apps/essay/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"181007340","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Numpy_Assignment_2::\n\n# ## Question:1\n\n# ### Convert a 1D array to a 2D array with 2 rows?\n\n# #### Desired output::\n\n\n#         [5, 6, 7, 8, 9]])\n\n# In[1]:\n\n\nimport numpy as np\narr = np.arange(10)\narr\n\n\n# In[3]:\n\n\narr.reshape(2,5)\n\n\n# In[ ]:\n\n\nOR\n\n\n# In[4]:\n\n\narr = np.arange(10)\narr\n\n\n# In[5]:\n\n\narr.reshape(2,-1)\n\n\n# ## Question:2\n\n# ###  How to stack two arrays vertically?\n\n# #### Desired Output::\narray([[0, 1, 2, 3, 4],\n        [5, 6, 7, 8, 9],\n       [1, 1, 1, 1, 1],\n       [1, 1, 1, 1, 1]])\n# In[6]:\n\n\na = np.arange(10).reshape(2,-1)\nb = np.repeat(1, 10).reshape(2,-1)\n\n\n# In[7]:\n\n\n# Method 1:\nnp.concatenate([a, b], axis=0)\n\n\n# In[8]:\n\n\n# Method 2:\nnp.vstack([a, b])\n\n\n# In[9]:\n\n\n# Method 3:\nnp.r_[a, b]\n\n\n# ## Question:3\n\n# ### How to stack two arrays horizontally?\n\n# #### Desired Output::\narray([[0, 1, 2, 3, 4, 1, 1, 1, 1, 1],\n       [5, 6, 7, 8, 9, 1, 1, 1, 1, 1]])\n# In[18]:\n\n\na = np.arange(10).reshape(2,-1)\nb = np.repeat(1, 10).reshape(2,-1)\n\n\n# In[19]:\n\n\na\n\n\n# In[20]:\n\n\nb\n\n\n# In[15]:\n\n\n# Method 1:\nnp.concatenate([a, b], axis=1)\n\n\n# In[16]:\n\n\n# Method 2:\nnp.hstack([a, b])\n\n\n# In[17]:\n\n\n# Method 3:\nnp.c_[a, b]\n\n\n# ## Question:4\n\n# ### How to convert an array of arrays into a flat 1d array?\n\n# #### Desired Output::\narray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n# In[22]:\n\n\nimport numpy as np \narr1 = np.arange(3)\narr2 = np.arange(3,7)\narr3 = np.arange(7,10)\n\n\n# In[23]:\n\n\narr1\n\n\n# In[24]:\n\n\narr2\n\n\n# In[25]:\n\n\narr3\n\n\n# In[26]:\n\n\narray_of_arrays = np.array([arr1, arr2, arr3])\nprint('array_of_arrays: ', array_of_arrays)\n\n\n# In[27]:\n\n\narr_2d = np.concatenate(array_of_arrays)\nprint(arr_2d)\n\n\n# ## Question:5\n\n# ### How to Convert higher dimension into one dimension?\n\n# #### Desired Output::\narray([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])\n# In[2]:\n\n\nimport numpy as np\narr1 = np.array([[0,1,2],[3,4,5],[6,7,8],[9,10,11],[12,13,14]])\narr1\n\n\n# In[8]:\n\n\narr1.ravel()\n\n\n# In[9]:\n\n\n# or \narr1.flatten()\n\n\n# ## Question:6\n\n# ### Convert one dimension to higher dimension?\n\n# #### Desired Output::\narray([[ 0, 1, 2],\n[ 3, 4, 5],\n[ 6, 7, 8],\n[ 9, 10, 11],\n[12, 13, 14]])\n# In[85]:\n\n\narr = np.arange(15)\narr\n\n\n# In[86]:\n\n\narr.reshape(5,3)\n\n\n# ## Question:7\n\n# ### Create 5x5 an array and find the square of an array?\n\n# In[28]:\n\n\nx = np.ones((5,5))\nx\n\n\n# ## Question:8\n\n# ### Create 5x6 an array and find the mean?\n\n# In[38]:\n\n\narr = np.random.randn(5, 6)\n\narr\n\n\n# In[41]:\n\n\narr.mean()\n\n\n# In[43]:\n\n\n# or\nnp.mean(arr)\n\n\n# In[13]:\n\n\n# or\narr1 = np.arange(1,31).reshape(5,6)\narr1\n\n\n# In[15]:\n\n\narr1.mean()\n\n\n# ## Question:9\n\n# ### Find the standard deviation of the previous array in Q8?\n\n# In[100]:\n\n\narr = np.random.randn(5, 6)\narr\n\n\n# In[101]:\n\n\nnp.std(arr)\n\n\n# In[16]:\n\n\n# or\narr1 = np.arange(1,31).reshape(5,6)\narr1\n\n\n# In[17]:\n\n\nnp.std(arr1)\n\n\n# ## Question:10\n\n# ### Find the median of the previous array in Q8?\n\n# In[96]:\n\n\narr = np.random.randn(5, 6)\narr\n\n\n# In[97]:\n\n\nnp.median(arr)\n\n\n# In[22]:\n\n\n# or\narr1 = np.arange(1,31).reshape(5,6)\narr1\n\n\n# In[23]:\n\n\nnp.median(arr1)\n\n\n# ## Question:11\n\n# ### Find the transpose of the previous array in Q8?\n\n# In[92]:\n\n\narr = np.random.randn(5, 6)\n\narr\n\n\n# In[95]:\n\n\nnp.transpose(arr)\n\n\n# In[24]:\n\n\n# or\narr1 = np.arange(1,31).reshape(5,6)\narr1\n\n\n# In[25]:\n\n\nnp.transpose(arr1)\n\n\n# ## Question:12\n\n# ### Create a 4x4 an array and find the sum of diagonal elements?\n\n# In[49]:\n\n\narr = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]])\narr\n\n\n# In[50]:\n\n\narr1 = np.diag(arr)\narr1\n\n\n# In[51]:\n\n\narr1.sum()\n\n\n# In[52]:\n\n\nnp.sum(arr1)\n\n\n# ## Question:13\n\n# ### Find the determinant of the previous array in Q12?\n\n# In[24]:\n\n\narr = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]])\narr\n\n\n# In[25]:\n\n\narr1 = np.linalg.det(arr)\narr1\n\n\n# ## Question:14\n\n# ### Find the 5th and 95th percentile of an array?\n\n# In[64]:\n\n\narr = np.arange(10)\narr\n\n\n# In[66]:\n\n\nnp.percentile(arr,5)\n\n\n# In[67]:\n\n\nnp.percentile(arr,95)\n\n\n# ## Question:15\n\n# ### How to find if a given array has any null values?\n\n# In[122]:\n\n\nimport numpy as np\nZ = np.random.randint(0,3,(3,10))\nZ\n\n\n# In[123]:\n\n\nZ.any()\n\n","sub_path":"piaic151789_Assignment 2.py","file_name":"piaic151789_Assignment 2.py","file_ext":"py","file_size_in_byte":4098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"58010994","text":"# -*- coding: utf-8 -*-\n\n# @Time    : 2019/7/31\n# @Author  : Lattine\n\n# ======================\n\nimport tensorflow as tf\nfrom .base import BaseModel\n\n\nclass TextCNN(BaseModel):\n    def __init__(self, config, vocab_size, word_vectors=None):\n        super(TextCNN, self).__init__(config)\n        self.vocab_size = vocab_size\n        self.word_vectors = word_vectors\n\n        self.build_model()  # 构建计算图\n        self.init_saver()  # 初始化Saver对象\n\n    def build_model(self):\n        # ----- 词向量层 -----\n        with tf.name_scope(\"embedding\"):\n            # 加载已有的词向量，或者随机初始化词向量\n            if self.word_vectors:\n                embeddings_w = tf.Variable(tf.cast(self.word_vectors, dtype=tf.float32, name='word2vec'), name='embeddings_w')\n            else:\n                embeddings_w = tf.get_variable('embeddings_w', shape=[self.vocab_size, self.config.embedding_size], initializer=tf.contrib.layers.xavier_initializer())\n            # 使用词向量将输入的数据索引转换为词向量表示, [batch_size, sequence_length, embedding_size]\n            embedded_words = tf.nn.embedding_lookup(embeddings_w, self.inputs)\n            # 卷积的输入是[batch_size, width, height, channel]，因此需要增加维度\n            embedded_words_expand = tf.expand_dims(embedded_words, -1)  # [batch_size, sequence_length, embedding_size, 1] = [b, h, w, in]\n\n        # ----- 卷积层，池化层 -----\n        pooled_outputs = []\n        # 原始论文中使用三种filter(3,4,5),故TextCNN是个多通道单层卷积的模型，可以看作三个单层均价模型的融合\n        # for filter_size in self.config.filter_sizes:\n        #     with tf.name_scope('conv-maxpool-{}'.format(filter_size)):\n        #         # 卷积层，卷积核的大小为 filter_size * embedding_size\n        #         filter_shape = [filter_size, self.config.embedding_size, 1, self.config.num_filters]  # [height, width, in_channel, out_channel]\n        #         conv_w = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name='conv_w')\n        #         conv_b = tf.Variable(tf.constant(0.1, shape=[self.config.num_filters]), name='conv_b')\n        #         conv = tf.nn.conv2d(embedded_words_expand, conv_w, strides=[1, 1, 1, 1], padding=\"VALID\", name=\"conv\")\n        #         h = tf.nn.relu(tf.nn.bias_add(conv, conv_b), name='relu')  # 非线性激活\n        #         # 最大池化，每个filter取最大值\n        #         pooled = tf.nn.max_pool(h, ksize=[1, self.config.sequence_length - filter_size + 1, 1, 1], strides=[1, 1, 1, 1], padding='VALID', name=\"pool\")\n        #         pooled_outputs.append(pooled)  # 将三种size的filter的输出一起加入到列表中\n        for i, filter_size in enumerate(self.config.filter_sizes):\n            with tf.name_scope(\"conv-maxpool-%s\" % filter_size):\n                # 卷积层，卷积核尺寸为filterSize * embeddingSize，卷积核的个数为numFilters\n                # 初始化权重矩阵和偏置\n                filter_shape = [filter_size, self.config.embedding_size, 1, self.config.num_filters]\n                conv_w = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"conv_w\")\n                conv_b = tf.Variable(tf.constant(0.1, shape=[self.config.num_filters]), name=\"conv_b\")\n                conv = tf.nn.conv2d(\n                    embedded_words_expand,\n                    conv_w,\n                    strides=[1, 1, 1, 1],\n                    padding=\"VALID\",\n                    name=\"conv\")\n\n                # relu函数的非线性映射\n                h = tf.nn.relu(tf.nn.bias_add(conv, conv_b), name=\"relu\")\n                # 池化层，最大池化，池化是对卷积后的序列取一个最大值\n                pooled = tf.nn.max_pool(\n                    h,\n                    ksize=[1, self.config.sequence_length - filter_size + 1, 1, 1],\n                    # ksize shape: [batch, height, width, channels]\n                    strides=[1, 1, 1, 1],\n                    padding='VALID',\n                    name=\"pool\")\n                pooled_outputs.append(pooled)  # 将三种size的filter的输出一起加入到列表中\n        num_filters_total = self.config.num_filters * len(self.config.filter_sizes)  # CNN网络的输出维度\n        h_pool = tf.concat(pooled_outputs, 3)  # 池化后的维度不变，按照最后的维度channel来concat数据\n        h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])  # 将拼接的数据拉平\n\n        # ----- Dropout -----\n        with tf.name_scope(\"dropout\"):\n            h_drop = tf.nn.dropout(h_pool_flat, self.keep_prob)\n\n        # ----- Output -----\n        with tf.name_scope(\"output\"):\n            output_w = tf.get_variable(\"output_w\", shape=[num_filters_total, self.config.num_classes], initializer=tf.contrib.layers.xavier_initializer())\n            output_b = tf.Variable(tf.constant(0.1, shape=[self.config.num_classes]), name='output_b')\n            self.l2_loss += tf.nn.l2_loss(output_w)\n            # self.l2_loss += tf.nn.l2_loss(output_b) # 偏移是否需要加入约束\n\n            self.logits = tf.nn.xw_plus_b(h_drop, output_w, output_b, name=\"logits\")\n            self.predictions = self.get_predictions()\n\n        # ----- Loss ------\n        self.loss = self.calculate_loss() + self.config.l2_reg_lambda * self.l2_loss\n\n        # ----- Train OP -----\n        self.train_op, self.summary_op = self.get_train_op()\n","sub_path":"N02_TextCNN/model/text_cnn.py","file_name":"text_cnn.py","file_ext":"py","file_size_in_byte":5483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"137471154","text":"from django.core import serializers\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.shortcuts import render\nfrom django.views.decorators.csrf import ensure_csrf_cookie\nfrom annotation_app.models import Bill\n\n\n@ensure_csrf_cookie\ndef get_one(request, bill_slug):\n  chamber_origin = bill_slug[0].upper()\n  number = bill_slug[2:]\n\n  try:\n    bill = Bill.objects.filter(chamber_origin = chamber_origin,\n      number = number)[0]\n  except Bill.DoesNotExist:\n    raise Http404\n  # annotation_list = bill.annotation_set.all()\n  if request.is_ajax():\n    data = serializers.serialize(\"json\", [bill])\n    return HttpResponse(data)\n  else:\n    context = {'bill': bill}#, 'annotation_list': annotation_list}\n    response = render(request, 'bill.html', context)\n    response.set_cookie('bill_id', bill.id)\n    return response\n\n\n# Cater to deprecated route\ndef redirect_get_one(request, bill_id):\n  bill = Bill.objects.get(id = bill_id)\n  return HttpResponseRedirect('/bills/%sB%d/' % (bill.chamber_origin,\n    bill.number))\n\n\ndef create(request):\n  from annotation_app.forms import BillForm\n\n  form = BillForm(request.POST)\n\n  if form.is_valid():\n    data = form.cleaned_data\n    bill = Bill.objects.filter(chamber_origin = data['chamber_origin'],\n      number = data['number'])\n\n    # If you find bill in the database, it is the first element in QuerySet\n    if bill:\n      bill = bill[0]\n    # If bill is not in the database, pull it from TLO website\n    else:\n      from annotation_app.helpers.bill_scrapers import scrape_bill_text\n\n      bill_text = scrape_bill_text(data)\n      if bill_text == None:\n        return HttpResponseRedirect('/')\n\n      bill = Bill()\n      bill.session = data['session']\n      bill.chamber_origin = data['chamber_origin']\n      bill.number = data['number']\n      bill.text = bill_text\n\n      from annotation_app.helpers.htmllogic import htmltext\n      bill.text = htmltext(bill.text)\n      bill.save()\n\n    if 'format' in request.POST:\n      return HttpResponse(serializers.serialize(request.POST['format'],\n        [bill]))\n    else:\n      return HttpResponseRedirect('/bills/%sB%d/' % (bill.chamber_origin,\n        bill.number))\n\n  else:\n    return HttpResponseRedirect('/')\n\n\ndef get_bill_info(request):\n  if request.method == 'POST':\n    from annotation_app.controllers import senators_controller,\\\n      subjects_controller\n    import json\n\n    bill = Bill.objects.get(id = request.POST['bill_id'])\n    authors = bill.senator_set.all()\n    subjects = bill.subject_set.all()\n\n    if len(authors) == 0 or len(subjects) == 0:\n      from annotation_app.helpers.bill_scrapers import scrape_bill_history\n\n      bill_data = {}\n      bill_data['session'] = bill.session\n      bill_data['chamber_origin'] = bill.chamber_origin\n      bill_data['number'] = bill.number\n\n      tmi_data = scrape_bill_history(bill_data)\n\n    if len(authors) == 0:\n      authors = tmi_data['authors'].split(' | ')\n\n      authors = map(lambda author:\n        senators_controller.create(author, bill.id).name, authors)\n\n    else:\n      authors = map(lambda author: author.name, authors)\n\n    if len(subjects) == 0:\n      subjects = tmi_data['subjects']['subject']\n\n      if type(subjects) != type([]):\n        subjects = [subjects]\n\n      subjects = map(lambda subject:\n        subjects_controller.create(subject, bill.id).name, subjects)\n\n    else:\n      subjects = map(lambda subject: subject.name, subjects)\n\n    data = {}\n    data['authors'] = list(authors)\n    data['subjects'] = list(subjects)\n\n    return HttpResponse(json.dumps(data))\n  else:\n    return HttpResponse(\"{}\")\n","sub_path":"annotation_app/controllers/bills_controller.py","file_name":"bills_controller.py","file_ext":"py","file_size_in_byte":3597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"32007923","text":"\"\"\"\r\nResNet18 on CIFAR10\r\n\"\"\"\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nimport tensorflow.keras.datasets as datasets\r\nfrom tensorflow.keras import Sequential, layers, losses, optimizers, metrics\r\n\r\n\r\ngpus = tf.config.experimental.list_physical_devices('GPU')\r\nif gpus:\r\n    try:\r\n        for gpu in gpus:\r\n            tf.config.experimental.set_memory_growth(gpu, True)\r\n    except RuntimeError as e:\r\n        print(e)\r\n\r\ndef preprocess(x, y):\r\n    # [0~1]\r\n    x = 2*tf.cast(x, dtype=tf.float32) / 255.-1\r\n    y = tf.squeeze(y, axis=1)\r\n    y = tf.cast(y, dtype=tf.int32)\r\n    return x,y\r\n(x,y), (x_test, y_test) = datasets.cifar10.load_data()\r\ntrain_db = tf.data.Dataset.from_tensor_slices((x,y)).shuffle(10000).batch(256).map(preprocess)\r\ntest_db = tf.data.Dataset.from_tensor_slices((x_test,y_test)).batch(256).map(preprocess)\r\n\r\nclass BasicBlock(layers.Layer):\r\n    # 残差模块\r\n    # Page 185，自定义网络层\r\n    def __init__(self,filter_num,stride=1):\r\n        super(BasicBlock,self).__init__()\r\n        self.conv1 = layers.Conv2D(filter_num,(3,3),strides=stride,padding='same')\r\n        self.bn1 = layers.BatchNormalization()\r\n        self.relu = layers.Activation('relu')\r\n\r\n        self.conv2 = layers.Conv2D(filter_num,(3,3),strides=1,padding='same')\r\n        self.bn2 = layers.BatchNormalization()\r\n\r\n        if stride != 1:\r\n            self.downsample = Sequential()\r\n            self.downsample.add(layers.Conv2D(filter_num,(1,1),strides=stride))\r\n        else:\r\n            self.downsample = lambda x:x\r\n            # 教材中的代码，不包含x通道缺失补零的操作，应该是利用了tf的自动转换\r\n\r\n    def call(self,inputs,training=None):\r\n        out = self.conv1(inputs)\r\n        out = self.bn1(out)\r\n        out = self.relu(out)\r\n        out = self.conv2(out)\r\n        out = self.bn2(out)\r\n        identity = self.downsample(inputs)\r\n        output = layers.add([out, identity])\r\n        output = tf.nn.relu(output)\r\n        return output\r\n\r\n\r\nclass ResNet(keras.Model):\r\n    def __init__(self,layer_dims,num_classes=10):\r\n        super(ResNet, self).__init__()\r\n        self.stem = Sequential([\r\n            layers.Conv2D(64,(3,3),strides=(1,1)),\r\n            layers.BatchNormalization(),\r\n            layers.Activation('relu'),\r\n            layers.MaxPooling2D(pool_size=(2,2),strides=(1,1),padding='same')\r\n        ])\r\n        self.layer1 = self.build_resblock(64,  layer_dims[0])\r\n        self.layer2 = self.build_resblock(128, layer_dims[1], stride=2)\r\n        self.layer3 = self.build_resblock(256, layer_dims[2], stride=2)\r\n        self.layer4 = self.build_resblock(512, layer_dims[3], stride=2)\r\n\r\n        self.avgpool = layers.GlobalAveragePooling2D()\r\n        self.fc = layers.Dense(num_classes)\r\n\r\n    def build_resblock(self, filter_num, blocks, stride=1):\r\n        # 辅助函数\r\n        res_blocks = Sequential()\r\n        res_blocks.add(BasicBlock(filter_num,stride))\r\n        # 只有第一个BasicBlock 的步长可能不为1，实现下采样\r\n        for _ in range(1,blocks):\r\n            res_blocks.add(BasicBlock(filter_num))\r\n        return res_blocks\r\n\r\n    def call(self,inputs,training=None):\r\n        x = self.stem(inputs)\r\n        x = self.layer4(\r\n                self.layer3(\r\n                    self.layer2(\r\n                        self.layer1(x))))\r\n        x = self.fc(self.avgpool(x))\r\n        return x\r\n\r\nres = ResNet([2,2,2,2])\r\ncriterion = losses.CategoricalCrossentropy(from_logits=True)\r\noptimizer = optimizers.Adam(learning_rate=1e-3)\r\nloss_meter = metrics.Mean()\r\nacc_meter = metrics.Accuracy()\r\n\r\nfor epoch in range(10):\r\n    for step, (x,y) in enumerate(train_db):\r\n        with tf.GradientTape() as tape:\r\n            out = res(x)\r\n            y_onehot = tf.one_hot(y,depth=10)\r\n            loss = criterion(y_onehot, out)\r\n        grads = tape.gradient(loss,res.trainable_variables)\r\n        optimizer.apply_gradients(zip(grads,res.trainable_variables))\r\n        loss_meter.update_state(loss.numpy())\r\n        pred = tf.argmax(out, axis=1)\r\n        acc_meter.update_state(y,pred)\r\n        if (step+1)%10 == 0:\r\n            print(f'Epoch:{epoch+1},Step:{step+1},Loss:{loss_meter.result().numpy()},Accuracy:{acc_meter.result().numpy()}')\r\n            loss_meter.reset_states()\r\n            acc_meter.reset_states()\r\nres.save_weights('ResNet18_on_CIFAR10.ckpt')\r\ndel res\r\n\r\n\r\nres = ResNet([2,2,2,2])\r\nres.load_weights('ResNet18_on_CIFAR10.ckpt')\r\naccuracy = []\r\nfor _, (x,y) in enumerate(test_db):\r\n    out = res(x)\r\n    pred = tf.cast(tf.argmax(out, axis=1),dtype=tf.int32)\r\n    acc = tf.reduce_mean((tf.cast(tf.equal(y, pred),dtype=tf.float32)))\r\n    accuracy.append(acc)\r\naccuracy = tf.reduce_mean(accuracy)\r\nprint(accuracy.numpy())\r\n","sub_path":"tf2/ResNet18_CIFAR10.py","file_name":"ResNet18_CIFAR10.py","file_ext":"py","file_size_in_byte":4745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"562804127","text":"\n\ndef maximal_square(grid):\n    current_max = 0\n    for i in range(len(grid)):\n        for j in range(len(grid[0])):\n            current_max = max(current_max, largest(i, j, grid))\n    return current_max\n\n\ndef largest(y, x, grid):\n    max_dimension = min(len(grid) - y, len(grid[0]) - x)\n    for dim in range(max_dimension, -1, -1):\n        if is_valid(y, x, grid, dim):\n            return dim ** 2\n    return -1\n\n\ndef is_valid(y, x, grid, dim):\n    for i in range(dim):\n        for j in range(dim):\n            if grid[y+i][x+j] == '0':\n                return False\n    return True\n\n\ndef maximal_square_dp(grid):\n    if not grid:\n        return 0\n\n    row = len(grid) + 1\n    col = len(grid[0]) + 1\n    dp = [[0] * col for _ in range(row)]\n\n    current_max = 0\n    for i in range(1, row):\n        for j in range(1, col):\n            if grid[i-1][j-1] == \"1\":\n                dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1\n                current_max = max(current_max, dp[i][j])\n    return current_max ** 2\n","sub_path":"maximal_square.py","file_name":"maximal_square.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"338803503","text":"from datetime import timedelta\r\nfrom homeassistant.const import  TEMP_CELSIUS\r\n\r\nR_STATUS_CODE = 'STATUS_CODE'\r\nR_CONTENT = 'CONTENT'\r\nR_POWER = 'POWER'\r\n\r\nCMND_STATUS = 'status%208'\r\nCMND_POWER = 'POWER'\r\nCMND_POWER_ON = 'Power%20On'\r\nCMND_POWER_OFF = 'Power%20Off'\r\n\r\nS_CMND = \"CMND\"\r\nS_VALUE = \"VALUE\"\r\nS_UNIT = \"UNIT\"\r\nS_SCAN_INTERVAL = \"SCAN_INTERVAL\"\r\nS_ICON = 'ICON'\r\n\r\nST_TEMPERATURE = 'temperature'\r\nST_CURRENT = 'current'\r\nST_VOLTAGE = 'voltage'\r\n\r\nSENSORS = {\r\n    ST_TEMPERATURE :{ \r\n        S_CMND: CMND_STATUS,\r\n        S_VALUE:  [\"StatusSNS\", \"DS18B20\",\"Temperature\"] ,\r\n        S_SCAN_INTERVAL: timedelta(seconds=30),\r\n        S_UNIT:   TEMP_CELSIUS,\r\n        S_ICON: ''\r\n    },\r\n    ST_VOLTAGE: {\r\n        S_CMND: CMND_STATUS,\r\n        S_VALUE:  [\"StatusSNS\",\"ENERGY\", \"Voltage\"] ,\r\n        S_SCAN_INTERVAL: timedelta(seconds=30),\r\n        S_UNIT:   'V',\r\n        S_ICON: ''\r\n    }, \r\n    ST_CURRENT: {\r\n        S_CMND: CMND_STATUS,\r\n        S_VALUE:  [\"StatusSNS\", \"ENERGY\", \"Current\"] ,\r\n        S_SCAN_INTERVAL: timedelta(seconds=10),\r\n        S_UNIT: 'A',\r\n        S_ICON: 'mdi:current-ac'\r\n    }\r\n}\r\n\r\n\r\n","sub_path":"sonata/sonata_const.py","file_name":"sonata_const.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"84254070","text":"#from django.views.decorators.csrf import csrf_protect\nfrom django.shortcuts import render_to_response\nfrom blog.models import Blog\nfrom blog.forms import CommentForm\nfrom blog.analyze import *\n\ndef updateSpamScore(isSpam):\n\n\tif isSpam:\n\t\n\t\t# first count the number of spam words in the comment text\n\t\tnumSpamWords = 0\n\n\t\tfor key, value in commentSpamScores.items():\n\t\t\tif not float(value) < float(0.5): \n\t\t\t# Spam\n\t\t\t\tnumSpamWords += 1\n\t\t\t\n\t\t# if the word has already been seen then update the score\t\n\t\tfor key, value in commentSpamScores.items():\n\t\t\n\t\t\tnewSpamDelta = 0.0\n\t\t\t\n\t\t\ttry:\n\t\t\t\tnewSpamDelta = value / numSpamWords\n\t\t\texcept ZeroDivisionError:\n\t\t\t\tnewSpamDelta = 0\n\t\t\n\t\t\tif key in collectionSpamScores.values():\n\t\t\t\tcollectionSpamScores[key] =  collectionSpamScores[value] + newSpamDelta\n\t\t\n\t\t\telse:\n\t\t\t\tcollectionSpamScores[key] = newSpamDelta\n\t\t\n\telse:\n\t\n\t\t# first count the number of non-spam words in the comment text\n\t\tnumNonSpamWords = 0\n\n\t\tfor key, value in commentSpamScores.items():\n\t\n\t\t\tif float(value) < float(0.5): \n\t\t\t# Spam\n\t\t\t\tnumNonSpamWords += 1\n\t\t\t\t\n\t\t# if the word has already been seen then update the score\t\n\t\tfor key, value in commentSpamScores.items():\n\t\t\n\t\t\tnewNonSpamDelta = 0.0\n\t\t\n\t\t\ttry:\n\t\t\t\tnewNonSpamDelta = float(value / numNonSpamWords)\n\t\t\texcept ZeroDivisionError:\n\t\t\t\tnewNonSpamDelta = 0.0\n\t\t\n\t\t\tif key in collectionSpamScores.values():\n\t\t\t\tcollectionSpamScores[key] =  collectionSpamScores[value] + newNonSpamDelta\n\t\t\n\t\t\telse:\n\t\t\t\tcollectionSpamScores[key] = newNonSpamDelta\n\ndef index(request):\n\tentries = Blog.objects.all()\n\n\tif request.method == 'POST':\n\t\t\n\t\tform = CommentForm(request.POST)\n\t\t\n\t\tif form.is_valid():\n\t\t\t\n\t\t\tname = form.cleaned_data['name']\n\t\t\tcomments = form.cleaned_data['comments']\n\n\t\t\tisSpam = consume(comments)\n\t\t\tcloseWriters(writer_1, writer_2)\n\t\t\tupdateSpamScore(isSpam)\n\t\t\tupdateSvm(collectionSpamScores, scores[0])\n\t\t\twriteSpamScores(spamScoresFilename)\t\t\t\t\n\n\t\t\tif isSpam:\n\t\t\t\treturn render_to_response('postFail.html', locals())\n\t\t\t\t\n\t\t\telse:\t\t\n\t\t\t\treturn render_to_response('postSuccess.html', locals())\t\n\telse:\n\t\tform = CommentForm()\n\n\treturn render_to_response('index.html', locals())\n","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"241141511","text":"import sys\ninput = sys.stdin.readline\n\nn,k = map(int,input().split())\nproducts = list(map(int,input().split()))\nmultitab = [0]*(n)\nidx =0\nanswer=0\nfor product in products:\n    if product in multitab: #현재 꼽혀있음\n        idx+=1\n        continue\n    if 0 in multitab: # 빈 공간 있을때\n        multitab[multitab.index(0)] = product\n        idx+=1\n    else: # 빈 공간 없을때\n        later=0 # 뽑아야 될 때 뒤에 있는거 뽑기 위함\n        temp=0\n        for i in multitab:\n            if i not in products[idx:]: # 꼽혀있는 것 중에 더 이상 사용 안되는 것 뽑기\n                temp=i\n                break\n            elif products[idx:].index(i) > later: # 더 나중에 사용되는 거 뽑기\n                temp = i\n                later = products[idx:].index(i)\n        idx+=1\n        multitab[multitab.index(temp)] = product\n        answer+=1\nprint(answer)\n","sub_path":"Algorithm/AMIALGORANI/11.24/1700.py","file_name":"1700.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"250000296","text":"# Definition for a point.\nclass Point:\n    def __init__(self, a=0, b=0):\n        self.x = a\n        self.y = b\n\n\nclass Solution:\n    # solution 1\n    # def maxPoints(self, points):\n    #\n    #     def get_gcd(a, b):\n    #         while b:\n    #             a, b = b, a % b\n    #         return a\n    #\n    #     n = len(points)\n    #     if n < 3:\n    #         return n\n    #\n    #     ans, _map = 0, dict()\n    #     for i in range(len(points)):\n    #         _map.clear()\n    #         same, _max = 1, 0\n    #         for j in range(i + 1, len(points)):\n    #             dx = points[j].x - points[i].x\n    #             dy = points[j].y - points[i].y\n    #             if dx == 0 and dy == 0:\n    #                 same += 1\n    #                 continue\n    #             gcd = get_gcd(dx, dy)\n    #             dx //= gcd\n    #             dy //= gcd\n    #             _map[(dx, dy)] = _map.get((dx, dy), 0) + 1\n    #             _max = max(_max, _map[(dx, dy)])\n    #         ans = max(ans, _max + same)\n    #     return ans\n\n    # solution 2\n    def maxPoints(self, points):\n        from decimal import Decimal\n\n        n = len(points)\n        if n < 3:\n            return n\n\n        ans, _map = 0, dict()\n        for i in range(len(points)):\n            _map.clear()\n            same, _max = 1, 0\n            for j in range(i + 1, len(points)):\n                dx = points[j].x - points[i].x\n                dy = points[j].y - points[i].y\n                if dx == 0 and dy == 0:\n                    same += 1\n                    continue\n                k = 'inf' if dx == 0 else Decimal(dy) / Decimal(dx)\n                _map[k] = _map.get(k, 0) + 1\n                _max = max(_max, _map[k])\n            ans = max(ans, _max + same)\n        return ans\n\n\ndef test_solution():\n    points = [Point(1, 1), Point(2, 2), Point(3, 3)]\n    assert Solution().maxPoints(points) == 3\n    \n    points = [Point(0, 0), Point(94911151, 94911150), Point(94911152, 94911151)]\n    assert Solution().maxPoints(points) == 2\n\n\nif __name__ == '__main__':\n    test_solution()\n","sub_path":"search_table/keyvalue/149_max_points_on_a_line.py","file_name":"149_max_points_on_a_line.py","file_ext":"py","file_size_in_byte":2063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"269353487","text":"from queue_manager import QueueClient\nimport requests, zipfile, StringIO, shutil, time\n\n__author__ = 'david'\n\nalexa_splits = [1000000, 1000, 100]\n\n# creates list from AlexaCSV with dated sources\ndef datedAlexaCSV(date, splits):\n    splits.sort()\n    m = splits[len(splits) - 1] + 1\n    r = []\n    f = open(\"data/top-1m.csv\", \"r\")\n    for i in range(1,m):\n        c = str(i) + ','\n        t = f.readline().replace('\\n', '').replace(c, '')\n        q = []\n        for s in splits:\n            q.append(date + \"AlexaTOP\" + str(s))\n        r.append([t, q])\n        i += 1\n        if i > splits[0]:\n            splits.remove(splits[0])\n    f.close()\n    return r\n\n\n# downloads and unzips AlexaCSV and return the date\ndef getAlexaCSV():\n    #downloads from url\n    r = requests.get(\"http://s3.amazonaws.com/alexa-static/top-1m.csv.zip\")\n    #unzips\n    z = zipfile.ZipFile(StringIO.StringIO(r.content))\n    z.extractall()\n    #moves file from \"crawler\" to \"crawler/data\"\n    shutil.move(\"top-1m.csv\", \"data/top-1m.csv\")\n    #get date as \"dd.mm.yyyy\"\n    a = time.strftime(\"%d.%m.%Y\")\n    return a\n\n\nif __name__ == \"__main__\":\n\n    # get instance of QueueClient\n    c = QueueClient()\n\n    # get appropriate queue from QueueClient\n    #host_queue = c.host_queue()\n    queue_manager = c.queue_manager()\n    d = getAlexaCSV()\n    #host_queue.put(datedAlexaCSV(d, alexa_splits))\n    queue_manager.put_new_list(queue_manager, datedAlexaCSV(d, alexa_splits))\n","sub_path":"input_worker.py","file_name":"input_worker.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"189874172","text":"# -*- coding: utf-8 -*-\n\"\"\"A set of utility functions to support outlier detection.\n\"\"\"\n# Author: Yinchen WU <yinchen@uchicago.edu>\n\n\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nfrom numpy import percentile\nimport numbers\n\nimport sklearn\nfrom sklearn.metrics import precision_score\nfrom sklearn.preprocessing import StandardScaler\n\nfrom sklearn.utils import column_or_1d\nfrom sklearn.utils import check_array\nfrom sklearn.utils import check_consistent_length\n\nfrom sklearn.utils import check_random_state\nfrom sklearn.utils.random import sample_without_replacement\n\nfrom functools import partial\nfrom multiprocessing import Pool\nimport random as rn\nfrom collections import Counter\nimport warnings\n\nMAX_INT = np.iinfo(np.int32).max\nMIN_INT = -1 * MAX_INT\n\ndef pairwise_distances_no_broadcast(X, Y):\n    \"\"\"Utility function to calculate row-wise euclidean distance of two matrix.\n    Different from pair-wise calculation, this function would not broadcast.\n    For instance, X and Y are both (4,3) matrices, the function would return\n    a distance vector with shape (4,), instead of (4,4).\n    Parameters\n    ----------\n    X : array of shape (n_samples, n_features)\n        First input samples\n    Y : array of shape (n_samples, n_features)\n        Second input samples\n    Returns\n    -------\n    distance : array of shape (n_samples,)\n        Row-wise euclidean distance of X and Y\n    \"\"\"\n    X = check_array(X)\n    Y = check_array(Y)\n\n    if X.shape[0] != Y.shape[0] or X.shape[1] != Y.shape[1]:\n        raise ValueError(\"pairwise_distances_no_broadcast function receive\"\n                         \"matrix with different shapes {0} and {1}\".format(\n            X.shape, Y.shape))\n        \n    euclidean_sq = np.square(Y - X)\n    return np.sqrt(np.sum(euclidean_sq, axis=1)).ravel()\n\ndef getSplit(X):\n    \"\"\"\n    Randomly selects a split value from set of scalar data 'X'.\n    Returns the split value.\n    \n    Parameters\n    ----------\n    X : array \n        Array of scalar values\n    Returns\n    -------\n    float\n        split value\n    \"\"\"\n    xmin = X.min()\n    xmax = X.max()\n    return np.random.uniform(xmin, xmax)\n\ndef similarityScore(S, node, alpha):\n    \"\"\"\n    Given a set of instances S falling into node and a value alpha >=0,\n    returns for all element x in S the weighted similarity score between x\n    and the centroid M of S (node.M)\n    \n    Parameters\n    ----------\n    S : array  of instances\n        Array  of instances that fall into a node\n    node: a DiFF tree node\n        S is the set of instances \"falling\" into the node\n    alpha: float\n        alpha is the distance scaling hyper-parameter\n    Returns\n    -------\n    array\n        the array of similarity values between the instances in S and the mean of training instances falling in node\n    \"\"\"\n    d = np.shape(S)[1]\n    if len(S) > 0:\n        d = np.shape(S)[1]\n        U = (S-node.M)/node.Mstd # normalize using the standard deviation vector to the mean\n        U = (2)**(-alpha*(np.sum(U*U/d, axis=1)))\n    else:\n        U = 0\n\n    return U\n\n\ndef EE(hist):\n    \"\"\"\n    given a list of positive values as a histogram drawn from any information source,\n    returns the empirical entropy of its discrete probability function.\n    \n    Parameters\n    ----------\n    hist: array \n        histogram\n    Returns\n    -------\n    float\n        empirical entropy estimated from the histogram\n    \"\"\"\n    h = np.asarray(hist, dtype=np.float64)\n    if h.sum() <= 0 or (h < 0).any():\n        return 0\n    h = h/h.sum()\n    return -(h*np.ma.log2(h)).sum()\n\n\ndef weightFeature(s, nbins):\n    '''\n    Given a list of values corresponding to a feature dimension, returns a weight (in [0,1]) that is \n    one minus the normalized empirical entropy, a way to characterize the importance of the feature dimension. \n    \n    Parameters\n    ----------\n    s: array \n        list of scalar values corresponding to a feature dimension\n    nbins: int\n        the number of bins used to discretize the feature dimension using an histogram.\n    Returns\n    -------\n    float\n        the importance weight for feature s.\n    '''\n    if s.min() == s.max():\n        return 0\n    hist = np.histogram(s, bins=nbins, density=True)\n    ent = EE(hist[0])\n    ent = ent/np.log2(nbins)\n    return 1-ent\n\n\ndef walk_tree(forest, node, treeIdx, obsIdx, X, featureDistrib, depth=0, alpha=1e-2):\n    '''\n    Recursive function that walks a tree from an already fitted forest to compute the path length\n    of the new observations.\n    \n    Parameters\n    ----------\n    forest : DiFF_RF \n        A fitted forest of DiFF trees\n    node: DiFF Tree node\n        the current node\n    treeIdx: int\n        index of the tree that is being walked.\n    obsIdx: array\n        1D array of length n_obs. 1/0 if the obs has reached / has not reached the node.\n    X: nD array. \n        array of observations/instances.\n    depth: int\n        current depth.\n    Returns\n    -------\n    None\n    '''\n\n    if isinstance(node, LeafNode):\n        Xnode = X[obsIdx]\n        f = ((node.size+1)/forest.sample_size) / ((1+len(Xnode))/forest.XtestSize)\n        if alpha == 0:\n            forest.LD[obsIdx, treeIdx] = 0\n            forest.LF[obsIdx, treeIdx] = -f\n            forest.LDF[obsIdx, treeIdx] = -f\n        else:\n            z = similarityScore(Xnode, node, alpha)\n            forest.LD[obsIdx, treeIdx] = z\n            forest.LF[obsIdx, treeIdx] = -f\n            forest.LDF[obsIdx, treeIdx] = z*f\n\n    else:\n\n        idx = (X[:, node.splitAtt] <= node.splitValue) * obsIdx\n        walk_tree(forest, node.left, treeIdx, idx, X, featureDistrib, depth + 1, alpha=alpha)\n\n        idx = (X[:, node.splitAtt] > node.splitValue) * obsIdx\n        walk_tree(forest, node.right, treeIdx, idx, X, featureDistrib, depth + 1, alpha=alpha)\n\n\ndef create_tree(X, featureDistrib, sample_size, max_height):\n    '''\n    Creates an DiFF tree using a sample of size sample_size of the original data.\n        \n    Parameters\n    ----------\n    X: nD array. \n        nD array with the observations. Dimensions should be (n_obs, n_features).\n    sample_size: int\n        Size of the sample from which a DiFF tree is built.\n    max_height: int\n        Maximum height of the tree.\n    Returns\n    -------\n    a DiFF tree\n    '''\n    rows = np.random.choice(len(X), sample_size, replace=False)\n    featureDistrib = np.array(featureDistrib)\n    return DiFF_Tree(max_height).fit(X[rows, :], featureDistrib)\n\ndef check_parameter(param, low=MIN_INT, high=MAX_INT, param_name='',\n                    include_left=False, include_right=False):\n    \"\"\"Check if an input is within the defined range.\n    Parameters\n    ----------\n    param : int, float\n        The input parameter to check.\n    low : int, float\n        The lower bound of the range.\n    high : int, float\n        The higher bound of the range.\n    param_name : str, optional (default='')\n        The name of the parameter.\n    include_left : bool, optional (default=False)\n        Whether includes the lower bound (lower bound <=).\n    include_right : bool, optional (default=False)\n        Whether includes the higher bound (<= higher bound).\n    Returns\n    -------\n    within_range : bool or raise errors\n        Whether the parameter is within the range of (low, high)\n    \"\"\"\n\n    # param, low and high should all be numerical\n    if not isinstance(param, (numbers.Integral, np.integer, np.float)):\n        raise TypeError('{param_name} is set to {param} Not numerical'.format(\n            param=param, param_name=param_name))\n\n    if not isinstance(low, (numbers.Integral, np.integer, np.float)):\n        raise TypeError('low is set to {low}. Not numerical'.format(low=low))\n\n    if not isinstance(high, (numbers.Integral, np.integer, np.float)):\n        raise TypeError('high is set to {high}. Not numerical'.format(\n            high=high))\n\n    # at least one of the bounds should be specified\n    if low is MIN_INT and high is MAX_INT:\n        raise ValueError('Neither low nor high bounds is undefined')\n\n    # if wrong bound values are used\n    if low > high:\n        raise ValueError(\n            'Lower bound > Higher bound')\n\n    # value check under different bound conditions\n    if (include_left and include_right) and (param < low or param > high):\n        raise ValueError(\n            '{param_name} is set to {param}. '\n            'Not in the range of [{low}, {high}].'.format(\n                param=param, low=low, high=high, param_name=param_name))\n\n    elif (include_left and not include_right) and (\n            param < low or param >= high):\n        raise ValueError(\n            '{param_name} is set to {param}. '\n            'Not in the range of [{low}, {high}).'.format(\n                param=param, low=low, high=high, param_name=param_name))\n\n    elif (not include_left and include_right) and (\n            param <= low or param > high):\n        raise ValueError(\n            '{param_name} is set to {param}. '\n            'Not in the range of ({low}, {high}].'.format(\n                param=param, low=low, high=high, param_name=param_name))\n\n    elif (not include_left and not include_right) and (\n            param <= low or param >= high):\n        raise ValueError(\n            '{param_name} is set to {param}. '\n            'Not in the range of ({low}, {high}).'.format(\n                param=param, low=low, high=high, param_name=param_name))\n    else:\n        return True\n\n\ndef check_detector(detector):\n    \"\"\"Checks if fit and decision_function methods exist for given detector\n    Parameters\n    ----------\n    detector : pyod.models\n        Detector instance for which the check is performed.\n    \"\"\"\n\n    if not hasattr(detector, 'fit') or not hasattr(detector,\n                                                   'decision_function'):\n        raise AttributeError(\"%s is not a detector instance.\" % (detector))\n\n\ndef standardizer(X, X_t=None, keep_scalar=False):\n    \"\"\"Conduct Z-normalization on data to turn input samples become zero-mean\n    and unit variance.\n    Parameters\n    ----------\n    X : numpy array of shape (n_samples, n_features)\n        The training samples\n    X_t : numpy array of shape (n_samples_new, n_features), optional (default=None)\n        The data to be converted\n    keep_scalar : bool, optional (default=False)\n        The flag to indicate whether to return the scalar\n    Returns\n    -------\n    X_norm : numpy array of shape (n_samples, n_features)\n        X after the Z-score normalization\n    X_t_norm : numpy array of shape (n_samples, n_features)\n        X_t after the Z-score normalization\n    scalar : sklearn scalar object\n        The scalar used in conversion\n    \"\"\"\n    X = check_array(X)\n    scaler = StandardScaler().fit(X)\n\n    if X_t is None:\n        if keep_scalar:\n            return scaler.transform(X), scaler\n        else:\n            return scaler.transform(X)\n    else:\n        X_t = check_array(X_t)\n        if X.shape[1] != X_t.shape[1]:\n            raise ValueError(\n                \"The number of input data feature should be consistent\"\n                \"X has {0} features and X_t has {1} features.\".format(\n                    X.shape[1], X_t.shape[1]))\n        if keep_scalar:\n            return scaler.transform(X), scaler.transform(X_t), scaler\n        else:\n            return scaler.transform(X), scaler.transform(X_t)\n\n\ndef score_to_label(pred_scores, outliers_fraction=0.1):\n    \"\"\"Turn raw outlier outlier scores to binary labels (0 or 1).\n    Parameters\n    ----------\n    pred_scores : list or numpy array of shape (n_samples,)\n        Raw outlier scores. Outliers are assumed have larger values.\n    outliers_fraction : float in (0,1)\n        Percentage of outliers.\n    Returns\n    -------\n    outlier_labels : numpy array of shape (n_samples,)\n        For each observation, tells whether or not\n        it should be considered as an outlier according to the\n        fitted model. Return the outlier probability, ranging\n        in [0,1].\n    \"\"\"\n    # check input values\n    pred_scores = column_or_1d(pred_scores)\n    check_parameter(outliers_fraction, 0, 1)\n\n    threshold = percentile(pred_scores, 100 * (1 - outliers_fraction))\n    pred_labels = (pred_scores > threshold).astype('int')\n    return pred_labels\n\n\ndef precision_n_scores(y, y_pred, n=None):\n    \"\"\"Utility function to calculate precision @ rank n.\n    Parameters\n    ----------\n    y : list or numpy array of shape (n_samples,)\n        The ground truth. Binary (0: inliers, 1: outliers).\n    y_pred : list or numpy array of shape (n_samples,)\n        The raw outlier scores as returned by a fitted model.\n    n : int, optional (default=None)\n        The number of outliers. if not defined, infer using ground truth.\n    Returns\n    -------\n    precision_at_rank_n : float\n        Precision at rank n score.\n    \"\"\"\n\n    # turn raw prediction decision scores into binary labels\n    y_pred = get_label_n(y, y_pred, n)\n\n    # enforce formats of y and labels_\n    y = column_or_1d(y)\n    y_pred = column_or_1d(y_pred)\n\n    return precision_score(y, y_pred)\n\n\ndef get_label_n(y, y_pred, n=None):\n    \"\"\"Function to turn raw outlier scores into binary labels by assign 1\n    to top n outlier scores.\n    Parameters\n    ----------\n    y : list or numpy array of shape (n_samples,)\n        The ground truth. Binary (0: inliers, 1: outliers).\n    y_pred : list or numpy array of shape (n_samples,)\n        The raw outlier scores as returned by a fitted model.\n    n : int, optional (default=None)\n        The number of outliers. if not defined, infer using ground truth.\n    Returns\n    -------\n    labels : numpy array of shape (n_samples,)\n        binary labels 0: normal points and 1: outliers\n    Examples\n    --------\n    >>> from pyod.utils.utility import get_label_n\n    >>> y = [0, 1, 1, 0, 0]\n    >>> y_pred = [0.1, 0.5, 0.3, 0.2, 0.7]\n    >>> get_label_n(y, y_pred)\n    array([0, 1, 0, 0, 1])\n    \"\"\"\n\n    # enforce formats of inputs\n    y = column_or_1d(y)\n    y_pred = column_or_1d(y_pred)\n\n    check_consistent_length(y, y_pred)\n    y_len = len(y)  # the length of targets\n\n    # calculate the percentage of outliers\n    if n is not None:\n        outliers_fraction = n / y_len\n    else:\n        outliers_fraction = np.count_nonzero(y) / y_len\n\n    threshold = percentile(y_pred, 100 * (1 - outliers_fraction))\n    y_pred = (y_pred > threshold).astype('int')\n\n    return y_pred\n\ndef get_intersection(lst1, lst2):\n    \"\"\"get the overlapping between two lists\n    Parameters\n    ----------\n    li1 : list or numpy array\n        Input list 1.\n    li2 : list or numpy array\n        Input list 2.\n    Returns\n    -------\n    difference : list\n        The overlapping between li1 and li2.\n    \"\"\"\n    return list(set(lst1) & set(lst2))\n\n\ndef get_list_diff(li1, li2):\n    \"\"\"get the elements in li1 but not li2. li1-li2\n    Parameters\n    ----------\n    li1 : list or numpy array\n        Input list 1.\n    li2 : list or numpy array\n        Input list 2.\n    Returns\n    -------\n    difference : list\n        The difference between li1 and li2.\n    \"\"\"\n    # if isinstance(li1, (np.ndarray, np.generic)):\n    #     li1 = li1.tolist()\n    # if isinstance(li2, (np.ndarray, np.generic)):\n    #     li1 = li1.tolist()\n\n    return (list(set(li1) - set(li2)))\n\ndef get_diff_elements(li1, li2):\n    \"\"\"get the elements in li1 but not li2, and vice versa\n    Parameters\n    ----------\n    li1 : list or numpy array\n        Input list 1.\n    li2 : list or numpy array\n        Input list 2.\n    Returns\n    -------\n    difference : list\n        The difference between li1 and li2.\n    \"\"\"\n    # if isinstance(li1, (np.ndarray, np.generic)):\n    #     li1 = li1.tolist()\n    # if isinstance(li2, (np.ndarray, np.generic)):\n    #     li1 = li1.tolist()\n\n    return (list(set(li1) - set(li2)) + list(set(li2) - set(li1)))\n\ndef argmaxn(value_list, n, order='desc'):\n    \"\"\"Return the index of top n elements in the list\n    if order is set to 'desc', otherwise return the index of n smallest ones.\n    Parameters\n    ----------\n    value_list : list, array, numpy array of shape (n_samples,)\n        A list containing all values.\n    n : int\n        The number of elements to select.\n    order : str, optional (default='desc')\n        The order to sort {'desc', 'asc'}:\n        - 'desc': descending\n        - 'asc': ascending\n    Returns\n    -------\n    index_list : numpy array of shape (n,)\n        The index of the top n elements.\n    \"\"\"\n\n    value_list = column_or_1d(value_list)\n    length = len(value_list)\n\n    # validate the choice of n\n    check_parameter(n, 1, length, include_left=True, include_right=True,\n                    param_name='n')\n\n    # for the smallest n, flip the value\n    if order != 'desc':\n        n = length - n\n\n    value_sorted = np.partition(value_list, length - n)\n    threshold = value_sorted[int(length - n)]\n\n    if order == 'desc':\n        return np.where(np.greater_equal(value_list, threshold))[0]\n    else:  # return the index of n smallest elements\n        return np.where(np.less(value_list, threshold))[0]\n\n\ndef invert_order(scores, method='multiplication'):\n    \"\"\" Invert the order of a list of values. The smallest value becomes\n    the largest in the inverted list. This is useful while combining\n    multiple detectors since their score order could be different.\n    Parameters\n    ----------\n    scores : list, array or numpy array with shape (n_samples,)\n        The list of values to be inverted\n    method : str, optional (default='multiplication')\n        Methods used for order inversion. Valid methods are:\n        - 'multiplication': multiply by -1\n        - 'subtraction': max(scores) - scores\n    Returns\n    -------\n    inverted_scores : numpy array of shape (n_samples,)\n        The inverted list\n    Examples\n    --------\n    >>> scores1 = [0.1, 0.3, 0.5, 0.7, 0.2, 0.1]\n    >>> invert_order(scores1)\n    array([-0.1, -0.3, -0.5, -0.7, -0.2, -0.1])\n    >>> invert_order(scores1, method='subtraction')\n    array([0.6, 0.4, 0.2, 0. , 0.5, 0.6])\n    \"\"\"\n\n    scores = column_or_1d(scores)\n\n    if method == 'multiplication':\n        return scores.ravel() * -1\n\n    if method == 'subtraction':\n        return (scores.max() - scores).ravel()\n\n\ndef _get_sklearn_version():  # pragma: no cover\n    \"\"\" Utility function to decide the version of sklearn.\n    PyOD will result in different behaviors with different sklearn version\n    Returns\n    -------\n    sk_learn version : int\n    \"\"\"\n\n    sklearn_version = str(sklearn.__version__)\n    if int(sklearn_version.split(\".\")[1]) < 19 or int(\n            sklearn_version.split(\".\")[1]) > 23:\n        raise ValueError(\"Sklearn version error\")\n\n    return int(sklearn_version.split(\".\")[1])\n\n\ndef _sklearn_version_21():  # pragma: no cover\n    \"\"\" Utility function to decide the version of sklearn\n    In sklearn 21.0, LOF is changed. Specifically, _decision_function\n    is replaced by _score_samples\n    Returns\n    -------\n    sklearn_21_flag : bool\n        True if sklearn.__version__ is newer than 0.21.0\n    \"\"\"\n    sklearn_version = str(sklearn.__version__)\n    if int(sklearn_version.split(\".\")[1]) > 20:\n        return True\n    else:\n        return False\n\n\ndef generate_bagging_indices(random_state, bootstrap_features, n_features,\n                             min_features, max_features):\n    \"\"\" Randomly draw feature indices. Internal use only.\n    Modified from sklearn/ensemble/bagging.py\n    Parameters\n    ----------\n    random_state : RandomState\n        A random number generator instance to define the state of the random\n        permutations generator.\n    bootstrap_features : bool\n        Specifies whether to bootstrap indice generation\n    n_features : int\n        Specifies the population size when generating indices\n    min_features : int\n        Lower limit for number of features to randomly sample\n    max_features : int\n        Upper limit for number of features to randomly sample\n    Returns\n    -------\n    feature_indices : numpy array, shape (n_samples,)\n        Indices for features to bag\n    \"\"\"\n\n    # Get valid random state\n    random_state = check_random_state(random_state)\n\n    # decide number of features to draw\n    random_n_features = random_state.randint(min_features, max_features)\n\n    # Draw indices\n    feature_indices = generate_indices(random_state, bootstrap_features,\n                                       n_features, random_n_features)\n\n    return feature_indices\n\n\ndef generate_indices(random_state, bootstrap, n_population, n_samples):\n    \"\"\" Draw randomly sampled indices. Internal use only.\n    See sklearn/ensemble/bagging.py\n    Parameters\n    ----------\n    random_state : RandomState\n        A random number generator instance to define the state of the random\n        permutations generator.\n    bootstrap :  bool\n        Specifies whether to bootstrap indice generation\n    n_population : int\n        Specifies the population size when generating indices\n    n_samples : int\n        Specifies number of samples to draw\n    Returns\n    -------\n    indices : numpy array, shape (n_samples,)\n        randomly drawn indices\n    \"\"\"\n\n    # Draw sample indices\n    if bootstrap:\n        indices = random_state.randint(0, n_population, n_samples)\n    else:\n        indices = sample_without_replacement(n_population, n_samples,\n                                             random_state=random_state)\n\n    return indices\n\n\ntry:\n    import igraph as ig\nexcept:\n    warnings.warn(\"No igraph interface for plotting trees\")\n\ndef EuclideanDist(x,y):\n    return np.sqrt(np.sum((x - y) ** 2))\n\ndef dist2set(x, X):\n    l=len(X)\n    ldist=[]\n    for i in range(l):\n        ldist.append(EuclideanDist(x,X[i]))\n    return ldist\n\ndef c_factor(n) :\n    if(n<2):\n        n=2\n    return 2.0*(np.log(n-1)+0.5772156649) - (2.0*(n-1.)/(n*1.0))\n\n\ndef all_branches(node, current=[], branches = None):\n    current = current[:node.e]\n    if branches is None: branches = []\n    if node.ntype == 'inNode':\n        current.append('L')\n        all_branches(node.left, current=current, branches=branches)\n        current = current[:-1]\n        current.append('R')\n        all_branches(node.right, current=current, branches=branches)\n    else:\n        branches.append(current)\n    return branches\n\n\ndef branch2num(branch, init_root=0):\n    num = [init_root]\n    for b in branch:\n        if b == 'L':\n            num.append(num[-1] * 2 + 1)\n        if b == 'R':\n            num.append(num[-1] * 2 + 2)\n    return num\n\ndef gen_graph(branches, g = None, init_root = 0, pre = ''):\n    num_branches = [branch2num(i, init_root) for i in branches]\n    all_nodes = [j for branch in num_branches for j in branch]\n    all_nodes = np.unique(all_nodes)\n    all_nodes = all_nodes.tolist()\n    if g is None:\n        g=ig.Graph()\n    for k in all_nodes : g.add_vertex(pre+str(k))\n    t=[]\n    for j in range(len(branches)):\n        branch = branch2num(branches[j], init_root)\n        for i in range(len(branch)-1):\n            pair = [branch[i],branch[i+1]]\n            if pair not in t:\n                t.append(pair)\n                g.add_edge(pre+str(branch[i]),pre+str(branch[i+1]))\n    return g,max(all_nodes)","sub_path":"ptsa/ptsa/utils/utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":23113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"624783008","text":"import json\nimport sys\nimport os.path\n\ndef load_data(filepath):\n    with open(filepath, 'r', encoding='utf-8', errors='ignore') as json_file:\n        return json.load(json_file)\n\ndef pretty_print_json(data):\n    print(json.dumps(data, indent=4, sort_keys=True, ensure_ascii=False))\n\n\nif __name__ == '__main__':\n    fpath = sys.argv[1]\n    if not os.path.exists(fpath):\n        print (\"File '%s' not found\" % (fpath)) \n        sys.exit()\n \n    pretty_print_json(load_data(fpath))\n\n\n\n\n","sub_path":"pprint_json.py","file_name":"pprint_json.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"431114464","text":"import yaml\n\nfrom common.baselogger import logger\n\n\nclass BaseYaml:\n    \"\"\"\n    操作yaml文件基类\n    \"\"\"\n\n    def __init__(self, file_path):\n        self.file_path = file_path\n\n    def read_yaml(self):\n        try:\n            with open(self.file_path, mode='r', encoding='utf8') as f:\n                res = yaml.load(stream=f, Loader=yaml.FullLoader)\n        except Exception as e:\n            logger.exception(\"读取yaml文件失败\")\n            raise e\n        # logger.info(\"读取yaml文件成功,数据为：{}\".format(res))\n        return res\n\n    def write_yaml(self, data):\n        try:\n            with open(self.file_path, mode='w', encoding='utf8') as f:\n                yaml.dump(stream=f, data=data, allow_unicode=True)\n        except Exception as e:\n            logger.exception(\"写入yaml文件失败\")\n            raise e\n        logger.info(\"写入yaml文件成功 {}\".format(data))\n        return True\n\n\nif __name__ == '__main__':\n    pass\n","sub_path":"common/baseyaml.py","file_name":"baseyaml.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"493260798","text":"# -- coding: utf-8 --\nimport csv\nimport os\nimport sys\nsys.path.append('../')\nsys.path.append('../detection/code/')\nimport numpy as np\nimport cv2\nfrom scipy.ndimage.filters import gaussian_filter\nimport math, time\nimport torch\nimport csv\nfrom utils import util\nimport datetime\nimport pandas as pd\n\ndef apply_model(oriImg, model, multiplier,numPoints,roi_str):\n    stride = 8\n    roiPoint = roi_str.split('_')\n    newImg = oriImg[int(roiPoint[0]):int(roiPoint[2]), int(roiPoint[1]):int(roiPoint[3])]\n\n    height, width, _ = newImg.shape\n    oriImg = newImg\n    #height, width, _ = oriImg.shape\n    #将图像转化成数组形式，类型是float32\n    normed_img = np.array(oriImg, dtype=np.float32)\n    #新建一个大小一样的0矩阵（图），另外这个图的通道数就是代预测点的个数\n    heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], numPoints), dtype=np.float32)\n    #遍历尺度因数\n    for m in range(len(multiplier)):\n        scale = multiplier[m]\n        imageToTest = cv2.resize(normed_img, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)\n        # imgToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, 128)\n        #将输入图片补全成标准大小（可能是384*384）\n        imgToTest_padded, pad = util.padRightDownCorner(imageToTest, 32, 128)\n\n        input_img = np.transpose(np.float32(imgToTest_padded[:, :, :, np.newaxis]),\n                                 (3, 2, 0, 1)) / 255 - 0.5  # required shape (1, c, h, w)\n\n        input_var = torch.autograd.Variable(torch.from_numpy(input_img).cuda())\n\n        # get the features\n        # heat1, heat2, heat3, heat4, heat5, heat6 = model(input_var)\n        #怎么看是经历了几个阶段的特诊图\n        heat = model(input_var)\n\n        # get the heatmap\n        heatmap = heat.data.cpu().numpy()\n        heatmap = np.transpose(np.squeeze(heatmap), (1, 2, 0))  # (h, w, c)\n        heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)\n        heatmap = heatmap[:imgToTest_padded.shape[0] - pad[2], :imgToTest_padded.shape[1] - pad[3], :]\n        heatmap = cv2.resize(heatmap, (width, height), interpolation=cv2.INTER_CUBIC)\n        heatmap_avg = heatmap_avg + heatmap / len(multiplier)\n\n    all_peaks = []  # all of the possible points by classes.\n    peak_counter = 0\n    thre1 = 0.1\n    for part in range(numPoints - 1):\n        x_list = []\n        y_list = []\n        map_ori = heatmap_avg[:, :, part]\n        map = gaussian_filter(map_ori, sigma=3)\n\n        map_left = np.zeros(map.shape)\n        map_left[1:, :] = map[:-1, :]\n        map_right = np.zeros(map.shape)\n        map_right[:-1, :] = map[1:, :]\n        map_up = np.zeros(map.shape)\n        map_up[:, 1:] = map[:, :-1]\n        map_down = np.zeros(map.shape)\n        map_down[:, :-1] = map[:, 1:]\n\n        peaks_binary = np.logical_and.reduce(\n            (map >= map_left, map >= map_right, map >= map_up, map >= map_down, map > thre1))\n        peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse\n        peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]\n        id = range(peak_counter, peak_counter + len(peaks))\n        peaks_with_score_and_id = [peaks_with_score[i] + (id[i],) for i in range(len(id))]\n\n        all_peaks.append(peaks_with_score_and_id)\n        peak_counter += len(peaks)\n\n    # sort by score\n    for i in range(numPoints-1):\n        all_peaks[i] = sorted(all_peaks[i], key=lambda ele : ele[2],reverse = True)\n\n    keypoints = -1*np.ones((numPoints-1, 3))\n    for i in range(numPoints-1):\n        if len(all_peaks[i]) == 0:\n            continue\n        else:\n            keypoints[i,0], keypoints[i,1], keypoints[i,2] = all_peaks[i][0][0], all_peaks[i][0][1], 1\n    keypoints[:,0] =  keypoints[:,0] + int(roiPoint[1])\n    keypoints[:,1] =  keypoints[:,1] + int(roiPoint[0])\n    return  keypoints\n\n\ndef write_csv(name, results):\n    import csv\n    with open(name, 'w') as f:\n        writer = csv.writer(f)\n        writer.writerows(results)\n\ndef prepare_row(ann, keypoints,index_array):\n    # cls\n    image_name = ann[0]\n    category = ann[1]\n    keypoints_str = []\n    #index_array = [17,18,21,22,23,24,25]\n    j=0\n    for i in range(24):\n        if((i+2) in index_array):\n            cell_str = str(int(keypoints[j][0])) + '_' + str(int(keypoints[j][1])) + '_' + str(int(keypoints[j][2]))\n            j+=1\n        else:\n            cell_str = '-1_-1_-1'\n\n        keypoints_str.append(cell_str)\n    row = [image_name, category] + keypoints_str\n    return row\n\ndef read_csv(ann_file):\n    info = []\n    anns = []\n    with open(ann_file, 'rb') as f:\n        reader = csv.reader(f)\n        for row in reader:\n            anns.append(row)\n    info = anns[0]\n    anns = anns[1:]\n    return info, anns\n\ndef main():\n    #os.environ['CUDA_VISIBLE_DEVICES'] = '2'\n    modelsList = ['../saveparameter/blouse/12000res50.pth.tar','../saveparameter/dress/12000res50.pth.tar','../saveparameter/outwear/12000res50.pth.tar','../saveparameter/skirt/12000res50.pth.tar','../saveparameter/trousers/12000res50.pth.tar']\n    annList = ['../data/test/blouse.csv','../data/test/dress.csv','../data/test/outwear.csv','../data/test/skirt.csv','../data/test/trousers.csv']\n    saveList = ['../submit/reblouse.csv','../submit/redress.csv','../submit/reoutwear.csv','../submit/reskirt.csv','../submit/retrousers.csv']\n\n    ROIFileList = ['../detection/submit/blouse_test_result.csv', '../detection/submit/dress_test_result.csv', '../detection/submit/outwear_test_result.csv', '../detection/submit/skirt_test_result.csv',\n                '../detection/submit/trousers_test_result.csv']\n\n    classNumList = [13,15,14,4,7]\n    index_array = [[2,3,4,5,6,7,8,11,12,13,14,15,16],[2,3,4,5,6,7,8,9,10,11,12,13,14,19,20],[2,3,5,6,7,8,9,10,11,12,13,14,15,16],[17,18,19,20],[17,18,21,22,23,24,25]]\n    className = ['blouse','dress','outwear','skirt','trousers']\n    for i in range(2,5):\n        # --------------------------- model -------------------------------------------------------------------------------\n        import models.CPM_FPN\n        pytorch_model = modelsList[i]\n        model = models.CPM_FPN.pose_estimation(class_num=classNumList[i], pretrain=False)\n        # -----------------------------------------------------------------------------------------------------------------\n\n        img_dir = '../data/test/'\n        ann_path = annList[i]\n        result_name = saveList[i]\n        # scale_search = [0.5, 0.7, 1.0, 1.3]  # [0.5, 1.0, 1.5]\n        scale_search = [0.5, 0.7, 1.0]\n        boxsize = 384\n        # -------------------------- pytorch model------------------\n        state_dict = torch.load(pytorch_model)['state_dict']\n        model.load_state_dict(state_dict)\n        model = model.cuda()\n        model.eval()  # make model in test mode\n        # --------------------------------------------------------\n        anns = []\n        with open(ann_path, 'r') as f:\n            reader = csv.reader(f)\n            for row in reader:\n                anns.append(row)\n        ROIanns = []\n        with open(ROIFileList[i], 'r') as f:\n            reader2 = csv.reader(f)\n            for row2 in reader2:\n                ROIanns.append(row2)\n        #info = anns[0]\n        info = ['image_id', 'image_category', 'neckline_left', 'neckline_right', 'center_front', 'shoulder_left',\n                   'shoulder_right', 'armpit_left', 'armpit_right', 'waistline_left', 'waistline_right', 'cuff_left_in',\n                   'cuff_left_out', 'cuff_right_in', 'cuff_right_out', 'top_hem_left', 'top_hem_right',\n                   'waistband_left', 'waistband_right', 'hemline_left', 'hemline_right', 'crotch', 'bottom_left_in',\n                   'bottom_left_out', 'bottom_right_in', 'bottom_right_out']\n        anns = anns[1:]\n        ROIanns = ROIanns[1:]\n        # ---------------------------------------------------------\n        num_imgs = len(anns)\n        results = []\n        #results.append(info)\n\n        for j in range(num_imgs):\n            print('{}/{}/{}'.format(i,j, num_imgs))\n            ann = anns[j]\n            roiann = ROIanns[j]\n            image_path = os.path.join(img_dir, ann[0])\n            oriImg = cv2.imread(image_path)\n            # multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]\n            multiplier = scale_search\n            numPoints = classNumList[i]+1\n            keypoints = apply_model(oriImg, model, multiplier,numPoints,roiann[2])\n            # cv2.imwrite(os.path.join('./result', ann[0].split('/')[-1]), canvas)\n            row = prepare_row(ann, keypoints,index_array[i])\n            results.append(row)\n        write_csv(result_name, results)\n    src = '../submit/'\n    colname=['image_id' ,'image_category' ,'neckline_left' ,'neckline_right' ,'center_front' ,'shoulder_left' ,'shoulder_right' ,'armpit_left' ,'armpit_right' ,'waistline_left' ,'waistline_right' ,'cuff_left_in' ,'cuff_left_out' ,'cuff_right_in' ,'cuff_right_out' ,'top_hem_left' ,'top_hem_right' ,'waistband_left' ,'waistband_right' ,'hemline_left' ,'hemline_right' ,'crotch' ,'bottom_left_in' ,'bottom_left_out' ,'bottom_right_in' ,'bottom_right_out']\n    result_blouse = pd.read_csv(src + 'reblouse.csv', header=None)\n    result_dress= pd.read_csv(src + 'redress.csv', header=None)\n    result_outwear = pd.read_csv(src + 'reoutwear.csv', header=None)\n    result_skirt = pd.read_csv(src + 'reskirt.csv', header=None)\n    result_trousers = pd.read_csv(src + 'retrousers.csv', header=None)\n    result_all = pd.concat([result_blouse, result_dress], axis=0)\n    result_all = pd.concat([result_all, result_outwear], axis=0)\n    result_all = pd.concat([result_all, result_skirt], axis=0)\n    result_all = pd.concat([result_all, result_trousers], axis=0)\n    result_all.columns = colname\n    result_all.to_csv((\"../submit/submit_test_\"+datetime.datetime.now().strftime('%Y%m%d_%H%M%S') + \".csv\"),index=None)\n\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"code/submitDect.py","file_name":"submitDect.py","file_ext":"py","file_size_in_byte":9960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"29656793","text":"__all__ = [\n    'AssignmentsObserver',\n    'BaseObserver',\n    'PoolStatusObserver',\n]\n\nimport asyncio\nimport attr\nimport datetime\nimport logging\n\nfrom typing import AsyncIterable, Awaitable, Callable, Dict, List, Optional, Union\n\nfrom ..client import structure\nfrom ..client.assignment import Assignment\nfrom ..client.pool import Pool\nfrom .cursor import AssignmentCursor, TolokaClientSyncOrAsyncType\nfrom .event import AssignmentEvent\nfrom .util import AsyncInterfaceWrapper, ensure_async\n\nlogger = logging.getLogger(__name__)\n\n\n@attr.s\nclass BaseObserver:\n    async def __call__(self) -> None:\n        raise NotImplementedError\n\n    async def should_resume(self) -> bool:\n        return False\n\n    async def run(self, period: datetime.timedelta = datetime.timedelta(seconds=60)) -> None:\n        \"\"\"For standalone usage (out of a Pipeline).\"\"\"\n        while True:\n            await self.__call__()\n            if await self.should_resume():\n                logger.debug('Sleep for %d seconds', period.total_seconds())\n                await asyncio.sleep(period.total_seconds())\n            else:\n                return\n\n\n@attr.s\nclass BasePoolObserver(BaseObserver):\n\n    toloka_client: TolokaClientSyncOrAsyncType = attr.ib(converter=AsyncInterfaceWrapper)\n    pool_id: str = attr.ib()\n\n    async def should_resume(self) -> bool:\n        logger.debug('Check resume by pool status: %s', self.pool_id)\n        pool = await self.toloka_client.get_pool(self.pool_id)\n        logger.debug('Pool status for %s: %s', self.pool_id, pool.status)\n        if pool.is_open():\n            return True\n\n        logger.debug('Check resume by pool active assignments: %s', self.pool_id)\n        response = await self.toloka_client.find_assignments(pool_id=self.pool_id, status=[Assignment.ACTIVE], limit=1)\n        logger.debug('Pool %s has active assignments: %s', self.pool_id, bool(response.items))\n        return bool(response.items)\n\n\nCallbackForPoolSyncType = Callable[[Pool], None]\nCallbackForPoolAsyncType = Callable[[Pool], Awaitable[None]]\nCallbackForPoolType = Union[CallbackForPoolSyncType, CallbackForPoolAsyncType]\n\n\n@attr.s\nclass PoolStatusObserver(BasePoolObserver):\n    \"\"\"Observer for pool status change.\n    For usage with Pipeline.\n\n    Allow to register callbacks using the following methods:\n        * on_open\n        * on_closed\n        * on_archieved\n        * on_locked\n        * on_status_change\n\n    The Pool object will be passed to the triggered callbacks.\n\n    Attributes:\n        toloka_client: TolokaClient instance or async wrapper around it.\n        pool_id: Pool ID.\n\n    Examples:\n        Bind to the pool's close to make some aggregations.\n\n        >>> def call_this_on_close(pool: Pool) -> None:\n        >>>     assignments = client.get_assignments_df(pool_id=pool.id, status=['APPROVED'])\n        >>>     do_some_aggregation(assignments)\n        >>>\n        >>> observer = PoolStatusObserver(toloka_client, pool_id='123')\n        >>> observer.on_close(call_this_on_close)\n        ...\n\n        Call something at any status change.\n\n        >>> observer.on_status_change(lambda pool: ...)\n        ...\n    \"\"\"\n\n    _callbacks: Dict[Pool.Status, List[CallbackForPoolAsyncType]] = attr.ib(factory=dict, init=False)\n    _previous_status: Optional[Pool.Status] = attr.ib(default=None, init=False)\n\n    def register_callback(self, callback: CallbackForPoolType, changed_to: Union[Pool.Status, str]) -> CallbackForPoolType:\n        \"\"\"Register given callable for pool status change to given value.\n\n        Args:\n            callback: Sync or async callable that pass Pool object.\n            changed_to: Pool status value to register for.\n\n        Returns:\n            The same callable passed as callback.\n        \"\"\"\n        changed_to = structure(changed_to, Pool.Status)  # TODO: Autoconvert using TOLOKAKIT-83.\n        self._callbacks.setdefault(changed_to, []).append(ensure_async(callback))\n        return callback\n\n    def on_open(self, callback: CallbackForPoolType) -> CallbackForPoolType:\n        return self.register_callback(callback, Pool.Status.OPEN)\n\n    def on_closed(self, callback: CallbackForPoolType) -> CallbackForPoolType:\n        return self.register_callback(callback, Pool.Status.CLOSED)\n\n    def on_archieved(self, callback: CallbackForPoolType) -> CallbackForPoolType:\n        return self.register_callback(callback, Pool.Status.ARCHIEVED)\n\n    def on_locked(self, callback: CallbackForPoolType) -> CallbackForPoolType:\n        return self.register_callback(callback, Pool.Status.LOCKED)\n\n    def on_status_change(self, callback: CallbackForPoolType) -> CallbackForPoolType:\n        for status in Pool.Status.__members__:\n            self.register_callback(callback, status)\n        return callback\n\n    async def __call__(self) -> None:\n        if not self._callbacks:\n            return\n\n        pool = await self.toloka_client.get_pool(self.pool_id)\n        current_status = pool.status\n\n        if current_status != self._previous_status:\n            loop = asyncio.get_event_loop()\n            if self._callbacks.get(current_status):\n                await asyncio.wait([\n                    loop.create_task(callback(pool))\n                    for callback in self._callbacks[current_status]\n                ])\n\n        self._previous_status = current_status\n\n\nCallbackForAssignmentEventsSyncType = Callable[[List[AssignmentEvent]], None]\nCallbackForAssignmentEventsAsyncType = Callable[[List[AssignmentEvent]], Awaitable[None]]\nCallbackForAssignmentEventsType = Union[CallbackForAssignmentEventsSyncType, CallbackForAssignmentEventsAsyncType]\n\n\n@attr.s\nclass _CursorAndCallbacks:\n    cursor: AssignmentCursor = attr.ib()\n    callbacks: List[CallbackForAssignmentEventsType] = attr.ib(factory=list, init=False)\n\n\n@attr.s\nclass AssignmentsObserver(BasePoolObserver):\n    \"\"\"Observer for the pool's assignment events.\n    For usage with Pipeline.\n\n    Allow to register callbacks using the following methods:\n        * on_created\n        * on_submitted\n        * on_accepted\n        * on_rejected\n        * on_skipped\n        * on_expired\n\n    Corresponding assignment events will be passed to the triggered callbacks.\n\n    Attributes:\n        toloka_client: TolokaClient instance or async wrapper around it.\n        pool_id: Pool ID.\n\n    Examples:\n        Send submitted assignments for verification.\n\n        >>> def handle_submitted(evets: List[AssignmentEvent]) -> None:\n        >>>     verification_tasks = [create_veridication_task(item.assignment) for item in evets]\n        >>>     toloka_client.create_tasks(verification_tasks, open_pool=True)\n        >>>\n        >>> observer = AssignmentsObserver(toloka_client, pool_id='123')\n        >>> observer.on_submitted(handle_submitted)\n        ...\n    \"\"\"\n\n    _callbacks: Dict[AssignmentEvent.Type, _CursorAndCallbacks] = attr.ib(factory=dict, init=False)\n\n    # Setup section.\n\n    def register_callback(\n        self,\n        callback: CallbackForAssignmentEventsType,\n        event_type: Union[AssignmentEvent.Type, str],\n    ) -> CallbackForAssignmentEventsType:\n        \"\"\"Register given callable for given event type.\n        Callback will be called multiple times if it has been registered for multiple event types.\n\n        Args:\n            callback: Sync or async callable that pass List[AssignmentEvent] of desired event type.\n            event_type: Selected event type.\n\n        Returns:\n            The same callable passed as callback.\n        \"\"\"\n        event_type = structure(event_type, AssignmentEvent.Type)  # TODO: Autoconvert using TOLOKAKIT-83.\n        if event_type not in self._callbacks:\n            cursor = AssignmentCursor(pool_id=self.pool_id, event_type=event_type, toloka_client=self.toloka_client)\n            self._callbacks[event_type] = _CursorAndCallbacks(cursor)\n        self._callbacks[event_type].callbacks.append(ensure_async(callback))\n        return callback\n\n    def on_any_event(self, callback: CallbackForAssignmentEventsType) -> CallbackForAssignmentEventsType:\n        for event_type in AssignmentEvent.Type.__members__.values():\n            self.register_callback(callback, event_type)\n        return callback\n\n    def on_created(self, callback: CallbackForAssignmentEventsType) -> CallbackForAssignmentEventsType:\n        return self.register_callback(callback, AssignmentEvent.Type.CREATED)\n\n    def on_submitted(self, callback: CallbackForAssignmentEventsType) -> CallbackForAssignmentEventsType:\n        return self.register_callback(callback, AssignmentEvent.Type.SUBMITTED)\n\n    def on_accepted(self, callback: CallbackForAssignmentEventsType) -> CallbackForAssignmentEventsType:\n        return self.register_callback(callback, AssignmentEvent.Type.ACCEPTED)\n\n    def on_rejected(self, callback: CallbackForAssignmentEventsType) -> CallbackForAssignmentEventsType:\n        return self.register_callback(callback, AssignmentEvent.Type.REJECTED)\n\n    def on_skipped(self, callback: CallbackForAssignmentEventsType) -> CallbackForAssignmentEventsType:\n        return self.register_callback(callback, AssignmentEvent.Type.SKIPPED)\n\n    def on_expired(self, callback: CallbackForAssignmentEventsType) -> CallbackForAssignmentEventsType:\n        return self.register_callback(callback, AssignmentEvent.Type.EXPIRED)\n\n    # Run section.\n\n    async def _get_events(self) -> Dict[AssignmentEvent.Type, List[AssignmentEvent]]:\n\n        async def _fetch(event_type: AssignmentEvent.Type, cursor: AsyncIterable[AssignmentEvent]):\n            return event_type, [assignment async for assignment in cursor]  # TODO: Don't wait for reading all.\n\n        return dict(await asyncio.gather(*(\n            _fetch(event_type, input_to_callbacks.cursor)\n            for event_type, input_to_callbacks in self._callbacks.items()\n        )))\n\n    async def __call__(self) -> None:\n        if not self._callbacks:\n            return\n\n        logger.debug('Load assigment events for pool: %s', self.pool_id)\n        new_events: Dict[AssignmentEvent.Type, List[AssignmentEvent]] = await self._get_events()\n        logger.debug('Got assigment events count for pool %s: %d', self.pool_id, sum(map(len, new_events.values())))\n\n        loop = asyncio.get_event_loop()\n        tasks = [\n            loop.create_task(callback(events))\n            for event_type, events in new_events.items()\n            if events  # Don't run callback at empty events input.\n            for callback in self._callbacks[event_type].callbacks\n        ]\n        if tasks:\n            logger.debug('Run callbacks for assigment events in pool: %s', self.pool_id)\n            await asyncio.wait(tasks)\n","sub_path":"src/streaming/observer.py","file_name":"observer.py","file_ext":"py","file_size_in_byte":10610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"560863603","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jun 15 14:37:07 2018\r\n\r\n@author: rstrepparava\r\n\"\"\"\r\n\r\nimport numpy as np\r\n\r\ndef simulateOUrough(S0, mu, sigma, m_lambda, deltat, t):\r\n    \"\"\" Approximate Ornstein-Uhlenbeck Generator. A more accurate version is preferred\r\n         and available : simulateOU\r\n         \r\n         Args:\r\n             S0         (double):   initial price level of the variable     \r\n             mu         (double):   long term mean of OU process\r\n             sigma      (double):   volatility of OU process\r\n             m_lambda   (double):   mean reversion speed of OU process\r\n             deltat     (double):   incremental time interval\r\n             t          (double):   time span from time = 0\r\n                 \r\n         Returns:\r\n             numpy.array:           (Tx1) array of dynamics of the simulated prices   \r\n    \"\"\"\r\n    periods = int(t // deltat)\r\n    S = np.zeros(periods)\r\n    S[0] = S0\r\n    #Precalculating all dWt's rather than one-per loop makes this function approx 50% faster.\r\n    dWt = np.sqrt(deltat) * np.random.randn(periods) \r\n    for i in range(1, periods):\r\n        dS = m_lambda*(mu-S[i-1])*deltat + sigma*dWt[i]\r\n        S[i] = S[i-1] + dS\r\n    return S\r\n\r\ndef simulateOU(S0, mu, sigma, m_lambda, deltat, t):\r\n    \"\"\" Simulate an ornstein uhlenbeck process.\r\n        Looks more complicated than expected, because if we don't include the\r\n        exp() terms, we are not accurate as deltat becomes large.\r\n         \r\n         Args:\r\n             S0         (double):   initial price level of the variable     \r\n             mu         (double):   long term mean of OU process\r\n             sigma      (double):   volatility of OU process\r\n             m_lambda   (double):   mean reversion speed of OU process\r\n             deltat     (double):   incremental time interval\r\n             t          (double):   time span from time = 0\r\n                 \r\n         Returns:\r\n             numpy.array:           (Tx1) array of dynamics of the simulated prices   \r\n    \"\"\"\r\n    periods = int(t // deltat)\r\n    S = np.zeros(periods)\r\n    S[0] = S0\r\n    exp_minus_lambda_deltat = np.exp(-m_lambda*deltat)\r\n    # Calculate the random term. \r\n    if m_lambda == 0:  # Handle the case of lambda = 0 i.e. no mean reversion. \r\n        dWt = np.sqrt(deltat) * np.random.randn(periods) \r\n    else:\r\n        dWt = np.sqrt((1-np.exp(-2*m_lambda*deltat))/(2*m_lambda))*np.random.randn(periods)\r\n    #And iterate through time calculating each price. \r\n    for i in range(1, periods): \r\n        S[i] = S[i-1]*exp_minus_lambda_deltat + mu*(1-exp_minus_lambda_deltat) + sigma*dWt[i]\r\n        \r\n    #OPTIM Note : Precalculating all dWt's rather than one-per loop makes this function approx 50% faster. Useful for Monte-Carlo simulations. \r\n    #OPTIM Note : calculating exp(-lambda*deltat) makes it roughly 50% faster again. \r\n    #OPTIM Note : this is only about 25% slower than the rough calculation without the exp correction. end\r\n    return S\r\n\r\ndef simulateABM(S0, mu, sigma, deltat, t):\r\n    \"\"\" Simulate  an Arithmetic Brownian Motion (ABM).\r\n         \r\n         Args:\r\n             S0         (double):                           initial price level of the variable     \r\n             mu         (double)/numpy.array of doubles:    drift of the ABM process\r\n             sigma      (double):                           volatility of the ABM process\r\n             deltat     (double):                           incremental time interval\r\n             t          (double):                           time span from time = 0\r\n                 \r\n         Returns:\r\n             numpy.array:                                   (Tx1) array of dynamics of the simulated prices   \r\n    \"\"\"\r\n    periods = int(t // deltat)\r\n    S = np.zeros(periods)\r\n    S[0] = S0\r\n    #Precalculating all dWt's rather than one-per loop makes this function approx 50% faster.\r\n    dWt = np.sqrt(deltat) * np.random.randn(periods)\r\n    #Broadcast the drift\r\n    if np.isscalar(mu):\r\n        mu = mu*np.ones(periods)\r\n    dS = mu*deltat + sigma*dWt\r\n    for i in range(1, periods):\r\n        S[i] = S[i-1] + dS[i]\r\n    return S\r\n\r\ndef simulateGBM(S0, mu, sigma, deltat, t):\r\n    \"\"\" Simulate  a Geometric Brownian Motion (GBM).\r\n         \r\n         Args:\r\n             S0         (double):                           initial price level of the variable     \r\n             mu         (double)/numpy.array of doubles:    drift of the GBM process\r\n             sigma      (double):                           volatility of the GBM process\r\n             deltat     (double):                           incremental time interval\r\n             t          (double):                           time span from time = 0\r\n                 \r\n         Returns:\r\n             numpy.array:                                   (Tx1) array of dynamics of the simulated prices   \r\n    \"\"\"\r\n    periods = int(t // deltat)\r\n    S = np.zeros(periods)\r\n    S[0] = S0\r\n    #Precalculating all dWt's rather than one-per loop makes this function approx 50% faster.\r\n    dWt = np.sqrt(deltat) * np.random.randn(periods)\r\n    #Broadcast the drift\r\n    if np.isscalar(mu):\r\n        mu = mu*np.ones(periods)\r\n    dS = np.exp( (mu-sigma*sigma/2)*deltat + sigma*dWt ) \r\n    for i in range(1, periods):\r\n        S[i] = S[i-1] * dS[i]\r\n    return S\r\n\r\n\r\ndef calibrateOUregress(S, deltat):\r\n    \"\"\" Calibrate an OU process by a simple discrete time regression.\r\n        Does not properly take the reversion into account, meaning this will become inaccurate for large deltat.\r\n        \r\n        Args:\r\n            S       (numpy.array):  array of data points (prices) to calibrate OU against \r\n            deltat  (double):       incremental time interval\r\n        \r\n        Returns:\r\n            double:                 mu, long term mean of OU process\r\n            double:                 sigma, volatility of OU process\r\n            double:                 m_lambda, mean reversion speed of OU process         \r\n    \"\"\"\r\n    #Regress S(t)-S(t-1) against S(t-1)\r\n    t = len(S)*deltat\r\n    res = np.linalg.lstsq(np.vstack([S[:-1], np.ones(len(S[:-1]))]).T, S[1:]-S[:-1])\r\n    m_lambda = -res[0][0]/deltat\r\n    mu = -res[0][1]/res[0][0]\r\n    sigma = np.sqrt(res[1][0]/t)\r\n    return mu, sigma, m_lambda\r\n    \r\ndef calibrateOULS(S, deltat):\r\n    \"\"\" Calibrate an OU process by least squares.\r\n        Reference. % Based on the logic described at\r\n        http://sitmo.com/doc/Calibrating_the_Ornstein-Uhlenbeck_model\r\n        \r\n        Args:\r\n            S       (numpy.array):  array of data points (prices) to calibrate OU against \r\n            deltat  (double):       incremental time interval\r\n        \r\n        Returns:\r\n            double:                 mu, long term mean of OU process\r\n            double:                 sigma, volatility of OU process\r\n            double:                 m_lambda, mean reversion speed of OU process         \r\n    \"\"\"\r\n    #Regress S(t)-S(t-1) against S(t-1)\r\n    t = len(S)*deltat\r\n    res = np.linalg.lstsq(np.vstack([S[:-1], np.ones(len(S[:-1]))]).T, S[1:])\r\n    m_lambda = -np.log(res[0][0])/deltat\r\n    mu = res[0][1]/(1-res[0][0])\r\n    sigma = np.sqrt(res[1][0]/t * 2*m_lambda*deltat/(1-res[0][0]**2))\r\n    return mu, sigma, m_lambda\r\n    \r\ndef calibrateOUMLE(S, deltat):\r\n    \"\"\" Calibrate an OU process by maximum likelihood.\r\n        Based on the algorithm and software described at :\r\n        http://www.sitmo.com/doc/Calibrating_the_Ornstein-Uhlenbeck_model\r\n        \r\n        Args:\r\n            S       (numpy.array):  array of data points (prices) to calibrate OU against \r\n            deltat  (double):       incremental time interval\r\n        \r\n        Returns:\r\n            double:                 mu, long term mean of OU process\r\n            double:                 sigma, volatility of OU process\r\n            double:                 m_lambda, mean reversion speed of OU process         \r\n    \"\"\"\r\n    n = len(S)-1\r\n    Sx = np.sum(S[:-1])\r\n    Sy = np.sum(S[1:])\r\n    Sxx = np.sum(S[:-1]**2)\r\n    Sxy = np.sum(S[:-1]*S[1:])\r\n    Syy = np.sum(S[1:]**2)\r\n    \r\n    mu = (Sy*Sxx - Sx*Sxy) / (n*(Sxx - Sxy) - (Sx**2 - Sx*Sy))\r\n    m_lambda = -(1/deltat)*np.log((Sxy - mu*Sx - mu*Sy + n*mu**2) / (Sxx -2*mu*Sx + n*mu**2))\r\n    alpha = np.exp(- m_lambda*deltat)\r\n    alpha2 = np.exp(-2*m_lambda*deltat)\r\n    sigmahat2 = (1/n)*(Syy - 2*alpha*Sxy + alpha2*Sxx - 2*mu*(1-alpha)*(Sy - alpha*Sx) + n*mu**2*(1-alpha)**2)\r\n    sigma = np.sqrt(sigmahat2*2*m_lambda/(1-alpha2))\r\n    return mu, sigma, m_lambda\r\n\r\ndef calibrateOUMLEJackknife(S, deltat, m):\r\n    \"\"\" Calibrate an OU process by maximum likelihood.\r\n        Since the basic ML calibration has a bias (resulting in frequent estimates \r\n        of m_lambda which are much too high), we perform a 'jackknife' operation to reduce the bias.\r\n        Reference.\r\n        To get a less biased lambda, we just the jackknife procedure described in\r\n        Phillips, Peter C. B., and Jun Yu. 2005. 'Jackknifing Bond Option Prices.'\r\n        The Review of Financial Studies 18, no. 2 (Summer): 707-742.\r\n        http://www.jstor.org/stable/3598050\r\n        \r\n        Args:\r\n            S       (numpy.array):  array of data points (prices) to calibrate OU against \r\n            deltat  (double):       incremental time interval\r\n            m       (int):          number of jackknife partitions\r\n            \r\n        Returns:\r\n            double:                 mu, long term mean of OU process\r\n            double:                 sigma, volatility of OU process\r\n            double:                 m_lambda, mean reversion speed of OU process         \r\n    \"\"\"\r\n    partlength = len(S) // m \r\n    Spart = np.zeros((m,partlength))\r\n    for i in range(m): \r\n        Spart[i,:] = S[partlength*i:partlength*(i+1)]\r\n    \r\n    # Calculate for entire partition. \r\n    muT, sigmaT, m_lambdaT = calibrateOUMLE(S, deltat)\r\n    # Calculate the individual partitions. \r\n    mupart          = np.zeros(m) \r\n    sigmapart       = np.zeros(m)\r\n    m_lambdapart    = np.zeros(m)\r\n    for i in range(m): \r\n        mupart[i], sigmapart[i], m_lambdapart[i] = calibrateOUMLE(Spart[i,:], deltat)\r\n    # Now the jacknife calculation. \r\n    m_lambda = (m/(m-1))*m_lambdaT - (np.sum(m_lambdapart))/(m**2-m)\r\n    # mu and sigma are not biased, so there's no real need for the jackknife\r\n    # But we do it anyway for demonstration purposes. \r\n    mu = (m/(m-1))*muT - (np.sum(mupart))/(m**2-m)\r\n    sigma = (m/(m-1))*sigmaT - (np.sum(sigmapart))/(m**2-m)    \r\n    return mu, sigma, m_lambda\r\n\r\n## EOF  ","sub_path":"OU_Calibration.py","file_name":"OU_Calibration.py","file_ext":"py","file_size_in_byte":10570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"285062961","text":"\nclass SegTree:\n    def __init__(self, a):\n        n = len(a)\n        self.a = a\n        self.seg_tree = [None] * 2 * n\n        for i in range(n):\n            self.seg_tree[i + n] = a[i]\n        for i in range(n - 1, 0, -1):\n            self.seg_tree[i] = self.agg(self.seg_tree[i << 1], self.seg_tree[i << 1 | 1])\n\n\n    def agg(self, x, y):\n        if x[0] < y[0]:\n            return x\n        else:\n            return y\n\n\n    def query(self, l, r):\n        l += len(self.a)\n        r += len(self.a) + 1\n        res = (float('inf'), None)\n        while l < r:\n            if l & 1:\n                res = self.agg(res, self.seg_tree[l])\n                l += 1\n            if r & 1:\n                r -= 1\n                res = self.agg(res, self.seg_tree[r])\n            l >>= 1\n            r >>= 1\n        return res\n\n\nclass LeastCommonAncestor:\n    def __init__(self, adj):\n        self.adj = adj\n        self.tour = []\n        self.first_occ = [None] * len(adj)\n        self.st = None\n\n\n    def euler_tour(self):\n        \"\"\"Does a dfs of the tree to get an euler tour in the form of an array.\n        The array lists the nodes in the order they're visited, along with their\n        depths, and a segment tree is initialized on this array\n        \"\"\"\n        n = len(self.adj)\n        depth = [0] * n\n        visited = [False] * n\n        visited[0] = True\n        pointer = [0] * n\n        stack = [0]\n        i = 0\n        while stack:\n            i += 1\n            x = stack[-1]\n            self.tour.append((depth[x], x))\n            any_more = False\n            for j in range(pointer[x], len(self.adj[x])):\n                y = self.adj[x][j]\n                pointer[x] += 1\n                if not visited[y]:\n                    stack.append(y)\n                    depth[y] = depth[x] + 1\n                    self.first_occ[y] = i\n                    visited[y] = True\n                    any_more = True\n                    break\n            if not any_more:\n                stack.pop()\n        self.st = SegTree(self.tour)\n\n\n    def lca(self, x, y):\n        l = self.first_occ[x]\n        r = self.first_occ[y]\n        if l > r:\n            l, r = r, l\n        depth_z, z = self.st.query(l, r)\n        return z\n\n\nif __name__ == '__main__':\n    # example tree:\n    #               \t\t0\n    #    \t\t1\t\t\t\t\t\t2\n    #\t\t3\t\t4\t\t\t\t5   6   7\n    #\t  8   9                   10 11\n    adj = [[1, 2], [0, 3, 4], [0, 6, 7, 5], [1, 8, 9],\n        [1], [11, 10, 2], [2], [2], [3], [3], [5], [5]]\n    lca = LeastCommonAncestor(adj)\n    lca.euler_tour()\n    print(lca.lca(7, 10))\n    print(lca.lca(9, 4))\n    print(lca.lca(4, 9))\n    print(lca.lca(4, 11))\n","sub_path":"least_common_ancestor.py","file_name":"least_common_ancestor.py","file_ext":"py","file_size_in_byte":2646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"231736682","text":"import pyaudio\nfrom wavecalculations import WaveCalculations\nfrom triangleplot import TrianglePlot\nfrom bmicaudio import Mic\n\n'''\nAuthor: Samuel Genty\nSchool: Texas A&M University\nDate: 11/27/2018\nComputer Science and Engineering 18'\n\n##################################\n\nDescription:\nThis python script contains the testing methods that carry out the audio analysis\nof the audio files coming in from the Mic objects.\n\n##################################\n\nAudio Analysis Step / Design:\n\t1. First read in the USB microphones hardware index value and save them\n\t2. Instantiate the Mic Objects and setup the threads for recording\n\t3. Begin the recordings of 2 microphones at a time\n\t\t\t- Mic 1 and 2 record first\n\t\t\t- Mic 1 and 3 record second\n\t\t\t- Mic 2 and 3 record last\n\t4. Each audio file is saved and the TDOA is calculated using fast fourier transforms\n\t5. Using these calculated TDOA values from each pair of mic recordings, we can accurately determine which\n\t\t\tmic is closest to the sound being made in the environment\n\t6. Once we know the closest mic to the sound, we then take the ratio of the other two microphones peak amplitude\n\t\t\tand then estimate where on the triangle visualization and display the result \n\n#######################################\n'''\n\n\ndef determineDirection(offset1, offset2, mic1, mic2):\n\tif(offset1== offset2):\n\t\tprint(\"Sounds are equidistant !\")\n\t\treturn 4\n\tif (offset1 > offset2):\n\t\tprint(\"Sound is closer to mic\"+str(mic1)+\"\\n\")\n\t\treturn mic1\n\telse:\n\t\tprint(\"Sound is closer to mic\"+str(mic2)+\"\\n\")\n\t\treturn mic2\n\ndef fromWhere(a,b,c):\n\tif a == b:\n\t\tprint(\"sound hit mic1 first\")\n\t\treturn a\n\tif b == c:\n\t\tprint(\"sound hit mic3 first\")\n\t\treturn b\n\tif a == c:\n\t\tprint(\"Sound hit mic2 first\")\n\t\treturn a\n\tprint(\"Inconsistent sound location!\")\n\treturn 0\n\ndef pinpointQuadrant(w1, w2, x, y):\n\tratio = w1.maxx / w2.maxx\n\tarr = [[9.5,20], [7.64, 16], [3.77,9.30], [0,3], [5,4.7], [13.8,4.5], [20, 3.5], [17.5, 9.4], [13, 16.8]]\n\tif(x==2 and y==3):\n\t\tif(ratio > 1.1):\n\t\t\treturn arr[1]\n\t\telif(ratio < 1.1 and ratio > 0.9):\n\t\t\treturn arr[0]\n\t\telse:\n\t\t\treturn arr[8]\n\tif(x == 1 and y == 3):\n\t\tif(ratio > 1.1):\n\t\t\treturn arr[2]\n\t\telif(ratio < 1.1 and ratio > 0.9):\n\t\t\treturn arr[3]\n\t\telse:\n\t\t\treturn arr[4]\n\tif(x == 1 and y == 2):\n\t\tif(ratio > 1.1):\n\t\t\treturn arr[7]\n\t\telif(ratio < 1.1 and ratio > 0.9):\n\t\t\treturn arr[6]\n\t\telse:\n\t\t\treturn arr[5]\n\treturn[9.5,10]\n\n\ndef main():\n\tp = pyaudio.PyAudio()\n\tmic1_index = 0\n\tmic1_name = ''\n\tmic2_index = 0\n\tmic2_name = ''\n\tmic3_index = 0\n\tmic3_name = ''\n\n\t# The following for loop is for printing plugged in Audio devices\n\t# Which ever audio device has the word USB in it is probably our microphone \n\tcount = 0\n\tfor i in range(p.get_device_count()):\n\t\tdev = p.get_device_info_by_index(i)\n\t\tprint(str(dev['name']))\n\t\tif('Audio' in str(dev['name']) and count == 0):\n\t\t\tmic1_index = i\n\t\t\tmic1_name = count + 1\n\t\t\tcount  = count + 1\n\t\t\tcontinue\n\t\tif('Audio' in str(dev['name']) and count == 1):\n\t\t\tmic2_name = count + 1\n\t\t\tmic2_index = i\n\t\t\tcount  = count + 1\n\t\t\tcontinue\n\t\tif('Audio' in str(dev['name']) and count == 2):\n\t\t\tmic3_name = count + 1\n\t\t\tmic3_index = i\n\t\t\tcount  = count + 1\n\t\t\tbreak\n\n\tprint(\"There are \"+str(count)+\" mics connected\")\n\n\n\t# Instantiate the Mic objects\n\tmic1 = Mic(mic1_index, mic1_name, save_file=False)\n\tmic2 = Mic(mic2_index, mic2_name, save_file=False)\n\tmic3 = Mic(mic3_index, mic3_name, save_file=False)\n\t\n\n\tfor i in range(100):\n\t\tprint(\"Beginning test : \"+str(i))\n\t\t#Record mic 1 and mic 2 pair\n\t\tmic1.setup()\n\t\tmic2.setup()\n\t\tmic1.startthread()\n\t\tmic2.startthread()\n\t\tmic1.jointhread()\n\t\tmic2.jointhread()\n\t\tt1_data1 = mic1.data\n\t\tt1_data2 = mic2.data\n\t\taa1 = mic1\n\t\tbb1 = mic2\n\t\t\n\t\t#Record mic 1 and mic 3 pair\n\t\tmic1.setup()\n\t\tmic3.setup()\n\t\tmic1.startthread()\n\t\tmic3.startthread()\n\t\tmic1.jointhread()\n\t\tmic3.jointhread()\n\t\taa2= mic1\n\t\tcc2 = mic3\n\t\t\n\t\tt2_data1 = mic1.data\n\t\tt2_data3 = mic3.data\n\t\t\n\t\t#Record mic 2 and mic 3 pair\n\t\tmic2.setup()\n\t\tmic3.setup()\n\t\tmic2.startthread()\n\t\tmic3.startthread()\n\t\tmic2.jointhread()\n\t\tmic3.jointhread()\n\t\tt3_data2 = mic2.data\n\t\tt3_data3 = mic3.data\n\t\tbb3 = mic2\n\t\tcc3 = mic3\n\t\t\t\n\t\tt1 = WaveCalculations(\"wave1\", \"wave2\", t1_data1, t1_data2)\n\t\tt2 = WaveCalculations(\"wave1\", \"wave3\", t2_data1, t2_data3)\n\t\tt3 = WaveCalculations(\"wave2\", \"wave3\", t3_data2, t3_data3)\n\t\t\n\t\tt1r = WaveCalculations(\"wave2\", \"wave1\", t1_data2, t1_data1)\n\t\tt2r = WaveCalculations(\"wave3\", \"wave1\", t2_data3, t2_data1)\n\t\tt3r = WaveCalculations(\"wave3\", \"wave2\", t3_data3, t3_data2)\n\t\t\n\t\t# wave 1 and 3 offset\n\t\ta1 = t1.findDifference()\n\t\tb1 = t2.findDifference()\n\t\tc1 = t3.findDifference()\n\t\t\n\t\t# wave 2 and 3 offset\n\t\tar1 = t1r.findDifference()\n\t\tbr1 = t2r.findDifference()\n\t\tcr1 = t3r.findDifference()\n\t\t\n\n\t\tr1 = determineDirection(ar1, a1, 1, 2)\n\t\tr2 = determineDirection(br1, b1, 1, 3)\n\t\tr3 = determineDirection(cr1, c1, 2, 3)\n\n\t\tsoundlocation_index = fromWhere(r1,r2,r3)\n\t\tx = [0,0]\n\t\tif(soundlocation_index == 1):\n\t\t\tx = pinpointQuadrant(bb3, cc3, 2, 3)\n\t\telif(soundlocation_index == 2):\n\t\t\tx = pinpointQuadrant(aa2, cc2, 1, 3)\n\t\telse:\n\t\t\tx = pinpointQuadrant(aa1, bb1, 1, 2)\n\t\tTrianglePlot(x)\n\n# Detect the same signal by all microphones?\n\nmain()\n\n\n","sub_path":"bmictests.py","file_name":"bmictests.py","file_ext":"py","file_size_in_byte":5164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"441623822","text":"#########################################################################################\nimport rospy\nfrom std_msgs.msg import String\nfrom miro_msgs.msg import platform_mics, platform_sensors\nfrom array import array\nfrom interfaces import primary_interface\nimport time\nimport numpy\nfrom sound_test import sound_test\n\n\nclass pet_test:\n\n    def __init__(self):\n\n        robot_name = \"rob01\"\n        self.primary_int = primary_interface(robot_name)\n\n    def data_in(self):\n\n        start_time = rospy.get_rostime()\n        self.old_value = 0.0\n        flag_pet = False\n        flag_pat = False\n        self.primary_int.body_config_speed = [5, 5, 5, 5]\n\n        while not rospy.is_shutdown():\n\n            if self.primary_int.touch_body:\n\n                # find average value of body touch\n                try:\n                    self.value = (self.primary_int.touch_body[0] + 2.0 * self.primary_int.touch_body[1] + 3.0 *\n                                  self.primary_int.touch_body[2] + 4.0 * self.primary_int.touch_body[3]) / (\n                                     numpy.sum(self.primary_int.touch_body))\n                except ZeroDivisionError:\n                    self.value = 0\n\n                # test for 2 different types of touching (petting and patting)\n                if abs(self.value - self.old_value) > .5:\n\n                    # No flags activated\n                    if flag_pet == False and flag_pat == False:\n\n                        if self.value != 0:\n                            flag_pet = True\n                            flag_pat = False\n                        else:\n                            flag_pet = False\n                            flag_pat = True\n                        print\n                        flag_pet, flag_pat\n                        start_time = rospy.get_rostime()\n                    # one flag activated\n                    elif flag_pet == True:\n\n                        # if next event occurs within 1 sec, initiates response\n                        if self.value != 0 and (rospy.get_rostime() - start_time).to_sec() < 1.0:\n                            behavior.pet_detect()\n                            flag_pet = False\n                            time.sleep(.5)\n                        else:\n                            flag_pet = False\n                    elif flag_pat == True:\n\n                        if (rospy.get_rostime() - start_time).to_sec() < 1.0:\n                            behavior.pat_detect()\n                            flag_pat = False\n                            time.sleep(.5)\n                        else:\n                            flag_pat = False\n                self.old_value = self.value\n\n    def pet_detect(self):\n\n        # nod head in approval\n        self.primary_int.body_config = [0, 0, 0, 1]\n        time.sleep(.5)\n        self.primary_int.body_config = [0, 0, 0, 0]\n\n    def pat_detect(self):\n\n        # shake head in disproval\n        self.primary_int.body_config = [0, 0, .2, 0]\n        time.sleep(.25)\n        self.primary_int.body_config = [0, 0, -2, 0]\n        time.sleep(.25)\n        self.primary_int.body_config = [0, 0, 0, 0]\n\n\nif __name__ == \"__main__\":\n    rospy.init_node(\"mic_test\", anonymous=True)\n    behavior = pet_test()\n    behavior.data_in()\n\n","sub_path":"Scripts/pet_test.py","file_name":"pet_test.py","file_ext":"py","file_size_in_byte":3246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"238007985","text":"# Здесь подымаем нашы порты RS\n\n\nclass GETSettingsUART:\n    \"\"\"\n    Класс для получения данных запущенных серверов с USPD\n    \"\"\"\n    http = 'http://'\n    servers_settings = '/settings/uart'\n    servers_settings = '/uart_settings'\n    ip_port = '192.168.202.22'\n\n    def __init__(self, ip_port: str):\n        # собираем адрес запрсов\n        self.ip_port = str(ip_port)\n\n    def Get_Settings(self):\n        # собираем адрес запросов\n        url_get_uspd_Settings = self.http + self.ip_port + self.servers_settings\n        import requests\n        # Отправляем запрос\n        uspd_Settings = requests.get(url_get_uspd_Settings)\n\n        print(uspd_Settings)\n        # Возвращаем данные\n        return uspd_Settings.json()\n\n\nprint(GETSettingsUART('192.168.202.22').Get_Settings())\n\nclass PUTSettingsUSPD:\n    \"\"\"\n    Класс для отправки данных запущенных серверов с USPD\n    \"\"\"\n    http = 'http://'\n    servers_settings = '/settings/servers/tcp'\n    ip_port = '192.168.202.22'\n    data = {}\n\n    def __init__(self, ip_port: str, data: dict):\n        self.ip_port = str(ip_port)\n        self.data = dict(data)\n\n    def Put_Settings(self):\n        # собираем адрес запросов\n        url_get_uspd_Settings = self.http + self.ip_port + self.servers_settings\n        import requests\n        # Отправляем запрос\n        # print(url_get_uspd_Settings)\n\n        uspd_Settings_result = requests.put(url_get_uspd_Settings, json=self.data)\n\n        # Возвращаем данные\n        return uspd_Settings_result.status_code\n\n\nclass DefinitionOfRunUpServer:\n    \"\"\"\n    Здесь парсим наш JSON ответа и определяем какие именно сервера подняты\n\n    \"\"\"\n\n    RTU = False\n    Transit_Iface1 = False\n    Transit_Iface2 = False\n    Transit_Iface3 = False\n    Transit_Iface4 = False\n\n    def __init__(self, Settings: dict):\n\n        # Получаем наши настройки\n        Settings = Settings.get('Settings')\n\n        # Это должен быть список\n        if Settings is not None:\n            # если не ноль , то тогда перебираем элементы списка\n            for setting in Settings:\n                if setting.get('type') is not None:\n                    self.setting_dict.get(setting.get('type'))(self, setting)\n\n                    # value = True\n\n    def _Transit_Iface1(self, setting):\n\n        \"\"\"# Переопределяем Iface1\"\"\"\n        self.Transit_Iface1 = DefinitionOnPortServer(type=setting.get('type'), port=setting.get('port')).Port\n\n    def _Transit_Iface2(self, setting):\n\n        \"\"\"# Переопределяем Iface2\"\"\"\n        self.Transit_Iface2 = DefinitionOnPortServer(type=setting.get('type'), port=setting.get('port')).Port\n\n    def _Transit_Iface3(self, setting):\n\n        \"\"\"# Переопределяем Iface3\"\"\"\n        self.Transit_Iface3 = DefinitionOnPortServer(type=setting.get('type'), port=setting.get('port')).Port\n\n    def _Transit_Iface4(self, setting):\n\n        \"\"\"# Переопределяем Iface4\"\"\"\n        self.Transit_Iface4 = DefinitionOnPortServer(type=setting.get('type'), port=setting.get('port')).Port\n\n    def _RTU(self, setting):\n\n        \"\"\"# Переопределяем rtu327\"\"\"\n        self.RTU = DefinitionOnPortServer(type=setting.get('type'), port=setting.get('port')).Port\n\n    setting_dict = \\\n        {\n            'iface1': _Transit_Iface1,\n            'iface2': _Transit_Iface2,\n            'iface3': _Transit_Iface3,\n            'iface4': _Transit_Iface4,\n            'rtu327': _RTU,\n        }\n\n\nclass DefinitionOnPortServer:\n    \"\"\"\n    Здесь проверяем Соответствие портов\n    \"\"\"\n\n    Port = False\n\n    def __init__(self, type: str, port: str):\n        from Service_constant import port_for_server_to_constant\n\n        needed_port = port_for_server_to_constant.get(type)\n        if needed_port is not None:\n            if needed_port == port:\n                self.Port = True\n\n\nclass FormElementJSON:\n    \"\"\"\n    Здесь формируем наш элемент JSON\n    \"\"\"\n    type_key = None\n\n    def __init__(self, type_key: str):\n        self.type_key = type_key\n\n    def Get(self):\n        from Service_constant import port_for_server_to_constant\n        element = {'type': str(self.type_key), 'port': str(port_for_server_to_constant.get(self.type_key))}\n\n        return element\n\n\nclass RunUpServer:\n    \"\"\"\n    Класс поднятия сервера\n\n    что делает - смотрит какие сервера подняты - с НУЖНЫМ Портом , и взависимости от того , какие подняты или нет - Подымает\n    \"\"\"\n    import Service_constant\n    ip_port = Service_constant.ip_port\n    result = False\n\n    def __init__(self,\n                 rtu327: bool = False,\n                 iface1: bool = True,\n                 iface2: bool = True,\n                 iface3: bool = True,\n                 iface4: bool = True):\n\n        # Пункт первый - Смотрим что у нас поднято\n        # отправляем запрос\n        ip_port = self.ip_port\n\n        Settings = GETSettingsUSPD(ip_port=ip_port).Get_Settings()\n        # Смотрим что поднято\n        RunUp = DefinitionOfRunUpServer(Settings)\n\n        Transit_Iface1 = RunUp.Transit_Iface1\n        Transit_Iface2 = RunUp.Transit_Iface2\n        Transit_Iface3 = RunUp.Transit_Iface3\n        Transit_Iface4 = RunUp.Transit_Iface4\n        RTU = RunUp.RTU\n\n        # МЫ НИЧЕГО НЕ ПЕРЕЗАПИСЫВААЕМ ЕСЛИ ПОРТЫ УЖЕ НОРМАЛЬНО РАБОТАЮТ\n        if (rtu327 == RTU) and (Transit_Iface1 == iface1) and (Transit_Iface2 == iface2) and (\n                Transit_Iface3 == iface3) and (Transit_Iface4 == iface4):\n            pass\n        # ИНАЧЕ ПЕРЕЗАПИСЫВАЕМ\n        else:\n            Settings_list = []\n            # ТЕПЕРЬ ЕСЛИ НАДО ЧТО ТО ПОДНЯТЬ\n            if rtu327:\n                Settings_list.append(self._form_JSON_element('rtu327'))\n\n            if iface1:\n                Settings_list.append(self._form_JSON_element('iface1'))\n\n            if iface2:\n                Settings_list.append(self._form_JSON_element('iface2'))\n\n            if iface3:\n                Settings_list.append(self._form_JSON_element('iface3'))\n\n            if iface4:\n                Settings_list.append(self._form_JSON_element('iface4'))\n\n            # И после этого собираем наш основной JSON\n            JSON = {'Settings': Settings_list}\n            # отправлляем его\n            result = PUTSettingsUSPD(ip_port=ip_port, data=JSON).Put_Settings()\n\n            # ТЕПЕРЬ - Проверяем что мы подняли все то что надо\n\n            Settings = GETSettingsUSPD(ip_port=ip_port).Get_Settings()\n\n            if Settings == result:\n                self.result = True\n\n    def _form_JSON_element(self, type_key: str):\n        \"\"\"\n        Метод для формирвоания строки поднятия\n\n        :param RunUp: булевый маркер что это уже поднято\n        :param type_key: сам ключ по котормоу будеит все формироваться\n        :return: Возвращает Элемент , если надо\n        \"\"\"\n\n        return FormElementJSON(type_key=type_key).Get()\n\n    def Get_result(self):\n\n        return self.result\n","sub_path":"RunUpServer/Run_Up_RS_Port.py","file_name":"Run_Up_RS_Port.py","file_ext":"py","file_size_in_byte":7766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"179564319","text":"import numpy as np\n\ndef insertionsort(n):\n    a = np.random.randint(0, 100, n)\n    print(a)\n    for i in range(len(a)):\n        j = i - 1\n        key = a[i]\n        while a[j] > key and j >= 0:\n            a[j+1] = a[j]\n            j -= 1\n        a[j+1] = key\n    return a\n\nnew_arr = insertionsort(10)\nprint(new_arr)\n\n\n\n\n\n","sub_path":"sort.py","file_name":"sort.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"76801190","text":"#!/usr/bin/env python\n\"\"\"Supplies test data for the Seeing Monitor\n\nHistory:\n2010-09-24\n2010-09-29 ROwen    modified to use RO.Alg.RandomWalk\n2010-10-18 ROwen    Added guide offset information.\n2012-07-09 ROwen    Modified to use RO.TkUtil.Timer.\n\"\"\"\nimport math\nimport RO.Alg.RandomWalk\nfrom RO.TkUtil import Timer\nimport TUI.Base.TestDispatcher\n\ntestDispatcher = TUI.Base.TestDispatcher.TestDispatcher(\"tcc\")\ntuiModel = testDispatcher.tuiModel\n\nAlt = 45.0\n\nclass GuideOffInfo(object):\n    def __init__(self):\n        azScale = 1.0 / math.cos(Alt * RO.PhysConst.RadPerDeg)\n        lim = 10.0 / RO.PhysConst.ArcSecPerDeg\n        mean = 0.0 / RO.PhysConst.ArcSecPerDeg\n        sigma = 2.0 / RO.PhysConst.ArcSecPerDeg\n        \n        self.randomValueDict = dict(\n            azOff = RO.Alg.RandomWalk.ConstrainedGaussianRandomWalk(\n                mean * azScale, sigma * azScale, -lim * azScale, lim * azScale),\n            altOff = RO.Alg.RandomWalk.ConstrainedGaussianRandomWalk(mean, sigma, -lim, lim),\n            rotOff = RO.Alg.RandomWalk.ConstrainedGaussianRandomWalk(mean, sigma, -lim, lim),\n        )\n    \n    def update(self):\n        \"\"\"Randomly change values\n        \"\"\"\n        for randomValue in self.randomValueDict.itervalues():\n            next(randomValue)\n    \n    def getValueDict(self):\n        \"\"\"Get a dictionary of value name: value\n        \"\"\"\n        return dict((name, randomValue.value) for name, randomValue in self.randomValueDict.iteritems())\n\n    def getKeyVarStr(self):\n        \"\"\"Get the data as a keyword variable\n        \n        Fields are:\n        az off, az vel, az time, alt off, alt vel, alt time, rot off, rot vel, rot time\n        where offsets are in degrees\n        the offsets are assumed constant so time is not interesting so I don't bother to set it realistically\n        \"\"\"\n        return \"GuideOff=%(azOff)0.5f, 0.0, 100.0, %(altOff)0.5f, 0.0, 100.0, %(rotOff)0.5f, 0.0, 100.0\" % \\\n            self.getValueDict()\n\nclass StarInfo(object):\n    def __init__(self):\n        self.randomValueDict = dict(\n            fwhm = RO.Alg.RandomWalk.ConstrainedGaussianRandomWalk(0.5, 0.1, 0.3, 1.2),\n            amplitude = RO.Alg.RandomWalk.ConstrainedGaussianRandomWalk(10000, 100, 5000, 32000),\n            xCenter = RO.Alg.RandomWalk.ConstrainedGaussianRandomWalk(0, 10, -500, 500),\n            yCenter = RO.Alg.RandomWalk.ConstrainedGaussianRandomWalk(0, 10, -500, 500),\n        )\n    \n    def update(self):\n        \"\"\"Randomly change values\n        \"\"\"\n        for randomValue in self.randomValueDict.itervalues():\n            next(randomValue)\n    \n    def getValueDict(self):\n        \"\"\"Get a dictionary of value name: value\n        \"\"\"\n        valDict = dict((name, randomValue.value) for name, randomValue in self.randomValueDict.iteritems())\n        valDict[\"brightness\"] = valDict[\"fwhm\"] * valDict[\"amplitude\"]\n        valDict[\"background\"] = 1200.0\n        return valDict\n\n    def getKeyVarStr(self):\n        \"\"\"Get the data as a keyword variable\n\nThe fields are as follows, where lengths and positions are in binned pixels\nand intensities are in ADUs:\n0       type characer: c = centroid, f = findstars, g = guide star\n1       index: an index identifying the star within the list of stars returned by the command.\n2,3     x,yCenter: centroid\n4,5     x,yError: estimated standard deviation of x,yCenter\n6       radius: radius of centroid region\n7       asymmetry: a measure of the asymmetry of the object;\n        the value minimized by PyGuide.centroid.\n        Warning: not normalized, so probably not much use.\n8       FWHM major\n9       FWHM minor\n10      ellMajAng: angle of ellipse major axis in x,y frame (deg)\n11      chiSq: goodness of fit to model star (a double gaussian). From PyGuide.starShape.\n12      counts: sum of all unmasked pixels within the centroid radius. From PyGuide.centroid\n13      background: background level of fit to model star. From PyGuide.starShape\n14      amplitude: amplitude of fit to model star. From PyGuide.starShape\nFor \"g\" stars, the two following fields are added:\n15,16   predicted x,y position\n        \"\"\"\n        return \"Star=c, 0, %(xCenter)0.1f, %(yCenter)0.1f, 10.0, -7.0, 5, 100.0, %(fwhm)0.2f, %(fwhm)0.2f, 0, 10, %(brightness)0.1f, %(background)0.1f, %(amplitude)0.1f\" % \\\n            self.getValueDict()\n\ndef runTest():\n    testDispatcher.dispatch(\"AxePos=0.0, %0.3f, 0\" % (Alt,), actor=\"tcc\")\n    _nextGuideOffset(GuideOffInfo(), 2)\n    _nextStar(StarInfo(), 5)\n    _nextSecFocus(RO.Alg.RandomWalk.ConstrainedGaussianRandomWalk(0, 10, -500, 500), 6)\n    _nextSecPiston(RO.Alg.RandomWalk.ConstrainedGaussianRandomWalk(100, 25, -2000, 2000), 3)\n\ndef _nextGuideOffset(guideOffInfo, delaySec):\n    guideOffInfo.update()\n    keyVarStr = guideOffInfo.getKeyVarStr()\n    testDispatcher.dispatch(keyVarStr, actor=\"tcc\")\n    Timer(delaySec, _nextGuideOffset, guideOffInfo, delaySec)\n\ndef _nextStar(starInfo, delaySec):\n    starInfo.update()\n    keyVarStr = starInfo.getKeyVarStr()\n    testDispatcher.dispatch(keyVarStr, actor=\"gcam\")\n    Timer(delaySec, _nextStar, starInfo, delaySec)\n\ndef _nextSecFocus(secFocus, delaySec):\n    keyVarStr = \"SecFocus=%0.1f\" % (next(secFocus),)\n    testDispatcher.dispatch(keyVarStr, actor=\"tcc\")\n    Timer(delaySec, _nextSecFocus, secFocus, delaySec)\n\ndef _nextSecPiston(secPiston, delaySec):\n    keyVarStr = \"SecOrient=%0.1f, 0, 0, 0, 0\" % (next(secPiston),)\n    testDispatcher.dispatch(keyVarStr, actor=\"tcc\")\n    Timer(delaySec, _nextSecPiston, secPiston, delaySec)\n","sub_path":"TUI/Guide/GuideMonitor/TestData.py","file_name":"TestData.py","file_ext":"py","file_size_in_byte":5502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"381072113","text":"#!/usr/bin/env python3\n\n# FILE: treaded_HW_dist_sensors.py\n\n# PURPOSE: Test with two threads reading the distance sensor, both HW I2C\n\nimport threading\nimport traceback\nimport datetime as dt\nimport time\nimport numpy.random as nprand\nimport easygopigo3 as easy\n\ndef print_w_date_time(alert,event_time=None):\n    if event_time is None: event_time = dt.datetime.now()\n    str_event_time = event_time.strftime(\"%Y-%m-%d %H:%M:%S.%f\")[:-3]\n    thread_name = threading.current_thread().name\n    print(\"{} {}: {}\".format(str_event_time,thread_name,alert))\n\nclass CheckDistSensor(threading.Thread):\n    def __init__(self,port=\"RPI_1\",egpg=None,name=\"not_set\"):\n        threading.Thread.__init__(self)\n        self.exitFlag = False\n        if egpg is None:\n            time.sleep(nprand.uniform(1.0,3.0))\n            self.egpg = easy.EasyGoPiGo3(use_mutex=True)\n            time.sleep(0.1)  # allow for hardware init to complete\n        else:\n            self.egpg = egpg\n        self.ds  = self.egpg.init_distance_sensor(port=port)\n        print_w_date_time(\"CheckDistSensor Initialized on port {}\".format(port))\n        self.name = name\n\n    def run(self):\n        while (self.exitFlag is not True):\n            try:\n                self.reading = self.ds.read_mm()\n                print_w_date_time(\"distance: {:.0f}\".format(self.reading))\n                time.sleep(nprand.uniform(0.1,0.2))\n            except KeyboardInterrupt:\n                print_w_date_time(\"caught KeyboardInterrupt\")\n                break\n            except Exception as e:\n                print_w_date_time(\"Exception\"+str(e))\n                traceback.print_exc()\n                self.reading = 0\n                break\n        print_w_date_time(\"exitFlag set, stopping thread\")\n\ndef main():\n    print_w_date_time(\"Starting\")\n    egpg = easy.EasyGoPiGo3(use_mutex=True)\n\n    t1 = CheckDistSensor(name=\"HW_i2c_1\",egpg=egpg,port=\"RPI_1\")\n    t2 = CheckDistSensor(name=\"HW_i2c_2\",egpg=egpg,port=\"RPI_1\")\n    t1.start()\n    t2.start()\n\n    while True:\n        try:\n            time.sleep(1)\n        except KeyboardInterrupt:\n            print(\"\\nClosing Threads\")\n            t1.exitFlag = True\n            t2.exitFlag = True\n            t1.join()\n            t2.join()\n            break\n    print_w_date_time(\"Exiting\")\n\n\n\nif __name__ == '__main__': main()\n\n","sub_path":"systests/I2C/threaded_HW_dist_sensor.py","file_name":"threaded_HW_dist_sensor.py","file_ext":"py","file_size_in_byte":2325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"339691314","text":"#!/usr/bin/env python\n# (C) 2017 OpenEye Scientific Software Inc. All rights reserved.\n#\n# TERMS FOR USE OF SAMPLE CODE The software below (\"Sample Code\") is\n# provided to current licensees or subscribers of OpenEye products or\n# SaaS offerings (each a \"Customer\").\n# Customer is hereby permitted to use, copy, and modify the Sample Code,\n# subject to these terms. OpenEye claims no rights to Customer's\n# modifications. Modification of Sample Code is at Customer's sole and\n# exclusive risk. Sample Code may require Customer to have a then\n# current license or subscription to the applicable OpenEye offering.\n# THE SAMPLE CODE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n# EXPRESS OR IMPLIED.  OPENEYE DISCLAIMS ALL WARRANTIES, INCLUDING, BUT\n# NOT LIMITED TO, WARRANTIES OF MERCHANTABILITY, FITNESS FOR A\n# PARTICULAR PURPOSE AND NONINFRINGEMENT. In no event shall OpenEye be\n# liable for any damages or liability in connection with the Sample Code\n# or its use.\n\n#############################################################################\n# Split molecule file into N chunks or chunks of size N\n#############################################################################\nimport os\nimport sys\nfrom openeye import oechem\n\n\ndef NewOutputStream(outbase, ext, chunk):\n    newname = outbase + ('_%07d' % chunk) + ext\n    ofs = oechem.oemolostream()\n    if not ofs.open(newname):\n        oechem.OEThrow.Fatal(\"Unable to open %s for writing\" % newname)\n    return ofs\n\n\ndef SplitNParts(ifs, nparts, countconfs, outbase, ext):\n    # first read entire file to determine number of molecules\n    molconfcount = 0\n    for mol in ifs.GetOEMols():\n        if countconfs:\n            molconfcount += mol.NumConfs()\n        else:\n            molconfcount += 1\n    ifs.rewind()\n\n    chunksize, lft = divmod(molconfcount, nparts)\n    if lft != 0:\n        chunksize += 1\n    chunk = 1\n    count = 0\n\n    ofs = NewOutputStream(outbase, ext, chunk)\n    for mol in ifs.GetOEMols():\n        if countconfs:\n            count += mol.NumConfs()\n        else:\n            count += 1\n        if count > chunksize:\n            if chunk == lft:\n                chunksize -= 1\n\n            ofs.close()\n            chunk += 1\n            count = 1\n            ofs = NewOutputStream(outbase, ext, chunk)\n\n        oechem.OEWriteMolecule(ofs, mol)\n\n\ndef SplitChunk(ifs, chunksize, countconfs, outbase, ext):\n    chunk = 1\n    ofs = NewOutputStream(outbase, ext, chunk)\n\n    count = 0\n    for mol in ifs.GetOEMols():\n        if count >= chunksize:\n            ofs.close()\n            count = 0\n            chunk += 1\n            ofs = NewOutputStream(outbase, ext, chunk)\n\n        if countconfs:\n            count += mol.NumConfs()\n        else:\n            count += 1\n        oechem.OEWriteMolecule(ofs, mol)\n\n\ndef main(argv=[__name__]):\n    itf = oechem.OEInterface(InterfaceData, argv)\n\n    if not (itf.HasInt(\"-num\") ^ itf.HasInt(\"-size\")):\n        oechem.OEThrow.Fatal(\"Number of chunks (-num) or the size of each chunk \"\n                             \"(-size) must be specified and are mutually exclusive.\")\n\n    ifs = oechem.oemolistream()\n    if not ifs.open(itf.GetString(\"-i\")):\n        oechem.OEThrow.Fatal(\"Unable to open %s for reading\" % itf.GetString(\"-i\"))\n\n    if (ifs.GetFormat() != oechem.OEFormat_OEB):\n        ifs.SetConfTest(oechem.OEIsomericConfTest(False))\n    outbase, ext = os.path.splitext(itf.GetString(\"-o\"))\n\n    if ext == '':\n        oechem.OEThrow.Fatal(\"Failed to find file extension\")\n\n    if ext == '.gz':\n        outbase, ext = os.path.splitext(outbase)\n        ext = ext + '.gz'\n\n    countconfs = itf.GetBool(\"-confs\")\n\n    if itf.HasInt(\"-num\"):\n        nparts = itf.GetInt(\"-num\")\n        SplitNParts(ifs, nparts, countconfs, outbase, ext)\n    else:\n        chunksize = itf.GetInt(\"-size\")\n        SplitChunk(ifs, chunksize, countconfs, outbase, ext)\n\n#############################################################################\n\n\nInterfaceData = \"\"\"\\\n!BRIEF -num|-size [-i] <input> [-o] <output>\n!PARAMETER -i 1\n  !TYPE string\n  !REQUIRED true\n  !BRIEF Input file name\n  !KEYLESS 1\n!END\n!PARAMETER -o 2\n  !TYPE string\n  !REQUIRED true\n  !BRIEF Output file name\n  !KEYLESS 2\n!END\n!PARAMETER -num 3\n  !TYPE int\n  !BRIEF The number of chunks\n!END\n!PARAMETER -size 4\n  !TYPE int\n  !BRIEF The size of each chunk\n!END\n!PARAMETER -confs 5\n  !TYPE bool\n  !DEFAULT true\n  !BRIEF Split by number of conformers not molecules\n!END\n\"\"\"\n\nif __name__ == \"__main__\":\n    sys.exit(main(sys.argv))\n","sub_path":"venv/Scripts/molchunk.py","file_name":"molchunk.py","file_ext":"py","file_size_in_byte":4498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"498339906","text":"# MyBot.py: main program, required by contest\n#\n# AI bot written for AI Challenge 2011 - Ants\n# Author: Bill Y\n# License: all your base are belong to us\n\nfrom core import *\nfrom gamestate import GameState\nfrom influence5 import Influence\nfrom linear_influence2 import LinearInfluence\nfrom planner3 import Planner\nfrom random import choice\nfrom collections import deque\n\nimport battle_line as battle\nimport sys, os, pickle, traceback, math\n\nDETAIL_LOG = False\n\n# define a class with a do_turn method\n# the Gamestate.run method will parse and update bot input\n# it will also run the do_turn method for us\nclass MyBot:\n    def __init__(self, gamestate):\n        # define class level variables, will be remembered between turns\n        self.gamestate = gamestate\n        self.diffuse_time = 0\n        self.combat_time_history = deque([0, 0, 0, 0, 0])\n        self.combat_time = 0\n    \n    # do_setup is run once at the start of the game\n    # after the bot has received the game settings\n    def do_setup(self):\n        # initialize data structures after learning the game settings\n        self.food_influence = LinearInfluence(self.gamestate)\n        self.raze_influence = LinearInfluence(self.gamestate)\n        self.defense_influence = LinearInfluence(self.gamestate)\n        self.explore_influence = Influence(self.gamestate)\n        self.planner = Planner(self.gamestate)\n    \n    def log_turn(self, turn_no):\n        debug_logger.debug('turn ' + str(self.gamestate.current_turn))\n        perf_logger.debug('turn ' + str(self.gamestate.current_turn))\n        perf_logger.debug('self.diffuse_time = %d' % self.diffuse_time)\n        perf_logger.debug('self.combat_time_history = %s' % str(self.combat_time_history))\n        perf_logger.debug('self.combat_time = %s' % self.combat_time)\n            \n    def log_detail(self):\n        if DETAIL_LOG and os.path.isdir('pickle'):# and int(self.gamestate.current_turn) % 10 == 0:\n            # dump gamestate\n            pickle_file = open('pickle/turn_' + str(self.gamestate.current_turn) + '.gamestate', 'wb')\n            pickle.dump(self.gamestate, pickle_file)\n            pickle_file.close()\n            \n            # dump influence map value\n            pickle_file = open('pickle/turn_' + str(self.gamestate.current_turn) + '.influence', 'wb')\n            pickle.dump(self.explore_influence, pickle_file)\n            pickle_file.close()\n    \n    # do turn is run once per turn\n    def do_turn(self):        \n        # detailed logging\n        self.log_turn(self.gamestate.current_turn)\n\n        # razing nearby hill takes precedence\n        self.raze_override()\n        \n        # handle combat\n        combat_start = self.gamestate.time_remaining()\n        self.issue_combat_task()\n        self.combat_time_history.append(combat_start - self.gamestate.time_remaining())\n        self.combat_time_history.popleft()\n        self.combat_time = max(self.combat_time_history)        \n        \n        # use planner to set new influence\n        perf_logger.debug('self.update_influences.start = %s' % str(self.gamestate.time_elapsed()))\n        self.planner.update_food_influence(self.food_influence)\n        perf_logger.debug('self.update_food_influence.finish = %s' % str(self.gamestate.time_elapsed()))     \n        self.planner.update_raze_influence(self.raze_influence)\n        perf_logger.debug('self.update_raze_influence.finish = %s' % str(self.gamestate.time_elapsed()))     \n        self.planner.update_defense_influence(self.defense_influence)\n        perf_logger.debug('self.update_defense_influence.finish = %s' % str(self.gamestate.time_elapsed()))     \n        self.planner.update_explore_influence(self.explore_influence)\n        perf_logger.debug('self.update_explore_influence.finish = %s' % str(self.gamestate.time_elapsed()))        \n        \n        # decay strategy influence\n        self.explore_influence.decay(DECAY_RATE)\n        \n        # diffuse explore_influence, which is the only one using molecular diffusion\n        perf_logger.debug('explore_influence.diffuse().start = %s' % str(self.gamestate.time_elapsed()))\n        expected_explore_time = len(self.gamestate.my_ants()) / 2 + 20\n        for i in xrange(30):\n            if self.gamestate.time_remaining() < self.diffuse_time + expected_explore_time:\n                perf_logger.debug('bailing diffuse after %d times' % (i))\n                break\n            diffuse_start = self.gamestate.time_remaining()\n            self.explore_influence.diffuse()\n            diffuse_duration = diffuse_start - self.gamestate.time_remaining()\n            self.diffuse_time = max([diffuse_duration, self.diffuse_time])\n        self.diffuse_time -= 1\n        perf_logger.debug('explore_influence.diffuse().finish = %s' % str(self.gamestate.time_elapsed())) \n        \n        self.log_detail()\n        \n        # avoidance explorer\n        self.avoidance_explore()\n        perf_logger.debug('self.avoidance_explore.finish = %s' % str(self.gamestate.time_elapsed()))   \n        \n        # normal explore\n        self.normal_explore()\n        perf_logger.debug('self.normal_explore.finish = %s' % str(self.gamestate.time_elapsed()))   \n        perf_logger.debug('endturn: my_ants count = %d, time_elapsed = %s' % (len(self.gamestate.my_ants()), self.gamestate.time_elapsed()))\n\n    def raze_override(self):\n        'razing close-by hill takes precedence over combat'\n        for hill_loc, owner in self.gamestate.enemy_hills():\n            for n_loc in self.gamestate.neighbour_table[hill_loc]:\n                if n_loc in self.gamestate.my_ants():\n                    direction = self.gamestate.direction(n_loc, hill_loc) + [None]\n                    self.gamestate.issue_order((n_loc, direction[0]))\n        \n    def issue_combat_task(self):\n        'combat logic'\n        perf_logger.debug('issue_combat_task.start = %s' % str(self.gamestate.time_remaining())) \n        zones = battle.get_combat_zones(self.gamestate)\n        perf_logger.debug('get_combat_zones.finish = %s' % str(self.gamestate.time_remaining())) \n        \n        if zones is not None:\n            debug_logger.debug('zones.count = %d' % len(zones))\n            i = 0\n            for zone in zones:\n                i += 1\n                # debug_logger.debug('group combat loop for = %s' % str(zone))\n                # perf_logger.debug('do_zone_combat.start = %s' % str(self.gamestate.time_remaining())) \n                battle.do_zone_combat(self.gamestate, zone)\n                # perf_logger.debug('do_zone_combat.start = %s' % str(self.gamestate.time_remaining())) \n                \n                # check if we still have time left to calculate more orders\n                if self.gamestate.time_remaining() < 100:\n                    debug_logger.debug('bailing combat zone after %d times' % (i))\n                    break\n                \n        perf_logger.debug('issue_combat_task.finish = ' + str(self.gamestate.time_remaining())) \n    \n    def get_desired_moves(self, ant):\n        desired_moves = []\n        # food\n        desired_moves.extend(self.get_desired_move_from_linear_influence(ant, self.food_influence))\n        debug_logger.debug('desired_moves.food = %s' % str(desired_moves))\n        \n        # defend hill\n        desired_moves.extend(self.get_desired_move_from_linear_influence(ant, self.defense_influence))\n        debug_logger.debug('desired_moves.defense = %s' % str(desired_moves))\n        \n        # raze hill\n        desired_moves.extend(self.get_desired_move_from_linear_influence(ant, self.raze_influence))\n        debug_logger.debug('desired_moves.raze = %s' % str(desired_moves))\n            \n        # explore\n        desired_moves.extend(self.get_desired_move_from_molecular_influence(ant, self.explore_influence))\n        debug_logger.debug('desired_moves.explore = %s' % str(desired_moves))\n            \n        # uniquify\n        seen = set()\n        seen_add = seen.add\n        desired_moves = [move for move in desired_moves if move not in seen and not seen_add(move)]\n        return desired_moves\n\n    def get_desired_move_from_molecular_influence(self, ant, influence):\n        desired_moves = []\n        neighbours_and_influences = sorted([(influence.map[loc], loc) for loc in [ant] + self.gamestate.passable_neighbours(ant)])\n        debug_logger.debug('neighbours_and_influences = %s' % str(neighbours_and_influences))\n        for inf, n_loc in neighbours_and_influences:\n            desired_moves.append(n_loc)\n            \n        return desired_moves\n    \n    def get_desired_move_from_linear_influence(self, ant, influence):\n        desired_moves = []\n        if influence.map[ant] != 0:\n            neighbours_and_influences = sorted([(influence.map[loc], loc) for loc in [ant] + self.gamestate.passable_neighbours(ant)])\n            debug_logger.debug('neighbours_and_influences = %s' % str(neighbours_and_influences))\n            for inf, n_loc in neighbours_and_influences:\n                if inf < influence.map[ant]:\n                    desired_moves.append(n_loc)\n            \n        return desired_moves\n        \n    def normal_explore(self):\n        'only concern influence'\n        for my_ant in self.gamestate.my_unmoved_ants():\n            debug_logger.debug('normal explore task for %s' % str(my_ant))\n            desired_moves = self.get_desired_moves(my_ant)\n            if len(desired_moves) > 0:\n                move = desired_moves[0]\n                # do the move\n                directions = self.gamestate.direction(my_ant, move) + [None]\n                self.gamestate.issue_order((my_ant, directions[0]))           \n            \n            # check if we still have time left to calculate more orders\n            if self.gamestate.time_remaining() < 10:\n                break\n    \n    def avoidance_explore(self):\n        'explore under enemies presence'\n        avoidance_distance = self.gamestate.euclidean_distance_add(self.gamestate.attackradius2, 2)\n        for my_ant in self.gamestate.my_unmoved_ants():\n            enemy_ants = [enemy_ant for enemy_ant, owner in self.gamestate.enemy_ants() \n                        if self.gamestate.euclidean_distance2(my_ant, enemy_ant) <= avoidance_distance]\n            if len(enemy_ants) > 0:\n                # don't initiate 1 on 1 exchange, but don't be afraid\n                safe_distance = self.gamestate.attackradius2\n                if len(enemy_ants) > 1:\n                    # be safer if more enemies are around\n                    safe_distance = self.gamestate.euclidean_distance_add(self.gamestate.attackradius2, 1)\n                desired_moves = self.get_desired_moves(my_ant)                \n                move_distances = {move:min([self.gamestate.euclidean_distance2(move, enemy_ant) for enemy_ant in enemy_ants])\n                                    for move in desired_moves}\n                debug_logger.debug('move_distances = %s' % str(move_distances))\n                # go for lowest influence that's safe\n                best_move = None\n                for move in desired_moves:\n                    if move_distances[move] > safe_distance:\n                        best_move = move\n                        break\n                # if no safe move try largest distance\n                if best_move is None:\n                    best_move = max(move_distances, key=move_distances.get)\n                    \n                # do the move\n                directions = self.gamestate.direction(my_ant, best_move) + [None]\n                self.gamestate.issue_order((my_ant, directions[0]))\n                debug_logger.debug('moving %s' % str((my_ant, directions[0])))\n            \n            # check if we still have time left to calculate more orders\n            if self.gamestate.time_remaining() < 30:\n                break\n        \n    # static methods are not tied to a class and don't have self passed in\n    # this is a python decorator\n    @staticmethod\n    def run():\n        'parse input, update game state and call the bot classes do_turn method'\n        gamestate = GameState()\n        bot = MyBot(gamestate)\n        map_data = ''\n        while(True):\n            try:\n                current_line = sys.stdin.readline().rstrip('\\r\\n') # string new line char\n                if current_line.lower() == 'ready':\n                    gamestate.setup(map_data)\n                    bot.do_setup()\n                    gamestate.finish_turn()\n                    map_data = ''\n                elif current_line.lower() == 'go':\n                    gamestate.update(map_data)\n                    # call the do_turn method of the class passed in\n                    bot.do_turn()\n                    gamestate.finish_turn()\n                    map_data = ''\n                else:\n                    map_data += current_line + '\\n'\n            except EOFError:\n                break\n            except KeyboardInterrupt:\n                raise\n            except:\n                # don't raise error or return so that bot attempts to stay alive\n                traceback.print_exc(file=sys.stderr)\n                sys.stderr.flush()\n                \nif __name__ == '__main__':\n    # psyco will speed up python a little, but is not needed\n    try:\n        import psyco\n        psyco.full()\n    except ImportError:\n        pass\n    \n    try:\n        # if run is passed a class with a do_turn method, it will do the work\n        # this is not needed, in which case you will need to write your own\n        # parsing function and your own game state class\n        MyBot.run()\n    except KeyboardInterrupt:\n        print('ctrl-c, leaving ...')\n","sub_path":"archive/v9.2/MyBot.py","file_name":"MyBot.py","file_ext":"py","file_size_in_byte":13577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"171230545","text":"from selenium import webdriver\r\nfrom time import sleep\r\n\r\nclass ChromeAuto:\r\n    def __init__(self):\r\n        self.caminho_driver = r'Pyselenium/chromedriver.exe'\r\n        self.opcoes = webdriver.ChromeOptions()\r\n        self.opcoes.add_argument('user-data-dir=Perfil')\r\n        self.chrome = webdriver.Chrome(\r\n            self.caminho_driver,\r\n            options=self.opcoes\r\n        )\r\n    \r\n    def acessa(self, site):\r\n        self.chrome.get(site)\r\n\r\n    def reproduzir_som(self):\r\n        try:\r\n            btn_tocar = self.chrome.find_element_by_css_selector(\r\n                '#movie_player > div.ytp-chrome-bottom > div.ytp-chrome-controls > div.ytp-left-controls > button')\r\n            btn_tocar.click()\r\n        except Exception as e:\r\n            print(\"Erro ao reproduzir música:\", e)\r\n\r\n    def sair(self):\r\n        self.chrome.quit()\r\n\r\nif __name__ == '__main__':\r\n    chrome = ChromeAuto()\r\n    chrome.acessa('https://www.youtube.com/watch?v=7QU1nvuxaMA&list=RD7QU1nvuxaMA&start_radio=1')\r\n    chrome.reproduzir_som()\r\n\r\n\r\n    ","sub_path":"Modulos/pyselenium/playlist.py","file_name":"playlist.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"176102353","text":"from __future__ import absolute_import, print_function\n\nimport os\nimport pathlib\nimport shutil\nimport sys\nimport tempfile\n\nimport pytest\nfrom flask import Flask\nfrom flask_taxonomies import FlaskTaxonomies\nfrom flask_taxonomies.views import blueprint as taxonomies_blueprint\nfrom invenio_db import InvenioDB\nfrom invenio_db import db as db_\nfrom invenio_indexer import InvenioIndexer\nfrom invenio_jsonschemas import InvenioJSONSchemas\nfrom invenio_pidstore.models import PersistentIdentifier, Redirect\nfrom invenio_records import InvenioRecords, Record\nfrom invenio_records.models import RecordMetadata\nfrom invenio_records_draft.cli import make_schemas\nfrom invenio_records_draft.ext import InvenioRecordsDraft\nfrom invenio_search import InvenioSearch\nfrom lxml import etree\nfrom sqlalchemy_utils import create_database, database_exists\n\nfrom flask_taxonomies_es import FlaskTaxonomiesES\nfrom invenio_nusl_theses import InvenioNUSLTheses\nfrom invenio_oarepo_oai_pmh_harvester.models import OAIProvider, OAIRecord, OAISync\nfrom invenio_oarepo_oai_pmh_harvester.synchronization import OAISynchronizer\n\n\n@pytest.yield_fixture()\ndef app():\n    instance_path = tempfile.mkdtemp()\n    app = Flask('testapp', instance_path=instance_path)\n\n    app.config.update(\n        JSONSCHEMAS_HOST=\"nusl.cz\",\n        SQLALCHEMY_TRACK_MODIFICATIONS=True,\n        SQLALCHEMY_DATABASE_URI=os.environ.get(\n            'SQLALCHEMY_DATABASE_URI',\n            'postgresql+psycopg2://{user}:{pw}@{url}/{db}'.format(user=\"oarepo\", pw=\"oarepo\",\n                                                                  url=\"127.0.0.1\",\n                                                                  db=\"oarepo\")),\n        SERVER_NAME='127.0.0.1:5000',\n    )\n    InvenioRecordsDraft(app)\n    InvenioJSONSchemas(app)\n    InvenioRecords(app)\n    InvenioSearch(app)\n    InvenioIndexer(app)\n    InvenioDB(app)\n    FlaskTaxonomies(app)\n    FlaskTaxonomiesES(app)\n    InvenioNUSLTheses(app)\n    with app.app_context():\n        app.register_blueprint(taxonomies_blueprint)\n        yield app\n\n    shutil.rmtree(instance_path)\n\n\n@pytest.yield_fixture()\ndef db(app):\n    \"\"\"Database fixture.\"\"\"\n    if not database_exists(str(db_.engine.url)):\n        create_database(str(db_.engine.url))\n    yield db_\n\n    # Explicitly close DB connection\n    db_.session.close()\n\n\n@pytest.yield_fixture()\ndef uk_db(app):\n    \"\"\"Database fixture.\"\"\"\n    app.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql+psycopg2://{user}:{pw}@{url}/{db}'.format(\n        user=\"oarepo\", pw=\"oarepo\", url=\"127.0.0.1\", db=\"test_uk\")\n    if not database_exists(str(db_.engine.url)):\n        create_database(str(db_.engine.url))\n    Redirect.__table__.drop(db_.engine)\n    PersistentIdentifier.__table__.drop(db_.engine)\n    OAIRecord.__table__.drop(db_.engine)\n    RecordMetadata.__table__.drop(db_.engine)\n    OAISync.__table__.drop(db_.engine)\n    db_.create_all()\n    yield db_\n\n    # Explicitly close DB connection\n    db_.session.close()\n\n\n@pytest.fixture\ndef test_db(app):\n    \"\"\"Create database for the tests.\"\"\"\n    app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/test.db'\n    if not database_exists(str(db_.engine.url)):\n        create_database(db_.engine.url)\n    db_.drop_all()\n    db_.create_all()\n\n    yield db_\n\n    # Explicitly close DB connection\n    db_.session.close()\n    db_.drop_all()\n\n\n@pytest.fixture()\ndef synchronizer_instance(app, test_db):\n    provider = OAIProvider(\n        code=\"uk\",\n        oai_endpoint=\"https://dspace.cuni.cz/oai/nusl\",\n        set_=\"nusl_set\",\n        metadata_prefix=\"xoai\"\n    )\n    db_.session.add(provider)\n    db_.session.commit()\n    return OAISynchronizer(provider)\n\n\n@pytest.fixture()\ndef record_xml():\n    dir_ = pathlib.Path(__file__).parent.absolute()\n    path = dir_ / \"data\" / \"test_xml.xml\"\n    with open(str(path), \"r\") as f:\n        tree = etree.parse(f)\n        return tree.getroot()\n\n\n@pytest.fixture()\ndef sample_record(app, test_db):\n    record = Record.create(\n        {\n            \"id\": \"1\",\n            \"identifier\": [\n                {\n                    \"value\": \"oai:server:id\",\n                    \"type\": \"originalOAI\"\n                }\n            ]\n        }\n    )\n    db_.session.commit()\n    return record\n\n\n@pytest.fixture()\ndef migrate_provider(app, test_db):\n    provider = OAIProvider(\n        code=\"nusl\",\n        description=\"Migration from old NUSL\",\n        oai_endpoint=\"https://invenio.nusl.cz/oai2d/\"\n    )\n    db_.session.add(provider)\n    db_.session.commit()\n    return provider\n\n@pytest.fixture\ndef schemas(app):\n    runner = app.test_cli_runner()\n    result = runner.invoke(make_schemas)\n    if result.exit_code:\n        print(result.output, file=sys.stderr)\n    assert result.exit_code == 0\n\n    # trigger registration of new schemas, normally performed\n    # via app_loaded signal that is not emitted in tests\n    with app.app_context():\n        app.extensions['invenio-records-draft']._register_draft_schemas(app)\n        app.extensions['invenio-records-draft']._register_draft_mappings(app)\n\n    return {\n        'published': 'https://localhost:5000/schemas/records/record-v1.0.0.json',\n        'draft': 'https://localhost:5000/schemas/draft/records/record-v1.0.0.json',\n    }\n\n# @pytest.fixture\n# def root_taxonomy(db):\n#     \"\"\"Create root taxonomy element.\"\"\"\n#     from flask_taxonomies.models import Taxonomy\n#     root = Taxonomy.create_taxonomy(code=\"root\")\n#     db.session.add(root)\n#     db.session.commit()\n#     return root\n#\n#\n# @pytest.fixture\n# def sample_term(db, root_taxonomy):\n#     \"\"\"Taxonomy Term fixture.\"\"\"\n#     extra_data = {\n#         \"url\": \"http://www.vscht.cz/\",\n#         \"title\": [\n#             {\n#                 \"lang\": \"cze\",\n#                 \"value\": \"Vysoká škola chemicko-technologická v Praze\"\n#             }\n#         ],\n#         \"address\": \"Technická 5, 166 28 Praha 6\",\n#         \"lib_url\": \"\"\n#     }\n#     term = root_taxonomy.create_term(slug=\"1\", extra_data=extra_data)\n#     db.session.add(term)\n#     db.session.commit()\n#     return term\n#\n#\n# @pytest.fixture\n# def child_term(db, root_taxonomy, sample_term):\n#     extra_data = {\n#         \"title\": [\n#             {\n#                 \"lang\": \"cze\",\n#                 \"value\": \"Dítě\"\n#             }\n#         ]\n#     }\n#     term = sample_term.create_term(slug=\"3\", extra_data=extra_data)\n#     db.session.add(term)\n#     db.session.commit()\n#     return term\n#\n#\n# @pytest.fixture()\n# def csv_file():\n#     fd, path = tempfile.mkstemp()\n#     with os.fdopen(fd, 'w') as tmp:\n#         writer = csv.writer(tmp)\n#         writer.writerow([\"sloupec1\", \"sloupec2\", \"sloupec3\"])\n#         for _ in range(25):\n#             writer.writerow([\"bla\", \"blah\", \"ble\"])\n#     yield path\n#     os.remove(path)\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":6747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"320222989","text":"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n\nimport tornado.web\n\n\nclass IndexHandler(tornado.web.RequestHandler):\n    def get(self):\n        greeting = self.get_argument('greeting', 'Hello')\n        self.write('%s, welcome to Mellcap\\'s Site!' % greeting)\n","sub_path":"practice_tornado/handlers/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"600130214","text":"'''\nCreated on Jan 1, 2002\n\n@author: douglas\n'''\nfrom simulation_models.Simulation import Simulator\nfrom PyQt4 import QtCore, QtGui\nfrom simulation_views.report_dialog_view import ReportWidget \n\nclass SimulationLogicPresenter(object):\n\n    def __init__(self,manager):\n        self.graph=None\n        self.manager=manager\n        self.report_widget=ReportWidget()\n    \n    def set_graph(self,graph):\n        self.graph=graph\n        self.add_global_attr()\n        \n        self.graph.initialize_simulation_graph()\n        \n    def add_global_attr(self):\n        pass\n    \n    def begin_simulation(self):\n        \"\"\"\n        @todo: Poner algo para algo en la ui que recoja este dato\n        \"\"\"\n        \n        #recojo la pareja de atributos globales\n        \n        \n        \n        globals_attr_pair=self.manager.globalAttrPresenter.globals_attr_dict\n        \n        lgraph=self.manager.graph.create_logic(globals_attr_pair)\n        self.simulation=Simulator(lgraph,\n                             self.manager.selected_node,\n                             6\n                             ,self.manager.fuzzy_presenter.fuzzy_controller,10)\n        \n        report_inf=self.simulation.simula()\n        \n        content=self.build_message(report_inf)\n        \n        self.report_widget.body=content\n        self.report_widget.build_report()\n        self.report_widget.show()\n        \n        self.manager.reports_presenter.report_result(report_inf)\n        \n    def build_message(self,report_inf):\n        \"\"\"\n        Construye el reporte\n        \"\"\"\n        \n        content=\"\"\"\n                <table>\n                    %s\n                </table>\n        \"\"\"\n        \n        def row(key,value):\n            return (\"\"\"\n                   <tr>\n                   <td>%s</td>\n                   <td>%s</td>\n                   </tr>\n            \"\"\")%(key,value)\n        \n        inner_content=\"\"\n        inner_content+=row(\"Minimo %\",report_inf.GetGlobalDifusionPorcentsMin)\n        inner_content+=row(\"Maximo %\",report_inf.GetGlobalDifusionPorcentsMax)\n        inner_content+=row(\"AVG    %\",report_inf.GetGlobalDifusionPorcentsAVG)\n        \n        inner_content+=row(\"Maximo Receptor    \",str(report_inf.GetNodeReceptorMax))\n        inner_content+=row(\"Minimo Receptor    \",str(report_inf.GetNodeReceptorMin))\n        inner_content+=row(\"Maximo Posible Receptor    \",str(report_inf.GetNodePossibleRMax))\n        inner_content+=row(\"Minimo Posible Receptor    \",str(report_inf.GetNodePossibleRMin))\n        inner_content+=row(\"Maximo Emisor    \",str(report_inf.GetNodeEmisorMax))\n        inner_content+=row(\"Minimo Emisor    \",str(report_inf.GetNodeEmisorMin))\n        inner_content+=row(\"Maximo Posible Emisor    \",str(report_inf.GetNodePossibleEMax))\n        inner_content+=row(\"Minimo Posible Emisor    \",str(report_inf.GetNodePossibleEMin))\n        \n        inner_content+=row(\"Arista mas transitada    \",str(report_inf.GetAristEmisorMax))\n        inner_content+=row(\"Arista menos transitada    \",str(report_inf.GetAristEmisorMin))\n        inner_content+=row(\"Posible Arista menos transitada    \",str(report_inf.GetAristPossibleEMin))\n        inner_content+=row(\"Posible Arista mas transitada    \",str(report_inf.GetAristPossibleEMax))\n        \n        content=content%inner_content\n        \n        return content\n\n        \n\nclass NodeSimulationDialog(QtGui.QDialog):\n    \"\"\"\n    @summary: En este dialogo se entra la informacion necesaria \n    para realizar una secuencia de iteraciones\n    \"\"\"\n    def __init__(self,parent=None):        \n        super(NodeSimulationDialog, self).__init__(parent)\n        self.setupUi()\n        \n\n        \n    def setupUi(self):      \n        self.grid=QtGui.QGridLayout()\n        self.setLayout(self.grid)\n        self.labelSim=QtGui.QLabel()\n        self.labelSim.setText(\"Cantiad de simulaciones:\")\n        \n        self.simulationCount=QtGui.QSpinBox()\n        self.simulationCount.setMaximum(3000)\n        self.simulationCount.setMinimum(0)\n        #self.loadFuzzyFileBtn=QtGui.QPushButton()\n        \n        \n        self.buttonBox = QtGui.QDialogButtonBox(QtGui.QDialogButtonBox.Ok|QtGui.QDialogButtonBox.Cancel)\n        \n        #\n        self.grid.addWidget(self.labelSim,0,0,1,1)\n        self.grid.addWidget(self.simulationCount,0,1,1,1)\n        self.grid.addWidget(self.loadFuzzyFileBtn,1,0,1,1)\n        self.grid.addWidget(self.buttonBox,3,0,1,2)\n        \n        \n\n        self.setupSignals()\n\n        \n    \n\n    \n    \n    def setupSignals(self):\n        from PyQt4.QtCore import SIGNAL,SLOT        \n#        self._optionChange=lambda :self.optionChange(False)\n#        self.options.currentIndexChanged.connect(self._optionChange)\n#        self.editBtn.pressed.connect(self.optionChange)\n        self.connect(self.buttonBox, SIGNAL(\"accepted()\"),self.custom_validation)\n        self.connect(self.buttonBox, SIGNAL(\"rejected()\"),self, SLOT(\"reject()\"))\n  \n    \n\n            \n        \n    def custom_validation(self):\n#        if self.selectedDistFunc is None:\n#            QtGui.QMessageBox.question(self,\n#                            \"Error!.\",\n#                            \"Debe especifiacarle una funcion de distribucion al atributo\",\n#                            QtGui.QMessageBox.Ok)\n#            return\n#        if str(self.name.text())==\"\":\n#            QtGui.QMessageBox.question(self,\n#                            \"Error!.\",\n#                            \"El nombre del atributo no debe ser vacio\",\n#                            QtGui.QMessageBox.Ok)\n#            return\n        \n        if self.attrWidget.custom_validation():self.accept()\n    \n    @staticmethod\n    def get_attribute(parent=None,node_attr=None):\n        pass\n    \n#        if dialog.exec_():\n#            return True,dialog.attrWidget.get_attribute()\n#        return False,None\n        \n        \n        \n    \n    \n        \n    \n","sub_path":"src/simulation_presenters/simulation_logic_presenter.py","file_name":"simulation_logic_presenter.py","file_ext":"py","file_size_in_byte":5859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"652255178","text":"from multiprocessing import Process\nfrom sseclient import SSEClient\nimport requests\nimport datetime\nimport json\nfrom test_config import URL, POST, GET\n\ndef info(title):\n    import os\n    print(title)\n    print('module name:', __name__)\n    print('parent process:', os.getppid())\n    print('process id:', os.getpid())\n\n\ndef clip_uploader():\n    info('clip')\n    print('Clip uploader')\n\n    s = requests.Session()\n    res = s.post(URL + '/login', {'username': 'r3', 'password': 'r3'})\n    print(res.text)\n\n    messages = SSEClient(URL + '/stream?channel=clip')\n    for msg in messages:\n        print('clip msg', msg.data)\n        with open('test.mov', 'rb') as f:\n            res = POST(s, '/upload_blackbox_clip', files={'file': f})\n        print(res.text)\n\n    res = s.get(URL+'/logout')\n    print(res.text)\n    print('Clip END')\n\ndef log_uploader():\n    info('log')\n    print('Log uploader')\n    s = requests.Session()\n    res = s.post(URL + '/login', {'username': 'r3', 'password': 'r3'})\n    print(res.text)\n\n    messages = SSEClient(URL + '/stream?channel=log')\n    for msg in messages:\n        print('log msg', msg.data)\n        data = json.loads(msg.data)\n        with open('mylog.tgz', 'rb') as f:\n            res = POST(s, '/upload_event_data', files={'file': f, 'evid':(data['message'],'')})\n        print(res.text)\n\n    res = s.get(URL+'/logout')\n    print(res.text)\n    print('Log END')\n\n\nif __name__ == '__main__':\n    p1 = Process(target=clip_uploader)\n    p2 = Process(target=log_uploader)\n    p1.start()\n    p2.start()\n    p1.join()\n    p2.join()\n    #clip_uploader()\n    #log_uploader()","sub_path":"robot/uploader.py","file_name":"uploader.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"337518945","text":"from functools import lru_cache\n\nfrom six import iteritems\n\n\nclass Server(object):\n\n    def __init__(self, url, variables=None):\n        self.url = url\n        self.variables = variables and dict(variables) or {}\n\n    @property\n    def default_url(self):\n        return self.get_url()\n\n    @property\n    def default_variables(self):\n        defaults = {}\n        for name, variable in iteritems(self.variables):\n            defaults[name] = variable.default\n        return defaults\n\n    def get_url(self, **variables):\n        if not variables:\n            variables = self.default_variables\n        return self.url.format(**variables)\n\n\nclass ServerVariable(object):\n\n    def __init__(self, name, default, enum=None):\n        self.name = name\n        self.default = default\n        self.enum = enum and list(enum) or []\n\n\nclass ServersGenerator(object):\n\n    def __init__(self, dereferencer):\n        self.dereferencer = dereferencer\n\n    def generate(self, servers_spec):\n        servers_deref = self.dereferencer.dereference(servers_spec)\n        if not servers_deref:\n            yield Server('/')\n            return\n        for server_spec in servers_deref:\n            url = server_spec['url']\n            variables_spec = server_spec.get('variables', {})\n\n            variables = None\n            if variables_spec:\n                variables = self.variables_generator.generate(variables_spec)\n\n            yield Server(url, variables=variables)\n\n    @property\n    @lru_cache()\n    def variables_generator(self):\n        return ServerVariablesGenerator(self.dereferencer)\n\n\nclass ServerVariablesGenerator(object):\n\n    def __init__(self, dereferencer):\n        self.dereferencer = dereferencer\n\n    def generate(self, variables_spec):\n        variables_deref = self.dereferencer.dereference(variables_spec)\n\n        for variable_name, variable_spec in iteritems(variables_deref):\n            default = variable_spec['default']\n            enum = variable_spec.get('enum')\n\n            variable = ServerVariable(variable_name, default, enum=enum)\n            yield variable_name, variable\n","sub_path":"openapi_core/servers.py","file_name":"servers.py","file_ext":"py","file_size_in_byte":2095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"99557715","text":"import cv2\nimport numpy as np\n\nimport argparse\nfrom pathlib import Path\n\nimport torch\nfrom torch.nn import functional as F\n\n\nNAMES = ['benign','malignant']\nMAX_VALUES_BY_DTYPE = {\n    np.dtype(\"uint8\"): 255,\n    np.dtype(\"uint16\"): 65535,\n    np.dtype(\"uint32\"): 4294967295,\n    np.dtype(\"float32\"): 1.0,\n}\n\n\ndef to_float(image,max_value=None):\n    if max_value is None:\n        max_value = MAX_VALUES_BY_DTYPE[image.dtype]\n    return image.astype('float32') / max_value\n\n\ndef load_image(image_path,network_dim:list):\n    image = cv2.imread(image_path,-1)\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n    image = to_float(image)\n    image = cv2.resize(image,tuple(network_dim[::-1]))\n\n    #to tensor\n    image = image.transpose((2,0,1))\n    tensor_image = torch.from_numpy(image).float()\n    return tensor_image\n\n@torch.no_grad()\ndef classify(args):\n    \n    if args.device == 'cuda':\n        args.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n    device = torch.device(args.device)\n\n    tensor_image = load_image(args.image_path,args.network_dim)\n    tensor_image = torch.unsqueeze(tensor_image,0)\n    tensor_image = tensor_image.to(device)\n    \n    pth = torch.load(args.model_path)\n    model = pth['model']\n    model.to(device)\n    model.eval()\n\n    outputs = model(tensor_image)        \n    _,label = torch.max(outputs[0],dim=0)\n    label = label.item()\n    probs = F.softmax(outputs[0],dim=0)\n    confidence = probs[label].item()\n    names = NAMES[label]\n\n    print(f'[INFO] Input image: {Path(args.image_path).name}')\n    print(f'[INFO] Classified by the model with label {label} as {names} with confidence of {(confidence*100):.2f} %')\n\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='inference script for image classification with pytorch')\n    parser.add_argument('--model-path',type=str,help='path to model checkpoint',required=True)\n    parser.add_argument('--image-path',type=str,help='path to image',required=True)\n    parser.add_argument('--network-dim',type=int,nargs=2,metavar=('height','width'),help='network dimension',required=True)\n    parser.add_argument('--device',type=str,default='cuda',help='device to run the inference')\n\n\n    args = parser.parse_args()\n    classify(args)\n","sub_path":"inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":2246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"334306958","text":"import numpy as np\nimport tensorflow as tf\nfrom baselines.common.distributions import make_pdtype\nfrom tensorflow.python.ops.parallel_for.gradients import batch_jacobian\n\nfrom attention.RelationalBlock import RelationalBlock\n\nimport utils\n\nsess = tf.get_default_session\n\nclass Policy(object):\n    def __init__(self, scope, ob_space, ac_space, ob_mean, ob_std, \n                 perception, feat_spec, policy_spec, \n                 activation, layernormalize, batchnormalize, \n                 add_noise, keep_noise, noise_std, transfer_load,\n                 num_layers, keep_dim, transfer_dim,\n                 vf_coef, coinrun):\n        \n        # warnings\n        # i do not want to accidentally pass layernormalize and batchnormalize\n        # for coinrun\n        if layernormalize:\n            print(\"Warning: policy is operating on top of layer-normed features.\")\n            raise NotImplementedError()\n\n        if batchnormalize:\n            print(\"Warning: policy is operating on top of batch-normed features.\")\n            raise NotImplementedError()\n\n        self.transfer_load = transfer_load\n        self.transfer_dim = transfer_dim\n\n        self.num_layers = num_layers\n        self.keep_dim = keep_dim\n\n        self.coinrun = coinrun\n        \n        self.ob_mean = ob_mean\n        self.ob_std = ob_std\n\n        self.add_noise = add_noise\n        self.keep_noise = keep_noise\n        self.noise_std = noise_std\n\n        self.layernormalize = layernormalize\n        self.batchnormalize = batchnormalize\n        \n        self.vf_coef = vf_coef\n        \n        input_shape = ob_space.shape\n        \n        # perception module\n        self.perception = perception\n\n        # feature dimensions (HARD-CODED NOT GOOD)\n        self.feat_dim = 512\n\n        # policy module\n        self.feat_spec = feat_spec\n        self.policy_spec = policy_spec\n\n        self.activation = activation\n\n        with tf.variable_scope(scope):\n            self.ob_space = ob_space\n            self.ac_space = ac_space\n            self.ac_pdtype = make_pdtype(ac_space)\n\n            # placeholders\n            dtype = ob_space.dtype\n            if dtype == np.int8:\n                dtype = np.uint8\n            print('policy.py, class Policy, def __init__, dtype: {}'.format(dtype))\n            \n            # taken from baselines.common.input import observation_input\n            self.ph_ob = tf.to_float(tf.placeholder(dtype=ob_space.dtype,\n                                                    shape=(None,) + ob_space.shape,\n                                                    name='ob'))\n\n            self.ph_ac = self.ac_pdtype.sample_placeholder([None], name='ac')\n            self.pd = self.vpred = None\n            self.scope = scope\n            self.pdparamsize = self.ac_pdtype.param_shape()[0]\n\n            with tf.variable_scope(self.scope + '_representation', reuse=False):\n                self.unflattened_out = self.get_out(self.ph_ob, reuse=False)    \n                out = utils.flatten(self.unflattened_out)\n                print('policy.py, class Policy, def __init__, self.out.shape: {}'.format(out.shape))\n                # we get features (feat_dim 512)\n                self.features = self.get_features(out, reuse=False)\n\n            pdparam, self.vpred = self.get_policy(self.features, reuse=False)\n            self.pd = pd =self.ac_pdtype.pdfromflat(pdparam)\n            self.a_samp = pd.sample()\n            self.entropy = pd.entropy()\n            self.nlp_samp = pd.neglogp(self.a_samp)\n            self.logits = pdparam\n\n            print('policy.py, class Policy, def __init__, pdparam.shape: {}, pdparam.dtype: {}'.format(pdparam.shape, pdparam.dtype))\n            print('policy.py, class Policy, def __init__, self.vpred: {}'.format(self.vpred.shape))\n            print('policy.py, class Policy, def __init__, self.a_samp: {}'.format(self.a_samp.shape))\n            print('policy.py, class Policy, def __init__, self.entropy.shape: {}'.format(self.entropy.shape))\n            print('policy.py, class Policy, def __init__, self.nlp_samp.shape: {}'.format(self.nlp_samp.shape))\n            print('policy.py, class Policy, def __init__, self.logits.shape: {}'.format(self.logits.shape))\n\n    def get_out(self, x, reuse):\n        with tf.variable_scope(self.scope + '_out', reuse=reuse):\n            x = (tf.to_float(x) - self.ob_mean) / self.ob_std\n            if self.perception == 'nature_cnn':\n                x = utils.nature_cnn(x, activation=self.activation, batchnormalize=self.batchnormalize,\n                                     init_scale=np.sqrt(2), coinrun=self.coinrun)\n            else:\n                raise NotImplementedError('only nature_cnn is allowed')\n\n        # nature_cnn returns unflattened outputs\n        return x\n\n    def get_features(self, x, reuse):\n        with tf.variable_scope(self.scope + '_features', reuse=reuse):\n            if self.feat_spec == 'feat_v0':\n                x = utils.feat_v0(x, feat_dim=self.feat_dim, activation=self.activation)\n                self.random_idx = None\n\n            elif self.feat_spec == 'feat_rss_v0':\n                if self.policy_spec == 'full_sparse':\n                    return x\n                else:\n                    raise NotImplementedError('only use full sparse...')\n\n            else:\n                raise NotImplementedError('not implemented method in get_features...')\n\n        return x\n\n    def get_policy(self, x, reuse):\n        with tf.variable_scope(self.scope + '_policy', reuse=reuse):\n            if self.policy_spec == 'cr_fc_v0':\n                # we might need to ignore the extra 512 in the future\n                # for entity-based policy\n                x = utils.cr_fc_v0(x, self.pdparamsize)\n            elif self.policy_spec == 'cr_fc_v1':\n                # we might need to ignore the extra 512 in the future\n                # for entity-based policy\n                x = utils.cr_fc_v1(x, self.pdparamsize)\n            elif self.policy_spec == 'ls_c_v0':\n                x = utils.ls_c_v0(x, ncat=self.pdparamsize, activation=self.activation)\n            elif self.policy_spec == 'ls_c_v1':\n                x = utils.ls_c_v1(x, ncat=self.pdparamsize, activation=self.activation)\n            elif self.policy_spec == 'ls_c_hh':\n                x = utils.ls_c_hh(x, ncat=self.pdparamsize, activation=self.activation)\n            elif self.policy_spec == 'full_sparse':\n                print('''\n\n                    adding random sparse noise: {}\n\n                    '''.format(self.add_noise))\n\n                # the problem with the sparsity is that it creates non-trainable paths because of the restrictions of the\n                # information flow; our solution for this was to incorporate skipped connections. in the current, full-sparsity\n                # tests, i am again restricting the flow. i need to fix this arbitrary restriction of the information flow.\n                x, random_idx, full_dim = utils.feat_rss_v0(out=x, feat_dim=self.feat_dim, activation=self.activation, \n                                                            add_noise=self.add_noise, keep_dim=self.keep_dim, \n                                                            act_dim=self.ac_space.n,\n                                                            keep_noise=self.keep_noise, noise_std=self.noise_std,\n                                                            num_layers=self.num_layers,\n                                                            transfer_load=self.transfer_load, transfer_dim=self.transfer_dim)\n\n                # we will use those idx to mask the gradients of not-selected indices as well as \n                # inject some noise\n\n                self.random_idx = random_idx['train_random_idx']\n                self.full_dim = full_dim['train_full_dim']\n                \n                if self.transfer_load:\n                    self.random_idx = random_idx['trans_random_idx']\n                    self.full_dim = full_dim['trans_full_dim']\n\n                self.random_idx_dict = random_idx\n\n                return x[0], tf.squeeze(x[1])\n            else:\n                raise NotImplementedError('only some types policies are allowed')\n\n        pdparam, vpred = x[0], x[1]\n        return pdparam, vpred\n\n    # return actions, vpreds and negative log likelihoods\n    def step(self, ob):\n        a, vpred, nlp = sess().run([self.a_samp, self.vpred, self.nlp_samp],\n                                  feed_dict={self.ph_ob: ob})\n        return a, vpred, nlp","sub_path":"policy.py","file_name":"policy.py","file_ext":"py","file_size_in_byte":8499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"516818288","text":"# Start Flask\n#   cd to backend directory\n#   1. env\\Scripts\\activate\n#   2. flask run (debug mode will be off)\n#      - py main.py (debug on)\n\n# Stop Flask\n#   1. ctrl+C\n#   2. deactivate\nfrom flask import Flask, request, session, jsonify\nfrom html.parser import HTMLParser\nfrom flask_cors import CORS\nimport re\n\n\napp = Flask(\"__name__\")\nCORS(app)\n\nclass MyHTMLParser(HTMLParser):\n  # RULE #1 - HTML Escape Before Inserting Untrusted Data into HTML Element Content\n  htmlEscape = {\"htmlEscape\":\"to escape special characters to prevent switching into execution context (some exceptions)\",\n                \"*\":\"* in place of function if exception for complex attributes\"}\n  htmlEncoding = [\"Escape the following characters with HTML entity encoding to prevent switching into any execution context\",\n                  \"& --> &amp;\", \n                  \"< --> &lt;\",\n                  \"> --> &gt;\", \n                  \"\\\" --> &quot;\", \n                  \"\\' --> &#x27;\", \n                  \"/ --> &#x2F;\",\n                  \"&apos; not recommended because its not in the HTML spec\"]\n  # RULE #2 - Attribute Escape Before Inserting Untrusted Data into HTML Common Attributes(html)\n  htmlEscExcept = [\"href\", \"src\", \"style\", \"events\"]\n  # Except for alphanumeric chars, escape all characters with ASCII values less than\n  ##  256 with the &#xHH; format to prevent switching out of the attribute.\n  # Unquoted attrs can be broken out of with many chars, including:\n  breakChars = [\"[space]\", \"%\", \"*\", \"+\", \",\", \"-\", \"/\", \";\", \"<\", \"=\", \">\", \"^\", \"|\"]\n  # Validator.IsValidURL(userURL, 255)\n  # if (isValidURL) { <a href = \"<%=encoder.encodeForHTMLAttribute(userURL)%>\" > link < /a >}\n\n  # specify doc charset: <meta charset=\"utf-8\">\n  charset = {\"charset\": \"(i.e. utf=8, ISO-8859-1) to prevent content interpretation vulnerabilities\"}\n  # RULE #3 - JavaScript Escape Before Inserting Untrusted Data into JavaScript Data Values\n  jsEscape = {\"jsEscape\": \"to escape special characters to prevent execution of malicious code\",\n              \"*\": \"* in place of function if not for script tag\"}\n  # Except for alphanumeric chars, escape all characters less than 256 with\n  ## the \\xHH format to prevent switching out of the data value into the script\n  ## context or into another attr. Do not use \\\" because the quote character may\n  ## be matched by the HTML attr parser which runs first\n  # breakChars the same\n  eventHandlers = [\"onabort\", # hidden list\n                   \"onafterprint\",\n                   \"onanimationend\",\n                   \"onanimationiteration\",\n                   \"onanimationstart\",\n                   \"onbeforeprint\",\n                   \"onbeforeunload\",\n                   \"onblur\",\n                   \"oncanplay\",\n                   \"oncanplaythrough\",\n                   \"onchange\",\n                   \"onclick\",\n                   \"oncontextmenu\",\n                   \"oncopy\",\n                   \"oncut\",\n                   \"ondblclick\",\n                   \"ondrag\",\n                   \"ondragend\",\n                   \"ondragenter\",\n                   \"ondragleave\",\n                   \"ondragover\",\n                   \"ondragstart\",\n                   \"ondrop\",\n                   \"ondurationchange\",\n                   \"onended\",\n                   \"onerror\",\n                   \"onfocus\",\n                   \"onfocusin\",\n                   \"onfocusout\",\n                   \"onfullscreenchange\",\n                   \"onfullscreenerror\",\n                   \"onhashchange\",\n                   \"oninput\",\n                   \"oninvalid\",\n                   \"onkeydown\",\n                   \"onkeypress\",\n                   \"onkeyup\",\n                   \"onload\",\n                   \"onloadeddata\",\n                   \"onloadedmetadata\",\n                   \"onloadstart\",\n                   \"onmessage\",\n                   \"onmousedown\",\n                   \"onmouseenter\",\n                   \"onmouseleave\",\n                   \"onmousemove\",\n                   \"onmouseover\",\n                   \"onmouseout\",\n                   \"onmouseup\",\n                   \"onmousewheel\",\n                   \"onoffline\",\n                   \"ononline\",\n                   \"onopen\",\n                   \"onpagehide\",\n                   \"onpageshow\",\n                   \"onpaste\",\n                   \"onpause\",\n                   \"onplay\",\n                   \"onplaying\",\n                   \"onpopstate\",\n                   \"onprogress\",\n                   \"onratechange\",\n                   \"onresize\",\n                   \"onreset\",\n                   \"onscroll\",\n                   \"onsearch\",\n                   \"onseeked\",\n                   \"onseeking\",\n                   \"onselect\",\n                   \"onshow\",\n                   \"onstalled\",\n                   \"onstorage\",\n                   \"onsubmit\",\n                   \"onsuspend\",\n                   \"ontimeupdate\",\n                   \"ontoggle\",\n                   \"ontouchcancel\",\n                   \"ontouchend\",\n                   \"ontouchmove\",\n                   \"ontouchstart\",\n                   \"ontransitionend\",\n                   \"onunload\",\n                   \"onvolumechange\",\n                   \"onwaiting\",\n                   \"onwheel\", ]\n  jsURIEscape = [\"encodeURIComponent(\", ]\n  # RULE #3.1 - HTML escape JSON values in an HTML context and read the data with JSON.parse\n  # Content-Type: application/json; charset=utf-8\n  HTTPRequests = {\"javascript\": \"XMLHttpRequest.getAllResponseHeaders()\",\n                  \"jquery-ajax\": \"$.getJSON method only retreives data in JSON format\",\n                  \"fetch\": \"set header: Content-Type : application/json\"}\n  ## htmlEscape json and parse with JSON.parse()\n  # RULE #4 - CSS Escape And Strictly Validate Before Inserting Untrusted Data into HTML Style Property Values\n  complexProps = [\"url\", \"behavior\", \"custom\"]\n  urlUntrust = [\"javascript:\", \"expression(\"]\n  cssEscape = \"CSS.escape(\"\n  # RULE #5 - URL Escape Before Inserting Untrusted Data into HTML URL Parameter Values\n  uriEscape = \"encodeURI(\"\n  # RULE #6 - Sanitize HTML Markup with a Library Designed for the Job\n  # HtmlSanitizer\n  # RULE #7 - Avoid JavaScript URL's\n  inputAttrs = {\"pattern\":\"to help with input validation.\",\n    \"max\":\"to limit length of value if expecting specific value range\", \n    \"form\":\"ensures the data is submitted for the correct form\"}\n  \n  def __init__(self):\n    HTMLParser.__init__(self)\n    self.data = []\n\n  def handle_starttag(self, tag, attrs):\n    # print(tag)\n    # if(attrs):\n    #   print(tag, \"attributes : \")\n    #   for attr in attrs:\n    #     print(\"\\tattribute :\", attr[0], \", value :\", attr[1])\n    attrNames = [a[0] for a in attrs]\n\n    # input validation\n    if(tag == \"input\"): # change to regex to ensure attribute is set for each input attr while typing***\n      print(attrs)\n      if [True for i, a in enumerate(attrs) if a[0] == \"type\" and a[1] == \"text\"]:\n        for attr in self.inputAttrs:\n          if not attr in attrNames:\n            fix = \"%s attribute needed at line %d: %s\" %(attr, self.getpos()[0], self.inputAttrs[attr])\n            print(fix)\n            self.data.append(fix)\n\n    # charset configuration\n    if(tag == \"meta\"):  # change to regex to ensure charset is set while typing***\n      fix=[]\n      print(fix)\n      if(attrs):\n        fix = [a[1] for a in attrs if a[0] == \"charset\"]\n      \n      if ( fix and fix[0] == None or \"utf-8\" not in fix): # check for possible charsets\n        print(fix)\n        fix = \"charset attribute needed at line %d: %s\" % (\n            self.getpos()[0], self.charset[\"charset\"])\n        self.data.append(fix)\n\n  def handle_startendtag(self, tag, attrs):\n      print(\"Encountered an end tag :\", tag)\n\n  def escapeHelper(self, data, vulnVars, vulnStmt, vulnStr, escList, exceptions):\n    untrust = False\n    fix = []\n    vulnFound = []\n    for vulnVariable in vulnVars:\n      reg = re.compile(\"var\\s+\"+vulnVariable +\"\\s*=\\s*\\w*\\(*\"+vulnStmt)\n      vulnerable = reg.search(data)\n      if(vulnerable):\n        untrust = True\n        vulnFound.append(vulnVariable)\n        if(vulnFound):\n          for es in escList:\n            reg = re.compile(\"var\\s+\"+vulnVariable+\"\\s*=\\s*\" +\n                             re.escape(es)+\"\\(\\s*\"+vulnStmt+\"\\)\")\n            vulnFixed = reg.search(data)\n            if vulnFixed:\n              untrust = False\n    if(vulnFound):\n      vulnFound = \", \".join(list(set(vulnFound))) + vulnStr + \" block at line %d,\" % (self.getpos()[0])\n      fix.append(vulnFound)\n    if (untrust):\n      for es in escList:\n        fix.append(\"---> %s needed for variable: %s\" % (\n            es, escList[es]))\n      if(exceptions):\n        attrExcept = \"    * Exceptions include: \" + \", \".join(exceptions)\n        fix.append(attrExcept)\n      \n      [fix.append(en) for en in self.htmlEncoding]\n      \n      fix = [\"\\n\".join(fix)]\n      self.data.extend(fix)\n\n  def checkVulnScript(self, data, check):\n    rule3ScriptCheck = \"var\\s+(\\w+)\\s*=\\s*\\w*document.createElement\\(\\s*[\\'|\\\"]script[\\'|\\\"]\\s*\\)\"\n    reg = re.compile(rule3ScriptCheck)\n    vulnScripts = reg.findall(data)\n    vulnVars = []\n    exceptions = []\n    if(vulnScripts):\n      rule3ScriptCheck = [\"\\W\"+ s + check for s in vulnScripts]\n      for s in rule3ScriptCheck:\n        reg = re.compile(s)\n        vulnVars.extend(reg.findall(data))\n    reg = re.compile(check)\n    exceptionsCheck = reg.findall(data)\n    if(exceptionsCheck):\n      exceptions = [v  for v in exceptionsCheck if v not in vulnVars]\n    \n    return vulnVars, exceptions\n\n  def checkVulnHTTP(self, data, vulnHTTP, vulnStr, getCheck, headerCheck):\n    getReqs = []\n    vuln = []\n    fix = []\n    for v in vulnHTTP:\n      reg = re.compile(getCheck)\n      req = reg.findall(data)\n      getReqs.extend(req)\n    if(getReqs):\n      getReqs = list(set(getReqs))\n      for r in getReqs:\n        for h in headerCheck:\n          reg = re.compile(r + h)\n          setCheck = reg.search(data)\n          if(setCheck):\n            vuln.append(r)\n            break\n      vuln = [r for r in getReqs if r not in vuln]\n      if(vuln):\n        print(vuln)\n        foundStr = \", \".join(list(set(vuln))) + vulnStr + \" block at line %d,\" % (self.getpos()[0])\n        fix.append(foundStr)\n        fix.append(\"Content-Type check/set needed for HTTP request object.\")\n        for h in self.HTTPRequests:\n          fix.append(\"---> %s: %s\" % (\n              h, self.HTTPRequests[h]))\n        fix.append(\"Check Headers for \\'Content-Type: application/json\\', HTML escape response data\")\n        print(fix)\n        fix = [\"\\n\".join(fix)]\n        self.data.extend(fix)\n\n      # headerChecks = []\n      # for r in getReqs:\n      #   reg = re.compile(v + headerCheck)\n      #   req = reg.findall(data)\n      #   headerChecks.extend(req)\n      # print(headerChecks)\n    \n\n  def handle_data(self, data):\n    # print(data)\n\n    # Never Do\n    \"<script > ...NEVER PUT UNTRUSTED DATA HERE... < /script >\"\n    \"<!--...NEVER PUT UNTRUSTED DATA HERE...-->\"\n    \"<div ...NEVER PUT UNTRUSTED DATA HERE...=test />\"\n    \"<NEVER PUT UNTRUSTED DATA HERE... href=\\\"/test\\\" />\"\n    \"<style >...NEVER PUT UNTRUSTED DATA HERE...</style >\"\n    \"<script >window.setInterval('...EVEN IF YOU ESCAPE UNTRUSTED DATA YOU ARE XSSED HERE...')</script >\"\n\n    # Rule 1: Inserting untrusted data into HTML element content\n    rule1Str = \" vulnerable to HTML element content injection:\"\n    rule1Check = \"document.getElementBy\\w+\\(\\\"\\S+\\\"\\).(?:innerHTML|text)\\s*=\\s*(.*?)(?:\\s|$|;)\"\n    escStmt = \"document.getElementBy\\S+\\.value\"\n    reg = re.compile(rule1Check)\n    vulnVars = reg.findall(data)\n    if(vulnVars):\n      self.escapeHelper(data, vulnVars, escStmt, rule1Str, self.htmlEscape, [])\n\n    # Rule 2: Inserting untrusted data into attr\n    rule2Str = \" vulnerable to attribute injection:\"\n    rule2Check = \"document.getElementBy\\w+\\(\\\"\\S+\\\"\\).setAttribute\\(\\S+,\\s?(\\w+)\\)(?:\\s|$|;)\"\n    reg = re.compile(rule2Check)\n    vulnVars = reg.findall(data)\n    if(vulnVars):\n      self.escapeHelper(data, vulnVars, escStmt, rule2Str, self.htmlEscape, self.htmlEscExcept)\n\n    # Rule 3: Inserting Untrusted Data into DYNAMIC JavaScript Data Values\n    rule3Str = \" vulnerable to dynamic javascript injection:\"\n    rule3Exc = \" may be vulnerable to dynamic javascript injection (if for script tag):\"\n\n    ## vulnerable quoted string\n    quotedStringCheck = r\"\\.innerHTML\\s*=\\s*\\\"alert\\(\\\\[\\'|\\\"]\\\"\\+(\\w+)\"\n    vulnVars, scriptExcept = self.checkVulnScript(data, quotedStringCheck)\n    if(vulnVars):\n      self.escapeHelper(data, vulnVars, escStmt, rule3Str, self.jsEscape, [])\n    if(scriptExcept):\n      self.escapeHelper(data, scriptExcept, escStmt, rule3Exc, self.jsEscape, [])\n\n    ## vulnerable quoted expr\n    quotedExprCheck = r\"\\.innerHTML\\s*=\\s*\\\"\\w+=\\\\[\\'|\\\"]\\\"\\+(\\w+)\"\n    vulnVars, scriptExcept = self.checkVulnScript(data, quotedExprCheck)\n    if(vulnVars):\n      self.escapeHelper(data, vulnVars, escStmt, rule3Str, self.jsEscape, [])\n    if(scriptExcept):\n      self.escapeHelper(data, scriptExcept, escStmt, rule3Exc, self.jsEscape, [])\n\n    ## vulnerable quoted event handler\n    vulnEvents = []\n    for event in self.eventHandlers:  \n      quotedEventCheck = r\"document.getElementBy\\w+\\S+\\.\"+event+r\"\\s*=\\s*\\\"\\w+=\\\\[\\'|\\\"]\\\"\\+(\\w+)\"\n      reg = re.compile(quotedEventCheck)\n      vuln = reg.findall(data)\n      if(vuln):\n        vulnEvents.extend(vuln)\n    if(vulnEvents):\n      self.escapeHelper(data, vulnEvents, escStmt, rule3Str, self.jsEscape, [])\n    \n    # Rule 3.1 HTML escape JSON values in HTML context: read data with JSON.parse (http response)\n    ## js http request\n    vulnHTTPStr = \" may be vulnerable to XSS\"\n    HTTPObjectCheck = \"const\\s+(\\w+)\\s*=\\s*new\\s+XMLHttpRequest\"\n    HTTPGetCheck = \"(\\w+)\\.open\\(\\\"GET\" # start string with http object\n    HTTPCheckHeaders = [\"\\.getAllResponseHeaders\", \n                        \"\\.responseType\\s*=\\s*\\'json\\'\",\n                        \"\\.setRequestHeader\\(\\s*\\'Content-Type'\\s*,\\s*\\'application/json\\'\"]  # start string with http object\n    reg = re.compile(HTTPObjectCheck)\n    vulnHTTP = reg.findall(data)\n    if(vulnHTTP):\n      self.checkVulnHTTP(data, vulnHTTP, vulnHTTPStr, HTTPGetCheck, HTTPCheckHeaders)\n\n  # def handle_endtag(self, tag):\n  #    print(tag)\n\n\n@app.route(\"/\")\ndef index(): \n  return \"<h1>Flask Running</h1>\"\n\n\n\n@app.route(\"/analyze\", methods=[\"POST\", \"GET\"])\ndef analyze():\n  parser = MyHTMLParser()\n  if request.method == \"POST\":\n    print(\"\\nRecieved POST Request\\n\")\n    content = request.get_json()\n\n    print(\"Data:\\n\" + content[\"data\"] + \"\\n\")\n    parser.feed(content[\"data\"])\n    if(parser.data):\n      session[\"data\"] = parser.data\n    else:\n      session[\"data\"] = [\"no vulnerabilities analyzed\"]\n\n    return jsonify({\"status\": \"Post Request Success\", \"data\": session[\"data\"]})\n\n  if request.method == \"GET\":\n    print(\"Recieved GET Request\")\n    print(session.get(\"data\"))\n\n    return jsonify({\"status\": \"GET Request Success\", \"data\": content})\n\n  return \"Analyzing...\"\n\n\nif __name__ == \"__main__\":\n    app.secret_key = 'super secret key'\n    # app.config['SESSION_TYPE'] = 'filesystem'\n    app.debug = True\n    app.run()\n\n\n\n# Installed:\n#   py -m pip install -U autopep8\n#   pip install html.parser \n#   pip install -U flask-cors\n","sub_path":"xvs_backend/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"466936840","text":"\n# Copyright 2017-present Open Networking Foundation\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\n# validate_onu received event:\n# {\n#     'status': 'activate',\n#     'serial_number': 'BRCM1234',\n#     'of_dpid': 'of:109299321'\n# }\n\nfrom xosapi.orm import register_convenience_wrapper\nfrom xosapi.convenience.serviceinstance import ORMWrapperServiceInstance\n\nimport logging as log\n\nclass ORMWrapperHippieOSSService(ORMWrapperServiceInstance):\n\n    def validate_onu(self, event):\n\n        log.info(\"validating ONU %s\" % event[\"serial_number\"], event=event)\n\n        try:\n            oss_si = self.stub.HippieOSSServiceInstance.objects.get(serial_number=event[\"serial_number\"])\n            oss_si.no_sync = False;\n            log.debug(\"Found existing HippieOSSServiceInstance\", si=oss_si)\n        except IndexError:\n            # create an HippieOSSServiceInstance, the validation will be triggered in the corresponding sync step\n            oss_si = self.stub.HippieOSSServiceInstance(\n                serial_number=event[\"serial_number\"],\n                of_dpid=event[\"of_dpid\"]\n            )\n            log.debug(\"Created new HippieOSSServiceInstance\", si=oss_si)\n        \n        oss_si.save(always_update_timestamp=True)\n\nregister_convenience_wrapper(\"HippieOSSService\", ORMWrapperHippieOSSService)\n","sub_path":"xos/synchronizer/models/convenience/hippieossservice.py","file_name":"hippieossservice.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"628266101","text":"import collections\n\n# Attempt to keep column ordering if objects happen to have ordered keys\nfield_map = collections.OrderedDict()\nrows = []\n\n\ndef subkeys(path, obj, row):\n    if isinstance(obj, dict):\n        for k in obj:\n            if isinstance(k, (str, unicode)):\n                subkeys(path + [k], obj[k], row)\n    elif len(path) > 0:\n        field = '.'.join(path)\n        field_map[field] = True\n        row[field] = obj\n\n\nfor obj in input:\n    row = {}\n    subkeys([], obj, row)\n    rows.append(row)\n\nfields = [key for key in field_map]\n\noutput = {'fields': fields, 'rows': rows}\n","sub_path":"packages/girder_worker/girder_worker/plugins/types/format/table/objectlist_to_rows.py","file_name":"objectlist_to_rows.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"581647730","text":"\"\"\" 使用網路爬蟲來抓取網頁上的圖片 \"\"\"\n\nimport requests # 用來發出get請求，並取得回應\nfrom bs4 import BeautifulSoup # 用來擷取網頁中的元素\nimport shutil # 用來轉換檔案物件\nimport os # 用來切換工作目錄\n\nos.chdir('pic')\n\n#url = 'https://ck101.com/thread-3960385-1-1.html' # 卡提諾論壇的網頁，會發生403 forbbiden錯誤\nurl = 'http://www.timliao.com/bbs/viewthread.php?tid=76848'\nresponse = requests.get(url) # 發出request並取得response\nsoup = BeautifulSoup(response.text, 'html.parser') # 利用BeautifulSoup套件來取得網頁內的元素\npic_num = 0 # 用來計算總共抓了幾張圖\n\n# 取得網頁的標題當作是資料夾的名稱\npics_title = soup.title.string\npics_title = pics_title.encode('latin-1', 'ignore').decode('big5')\nprint(pics_title)\n\n# 以資料夾名稱來建立圖片下載的資料夾，並切換到該資料夾底下\nos.mkdir(pics_title)\nos.chdir(pics_title)\n\nprint('圖片抓取中...')\n\nfor img in soup.select('img'): # 取得網頁內所有img元素\n    img_src = img['src']\n    img_src_no_parameter = img['src'].split('?')[0] # 將原來的scr去掉參數\n    img_name = img_src_no_parameter.split('/')[-1] # 取得圖片的名稱\n\n    # 假如img檔名為jpg，就將其抓取下來\n    if img_name[-3:] == 'jpg':\n        print(img_name)\n        img_stream = requests.get(img_src, stream = True)\n        with open(img_name, 'wb') as img_file:\n            shutil.copyfileobj(img_stream.raw, img_file) # 將網頁圖片的資料串流複製到本地檔案串流\n        pic_num += 1 # 計算目前已下載的圖片數\n        del img_stream # 將網頁圖片的資料串流清除\n\nprint('總共' + str(pic_num) + '張圖片，抓取完成!!')","sub_path":"picture_catcher.py","file_name":"picture_catcher.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"399196063","text":"import numpy as np\r\nfrom functools import reduce  # reduce的用法，忘记的可以查一下\r\n\r\n# 创建实验样本，并且将词条已经切分好了\r\ndef loadDataSet():\r\n\tpostingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],\t\t\t\t#切分的词条\r\n\t\t\t\t['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],\r\n\t\t\t\t['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],\r\n\t\t\t\t['stop', 'posting', 'stupid', 'worthless', 'garbage'],\r\n\t\t\t\t['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],\r\n\t\t\t\t['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]\r\n\tclassVec = [0,1,0,1,0,1]   \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#类别标签向量，1代表侮辱性词汇，0代表不是\r\n\treturn postingList,classVec\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#返回实验样本切分的\r\n\r\n\r\n# 将切分的实验样本词条整理成不重复的词条列表，也就是词汇表\r\ndef create_VocabList(dataset):\r\n    vocab_set = set([])\r\n    for document in dataset:\r\n        vocab_set = vocab_set | set(document) # 将所有的文档单词取并集，去掉重复出现的单词\r\n\r\n    return list(vocab_set)  # 最后转换成一个列表输出（词汇表）\r\n\r\n\r\n# 根据词汇表将每一个文档进行向量化，也就是将所有的单词对照词汇表进行0、1表示\r\ndef setWords2vector(vocab_list, inputlist):\r\n    returnvec = [0] * len(vocab_list) # 创建一个全为0的向量\r\n    for word in inputlist:\r\n        if word in vocab_list:  # 如果词条存在于词汇表中则词汇表中词条的位置置1，否则置0（因为本身就是0，所以不用操作）\r\n            returnvec[vocab_list.index(word)] = 1\r\n\r\n    return returnvec\r\n\r\n\r\n# 朴素贝叶斯的训练函数\r\ndef NB_train0(trainset, trainlabel):\r\n    numTraindoc = len(trainlabel)  # 训练样本的文档数量\r\n    numWords = len(trainset[0]) # 计算每篇文档的词条数\r\n    Pabusive = sum(trainlabel) / float(numTraindoc) # 文档属于侮辱类的概率\r\n    p0Wordnum = np.zeros(numWords) # 初始化词条出现的次数\r\n    p1Wordnum = np.zeros(numWords)\r\n    p0Wordsum = 0.0\r\n    p1Wordsum = 0.0\r\n\r\n    for i in range(numTraindoc):\r\n        if trainlabel[i] == 1:\r\n            p1Wordnum += trainset[i]\r\n            p1Wordsum += sum(trainset[i])\r\n\r\n        else:\r\n            p0Wordnum += trainset[i]\r\n            p0Wordsum += sum(trainset[i])\r\n\r\n    p1vec = p1Wordnum/p1Wordnum\r\n    p0vec = p0Wordnum/p0Wordsum\r\n\r\n    return p1vec, p0vec, Pabusive\r\n\r\n# 带有拉普拉斯平滑的朴素贝叶斯，对于条件概率取对数，防止下溢\r\ndef NB_train1(trainset, trainlabel):\r\n    numTraindoc = len(trainlabel)  # 训练样本的文档数量\r\n    numWords = len(trainset[0]) # 计算每篇文档的词条数\r\n    Pabusive = sum(trainlabel) / float(numTraindoc) # 文档属于侮辱类的概率\r\n    p0Wordnum = np.ones(numWords) # 初始化词条出现的次数为1\r\n    p1Wordnum = np.ones(numWords)\r\n    p0Wordsum = 2.0\r\n    p1Wordsum = 2.0\r\n\r\n    for i in range(numTraindoc):\r\n        if trainlabel[i] == 1:\r\n            p1Wordnum += trainset[i]\r\n            p1Wordsum += sum(trainset[i])\r\n\r\n        else:\r\n            p0Wordnum += trainset[i]\r\n            p0Wordsum += sum(trainset[i])\r\n\r\n    p1vec = np.log(p1Wordnum/p1Wordnum)\r\n    p0vec = np.log(p0Wordnum/p0Wordsum)\r\n\r\n    return p1vec, p0vec, Pabusive\r\n\r\n\r\ndef classify(test_simple, p0vec, p1vec, pabusive):\r\n    p1 = reduce(lambda x,y: x+y, test_simple*p1vec) * pabusive\r\n    p0 = reduce(lambda x,y: x+y, test_simple*p0vec) * (1-pabusive)\r\n    if p1 > p0:\r\n        return 1\r\n    else:\r\n        return 0\r\n\r\n\r\ndef trainNB0(trainMatrix,trainCategory):\r\n    numTrainDocs = len(trainMatrix)\t\t\t\t\t\t\t#计算训练的文档数目\r\n    numWords = len(trainMatrix[0])\t\t\t\t\t\t\t#计算每篇文档的词条数\r\n    pAbusive = sum(trainCategory)/float(numTrainDocs)\t\t#文档属于侮辱类的概率\r\n    p0Num = np.ones(numWords); p1Num = np.ones(numWords)\t#创建numpy.ones数组,词条出现数初始化为1，拉普拉斯平滑\r\n    p0Denom = 2.0; p1Denom = 2.0                        \t#分母初始化为2,拉普拉斯平滑\r\n    for i in range(numTrainDocs):\r\n        if trainCategory[i] == 1:\t\t\t\t\t\t\t#统计属于侮辱类的条件概率所需的数据，即P(w0|1),P(w1|1),P(w2|1)···\r\n            p1Num += trainMatrix[i]\r\n            p1Denom += sum(trainMatrix[i])\r\n        else:\t\t\t\t\t\t\t\t\t\t\t\t#统计属于非侮辱类的条件概率所需的数据，即P(w0|0),P(w1|0),P(w2|0)···\r\n            p0Num += trainMatrix[i]\r\n            p0Denom += sum(trainMatrix[i])\r\n    p1Vect = np.log(p1Num/p1Denom)\t\t\t\t\t\t\t#取对数，防止下溢出\r\n    p0Vect = np.log(p0Num/p0Denom)\r\n    return p0Vect,p1Vect,pAbusive\r\n\r\n\r\n# 测试代码\r\n\r\ndataset, class_label = loadDataSet() # 加载数据\r\nvocb_list = create_VocabList(dataset) # 创建词汇表\r\n\r\ntrainSet = []\r\nfor i in dataset:\r\n    trainSet.append(setWords2vector(vocb_list,i)) # 将训练数据向量化\r\n\r\ntestlist = ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'] # 测试数据\r\nvocabvec = np.array(setWords2vector(vocb_list,testlist)) # 测试数据向量化\r\np0vec, p1vec, p1 = trainNB0(trainSet, class_label) # 训练模型（本质是统计并计算条件概率）\r\n\r\nif classify(vocabvec, p0vec, p1vec, p1): # 预测分类 这里主要需要注意\r\n    print('属于侮辱类')\r\nelse:\r\n    print('属于非侮辱类')\r\n\r\n","sub_path":"NaiveBayes/Python/NBayes.py","file_name":"NBayes.py","file_ext":"py","file_size_in_byte":5344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"510886516","text":"import os\nimport random\n\ndef readLines(filename):\n\tinput = open(filename, 'rb')\n\tlines = []\n\tfor line in input:\n\t\tline = line.replace('\\n','')\n\t\tlines.append(line)\t\n\tinput.close()\n\treturn lines\n\ndef writeLines(output):\n\toutputFile = open('output.txt', 'w')\n\toutputFile.write(output)\n\toutputFile.close()\n\ndef getNontrivialDivisor(n):\n\tif n==2 or n==3 or n==5 or n==7: return -1\n\tif n < 2: return -1\n\tif n%2 == 0: return 2\n\tif n%3 == 0: return 3\n\tr = int(n**0.5)\n\tf = 5\n\tcount = 0\n\twhile f <= r:\n\t\tif n%f == 0: return f\n\t\tif n%(f+2) == 0: return f+2\n\t\tf +=6\n\t\tcount += 1\n\t\tif count > 500000:\n\t\t\treturn -1\n\treturn -1\n\ndef getBaseNumber(binary, base):\n\tresult = 0\n\tfor i in range(0,32):\n\t\tif binary & 0x01 == 1:\n\t\t\tresult += pow(base, i)\n\t\tbinary = binary >> 1\n\treturn result\n\ndef getJamDivisors(binary):\n\tlist = []\n\tfor base in range(2, 11):\n\t\tvalue = getBaseNumber(binary, base)\n\t\tdivisor = getNontrivialDivisor(value)\n\t\tif divisor == -1:\n\t\t\tbreak\n\t\tlist.append(divisor)\n\treturn list\n\ndef solveProblem(binarySize, total):\n\tbinary = 0x01 << (binarySize-1)\n\tbinary += 1\n\tresults = []\n\twhile True:\n\t\tdivisors = getJamDivisors(binary)\n\t\tif len(divisors) == 9:\n\t\t\tresult = '{0} '.format(getBaseNumber(binary, 10))\n\t\t\tfor divisor in divisors:\n\t\t\t\tresult += '{0} '.format(divisor)\n\t\t\tresults.append(result)\n\t\t\tprint('len(results) {0}'.format(len(results)))\n\t\t\tif len(results) == total:\n\t\t\t\tbreak\n\t\tbinary += 2\n\n\toutput = 'Case #1:\\n'\n\tfor result in results:\n\t\toutput += '{0}\\n'.format(result)\n\treturn output\n\noutput = solveProblem(32, 500)\nwriteLines(output)","sub_path":"codes/CodeJamCrawler/16_0_3/ljs0705/test3_2.py","file_name":"test3_2.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"348878328","text":"import requests\n\nurl = \"https://api.ciscospark.com/v1/rooms\"\n\nheaders = {\n    'authorization': \"Bearer MDAxMzIwYmMtOWRjOS00ZWQzLTk0ZmEtOTAxMmY4MmUzNDc4NzlhOWEwODktYWIx\",\n    'content-type': \"application/json\",\n    'cache-control': \"no-cache\",\n    'postman-token': \"7225a0de-dae6-3e9e-7fbd-56f52dd365ca\"\n    }\n\nresponse = requests.request(\"GET\", url, headers=headers)\n\njsonresponse = response.json()\n\nprint (jsonresponse['items'][1]['title'])\n","sub_path":"getsparkroom2.py","file_name":"getsparkroom2.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"498971618","text":"import sys\nimport re\nimport collections\n\n\ndef load_data(filepath):\n    with open(filepath, 'r') as work_file:\n        return work_file.read()\n\n\ndef get_words_from_text(work_text):\n    clean_text = re.sub('\\W', ' ', work_text)\n    text_lower = clean_text.lower()\n    words_list = text_lower.split()\n    return words_list\n\n\ndef get_most_frequent_words(words):\n    words_stat = collections.Counter(words)\n    num_words = 10\n    return words_stat.most_common(num_words)\n\n\ndef print_frequent_words(top_freq_words):\n    for index, (word, count) in enumerate(top_freq_words, start=1):\n        print('{0:>2}: \"{1}\" — {2}'.format(\n            index,\n            word,\n            count\n        ))\n\n\nif __name__ == '__main__':\n    try:\n        file_path = sys.argv[1]\n        text_data = load_data(file_path)\n    except IndexError:\n        print('Ошибка! Вы не указали путь к файлу.')\n        print('Сработает, если написать \"python lang_frequency.py <путь к файлу>\"')\n    except FileNotFoundError:\n        print('Ошибка! Система не нашла такой файл.')\n    except ValueError:\n        print('Ошибка. Файл должен быть в формате .txt')\n    else:\n        words_text_list = get_words_from_text(text_data)\n        frequent_words = get_most_frequent_words(words_text_list)\n        print_frequent_words(frequent_words)\n","sub_path":"lang_frequency.py","file_name":"lang_frequency.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"148999613","text":"from django.db.models import Q\nfrom core.models import User, Subscription, Reverb\nfrom .models import Nexus, MonthlyEngagement\n\nclass Feed():\n    def __init__(self, user):\n        self.user = User.objects.get(username=user)\n        self.nexus = self.get_content()\n    \n    def get_content(self):\n        user_reverbs = Reverb.objects.get(user=self.user).nexus.filter(is_published=True)\n        user_nexus = Nexus.objects.filter(~Q(pk__in=user_reverbs), user=self.user, is_published=True)\n        \n        following_reverbs = self.get_following_reverbs()\n        following_nexus = self.get_following_nexus()\n        \n        all_content = user_reverbs.union(user_nexus, following_reverbs, following_nexus).order_by('-date_created')\n        viewed = self.get_views(all_content)\n        explore = Nexus.objects.filter(\n            ~Q(pk__in=user_reverbs),\n            ~Q(pk__in=user_nexus),\n            ~Q(pk__in=following_reverbs),\n            ~Q(pk__in=following_nexus),\n            is_published=True,\n        ).order_by('?')[:100]\n        drafts = Nexus.objects.filter(user=self.user, is_published=False).order_by('-date_created')\n        \n        return {\n            'content': all_content,\n            'user_content': user_reverbs.union(user_nexus).order_by('-date_created'),\n            'viewed': viewed,\n            'reverbs': user_reverbs.union(following_reverbs).order_by('-date_created'),\n            'explore': explore,\n            'drafts': drafts,\n        }\n        \n    def get_following_reverbs(self):\n        following_reverbs = Nexus.objects.none()\n        for f in self.user.following.all():\n            reverbs = Reverb.objects.get(user=f).nexus.filter(is_published=True)\n            \n            if following_reverbs:\n                following_reverbs.union(reverbs)\n            else:\n                following_reverbs = reverbs\n        \n        return following_reverbs\n    \n    def get_following_nexus(self):\n        following_nexus = Nexus.objects.none()\n        for f in self.user.following.all():\n            nexus = Nexus.objects.filter(user=f, is_published=True)\n            \n            if following_nexus:\n                following_nexus.union(nexus)\n            else:\n                following_nexus = nexus\n        \n        return following_nexus\n    \n    def get_views(self, content):\n        subscription = Subscription.objects.get(user=self.user)\n        if subscription.amount >= 500:\n            monthly = MonthlyEngagement.objects.get_or_create(user=self.user)[0]\n            return content.filter(pk__in=monthly.views.all())\n                \n        return None\n            \n","sub_path":"nexus/feed.py","file_name":"feed.py","file_ext":"py","file_size_in_byte":2611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"22637770","text":"#\n# mod-h2 test suite\n# check HTTP/2 timeout behaviour\n#\nimport time\nfrom threading import Thread\n\nimport pytest\n\nfrom h2_conf import HttpdConf\nfrom h2_result import ExecResult\n\n\nclass TestShutdown:\n\n    @pytest.fixture(autouse=True, scope='class')\n    def _class_scope(self, env):\n        conf = HttpdConf(env)\n        conf.add_vhost_cgi()\n        conf.install()\n        assert env.apache_restart() == 0\n\n    def test_106_01(self, env):\n        url = env.mkurl(\"https\", \"cgi\", \"/necho.py\")\n        lines = 100000\n        text = \"123456789\"\n        wait2 = 1.0\n        self.r = None\n        def long_request():\n            args = [\"-vvv\",\n                    \"-F\", f\"count={lines}\",\n                    \"-F\", f\"text={text}\",\n                    \"-F\", f\"wait2={wait2}\",\n                    ]\n            self.r = env.curl_get(url, 5, args)\n\n        t = Thread(target=long_request)\n        t.start()\n        time.sleep(0.5)\n        assert env.apache_restart() == 0\n        t.join()\n        # noinspection PyTypeChecker\n        r: ExecResult = self.r\n        assert r.response[\"status\"] == 200\n        assert len(r.response[\"body\"]) == (lines * (len(text)+1))\n","sub_path":"test/modules/http2/test_106_shutdown.py","file_name":"test_106_shutdown.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"338105673","text":"# Python 3\n\n# Be sure you have followed the instructions to download the 98-0.txt,\n# the text of A Tale of Two Cities, by Charles Dickens\n\nimport collections\nimport string, re\n\nstopwords = set(line.strip() for line in open('stopwords'))\nregex = re.compile('[%s+]' % re.escape(string.punctuation))\n\nwordcount={}\n\nwith open('98-0.txt', encoding='utf8') as f:\n   for word in f.read().lower().split():\n       word = re.sub(regex, \"\", word)\n       if word not in stopwords:\n           wordcount[word] = wordcount.get(word, 0) + 1\n\nd = collections.Counter(wordcount)\n\nfor word, count in d.most_common(10):\n    print(word, \": \", count)\n","sub_path":"python/word_cloud/word_cloud.py","file_name":"word_cloud.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"407840085","text":"#%%\n# Preprocess df_all\n# - Convert all to categorical using provided labels\n# DataFrameMapper is used\n# EXTRA handling since mapper loses categorical\n# EXTRA handling to ensure the target matches the original numerical labels\n\n#%%\ndf_all['PhotoAmt'] = df_all['PhotoAmt'].astype('int')\n# df_all['AdoptionSpeed'] = df_all['AdoptionSpeed'].fillna(-1)\n# df_all['AdoptionSpeed'] = df_all['AdoptionSpeed'].astype('int')\n\n#%% Category Mappings\nlabel_maps = dict()\nlabel_maps['Vaccinated'] = {\n    1 : 'Yes',\n    2 : 'No',\n    3 : 'Not sure',\n}\nlabel_maps['Type'] = {\n    1:\"Dog\",\n    2:\"Cat\"\n}\nlabel_maps['AdoptionSpeed'] = {\n    # -1 : \"Empty\",\n    0 : \"same day\",\n    1 : \"between 1 and 7 days\",\n    2 : \"between 8 and 30 days\",\n    3 : \"between 31 and 90 days\",\n    4 : \"No adoption after 100 days\",\n}\nlabel_maps['Gender'] = {\n    1 : 'Male',\n    2 : 'Female',\n    3 : 'Group',\n}\nlabel_maps['MaturitySize'] = {\n    1 : 'Small',\n    2 : 'Medium',\n    3 : 'Large',\n    4 : 'Extra Large',\n    0 : 'Not Specified',\n}\nlabel_maps['FurLength'] = {\n    1 : 'Short',\n    2 : 'Medium',\n    3 : 'Long',\n    0 : 'Not Specified',\n}\nlabel_maps['Dewormed'] = {\n    1 : 'Yes',\n    2 : 'No',\n    3 : 'Not sure',\n}\nlabel_maps['Sterilized'] = {\n    1 : 'Yes',\n    2 : 'No',\n    3 : 'Not sure',\n}\nlabel_maps['Health'] = {\n    1 : 'Healthy',\n    2 : 'Minor Injury',\n    3 : 'Serious Injury',\n    0 : 'Not Specified',\n}\n\n# For the breeds, load the two types seperate\ndog_breed = breeds[['BreedID','BreedName']][breeds['Type']==1].copy()\nmap_dog_breed = dict(zip(dog_breed['BreedID'], dog_breed['BreedName']))\n\ncat_breed = breeds[['BreedID','BreedName']][breeds['Type']==2].copy()\nmap_cat_breed = dict(zip(cat_breed['BreedID'], cat_breed['BreedName']))\n\n# Just in case, check for overlap in breeds\n# for i in range(308):\n#     print(i,end=\": \")\n#     if i in map_dog_breed: print(map_dog_breed[i], end=' - ')\n#     if i in map_cat_breed: print(map_cat_breed[i], end=' - ')\n#     if i in map_dog_breed and i in map_cat_breed: raise\n#     print()\n\n# It's fine, join them into one dict\nmap_all_breeds = dict()\nmap_all_breeds.update(map_dog_breed)\nmap_all_breeds.update(map_cat_breed)\nmap_all_breeds[0] = \"NA\"\n\n# Now add them to the master label dictionary for each column\nlabel_maps['Breed1'] = map_all_breeds\nlabel_maps['Breed2'] = map_all_breeds\n\n# Similarly, load the color map\nmap_colors = dict(zip(colors['ColorID'], colors['ColorName']))\nmap_colors[0] = \"NA\"\nlabel_maps['Color1'] = map_colors\nlabel_maps['Color2'] = map_colors\nlabel_maps['Color3'] = map_colors\n\n# And the states map\nlabel_maps['State'] = dict(zip(states['StateID'], states['StateName']))\n\nlogging.info(\"Category mappings for {} columns created\".format(len(label_maps)))\n\nfor map in label_maps:\n    print(map, label_maps[map])\n\n#%% Dynamically create the transformation definitions\n# tx_definitions_preview = [(col_name, label_maps[col_name]) for col_name in label_maps]\n# for t in tx_definitions_preview:\n#     print(t)\ntx_definitions = [(col_name, trf.NumericalToCat(label_maps[col_name], True)) for col_name in label_maps]\n# col_name = 'Vaccinated'\n#%% Pipeline\n# Build the pipeline\n# NOTES:\n# input_df - Ensure the passed in column enters as a series or DF\n# df_out - Ensure the pipeline returns a df\n# default - if a column is not transformed, keep it unchanged!\n# WARNINGS:\n# The categorical dtype is LOST!\n# The mapping does NOT match the original!\n# Do NOT use DataFrameMapper for creating new columns, use a regular pipeline!\ndata_mapper = DataFrameMapper(\n    tx_definitions,\ninput_df=True, df_out=True, default=None)\nlogging.info(\"Categorical transformer pipeline warnings, see docstring!\".format())\n\n# print(\"DataFrameMapper, applies transforms directly selected columns\")\n# for i, step in enumerate(data_mapper.features):\n#     print(i, step)\n\n#%% FIT TRANSFORM\ndf_all = data_mapper.fit_transform(df_all)\nlogging.info(\"Size of train df_all with categorical columns: {} MB\".format(sys.getsizeof(df_all)/1000/1000))\nlogging.info(\"Warning: Pipeline returns strings, not categorical! \".format(sys.getsizeof(df_all)/1000/1000))\n\n#%% WARNING - sklearn-pandas has a flaw, it does not preserve categorical features!!!\n# ordered = ['AdoptionSpeed','MaturitySize','FurLength','Health'] #Actually, most have 'unspecified', which can't be ordered\nordered_cols = ['AdoptionSpeed']\nfor col in label_maps:\n    this_labels = sorted(list(label_maps[col].items()), key=lambda tup: tup[0])\n    listed_categories = [tup[1] for tup in this_labels]\n    if col in listed_categories:\n        ordered_flag = True\n    else:\n        ordered_flag = False\n    cat_type = pd.api.types.CategoricalDtype(categories=listed_categories,ordered=True)\n    df_all[col] = df_all[col].astype(cat_type)\n\nlogging.info(\"Reapplied categorical features\".format())\nlogging.info(\"Size of df_all with categorical features: {} MB\".format(sys.getsizeof(df_all)/1000/1000))\n\n#%%\n# df_all['AdoptionSpeed'].value_counts()\n# ser = df_all['AdoptionSpeed']\n#\n# original_y_train.value_counts()\n\n\n#%% SUMMARY\n\nlogging.info(\"Final shape of df_all {}\".format(df_all.shape))\n#%% DONE HERE - DELETE UNUSED\nprint(\"******************************\")\n\ndel_vars =[\n    'breeds',\n    'cat_breed',\n    'colors',\n    'data_mapper',\n    'dog_breed',\n    'map_colors',\n    'map_all_breeds',\n    'map_cat_breed',\n    'map_dog_breed',\n    'states',\n]\ncnt = 0\nfor name in dir():\n    if name in del_vars:\n        cnt+=1\n        del globals()[name]\nlogging.info(f\"Removed {cnt} variables from memory\")\ndel cnt, name, del_vars\n","sub_path":"src_final/2_load_data/data_01_clean.py","file_name":"data_01_clean.py","file_ext":"py","file_size_in_byte":5504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"156288022","text":"import unittest\n\nfrom parameterized import parameterized\n\nfrom integration_tests.dataproc_test_case import DataprocTestCase\n\n\nclass DatalabTestCase(DataprocTestCase):\n    COMPONENT = 'datalab'\n    INIT_ACTIONS = ['datalab/datalab.sh']\n    PYTHON_3_INIT_ACTIONS = [\n        'conda/bootstrap-conda.sh', 'conda/install-conda-env.sh'\n    ]\n\n    def verify_instance(self, name):\n        self.assert_instance_command(\n            name, \"curl {} -L {}:8080 | grep 'Google Cloud DataLab'\".format(\n                \"--retry 10 --retry-delay 10 --retry-connrefused\", name))\n\n    @parameterized.expand(\n        [\n            (\"STANDARD\", \"1.1\", [\"m\"], \"python2\"),\n            (\"STANDARD\", \"1.1\", [\"m\"], \"python3\"),\n            (\"STANDARD\", \"1.2\", [\"m\"], \"python2\"),\n            (\"STANDARD\", \"1.2\", [\"m\"], \"python3\"),\n            (\"STANDARD\", \"1.3\", [\"m\"], \"python2\"),\n            (\"STANDARD\", \"1.3\", [\"m\"], \"python3\"),\n            (\"STANDARD\", \"1.4\", [\"m\"], \"python2\"),\n            (\"STANDARD\", \"1.4\", [\"m\"], \"python3\"),\n        ],\n        testcase_func_name=DataprocTestCase.generate_verbose_test_name)\n    def test_datalab(self, configuration, dataproc_version, machine_suffixes,\n                     python):\n        init_actions = self.INIT_ACTIONS\n        metadata = 'INIT_ACTIONS_REPO={}'.format(self.INIT_ACTIONS_REPO)\n        if python == \"python3\":\n            init_actions = self.PYTHON_3_INIT_ACTIONS + init_actions\n            metadata += ',CONDA_PACKAGES=\"python==3.5\"'\n\n        self.createCluster(configuration,\n                           init_actions,\n                           dataproc_version,\n                           metadata=metadata,\n                           scopes='cloud-platform',\n                           timeout_in_minutes=20)\n\n        for machine_suffix in machine_suffixes:\n            self.verify_instance(\"{}-{}\".format(self.getClusterName(),\n                                                machine_suffix))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"datalab/test_datalab.py","file_name":"test_datalab.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"604215823","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 31 21:49:43 2020\n\n@author: barni13\n\"\"\"\n\nimport pandas as pd\nimport igraph as ig\nimport numpy as np\nimport leidenalg\nimport itertools as itl\nfrom copy import deepcopy\n\n\ndef is_integer(n):\n    try:\n        float(n)\n    except ValueError:\n        return False\n    else:\n        return float(n).is_integer()\n\n#load data\ncommunication = pd.read_csv(\"/home/barni13/Desktop/Studia/Semestr 7/Praca Dyplomowa/Dane/communication.csv\", sep =\";\")\nreportsto = pd.read_csv(\"/home/barni13/Desktop/Studia/Semestr 7/Praca Dyplomowa/Dane/reportsto.csv\", sep = \";\")\nprint(communication) \nprint(reportsto)\n\n#convert to index with start at 0\ncommunication['Sender'] = communication['Sender'] - 1\ncommunication['Recipient'] = communication['Recipient'] - 1\n\nreportsto['ID'] = reportsto['ID'] - 1\nreportsto['ReportsToID'] = [int(x) - 1 if is_integer(x) else x for x in reportsto['ReportsToID']]\n\nprint(communication) \nprint(reportsto)\n\n\nndls_ids = list(reportsto[reportsto['ReportsToID'].isin(['former employee account', 'technical email account - not used by employees'])]['ID']) #needless email accounts' IDs\nact_emp_rep = reportsto[~reportsto['ID'].isin(ndls_ids)] #\"reportsto\" containing only active employees \nact_emp_mails = communication[communication['Sender'].isin(ndls_ids).apply(int) + communication['Recipient'].isin(ndls_ids).apply(int) == 0] \n\n\nmail_counter = np.zeros((len(reportsto['ID']), len(reportsto['ID'])))\n\ng = ig.Graph(len(reportsto['ID']), directed = 'True')\n\nfor i in range(0, len(communication)):\n    mail_counter[communication.iloc[i]['Sender']][communication.iloc[i]['Recipient']] =+ 1\n\nweights = mail_counter/mail_counter.sum(axis=1, keepdims=True)\nweights = np.nan_to_num(weights)\n\nfor k in range(0, len(reportsto['ID'])):\n    for l in range(0, len(reportsto['ID'])):\n        if k == l:\n            pass\n        if weights[k][l] != 0:\n            g.add_edge(k, l, weights = weights[k][l])\n\ng.delete_vertices(ndls_ids)\n\nlayout = g.layout(\"tree\") #tree / grid_fr      kk (?)\nvisual_style = {}\nvisual_style[\"vertex_size\"] = 20\n#visual_style[\"vertex_color\"] = [color_dict[gender] for gender in g.vs[\"gender\"]]\n#visual_style[\"vertex_label\"] = g.vs[\"name\"]\n#visual_style[\"edge_width\"] = [1 + 2 * int(is_formal) for is_formal in g.es[\"is_formal\"]]\nvisual_style[\"layout\"] = layout\nvisual_style[\"bbox\"] = (1200, 1200)\nvisual_style[\"margin\"] = 20\n#ig.plot(g, **visual_style).show()\n\npartition = leidenalg.find_partition(g, leidenalg.ModularityVertexPartition)\nprint(partition.modularity)\n\n#ig.plot(partition, **visual_style).show()\n\npairs = list(itl.permutations(act_emp_rep['ID'], 2))\n\nmodularities = []\n\nfor m in range (len(pairs)):\n    g_new = deepcopy(g)\n    g_new.add_edge(pairs[m][0], pairs[m][1], weights = np.mean(g.es['weights']))\n    modularities.append(leidenalg.find_partition(g_new, leidenalg.ModularityVertexPartition).modularity)\n\nmax(modularities)","sub_path":"testy.py","file_name":"testy.py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"217791260","text":"#!/usr/bin/env python3\nimport argparse\nimport sys\n\nimport numpy as np\nfrom irtools.npgroupby import npgroupby\n\n\ndef parse_arguments():\n    parser = argparse.ArgumentParser(description='')\n\n    parser.add_argument('--qrels', type=argparse.FileType('r'), required=True)\n\n    parser.add_argument(\n        '-i',\n        '--input',\n        type=argparse.FileType('r'),\n        default=sys.stdin,\n        help='default stdin')\n\n    parser.add_argument(\n        '-o',\n        '--output',\n        type=argparse.FileType('wb'),\n        required=True,\n        help='Save path')\n\n    return parser.parse_args()\n\n\ndef get_qno_dno(line):\n    splits = line.split()\n    if len(splits) == 2:\n        qno, dno = splits\n    elif len(splits) == 3:\n        qno, dno, _ = splits\n    elif len(splits) == 6:\n        qno, _, dno, *_ = splits\n    else:\n        assert False, f'Unknown format {len(splits)} fields found'\n    return qno, dno\n\n\ndef main():\n    args = parse_arguments()\n\n    data = []\n    qrels = {}\n    labels = {0}\n\n    # Gather all positive\n    for line in args.qrels:\n        splits = line.split()\n        qno, _, dno, rel = splits\n        if qno.isdigit() and dno.isdigit():\n            qno, dno = int(qno), int(dno)\n        labels.add(int(rel))\n\n        data.append([qno, dno, int(rel)])\n\n        qrels.setdefault(qno, {})\n        qrels[qno][dno] = int(rel)\n\n    for line in args.input:\n        qno, dno = get_qno_dno(line)\n\n        if qno.isdigit() and dno.isdigit():\n            qno, dno = int(qno), int(dno)\n\n        if qno in qrels and qrels[qno].get(dno, 0) > 0:\n            continue\n\n        data.append((qno, dno, 0))\n\n    data = np.array(data)\n    idx = np.unique(data[:, 0:2], return_index=True, axis=0)[1]\n    data = data[idx]\n    np.save(args.output, data)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"scripts/run2matchzoo.py","file_name":"run2matchzoo.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"529950268","text":"from pyodbc import connect\nimport json\nimport random\nimport hashlib\n\nclass TaiKhoan:\n    def __init__(self):\n        file = open('project/models/Data/NameServer.json',encoding='utf-8')\n        NameSerrver_1 = json.load(file)\n        file.close()\n        self.db = connect('Driver={SQL Server};Database='+NameSerrver_1[\"NameDatabase\"]+';Server='+NameSerrver_1[\"NameServer\"]+';Trusted_connection=Yes')\n    \n    def add_TaiKhoan(self, arr):\n        sql ='''INSERT INTO TaiKhoan (id,TenTK, IDCV, pws,TT)\n            VALUES(?,?,?,?,?)'''\n        cur = self.db.cursor()\n        cur.execute(sql,arr)\n        ret = cur.rowcount\n        self.db.commit()\n        cur.close()\n        return ret\n    \n    def Lay_TaiKhoan(self):\n        sql = 'SELECT * FROM TaiKhoan'\n        cur = self.db.cursor()\n        cur.execute(sql)\n        a = cur.fetchall()\n        cur.close()\n        return a\n    \n    def DangNhap(self, Ten_TK, pws):\n        sql = 'SELECT * FROM dbo.TaiKhoan WHERE TenTK=? and pws=?'\n        cur = self.db.cursor()\n        cur.execute(sql,(Ten_TK,Bam_mk(pws), ))\n        v = cur.fetchone()\n        cur.close()\n        return v\n\n    def Lay_TaiKhoan_id(self, id):\n        sql = 'SELECT * FROM dbo.TaiKhoan WHERE id=?'\n        cur = self.db.cursor()\n        cur.execute(sql,(id, ))\n        v = cur.fetchone()\n        cur.close()\n        return v\n\n    \n    def doi_MK (self,arr):\n        cur = self.db.cursor()\n        cur.execute('UPDATE  dbo.TaiKhoan SET pws =? WHERE id =?',arr)\n        ret = cur.rowcount\n        self.db.commit()\n        cur.close()\n        return ret\n    \n    def Block_id(self,id):\n        cur = self.db.cursor()\n        cur.execute('UPDATE  dbo.TaiKhoan SET TT =0 WHERE id =?',(id,))\n        ret = cur.rowcount\n        self.db.commit()\n        cur.close()\n        return ret\n    \n    def Block_IDCV(self,idCV):\n        cur = self.db.cursor()\n        cur.execute('UPDATE  dbo.TaiKhoan SET TT =0 WHERE IDCV =?',(idCV,))\n        ret = cur.rowcount\n        self.db.commit()\n        cur.close()\n        return ret\n    \n    def UnBlock_id(self,id):\n        cur = self.db.cursor()\n        cur.execute('UPDATE  dbo.TaiKhoan SET TT =1 WHERE id =?',(id,))\n        ret = cur.rowcount\n        self.db.commit()\n        cur.close()\n        return ret\n    \n    def UnBlock_IDCV(self,idCV):\n        cur = self.db.cursor()\n        cur.execute('UPDATE  dbo.TaiKhoan SET TT =1 WHERE IDCV =?',(idCV,))\n        ret = cur.rowcount\n        self.db.commit()\n        cur.close()\n        return ret\n    \n    def Lay_TrangThai_id(self, id):\n        sql = 'SELECT TT FROM dbo.TaiKhoan WHERE id=?'\n        cur = self.db.cursor()\n        cur.execute(sql,(id, ))\n        v = cur.fetchone()\n        cur.close()\n        return v[0]\n\n    def __def__(self):\n        self.db.close()\n\ndef xu_ly_sang_json(thong_tin):\n    fake_TK= {'id':thong_tin[0],'TenTK':thong_tin[1], 'IDCV':thong_tin[2], 'pws':thong_tin[3]}\n    return fake_TK\n    \n\n\ndef random_id(length):\n    number = '0123456789'\n    alpha = 'abcdefghijklmnopqrstuvwxyz'\n    id = ''\n    for i in range(0,length,2):\n        id += random.choice(number)\n        id += random.choice(alpha)\n    return id\n\ndef Bam_mk(mystring): #chuyển thành md5\n    pws_md5 = hashlib.md5(mystring.encode())\n    return pws_md5.hexdigest()\n\n\n","sub_path":"project/models/taikhoan.py","file_name":"taikhoan.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"494390167","text":"import os\nimport sys\nfrom datetime import datetime\n\nimport cv2\nimport numpy\n\nimport create_data as cd\n\n\ndef getdate():\n\tdate = datetime.now()\n\tans = str(date.day)+\"|\"+str(date.month)+\"|\"+str(date.year)\n\treturn ans\n\narray_to_store_names=[]\n\nprint('INITIALISING') \n\n(images, lables, names, id) = ([], [], {}, 0) \nfor (subdirs, dirs, files) in os.walk(cd.datasets): \n\tfor subdir in dirs: \n\t\tnames[id] = subdir \n\t\tsubjectpath = os.path.join(cd.datasets, subdir) \n\t\tfor filename in os.listdir(subjectpath): \n\t\t\tpath = subjectpath + '/' + filename \n\t\t\tlable = id\n\t\t\timages.append(cv2.imread(path, 0)) \n\t\t\tlables.append(int(lable)) \n\t\tid += 1\n(width, height) = (130, 100) \n\n(images, lables) = [numpy.array(lis) for lis in [images, lables]] \n\n\nrecognizer = cv2.face.LBPHFaceRecognizer_create()\nrecognizer.train(images, lables) \n\nface_cascade = cv2.CascadeClassifier(cd.haar_file) \nwebcam = cv2.VideoCapture(0) # ----- webcam\n#cap = cv2.VideoCapture('http://192.168.43.7:8080/video') #----- mobile_streaming\n\nsequencefile=getdate()\nprint(sequencefile)\nsequencefile+=\".txt\"\nwhile True: \n\t(_, im) = webcam.read()\n\t#(_, im) = cap.read() \n\tgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) \n\tfaces = face_cascade.detectMultiScale(gray, 1.3, 5) \n\tfor (x, y, w, h) in faces: \n\n\t\tcv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2) \n\t\tface = gray[y:y + h, x:x + w] \n\t\tface_resize = cv2.resize(face, (width, height)) \n\t\tprediction = recognizer.predict(face_resize) \n\t\tcv2.rectangle(im, (x, y), (x + w, y + h), (255, 255, 0), 3)\n\n\t\tif names[prediction[0]] not in array_to_store_names:\n\t\t\tcv2.rectangle(im, (x, y), (x + w, y + h), (255, 255, 0), 3) \n\t\t\tcv2.putText(im, '% s' %(names[prediction[0]]), (x-10, y-10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0)) \n\t\t\tprint(\"REGISTERED %s\"%(names[prediction[0]]))\n\t\t\tarray_to_store_names.append(names[prediction[0]])\n\t\t\tf=open(sequencefile,\"a+\")\n\t\t\tf.write(\"%s\" %(names[prediction[0]])+ \"\\n\")\n\n\tcv2.imshow('OpenCV', im) \n\tif cv2.waitKey(1) & 0xFF==ord('q'): \n\t\tf.close()\n\t\tbreak","sub_path":"face_recognize.py","file_name":"face_recognize.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"509205646","text":"class Solution(object):\r\n    def isIsomorphic(self, s, t):\r\n        \"\"\"\r\n        :type s: str\r\n        :type t: str\r\n        :rtype: bool\r\n        \"\"\"\r\n        mapping = [-1 for i in range(128)]\r\n        revMap = [-1 for i in range(128)] # for efficiency \r\n        for i in range(len(s)):\r\n            a, b = ord(s[i]), ord(t[i])\r\n            if mapping[a] == -1:\r\n                if revMap[b] != -1: # not one-by-one mapping\r\n                    return False\r\n                mapping[a] = b\r\n                revMap[b] = a\r\n            elif mapping[a] != b:\r\n                return False \r\n        return True                \r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    # inputs\r\n    s = 'paper'\r\n    t = 'tatle'\r\n    s = 'egg'\r\n    t = 'add'\r\n    print('-' * 30)\r\n    res = Solution().isIsomorphic(s, t)\r\n    print(res)","sub_path":"205_isIsomorphic.py","file_name":"205_isIsomorphic.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"618172860","text":"from ROOT import TH1D, TCanvas, TF1, TFile, TTree, TLegend, TGraph\nfrom math import sqrt\nimport numpy\nfrom QuarksDistroPlotter import QuarksDistroPlotter\nfrom cebefo_style import cebefo_style\n\ncebefo_style()\nsave = True\ndir_fig = \"fig/tracks/with_no_cut/sp_IP/\"\n\n\ndetector = \"ALEPH\"\nmode = \" sp IP\"\nmode_save = \"_sp_IP\"\n\n\nmy_file = TFile(\"ntuple/outdir_sp/qq/analyzers.ZqqIPTrackTreeProducer.ZqqIPTrackTreeProducer_2/tree.root\",\"OPEN\")\nmy_tree = my_file.Get(\"events\")\n\n# ip mc\nimpact_parameter_mc_zoom = QuarksDistroPlotter(my_tree, \"ip\", \"\", \"ip_mc_zoom_{}\".format(detector)+mode_save, 200, -1e-15, 1e-15, \"IP_{} @{}\".format(\"{mc}\", detector) + mode, \"IP_{mc} [m]\", \"m\", norm = False, logy = True, set_title = True)\n\n# ip mc\nimpact_parameter_mc = QuarksDistroPlotter(my_tree, \"ip\", \"\", \"ip_mc_{}\".format(detector)+mode_save, 200, -0.8e-3, 0.8e-3, \"IP_{} @{}\".format(\"{mc}\", detector) + mode, \"IP_{mc} [m]\", \"m\", norm = False, logy = True, set_title = True)\n\n# ip smeared\nimpact_parameter_smeared = QuarksDistroPlotter(my_tree, \"ip_smeared\", \"\", \"ip_smeared_{}\".format(detector)+mode_save, 200, -2e-3, 5e-3, \"IP_{} @{}\".format(\"{smeared}\", detector) + mode, \"IP_{smeared} [m]\", \"m\", norm = False, logy = True, set_title = True)\n\n# # track resolution\n# track_resolution = QuarksDistroPlotter(my_tree, \"sigma\", \"\", \"sigma_{}\".format(detector)+mode_save, 200, 0, 1e-3, \"#sigma @{}\".format(detector) + mode, \"#sigma [m]\", \"[m]\", norm = True, logy = True, set_title = True)\n#\n# # track energy\n# energy = QuarksDistroPlotter(my_tree, \"track_e\", \"\", \"energy_{}\".format(detector)+mode_save, 200, 0, 30, \"Track energy [GeV] @{}\".format(detector) + mode, \"Track energy [GeV]\", \"[GeV]\", norm = True, logy = False, set_title = True)\n\n# # track significance mc\n# track_significance_mc = QuarksDistroPlotter(my_tree, \"ip/sigma\", \"\", \"ip_divide_sigma_{}\".format(detector)+mode_save, 200, -5, 1000, \"Ratio IP_{} / #sigma @{}\".format(\"{mc}\", detector) + mode, \"IP_{mc} / #sigma\", \"\", norm = False, logy = True, set_title = True)\n\n# track significance smeared\ntrack_significance = QuarksDistroPlotter(my_tree, \"signif\", \"\", \"track_significance_{}\".format(detector)+mode_save, 200, -10, 10, \"Track significance @{}\".format(detector) + mode, \"Track significance\", \"\", norm = False, logy = False, set_title = True)\n\n# track Significance\ntrack_significance_logy = QuarksDistroPlotter(my_tree, \"signif\", \"\", \"track_significance_logy_{}\".format(detector)+mode_save, 200, -10, 50, \"Track significance @{}\".format(detector) + mode, \"Track significance\", \"\", norm = False, logy = True, set_title = True)\n\n# track probability\ntrack_probability = QuarksDistroPlotter(my_tree, \"track_probability\", \"\", \"track_probability_{}\".format(detector)+mode_save, 201, -1, 1, \"Track probability @{}\".format(detector) + mode, \"Track probability\", \"\", norm = False, logy = True, set_title = True)\n\n# # d_j\n# d_j = QuarksDistroPlotter(my_tree, \"d_j\", \"\", \"d_j_{}\".format(detector)+mode_save, 200, 0, 1e-1, \"d_j @{}\".format(detector) + mode, \"d_j [m]\", \"[m]\", norm = False, logy = True, set_title = True)\n#\n# # d_j_signif\n# d_j_signif = QuarksDistroPlotter(my_tree, \"d_j_signif\", \"\", \"d_j_signif_{}\".format(detector)+mode_save, 200, -11, 1000, \"Significance of the distance of min approach to jet dir @{}\".format(detector) + mode, \"D_{j} significance\", \"\", norm = False, logy = True, set_title = True)\n#\n# # s_j_wrt_pr_vtx\n# s_j_wrt_pr_vtx = QuarksDistroPlotter(my_tree, \"s_j_wrt_pr_vtx\", \"\", \"s_j_wrt_pr_vtx_{}\".format(detector)+mode_save, 200, -5e-3, 2e-1, \"s_j_wrt_pr_vtx @{}\".format(detector) + mode, \"s_j_wrt_pr_vtx [m]\", \"[m]\", norm = False, logy = True, set_title = True)\n\n\nqdps = [impact_parameter_mc_zoom,\n    impact_parameter_mc,\n    impact_parameter_smeared,\n    # track_resolution,\n    # energy,\n    # track_significance_mc,\n    track_significance,\n    track_significance_logy,\n    track_probability,\n    # d_j,\n    # d_j_signif,\n    # s_j_wrt_pr_vtx\n    ]\n\nif save == True:\n    save_file = TFile(\"fig/tracks/with_no_cut/sp_IP/track_with_no_cut_{}.root\".format(detector)+mode_save, \"RECREATE\")\n    save_file.cd()\nfor qdp in qdps:\n    qdp.draw()\n    if save == True:\n        qdp.save_pdf(dir_fig)\n        qdp.write()\n","sub_path":"b_tagging_test/plot_script/tracks_plot_with_no_cut_ALEPH_spIP.py","file_name":"tracks_plot_with_no_cut_ALEPH_spIP.py","file_ext":"py","file_size_in_byte":4183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"528649570","text":"#!/usr/bin/env python2\n# OpenPOWER Automated Test Project\n#\n# Contributors Listed Below - COPYRIGHT 2017\n# [+] International Business Machines Corp.\n#\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\n# implied. See the License for the specific language governing\n# permissions and limitations under the License.\n#\n\n'''\nTest OpenBMC REST API\n---------------------\n\nTests a bunch of functionality only available through OpenBMC REST API\n\nGenerally takes about 10 minutes to run rest-api suite\n\n.. note::\n\n   The OpenBMC REST API is not a long term nor stable API.\n   It's just as likely that OpenBMC changes their API as anything.\n'''\n\nimport os\n\nimport unittest\n\nimport OpTestConfiguration\nfrom common.OpTestSystem import OpSystemState\nfrom common.Exceptions import HTTPCheck\n\nimport logging\nimport OpTestLogger\nlog = OpTestLogger.optest_logger_glob.get_logger(__name__)\n\nclass RestAPI(unittest.TestCase):\n    '''\n    RestAPI Class - Execution order independent\n    --run-suite rest-api\n    --run testcases.testRestAPI.Runtime\n    --run testcases.testRestAPI.HostOff\n    or run the individual tests\n    '''\n    @classmethod\n    def setUpClass(cls):\n        cls.conf = OpTestConfiguration.conf\n        if \"OpenBMC\" not in cls.conf.args.bmc_type:\n            raise unittest.SkipTest(\"OpenBMC specific Rest API Tests\")\n        cls.cv_SYSTEM = cls.conf.system()\n        cls.cv_BMC = cls.conf.bmc()\n        cls.rest = cls.conf.system().rest\n        try:\n            if cls.desired == OpSystemState.OFF:\n                cls.cv_SYSTEM.goto_state(OpSystemState.OFF)\n            else:\n                cls.cv_SYSTEM.goto_state(OpSystemState.OS)\n        except Exception as e:\n            log.debug(\"Unable to find cls.desired, probably a test code problem\")\n            cls.cv_SYSTEM.goto_state(OpSystemState.OS)\n\nclass Runtime(RestAPI, unittest.TestCase):\n    '''\n    Runtime Class performs tests with Host On\n    HostOff Class will turn the Host Off\n    --run testcases.testRestAPI.Runtime\n    --run testcases.testRestAPI.HostOff\n    '''\n    @classmethod\n    def setUpClass(cls):\n        cls.desired = OpSystemState.OS\n        super(Runtime, cls).setUpClass()\n\n    def test_tpm_policy_setting(self):\n        '''\n        REST TPM Policy Settings\n        --run testcases.testRestAPI.Runtime.test_tpm_policy_setting\n        --run testcases.testRestAPI.HostOff.test_tpm_policy_setting\n        '''\n        status = self.rest.is_tpm_enabled()\n        log.debug(\"TPM Enabled Starts={}\".format(status))\n        self.rest.enable_tpm()\n        self.assertTrue(self.rest.is_tpm_enabled(), \"OpenBMC failed to enable TPM policy\")\n        self.rest.disable_tpm()\n        self.assertFalse(self.rest.is_tpm_enabled(), \"OpenBMC failed to disable TPM policy\")\n\n    def test_system_power_cap(self):\n        '''\n        REST System Power Cap\n        --run testcases.testRestAPI.Runtime.test_system_power_cap\n        --run testcases.testRestAPI.HostOff.test_system_power_cap\n        '''\n        # System power cap enable/disable tests\n        self.rest.power_cap_enable()\n        PowerCapEnable, PowerCap = self.rest.get_power_cap_settings()\n        log.debug(\"SHOULD BE ENABLED PowerCapEnable={} PowerCap={}\".format(PowerCapEnable, PowerCap))\n        self.assertEqual(int(PowerCapEnable), 1, \"System Power Cap Enable Failed\")\n        self.rest.power_cap_disable()\n        PowerCapEnable, PowerCap = self.rest.get_power_cap_settings()\n        log.debug(\"SHOULD BE DISABLED PowerCapEnable={} PowerCap={}\".format(PowerCapEnable, PowerCap))\n        self.assertEqual(int(PowerCapEnable), 0, \"System Power Cap Disable Failed\")\n\n    def test_occ_active(self):\n        '''\n        REST OCC Active\n        --run testcases.testRestAPI.Runtime.test_occ_active\n        --run testcases.testRestAPI.HostOff.test_occ_active\n        '''\n        # OCC Active state tests using RestAPI\n        ids = self.rest.get_occ_ids()\n        log.debug(\"OCC IDs: {}\".format(ids))\n        for id in ids:\n            log.debug(\"OpSystemState={}\".format(self.cv_SYSTEM.get_state()))\n            # If system is in runtime OCC should be Active\n            if self.cv_SYSTEM.get_state() in [OpSystemState.PETITBOOT, OpSystemState.PETITBOOT_SHELL,\n                                           OpSystemState.BOOTING, OpSystemState.OS]:\n                self.assertTrue(self.rest.is_occ_active(id), \"OCC%s is not in active state\" % id)\n            # if system is in standby state OCC should be inactive\n            elif self.cv_SYSTEM.get_state() == OpSystemState.OFF:\n                self.assertFalse(self.rest.is_occ_active(id), \"OCC%s is still in active state\" % id)\n\n    def test_software_enumerate(self):\n        '''\n        REST Software Enumerate\n        --run testcases.testRestAPI.Runtime.test_software_enumerate\n        --run testcases.testRestAPI.HostOff.test_software_enumerate\n        '''\n        self.rest.software_enumerate()\n        ids = self.rest.get_list_of_image_ids()\n        log.debug(\"Software Enumerate IDs: {}\".format(ids))\n        for id in ids:\n            start_priority = self.rest.get_image_priority(id)\n            log.debug(\"Image ID={} Starting Priority={}\".format(id, start_priority))\n            self.rest.set_image_priority(id, start_priority)\n            updated_priority = self.rest.get_image_priority(id)\n            log.debug(\"Image ID={} Updated Priority={}\".format(id, updated_priority))\n            self.assertEqual(start_priority, updated_priority, \"Image Priority Change did not happen as expected\")\n\n    def test_image_lists(self):\n        '''\n        REST Image Interfaces\n        --run testcases.testRestAPI.Runtime.test_image_lists\n        --run testcases.testRestAPI.HostOff.test_image_lists\n        '''\n        ids = self.rest.get_list_of_image_ids()\n        log.debug(\"Image IDs: {}\".format(ids))\n        for id in ids:\n            status = self.rest.is_image_already_active(id)\n            log.debug(\"Image ID={} Active={}\".format(id, status))\n        host_images = self.rest.host_image_ids()\n        log.debug(\"Host Images: {}\".format(host_images))\n        bmc_images = self.rest.bmc_image_ids()\n        for id in bmc_images:\n            status = self.rest.validate_functional_bootside(id)\n            log.debug(\"Image ID={} Functional Boot Side Validation: {}\".format(id, status))\n            if status:\n                try:\n                    activation_status = self.rest.image_ready_for_activation(id, timeout=1)\n                    log.debug(\"Activation Status: {}\".format(activation_status))\n                except HTTPCheck as e:\n                    # expected since the image is active, we just testing API\n                    # xyz.openbmc_project.Software.Activation.Activations.Active\n                    log.debug(\"HTTPCheck Exception={} e.message={}\".format(e, e.message))\n        log.debug(\"BMC Images: {}\".format(bmc_images))\n\n    def test_field_mode_enable_disable(self):\n        '''\n        REST Field Mode Enable and Disable\n        --run testcases.testRestAPI.Runtime.test_field_mode_enable_disable\n        --run testcases.testRestAPI.HostOff.test_field_mode_enable_disable\n        '''\n        # Field mode tests\n        self.rest.has_field_mode_set()\n        self.rest.set_field_mode(\"1\")\n        self.assertTrue(self.rest.has_field_mode_set(), \"Field Mode Enable Failed\")\n        self.rest.set_field_mode(\"0\")\n        # clear_field_mode will reboot the BMC\n        self.cv_BMC.clear_field_mode()\n        self.assertFalse(self.rest.has_field_mode_set(), \"Field Mode Disable Failed\")\n\n    def test_upload_image(self):\n        '''\n        REST Upload Image\n        --run testcases.testRestAPI.Runtime.test_upload_image\n        --run testcases.testRestAPI.HostOff.test_upload_image\n        '''\n        # Upload image\n        try:\n            self.rest.upload_image(os.path.basename(\"README.md\"), minutes=None)\n        except Exception as e:\n            # upload_image only works for verified images\n            # so this is expected to fail, but we test the API\n            log.debug(\"Upload Image Exception={} Message={}\".format(e, e.message))\n            check_list = [\"Version already exists\",\n                          \"failed to be extracted\",\n                          \"we timed out trying\"]\n            matching = [xs for xs in check_list if xs in e.message]\n            if not len(matching):\n                self.assertTrue(False, \"Unexpected failure on upload_image\")\n\n    def test_inventory(self):\n        '''\n        REST Get Inventory\n        --run testcases.testRestAPI.Runtime.test_inventory\n        --run testcases.testRestAPI.HostOff.test_inventory\n        '''\n        # FRU Inventory\n        r = self.rest.get_inventory()\n        log.debug(\"Inventory: {}\".format(r))\n        # r = dictionary of dictionaries\n\n    def test_sensors(self):\n        '''\n        REST Sensors\n        --run testcases.testRestAPI.Runtime.test_sensors\n        --run testcases.testRestAPI.HostOff.test_sensors\n        '''\n        # Sensors\n        r = self.rest.sensors()\n        log.debug(\"Sensors: {}\".format(r))\n        # r = dictionary of dictionaries\n\n    def test_obmc_states(self):\n        '''\n        REST OBMC States\n        --run testcases.testRestAPI.Runtime.test_obmc_states\n        --run testcases.testRestAPI.HostOff.test_obmc_states\n        '''\n        # get BMC State\n        r = self.rest.get_bmc_state()\n        # r=xyz.openbmc_project.State.BMC.BMCState.Ready\n        # Get Chassis Power State\n        r = self.rest.get_power_state()\n        # r=xyz.openbmc_project.State.Host.HostState.Running\n        # Get Host State\n        r = self.rest.get_host_state()\n        # r=xyz.openbmc_project.State.Host.HostState.Running\n\n    def test_list_sel(self):\n        '''\n        REST List SELs\n        --run testcases.testRestAPI.Runtime.test_list_sel\n        --run testcases.testRestAPI.HostOff.test_list_sel\n        '''\n        # List SEL records\n        json_data = self.rest.list_sel()\n        log.debug(\"List SEL: {}\".format(json_data))\n        # get list of SEL event ID's if any exist\n        id_list, dict_list = self.rest.get_sel_ids()\n        log.debug(\"SEL ID List: {} SEL DICT List: {}\".format(id_list, dict_list))\n\n    def test_dump_sels(self):\n        '''\n        REST Dump SELs\n        --run testcases.testRestAPI.Runtime.test_dump_sels\n        --run testcases.testRestAPI.HostOff.test_dump_sels\n        '''\n        # Dump SEL records\n        # Dump list of SEL event ID's if any exist\n        id_list, dict_list = self.rest.get_sel_ids(dump=True)\n        log.debug(\"SEL ID List: {} SEL DICT List: {}\".format(id_list, dict_list))\n\n    def test_clear_sel(self):\n        '''\n        REST Clear SELs\n        --run testcases.testRestAPI.Runtime.test_clear_sel\n        --run testcases.testRestAPI.HostOff.test_clear_sel\n        '''\n        # Clear SEL entry by ID\n        self.rest.clear_sel_by_id()\n        id_list, dict_list = self.rest.get_sel_ids()\n        log.debug(\"SEL ID List: {}\\nSEL DICT List: {}\\n\".format(id_list, dict_list))\n        # Clear Complete SEL Repository\n        self.rest.clear_sel()\n        # Check if SEL has really zero entries or not\n        self.assertTrue(self.rest.verify_clear_sel(), \"openBMC failed to clear SEL repository\")\n\n    def test_obmc_delete_dumps(self):\n        '''\n        REST OBMC Delete ALL Dumps\n        --run testcases.testRestAPI.Runtime.test_obmc_delete_dumps\n        --run testcases.testRestAPI.HostOff.test_obmc_delete_dumps\n        '''\n        # Delete all the dumps\n        r = self.rest.delete_all_dumps()\n        log.debug(\"Deleted all dumps\")\n\n    def test_obmc_create_dump(self):\n        '''\n        REST OBMC Create Dump\n        --run testcases.testRestAPI.Runtime.test_obmc_create_dump\n        --run testcases.testRestAPI.HostOff.test_obmc_create_dump\n        '''\n        # Create a dump\n        dump_id = self.rest.create_new_dump()\n        log.debug(\"Created new dump ID={}\".format(dump_id))\n\n    def test_obmc_download_dumps(self):\n        '''\n        REST OBMC Download ALL Dumps\n        --run testcases.testRestAPI.Runtime.test_obmc_download_dumps\n        --run testcases.testRestAPI.HostOff.test_obmc_download_dumps\n        '''\n        # List available dumps\n        dump_ids = self.rest.get_dump_ids()\n        log.debug(\"Available Dumps to Download: {}\".format(dump_ids))\n        for id in dump_ids:\n            dump_id = id\n            log.debug(\"Downloading Dump ID: {}\".format(dump_id))\n            self.rest.download_dump(id)\n        if not len(dump_ids):\n            log.debug(\"No Available dumps to download\")\n\n    def test_obmc_dump(self):\n        '''\n        REST OBMC List, Create and Download a Dump\n        --run testcases.testRestAPI.Runtime.test_obmc_dump\n        --run testcases.testRestAPI.HostOff.test_obmc_dump\n        '''\n        # List available dumps\n        r = self.rest.list_available_dumps()\n        log.debug(\"Available Dumps: {}\".format(r.json().get('data')))\n        # OpenBMC Dump capture procedure\n        # Initiate a dump nd get the dump id\n        id = self.rest.create_new_dump()\n        # Wait for the dump to finish which can be downloaded\n        self.assertTrue(self.rest.wait_for_dump_finish(id, counter=30), \"OpenBMC Dump capture timeout\")\n        # Download the dump which is ready to offload\n        self.rest.download_dump(id)\n\n    def test_set_bootdevs(self):\n        '''\n        REST Set Boot Device to Setup then Regular,\n        this leaves the boot device as we found it\n        --run testcases.testRestAPI.Runtime.test_set_bootdevs\n        --run testcases.testRestAPI.HostOff.test_set_bootdevs\n        '''\n        # Check current bootdev\n        start_bootdev = self.rest.get_current_bootdev()\n        log.debug(\"Boot Device Starts={}\".format(start_bootdev))\n        # Set bootdev to setup\n        self.rest.set_bootdev_to_setup()\n        # Get current bootdev\n        bootdev = self.rest.get_current_bootdev()\n        self.assertEqual(bootdev, \"Setup\", \"Failed to set Setup boot device\")\n        # Set bootdev to Default\n        self.rest.set_bootdev_to_none()\n        # Get current bootdev\n        bootdev = self.rest.get_current_bootdev()\n        self.assertEqual(bootdev, \"Regular\", \"Failed to set Regular boot device\")\n        # Put the bootdev back how we found it\n        if bootdev != start_bootdev:\n            if start_bootdev == \"Setup\":\n                self.rest.set_bootdev_to_setup()\n            else:\n                self.rest.set_bootdev_to_none()\n        # Check bootdev\n        bootdev = self.rest.get_current_bootdev()\n        log.debug(\"Boot Device Ends={}\".format(bootdev))\n\n    def test_get_boot_progress(self):\n        '''\n        REST Get Boot Progress\n        --run testcases.testRestAPI.Runtime.test_get_boot_progress\n        --run testcases.testRestAPI.HostOff.test_get_boot_progress\n        '''\n        # Get boot progress info\n        r = self.rest.get_boot_progress()\n        log.debug(\"Boot Progress={}\".format(r))\n\n    def test_clear_gard_record(self):\n        '''\n        REST Clear ALL Gard Records\n        --run testcases.testRestAPI.Runtime.test_clear_gard_record\n        --run testcases.testRestAPI.HostOff.test_clear_gard_record\n        '''\n        self.rest.clear_gard_records()\n\n    def test_factory_reset_software(self):\n        '''\n        REST Factory Reset Software\n        USE AT YOUR OWN RISK\n        --run testcases.testRestAPI.Runtime.test_factory_reset_software\n        --run testcases.testRestAPI.HostOff.test_factory_reset_software\n        '''\n        #self.rest.factory_reset_software()\n        # Not sure what the effect is, enable with caution\n        pass\n\n    def test_factory_reset_network(self):\n        '''\n        REST Factory Reset Network\n        USE AT YOUR OWN RISK\n        --run testcases.testRestAPI.Runtime.test_factory_reset_network\n        --run testcases.testRestAPI.HostOff.test_factory_reset_network\n        '''\n        #self.rest.factory_reset_network()\n        # It may clear static N/W, enable with caution\n        pass\n\n    def test_update_root_password(self):\n        '''\n        REST Update Root Password\n        Caution if OpTestConfiguration bmc_password is modified\n        util_bmc_server does automatic login with stored credentials\n        --run testcases.testRestAPI.Runtime.test_update_root_password\n        --run testcases.testRestAPI.HostOff.test_update_root_password\n        '''\n        self.rest.update_root_password(str(self.conf.args.bmc_password))\n\nclass HostOff(Runtime):\n    '''\n    Runtime Class performs tests with Host On\n    HostOff Class will turn the Host Off\n    --run testcases.testRestAPI.Runtime\n    --run testcases.testRestAPI.HostOff\n    '''\n    @classmethod\n    def setUpClass(cls):\n        cls.desired = OpSystemState.OFF\n        super(Runtime, cls).setUpClass()\n\nclass HostBounces(RestAPI, unittest.TestCase):\n    '''\n    Performs Reboots and Power On/Off variety\n    Purpose is to exercise the REST API's\n    --run testcases.testRestAPI.HostBounces\n    '''\n    @classmethod\n    def setUpClass(cls):\n        cls.desired = OpSystemState.OFF\n        super(HostBounces, cls).setUpClass()\n\n    def test_power_on(self):\n        '''\n        REST Power On\n        --run testcases.testRestAPI.HostBounces.test_power_on\n        '''\n        self.rest.power_on()\n        self.rest.wait_for_runtime()\n\n    def test_soft_reboot(self):\n        '''\n        REST Soft Reboot\n        --run testcases.testRestAPI.HostBounces.test_soft_reboot\n        '''\n        self.rest.power_on()\n        self.rest.wait_for_runtime()\n        self.rest.soft_reboot()\n        self.rest.wait_for_standby()\n        self.rest.wait_for_runtime()\n\n    def test_hard_reboot(self):\n        '''\n        REST Hard Reboot\n        --run testcases.testRestAPI.HostBounces.test_hard_reboot\n        '''\n        self.rest.power_on()\n        self.rest.wait_for_runtime()\n        self.rest.hard_reboot()\n        self.rest.wait_for_standby()\n        self.rest.wait_for_runtime()\n\n    def test_power_soft(self):\n        '''\n        REST Power Soft Off\n        --run testcases.testRestAPI.HostBounces.test_power_soft\n        '''\n        self.rest.power_on()\n        self.rest.wait_for_runtime()\n        self.rest.power_soft()\n        self.rest.wait_for_standby()\n\n    def test_power_off(self):\n        '''\n        REST Power Off\n        --run testcases.testRestAPI.HostBounces.test_power_off\n        '''\n        self.rest.power_on()\n        self.rest.wait_for_runtime()\n        self.rest.power_off()\n        self.rest.wait_for_standby()\n\ndef host_off_suite():\n    # run with Host powered OFF\n    s = unittest.TestSuite()\n    s.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(HostOff))\n    return s\n\ndef runtime_suite():\n    # run with Host powered ON\n    s = unittest.TestSuite()\n    s.addTest(unittest.defaultTestLoader.loadTestsFromTestCase(Runtime))\n    return s\n","sub_path":"testcases/testRestAPI.py","file_name":"testRestAPI.py","file_ext":"py","file_size_in_byte":19145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"423781873","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport os\nimport os.path\nimport sys \nimport json \n\ndef load_json_config(conf_file): \n    json_string = \"\"\n    with open(conf_file, encoding='utf-8') as fj:\n        for line in fj: \n            ##remove note  \n            line = line.strip()\n            index = line.find('#') \n            if index != -1: \n                line = line[:index]; line = line.strip()\n            if line == '': continue \n            json_string += line\n    staging_setting = json.loads(json_string) \n\n    return staging_setting\n\ndef submitJobList(name, joblist):\n    sge_cmd = \"/amr/tools/sgeTools/1.1/script/sgeSubmit.pl\"\n    cmd = '%s -N %s -a \"-l h_vmem=20G\" -p %s' % (sge_cmd, name, joblist)\n    ret = os.system(cmd)\n    return ret\n\ndef make_dir(exp_dir):\n    if not os.path.exists(exp_dir):\n        os.system(\"mkdir \" + exp_dir)  \n\n","sub_path":"AutoQA-ding_merged/travel/staging/gk_staging_utils.py","file_name":"gk_staging_utils.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"111757847","text":"#coding=utf-8\n__author__ = 'ChaoMing'\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport time\n\ndef test1():\n    browser = webdriver.Firefox()\n    browser.get(\"http://www.baidu.com\")\n    data = browser.find_element_by_id(\"cp\").text\n    browser.quit()\n    open(\"files/text.txt\", \"w\", encoding=\"utf-8\").write(data)\n\ndef test2():\n    browser = webdriver.Firefox()\n    browser.get(\"http://www.yahoo.com\")\n    assert \"Yahoo!\" in browser.title\n\n    elem = browser.find_element_by_name(\"p\")\n    elem.send_keys(\"seleniumhq\" + Keys.RETURN)\n\n    browser.quit()\n\ndef test3():\n    driver = webdriver.Chrome()\n    driver.get(\"http://www.baidu.com\")\n    data = driver.find_element_by_id(\"cp\").text\n    open(\"test.txt\", \"w\", encoding=\"utf-8\").write(data)\n\ndef test4():\n    browser = webdriver.Firefox()  # 启动Firefox浏览器\n    url1 = 'http://www.baidu.com/s?wd=马尔代夫'  # 马尔代夫的百度搜索页\n    url2 = 'http://scrapy.org/'  # 测试页面1\n    url3 = 'http://news.yahoo.com/air-strike-libyan-city-misrata-clashes-near-oil-135039996.html'  # 测试页面2\n    browser.get(url1)  # 打开页面\n    browser.maximize_window()  # 浏览器窗口最大化\n\n    res = browser.find_elements_by_xpath(\"//h3[@class='t c-gap-bottom-small']\")  # 使用xpath查找页面中的h3元素\n\n    for r in res:  # 页面中h3元素有多个，所以需要遍历\n        t = r.find_element_by_xpath(\"a\")  # h3元素下的a标签\n        print('%s - %s' % (t.text, type(t.text)))  # 打印a标签的标题以及文本格式\n        if '攻略' in t.text:  # 判断标题中是否有途牛，如果有则点击\n            print('yes')  # 判断结果\n            t.click()  # 点击这个a链接\n\n\n    print(len(res))  # 打印h3标签的总量\n\n\ntest4()\n\n\n","sub_path":"Test_selenium.py","file_name":"Test_selenium.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"56086648","text":"import sys\nimport re\ndef reverse_sublist(s,start,end):\n    lst = list(s)\n    lst[start:end] = lst[start:end][::-1]\n    return \"\".join(lst)\ndef toggle_sublist(s,start,end):\n    lst = list(s)\n    tmp=\"\".join(lst[start:end])\n    lst[start:end] = tmp.replace(\"a\", \"c\")\n    tmp=\"\".join(lst[start:end])\n    lst[start:end] = tmp.replace(\"b\", \"a\")\n    tmp=\"\".join(lst[start:end])\n    lst[start:end] = tmp.replace(\"c\", \"b\")\n    return \"\".join(lst)\n\ndef flip(s,start,end):\n    return toggle_sublist(reverse_sublist(s,start,end),start,end)\n\ndef get_longest_seq(my_str, l):\n    r = re.compile(\"(\"+l+\"+\"+l+\")\").findall(my_str)\n    if len(r):return max(r)\n    else: return None\n\ndef solve(s):\n    #print(\"STATE:\",file=sys.stderr)\n    #print(s,file=sys.stderr)\n    if s.find(\"b\")==-1 or len(s)==0:\n        return 0\n    if s[0]==\"a\":\n        pos = s.rfind(\"b\")\n        seq = get_longest_seq(s[0:pos],\"a\")\n        if seq == None:\n            pos = s.find(\"b\")\n            s = flip(s,0,pos)\n            return 1 + solve(s)\n        else:\n            pos = s.find(seq)\n            pos += len(seq)\n            s = flip(s,0,pos)\n            return 1 + solve(s)\n    else:\n        pos = s.rfind(\"b\")+1\n        s=flip(s,0,pos)\n        return 1 + solve(s)\n\n\n\nt = int(input())\nfor i in range(1,t+1):\n    s = input()\n    s = s.replace(\"+\",\"a\")\n    s = s.replace(\"-\",\"b\")\n    print(\"Case #\"+str(i)+\": \"+str(solve(s)))\n\n#print(toggle_sublist(\"aaabbbaaa\",0,6))\n\n#print(get_longest_seq(\"aaaabbbaba\"))\n#print(flip(\"abba\",0,5))\n","sub_path":"solutions_5634697451274240_0/Python/EpicSound/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":1494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"217830429","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom django.http import HttpResponse, JsonResponse\nfrom .models import MasterTable\nfrom .forms import MasterForm\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.core import serializers\nimport json\n\ndef table_content(request):\n    if request.method == \"POST\":\n        form = MasterForm(request.POST)\n        if form.is_valid():\n            record = form.save(commit=False)\n            record.save()\n            response = 'Saved'\n    else:\n        form = MasterForm()\n        response = ''\n    return render(request, 'testtable/table_content.html', {'form': form, 'response': response})\n\ndef json_request(request):\n    #data = serializers.serialize('python', MasterTable.objects.all()) # first convert the data to a python format, then json format\n    #data = [d['fields'] for d in data]\n    #data = json.dumps(data)\n    data = json.dumps([a.get_json() for a in MasterTable.objects.all()])\n    return HttpResponse(data, content_type='json')\n\n\n#https://simpleisbetterthancomplex.com/tutorial/2016/08/29/how-to-work-with-ajax-request-with-django.html\n@csrf_exempt\ndef save_to_django(request):\n    message = []\n    if request.method == \"POST\":\n        json_result = request.body # Write POST request into json array\n        dictionaries = json.loads(json_result) # Write json array into list of dictionaries\n        for dictionary in dictionaries:\n            save = True\n            if MasterTable.objects.filter(pk=dictionary.get('id', 0)).exists(): # Check if pk exists\n                existing_entry = get_object_or_404(MasterTable, pk=dictionary.get('id'))\n                form = MasterForm(dictionary, instance=existing_entry) # Tell Django to update attributes of existing model object\n                if form.has_changed():\n                    message.append(form.as_p)\n                    message.append(\"PK {}: fields {} changed.\".format(dictionary.get('id', 0), \", \".join(form.changed_data)))\n                else:\n                    save = False\n            else:\n                form = MasterForm(dictionary) # Create new model object\n            if form.is_valid() and save:\n                form.save()\n\n    return render(request, 'testtable/ajax_result.html', {'datasets': message})\n\ndef validate_name(request):\n    name = request.GET.get('name', None)\n    data = {\n        'is_taken': MasterTable.objects.filter(name__iexact=name).exists()\n    }\n    if data['is_taken']:\n        data['error_message'] = 'A user with this name already exists.'\n    return JsonResponse(data)\n","sub_path":"testtable/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"458807514","text":"import requests\nimport RiotConstants as RC\n\nclass LoLException(Exception):\n    def __init__(self, error):\n        self.error = error\n\n    def __str__(self):\n        return self.error\n\nerror_400 = LoLException(\"Bad request\")\nerror_401 = LoLException(\"Unauthorized\")\nerror_404 = LoLException(\"Game data not found\")\nerror_429 = LoLException(\"Too many requests\")\nerror_500 = LoLException(\"Internal server error\")\nerror_503 = LoLException(\"Service unavailable\")\n\ndef raise_status(response):\n    if response.status_code == 400:\n        raise error_400\n    elif response.status_code == 401:\n        raise error_401\n    elif response.status_code == 404:\n        raise error_404\n    elif response.status_code == 429:\n        raise error_429\n    elif response.status_code == 500:\n        raise error_500\n    elif response.status_code == 503:\n        raise error_503\n    else:\n        response.raise_for_status()\n        \nclass RiotAPI(object):\n    def __init__(self, api_key, defaultregion=RC.REGIONS['north_america']):\n        self.api_key = api_key\n        self.defaultregion = defaultregion\n        \n    def _request(self, api_url, region, params={}):\n        if region is None:\n            region = self.defaultregion\n        \n        entries = {'api_key': self.api_key}\n        for key, value in params.items():\n            if key not in entries:\n                entries[key] = value\n        \n        reply = requests.get(\n            RC.URL['base'].format(\n                proxy = region,\n                region = region,\n                url = api_url),\n            params = entries)\n        \n        raise_status(reply)\n        print(reply.url) #comment out if I don't want to see the URL request made in the shell (good to keep track of progress), will remove if/when new api is used\n        return reply.json()\n    \n    def get_summoner_by_name(self, region, sumname):\n        api_url = RC.URL['summoner_by_name'].format(\n            version = RC.API_VERSIONS['summoner'],\n            names = sumname)\n        return self._request(api_url, region)\n    \n    def stats_by_summoner_summary(self, region, sumid):\n        api_url = RC.URL['stats_by_summoner_summary'].format(\n            version = RC.API_VERSIONS['stats'],\n            summonerId = sumid)\n        return self._request(api_url, region)\n    \n    def stats_by_summoner_ranked(self, region, sumid):\n        api_url = RC.URL['stats_by_summoner_ranked'].format(\n            version = RC.API_VERSIONS['stats'],\n            summonerId = sumid)\n        return self._request(api_url, region)\n    \n    def get_matchhistory_by_summoner(self, region, sumid):\n        api_url = RC.URL['matchhistory_by_summoner'].format(\n            version = RC.API_VERSIONS['matchhistory'],\n            summonerId = sumid)\n        return self._request(api_url, region)\n    \n    def get_ranked_league_by_summoner(self, region, sumid):\n        api_url = RC.URL['ranked_league_by_summoner'].format(\n            version = RC.API_VERSIONS['league'],\n            summonerId = sumid)\n        return self._request(api_url, region)\n    \n    def get_masteries_by_summoner(self, region, sumid):\n        api_url = RC.URL['masteries_by_summoner'].format(\n            version = RC.API_VERSIONS['summoner'],\n            summonerId = sumid)\n        return self._request(api_url, region)\n    \n    def get_names_by_summoner(self, region, sumid):\n        api_url = RC.URL['names_by_summoner'].format(\n            version = RC.API_VERSIONS['summoner'],\n            summonerId = sumid)\n        return self._request(api_url, region)\n    \n    def get_runes_by_summoner(self, region, sumid):\n        api_url = RC.URL['runes_by_summoner'].format(\n            version = RC.API_VERSIONS['summoner'],\n            summonerId = sumid)\n        return self._request(api_url, region)\n    \n    def get_match_by_matchid(self, region, matchid):\n        api_url = RC.URL['match_by_matchid'].format(\n            version = RC.API_VERSIONS['match'],\n            matchId = matchid)\n        return self._request(api_url, region)","sub_path":"RiotAPI.py","file_name":"RiotAPI.py","file_ext":"py","file_size_in_byte":4018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"495458055","text":"\nfrom elasticsearch import Elasticsearch\n\n# by default we connect to localhost:9200\nes = Elasticsearch()\n\n\nimport random\n\n\n#\n# sample_header='Date    Content-Type    Content-Length    Connection    Server    X-Powered-By    Vary    X-UA-Compatible    Content-language    Content-Encoding    P3P    X-Content-Type-Options    Last-Modified    Backend-Timing    X-Varnish    Via    Accept-Ranges    Age    X-Cache    Strict-Transport-Security    Set-Cookie    X-Analytics    X-Client-IP    Cache-Control'\n# sample_html='<!DOCTYPE html><html><body><h1>My First Heading</h1><p>My first paragraph.</p></body></html>'\n#\n\n#\n# link11 = 'http://www.theatlantic.com/international/archive/2016/04/world-response-terrorism/476337/'\n# link1 = 'http://en.wikipedia.org/wiki/List_of_terrorist_incidents'\n# link2 = 'http://en.wikipedia.org/wiki/September_11_attacks'\n# link3 = 'http://en.wikipedia.org/wiki/Collapse_of_the_World_Trade_Center'\n# link4 = 'http://en.wikipedia.org/wiki/World_Trade_Center'\n# link5 = 'https://en.wikipedia.org/wiki/2016_Orlando_nightclub_shooting'\n# link6 = 'https://en.wikipedia.org/wiki/2015_San_Bernardino_attack'\n# link8 = 'http://en.wikipedia.org/wiki/Patriot_Act'\n# link9 = 'http://www.terrorism-research.com'\n# link10 = 'https://www.washingtonpost.com/news/monkey-cage/wp/2015/11/23/youre-more-likely-to-be-fatally-crushed-by-furniture-than-killed-by-a-terrorist/'\n# link12='http://fas.org/irp/threat/cyber/docs/npgs/terror.htm'\n#\n#\n# init_links = [link1, link2, link3, link4, link5, link6, link8, link9, link10,link11,link12]\n#\n#\n\n\ndef check_repetition():\n    pass\n\n\n\n\ndef save_dictionary(path,data):\n    print('saving catalog...')\n    #open('u.item', encoding=\"utf-8\")\n    import json\n    with open(path,'w') as outfile:\n        json.dump(data, fp=outfile)\n    # save to file:\n    print(' catalog saved')\n########################################################################################\n########################################################################################\n########################################################################################\n########################################################################################\n\ndef read_dictionary(path):\n\n    import json\n    # load from file:\n    g = open(path, 'r')\n    print('reading ...')\n\n    try:\n        data = json.load(g)\n    # if the file is empty the ValueError will be thrown\n    except ValueError:\n        data = {}\n    print('reading finished!')\n    return(data)\n\n########################################################################################\n########################################################################################\n########################################################################################\n########################################################################################\n\ndef file_format_correction():\n\n    import os\n    path='/Users/mohsennabian/Infomation_Retrieval_4rdtrial'\n\n    all_files_name=os.listdir(path)\n\n    final_links=read_dictionary(path+'/'+'final_links.txt')\n    head_links = read_dictionary(path + '/' + 'all_heads.txt')\n\n    print(len(final_links))\n\n\n    author='Mohsen Nabian'\n\n\n    # count=0\n    # for i in range(0, 282):\n    #     print(i)\n    #\n    #\n    #     canonical_dict = read_dictionary(path + '/' + 'canonical_' + str(i) + '.txt')\n    #\n    #\n    #     can=canonical_dict.keys()\n    #     org=canonical_dict.values()\n    #     # can=list(set(can))\n    #     # org=list(set(org))\n    #     count=count+len(can)\n    #\n    #     # print(len(org)-len(can))\n    #\n    # print(count)\n\n\n    error_count=0\n    t=0\n    for i in range(0,282):\n\n\n        target2 = open(path + '/full2_'+str(i)+'.txt', 'w')\n        txt_dict = read_dictionary(path+'/'+'txt_'+str(i)+'.txt')\n        canonical_dict = read_dictionary(path+'/'+'canonical_'+str(i)+'.txt')\n        head_dict = read_dictionary(path+'/'+'head_'+str(i)+'.txt')\n        out_link_dict=read_dictionary(path+'/'+'outlinks_'+str(i)+'.txt')\n        l= len(txt_dict)\n\n        LINKS=canonical_dict.keys()  #original links\n        m=1\n        for link in LINKS:\n            print(m)\n            m=m+1\n            try:\n                print('folder '+str(i)+'link '+str(t))\n\n\n\n                target2.write('<DOC>')\n                target2.write('\\n')\n\n\n                target2.write('<DOCNO>'+ canonical_dict[link] +'</DOCNO>')\n                target2.write('\\n')\n\n\n\n                target2.write('<DOCHDR>' + head_links[link] + '</DOCHDR>')\n                target2.write('\\n')\n\n\n                target2.write('<HEAD>'+head_dict[link]+'</HEAD>')\n\n                target2.write('\\n')\n\n\n                target2.write('<URL>'+link+'/<URL>')\n                target2.write('\\n')\n\n\n\n\n                target2.write('<OUTLINK>'+' '.join(map(str,out_link_dict[link]))+'</OUTLINK>')\n\n                target2.write('\\n')\n\n\n                target2.write('<AUTHOR>' + ' Mohsen Nabian '+ '</AUTHOR>')\n                target2.write('\\n')\n\n\n                target2.write('<DEPTH>' + str(final_links[link])+ '</DEPTH>')\n\n                target2.write('\\n')\n\n\n                target2.write('</DOC>')\n                t = t + 1\n                print('good')\n                print('error = '+ str(error_count))\n\n            except:\n                error_count = error_count+1\n\n\n\n\n\n\ndef extract_head_and_html_save():\n    from BeautifulSoup import BeautifulSoup, SoupStrainer\n    from requests import request\n    path = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial'\n    path2 = path +'/html'\n    html_link={}\n    head_link={}\n    final_links = read_dictionary(path + '/' + 'final_links.txt')\n\n    LINKS=final_links.keys()\n    delta=len(LINKS)\n    index=0\n    count=0\n    t=0\n    eror_no=0\n    parse_error=0\n    for link in LINKS:\n        count=count+1\n        print(count)\n        print('Mohsen eror =  ' +str(eror_no))\n        print('Ali eror = ' +str(parse_error))\n\n        t=t+1\n        if t>9:\n\n            t=0\n            try:\n\n                save_dictionary(path2+'/html'+str(index)+'.txt',html_link)\n\n            except:\n                eror_no=eror_no+1\n                print('error in save')\n            index = index + 1\n            html_link={}\n        try:\n            response = request('GET', link,timeout=10)\n        except:\n            print(link)\n            parse_error = parse_error + 1\n            html_link[link] = '<!DOCTYPE html><html><body><h1>My First Heading</h1><p>My first paragraph.</p></body></html>'\n            head_link[link] = 'Date    Content-Type    Content-Length    Connection    Server    X-Powered-By    Vary    X-UA-Compatible    Content-language    Content-Encoding    P3P    X-Content-Type-Options    Last-Modified    Backend-Timing    X-Varnish    Via    Accept-Ranges    Age    X-Cache    Strict-Transport-Security    Set-Cookie    X-Analytics    X-Client-IP    Cache-Control'\n            continue\n\n        html_link[link] = str(response.content)\n        head_link[link] = str(response.headers)\n\n\n\n    save_dictionary(path2 + '/html' + str(index) + '.txt', html_link)\n    save_dictionary(path + '/all_heads' + '.txt', head_link)\n\n\n#extract_head_and_html_save()\n\n\ndef inlink_tab_format():\n\n    path = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial'\n    inlink_dict = read_dictionary(path+'/inlinks.txt')\n    new_inlinks=open(path+'/new_inlinks.txt', 'w')\n    LINKS=inlink_dict.keys()\n    i=0\n    for link in LINKS:\n        i=i+1\n        print(i)\n        new_inlinks.write(link + '  '.join(map(str, inlink_dict[link])))\n\n\n# file_format_correction()\n\n#inlink_tab_format()\n\n\n\n\n\n\ndef term_search(word):  # returns the term frequency of the given word in all documents as well as the document id\n    es = Elasticsearch()\n    tf = []\n    doc_id = []\n\n    total_hit = es.search(index='ap_dataset', doc_type='document', body={\"query\": {\n        \"match\": {\n            \"text\": word\n        }\n    },\n        \"explain\": \"true\"})['hits']['total']\n\n    print('number of hits for ',word, 'is ',total_hit)\n    total_hit=int(total_hit)\n    orig_total_hit=total_hit\n    if (total_hit>10000):\n        total_hit=10000\n    xx_root = es.search(index='ap_dataset', doc_type='document', size=total_hit, body={\"query\": {\n        \"match\": {\n            \"text\": word\n        }\n    },\n        \"explain\": \"true\"})['hits']['hits']\n\n    for id in range(0, total_hit):\n        print(id)\n        Jlist = xx_root[id]\n        Jlist = str(Jlist)\n        import re\n        m = re.search(\"description': u'tf.freq=([0-9]+)\", Jlist)\n        found = m.group(1)\n        print(found)\n        xx = int(found)+0.0\n        yy = xx_root[id]['_id']\n        tf.append(xx)\n        doc_id.append(yy)\n\n    print(total_hit)\n    print(len(tf))\n    print(len(doc_id))\n    return(orig_total_hit,doc_id,tf)\n\n\n\n\ndef location_html():\n    import os\n    path = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial'\n    pass\n\ndef location_string():\n    path = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial'\n    path_new = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial/content_with_address'\n    g = open(path_new + '/content.txt', 'w')\n    catalog_string={}\n    error_no=0\n    for i in range(0, 282):\n        print(i)\n        txt_dict = read_dictionary(path + '/' + 'txt_' + str(i) + '.txt')\n\n        for url in txt_dict.keys():\n\n                start_bit = g.tell()\n                g.write(txt_dict[url] + '\\n')\n                end_bit = g.tell()\n                location = [start_bit, end_bit]\n                catalog_string[url]= location\n\n                # error_no=error_no+1\n                # start_bit = g.tell()\n                # g.write('' + '\\n')\n                # end_bit = g.tell()\n                # location = [start_bit, end_bit]\n                # catalog_string[url] = location\n                # print('error_no = '+str(error_no))\n\n    catalog_path=path_new+'/catalog_string.txt'\n    print(len(catalog_string))\n    save_dictionary(catalog_path,catalog_string)\n\n\n\ndef location_string2():\n    path = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial'\n    path_new = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial/content_with_address'\n    g = open(path_new + '/content.txt', 'w')\n    catalog_string={}\n    error_no=0\n    for i in range(0, 282):\n        print(i)\n        filepath=path + '/' + 'txt_' + str(i) + '.txt'\n        txt_dict = read_dictionary(filepath)\n\n        for url in txt_dict.keys():\n\n                # start_bit = g.tell()\n                # g.write(txt_dict[url] + '\\n')\n                # end_bit = g.tell()\n                # location = [start_bit, end_bit]\n                catalog_string[url]= filepath\n\n                # error_no=error_no+1\n                # start_bit = g.tell()\n                # g.write('' + '\\n')\n                # end_bit = g.tell()\n                # location = [start_bit, end_bit]\n                # catalog_string[url] = location\n                # print('error_no = '+str(error_no))\n\n    catalog_path=path_new+'/catalog_string.txt'\n    print(len(catalog_string))\n    save_dictionary(catalog_path,catalog_string)\n\n\n\n\ndef location_html2():\n    path = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial/html'\n    path_new = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial/catalogs'\n    g = open(path_new + '/content.txt', 'w')\n    catalog_html={}\n    error_no=0\n    for i in range(0, 2001):\n        print(i)\n        filepath=path + '/' + 'html' + str(i) + '.txt'\n        txt_dict = read_dictionary(filepath)\n\n        for url in txt_dict.keys():\n\n                # start_bit = g.tell()\n                # g.write(txt_dict[url] + '\\n')\n                # end_bit = g.tell()\n                # location = [start_bit, end_bit]\n                catalog_html[url]= filepath\n\n                # error_no=error_no+1\n                # start_bit = g.tell()\n                # g.write('' + '\\n')\n                # end_bit = g.tell()\n                # location = [start_bit, end_bit]\n                # catalog_string[url] = location\n                # print('error_no = '+str(error_no))\n\n    catalog_path=path_new+'/catalog_html.txt'\n    print(len(catalog_html))\n    save_dictionary(catalog_path,catalog_html)\n\n\n#location_html2()\n\n\n\ndef extract_html(url):\n    file_location=catolog_html[url]\n    p=read_dictionary(file_location)\n    return(p[url])\n\n\n\ndef extract_string(url):\n    file_location=catolog_string[url]\n    p=read_dictionary(file_location)\n    return(p[url])\n\n\n\n#print(extract_string(\"http://www.care.org/impact/careknowshow\"))\n\n# for i in range(0,20000):\n#     a=extract_string(\"http://www.care.org/impact/careknowshow\")\n#     print(i)\n#\n#\n\n\n\ndef make_things_as_a_dictionary():\n\n        from collections import Counter\n        import os\n        path = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial'\n\n        all_files_name = os.listdir(path)\n\n\n\n        print(len(final_links))\n\n        author = 'Mohsen Nabian'\n\n        # count=0\n        # for i in range(0, 282):\n        #     print(i)\n        #\n        #\n        #     canonical_dict = read_dictionary(path + '/' + 'canonical_' + str(i) + '.txt')\n        #\n        #\n        #     can=canonical_dict.keys()\n        #     org=canonical_dict.values()\n        #     # can=list(set(can))\n        #     # org=list(set(org))\n        #     count=count+len(can)\n        #\n        #     # print(len(org)-len(can))\n        #\n        # print(count)\n\n\n        error_count = 0\n        t = 0\n        canonical_dict={'a':1}\n        head_dict={'a':1}\n        out_link_dict={'a':1}\n\n        canonical_dict ={}\n        canonical_dict_keys=[]\n        canonical_dict_values = []\n\n        head_dict = {}\n        head_dict_keys=[]\n        head_dict_values=[]\n\n\n\n        out_link_dict = {}\n        out_link_dict_keys=[]\n        out_link_dict_values=[]\n\n\n        for i in range(0, 282):\n\n            print(i)\n            canonical_dict_keys = read_dictionary(path + '/' + 'canonical_' + str(i) + '.txt').keys()+canonical_dict_keys\n            canonical_dict_values = read_dictionary(path + '/' + 'canonical_' + str(i) + '.txt').values() + canonical_dict_values\n\n            head_dict_keys = read_dictionary(path + '/' + 'head_' + str(i) + '.txt').keys()+head_dict_keys\n            head_dict_values = read_dictionary(path + '/' + 'head_' + str(i) + '.txt').values() + head_dict_values\n\n\n\n            out_link_dict_keys = read_dictionary(path + '/' + 'outlinks_' + str(i) + '.txt').keys()+out_link_dict_keys\n            out_link_dict_values = read_dictionary(path + '/' + 'outlinks_' + str(i) + '.txt').values() + out_link_dict_values\n\n        canonical_dict=dict(zip(canonical_dict_keys, canonical_dict_values))\n        head_dict=dict(zip(head_dict_keys, head_dict_values))\n        out_link_dict=dict(zip(out_link_dict_keys,out_link_dict_values))\n\n\n        path='/Users/mohsennabian/Infomation_Retrieval_4rdtrial/catalogs/'\n\n        save_dictionary(path+'canonical_dict.txt', canonical_dict)\n        save_dictionary(path+'head_dict.txt', head_dict)\n        save_dictionary(path+'out_link_dict.txt', out_link_dict)\n\n\n\n\nlink11 = 'http://www.theatlantic.com/international/archive/2016/04/world-response-terrorism/476337/'\nlink1 = 'http://en.wikipedia.org/wiki/List_of_terrorist_incidents'\nlink2 = 'http://en.wikipedia.org/wiki/September_11_attacks'\nlink3 = 'http://en.wikipedia.org/wiki/Collapse_of_the_World_Trade_Center'\nlink4 = 'http://en.wikipedia.org/wiki/World_Trade_Center'\nlink5 = 'https://en.wikipedia.org/wiki/2016_Orlando_nightclub_shooting'\nlink6 = 'https://en.wikipedia.org/wiki/2015_San_Bernardino_attack'\nlink8 = 'http://en.wikipedia.org/wiki/Patriot_Act'\nlink9 = 'http://www.terrorism-research.com'\nlink10 = 'https://www.washingtonpost.com/news/monkey-cage/wp/2015/11/23/youre-more-likely-to-be-fatally-crushed-by-furniture-than-killed-by-a-terrorist/'\nlink12='http://fas.org/irp/threat/cyber/docs/npgs/terror.htm'\n\n\ninit_links = [link1, link2, link3, link4, link5, link6, link8, link9, link10,link11,link12]\n\n\n\npath='/Users/mohsennabian/Infomation_Retrieval_4rdtrial'\n\n#make_things_as_a_dictionary()\n\nfinal_links = read_dictionary(path + '/' + 'final_links.txt')\n\nl=len(final_links)\nprint(l)\nr=[]\nfor i in range(0,10):\n    r.append(random.randint(1,1))\n\nfor i in range(0,500):\n    r.append(random.randint(1,1))\n\nfor i in range(0,5000):\n    r.append(random.randint(2,2))\n\n\nfor i in range(0,14491):\n    r.append(random.randint(3,3))\n\nfor link in init_links:\n    final_links[link]=0\n\n\n\nprint(len(r))\n\n\n\nkeys=final_links.keys()\nvalues=r\nfinal_links = dict(zip(keys, values))\n\n\n\n\n\n\ncatolog_html=read_dictionary('/Users/mohsennabian/Infomation_Retrieval_4rdtrial/catalogs/catalog_html.txt')\ncatolog_string=read_dictionary('/Users/mohsennabian/Infomation_Retrieval_4rdtrial/catalogs/catalog_string.txt')\n\n\n\nbig_head_links = read_dictionary(path + '/' + 'all_heads.txt')\n\n\ncanonical_dict=read_dictionary(path + '/catalogs/' + 'canonical_dict.txt')\nhead_dict=read_dictionary(path + '/catalogs/' + 'head_dict.txt')\nout_link_dict=read_dictionary(path + '/catalogs/' + 'out_link_dict.txt')\ninlink_dict=read_dictionary(path + '/catalogs/' + 'inlinks.txt')\n\n\n\nprint('hhhhhhi')\n\n\nglobal ee\nee= 0\n\nglobal pushing_in_number\n\npushing_in_number=0\n\n\nglobal najoor\nnajoor =0\n\nglobal b\nb = 0\n\n\n\ndef push_elasticsearch(link):\n\n    global pushing_in_number\n    print(link)\n    global ee\n    global najoor\n    global b\n\n    try:\n\n        es.index(index=INDEX_NAME, doc_type=\"document\", id=link,\n                 body={\"docno\": canonical_dict[link].lower(), \"HTTPheader\": big_head_links[link].lower()\n                     , \"title\": head_dict[link].lower(), \"text\": extract_string(link).lower(), 'html_Source': extract_html(link).lower()\n                     , 'in_links': str('\\t'.join(inlink_dict[link])).lower(), 'out_links': str('\\t'.join(out_link_dict[link])).lower(),\n                       'author': \"mohsen\",\n                       'depth': int(final_links[link]), 'url': link.lower()})\n        b = b + 1\n        print('good  =  ' + str(b))\n    except:\n\n        link = 'http://www.jpost.com/Jerusalem-Report/Donald-Trump-Israel-and-the-Jews-438335'\n        ee = ee + 1\n        k = link + '/' + str(ee)\n        print('sorry_bad')\n\n        print(k)\n        es.index(index=INDEX_NAME, doc_type=\"document\", id=k,\n                 body={\"docno\": k.lower(), \"HTTPheader\": big_head_links[link].lower()\n                     , \"title\": head_dict[link].lower(), \"text\": extract_string(link).lower(), 'html_Source': extract_html(link).lower()\n                     , 'in_links': str('\\t'.join(inlink_dict[link])).lower(), 'out_links': str('\\t'.join(out_link_dict[link])).lower(), 'author': \"mohsen\",\n                       'depth': int(final_links[link]), 'url': k.lower()})\n\n        najoor = najoor + 1\n        print('najoor  =  ' + str(najoor))\n    pushing_in_number=pushing_in_number+1\n    print('number of iteration=  ' + str(pushing_in_number))\n\n\n\n\n\ndef elasticsearch_indexing_my_crawl():  # uses read_documents function.will create index file given the documents and ids.\n\n\n    LINKS = canonical_dict.keys()\n    print(len(LINKS))\n\n\n    for link in LINKS:\n        push_elasticsearch(link)\n\n\n\n\n\ndef update_fields(doc_no,inlinks,outlinks,authors,depth):\n    doc = {\n        'doc': {\n            'depth':depth,\n            'inlinks': inlinks,\n            'outlinks': outlinks,\n            'author': authors\n        }\n    }\n    es.update(index=INDEX_NAME, id=doc_no, doc_type='document',\n              body=doc)\n\n\n\ndef document_exists(doc_no):\n\n    return es.exists(index=INDEX_NAME,\n                     id=str(doc_no),\n                     doc_type='document')\n\n\n\n\ndef merging():\n\n    global pushing_in_number\n    LINKS = canonical_dict.keys()\n    print(len(LINKS))\n\n    path = '/Users/mohsennabian/Infomation_Retrieval_4rdtrial'\n\n    #path = '/Users/mohsennabian/Dropbox/AP_DATA/ap89_collection'\n    for link in LINKS:\n        print('number of pushing'+ str(pushing_in_number))\n        canlink=canonical_dict[link]\n\n\n\n\n        print(link)\n\n        try:\n            repeated=document_exists(canlink)\n\n        except:\n            print('najoor 2')\n            push_elasticsearch(link)\n            continue\n\n        if not repeated:\n            print('new')\n            push_elasticsearch(link)\n            pushing_in_number = pushing_in_number + 1\n\n        else:\n            print('repeat_merging....')\n            existing_data = es.get(index=INDEX_NAME, doc_type='document', id=canlink,\n                                   fields=['in_links', 'out_links', 'author', 'depth'])\n\n\n\n            current_inlinks=''.join(existing_data['fields']['in_links'])\n            current_outlinks=''.join(existing_data['fields']['out_links'])\n            current_author = ''.join(existing_data['fields']['author'])\n            current_depth = existing_data['fields']['depth']\n            try:\n                merged_inlinks='\\t'.join(list(set(current_inlinks.splite('\\t')+inlink_dict[link])))\n            except:\n                merged_inlinks = current_inlinks\n            try:\n                merged_outlinks='\\t'.join(list(set(current_outlinks.splite('\\t')+out_link_dict[link])))\n            except:\n                merged_outlinks=current_outlinks\n\n\n\n            if final_links[link]<current_depth:\n                merged_depth=final_links[link]\n            else:\n                merged_depth=current_depth\n\n            merged_author=str(current_author)+\" mohsen \"\n            try:\n                update_fields(canlink.lower(), merged_inlinks.lower(), merged_outlinks.lower(),merged_author.lower(), merged_depth)\n            except:\n                update_fields(canlink.lower(), merged_inlinks.lower(), merged_outlinks.lower(),merged_author.lower(), merged_depth)\n            pushing_in_number = pushing_in_number+1\n\n\n\n\n\n\nINDEX_NAME='mohsen1'\nelasticsearch_indexing_my_crawl()\n\n#merging()\n#\n#\n\n#\n# LINKS = canonical_dict.keys()\n# print(len(LINKS))\n# print(len(list(set(LINKS))))\n#\n#\n# canLINk=canonical_dict.values()\n# print(len(canLINk))\n# print(len(list(set(canLINk))))\n\n\n#1)remove all try and except\n#2)link to canonical\n#3) un omgh haye random ru avaz kon\n","sub_path":"Submissions/HW3/Final/File_format_correction_HW3.py","file_name":"File_format_correction_HW3.py","file_ext":"py","file_size_in_byte":21935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"573517086","text":"'''\nCopyright © 2020 Forescout Technologies, Inc.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n'''\nimport logging\nimport base64\nimport jwt\nimport hashlib\nimport time\nimport json\nimport ssl\nimport urllib.request\n\n# Use this region to setup the call info of the TMCM server (server url, application id, api key)\nuse_url_base = params['connect_apex_url']\nuse_application_id = params['connect_apex_appid']\nuse_api_key = params['connect_apex_apikey']\nuseRequestBody = ''\nid = params.get('connect_apex_id')\nip = params.get('ip')\n\n# currently Canonical-Request-Headers will always be empty\ncanonicalRequestHeaders = ''\nproductAgentAPIPath = '/WebApp/API/AgentResource/ProductAgents'\n\ndef create_checksum(http_method, raw_url, headers, request_body):\n    string_to_hash = http_method.upper() + '|' + raw_url.lower() + '|' + headers + '|' + request_body\n    base64_string = base64.b64encode(hashlib.sha256(str.encode(string_to_hash)).digest()).decode('utf-8')\n    return base64_string\n\ndef create_jwt_token(appication_id, api_key, http_method, raw_url, headers, request_body,\n                     iat=time.time(), algorithm='HS256', version='V1'):\n    checksum = create_checksum(http_method, raw_url, headers, request_body)\n    payload = {'appid': appication_id,\n               'iat': iat,\n               'version': version,\n               'checksum': checksum}\n    token = jwt.encode(payload, api_key, algorithm=algorithm).decode('utf-8')\n    return token\n\nlogging.debug('apex: Attempting to restore isolated client using following parameters: url={} application ID={} Client ID={} API path={} '.format(\nuse_url_base ,use_application_id,id,productAgentAPIPath))\nresponse = {}\n\nif id is not None and id != \"\":\n    useRequestBody = json.dumps({'entity_id': id, 'act': 'cmd_restore_isolated_agent', 'allow_multiple_match': False})\n\n    jwt_token = create_jwt_token(use_application_id, use_api_key, 'POST',\n                                  productAgentAPIPath,\n                                  canonicalRequestHeaders, useRequestBody, iat=time.time())\n    headers = {'Authorization': 'Bearer ' + jwt_token}\n    useRequestBodyBytes = bytes(useRequestBody, encoding='utf8')\n    request = urllib.request.Request(use_url_base + productAgentAPIPath, method='POST', data=useRequestBodyBytes, headers=headers)\n    try:\n        resp = urllib.request.urlopen(request, timeout=5, context=ssl_context)\n        request_response = json.loads(resp.read())\n        if request_response['result_code'] == 1:\n            response[\"succeeded\"]= True\n            response['result_msg'] = request_response['result_description']\n            logging.debug('apex: Restoration of isolated client succedded for, ' + ip)\n        else:\n            response[\"succeeded\"]= False\n            response[\"error\"] = \"Error restoring isolated client: unable to connect to server, please test settings\"\n            logging.info('apex: Error restoring isolated client for ip address: ' + ip + ', unable to connect to server: ' + str(request_response['result_description']))\n    except urllib.error.URLError as e:\n        response['succeeded'] = False\n        response[\"error\"] = \"Error restoring isolated client: unable to connect to server, please test settings\"\n        logging.info('apex: Error restoring isolated client for ip address: ' + ip + ', unable to connect to server: ' + str((e.reason)))\nelse:\n    response[\"succeeded\"]= False\n    response[\"error\"] = \"No Apex ID for client.\"\n    logging.info('apex: No Apex ID found to execute for client: ' + ip)\n","sub_path":"Trend Micro/Trend Micro Apex 1.0.0/apex_restore.py","file_name":"apex_restore.py","file_ext":"py","file_size_in_byte":4494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"491785665","text":"# Import the pathlib and csv library\nimport os\nimport sys\nfrom pathlib import Path\nimport csv \n\n# Set the file path\ncsvpath = Path(os.path.join(sys.path[0], 'Resources/budget_data.csv'))\n\n# Initializatione\nrecords = []\nnumMonths = 0\nprofit_total = 0\nmaxMonthlyProfitibility = None\nminMonthlyProfitability = None\npreviousMonthlyProfit = None\nmonthlyProfitChange = 0\ntotalMonthlyProfitChange = 0\n# method 1\nminMonthlyChange = float(\"inf\")\nminChangeMonth = \"\"\n# method 2\nmaxMonthlyChange = [-float(\"inf\"), \"\"]\n\n\n# Open the csv file as an object\nwith open(csvpath, 'r') as csvfile:\n        \n    # Read header\n    csvreader = csv.reader(csvfile, delimiter=',')\n    csv_header = next(csvreader)\n    \n    # Read each row of data after the header\n    for row in csvreader:\n    \n        # print(row)\n        \n        # Count number of months\n        numMonths+= 1\n        \n        # Calculate total Profit/Loss\n        monthlyProfitLoss = int(row[1])\n        profit_total += monthlyProfitLoss\n        \n        # Calculate monthly profit change\n        if previousMonthlyProfit is None:\n            # we are on the first row\n            previousMonthlyProfit = monthlyProfitLoss\n            # print(\"We are on the first row\")\n            # print(f\"previousMonthlyProfit = {previousMonthlyProfit}\")\n        else: \n            # we are not on the first row\n            # print(\"We are not on the first row\")\n            # print(f\"previousMonthlyProfit = {previousMonthlyProfit}\")\n            monthlyProfitChange = monthlyProfitLoss - previousMonthlyProfit\n            previousMonthlyProfit = monthlyProfitLoss\n        \n        # comment\n        # Calculate min / max Monthly Change\n\n        # method 1\n        if monthlyProfitChange < minMonthlyChange:\n            minMonthlyChange = monthlyProfitChange\n            minChangeMonth = row[0]\n        \n        # method 2\n        maxMonthlyChange = max(maxMonthlyChange, [monthlyProfitChange, row[0]])\n\n        totalMonthlyProfitChange += monthlyProfitChange\n        # print(monthlyProfitChange)\n        # print(totalMonthlyProfitChange)\n        \n# Average P/L\naveragePnl = round(profit_total / numMonths, 2)\n\n# Average monthly profit change\naverageMonthlyProfitChange = round(totalMonthlyProfitChange / (numMonths - 1), 2)\n\n# Print results\nprint(\"Financial Analysis\")\nprint(\"-\" * 25)\nprint(\"Total Months:\", numMonths)\nprint(\"Total: $\", profit_total, sep=\"\")\nprint(f\"Average Change: ${averageMonthlyProfitChange}\")\n \n# C-style formatting\n(\"My digit: %d, my string %s\" % (5, \"five\"))\n# .format() method\n(\"My digit: {}, my string {}\".format(5, \"five\"))\n# f-string\n(f\"My digit: {5}, my string {'five'}\")\n\nprint(\"Greatest increase in profits: {} (${})\".format(maxMonthlyChange[1], maxMonthlyChange[0]))\nprint(f\"Greatest decrease in profits: {minChangeMonth} (${minMonthlyChange})\")\n","sub_path":"02-Python/Homework/Instructions/PyBank/untitled.py","file_name":"untitled.py","file_ext":"py","file_size_in_byte":2812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"85478845","text":"import json, random, time\nfrom channels.generic.websocket import WebsocketConsumer\n\nfrom channels.generic.websocket import AsyncWebsocketConsumer\nfrom channels.db import database_sync_to_async\nfrom integers.models import Message\n\nclass WSConsumer(WebsocketConsumer):\n\tdef connect(self):\n\t\tself.accept()\n\n\t\tfor i in range(1000):\n\t\t\tself.send(json.dumps({'message': random.randint(1, 100)}))\n\t\t\ttime.sleep(1)\n\nclass ChatConsumer(AsyncWebsocketConsumer):\n\t\n\t# Метод подключения к WS\n\tasync def connect(self):\n\t\t# Назначим пользователя в комнату\n\t\tself.room_group_name = \"1\"\n\t\t# Добавляем новую комнату\n\t\tawait self.channel_layer.group_add(\n\t\t\tself.room_group_name,\n\t\t\tself.channel_name\n\t\t)\n\t\t# Принимаем подключаем\n\t\tawait self.accept()\n \n\t# Метод для отключения пользователя\n\tasync def disconnect(self, close_code):\n\t\t# Отключаем пользователя\n\t\tawait self.channel_layer.group_discard(\n\t\t\tself.room_group_name,\n\t\t\tself.channel_name\n\t\t)\n \n\t# Декоратор для работы с БД в асинхронном режиме\n\t@database_sync_to_async\n\t# Функция для создания нового сообщения в БД\n\tdef new_message(self, message):\n\t\t# Создаём сообщение в БД\n\t\tMessage.objects.create(text=message)\n \n\t# Принимаем сообщение от пользователя\n\tasync def receive(self, text_data=None, bytes_data=None):\n\t\t# Форматируем сообщение из JSON\n\t\ttext_data_json = json.loads(text_data)\n\t\t# Получаем текст сообщения\n\t\tmessage = text_data_json['message']\n\t\t\n\t\t# Добавляем сообщение в БД \n\t\tawait self.new_message(message=message)\n\t\t\n\t\t# Отправляем сообщение \n\t\tawait self.channel_layer.group_send(\n\t\t\tself.room_group_name,\n\t\t\t{\n\t\t\t\t'type': 'chat_message',\n\t\t\t\t'message': message,\n\t\t\t}\n\t\t)\n\t\n\t# Метод для отправки сообщения клиентам\n\tasync def chat_message(self, event):\n\t\t# Получаем сообщение от receive\n\t\tmessage = event['message']\n\t\t# Отправляем сообщение клиентам\n\t\tawait self.send(text_data=json.dumps({\n\t\t\t'message': message,\n\t\t}, ensure_ascii=False))\n\t","sub_path":"integers/consumers/chat_consumers.py","file_name":"chat_consumers.py","file_ext":"py","file_size_in_byte":2325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"156262449","text":"#!/usr/bin/env python3\nimport re\nfrom sys import stdin\n\n\nclass LightGrid:\n    def __init__(self):\n        self.g = [[0 for col in range(1000)] for row in range(1000)]\n\n    # all of these functions will behave badly\n    # if p2.col < p1.col or p2.row < p2.col\n    def turn_on(self, p1, p2):\n        for row in range(p1[0], p2[0]+1):\n            for col in range(p1[1], p2[1]+1):\n                self.g[row][col] = 1\n\n    def turn_off(self, p1, p2):\n        for row in range(p1[0], p2[0]+1):\n            for col in range(p1[1], p2[1]+1):\n                self.g[row][col] = 0\n\n    def toggle(self, p1, p2):\n        for row in range(p1[0], p2[0]+1):\n            for col in range(p1[1], p2[1]+1):\n                self.g[row][col] ^= 1\n\n    def num_lights_on(self):\n        return sum((col for row in self.g for col in row))\n\n\nif __name__ == '__main__':\n    lg = LightGrid()\n\n    for line in stdin.readlines():\n        # Avoid issues on empty lines...\n        if not line:\n            break\n\n        # Extract coord pairs.\n        m = re.search(r'(\\d+,\\d+)\\D*(\\d+,\\d+)', line)\n        p1 = tuple(int(x) for x in m.group(1).split(','))\n        p2 = tuple(int(x) for x in m.group(2).split(','))\n        assert p1 < p2\n\n        # Execute\n        if 'turn on' in line:\n            lg.turn_on(p1, p2)\n        elif 'turn off' in line:\n            lg.turn_off(p1, p2)\n        elif 'toggle' in line:\n            lg.toggle(p1, p2)\n\n    print(lg.num_lights_on())\n","sub_path":"2015/day06/p01.py","file_name":"p01.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"372437780","text":"# encoding: utf-8\nimport tkinter as tk\nimport tkinter.ttk as ttk\n\nfrom pygubu.binding import (\n    ApplicationLevelBindManager as BindManager,\n    remove_binding,\n)\n\n\nclass ScrolledFrame(ttk.Frame):\n    VERTICAL = \"vertical\"\n    HORIZONTAL = \"horizontal\"\n    BOTH = \"both\"\n    _framecls = ttk.Frame\n    _sbarcls = ttk.Scrollbar\n\n    def __init__(self, master=None, **kw):\n        self.scrolltype = kw.pop(\"scrolltype\", self.VERTICAL)\n        self.usemousewheel = tk.getboolean(kw.pop(\"usemousewheel\", False))\n        self._bindingids = []\n\n        # super(ScrolledFrame, self).__init__(master, **kw)\n        self._framecls.__init__(self, master, **kw)\n\n        self._container = self._framecls(self, width=200, height=200)\n        self._clipper = self._framecls(self._container, width=200, height=200)\n        self.innerframe = self._framecls(self._clipper)\n        self.vsb = self._sbarcls(self._container)\n        self.hsb = self._sbarcls(self._container, orient=\"horizontal\")\n\n        # variables\n        self.hsbOn = 0\n        self.vsbOn = 0\n        self.hsbNeeded = 0\n        self.vsbNeeded = 0\n        self._jfraction = 0.05\n        self._scrollTimer = None\n        self._scrollRecurse = 0\n        self._startX = 0\n        self._startY = 0\n\n        # configure scroll\n        self.hsb.set(0.0, 1.0)\n        self.vsb.set(0.0, 1.0)\n        self.vsb.config(command=self.yview)\n        self.hsb.config(command=self.xview)\n\n        # grid\n        self._container.pack(expand=True, fill=\"both\")\n        self._clipper.grid(row=0, column=0, sticky=tk.NSEW)\n        # self.vsb.grid(row=0, column=1, sticky=tk.NS)\n        # self.hsb.grid(row=1, column=0, sticky=tk.EW)\n\n        self._container.rowconfigure(0, weight=1)\n        self._container.columnconfigure(0, weight=1)\n\n        # Whenever the clipping window or scrolled frame change size,\n        # update the scrollbars.\n        self.innerframe.bind(\"<Configure>\", self._reposition)\n        self._clipper.bind(\"<Configure>\", self._reposition)\n        self.bind(\"<Configure>\", self._reposition)\n        self._configure_mousewheel()\n\n    # Set timer to call real reposition method, so that it is not\n    # called multiple times when many things are reconfigured at the\n    # same time.\n    def reposition(self):\n        if self._scrollTimer is None:\n            self._scrollTimer = self.after_idle(self._scrollBothNow)\n\n    # Called when the user clicks in the horizontal scrollbar.\n    # Calculates new position of frame then calls reposition() to\n    # update the frame and the scrollbar.\n    def xview(self, mode=None, value=None, units=None):\n        if isinstance(value, str):\n            value = float(value)\n        if mode is None:\n            return self.hsb.get()\n        elif mode == \"moveto\":\n            frameWidth = self.innerframe.winfo_reqwidth()\n            self._startX = value * float(frameWidth)\n        else:  # mode == 'scroll'\n            clipperWidth = self._clipper.winfo_width()\n            if units == \"units\":\n                jump = int(clipperWidth * self._jfraction)\n            else:\n                jump = clipperWidth\n            self._startX = self._startX + value * jump\n\n        self.reposition()\n\n    # Called when the user clicks in the vertical scrollbar.\n    # Calculates new position of frame then calls reposition() to\n    # update the frame and the scrollbar.\n    def yview(self, mode=None, value=None, units=None):\n\n        if isinstance(value, str):\n            value = float(value)\n        if mode is None:\n            return self.vsb.get()\n        elif mode == \"moveto\":\n            frameHeight = self.innerframe.winfo_reqheight()\n            self._startY = value * float(frameHeight)\n        else:  # mode == 'scroll'\n            clipperHeight = self._clipper.winfo_height()\n            if units == \"units\":\n                jump = int(clipperHeight * self._jfraction)\n            else:\n                jump = clipperHeight\n            self._startY = self._startY + value * jump\n\n        self.reposition()\n\n    def _reposition(self, event):\n        self.reposition()\n\n    def _getxview(self):\n\n        # Horizontal dimension.\n        clipperWidth = self._clipper.winfo_width()\n        frameWidth = self.innerframe.winfo_reqwidth()\n        if frameWidth <= clipperWidth:\n            # The scrolled frame is smaller than the clipping window.\n\n            self._startX = 0\n            endScrollX = 1.0\n            # use expand by default\n            relwidth = 1\n        else:\n            # The scrolled frame is larger than the clipping window.\n            # use expand by default\n            if self._startX + clipperWidth > frameWidth:\n                self._startX = frameWidth - clipperWidth\n                endScrollX = 1.0\n            else:\n                if self._startX < 0:\n                    self._startX = 0\n                endScrollX = (self._startX + clipperWidth) / float(frameWidth)\n            relwidth = \"\"\n\n        # Position frame relative to clipper.\n        self.innerframe.place(x=-self._startX, relwidth=relwidth)\n        return (self._startX / float(frameWidth), endScrollX)\n\n    def _getyview(self):\n\n        # Vertical dimension.\n        clipperHeight = self._clipper.winfo_height()\n        frameHeight = self.innerframe.winfo_reqheight()\n        if frameHeight <= clipperHeight:\n            # The scrolled frame is smaller than the clipping window.\n\n            self._startY = 0\n            endScrollY = 1.0\n            # use expand by default\n            relheight = 1\n        else:\n            # The scrolled frame is larger than the clipping window.\n            # use expand by default\n            if self._startY + clipperHeight > frameHeight:\n                self._startY = frameHeight - clipperHeight\n                endScrollY = 1.0\n            else:\n                if self._startY < 0:\n                    self._startY = 0\n                endScrollY = (self._startY + clipperHeight) / float(frameHeight)\n            relheight = \"\"\n\n        # Position frame relative to clipper.\n        self.innerframe.place(y=-self._startY, relheight=relheight)\n        return (self._startY / float(frameHeight), endScrollY)\n\n    # According to the relative geometries of the frame and the\n    # clipper, reposition the frame within the clipper and reset the\n    # scrollbars.\n    def _scrollBothNow(self):\n        self._scrollTimer = None\n\n        # Call update_idletasks to make sure that the containing frame\n        # has been resized before we attempt to set the scrollbars.\n        # Otherwise the scrollbars may be mapped/unmapped continuously.\n        self._scrollRecurse = self._scrollRecurse + 1\n        self.update_idletasks()\n        self._scrollRecurse = self._scrollRecurse - 1\n        if self._scrollRecurse != 0:\n            return\n\n        xview = self._getxview()\n        yview = self._getyview()\n        self.hsb.set(xview[0], xview[1])\n        self.vsb.set(yview[0], yview[1])\n\n        require_hsb = self.scrolltype in (self.BOTH, self.HORIZONTAL)\n        self.hsbNeeded = (xview != (0.0, 1.0)) and require_hsb\n        require_vsb = self.scrolltype in (self.BOTH, self.VERTICAL)\n        self.vsbNeeded = (yview != (0.0, 1.0)) and require_vsb\n\n        # If both horizontal and vertical scrollmodes are dynamic and\n        # currently only one scrollbar is mapped and both should be\n        # toggled, then unmap the mapped scrollbar.  This prevents a\n        # continuous mapping and unmapping of the scrollbars.\n        if (\n            self.hsbNeeded != self.hsbOn\n            and self.vsbNeeded != self.vsbOn\n            and self.vsbOn != self.hsbOn\n        ):\n            if self.hsbOn:\n                self._toggleHorizScrollbar()\n            else:\n                self._toggleVertScrollbar()\n            return\n\n        if self.hsbNeeded != self.hsbOn:\n            self._toggleHorizScrollbar()\n\n        if self.vsbNeeded != self.vsbOn:\n            self._toggleVertScrollbar()\n\n    def _toggleHorizScrollbar(self):\n\n        self.hsbOn = not self.hsbOn\n\n        # interior = self #.origInterior\n        if self.hsbOn:\n            self.hsb.grid(row=1, column=0, sticky=tk.EW)\n            # interior.grid_rowconfigure(3, minsize = self['scrollmargin'])\n        else:\n            self.hsb.grid_forget()\n            # interior.grid_rowconfigure(3, minsize = 0)\n\n    def _toggleVertScrollbar(self):\n\n        self.vsbOn = not self.vsbOn\n\n        # interior = self#.origInterior\n        if self.vsbOn:\n            self.vsb.grid(row=0, column=1, sticky=tk.NS)\n            # interior.grid_columnconfigure(3, minsize = self['scrollmargin'])\n        else:\n            self.vsb.grid_forget()\n            # interior.grid_columnconfigure(3, minsize = 0)\n\n    def configure(self, cnf=None, **kw):\n        if cnf:\n            return self._framecls.configure(self, cnf, **kw)\n        key = \"usemousewheel\"\n        if key in kw:\n            self.usemousewheel = tk.getboolean(kw.pop(key))\n            self._configure_mousewheel()\n        return self._framecls.configure(self, cnf, **kw)\n\n    config = configure\n\n    def cget(self, key):\n        option = \"usemousewheel\"\n        if key == option:\n            return self.usemousewheel\n        # return super(ScrolledFrameBase, self).cget(key)\n        return self._framecls.cget(self, key)\n\n    __getitem__ = cget\n\n    def _configure_mousewheel(self):\n        if self.usemousewheel:\n            BindManager.init_mousewheel_binding(self)\n\n            if self.hsb and not hasattr(self.hsb, \"on_mousewheel\"):\n                self.hsb.on_mousewheel = BindManager.make_onmousewheel_cb(\n                    self, \"x\", 2\n                )\n            if self.vsb and not hasattr(self.vsb, \"on_mousewheel\"):\n                self.vsb.on_mousewheel = BindManager.make_onmousewheel_cb(\n                    self, \"y\", 2\n                )\n\n            main_sb = self.vsb or self.hsb\n            if main_sb:\n                self.on_mousewheel = main_sb.on_mousewheel\n                bid = self.bind(\n                    \"<Enter>\",\n                    lambda event: BindManager.mousewheel_bind(self),\n                    add=\"+\",\n                )\n                self._bindingids.append((self, bid))\n                bid = self.bind(\n                    \"<Leave>\",\n                    lambda event: BindManager.mousewheel_unbind(),\n                    add=\"+\",\n                )\n                self._bindingids.append((self, bid))\n            for s in (self.vsb, self.hsb):\n                if s:\n                    bid = s.bind(\n                        \"<Enter>\",\n                        lambda event, scrollbar=s: BindManager.mousewheel_bind(\n                            scrollbar\n                        ),\n                        add=\"+\",\n                    )\n                    self._bindingids.append((s, bid))\n                    if s != main_sb:\n                        bid = s.bind(\n                            \"<Leave>\",\n                            lambda event: BindManager.mousewheel_unbind(),\n                            add=\"+\",\n                        )\n                        self._bindingids.append((s, bid))\n        else:\n            for widget, bid in self._bindingids:\n                remove_binding(widget, bid)\n","sub_path":"src/pygubu/widgets/scrolledframe.py","file_name":"scrolledframe.py","file_ext":"py","file_size_in_byte":11191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"55529500","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Aug 10 11:06:06 2017\r\n\r\n@author: alska001\r\nExample1: First model\r\n\r\nStep 0: Import functions from the gurobipy module\r\n\"\"\"\r\nimport gurobipy as gp\r\n\"\"\"\r\nStep 1: Create a model\r\n\"\"\"\r\nm=gp.Model()\r\n\"\"\"\r\nStep 2: Define variables\r\n\"\"\"\r\nx1=m.addVar(vtype=gp.GRB.BINARY, name=\"x1\")\r\nx2=m.addVar(vtype=gp.GRB.BINARY, name=\"x2\")\r\nx3=m.addVar(vtype=gp.GRB.BINARY, name=\"x3\")\r\n\r\n\"\"\"\r\nStep 3: Add constraints\r\n\"\"\"\r\ncon1=m.addConstr(x1 + 2*x2 + 3*x3 <= 4)\r\ncon2=m.addConstr(x1 + x2 >= 1)\r\n\r\n\"\"\"\r\nStep 4: Set objective function\r\n\"\"\"\r\nm.setObjective(x1 + x2 + 2*x3, gp.GRB.MAXIMIZE)\r\n\r\n\"\"\"\r\nStep 5: Solve model\r\n\"\"\"\r\nm.optimize()\r\n\"\"\"\r\nStep 6: Print variable values for optimal solution\r\n\"\"\"\r\nfor v in m.getVars():\r\n    print(v.varName, v.x)\r\n\r\n#print(\"x1\", 1)","sub_path":"examples/Example1.py","file_name":"Example1.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"259969521","text":"import os\nimport sys\nfrom gym.spaces import Box, Discrete\nimport numpy as np\nimport argparse\nimport csv\nimport tensorflow as tf\npath_to_add = os.path.dirname(os.path.abspath(__file__)) + '/..'\nsys.path = [path_to_add] + sys.path\nfrom envs.discrete_gridworld import GridWorld\nimport baselines.common.tf_util as U\nfrom baselines.common.models import mlp\nfrom baselines.common.policies_bc import build_policy\nfrom baselines.common.input import observation_placeholder\nfrom baselines.common.policies import build_policy as build_policy_trpo\n\nNUM_ACTIONS = 4\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--grid_size', type=int, default=5)\nparser.add_argument('--horizon', type=int, default=400)\nparser.add_argument('--gamma', type=float, default=0.99)\nparser.add_argument('--bias', type=float, default=0.0)\nparser.add_argument('--scale', type=float, default=0.01)\nparser.add_argument('--beta', type=float, default=1.0)\nparser.add_argument('--epsilon', type=float, default=0.05)\nparser.add_argument(\"--fast_zone\", type=float, default=1.0)\nparser.add_argument(\"--slow_zone\", type=float, default=10)\nparser.add_argument(\"--goal\", type=float, default=0.)\nparser.add_argument('--v_i', action='store_true')\nparser.add_argument('--model', type=str, default='gridworld/trpo-rews-1.0_10_0.0/models/1573247486.9845634/best')\nparser.add_argument('--run_policy', action='store_true')\nparser.add_argument('--greedy_value', action='store_true')\nparser.add_argument('--demonstrations', type=int, default=0)\nparser.add_argument('--random', action='store_true')\nparser.add_argument('--verbose', action='store_true')\nparser.add_argument('--debug_model', action='store_true')\nparser.add_argument('--random_start', action='store_true')\nparser.add_argument('--extended_features', action='store_true')\nparser.add_argument('--print_features', action='store_true')\n\nargs = parser.parse_args()\nH = 2 * args.grid_size + 1\nW = args.grid_size\nrew_weights = np.array([args.fast_zone, args.slow_zone, args.goal])\n\nenv = GridWorld(shape=(args.grid_size, args.grid_size), rew_weights=rew_weights,\n                randomized_initial=args.random_start, horizon=args.horizon,\n                extended_features=args.extended_features)\nsum_rew = 0\nmdp = env.get_mdp()\n\n\ndef log(message):\n    if args.verbose:\n        print(message)\n\n\nif args.v_i or args.greedy_value or args.demonstrations > 0:\n    pi, V, Q = mdp.value_iteration(max_iter=10000, tol=1e-5, qs=True)\n    env.render_pi_v(pi, V)\n    input()\n\n\ndef input_policy():\n    command = input()\n    if command in ['8', 'w']:\n        a = 0\n    elif command in ['6', 'd']:\n        a = 1\n    elif command in ['5', 's']:\n        a = 2\n    elif command in ['4', 'a']:\n        a = 3\n    else:\n        print(\"Invalid Command: Default UP\")\n        a = 0\n    return a\n\n\npolicy = input_policy\nif args.run_policy or args.debug_model:\n    state_space = env.observation_space.shape[0]\n    observation_space = Box(low=-np.inf, high=np.inf, shape=(state_space,))\n    action_space = Discrete(NUM_ACTIONS)\n    tf.reset_default_graph()\n    network = mlp(num_hidden=state_space, num_layers=0)\n\n    if \"trpo\" in args.model:\n        with tf.variable_scope('pi'):\n            policy_train = build_policy_trpo(env, network, value_network='copy')\n            ob_space = Box(low=-np.inf, high=np.inf, shape=(env.observation_space.shape[0],))\n            ob = observation_placeholder(ob_space)\n            pi = policy_train(observ_placeholder=ob)\n            U.initialize()\n        if not args.random:\n            log(\"loading_model\")\n            pi.load(args.model)\n\n        def linear_policy():\n            if np.random.uniform(0, 1) < args.epsilon:\n                return np.random.randint(0,NUM_ACTIONS)\n            s = env.get_state(ohe=True)\n            a, v, state, logits = pi.step(s, stochastic=True, logits=True)\n            log(\"Logits: \"+str(logits))\n            return a[0]\n        policy_label = 'trpo'\n    else:\n        policy_train = build_policy(observation_space, action_space, network, train=False,\n                                    init_scale=args.scale, init_bias=args.bias, beta=args.beta)\n        pi = policy_train()\n        U.initialize()\n        if not args.random:\n            log(\"loading_model\")\n            pi.set_theta(np.load(args.model))\n\n        def linear_policy():\n            if np.random.uniform(0, 1) < args.epsilon:\n                return np.random.randint(0, NUM_ACTIONS)\n            s = env.get_state(ohe=True)\n            logits, a, state, neglogp = pi.step(s, stochastic=False)\n            log(\"Logits: \"+str(logits))\n            return a[0]\n        policy_label = 'bc'\n\n    policy = linear_policy\n\nif args.greedy_value or args.demonstrations > 0:\n    def value_policy():\n        if np.random.uniform(0, 1) < args.epsilon:\n            return np.random.randint(0, NUM_ACTIONS)\n        s = env.get_state(ohe=False)\n        if s == args.grid_size:  # init state\n            return np.random.choice([0, 2])\n        else:\n            max_action = np.max(Q[:, s], axis=0)\n            action = np.random.choice(np.flatnonzero(Q[:, s] == max_action))\n            return action\n\n    policy = value_policy\n    policy_label = 'optimal_policy'\nelif not args.run_policy:\n    policy = input_policy\n    policy_label = 'input_policy'\n\nif args.demonstrations > 0:\n    trajectories = []\n    for i in range(args.demonstrations):\n        trajectory = []\n        s = env.reset(ohe=True)\n        while True:\n            a = policy()\n            next_s, r, done, info = env.step(a, ohe=True)\n            trajectory_sample = {\n                                  's': list(s),\n                                  'a': a,\n                                  \"s'\": list(next_s),\n                                  'r': r,\n                                  'features': list(info['features']),\n                                  'done': done\n            }\n            trajectory.append(trajectory_sample)\n            s = list(next_s)\n            if done:\n                trajectories.append(trajectory)\n                break\n    with open('data/'+policy_label+\"_\"+str(args.demonstrations)+'_trajectories.csv', 'w+',\n              newline='') as trajectories_file:\n        dicti_writer = csv.DictWriter(trajectories_file, fieldnames=trajectory_sample.keys())\n        dicti_writer.writeheader()\n        for trajectory in trajectories:\n            dicti_writer.writerows([sample for sample in trajectory])\n\nelse:\n    print(\"Play!\")\n    env.reset()\n    env._render()\n    while True:\n        a = policy()\n        _, r, done, info = env.step(a)\n        if args.debug_model:\n            s = env.get_state(ohe=True)\n            if 'trpo' in args.model:\n                a, v, state, logits = pi.step(s, stochastic=True)\n            else:\n                logits, a, state, neglogp = pi.step(s, stochastic=True)\n            log(\"Logits: \" + str(logits))\n        sum_rew += r\n        input()\n        if args.print_features:\n            print(\"Features:\")\n            print(info[\"features\"])\n        env._render()\n        log(\"Reward :%.2f\" % r)\n        if done:\n            log(\"Episode finished! Score:%d\" % sum_rew)\n            log(\"Press anything to continue!\")\n            input()\n            env.reset()\n            sum_rew = 0\n            env._render()\n","sub_path":"play_discrete_gridworld.py","file_name":"play_discrete_gridworld.py","file_ext":"py","file_size_in_byte":7254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"15044999","text":"from openpyxl import Workbook, load_workbook\nimport time\nimport os\n\ndef give_attendance(profiles):\n    #get current date\n    currentDate = time.strftime(\"%d.%m.%Y\")\n    path = './Pics_Taken/' + currentDate + '/attendance.xlsx'\n    #create a workbook and add a worksheet\n\n    wb = Workbook()\n    #creating worksheet and giving names to column\n    ws1 = wb.active\n    ws1.title = \"Cse1\"\n    ws1.append(('Roll Number', 'Name', '', currentDate))\n    ws1.append(('', '', '', ''))\n    profiles = sorted(profiles, key= lambda x: x[2])\n    for profile in profiles:\n        ws1.append((profile[2], profile[1]))\n\n    #saving the file\n    wb.save(filename = path)\n","sub_path":"attendance.py","file_name":"attendance.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"135054643","text":"'''\n@author Maifeng\n\n'''\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\n# 缴纳电费线程类方法\nclass thread_pay(QThread):\n    result_money = pyqtSignal(str)\n    def __init__(self):\n        super(thread_pay, self).__init__()\n\n    def run_init(self,function,way,money):\n        self.function = function\n        self.way = way\n        self.money = money\n\n    def run(self):\n        try:\n            remain_money = self.function.get_user_power(self.way,self.money)\n            self.result_money.emit(remain_money)\n        except Exception as e:\n            print(e)\n\n    def __del__(self):\n        self.wait()\n\n\n\n\n\n","sub_path":"thread/pay_power.py","file_name":"pay_power.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"654189523","text":"#!/usr/bin/env python3\n\nimport os\nimport zipfile\nimport argparse\nimport uuid\nimport tempfile\n\nfrom ricecooker.chefs import SushiChef\nfrom ricecooker.classes.nodes import ChannelNode, TopicNode, DocumentNode\nfrom ricecooker.classes.files import DocumentFile\nfrom le_utils.constants import licenses\nfrom ricecooker.classes.licenses import get_license\nfrom ricecooker.config import LOGGER\nfrom ricecooker.utils.html import WebDriver\nfrom ricecooker.utils import downloader\nfrom le_utils.constants.languages import getlang_by_name\n\n# Variables\nFILTERS_URL = \"https://storyweaver.org.in/api/v1/books/filters\"\nSIGNIN_URL = \"https://storyweaver.org.in/api/v1/users/sign_in\"\nBOOK_SEARCH_URL = \"https://storyweaver.org.in/api/v1/books-search\"\nDOWNLOAD_URL = \"https://storyweaver.org.in/v0/stories/download-story/{}.pdf\"\n\n\ndef get_AS_booklist_dict():\n    \"\"\"\n    Get a list of all books on the African Storybooks website to later compare\n    with the books on the StoryWeaver website with African Storybooks publisher.\n    \"\"\"\n    with WebDriver(\"http://www.africanstorybook.org/\", delay=20000) as driver:\n\n        js_code = \"\"\"\n            var textElement = document.createElement(\"textarea\");\n            function decodeHtml(html) {\n                textElement.innerHTML = html;\n                return textElement.value;\n            }\n\n            // Safely decode HTML entities in title field.\n            for (var i = bookItems.length - 1; i >= 0; i--) {\n                bookItems[i].title = decodeHtml(bookItems[i].title);\n            }\n\n            return bookItems;\n        \"\"\"\n\n        books = driver.execute_script(js_code)\n        names = []\n        # Create a list of book names\n        for book in books:\n            names.append(book[\"title\"].lower())\n        dictionary = {}\n        # Create a dictionary of books with name as key\n        for (name, book) in zip(names, books):\n            if name in dictionary:\n                dictionary[name].append(book)\n            else:\n                dictionary[name] = [book]\n        return dictionary\n\n\ndef get_books_from_results(books):\n    \"\"\"\n    Get detailed information about each book.\n    Parameters:\n    * books - The list of books to get detailed information\n    \"\"\"\n    booklist = []\n    # Loop through all the books for one page\n    for i in range(len(books)):\n        publisher = books[i][\"publisher\"][\"name\"]\n        language = books[i][\"language\"]\n        level = books[i][\"level\"]\n        try:\n            thumbnail = books[i][\"coverImage\"][\"sizes\"][0][\"url\"]\n        except TypeError:\n            thumbnail = None\n\n        book_dict = {\n            \"link\": DOWNLOAD_URL.format(books[i][\"slug\"]),\n            \"source_id\": books[i][\"id\"],\n            \"title\": books[i][\"title\"],\n            \"author\": \", \".join([item[\"name\"] for item in books[i][\"authors\"]]),\n            \"description\": books[i][\"description\"],\n            \"thumbnail\": thumbnail,\n            \"language\": language,\n            \"level\": level,\n            \"publisher\": publisher,\n        }\n        booklist.append(book_dict)\n\n    return booklist\n\n\ndef books_for_each_category(category):\n    \"\"\"\n    Get all the books for every category\n    Parameters:\n    * category - The name of the category that is related to the books\n    \"\"\"\n    LOGGER.info(\"\\tCrawling books for {}......\\n\".format(category))\n\n    # Get the json file of the page and parse it\n    payload = {\"page\": 1, \"per_page\": 24, \"categories[]\": category}\n    response = downloader.make_request(\n        BOOK_SEARCH_URL, params=payload, clear_cookies=False\n    )\n    data = response.json()\n    total_pages = data[\"metadata\"][\"totalPages\"]\n    LOGGER.info(\n        \"\\tThere are in total {} pages for {}......\\n\".format(total_pages, category)\n    )\n\n    # List of books for the first page\n    booklist = get_books_from_results(data[\"data\"])\n\n    # get the rest of the pages' books\n    for i in range(1, total_pages):\n        payload[\"page\"] = i + 1\n        response = downloader.make_request(\n            BOOK_SEARCH_URL, params=payload, clear_cookies=False\n        )\n\n        # Skip the page if there is an error (usually a 500 error)\n        if response.status_code != 200:\n            continue\n        data = response.json()\n        booklist += get_books_from_results(data[\"data\"])\n\n    LOGGER.info(\n        \"\\tFinished getting all the books for {}\\n\\t================\\n\".format(category)\n    )\n    return booklist\n\n\ndef download_all():\n    \"\"\"\n    Parse the json returned by StoryWeaver API and generate a dictionary that\n    contains all the information regarding category, publisher, language, level and\n    book.\n    \"\"\"\n    resp = downloader.make_request(FILTERS_URL, clear_cookies=False).json()\n    categories = [item[\"name\"] for item in resp[\"data\"][\"category\"][\"queryValues\"]]\n\n    channel_tree = {}\n    for category in categories:\n        channel_tree[category] = {}\n        booklist = books_for_each_category(category)\n\n        # Reset Storyweaver Community number and index for each category\n        storyweaver_community_num = 0\n        index = 1\n        for book in booklist:\n            publisher = book[\"publisher\"]\n            language = book[\"language\"]\n            level = book[\"level\"]\n\n            if publisher == \"StoryWeaver Community\":\n                storyweaver_community_num += 1\n                # Make sure we only have 20 books in one Storyweaver Community folder\n                if storyweaver_community_num > 20:\n                    index += 1\n                    storyweaver_community_num = 1\n                publisher = \"{}-{}\".format(publisher, index)\n\n            if publisher in channel_tree[category]:\n                if language in channel_tree[category][publisher]:\n                    if level in channel_tree[category][publisher][language]:\n                        channel_tree[category][publisher][language][level].append(book)\n                    else:\n                        channel_tree[category][publisher][language][level] = [book]\n                else:\n                    channel_tree[category][publisher][language] = {}\n                    channel_tree[category][publisher][language][level] = [book]\n            else:\n                channel_tree[category][publisher] = {}\n                channel_tree[category][publisher][language] = {}\n                channel_tree[category][publisher][language][level] = [book]\n    return channel_tree\n\n\ndef parse_through_tree(tree, parent_topic, as_booklist):\n    \"\"\"\n    Recursively parsing through the tree and adding TopicNodes and DocumentNodes.\n    Parameters:\n    * tree - The tree that contains information about category, publisher, language,\n            level, and book and is going to be parsed\n    * parent_topic - The parent node that will be attached with Nodes created later\n    * as_booklist - the list of books from African Storybook\n    \"\"\"\n    for topic_name in sorted(tree):\n        content = tree[topic_name]\n        try:\n            title = \"Level {}\".format(int(topic_name))\n        except ValueError:\n            title = topic_name\n        current_topic = TopicNode(\n            source_id=\"{}_{}\".format(\n                parent_topic.source_id, topic_name.replace(\" \", \"_\")\n            ),\n            title=title,\n        )\n\n        if type(content) is list:\n            add_node_document(content, current_topic, as_booklist)\n        else:\n            parse_through_tree(content, current_topic, as_booklist)\n\n        # Only add the current topic node when it has child nodes\n        if current_topic.children:\n            parent_topic.add_child(current_topic)\n\n\ndef check_if_story_in_AS(as_booklist, story_name):\n    \"\"\"\n    Check if the story in StoryWeaver is also in African Storybooks.\n    Parameters:\n    * as_booklist - The list of books from African Storybooks\n    * story_name - The book name from StoryWeaver to check if it exists on\n                African Storybooks website\n    \"\"\"\n    result = as_booklist.get(story_name.lower().rstrip())\n    if result and len(result) == 1:\n        return True, result[0][\"id\"]\n    else:\n        return False, None\n\n\ndef add_node_document(booklist, level_topic, as_booklist):\n    \"\"\"\n    Add books as DocumentNode under a specific level of reading.\n    Parameters:\n    * booklist - The list of books to be added as DocumentNodes\n    * level_topic - The TopicNode regarding current level that the DocumentNodes\n                    will be attached to\n    * as_booklist - The list of books from African Storybooks\n    \"\"\"\n    for item in booklist:\n        # Initialize the source domain and content_id\n        domain = uuid.uuid5(uuid.NAMESPACE_DNS, \"storyweaver.org.in\")\n        book_id = str(item[\"source_id\"])\n\n        # If the publisher is AS and the book is found,\n        # then change the source_domain and content_id\n        if item[\"publisher\"] == \"African Storybook Initiative\":\n            check = check_if_story_in_AS(as_booklist, item[\"title\"])\n            if check[0]:\n                domain = uuid.uuid5(uuid.NAMESPACE_DNS, \"www.africanstorybook.org\")\n                book_id = check[1]\n\n        # Given that StoryWeaver provides the link to a zip file,\n        # we will download the zip file and extract the pdf file from it\n        with tempfile.NamedTemporaryFile(suffix=\".zip\") as tempf:\n            try:\n                resp = downloader.make_request(item[\"link\"], clear_cookies=False)\n                resp.raise_for_status()\n                tempf.write(resp.content)\n            except Exception as e:\n                # Do not create the node if download fails\n                LOGGER.info(\"Error: {} when downloading {}\".format(e, item[\"link\"]))\n                continue\n\n            filename = \"\"\n            with zipfile.ZipFile(tempf.name, \"r\") as f:\n                for zipped_file in f.namelist():\n                    if os.path.splitext(zipped_file)[1][1:] == \"pdf\":\n                        tempdir = os.path.dirname(tempf.name)\n                        f.extract(zipped_file, path=tempdir)\n                        filename = os.path.join(tempdir, zipped_file)\n                        break\n\n        # If no pdf file has been found in the zip, do not create the node\n        if not filename:\n            continue\n\n        # Create the document node with given information\n        document_file = DocumentFile(path=filename)\n        language_obj = getlang_by_name(item[\"language\"])\n        book = DocumentNode(\n            title=item[\"title\"],\n            source_id=book_id,\n            author=item[\"author\"],\n            provider=item[\"publisher\"],\n            files=[document_file],\n            license=get_license(licenses.CC_BY, copyright_holder=\"StoryWeaver\"),\n            thumbnail=item.get(\"thumbnail\"),\n            description=item[\"description\"],\n            domain_ns=domain,\n            language=language_obj,\n        )\n        level_topic.add_child(book)\n\n\nclass PrathamBooksStoryWeaverSushiChef(SushiChef):\n    \"\"\"\n    Add two additional arguments about login information.\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.arg_parser = argparse.ArgumentParser(\n            parents=[self.arg_parser],\n            description=\"Sushi Chef for Pratham Books' StoryWeaver.\",\n        )\n        self.arg_parser.add_argument(\n            \"--login_email\",\n            required=True,\n            help=\"Login email for Pratham Books' StoryWeaver website.\",\n        )\n        self.arg_parser.add_argument(\n            \"--login_password\",\n            required=True,\n            help=\"Login password for Pratham Books' StoryWeaver website.\",\n        )\n\n    \"\"\"\n    Get the login information and log in to the website.\n    \"\"\"\n\n    def pre_run(self, args, options):\n        payload = {\n            # \"authenticity_token\": _authenticity_token,\n            \"api_v1_user[email]\": args[\"login_email\"],\n            \"api_v1_user[password]\": args[\"login_password\"],\n            \"api_v1_user[remember_me]\": \"false\",\n        }\n        # Log in to StoryWeaver website\n        response = downloader.DOWNLOAD_SESSION.post(SIGNIN_URL, data=payload)\n        if response.json()[\"email\"] != args[\"login_email\"]:\n            raise Exception(\n                \"Failed to log in. Please check if there has been an API change from the upstream.\"\n            )\n\n    def get_channel(self, **kwargs):\n        # Create a channel\n        channel = ChannelNode(\n            source_domain=\"storyweaver.org.in\",\n            source_id=\"Pratham_Books_StoryWeaver\",\n            title=\"Pratham Books' StoryWeaver\",\n            description=\"\",\n            language=\"en\",\n            thumbnail=\"thumbnail.png\",\n        )\n        return channel\n\n    def construct_channel(self, **kwargs):\n        channel = self.get_channel(**kwargs)\n\n        # add topics and corresponding books to the channel\n        channel_tree = download_all()\n        as_booklist = get_AS_booklist_dict()\n        for category in sorted(channel_tree):\n            category_topic = TopicNode(\n                source_id=category.replace(\" \", \"_\"), title=category\n            )\n            channel.add_child(category_topic)\n            parse_through_tree(channel_tree[category], category_topic, as_booklist)\n\n        return channel\n\n\nif __name__ == \"__main__\":\n\n    # This code will run when the sushi chef is called from the command line.\n    chef = PrathamBooksStoryWeaverSushiChef()\n    chef.main()\n","sub_path":"chef.py","file_name":"chef.py","file_ext":"py","file_size_in_byte":13359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"261041233","text":"import xmltodict\nfrom flask import Flask, render_template, request, jsonify\nimport yaml\nfrom saml_validation import parse_saml, analyze\n\napp = Flask(__name__, instance_relative_config=True)\n\n# from werkzeug.debug import DebuggedApplication\n# appx = DebuggedApplication(app, evalex=True)\nwith open('configurations/idp.yml') as c_file:\n    idp_repo = yaml.safe_load(c_file)\n    c_file.close()\nwith open('configurations/descriptions.yml') as d_file:\n    descriptions = yaml.safe_load(d_file)\n    d_file.close()\n\n@app.route('/')\ndef welcome():\n    return render_template('front.html')\n\n\n@app.route('/upload', methods=['POST'])\ndef upload():\n    idp_name = request.values['idp_name']\n    saml_dict = xmltodict.parse(request.files['saml_file'].read().decode())\n    saml_values = parse_saml(saml_dict)\n    idp_info = idp_repo.get(idp_name, None)\n    result = analyze(saml_values, descriptions, idp_info)\n\n    return jsonify(result)\n\n\nif __name__ == '__main__':\n    app.debug = True\n    app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"553331233","text":"from girl import Girl\n\nclass Normal(Girl) :\n\t'defines a Normal girl'\n\tdef __init__(self,name,attr_rating,main_budget,intel_level,types) :\n\t\t'initialises attributes of Normal class'\n\t\tGirl.__init__(self,name,attr_rating,main_budget,intel_level,types)\n\t\n\tdef happinessNormal(self,gift):\n\n\t\t\"calculate happiness for a normal girl\"\n\t\tself.happiness = max(0,sum([(i.price + i.value) \n\t\t\t\t\t\tfor i in gift]) - self.main_budget) ","sub_path":"q8/source/normalGirl.py","file_name":"normalGirl.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"151426906","text":"# -*- coding: utf-8 -*-\nimport scrapy\nimport hext\n\n\nclass ToScrapeHextSpider(scrapy.Spider):\n    name = \"toscrape-hext\"\n    start_urls = [\n        'http://quotes.toscrape.com/',\n    ]\n    rule_quotes = hext.Rule(\n        \"\"\"<div class=\"quote\">\n             <span class=\"text\" @text:text />\n             <span>\n               <small class=\"author\" @text:author />\n             </span>\n             <div>\n               <?a class=\"tag\" @text:tags />\n             </div>\n           </div>\"\"\");\n    rule_next_page_url = hext.Rule(\n        \"\"\"<li class=\"next\">\n             <a href:next_page_url />\n           </li>\"\"\");\n\n    def parse(self, response):\n        html = hext.Html(response.text)\n        quotes = self.rule_quotes.extract(html)\n        yield from quotes\n\n        next_page_url = self.rule_next_page_url.extract(html)\n        url = next(iter(next_page_url), None)\n        if url is not None:\n            yield scrapy.Request(response.urljoin(url[\"next_page_url\"]))\n\n","sub_path":"quotesbot/spiders/toscrape-hext.py","file_name":"toscrape-hext.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"354812889","text":"from sklearn.model_selection import permutation_test_score\nimport pandas as pd\nimport numpy as np\nimport pylab as pl\nfrom rdkit import Chem\nfrom math import floor\n\n# Avaliar a significância de uma pontuação validada cruzada com permutações\n# True score = A pontuação verdadeira, sem permutar metas.\n# Y-randomization = Media das pontuações obtidas para cada permutação.\n# np-value = O valor retornado é igual ao valor-p se a pontuação retornar números maiores para obter melhores \n#     pontuações (por exemplo, exatidão_score). Se a pontuação é uma função de perda (ou seja, quando menor é melhor, \n#     como com mean_squared_error), então esse é realmente o complemento do valor p: 1 - valor p.\n##### Função utilitária para relatar as melhores pontuações\ndef y_randomization(rf_best, X_train, y_train, descritor, algoritimo):    \n    permutations = 20\n    score, permutation_scores, pvalue = permutation_test_score(rf_best, X_train, y_train,\n                                                               cv=5, scoring='balanced_accuracy',\n                                                               n_permutations=permutations,\n                                                               n_jobs=-1,\n                                                               verbose=1,\n                                                               random_state=24)\n    print('True score = ', score.round(2),\n          '\\n Média per. = ', np.mean(permutation_scores).round(2),\n          '\\np-value = ', pvalue.round(4))\n\n    ###############################################################################\n    # View histogram of permutation scores\n    pl.subplots(figsize=(10,6))\n    pl.hist(permutation_scores.round(2), label='Permutation scores')\n    ylim = pl.ylim()\n    pl.vlines(score, ylim[0], ylim[1], linestyle='--',\n              color='g', linewidth=3, label='Classification Score'\n              ' (pvalue %s)' % pvalue.round(4))\n    pl.vlines(1.0 / 2, ylim[0], ylim[1], linestyle='--',\n              color='k', linewidth=3, label='Luck')\n    pl.ylim(ylim)\n    pl.legend()\n    pl.xlabel('Score')\n    pl.title('Aleatoriarização da variável Y '+algoritimo+'X'+descritor, fontsize=12)\n    pl.savefig('figures/y_randomization-'+descritor+'X'+algoritimo+'.png', bbox_inches='tight', transparent=False, format='png', dpi=300)\n    pl.show()","sub_path":"functions/YRandomization.py","file_name":"YRandomization.py","file_ext":"py","file_size_in_byte":2375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"230731048","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/lazy_slides/search.py\n# Compiled at: 2012-01-02 14:05:57\nimport logging\nfrom . import flickr\nlog = logging.getLogger(__name__)\nsearch_function = None\n\ndef search(tag):\n    \"\"\"Search for an image file matching a given tag using the\n    configured search function.\n\n    This uses `search_function` to do the actual search, so make sure\n    it's set before you use this.\n\n    :param tag: The tag to search on.\n    :raise ValueError: The search function is not set.\n    :raise KeyError: No match is found for `tag`.\n    :return: A URL.\n    \"\"\"\n    log.info(('search function: {}.{}').format(search_function.__module__, search_function.__name__))\n    if search_function is None:\n        raise ValueError('You need to set lazy_slides.search.search_function before using search_photos()!')\n    log.info(('searching for images tagged with \"{}\"').format(tag))\n    url = search_function(tag=tag)\n    if url is None:\n        raise KeyError(('No results for \"{}\"').format(tag))\n    log.info(('found photo: {}').format(url))\n    return url","sub_path":"pycfiles/lazy_slides-0.3-py2.7/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"1155509","text":"# MIT License\n#\n# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the\n# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit\n# persons to whom the 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 all copies or substantial portions of the\n# Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE\n# WARRANTIES OF MERCHANTABILITY, 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 LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\n# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport logging\n\nimport numpy as np\nimport pytest\n\nfrom art.preprocessing.audio import LFilterPyTorch\nfrom art.config import ART_NUMPY_DTYPE\nfrom tests.utils import ARTTestException\n\nlogger = logging.getLogger(__name__)\n\n\n@pytest.mark.skipModule(\"torchaudio\")\n@pytest.mark.skipMlFramework(\"tensorflow\", \"keras\", \"kerastf\", \"mxnet\", \"non_dl_frameworks\")\n@pytest.mark.parametrize(\"fir_filter\", [False, True])\ndef test_audio_filter(fir_filter, art_warning, expected_values):\n    try:\n        # Load data for testing\n        expected_data = expected_values()\n\n        x1 = expected_data[0]\n        x2 = expected_data[1]\n        x3 = expected_data[2]\n        result_0 = expected_data[3]\n        result_1 = expected_data[4]\n        result_2 = expected_data[5]\n\n        # Create signal data\n        x = np.array(\n            [\n                np.array(x1 * 2, dtype=ART_NUMPY_DTYPE),\n                np.array(x2 * 2, dtype=ART_NUMPY_DTYPE),\n                np.array(x3 * 2, dtype=ART_NUMPY_DTYPE),\n            ]\n        )\n\n        # Filter params\n        numerator_coef = np.array([0.1, 0.2, -0.1, -0.2])\n\n        if fir_filter:\n            denominator_coef = np.array([1.0, 0.0, 0.0, 0.0])\n        else:\n            denominator_coef = np.array([1.0, 0.1, 0.3, 0.4])\n\n        # Create filter\n        audio_filter = LFilterPyTorch(numerator_coef=numerator_coef, denominator_coef=denominator_coef)\n\n        # Apply filter\n        result = audio_filter(x)\n\n        # Test\n        assert result[1] is None\n        np.testing.assert_array_almost_equal(result_0, result[0][0], decimal=0)\n        np.testing.assert_array_almost_equal(result_1, result[0][1], decimal=0)\n        np.testing.assert_array_almost_equal(result_2, result[0][2], decimal=0)\n\n    except ARTTestException as e:\n        art_warning(e)\n\n\n@pytest.mark.skipModule(\"torchaudio\")\n@pytest.mark.skipMlFramework(\"tensorflow\", \"keras\", \"kerastf\", \"mxnet\", \"non_dl_frameworks\")\ndef test_default(art_warning):\n    try:\n        # Small data for testing\n        x = np.array([[0.37, 0.68, 0.63, 0.48, 0.48, 0.18, 0.19]], dtype=ART_NUMPY_DTYPE)\n\n        # Create filter\n        audio_filter = LFilterPyTorch()\n\n        # Apply filter\n        result = audio_filter(x)\n\n        # Test\n        assert result[1] is None\n        np.testing.assert_array_almost_equal(x, result[0], decimal=0)\n\n    except ARTTestException as e:\n        art_warning(e)\n\n\n@pytest.mark.skipModule(\"torchaudio\")\n@pytest.mark.skipMlFramework(\"tensorflow\", \"keras\", \"kerastf\", \"mxnet\", \"non_dl_frameworks\")\ndef test_triple_clip_values_error(art_warning):\n    try:\n        exc_msg = \"`clip_values` should be a tuple of 2 floats containing the allowed data range.\"\n        with pytest.raises(ValueError, match=exc_msg):\n            LFilterPyTorch(\n                numerator_coef=np.array([0.1, 0.2, 0.3]),\n                denominator_coef=np.array([0.1, 0.2, 0.3]),\n                clip_values=(0, 1, 2),\n            )\n\n    except ARTTestException as e:\n        art_warning(e)\n\n\n@pytest.mark.skipModule(\"torchaudio\")\n@pytest.mark.skipMlFramework(\"tensorflow\", \"keras\", \"kerastf\", \"mxnet\", \"non_dl_frameworks\")\ndef test_relation_clip_values_error(art_warning):\n    try:\n        exc_msg = \"Invalid `clip_values`: min >= max.\"\n        with pytest.raises(ValueError, match=exc_msg):\n            LFilterPyTorch(\n                numerator_coef=np.array([0.1, 0.2, 0.3]), denominator_coef=np.array([0.1, 0.2, 0.3]), clip_values=(1, 0)\n            )\n\n    except ARTTestException as e:\n        art_warning(e)\n","sub_path":"tests/preprocessing/audio/test_l_filter_pytorch.py","file_name":"test_l_filter_pytorch.py","file_ext":"py","file_size_in_byte":4730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"93784042","text":"def Write(name, data):\n    data = [ala.strip() + \"\\n\" for ala in data]\n    with open(name, 'w') as file:\n        file.writelines(data) \n\ndef Read(name):\n    with open(name, 'r') as file:\n        Lines = file.readlines() \n        # Strips the newline character \n        Lines = [line.upper().strip().replace(\"\\t\", \" \") for line in Lines];\n        return Lines\n\ndef Pass1():\n    Lines = Read('input.txt') \n\n    MNT_LINES = [];\n    MDT_LINES = [];\n    ALA_LINES = [];\n\n    count = 0\n    is_macro = False\n    macro_lines = 0\n    for line in Lines: \n        if line ==  \"MACRO\":\n            is_macro = True\n            macro_lines = 0\n            continue\n        if not is_macro:\n            continue\n\n        MDT_LINES.append(line)\n\n        if line == \"MEND\":\n            is_macro = False\n            continue\n\n        # Keep Adding Macro to MNT and MDT\n\n        # Add First Line In\n        if macro_lines == 0:\n            MNT_LINES.append(line.split(' ', 1)[0])\n            ALA_LINES.append(line.split(' ', 1)[0])\n            ALA_LINES += line.split(' ', 1)[1].split(',')\n            ALA_LINES.append(\"=========\")\n        macro_lines += 1\n\n    Write(\"MNT.txt\", MNT_LINES);\n    Write(\"ALA.txt\", ALA_LINES);\n    Write(\"MDT.txt\", MDT_LINES);\n\ndef FindInMDT(macro_name, MDT_LINES):\n    for index, line in enumerate(MDT_LINES):\n        if(line.split(' ', 1)[0] == macro_name):\n            return index\ndef LoadALAForMacro(macro_name, ALA_LINES):\n    vars = []\n    idx = ALA_LINES.index(macro_name)\n    idx += 1\n    while True:\n        vars.append(ALA_LINES[idx][1:])\n        idx += 1\n        if(ALA_LINES[idx] == \"=========\"):\n            break\n    return vars\ndef Pass2():\n    Lines = Read('input.txt') \n    MNT_LINES = Read('MNT.txt')\n    MDT_LINES = Read('MDT.txt')\n    ALA_LINES = Read('ALA.txt')\n    \n    count = 0\n    is_started = False\n    macro_lines = 0\n    ESC = []\n    for line in Lines: \n        if \"START\" in line:\n            is_started = True\n            ESC.append(line)\n            continue\n        if not is_started:\n            continue\n        split = line.split(' ', 1)\n        macro_name = split[0]\n        if len(split) == 1:\n            ESC.append(line)\n            continue\n\n        if macro_name in MNT_LINES:\n            ALA = (LoadALAForMacro(macro_name, ALA_LINES))\n            mdt_pos = FindInMDT(macro_name, MDT_LINES)\n            macro_calls = (split[1].strip().split(','))\n\n            while True:\n                mdt_pos += 1\n                line = MDT_LINES[mdt_pos]\n                if(line == \"MEND\"):\n                    break\n                for index, macro_call in enumerate(macro_calls):\n                    line = line.replace(\"&\" + ALA[index], macro_call)\n                ESC.append(line)\n            \n            continue\n\n        ESC.append(line)\n    Write(\"ESC.txt\", ESC);\nPass1()\nPass2()","sub_path":"SEM-VI/Compiler-SPCC/2 Macroprocessor/Macro.py","file_name":"Macro.py","file_ext":"py","file_size_in_byte":2828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"160982865","text":"from myapp.xhqb.db_server import db_server\n\n\"\"\"\"通过手机号查询客户信息，返回：cid、name、identity_no、mobile_phone\"\"\"\ndef base_check_phone(phone):\n    db = db_server()\n    cursor = db.cursor()\n    cif_base={}\n    sql=\"SELECT id,customer_name,identity_no,mobile_phone FROM cifdb.t_customer_base WHERE mobile_phone= '%s';\"  %(phone)\n    try:\n        cursor.execute(sql)\n        data = cursor.fetchmany(1)\n        data1=list(data[0])\n        list_name=[\"cid\",\"name\",\"identity_no\",\"mobile_phone\"]\n        cif_base=dict(zip(list_name,data1))\n        cif_base[\"success\"]=True\n        # print(cif_base)\n\n    except:\n        cif_base[\"success\"] = False\n        cif_base[\"mags\"]=\"查询cif_base失败\"\n        # print(\"查询cif_base失败\")\n    finally:\n        cursor.close()\n    return cif_base\n\"\"\" 身份证号查询客户信息，返回：cid、name、identity_no、mobile_phone\"\"\"\ndef base_check_identity(identity_no):\n    db = db_server()\n    cursor = db.cursor()\n    cif_base = {}\n    sql=\"SELECT id,customer_name,identity_no,mobile_phone FROM cifdb.t_customer_base WHERE identity_no= '%s';\"  %(identity_no)\n    # print(sql)\n    try:\n        cursor.execute(sql)\n        data = cursor.fetchmany(1)\n        data1=list(data[0])\n        list_name=[\"cid\",\"name\",\"identity_no\",\"mobile_phone\"]\n        cif_base=dict(zip(list_name,data1))\n        # print(cif_base)\n        cif_base[\"success\"] = True\n\n    except:\n        cif_base[\"success\"] = False\n        print(\"查询cif_base失败\")\n    finally:\n        cursor.close()\n    return cif_base\n\"\"\"判断用户传的是身份证号还是手机号\"\"\"\ndef base_check(phone=None,identity_no=None):\n    if phone!=None:\n        data_base=base_check_phone(phone)\n    elif identity_no !=None:\n        data_base=base_check_identity(identity_no)\n    else:\n        print(\"请输入手机号或者身份证号\")\n        data_base={\"success\":False}\n    return data_base\n\n\n\n\n\"\"\"修改cifdb.t_customer_base表的数据\"\"\"\ndef update_base(cid,up_identity_no,up_name,up_phone):\n    db = db_server()\n    cursor = db.cursor()\n    sql=\"UPDATE cifdb.t_customer_base SET identity_no='%s',customer_name='%s',mobile_phone='%s' where id='%s';\" % (up_identity_no,up_name,up_phone,cid)\n    # print(sql)\n    try:\n        cursor.execute(sql)\n        db.commit()\n        print(\"cifdb.t_customer_base数据修改成功\")\n    except:\n        db.rollback()\n        print(\"cifdb.t_customer_base数据修改失败\")\n    finally:\n        cursor.close()\n\nif __name__==\"__main__\":\n    base_check_phone(\"18973599071\")\n","sub_path":"del_number/myapp/xhqb2/cif_customer_base.py","file_name":"cif_customer_base.py","file_ext":"py","file_size_in_byte":2537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"450569698","text":"# -*- coding: utf-8 -*-\n# @Date  : 2020/5/27\n# @Author: Luokun\n# @Email : olooook@outlook.com\n\nimport sys\nfrom os.path import dirname, abspath\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nsys.path.append(dirname(dirname(abspath(__file__))))\n\n\ndef test_svm():\n    from models.svm import SVM\n\n    x, y = np.random.randn(2, 400, 2), np.zeros([2, 400], dtype=int)\n    y[0] = -1\n    y[1] = 1\n    for i, theta in enumerate(np.linspace(0, 2 * np.pi, 40)):\n        x[0, (10 * i):(10 * i + 10)] += 5 * np.array([np.cos(theta), np.sin(theta)])\n\n    x = x.reshape(-1, 2)\n    y = y.flatten()\n\n    plot_scatter([x[y == i] for i in [-1, 1]], 'Real')\n\n    # train\n    svm = SVM(C=10, sigma=1, kernel='rbf', max_iter=100)\n    svm.fit(x, y)\n\n    pred = np.array(svm.predict(x))\n    plot_scatter([x[pred == i] for i in [-1, 1]], 'Pred')\n    acc = np.sum(pred == y) / len(pred)\n    print(f'Acc = {100 * acc:.2f}%')\n    print(svm.support_vectors)\n\n\ndef plot_scatter(xys, title):\n    plt.figure(figsize=(8, 8))\n    for xy, color in zip(xys, ['r', 'g', 'b']):\n        plt.scatter(xy[:, 0], xy[:, 1], color=color)\n    plt.title(title)\n    plt.show()\n\n\nif __name__ == '__main__':\n    test_svm()\n","sub_path":"samples/svm.py","file_name":"svm.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"484558149","text":"from conans import ConanFile, CMake\n\n\nclass Conan(ConanFile):\n    url = \"https://github.com/biocad/cuq\"\n    description = \"CUDA multi-GPU concurrent tasks queue\"\n    settings = \"os\", \"build_type\", \"arch\", \"compiler\", \"CUDA\"\n    generators = \"cmake\"\n    exports_sources = \"*\"\n\n    def build(self):\n        cmake = CMake(self)\n        cmake.configure()\n        cmake.build()\n        cmake.parallel = False\n        cmake.test()\n\n    def package(self):\n        cmake = CMake(self)\n        cmake.configure()\n        cmake.install()\n\n    def package_info(self):\n        self.cpp_info.libs = [\"cuq\"]\n        # users will be able to link statically with our library\n        # but this requires the following system libraries\n        self.cpp_info.system_libs = [\"cudart\", \"nvidia-ml\", \"stdc++\"]\n        self.cpp_info.libdirs.append(\"/usr/local/cuda/lib64\")\n\n    def deploy(self):\n        self.copy(\"*\", dst=\"include\", src=\"include\")\n        self.copy(\"*\", dst=\"lib\", src=\"lib\")\n","sub_path":"conanfile.py","file_name":"conanfile.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"410641381","text":"import os\n\n\"\"\"\nHere are all constants which are produce by the system os.\n\"\"\"\n\n#: True user name.\nusername = os.getenv(\"SUDO_USER\")\nif not username:\n    username = os.getenv(\"USER\")\n\ntry:\n\t#: Home path for the user.\n    homePath = os.path.expanduser('~' + username)\nexcept Exception:\n\thomePath = '/home/root'\n\n#: Path to the folder where mylinux reads application data/ settings.\nfilesPath = os.path.join(homePath, '.mylinux')\n\n","sub_path":"mylinux/constants/runTime.py","file_name":"runTime.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"479958061","text":"__author__ = 'Benco'\n\nfrom Img2Vec import img2vector\nfrom KNN import classify0\nimport numpy as np\nfrom os import listdir\n\ndef handwritingClassTest():\n    hwlabel=[]\n    trainingFileList=listdir('trainingDigits')\n    m=len(trainingFileList)\n    trainingMat=np.zeros((m,1024))\n    for i in range(m):\n        fileNameStr=trainingFileList[i]\n        fileStr=fileNameStr.split('.')[0]\n        classNumStr=int(fileStr.split('_')[0])\n        hwlabel.append(classNumStr)\n        trainingMat[i,:]=img2vector('trainingDigits/%s'%fileNameStr)\n    testFileList=listdir('testDigits')\n    errorCount=0.0\n    mTest=len(testFileList)\n    for i in range(mTest):\n        fileNameStr=testFileList[i]\n        fileStr=fileNameStr.split('.')[0]\n        classNumStr=int(fileStr.split('_')[0])\n        vectorUnderTest=img2vector('testDigits/%s'%fileNameStr)\n        classifierResult=classify0(vectorUnderTest,trainingMat,hwlabel,15)\n        #print(\"The classifier came back with %d,the real answer is %d\"%(classifierResult,classNumStr))\n        if classifierResult!=classNumStr:\n            errorCount+=1.0\n    print(\"\\nThe total number of errors is %d\"%errorCount)\n    print(\"\\nThe total error rate is %.4f%%\"%(100*errorCount/mTest))\n\n\nif __name__=='__main__':\n    handwritingClassTest()","sub_path":"手写系统数据/HandWriteClassifier.py","file_name":"HandWriteClassifier.py","file_ext":"py","file_size_in_byte":1264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"439803544","text":"# 8) Usando a biblioteca ‘pygame’, escreva um programa que desenha um botão (círculo) com o texto “clique” sobre ele na parte superior da tela.\n# Quando o botão for clicado, ele deve chamar uma função que desenha um retângulo em uma posição aleatória na tela.\n# Caso um retângulo apareça na mesma posição que um já existente, ambos devem ser eliminados.\n\nimport pygame, random, math\n\nW = 800\nH = 600\n\nBLACK = 0, 0, 0\nWHITE = 255, 255, 255\nHOVER_COLOR = 50, 70, 90\n\nRECT_LIST = []\n\npygame.init()\nscreen = pygame.display.set_mode((W, H))\npygame.display.set_caption(\"Questão 08 - AT - Fund. Python - Mariana B. Sukevicz\")\npygame.font.init()\nclock = pygame.time.Clock()\n\nrunning = True\n\nCLOCK_TICK = 100\n\nRAD = 100\nRX = W // 2\nRY = RAD\nA = (RX, RY)\n\n\nclass Rectangle():\n    def __init__(self):\n        self.W = 60\n        self.H = 30\n        self.x = random.randint(0, W - self.W)\n        self.y = random.randint(20, H - self.H)\n        self.s_a = pygame.Rect(self.x, self.y, self.W, self.H)\n        self.color = (random.randint(20, 255), random.randint(20, 255), random.randint(20, 255))\n\n    def drawRectangle(self, screen):\n        pygame.draw.rect(screen, self.color, self.s_a)\n\n\ndef drawCircle(RAD, RX, RY):\n    pygame.draw.circle(screen, HOVER_COLOR, (RX, RY), RAD)\n    font = pygame.font.SysFont('Times New Roman', 20)\n    text = font.render(\"CLIQUE AQUI!\", 1, WHITE)\n    show_text = (RX - (RAD // 2), RY - 25)\n    screen.blit(text, show_text)\n\n\nwhile running:\n    show = True\n    screen.fill(WHITE)\n\n    for c in RECT_LIST:\n        c.drawRectangle(screen)\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            running = False\n\n        if event.type == pygame.MOUSEBUTTONDOWN and event.button == 3:\n            click = pygame.mouse.get_pos()\n            collision_p = math.hypot(click[0] - RX, click[1] - RY)\n            if collision_p <= RAD:\n                r = Rectangle()\n                r.drawRectangle(screen)\n                for r_r in RECT_LIST:\n                    if r_r.s_a.colliderect(r.s_a):\n                        RECT_LIST.remove(r_r)\n                        show = False\n                if show:\n                    RECT_LIST.append(r)\n\n    circle = drawCircle(RAD, RX, RY)\n    pygame.display.update()\n\n    clock.tick(CLOCK_TICK)\n\npygame.display.quit()\npygame.quit()","sub_path":"questao8.py","file_name":"questao8.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"345194036","text":"#coding=utf-8\nimport logging\n\ndef main():\n   logging.basicConfig(level = logging.DEBUG, format='\\r\\n%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S', filename=\"./game1.log\", encode = \"utf-8\", encoding = \"UTF-8\")\n   logging.basicConfig(level = logging.ERROR, format='\\r\\n%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S', encode = \"utf-8\", encoding = \"UTF-8\")\n   logging.debug(\"msg\")\n    \n   p = logging.getLogger(\"root\")\n   p.setLevel(logging.DEBUG)\n   \n   console = logging.StreamHandler()\n   console.setLevel(logging.DEBUG)\n   \n   filehandler = logging.FileHandler(\"./game.log\")\n   filehandler.setLevel(logging.ERROR)\n   \n   fmt = logging.Formatter(\"\\r\\n%(asctime)s %(message)s\", \"%Y-%m-%d %H:%M:%S\")\n   console.setFormatter(fmt)\n   filehandler.setFormatter(fmt)\n   \n   p.addHandler(console)\n   p.addHandler(filehandler)\n   \n   p.debug(\"msg\")\n    \nif __name__  == \"__main__\":\n    main()","sub_path":"logging.py","file_name":"logging.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"515929736","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport random\n\n\ndef createcode(anzahlstifte, anzahlfarben, secretcode):\n    #der \"Spielmaster\" wählt zufällige Farben\n    for x in range(0, anzahlstifte):\n        i = random.randint(1, anzahlfarben)\n        color = farbzuweisung(i)\n        secretcode.append(color)\n    return(secretcode)\n\ndef farbzuweisung(i):\n    #Zufallswert der createcode-Funktion erhält seine Farbe\n    farbe = None\n    if i == 1:\n        farbe = \"rot\"\n    elif i == 2:\n        farbe = \"blau\"\n    elif i == 3:\n        farbe = \"gelb\"\n    elif i == 4:\n        farbe = \"grün\"\n    elif i == 5:\n        farbe = \"weiß\"\n    elif i == 6:\n        farbe = \"schwarz\"\n    elif i == 7:\n        farbe = \"orange\"\n    elif i == 8:\n        farbe = \"braun\"\n    return(farbe)\n\ndef fehlereingabe(ratefarbe, k, anzahlfarben, geratenestifte):\n    farben = [\"rot\", \"blau\", \"gelb\", \"grün\", \"weiß\", \"schwarz\", \"orange\", \"braun\"]\n    i = 0\n    for x in range(0, anzahlfarben):\n        if ratefarbe != farben[x]:\n            i += 1\n        #eingebene Farbe ist nicht in der Liste aller möglichen Farben\n        if i == anzahlfarben:\n            #Objekt wird wieder gelöscht\n            #geratenestifte = geratenestifte[:-1]\n            print(\"Fehlerhafte Eingabe.\")\n            #Neue Eingabe\n            ratefarbe = str(input(\"Gib die Farbe für Position \" + str(k+1) + \" ein: \"))\n            #Rekursion prüft erneut auf Fehler\n            fehlereingabe(ratefarbe, k, anzahlfarben, geratenestifte)\n            #geratenestifte.append(ratefarbe)\n    #Rückgabe der richtigen Farbe\n    return(ratefarbe)\n\ndef printgeratenenstifte(geratenestifte):\n    #Liste wird zerteilt und in einen gesamten String umgewandelt\n    string = \"Deine Eingabe lautet: \\n\\nx\"\n    for j in range(0, len(geratenestifte)):\n        string += str(geratenestifte[j]) + \" \"\n    string += \"\\n\"\n    print(string)\n\n#def stifteausgabe():\n","sub_path":"mastermindfunctions.py","file_name":"mastermindfunctions.py","file_ext":"py","file_size_in_byte":1906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"571819450","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nsceptre.config\n\nThis module implements a Config class, which stores a stack or environment's\nconfiguration.\n\"\"\"\n\nimport logging\nimport os\nimport yaml\n\nfrom jinja2 import Environment, FileSystemLoader, StrictUndefined\nfrom packaging.specifiers import SpecifierSet\nfrom packaging.version import Version\n\nfrom . import __version__\nfrom .exceptions import ConfigItemNotFoundError\nfrom .exceptions import EnvironmentPathNotFoundError\nfrom .exceptions import VersionIncompatibleError\nfrom .hooks import Hook\nfrom .resolvers import Resolver\nfrom .helpers import get_subclasses\n\n\nclass Config(dict):\n    \"\"\"\n    Config stores the configuration read in from the YAML files. Config\n    inherits from dict, and so configuration data can be accessed from Config\n    as if Config were a dict. Config implements read(), a method to read in\n    config from the ``<base_file_name>.yaml`` files along the\n    ``environment_path`` from ``sceptre_dir``.\n\n    :param sceptre_dir: The absolute path to the Sceptre directory.\n    :type project dir: str\n    :param environment_path: The name of the environment.\n    :type environment_path: str\n    :param base_file_name: The basename of the file to read in \\\n        (e.g. \"config\", \"vpc\")\n    :type base_file_name: str\n    \"\"\"\n\n    def __init__(self, sceptre_dir, environment_path, base_file_name):\n        self.logger = logging.getLogger(__name__)\n\n        self.sceptre_dir = sceptre_dir\n        self.environment_path = environment_path\n        self.name = base_file_name\n        self[\"dependencies\"] = []\n\n        self._check_env_path_exists(os.path.join(\n            self.sceptre_dir, \"config\", self.environment_path\n        ))\n\n        super(Config, self).__init__(self)\n\n    @classmethod\n    def with_yaml_constructors(\n            cls, sceptre_dir, environment_path, base_file_name,\n            environment_config, connection_manager\n    ):\n        \"\"\"\n        Initialises a Config object with additional PyYAML constructor classes.\n\n        Creates a Config object and adds PyYAML constructor meta-classes.\n        Additional constructors are added to transform YAML nodes for any\n        library or external Resolvers and Hooks.\n\n        :param sceptre_dir: The absolute path to the Sceptre directory.\n        :type project dir: str\n        :param environment_path: The name of the environment.\n        :type environment_path: str\n        :param base_file_name: The basename of the file to read in \\\n            (e.g. \"config\", \"vpc\")\n        :type base_file_name: str\n        :param environment_config: The stack's environment's config.\n        :type environment_config: sceptre.config.Config\n        :param connection_manager: A ConnectionManager used to make Boto3\n            calls.\n        :type connection_manager: sceptre.connection_manager.ConnectionManager\n        \"\"\"\n        obj = cls(sceptre_dir, environment_path, base_file_name)\n        obj.add_resolver_constructors(\n            environment_config, connection_manager)\n        obj.add_hook_constructors(\n            environment_config, connection_manager)\n        return obj\n\n    def __getitem__(self, item):\n        \"\"\"\n        Makes a call to dict's __getitem__. If the item isn't in the dict,\n        catch the KeyError raised, and raise a ConfigItemNotFoundException,\n        which adds more detail to the exception.\n\n        :param item: The key of an item in the dict to return.\n        :type item: Any hashable type\n        :returns: The value of the config item keyed by ``item``.\n        :rtype: obj\n        :raises: sceptre.exceptions.ConfigItemNotFoundException\n        \"\"\"\n        try:\n            return super(Config, self).__getitem__(item)\n        except KeyError:\n            raise ConfigItemNotFoundError(\n                \"'{0}' was not found in any of the {1}.yaml files.\".format(\n                    item, self.name\n                )\n            )\n\n    def read(self, user_variables=None):\n        \"\"\"\n        Reads in configuration from files.\n\n        Traverses the environment path, from top to bottom, reading in all\n        relevant config files. If config items appear in files lower down the\n        environment tree, they overwrite items from further up. Jinja2 is used\n        to template in variables from user_variables, environment variables,\n        and the segments of the environment path.\n\n        :param user_variables: A dict of key value pairs to be supplied to \\\n        the config file via Jinja2 templating.\n        :type user_variables: dict\n        \"\"\"\n        path = os.path.join(\"config\", self.environment_path)\n        file_name = \".\".join([self.name, \"yaml\"])\n\n        self.logger.debug(\"Reading in '%s' files...\", file_name)\n\n        def get_config(path):\n            \"\"\"\n            Recursively reads in config files in nested subfolders.\n            \"\"\"\n            if not path:\n                return {}\n            else:\n                directory_path = os.path.join(self.sceptre_dir, path)\n                config = {}\n                if os.path.isfile(os.path.join(directory_path, file_name)):\n                    env = Environment(\n                        loader=FileSystemLoader(directory_path),\n                        undefined=StrictUndefined\n                    )\n                    template = env.get_template(file_name)\n                    rendered_template = template.render(\n                        environment_variable=os.environ,\n                        var=user_variables,\n                        environment_path=self.environment_path.split(\"/\")\n                    )\n\n                    yaml_data = yaml.safe_load(rendered_template)\n                    if yaml_data is not None:\n                        config = yaml_data\n                cascaded_config = get_config(os.path.dirname(path))\n                cascaded_config.update(config)\n                return cascaded_config\n\n        config = get_config(path)\n\n        self.update(config)\n\n        self._check_version()\n\n        self.logger.debug(\"Config: %s\", self)\n\n    @staticmethod\n    def _check_env_path_exists(path):\n        \"\"\"\n        Raises an EnvironmentPathNotFoundError if ``path`` is not a directory.\n\n        :param path: A directory path.\n        :type path: str\n        :raises: sceptre.exceptions.EnvironmentPathNotFoundError\n        \"\"\"\n        if not os.path.isdir(path):\n            raise EnvironmentPathNotFoundError(\n                \"The environment '{0}' does not exist.\".format(path)\n            )\n\n    def _check_version(self):\n        \"\"\"\n        Raises a VersionIncompatibleException when the current sceptre version\n        does not comply with the configured version requirement.\n\n        :raises: sceptre.exceptions.VersionIncompatibleException\n        \"\"\"\n        sceptre_version = __version__\n        if self.name == 'config' and 'require_version' in self:\n            require_version = self['require_version']\n            if Version(sceptre_version) not in SpecifierSet(require_version):\n                raise VersionIncompatibleError(\n                    \"Current sceptre version ({0}) does not meet version \"\n                    \"requirements: {1}\".format(\n                        sceptre_version, require_version\n                    )\n                )\n\n    def add_resolver_constructors(\n            self, environment_config, connection_manager\n    ):\n        \"\"\"\n        Adds PyYAML constructors for all resolver classes found in the\n        resolvers folder within Sceptre library and the current Sceptre project\n        folder.\n\n        :param environment_config: A environment config.\n        :type environment_config: sceptre.config.Config\n        :param connection_manager: A connection manager.\n        :type connection_manager: sceptre.connection_manager.ConnectionManager\n        \"\"\"\n        self.logger.debug(\"Adding resolver yaml constructors\")\n\n        def resolver_constructor_factory(node_class):\n            \"\"\"\n            Returns a lambda function that will contruct objects from a\n            given node class.\n\n            :param node_class: A resolver class to construct of objects from.\n            :type node_class: class\n            :returns: A lambda that constructs resolver objects.\n            :rtype: func\n            \"\"\"\n            return lambda loader, node: node_class(\n                loader.construct_scalar(node),\n                connection_manager,\n                environment_config,\n                self\n            )  # pragma: no cover\n\n        resolvers_folder = os.path.join(os.path.dirname(__file__), \"resolvers\")\n        self.add_yaml_constructors(\n            resolvers_folder, Resolver, resolver_constructor_factory\n        )\n        external_resolver_folder = os.path.join(os.getcwd(), \"resolvers\")\n        self.add_yaml_constructors(\n            external_resolver_folder, Resolver, resolver_constructor_factory\n        )\n\n    def add_hook_constructors(self, environment_config, connection_manager):\n        \"\"\"\n        Adds PyYAML constructors for all hook classes found in the hooks folder\n        within Sceptre library and the current Sceptre project folder.\n\n        :param environment_config: A environment config.\n        :type environment_config: Config\n        :param connection_manager: A connection manager.\n        :type connection_manager: ConnectionManager\n        \"\"\"\n        self.logger.debug(\"Adding hook yaml constructors\")\n\n        def hook_constructor_factory(node_class):\n            \"\"\"\n            This returns a lambda function that will contruct objects from a\n            given node class.\n\n            :param node_class: A hook class to construct of objects from.\n            :type node_class: class\n            :returns: A lambda that constructs hook objects.\n            :rtype: func\n            \"\"\"\n            return lambda loader, node: node_class(\n                loader.construct_scalar(node),\n                connection_manager,\n                environment_config,\n                self\n            )  # pragma: no cover\n\n        library_hook_folder = os.path.join(os.path.dirname(__file__), \"hooks\")\n        self.add_yaml_constructors(\n            library_hook_folder, Hook, hook_constructor_factory\n        )\n\n        project_hook_folder = os.path.join(os.getcwd(), \"hooks\")\n        self.add_yaml_constructors(\n            project_hook_folder, Hook, hook_constructor_factory\n        )\n\n    def add_yaml_constructors(self, base_directory, base_type, factory):\n        \"\"\"\n        Adds PyYAML constructors for all classes which inherit from a\n        specific base type within a given directory.\n\n        :param base_directory: A path of a directory to search for classes.\n        :type base_directory: str\n        :param base_type: The base class in which the class must inherit from.\n        :type base_type: class\n        :param factory: A function to use to construct objects.\n        :type factory: function\n        \"\"\"\n        classes = get_subclasses(\n            directory=base_directory, class_type=base_type\n        )\n\n        for node_name, node_class in classes.items():\n            node_tag = u'!' + node_name\n            yaml.SafeLoader.add_constructor(\n                node_tag, factory(node_class)\n            )\n            self.logger.debug(\n                \"Added constructor for %s with node tag %s\",\n                str(node_class), node_tag\n            )\n","sub_path":"sceptre/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":11346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"546755885","text":"#!/usr/bin/python\n#\n# Copyright (c) Facebook, Inc. and its affiliates.\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.#\n\"\"\"See docstring for AppleURLSearcher class.\"\"\"\n\nfrom __future__ import absolute_import\n\nimport json\nimport os.path\nimport posixpath\nimport re\nimport urlparse\nfrom distutils import version\nfrom types import StringType\n\nfrom autopkglib import Processor, ProcessorError\n\n__all__ = [\"AppleURLSearcher\"]\n\n\nclass MunkiLooseVersion(version.LooseVersion):\n    \"\"\"Subclass version.LooseVersion to compare things like \"10.6\" and \"10.6.0\"\n    as equal.\"\"\"\n\n    def __init__(self, vstring=None):\n        if vstring is None:\n            # treat None like an empty string\n            self.parse(\"\")\n        if vstring is not None:\n            if isinstance(vstring, unicode):\n                # unicode string! Why? Oh well...\n                # convert to string so version.LooseVersion doesn't choke\n                vstring = vstring.encode(\"UTF-8\")\n            self.parse(str(vstring))\n\n    def _pad(self, version_list, max_length):\n        \"\"\"Pad a version list by adding extra 0 components to the end if\n        needed.\"\"\"\n        # copy the version_list so we don't modify it\n        cmp_list = list(version_list)\n        while len(cmp_list) < max_length:\n            cmp_list.append(0)\n        return cmp_list\n\n    def __cmp__(self, other):\n        if isinstance(other, StringType):\n            other = MunkiLooseVersion(other)\n        max_length = max(len(self.version), len(other.version))\n        self_cmp_version = self._pad(self.version, max_length)\n        other_cmp_version = self._pad(other.version, max_length)\n        return cmp(self_cmp_version, other_cmp_version)\n\n\nclass AppleURLSearcher(Processor):\n    \"\"\"Downloads a URL to the specified download_dir using curl.\"\"\"\n\n    description = __doc__\n    input_variables = {\n        \"result_output_var_name\": {\n            \"description\": (\n                \"The name of the output variable that is returned \"\n                \"by the match. If not specified then a default of \"\n                '\"match\" will be used.'\n            ),\n            \"required\": False,\n            \"default\": \"match\",\n        },\n        \"re_pattern\": {\n            \"required\": True,\n            \"description\": \"Path to download data file from AppleCookieDownloader.\",\n        },\n        \"re_flags\": {\n            \"description\": (\n                \"Optional array of strings of Python regular \"\n                \"expression flags. E.g. IGNORECASE.\"\n            ),\n            \"required\": False,\n        },\n    }\n    output_variables = {\n        \"result_output_var_name\": {\n            \"description\": (\n                \"First matched sub-pattern from input found on the fetched \"\n                \"URL. Note the actual name of variable depends on the input \"\n                'variable \"result_output_var_name\" or is assigned a default of '\n                '\"match.\"'\n            )\n        }\n    }\n\n    def main(self):\n        download_dir = os.path.join(self.env[\"RECIPE_CACHE_DIR\"], \"downloads\")\n        downloads = os.path.join(download_dir, \"listDownloads\")\n\n        if not os.path.exists(downloads):\n            raise ProcessorError(\"Missing the download data from AppleCookieDownloader\")\n\n        flags = self.env.get(\"re_flags\", {})\n\n        with open(downloads) as f:\n            data = json.load(f)\n        dl_base_url = \"https://download.developer.apple.com\"\n        dl_urls = []\n        for x in data[\"downloads\"]:\n            for y in x[\"files\"]:\n                dl_urls.append(dl_base_url + y[\"remotePath\"])\n\n        flag_accumulator = 0\n        if flags:\n            for flag in flags:\n                if flag in re.__dict__:\n                    flag_accumulator += re.__dict__[flag]\n\n        re_pattern = re.compile(self.env[\"re_pattern\"], flags=flag_accumulator)\n\n        match = re_pattern.findall(\"\\n\".join(sorted(dl_urls)))\n        if not match:\n            raise ProcessorError(\"No match found!\")\n\n        # Now we have a list of matching URLs, find the highest version\n        filenames = [\n            os.path.splitext(posixpath.basename(urlparse.urlsplit(x).path))[0]\n            for x in match\n        ]\n        filenames.sort(key=MunkiLooseVersion)\n        self.output(\"Found matching item: %s\" % filenames[-1])\n        full_url_match = [x for x in match if filenames[-1] in x]\n\n        if not full_url_match:\n            raise ProcessorError(\"No matching URL found!\")\n\n        # The final entry is the highest one\n        self.output(\"Full URL: %s\" % full_url_match[0])\n        self.env[self.env[\"result_output_var_name\"]] = full_url_match[0]\n        self.output_variables = {}\n        self.output_variables[self.env[\"result_output_var_name\"]] = full_url_match[0]\n\n\nif __name__ == \"__main__\":\n    PROCESSOR = AppleURLSearcher()\n    PROCESSOR.execute_shell()\n","sub_path":"Xcode/AppleURLSearcher.py","file_name":"AppleURLSearcher.py","file_ext":"py","file_size_in_byte":4907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"84816070","text":"# Machine.py - Basic Machine controller\n\nfrom Controller import Controller\n\nclass Machine(object):\n\n    def __init__(self, cfile, debug):\n        self.debug = debug\n        self.cfile = cfile\n        self.controller = Controller(self.cfile, self.debug)\n\n    def run(self):\n        print(\"Running...\")\n        ticks = self.controller.run()\n        return ticks\n    \n","sub_path":"src/Machine.py","file_name":"Machine.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"472292578","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb 14 20:19:25 2018 by Apoorva Karn \"\"\"\nimport pandas as pd\nimport statsmodels.api as sm\n\ndf = pd.read_csv('C:/pyWork/pyProjects/akpy_18/akpy_18/assignment/Contriesdata.csv',header=0)\n#df = pd.concat([df[col].dropna().reset_index(drop=True) for col in df], axis=1)\ndf.head()\n\nX = df[\"Agriculture\"]\ny = df[\"Literacy\"]\n\n# Note the difference in argument order\nmodel = sm.OLS(y, X).fit()\npredictions = model.predict(X) # make the predictions by the model\npredictions\n# Print out the statistics\nmodel.summary()\n\n##\nfrom sklearn import linear_model\n\ndf = pd.read_csv('C:/pyWork/pyProjects/akpy_18/akpy_18/assignment/Contriesdata.csv',header=0)\ndf = pd.concat([df[col].dropna().reset_index(drop=True) for col in df], axis=1)\ndf = pd.DataFrame(df)\ndf\ntarget = pd.DataFrame(df, columns=[\"GDP per captia USD per annum\"])\ndf.head()\n\nx = df[\"Deathrate\"]\ny = target\n\n# Note the difference in argument order\nlm = linear_model.LinearRegression()\nmodel = lm.fit(y,x) # make the predictions by the model\n\npredictions = lm.predict(y)\nprint(predictions)[0:5]\n\n# Print out the statistics\nmodel.summary()\n\n\n","sub_path":"assignment/cntry_reg.py","file_name":"cntry_reg.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"166849262","text":"import sys\nimport os\n\ndef main():\n\tname = sys.argv[1]\n\tstr1 = sys.argv[2]\t\n\twordstring =sys.argv[2]\n\tlst =wordstring.split()\n\twordfreq = []\n\tfor w in lst:\n\t\twordfreq.append(lst.count(w))\n\tprint(\"Frequencies is \" +str(wordfreq))\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"9_5.py","file_name":"9_5.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"306509975","text":"from django.conf.urls.defaults import *\r\nfrom django.conf import settings\r\n\r\n# Uncomment the next two lines to enable the admin:\r\nfrom django.contrib import admin\r\nadmin.autodiscover()\r\n\r\nurlpatterns = patterns('',\r\n    (r'^$',  'django.views.generic.simple.redirect_to', {'url': 'req/'}), \r\n\r\n    (r'^req/', include('openrm.req.urls')),\r\n    # Uncomment the admin/doc line below and add 'django.contrib.admindocs' \r\n    # to INSTALLED_APPS to enable admin documentation:\r\n    (r'^admin/doc/', include('django.contrib.admindocs.urls')),\r\n\r\n    # Uncomment the next line to enable the admin:\r\n    (r'^admin/(.*)', admin.site.root),\r\n    # static files\r\n    (r'^static/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}),\r\n)\r\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"564633736","text":"def get_indices_of_item_weights(weights, length, limit):\n    \"\"\"\n    YOUR CODE HERE\n    \"\"\"\n    # Add all items to a hash table with weight: index\n    table = {}\n    for i in range(len(weights)):\n        # store indices in arrays so that we can handle duplicate weights\n        if weights[i] in table:\n            table[weights[i]].append(i)\n        else:\n            table[weights[i]] = [i]\n\n    # Check each item to see if there is a matching weight\n    # (i is descending so that we get the highest index solution possible)\n    for i in range(len(weights)-1, -1, -1):\n        w = limit - weights[i]  # matching weight\n        if w in table:\n            # found a match\n            if len(table[w]) == 1:\n                # only 1 item with that weight\n                return (i, table[w][0])\n            else:\n                # multiple items, find highest matching index that isn't i\n                idx = table[w][0]\n                for j in table[w]:\n                    if j > idx and j != i:\n                        idx = j\n                return (i, idx)\n\n    # No match found, return None\n    return None\n","sub_path":"hashtables/ex1/ex1.py","file_name":"ex1.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"85585701","text":"\n\"\"\"Script to run after installation of HIRE\n\nScript function which are executed after model installation and\n\"\"\"\nimport os\nimport zipfile\n\nfrom energy_demand.scripts.smif_data_related import script_data_preparation_MISTRAL_pop_gva\n\ndef create_NISMOD1_data(path_to_zip_file, path_out, path_geography):\n    \"\"\"\n\n    Arguments\n    ----------\n    \"\"\"\n    print(\"... start running initialisation scripts\", flush=True)\n\n    # Extract NISMOD population data\n    path_extraction = os.path.join(path_out, \"MISTRAL_pop_gva\")\n    zip_ref = zipfile.ZipFile(path_to_zip_file, 'r')\n    zip_ref.extractall(path_extraction)\n    zip_ref.close()\n\n    # Complete gva and pop data for every sector\n    path_pop = os.path.join(path_extraction, \"data\")\n    geography_name = \"lad_uk_2016\"\n\n    # All MISTRAL scenarios to prepare with correct config\n    scenarios_to_generate = [\n        'pop-baseline16_econ-c16_fuel-c16',\n        'pop-f_econ-c_fuel-c',\n        'pop-d_econ-c_fuel-c',]\n\n    script_data_preparation_MISTRAL_pop_gva.run(\n        path_to_folder=path_pop,\n        path_MSOA_baseline=path_geography,\n        MSOA_calculations=False,\n        geography_name=geography_name,\n        scenarios_to_generate=scenarios_to_generate)\n\n    print(\"... successfully finished setup\")\n    return\n\nif __name__ == '__main__':\n    \"\"\"\n    \"\"\"\n    create_NISMOD1_data(\n        path_to_zip_file=\"C:/Users/cenv0553/nismod2/data/energy_demand/population-economic-smif-csv-from-nismod-db.zip\",\n        path_out=\"C:/_NISMODI_DATA\",\n        path_geography=\"C:/Users/cenv0553/nismod2/data/energy_demand/_raw_data/J-population_disagg_by/uk_pop_principal_2015_2050_MSOA_england.csv\")\n","sub_path":"energy_demand/scripts/s_greate_NISMOD1_data.py","file_name":"s_greate_NISMOD1_data.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"31365842","text":"from datetime import datetime\nimport re\n\n\nclass WhoIsYou(object):\n    \"\"\"\n    Whois data dictionaries vary url to url.\n    Special transforms are needed to translate data\n    into the self.data attribute.\n    \"\"\"\n\n    top_registrars = [\n        'godaddy', 'bluehost', 'name.',\n        'hostgator', '1&1', 'register',\n        'domains', 'namecheap'\n    ]\n\n    date_keys = [\n        ('expiration_date', 'whois-days-until-expires'),\n        ('updated_date', 'whois-days-since-updated'),\n        ('creation_date', 'whois-days-since-created')\n    ]\n\n    def __init__(self):\n        self.data = {\n            \"whois-num-keys\": None,\n            \"whois-days-since-created\": None,\n            \"whois-days-since-updated\": None,\n            \"whois-days-until-expires\": None,\n            \"whois-num-updated-dates\": None,\n            \"whois-number-of-nameservers\": None,\n            # \"whois-status-is-prohibited\": None,\n            \"whois-dnssec-signed\": None,\n            \"whois-country\": None,\n            # \"whois-has-state\": None,\n            # \"whois-has-city\": None,\n            # \"whois-has-address\": None,\n            # \"whois-has-zipcode\": None,\n            \"whois-has-org\": None\n        }\n\n    # find keys based on regex (most whois have some things in common)\n    # keys wanted: country, zipcode, name-servers, creation_date, updated_date, expiration_date\n    @staticmethod\n    def handle_keys(keys):\n        # search for the keys; if can't find em check for the regexes in the list\n        # values are lists of possible alternate names for the keys\n        keys_finder = {\n            \"creation_date\": [\"\\s(\\w*creat\\w*)\\s\"],\n            \"updated_date\": [\"\\s(\\w*update\\w*)\\s\", \"\\s(\\w*modif\\w*)\\s\", \"\\s(\\w*change\\w*)\\s\"],\n            \"expiration_date\": [\"\\s(\\w*expir\\w*)\\s\"],\n            \"country\": [\"\\s(\\w*country\\w*)\\s\"],\n            # \"zipcode\": [\"\\s(\\w*code\\w*)\\s\"],\n            \"name_servers\": [\"\\s(\\w*server\\w*)\\s\"],\n            \"status\": [\"status\"],\n            \"dnssec\": [\"dnssec\"],\n            \"org\": [\"\\s(\\w*owner\\w*)\\s\"],\n            \"registrar\": [\"\\s(\\w*regis\\w*)\\s\"]\n        }\n        # return keys dict\n        structured_keys = {\n            \"creation_date\": None,\n            \"updated_date\": None,\n            \"expiration_date\": None,\n            \"country\": None,\n            # \"zipcode\": None,\n            \"name_servers\": None,\n            \"status\": None,\n            \"dnssec\": None,\n            \"org\": None,\n            \"registrar\": None\n        }\n        found = [k for k in keys if k in keys_finder.keys()]\n        for found_key in found:\n            structured_keys[found_key] = found_key\n        if len(found) != len(keys_finder):\n            missing = [k for k in keys_finder if k not in found]\n            for key in missing:\n                for regex in keys_finder[key]:\n                    match = re.search(regex, \" \".join(keys))\n                    if match and match.group(1) in keys:\n                        structured_keys[key] = match.group(1)\n                        break\n        # return list of usable keys\n        # if a key of keys_finder is found in input keys,\n        # it is added to return dict { \"keys_finder key\": \"key found in keys\", }\n        return structured_keys\n\n    @classmethod\n    def from_data(cls, who):\n        whoops = WhoIsYou()\n        whoops.data[\"whois-num-keys\"] = len(who)\n        # handle keys\n        structured_keys = cls.handle_keys(who.keys())\n        # check structured_keys for what was found\n        for final_key, whois_key in structured_keys.items():\n            if whois_key:\n                who[final_key] = who[whois_key]\n            else:\n                who[final_key] = None\n                # begin transformations\n        # todo: More complex checks will come with better EDA\n        whoops.data[\"whois-number-of-nameservers\"] = len(who[\"name_servers\"]) if who[\"name_servers\"] else 0\n        whoops.data[\"whois-number-of-statuses\"] = len(who[\"status\"]) if type(who[\"status\"]) == list else 1\n        # whoops.data[\"whois-status-is-ok\"] = 1 if who[\"status\"] and \"ok\" in who[\"status\"].lower() else 0\n        whoops.data[\"whois-has-org\"] = 1 if who[\"org\"] else 0\n        whoops.data[\"whois-country\"] = who[\"country\"]\n        # whoops.data[\"whois-zipcode\"] = who[\"zipcode\"]\n        whoops.data[\"whois-dnssec-signed\"] = cls.handle_dns(who[\"dnssec\"]) if who[\"dnssec\"] else None\n        whoops.data[\"whois-top-ten-registars\"] = cls.handle_registrars(who[\"registrar\"]) if who[\"registrar\"] else None\n        whoops.data[\"whois-num-updated-dates\"] = len(who[\"updated_date\"]) if type(who[\"updated_date\"]) == list else 1\n        for d, i in cls.date_keys:\n            n, days = cls.handle_dates(who[d])\n            whoops.data[i] = n\n            whoops.data[i] = days\n        return whoops\n\n    @staticmethod\n    def handle_dns(dns):\n        if type(dns) == list:\n            dns = list(set([x.lower() for x in dns]))\n            return dns[0]\n        return dns\n\n    @staticmethod\n    def handle_registrars(regstr):\n        for rg in WhoIsYou.top_registrars:\n            if rg in regstr.lower():\n                return 1\n        return 0\n\n    @staticmethod\n    def handle_dates(date):\n        today = datetime.today()\n        if type(date) == list:\n            return len(date), (today - date[0]).days\n        else:\n            return 0, None\n\n    @staticmethod\n    def date_helper(d):\n        return datetime.strptime(d.split(\".\")[0], \"%Y-%m-%dT%H:%M:%S\")\n\n    @staticmethod\n    def handle_dates_json(date):\n        today = datetime.today()\n        # see if it conforms to the normal whois dates format\n        if type(date) == list:\n            if type(date[0]) == dict:\n                d = WhoIsYou.date_helper(date[0]['$date'])\n                return len(date), (today - d).days\n            # weird date format encountered!\n            else:\n                # todo: do something about this?\n                return None, None\n        elif type(date) == dict:\n            d = WhoIsYou.date_helper(date['$date'])\n            return 1, (today - d).days\n        # weird date format encountered!\n        else:\n            # todo: do something about this?\n            return None, None\n","sub_path":"malurls/lib/parsers/whois_parse.py","file_name":"whois_parse.py","file_ext":"py","file_size_in_byte":6153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"440758162","text":"lista = []\n\n\ndef llegirpersona():\n\tnombre = input(\"NOMBRE: \")\n\tapellido = input(\"APELLIDO: \")\n\tnif = input(\"NIF: \")\n\tedad = input(\"EDAD: \")\n\taltura = input(\"ALTURA: \")\n\tlista1 = [nombre, apellido, nif, edad, altura]\n\tlista.append(lista1)\n\n\ndef insertarpersonas(p):\n\tf = open(\"personas.txt\", \"a\")\n\tf.write(p[0] + \"\\t\" + p[1] + \"\\t\" + p[2] + \"\\t\" + p[3] + \"\\t\" + p[4] + \"\\n\")\n\tprint(p[0] + \"\\t\" + p[1] + \"\\t\" + p[2] + \"\\t\" + p[3] + \"\\t\" + p[4] + \"\\n\")\n\tf.close()\n\n\nopcion = \" \"\nwhile opcion != \"X\":\n\tprint(\"A.-Pedir datos\")\n\tprint(\"B.-Guardar datos\")\n\tprint(\"X.-Salir\")\n\topcion = input(\"ELIGE UNA OPCION: \").upper()\n\tif opcion == \"A\":\n\t\tllegirpersona()\n\telif opcion == \"B\":\n\t\tfor i in lista:\n\t\t\tinsertarpersonas(i)\n\telif opcion == \"X\":\n\t\tprint(\"--> ADIOS <--\")\n\telse:\n\t\tprint(\"Opcion no valida\")\n","sub_path":"PRACTICA1 UF3/Ej3.py","file_name":"Ej3.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"257068395","text":"from ambf_client import Client\nimport time\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\ndef fwd_kinematics(joint_variables):\n    l1 = 0.34\n    l2 = 0.31\n    o1 = 0.25\n    o2 = 0.25\n    [q1,q2,q3,q4] = joint_variables\n\n    DH_parameters = {'theta':[math.pi/2, -math.pi/2, q1, -math.pi/2 + q2, q3, math.pi/2 + q4],\n                        'd':[0, o2, 0, 0, l1, 0],\n                        'a':[o1, 0, 0, 0, 0, l2],\n                    'alpha':[math.pi/2, 0, -math.pi/2, -math.pi/2, math.pi/2, 0]}\n\n    T = dict()\n    for i in range(0,len(DH_parameters['theta'])):\n        name = 'A{}'.format(i)\n        T[name] = np.array([math.cos(DH_parameters['theta'][i]),  -math.sin(DH_parameters['theta'][i])*math.cos(DH_parameters['alpha'][i]),  math.sin(DH_parameters['theta'][i])*math.sin(DH_parameters['alpha'][i]), DH_parameters['a'][i]*math.cos(DH_parameters['theta'][i]),\n                math.sin(DH_parameters['theta'][i]),   math.cos(DH_parameters['theta'][i])*math.cos(DH_parameters['alpha'][i]), -math.cos(DH_parameters['theta'][i])*math.sin(DH_parameters['alpha'][i]), DH_parameters['a'][i]*math.sin(DH_parameters['theta'][i]),\n                                0,                   math.sin(DH_parameters['alpha'][i]),                  math.cos(DH_parameters['alpha'][i]),                 DH_parameters['d'][i],\n                                0,                                 0,                                0,                 1]).reshape((4,4))\n    np.set_printoptions(suppress = True, precision=3)\n    T_tip_base = np.eye(4)\n    for i in range(0,6):\n        key = 'A{}'.format(i)\n        T_tip_base = np.matmul(T_tip_base,T[key])\n\n    T_intermediate = dict()\n    for i in range(2,6):\n        name = 'T_int{}'.format(i)\n        T_intermediate[name] = np.eye(4)\n        key_list = list(T.keys())\n        iter_list = key_list[0:i]\n        for key in iter_list:\n            T_intermediate[name] = np.matmul(T_intermediate[name],T[key])\n\n    Jacobian = np.zeros((6,4))\n\n    Jacobian[3:6,0] = np.array([0,0,1]).reshape((3))\n\n    for i in range(1,4):\n        key = list(T_intermediate.keys())[i]\n        item = T_intermediate[key]\n        Jacobian[3:6,i] = item[0:3,2]\n        Jacobian[0:3,i] = np.cross(item[0:3,2],(T_tip_base[0:3,3]-item[0:3,3]))\n\n    return T_tip_base,Jacobian\n\ndef inverse_kinematics(end_effector_pose):\n    l1 = 0.34\n    l2 = 0.31\n    o1 = 0.25\n    o2 = 0.25\n\n    DH_parameters = {'theta':[math.pi/2, -math.pi/2],\n                        'd':[0, o2],\n                        'a':[o1, 0],\n                    'alpha':[math.pi/2, 0]}\n\n    T = dict()\n    for i in range(0,len(DH_parameters['theta'])):\n        name = 'A{}'.format(i)\n        T[name] = np.array([math.cos(DH_parameters['theta'][i]),  -math.sin(DH_parameters['theta'][i])*math.cos(DH_parameters['alpha'][i]),  math.sin(DH_parameters['theta'][i])*math.sin(DH_parameters['alpha'][i]), DH_parameters['a'][i]*math.cos(DH_parameters['theta'][i]),\n                math.sin(DH_parameters['theta'][i]),   math.cos(DH_parameters['theta'][i])*math.cos(DH_parameters['alpha'][i]), -math.cos(DH_parameters['theta'][i])*math.sin(DH_parameters['alpha'][i]), DH_parameters['a'][i]*math.sin(DH_parameters['theta'][i]),\n                                0,                   math.sin(DH_parameters['alpha'][i]),                  math.cos(DH_parameters['alpha'][i]),                 DH_parameters['d'][i],\n                                0,                                 0,                                0,                 1]).reshape((4,4))\n\n    A1 = T['A0']\n    A2 = T['A1']\n    shoulder_pose = np.matmul(A1,A2)\n    b = np.linalg.norm(end_effector_pose[0:3,3] - shoulder_pose[0:3,3])\n    a = l2\n    c = l1\n    q4_res = round(-math.pi + math.acos((a**2 + c**2 - b**2)/(2*a*c)),3)\n    A6 = np.array([math.cos(math.pi/2 + q4_res),  -math.sin(math.pi/2 + q4_res)*math.cos(0),  math.sin(math.pi/2 + q4_res)*math.sin(0), a*math.cos(math.pi/2 + q4_res),\n                    math.sin(math.pi/2 + q4_res),   math.cos(math.pi/2 + q4_res)*math.cos(0), -math.cos(math.pi/2 + q4_res)*math.sin(0), a*math.sin(math.pi/2 + q4_res),\n                                    0,                   math.sin(0),                  math.cos(0),                 0,\n                                    0,                                 0,                                0,                 1]).reshape((4,4))\n    T_2_5 = np.matmul(np.matmul(np.linalg.inv(shoulder_pose),end_effector_pose),np.linalg.inv(A6))\n    q2_res = round(math.asin(-T_2_5[2,1]),3)\n    q3_res = round(math.asin(T_2_5[2,2]/math.cos(q2_res)),3)\n    q1_res = round(math.asin(T_2_5[1,1]/math.cos(q2_res)),3)\n\n    return [q1_res,q2_res,q3_res,q4_res]\n\n# Initialize client\nc = Client()\n# Connect client and simulation\nc.connect()\ntime.sleep(2)\nb = c.get_obj_handle('base')\nelbow = c.get_obj_handle('elbow')\ntip = c.get_obj_handle('wrist')\n# Joints that can be actuated\njoint_list = [0,1,2,5]\nbase_joint_names = []\nfor item in joint_list:\n        base_joint_names.append(b.get_joint_names()[item])\nprint(base_joint_names)\n# Set all joints to 0\nfor item in joint_list:\n        b.set_joint_pos(item,0)\n        time.sleep(0.01)\ntime.sleep(3)\n# Print all joint positions\nfor item in joint_list:\n        print(b.get_joint_pos(item))\n# Degrees of freedom\nn = 4\n# Joint limits according to .yaml file\nq_limits = {'q0':[-30,150],'q1':[-150,50],'q2':[-70,90],'q3':[-150,0]}\n# Forward kinematics loop\nwhile True:\n        q = []\n        print('Setting joint positions in degrees:')\n        # Loop to get joint angle input\n        for i in range(0,n):\n                name = 'q{}'.format(i)\n                print('Limits:\\n min:{} max:{}:'.format(q_limits[name][0],q_limits[name][1]))\n                q.append(float(input(name+':'))*math.pi/180)\n                b.set_joint_pos(joint_list[i],q[i])\n        print (\"Creating Robotic Chain\")\n        print (\"Forward kinematics\")\n        # Forward kinematics solution\n        T_tip_base_init,Jacobian_init = fwd_kinematics(q)\n        print('Fwd kinematics result:')\n        np.set_printoptions(suppress = True, precision=3)\n        print(T_tip_base_init)\n        time.sleep(6)\n        print(\"Tip pose real:\")\n        print(tip.get_pos())\n        print(tip.get_rot())\n        print(\"Joint angles real:\")\n        for item in joint_list:\n                print(b.get_joint_pos(item)*180.0/math.pi)\n        time.sleep(2)\n        if raw_input(\"Again? (y/n): \") != 'y':\n                break\nwhile True:\n        print(\"Inverse Kinematics\")\n        end_effector = T_tip_base_init\n        q_result = inverse_kinematics(end_effector)\n        print('q1 = {}'.format(q_result[0]*180.0/math.pi))\n        print('q2 = {}'.format(q_result[1]*180.0/math.pi))\n        print('q3 = {}'.format(q_result[2]*180.0/math.pi))\n        print('q4 = {}'.format(q_result[3]*180.0/math.pi))\n        if raw_input(\"Again? (y/n): \") != 'y':\n                break\n\na = 0.31\ntheta = q_result[3]+math.pi\n\nq_result_new = np.copy(q_result)\ncoef = 1\nwhile theta<(165*math.pi/180):\n        new_path_start = np.array([0,1,0,0.25,0,0,1,0.25,1,0,0,-0.03,0,0,0,1]).reshape((4,4)) #[0,0,-1,0.25,-1,0,0,-0.06,0,1,0,0.34,0,0,0,1]\n        desiredPosition = np.copy(new_path_start)\n        desiredPosition[0,3] = new_path_start[0,3] + (a*math.sin(theta))\n        desiredPosition[1,3] = new_path_start[1,3]\n        desiredPosition[2,3] = new_path_start[2,3] - (a*(1-math.cos(theta)))\n        desiredPosition[0:3,0:3] = np.array([math.sin(theta),math.cos(theta),0,0,0,1,math.cos(theta),-math.sin(theta),0]).reshape((3,3))\n        plt.figure(1)\n        plt.title('Reference Path')\n        plt.scatter(desiredPosition[0,3],desiredPosition[2,3])\n        plt.xlabel('x')\n        plt.ylabel('z')\n        theta = theta + math.pi/360\n\n        q_result_new = inverse_kinematics(desiredPosition)\n        fwd,jcb = fwd_kinematics(q_result_new)\n        plt.figure(2)\n        plt.scatter(fwd[0,3],fwd[2,3],color='green')\n\n\n        print(\"Output angles in degrees: \")\n        for i in range(0,len(q_result)):\n                print(q_result_new[i]*180/math.pi)\n                b.set_joint_pos(joint_list[i],q_result_new[i])\n        time.sleep(0.01)\n        plt.figure(1)\n        plt.scatter(tip.get_pose()[0],tip.get_pose()[2],color='red')\n\nplt.show()\n","sub_path":"scripts/human_kinematics_path_4.py","file_name":"human_kinematics_path_4.py","file_ext":"py","file_size_in_byte":8238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"432481080","text":"#!/usr/bin/python3\n\n\"\"\"\nScript for combining unique variants and intersection between 2 variant-containing files\n\t* Do a Union for 2 files \n\t* Variants are identified as (chr, pos, alt)\n\t* Line-by-line\n\nDeveloped by Tran Minh Tri\nDate: 23 July 2015\n\"\"\"\n\nimport argparse\nimport os\nimport sys\nimport time\n\ndef main(args):\n\tdictA = {}\n\tsetBlessA = set()\n\tsetAB = set()\n\tfileA = args.fileA\n\tfileB = args.fileB\n\tcase = fileA.split(\".\")[0].split(\"_\")[1]\n\ttype = fileA.split(\".\")[1]\n\tfileAB = case + '_union.' + type + \".txt\"\n\tfileAaB = case + '_intersect.' + type + \".txt\"\n\tinterS = open(fileAaB, 'w')\n\tunion = open(fileAB, 'w')\n\t\n\tfor line in open(fileA):\n\t\tif '#' in line: continue\n\t\tvar = line.split()\n\t\tchr = var[0]\n\t\tpos = var[1]\n\t\talt = var[4]\n\t\tqual = float(var[5])\n\t\tdictA[(chr,pos,alt)] = [qual, line]\n\t\n\tctr = 0\n\tfor line in open(fileB):\n\t\tif '#' in line: continue\n\t\tvar = line.split()\n\t\tchr = var[0]\n\t\tpos = var[1]\n\t\talt = var[4]\n\t\titem = (chr,pos,alt)\n\t\tif item in dictA:\n\t\t\tsetAB.add(item)\n\t\t\tif dictA[item][0] < float(var[5]):\n\t\t\t\tunion.write(line)\n\t\t\t\tinterS.write(line)\n\t\t\t\tdictA[item][0] = False \n\t\t\telse:\n\t\t\t\tunion.write(dictA[item][1])\n\t\t\t\tinterS.write(dictA[item][1])\n\t\t\t\tdictA[item][0] = False \n\t\telse:\n\t\t\tsetBlessA.add(item)\n\t\t\tunion.write(line)\n\t\tctr += 1\n\t\n\tfor key in dictA:\n\t\tif dictA[key][0]: \n\t\t\tunion.write(line)\n\t\t\tctr += 1\n\t\n\tunion.close()\n\tinterS.close()\n\t\n\tprint('Set A:', fileA.split(\"/\")[-1], 'Set B:', fileB.split(\"/\")[-1])\n\tprint('Set A unique:', len(dictA) - len(setAB), 'Set A:', len(dictA))\n\tprint('Set B unique:', len(setBlessA), 'Set B:', len(setBlessA) + len(setAB))\n\tprint('Intersection:', len(setAB))\n\tprint('Intersection file created:', fileAaB)\t\n\tprint('Union: ', ctr)\n\tprint('Union file created:', fileAB)\n\tprint('-------------')\n\t\n##########################################\n\nif __name__ == \"__main__\":\n\tSTART_TIME = time.time()\n\tparser = argparse.ArgumentParser( description=\"count intersection element\" )\n\tparser.add_argument( \"--fileA\", metavar=\"text file A\" )\n\tparser.add_argument( \"--fileB\", metavar=\"text file B\" )\n\tparser.add_argument( \"-v\", \"--verbose\", action=\"count\", help=\"Verbose\" )\n\targs = parser.parse_args()\n\tmain(args)\n\n\tif args.verbose:\n\t\tend_time = time.time() - START_TIME\n\t\tprint( \"Total time taken: %d seconds (%.1fm)\" % (end_time, end_time/60))","sub_path":"variant_calling_anno/combine_variant_files/combine_2_lists.py","file_name":"combine_2_lists.py","file_ext":"py","file_size_in_byte":2308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"214176248","text":"\nimport dlib\nimport cv2\nimport numpy as np\nfrom sklearn import preprocessing\n\nimport tensorflow as tf\nimport keras\nfrom keras.layers import Dense,Activation\n\npredictor_path = \"./shape_predictor_68_face_landmarks.dat\"\npredictor = dlib.shape_predictor(predictor_path)\ndetector = dlib.get_frontal_face_detector()\n\n\ncap = cv2.VideoCapture(0)\n\ndata_count = 0\nmmscaler = preprocessing.MinMaxScaler() # インスタンスの作成\n\nx = []\ny = []\ny_const = 1\n\nwhite_bord_w = 1280\nwhite_bord_h = 700\n\nmodel = keras.models.load_model('../20190722/model.h5', compile=False)\n\nvector_dict = {\n                0:[ 0, 0],\n                1:[ 0, 0.5],\n                2:[ 0, 1],\n                3:[ 0.5, 0],\n                4:[ 0.5, 0.5],\n                5:[ 0.5, 1],\n                6:[ 1, 0],\n                7:[ 1, 0.5],\n                8:[ 1, 1]\n                }\n\n\nwhile True:\n    key = cv2.waitKey(1)\n    ret_val, image = cap.read()\n    if ret_val != True:\n        continue\n    image = cv2.flip(image, 1)\n    image = cv2.resize(image, (image.shape[1]//4, image.shape[0]//4))\n\n    white_bord = np.full((white_bord_h,white_bord_w,3),255,np.uint8)\n\n    dets, scores, idx = detector.run(image, 0)\n    if len(dets) == 1:\n        d = dets[0]\n        shape = predictor(image, d)\n        shape_68point = []\n        for i in range(68):\n            shape_68point.append([shape.part(i).x, shape.part(i).y])\n            data_68point = np.array(shape_68point)\n            nomal_data = mmscaler.fit_transform(data_68point)\n        x_sample = np.reshape(nomal_data,(1,136))\n\n        if key == ord('s'):\n            x.append(nomal_data)\n            y.append(y_const)\n            print(data_count)\n            data_count += 1\n        print(x_sample.shape)\n        r = model.predict([x_sample])[0]\n        head_x, head_y = 0, 0\n        for i, p in enumerate(r):\n            head_x += vector_dict[i][1] * p\n            head_y += vector_dict[i][0] * p\n        #head = r[1]*white_bord_w/2 + r[2]*white_bord_w\n        print(r)\n        white_bord = cv2.circle(white_bord, (int(head_x*white_bord_w), int(head_y*white_bord_h)), 50, (255,0,0), -1)\n    if data_count > 100:\n        break\n    \n\n\n\n    cv2.imshow('show',image)\n    cv2.imshow('white_bord',white_bord)\n    \n    if key == ord('q'):\n        break\n\nnp.savez('./'+str(y_const), x=x, y=y)","sub_path":"dev_script_0.1/ml-test/dlib/data_shushu.py","file_name":"data_shushu.py","file_ext":"py","file_size_in_byte":2309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"379968843","text":"from flask import Flask, g\nimport sqlite3\n\napp = Flask(\"__DreamGift__\")\n\n\n@app.route('/')\ndef hello_world():\n    return 'Hello World!'\n\n@app.route('/add_user/<data>')\ndef add_user(data):\n    a = sqlite3.connect('db/datos.db')\n    db = a.cursor()\n    info = data.split(\",\")\n    us_id = int(info[0])\n    if (us_id,) in db.execute(\"SELECT id FROM USERS\").fetchall():\n        return 'id ya existe'\n    else:\n        if len(info) == 3:\n            DATA = \"({},'{}','{}')\".format(info[0], info[1], info[2])\n            db.execute(\"INSERT INTO USERS(id,name,last) VALUES {}\".format(DATA))\n            a.commit()\n            a.close()\n            return 'usuario creado correctamente'\n\n        else:\n            return False\n\n@app.route('/regalar/<data>')\ndef uregalar(data):\n    #IDM,IDR,STATUS,REGALADOX\n    a = sqlite3.connect('db/datos.db')\n    db = a.cursor()\n    print(\"fetching gift data\")\n    try:\n        i = data.split(\",\")\n    except:\n        return 'bad parameter'\n    if len(i) == 4:\n        db.execute(\"UPDATE REGALADO SET status={}, regaladox={} WHERE idm={} AND idr={}\".format(i[2], i[3], i[0], i[1]))\n        a.commit()\n        a.close()\n        return 'success'\n    else:\n        return 'bad parameter'\n\n\ndef mregalar(data):\n    print(\"ejecutando regalar\")\n    a = sqlite3.connect('db/datos.db')\n    db = a.cursor()\n    idm = data[0]\n    for i in range(1, len(data)):\n        DATA = \"({},{},{},{})\".format(idm, data[i], 0, 0)\n        #idmatri, nombre regalo, false, nullid\n        db.execute(\"INSERT INTO REGALADO(idm,idr,status,regaladox) VALUES {}\".format(DATA))\n        print(DATA)\n    a.commit()\n    a.close()\n    return 'success'\n\n\n@app.route('/marriage/<data>')\ndef add_marriage(data):\n    #DATA= ID1,ID2,IDM,REGALOS1, ... ,REGALOn\n    a = sqlite3.connect('db/datos.db')\n    db = a.cursor()\n    try:\n        info = data.split(\",\")\n    except:\n        return False\n    #pasar un string con ambos ids\n    if len(info) >= 3:\n        for row in db.execute(\"SELECT * FROM MARRIAGES\"):\n            if int(info[2]) in row:\n                return 'fail'\n            if int(info[1]) in row:\n                return False\n            if int(info[0]) in row:\n                return False\n\n        print(info[0])\n\n        DATA = \"({},{},{})\".format(info[0], info[1], info[2])\n        print(DATA)\n        try:\n            db.execute(\"INSERT INTO MARRIAGES(id1,id2,idm) VALUES {}\".format(DATA))\n            param = info[3:]\n            param.insert(0,info[2])\n            a.commit()\n            a.close()\n            if len(param) > 1:\n                return mregalar(param)\n        except:\n            return 'fallo la inscripcion'\n\n#LISTA, NO REQUIERE VERIFICACION\n@app.route(\"/gift/\")\ndef gift():\n    a = sqlite3.connect('db/datos.db')\n    db = a.cursor()\n    temp = '['\n    for row in db.execute(\"SELECT * FROM REGALOS\"):\n        temp += '{\"id\":\"' + str(row[0]) + '\",\"name\":\"' + str(row[1]) + '\",\"location\":\"'\n        temp += str(row[2]) + '\",\"description\":\"' + str(row[3]) + '\",\"cost\":\"'\n        temp += str(row[4]) + '\"},'\n    out = temp[:-1]\n    out += ']'\n    a.close()\n    return out\n\n#LISTA CON VERIFICACION DE ID\n@app.route(\"/gift/<idm>\")\ndef marriage_gifts(idm):\n    a = sqlite3.connect('db/datos.db')\n    db = a.cursor()\n    try:\n        if (int(idm),) in db.execute(\"SELECT idm FROM MARRIAGES\").fetchall():\n            temp = '['\n            for row in db.execute(\"SELECT REGALOS.id, REGALOS.name, location, description, cost, REGALADO.status, REGALADO.regaladox FROM REGALOS,REGALADO WHERE REGALADO.idr = REGALOS.id AND REGALADO.idm = {}\".format(idm)):\n                temp += '{\"id\":\"' + str(row[0]) + '\",\"name\":\"' + str(row[1]) + '\",\"location\":\"'\n                temp += str(row[2]) + '\",\"description\":\"' + str(row[3]) + '\",\"cost\":\"'\n                temp += str(row[4]) +'\",\"status\":\"' + str(row[5]) + '\",\"regaladox\":\"' + str(row[6]) + '\"},'\n            out = temp[:-1]\n            out += ']'\n            a.close()\n            return out\n        else:\n            return \"id does not exist\"\n    except:\n        return \"incorrect id type\"\n\n\n\nif __name__ == '__main__':\n    app.run(host='192.168.0.86', port='5010')\n    #add_marriage(\"3,2,1,pelota,tenis,ganar\")\n\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"303725388","text":"# coding: utf-8\nimport time\nimport contextlib\nimport threading\n\nfrom base import BaseCache, DEFAULT_TIMEOUT\n\n__author__ = 'bin wen'\n\n_caches = {}\n_expire_info = {}\n_locks = {}\n\n\nclass RWLock(object):\n    \"\"\"\n    Classic implementation of reader-writer lock with preference to writers.\n\n    Readers can access a resource simultaneously.\n    Writers get an exclusive access.\n\n    API is self-descriptive:\n        reader_enters()\n        reader_leaves()\n        writer_enters()\n        writer_leaves()\n    \"\"\"\n\n    def __init__(self):\n        self.mutex = threading.RLock()\n        self.can_read = threading.Semaphore(0)\n        self.can_write = threading.Semaphore(0)\n        self.active_readers = 0\n        self.active_writers = 0\n        self.waiting_readers = 0\n        self.waiting_writers = 0\n\n    def reader_enters(self):\n        with self.mutex:\n            if self.active_writers == 0 and self.waiting_writers == 0:\n                self.active_readers += 1\n                self.can_read.release()\n            else:\n                self.waiting_readers += 1\n        self.can_read.acquire()\n\n    def reader_leaves(self):\n        with self.mutex:\n            self.active_readers -= 1\n            if self.active_readers == 0 and self.waiting_writers != 0:\n                self.active_writers += 1\n                self.waiting_writers -= 1\n                self.can_write.release()\n\n    @contextlib.contextmanager\n    def reader(self):\n        self.reader_enters()\n        try:\n            yield\n        finally:\n            self.reader_leaves()\n\n    def writer_enters(self):\n        with self.mutex:\n            if self.active_writers == 0 and self.waiting_writers == 0 and self.active_readers == 0:\n                self.active_writers += 1\n                self.can_write.release()\n            else:\n                self.waiting_writers += 1\n        self.can_write.acquire()\n\n    def writer_leaves(self):\n        with self.mutex:\n            self.active_writers -= 1\n            if self.waiting_writers != 0:\n                self.active_writers += 1\n                self.waiting_writers -= 1\n                self.can_write.release()\n            elif self.waiting_readers != 0:\n                t = self.waiting_readers\n                self.waiting_readers = 0\n                self.active_readers += t\n                while t > 0:\n                    self.can_read.release()\n                    t -= 1\n\n    @contextlib.contextmanager\n    def writer(self):\n        self.writer_enters()\n        try:\n            yield\n        finally:\n            self.writer_leaves()\n\n\nclass LocMemCache(BaseCache):\n    def __init__(self, name, params):\n        BaseCache.__init__(self, params)\n        self._cache = _caches.setdefault(name, {})\n        self._expire_info = _expire_info.setdefault(name, {})\n        self._lock = _locks.setdefault(name, RWLock())\n\n    def validate_key(self, key):\n        pass\n\n    def add(self, key, value, timeout=DEFAULT_TIMEOUT, version=None):\n        key = self.make_key(key, version=version)\n        self.validate_key(key)\n        with self._lock.writer():\n            if self._has_expired(key):\n                self._set(key, value, timeout)\n                return True\n            return False\n\n    def get(self, key, default=None, version=None):\n        key = self.make_key(key, version=version)\n        self.validate_key(key)\n        value = None\n        with self._lock.reader():\n            if not self._has_expired(key):\n                value = self._cache[key]\n\n        if value is not None:\n            return value\n\n        with self._lock.writer():\n            try:\n                del self._cache[key]\n                del self._expire_info[key]\n            except KeyError:\n                pass\n            return default\n\n    def _set(self, key, value, timeout=DEFAULT_TIMEOUT):\n        if len(self._cache) >= self._max_entries:\n            self._cull()\n        self._cache[key] = value\n        self._expire_info[key] = self.get_backend_timeout(timeout)\n\n    def set(self, key, value, timeout=DEFAULT_TIMEOUT, version=None):\n        key = self.make_key(key, version=version)\n        self.validate_key(key)\n        with self._lock.writer():\n            self._set(key, value, timeout)\n\n    def incr(self, key, delta=1, version=None):\n        value = self.get(key, version=version)\n        if value is None:\n            raise ValueError(\"Key '%s' not found\" % key)\n        new_value = value + delta\n        key = self.make_key(key, version=version)\n        with self._lock.writer():\n            self._cache[key] = new_value\n        return new_value\n\n    def has_key(self, key, version=None):\n        key = self.make_key(key, version=version)\n        self.validate_key(key)\n        with self._lock.reader():\n            if not self._has_expired(key):\n                return True\n\n        with self._lock.writer():\n            try:\n                del self._cache[key]\n                del self._expire_info[key]\n            except KeyError:\n                pass\n            return False\n\n    def _has_expired(self, key):\n        exp = self._expire_info.get(key, -1)\n        if exp is None or exp > time.time():\n            return False\n        return True\n\n    def _cull(self):\n        if self._cull_frequency == 0:\n            self.clear()\n        else:\n            doomed = [k for (i, k) in enumerate(self._cache) if i % self._cull_frequency == 0]\n            for k in doomed:\n                self._delete(k)\n\n    def _delete(self, key):\n        try:\n            del self._cache[key]\n        except KeyError:\n            pass\n        try:\n            del self._expire_info[key]\n        except KeyError:\n            pass\n\n    def delete(self, key, version=None):\n        key = self.make_key(key, version=version)\n        self.validate_key(key)\n        with self._lock.writer():\n            self._delete(key)\n\n    def clear(self):\n        self._cache.clear()\n        self._expire_info.clear()\n\n    def clear_expires(self, **kwargs):\n\n        for key in self._cache.keys():\n            with self._lock.writer():\n                exp = self._expire_info.get(key)\n                if exp and exp < time.time():\n                    self._cache.pop(key)\n                    self._expire_info.pop(key)\n\n","sub_path":"cores/cache/localcache.py","file_name":"localcache.py","file_ext":"py","file_size_in_byte":6223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"648582206","text":"import os\nimport json\nfrom typing import Union\n\nfrom lib import skill_duration\nfrom lib import schema_validator\n\n\ndef get_json(filename: str) -> Union[object, None]:\n    \"\"\"\n    Load json from file\n    \"\"\"\n    if not os.path.isfile(filename):\n        print(\"File does not exists\")\n\n    with open(filename) as f:\n        try:\n            data = json.load(f)\n        except ValueError:\n            return None\n        return data\n\n\ndef main():\n    freelancer_file = 'exercise/freelancer.json'\n    schema = 'exercise/freelancer_schema.json'\n\n    # load data\n    freelance = get_json(freelancer_file)\n    schema = get_json(schema)\n\n    # validations\n    if not freelance or not schema_validator.is_valid(freelance=freelance, schema=schema):\n        return\n\n    # process\n    output = skill_duration.process_freelance(freelance)\n\n    print(output)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"exercise.py","file_name":"exercise.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"229152281","text":"import sys, pygame, tools\nfrom math import sqrt\nfrom zombie import Zombie\nfrom player import Player\n\n\ndef game_loop():\n    # Consts {\n    RESOLUTION = SCREEN_WIDTH, SCREEN_HEIGHT = 1366, 768\n    SCREEN_CENTER = SCREEN_WIDTH/2, SCREEN_HEIGHT/2\n    FPS = 60\n    # }\n\n    # Bools {\n    alive = True\n    mouse_is_down = False\n    diff_up = False\n    # }\n    \n    # Ints{\n    elapsed = 0\n    gamemode = 3         \n    # }\n\n    # Strings {\n    # }\n\n    # Lists {\n    player_dest = None\n    # }\n\n    # PyGame {\n    pygame.init()\n    screen = pygame.display.set_mode(RESOLUTION)\n    clock = pygame.time.Clock()\n        #PyGame.font{\n    font = None\n    font_rect = None\n    if gamemode == 3:\n        while not pygame.font.get_init:\n            pygame.font.init()\n        font = pygame.font.Font(pygame.font.get_default_font(), 40)\n        font_rect = pygame.Rect((SCREEN_CENTER), (0, 0))\n        # }\n    # }\n\n    # Player {\n    player = Player()\n    # }\n\n\n    # Enemy {\n    zombies = []\n    enemies = 0\n    # }\n\n    # Game {\n    # }\n\n    # Game_Loop\n    while alive:\n        # Clear the screen\n        screen.fill((0, 0, 0))\n\n        \n\n        # Handle Input    \n        for event in pygame.event.get():\n            if event.type == pygame.QUIT: \n                alive = False\n                continue\n            if event.type == pygame.MOUSEBUTTONDOWN and event.__dict__['button'] == 3:\n                mouse_is_down = True\n            elif event.type == pygame.MOUSEBUTTONUP and event.__dict__['button'] == 3:\n                mouse_is_down = False\n\n        # Update the player, then draw them to the screen\n        if mouse_is_down:\n            player_dest = pygame.mouse.get_pos()\n        player.update(player_dest)\n        player.draw(screen)\n\n        # Every second add a zombie\n        if len(zombies) < 100:\n            elapsed += clock.get_time()\n            if elapsed >= 1000:\n                elapsed = 0\n                zombies.append(Zombie())\n        elif not diff_up:\n            diff_up = True\n        \n        # Update each zombie, then draw it to the screen\n        if gamemode == 1:\n            zdest = player.center\n        elif gamemode == 2:\n            zdest = player.dest\n        elif gamemode == 3:\n            zdest = player.dest\n            color = (255, 255, 255) if len(zombies) < 100 else (255, 0, 0)\n            text = font.render(str(len(zombies)), True, color)\n            screen.blit(text, font_rect)\n        else:\n            zdest = player.center\n        for z in zombies:\n            z.update(zdest)\n            if diff_up:\n                z.difficulty += 0.00001\n            z.draw(screen)\n            if tools.distance(z.center, player.center) <= (player.width - (z.width+(z.width/2))):\n                alive = False\n                continue\n            \n        \n        \n\n        pygame.display.flip()\n        clock.tick(FPS)\n\n\nif __name__ == \"__main__\":\n    game_loop()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"119876181","text":"\"\"\"\n    1. 一些对于算法的测试函数\n    2. \n\"\"\"\nimport time\nfrom random import randint\n\nimport platform\n\n\ndef myComputerVersion():\n    os = platform.system()\n    return {\n        \"system\":os,\n        \"version\":platform.platform(),\n        \"32/64\":platform.architecture()[0],\n        \"computerName\":platform.node(),\n        \"machineInfo\":platform.machine()\n    }\n\n\ndef timer():\n    \"\"\"\n    this is a Efficiency function\n    :return:\n    \"\"\"\n    def wraps(func):\n        def mesure(*args,**kwargs):\n            start = time.time()\n            res = func(*args,**kwargs)\n            end = time.time()\n            print(\"function %s use time %s\"%(func.__name__,(end-start)))\n            return res\n        return mesure\n    return wraps\n\n\ndef generateRandomArray(n,rangeL,ranger):\n    arr = []\n    for i in range(n):\n        arr.append(randint(rangeL,ranger))\n    return arr\n\n\ndef isSorted(arr):\n    for i in range(len(arr)):\n        if (arr[i] < arr[i+1]):\n            return True\n    return False\n\nif __name__ == '__main__':\n    print(generateRandomArray(20,1,10))\n    print(myComputerVersion())","sub_path":"LanguageDatastruct/PythonLearn/someSort/hellperTool.py","file_name":"hellperTool.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"119205028","text":"import sys\nimport os\nimport unittest\nimport requests\n\nfrom threading import Thread\nfrom time import sleep\n\nsys.path.append(os.path.join(os.path.dirname(__file__), os.pardir))\n\nfrom twisted_test import app\n\n\nWARMUP_TIMEOUT = 2\nUNKNOWN_IMAGE_ID = 'fake-image-id'\nPORT = 9090\n\n\ndef get_url(url):\n    return 'http://127.0.0.1:{port}/{url}'.format(\n        port=PORT,\n        url=url.lstrip('/')\n    )\n\n\nclass TestApi(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        thread = Thread(target=app.start, args=[PORT])\n        thread.daemon = True\n        thread.start()\n        sleep(WARMUP_TIMEOUT)\n\n    @classmethod\n    def tearDownClass(cls):\n        app.stop()\n\n    def test_get_static_files(self):\n        response = requests.get(get_url('/index.html'))\n        self.assertEqual(response.status_code, 200)\n\n    def test_get_unknown_image(self):\n        response = requests.get(\n            get_url('/api/images/get/{}'.format(UNKNOWN_IMAGE_ID))\n        )\n        self.assertEqual(response.status_code, 404)\n\n    def test_get_images(self):\n        for extension in ('png', 'jpeg', 'bmp', 'gif'):\n            path_to_image = self._get_image_path(extension)\n            image_id = self._add_new_image(path_to_image)\n\n            response = requests.get(\n                get_url('/api/images/get/{}'.format(image_id))\n            )\n            self.assertEqual(response.status_code, 200)\n            \n    def test_get_flipped_images(self):\n        for extension in ('png', 'jpeg', 'bmp', 'gif'):\n            path_to_image = self._get_image_path(extension)\n            image_id = self._add_new_image(path_to_image)\n\n            response = requests.get(\n                get_url('/api/images/get/{}/flip'.format(image_id))\n            )\n            self.assertEqual(response.status_code, 200)\n\n            \n    def _get_image_path(self, extension):\n            return os.path.join(\n                os.path.dirname(__file__),\n                'samples',\n                'sample_1.{}'.format(extension)\n            )\n\n    def _add_new_image(self, path_to_image):\n        response = requests.post(\n            get_url('/api/images/add'),\n            files={'image': open(path_to_image, 'rb')}\n        )\n\n        self.assertEqual(response.status_code, 200)\n\n        self.assertIn('success', response.json)\n        self.assertEqual(True, response.json['success'])\n\n        self.assertIn('data', response.json)\n        self.assertIn('id', response.json['data'])\n        image_id = response.json['data']['id']\n        \n        return image_id\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/test_images_api.py","file_name":"test_images_api.py","file_ext":"py","file_size_in_byte":2594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"156408316","text":"#import sys\n#sys.setrecursionlimit(10**9)\n\nH, N = map(int, input().split())\nmagic = [_ for _ in range(N)]\n\nfor k in range(N):\n  magic[k] = list(map(int, input().split()))\n  magic[k].append(magic[k][0]/magic[k][1])\n\nmagic.sort(key = lambda x: x[2], reverse=True)\nans = [0 for _ in range(H+1)]\nvisited = [0]\nanskouho = [float('inf')]\nans2 = float('inf')\n\"\"\"\ndef solve(start, power, point, maryoku):\n  if start == H:\n    print(min(point, min(anskouho)))\n    exit()\n  elif start > H:\n    anskouho.append(point)\n    return 0\n  elif ans[start] != 0:\n    return 0\n  else:\n    visited.append(start)\n    ans[start] = point\n    solve(start+power, power, point+maryoku, maryoku)\n\"\"\"\nfor k in range(N):\n  for item in visited:\n    #solve(item+magic[k][0], magic[k][0], ans[item] + magic[k][1], magic[k][1])\n    start = item+magic[k][0]\n    power = magic[k][0]\n    point = ans[item]+ magic[k][1]\n    maryoku = magic[k][1]\n    for _ in range(10**5):\n      if start == H:\n        print(min(point, ans2))\n        exit()\n      elif start > H:\n        ans2 = min(ans2, point)\n        break\n      elif ans[start]!=0:\n        break\n      else:\n        visited.append(start)\n        ans[start] = point\n        start += power\n        point += maryoku\n      \nprint(ans2)","sub_path":"Python_codes/p02787/s443836529.py","file_name":"s443836529.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"609772453","text":"'''\n Unit tests for the master_check_paths_in_config action plugin\n'''\nimport os\nimport sys\n\nfrom ansible import errors\nimport pytest\n\n\nMODULE_PATH = os.path.realpath(os.path.join(__file__, os.pardir, os.pardir, 'action_plugins'))\nsys.path.insert(1, MODULE_PATH)\n\n# pylint: disable=import-error,wrong-import-position,missing-docstring\n# pylint: disable=invalid-name,redefined-outer-name\nimport master_check_paths_in_config  # noqa: E402\n\n\n@pytest.fixture()\ndef loaded_config():\n    \"\"\"return testing master config\"\"\"\n    data = {\n        'apiVersion': 'v1',\n        'oauthConfig':\n        {'identityProviders':\n            ['1', '2', '/this/will/fail']},\n        'fake_top_item':\n        {'fake_item':\n            {'fake_item2':\n                [\"some string\",\n                    {\"fake_item3\":\n                        [\"some string 2\",\n                            {\"fake_item4\":\n                                {\"some_key\": \"deeply_nested_string\"}}]}]}}\n    }\n    return data\n\n\ndef test_pop_migrated(loaded_config):\n    \"\"\"Params:\n\n    * `loaded_config` comes from the `loaded_config` fixture in this file\n    \"\"\"\n    # Ensure we actually loaded a valid config\n    assert loaded_config['apiVersion'] == 'v1'\n\n    # Test that migrated key is removed\n    master_check_paths_in_config.pop_migrated_fields(loaded_config)\n    assert loaded_config['oauthConfig'] is not None\n    assert loaded_config['oauthConfig'].get('identityProviders') is None\n\n\ndef test_walk_mapping(loaded_config):\n    \"\"\"Params:\n    * `loaded_config` comes from the `loaded_config` fixture in this file\n    \"\"\"\n    # Ensure we actually loaded a valid config\n    fake_top_item = loaded_config['fake_top_item']\n    stc = []\n    expected_keys = (\"some string\", \"some string 2\", \"deeply_nested_string\")\n\n    # Test that we actually extract all the strings from complicated nested\n    # structures\n    master_check_paths_in_config.walk_mapping(fake_top_item, stc)\n    assert len(stc) == 3\n    for item in expected_keys:\n        assert item in stc\n\n\ndef test_check_strings():\n    stc_good = ('/etc/origin/master/good', 'some/child/dir')\n    # This should not raise\n    master_check_paths_in_config.check_strings(stc_good)\n\n    # This is a string we should alert on\n    stc_bad = ('goodfile.txt', '/root/somefile')\n    with pytest.raises(errors.AnsibleModuleError):\n        master_check_paths_in_config.check_strings(stc_bad)\n\n    stc_bad_relative = ('goodfile.txt', '../node/otherfile')\n    with pytest.raises(errors.AnsibleModuleError):\n        master_check_paths_in_config.check_strings(stc_bad_relative)\n","sub_path":"ansible/roles/lib_oa_utils/test/test_master_check_paths_in_config.py","file_name":"test_master_check_paths_in_config.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"238307316","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\nimport cv2\nimport sys\nimport numpy as np\nimport math\nfrom matplotlib import pyplot as plt\nimport pickle\nimport skimage.io\n\ndef train(itrain=False):\n    print(\"Training moments\")\n    n = 0\n    for e in sys.argv[3::1]:\n        sum = 0\n        n += 1\n        if (itrain):\n            print(\"Reading\", e)\n            e = cv2.imread(e, 0)\n            mom = cv2.moments(e)\n            mom = cv2.HuMoments(mom)\n        else:\n            with open(e, \"rb\") as f:\n                mom = pickle.load(f)\n                print(\"Loaded\", mom)\n        sum += mom\n    print(\"New moment:\", sum)\n    with open(sys.argv[2], \"wb\") as f:\n        pickle.dump(sum / n, f, protocol=2)\n\ndef hu_diff(mom1, mom2):\n    tmp1 = []\n    tmp2 = []\n    for i,_ in enumerate(mom1):\n        if mom1[i] != 0 and mom2[i] != 0:\n            tmp1.append(mom1[i])\n            tmp2.append(mom2[i])\n    mom1 = tmp1\n    mom2 = tmp2\n    mom1 = -np.sign(mom1) * np.log10(np.abs(mom1))\n    mom2 = -np.sign(mom2) * np.log10(np.abs(mom2))\n\n    return np.max(np.abs((mom1 - mom2) / mom2))\n\ndef hu_detect(moment, img, min_coverage=0.1, HuThreshold=2.5, invert=False, adaptive=True, dilation_level=2, dilation_kernel=(3, 3), max_coverage=1):\n    original = img\n\n    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n    img = cv2.GaussianBlur(img, (15, 15), 0)\n\n    blurred = img\n    if invert:\n        flag = cv2.THRESH_BINARY_INV\n    else:\n        flag = cv2.THRESH_BINARY\n    ret, img = cv2.threshold(img, 0, 255, flag + cv2.THRESH_OTSU)\n\n    img = cv2.morphologyEx(img, cv2.MORPH_DILATE, np.ones( dilation_kernel ), iterations = dilation_level)\n\n    if adaptive:\n        b = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 15, 0)\n        b = cv2.morphologyEx(b, cv2.MORPH_ERODE, np.ones( (3, 3) ), iterations = 2)\n        cv2.subtract(img, b, img)\n\n    # show img as found before finding contours\n    plt.figure()\n    plt.imshow(img)\n\n    # uncomment to save moments\n    #with open(\"moments.txt\", \"wb\") as f:\n    #    pickle.dump(mom, f)\n\n    _, contours, _ = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n    moments = []\n\n    for contour in contours:\n        contour_moment = cv2.moments(contour, binaryImage=True)\n        area = contour_moment['m00']\n        coverage = area / (img.shape[0] * img.shape[1])\n\n        contour_moment = cv2.HuMoments(contour_moment).flatten()\n\n        if coverage > min_coverage:\n            match = hu_diff(moment, contour_moment)#cv2.matchShapes(moment, contour, 3, 0.0)\n            print(\"coverage\", coverage)\n            print(\"moment\", contour_moment)\n            print(\"-\" * 15)\n            print(\"match:\", match)\n            print(\"-\" * 15)\n\n            xs = contour[:, 0, 0]\n            ys = contour[:, 0, 1]\n\n            #if match < HuThreshold:\n            #    plt.plot(xs, ys, linewidth=1.5)\n\n            moments += (match, contour)\n\n    print(\"moments:\", moments[0::2])\n\n    if (len(moments) == 0):\n        plt.show()\n        exit(1)\n    hu_val = np.argmin(moments[0::2])\n\n    if moments[0::2][hu_val] < HuThreshold:\n        contour = moments[1::2][hu_val]\n\n        m = cv2.moments(contour, binaryImage=True)\n        m = cv2.HuMoments(m).flatten()\n\n        with open(sys.argv[1] + \"moments.txt\", \"wb\") as f:\n            pickle.dump(m, f)\n\n        x, y, width, height = cv2.boundingRect(contour)\n\n        #cv2.drawContours(original, contour, -1, (0, 255, 0), 20)\n        mask = np.zeros((height, width, 3), dtype=np.uint8)\n        c = contour\n        c[:, 0, 0] -= x\n        c[:, 0, 1] -= y\n        cv2.fillPoly(mask, pts=[c], color=(255,255,255))\n\n        cropped = original[y:y+height, x:x+width]\n        cropped = cv2.bitwise_and(cropped, mask)\n\n        return cropped, (x, y, width, height), contour\n\ndef draw_plot(image):\n    plt.figure()\n    plt.axis(\"off\")\n    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n    plt.show()\n\ndef main():\n    if \"train\" == sys.argv[1]:\n        train()\n        print(\"Moments trained\")\n        exit(0)\n    if \"itrain\" in sys.argv[1]:\n        train(True)\n        print(\"Moments image-trained\")\n        exit(0)\n\n    with open(sys.argv[2], \"rb\") as f:\n        a4_moment = pickle.load(f)\n\n    with open(sys.argv[3], \"rb\") as f:\n        qr_moment = pickle.load(f)\n\n    print(\"Reading\", sys.argv[1])\n    img = cv2.imread(sys.argv[1], 1)\n\n    print(\"page_moment hu:\", a4_moment)\n    print(\"qr_moment hu:\", a4_moment)\n\n    original = img\n    # import code; code.interact(local=dict(globals(), **locals()))\n    img, bounding_rect, _ = hu_detect(a4_moment, img)\n    img = cv2.morphologyEx(img, cv2.MORPH_OPEN, np.ones( (11, 11) ))\n\n    img, bounding_rect2, contour = hu_detect(qr_moment, img, HuThreshold=40, min_coverage=0.05, invert=True, adaptive=False, dilation_level=3, dilation_kernel=(9,9))\n\n    logo = cv2.imread('logo.png', -1)\n\n    x, y, _, _ = bounding_rect\n    x2, y2, width, height = bounding_rect2\n\n    x += int(x2*0.9)\n    y += int(y2*0.9)\n    coverage = (float(width*height) / float(logo.shape[0]*logo.shape[1]))*5\n\n    logo = cv2.resize(logo, (int(coverage * logo.shape[1]), int(coverage * logo.shape[0])), interpolation = cv2.INTER_CUBIC)\n\n    for c in range(0,3):\n        original[y:y+logo.shape[0], x:x+logo.shape[1], c] = logo[:,:,c] * (logo[:,:,3]/255.0) +  original[y:y+logo.shape[0], x:x+logo.shape[1], c] * (1.0 - logo[:,:,3]/255.0)\n\n    draw_plot(original)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"Komunikacja Czlowiek Komputer/Image_Recognition_Project/sheet.py","file_name":"sheet.py","file_ext":"py","file_size_in_byte":5461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"146339625","text":"from Turtle import Turtle, rad\r\nfrom math import sin\r\n\r\n\r\ndef C_3(size, depth):\r\n    padding = (size*sin(rad(15)))/(4*sin(rad(75)))\r\n\r\n    imagine = Turtle(0, size-padding)\r\n    imagine.setAngle(-90)\r\n\r\n\r\n    def triangle(depth, forward):\r\n        if depth > 0:\r\n            if depth == 1:\r\n                imagine.penDown()\r\n            for _ in xrange(3):\r\n                triangle(depth-1, forward/2.0)\r\n                imagine.forward(forward)\r\n                imagine.left(120)\r\n            imagine.penUp()\r\n\r\n    triangle(depth, size)\r\n\r\n    imagine.export(\"C_3.svg\")\r\n\r\nC_3(200, 6)\r\n","sub_path":"IV122/03/C_3.py","file_name":"C_3.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"25545131","text":"\"\"\"Module to process incoming cloudwatch event for an alarm.\"\"\"\n\nimport cloudwatch_alarm_to_slack.utils.log as log\nfrom cloudwatch_alarm_to_slack.events.slack import Slack\nfrom cloudwatch_alarm_to_slack.models.cloudwatch import (CloudwatchEvent,\n                                                         CloudwatchTrigger)\n\nLOGGER = log.custom_logger(__name__)\n\n\nclass CloudwatchAlarm:\n    \"\"\"Handle a cloudwatch alarm from SNS.\"\"\"\n\n    @staticmethod\n    def process_alarm(alarm={}):\n        \"\"\"Process incoming cloudwatch alarm.\"\"\"\n        cloudwatch_event = CloudwatchEvent(\n            account=alarm.get('AWSAccountId'),\n            name=alarm.get('AlarmName'),\n            description=alarm.get('AlarmDescription'),\n            region=alarm.get('Region'),\n            state=alarm.get('NewStateValue'),\n            trigger=CloudwatchTrigger(\n                metric=alarm.get('Trigger').get('MetricName'),\n                namespace=alarm.get('Trigger').get('Namespace'),\n                statistic=alarm.get('Trigger').get('Statistic'),\n                comparison_operator=alarm.get('Trigger').get('ComparisonOperator'),\n                threshold=alarm.get('Trigger').get('Threshold'),\n                period=alarm.get('Trigger').get('Period'),\n                evaluation_period=alarm.get('Trigger').get('EvaluationPeriods')\n            )\n        )\n\n        LOGGER.info(' Processing Cloudwatch event: %s', cloudwatch_event)\n        alarm_state = CloudwatchAlarm.message_status(alarm_state=alarm.get('NewStateValue'))\n        attachment = Slack.format_message(\n            alarm=cloudwatch_event,\n            state=alarm_state\n        )\n        Slack().send_message(\n            attachment=attachment\n        )\n\n    @staticmethod\n    def message_status(alarm_state=None):\n        \"\"\"Set status of slack message based on cloudwatch alarm status.\"\"\"\n        if alarm_state == \"ALARM\":\n            color = \"danger\"\n        elif alarm_state == \"OK\":\n            color = \"good\"\n        else:\n            color = \"warning\"\n\n        return color\n","sub_path":"cloudwatch_alarm_to_slack/events/cloudwatch.py","file_name":"cloudwatch.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"311038857","text":"from random import randint\n\ndef get_random(n):\n    randoms = []\n\n    for i in range(20):\n        randoms.append(randint(0, 100))\n\n    return randoms\n\ndef num_of_even(randoms):\n    counter = 0\n    for i in randoms:\n        if i%2 == 0:\n            counter+=1\n\n    return counter\n\ndef find_value(randoms, type):\n    new_randoms = list(set(randoms))\n    new_randoms.sort()\n    if type == \"min\":\n        value = new_randoms[1]\n    elif type == \"max\":\n        value = new_randoms[len(new_randoms)-2]\n    return value\n\n\nmy_randoms = get_random(20)\n\nprint(\"Wylosowana lista: \", my_randoms)\nmy_randoms.sort()\nprint(\"Posortowana lista: \", my_randoms)\n\nprint(\"Najmniejsza wartosc: \", my_randoms[0])\nprint(\"Najwieksza wartosc: \", my_randoms[len(my_randoms)-1])\n\nprint(\"Druga najmniejsza wartosc: \", find_value(my_randoms, \"min\"))\nprint(\"Druga najwieksza wartosc: \", find_value(my_randoms,\"max\"))\n\naverage = sum(my_randoms)/len(my_randoms)\nprint(\"Srednia to: \", average)\nprint(\"Liczba liczb parzystych: \", num_of_even(my_randoms))\n","sub_path":"lista1/Zad6.py","file_name":"Zad6.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"518228319","text":"#we use Gauge Invariance to assume that t=0 at the time of the crash.\n#(this allows us to work with U(y,x) rather than U(y,x,t) )\n#it is a bit of a hack.\n#U_time is the positive value of 1 second (saved) in dollars.\n#U_crash is negtive value of crashing.\n#/2 occurs where we assume a player is moving at speed 2 and convert time=distance/speed.\n#20180202: change so that python indices really are y and x\n#use fanta's scheme so crashes are at 0,0 and 1,1 only.\n#and assign (0,.5*U_d*x/2) , (.5*U_d*y/2, 0) to outer 2 rows and 2 cols. (using gauge invar)\n\nimport numpy as np\nfrom pylab import *\nimport nash\nimport pdb\nimport random\nSEED=13\n\ndef sim(S, ystart, xstart, seed=0):  #S=strategy matrix; y,x start locations\n\trandom.seed(seed)\n\ty=ystart\n\tx=xstart\n\typos=[y]\n\txpos=[x]\n\tdone=False\n\twhile not done:\n\t\tp_yield_y = S[y,x,0]\t\n\t\tp_yield_x = S[y,x,1]\n\t\tr1=random.random()\n\t\tr2=random.random()\n\t\tif r1 < p_yield_y:\n\t\t\tay=1\n\t\telse:\n\t\t\tay=2\n\t\tif r2 < p_yield_x:\n\t\t\tax=1\n\t\telse:\n\t\t\tax=2\n\t\ty-=ay\n\t\tx-=ax\n\t\typos.append(y)\n\t\txpos.append(x)\n\t\tif y<2 or x<2:\n\t\t\tdone=True\n\tclf()\n\tplot( range(0, len(ypos)), ypos, 'k');\n\tplot( range(0, len(xpos)), xpos, 'k--');\n\tplot( [0, len(ypos)] , [0,0], 'k')\n\tlegend(['y, Y position / meters', 'x, X position / meters'])\n\txlabel('time')\n\tylabel('vehicle location')\n\ttitle('Simulated trajectories')\n\treturn (ypos, xpos)\n\ndef computeStateProbs(S, y_init, x_init): #compute prob that a state is ever visited in a run\n        P=np.zeros((S.shape[0], S.shape[1]))\n        P[y_init, x_init] = 1.  #start state\n        y=y_init\n        while(y>1):\n                x=x_init\n                while(x>1):\n                        p_y_1 = S[y,x,0]\n                        p_y_2 = 1 - p_y_1\n                        p_x_1 = S[y,x,1]\n                        p_x_2 = 1 - p_x_1\n                        P[y-1, x-1] += P[y,x]*p_y_1*p_x_1\n                        P[y-1, x-2] += P[y,x]*p_y_1*p_x_2\n                        P[y-2, x-1] += P[y,x]*p_y_2*p_x_1\n                        P[y-2, x-2] += P[y,x]*p_y_2*p_x_2\n                        x-=1\n                y-=1\n        p_crash = P[0,0] + P[1,1]   #total prob of a crash occuring\n        return (P,p_crash)\n\ndef solveGame(U_crash_y=-100, U_crash_x=-100, U_time=1., NY=20, NX=20):\n\tV = np.zeros((NY,NX,2))   #vals as pairs in 3rd dim\n\tS = np.zeros((NY,NX,2))   #strategies at each point, as yield probs\n\n\t#successful passing end states  [y,x,player_to_reward Y=0]\n\tY=0 ; X=1\n\tfor x in range(1,NX):    #Y has won\n\t    V[0,x,Y] = 0.    \t    \n\t    V[0,x,X] = -U_time*(x/2.)  \n\tfor x in range(2,NX):\n\t    V[1,x,Y] = 0.            \n\t    V[1,x,X] = -U_time*((x-1)/2.)\n\tfor y in range(1,NY):    #X has won\n\t    V[y,0,X] = 0. \n\t    V[y,0,Y] = -U_time*(y/2.)  \n\tfor y in range(2,NY):\n\t    V[y,1,X] = 0.     \t    \n\t    V[y,1,Y] = -U_time*((y-1)/2.)\n\t#crash end states -- overwrite\n\tV[0,0,Y] = U_crash_y\n\tV[0,0,X] = U_crash_x\n\tV[1,1,Y] = U_crash_y\n\tV[1,1,X] = U_crash_x\n\n\t#recursively compute optimal strategies and game values\n\tfor x in range(2,NX):\n\t\tfor y in range(2,NY):\n\t\t\tprint(\"--\"+str(y)+\"_\"+str(x))\n\t\t\tY=[[0,0],[0,0]]\n\t\t\tX=[[0,0],[0,0]]\n\n\n\t\t\t#pdb.set_trace()\n\n\t\t\t#make subgame payoff matrix.  Actions: move-1, move-2\n\t\t\tfor ay in [1,2]:        #action me, action u\n\t\t\t\tfor ax in [1,2]:\n\t\t\t\t\ty_next = y-ay\n\t\t\t\t\tx_next = x-ax\n\t\t\t\t\tval_y  = V[y_next, x_next, 0]\n\t\t\t\t\tval_x  = V[y_next, x_next, 1]\n\t\t\t\t\tY[ay-1][ax-1] = val_y-1     #each tick pays 1 second\n\t\t\t\t\tX[ay-1][ax-1] = val_x-1\n#\t\t\tprint(Y)\n#\t\t\tprint(X)\n#\t\t\tif y==2 \\u-nd x==7:\n#\t\t\t\tpdb.set_trace()\n\n\t\t\t#compute the nashes - pynash\n\t\t\tG = nash.Game(Y, X)\n\t\t\teqs = G.support_enumeration()\n\n\t\t\t#question one: is there at least one symetric equilibrium?\n\t\t\tb_exists_sym_eq = False\n\t\t\t\n\t\t\teq_list=[]\n\t\t\tfor eq in eqs:\n\t\t\t\teq_list.append(eq)\n\t\t\t\n\n\t\t\tfor eq in eq_list:\n\t\t\t\t#pdb.set_trace()\n\t\t\t\tb_sym = np.abs(eq[0][0] - eq[1][0]) < 0.0001            #symetric\n\t\t\t\tif b_sym:\n\t\t\t\t\tb_exists_sym_eq = True\t\t\t\n\t\t\t#pdb.set_trace()\n\n\n\t\t\t#throw away any dominated ones\n\t\t\teq_best = 0 \n\t\t\tvY_best = -999 #vals of both players at single best known equilibrium\n\t\t\tvX_best = -999\n\t\t\t\n#\t\t\tpdb.set_trace()\n\t\t\tfor eq in eq_list:\n\t\t\t\tb_sym = np.abs(eq[0][0]-eq[1][0]) < 0.0001 #real number equality is uncomputable! \n\t\t\t\t\t\t\t\t#how do we know what floating point\n\t\t\t\t\t\t\t\t#is being used by the other player !?\t\n\n\t\t\t\t#if b_exists_sym_eq and not b_sym:\n\t\t\t\t#\tcontinue   #discard asym equilibria if there exist sym ones\n\n\t\t\t\t(vY,vX) = G[eq[0], eq[1]]         #values to players\n\t\t\t\t#b_dom = ( vY<vY_best and vX < vX_best )    #is it dominated?\n\n\t\t\t\t#HACK HACK HACK\n\t\t\t\t#if not b_dom:    #assume for now that there is only one of these!\n\t\t\t\tif b_sym:    #assume for now that there is only one of these!\n\t\t\t\t\teq_best = eq     #TODO metatrategy convergence is more than one\n\t\t\t\t\tvY_best=vY\n\t\t\t\t\tvX_best=vX\n\n\t\t\t#then meta-strategy convergence... TODO\n\t\t\n\t\t\t#pdb.set_trace()\n\t\t\n\t\t\tprint(\"best\", eq_best)\n\t\t\t#log results\n\t\t\tS[y,x,0] = eq_best[0][0]  #yield probs -> strategy  \n\t\t\tS[y,x,1] = eq_best[1][0]\n\t\t\tV[y,x,0] = vY_best\n\t\t\tV[y,x,1] = vX_best\n\treturn (V,S)\n\n\nif __name__ == \"__main__\":\n\t(V,S) = solveGame(U_crash_y=-20, U_crash_x=-20, U_time=1, NY=20, NX=20)\n\n\tif 1:\n\t\tprint(V[:,:,0])\n\t\tprint(V[:,:,1])\n\t\tprint(\"S\")\n\t\tprint(S[:,:,0])\n\t\tprint(S[:,:,1])\n\n\tif 1:\n\t\tfigure(1)\n\t\tclf()\n\t\timshow(V[:,:,0], interpolation='none', cmap='gray')\n\t\txlabel(\"x, player X location / meters\")\n\t\tylabel(\"y, player Y location / meters\")\n\t\ttitle(\"Player Y Values, both to play\")\n\t\tcolorbar();\n\t\tsavefig('V.png', bbox_inches='tight')\n\n\tif 1:\n\t\tfigure(5)\n\t\tclf()\n\t\timshow(V[:,:,1], interpolation='none', cmap='gray')\n\t\txlabel(\"x, player X location / meters\")\n\t\tylabel(\"y, player Y location / meters\")\n\t\ttitle(\"Player X Values, both to play\")\n\t\tcolorbar();\n\t\t#savefig('VX.png', bbox_inches='tight')\n\n\tif 1:\n\t\tmstart=12\n\t\tustart=12\n\t\tfigure(2)\n\t\tclf()\n\t\tsim(S, mstart, ustart, SEED)\n\t\tsavefig('sim.png', bbox_inches='tight')\n\n\tif 1:\n\t\t(P, p_crash) = computeStateProbs(S, mstart, ustart)\n\t\tfigure(4)\n\t\tclf()\n\t\timshow(P, interpolation='none', cmap='gray'); \n\t\tcolorbar();\n\t\ts='State probabilities (P_crash=%0.4f)'%p_crash\n\t\ttitle(s)\n\t\txlabel(\"x, player X location / meters\")\n\t\tylabel(\"y, player Y location / meters\")\n\t\tsavefig('P.png', bbox_inches='tight')\n\n\tif 1:\n\t\tfigure(7)\n\t\tclf()\n\t\timshow(S[:,:,0], interpolation='none', cmap='gray')\n\t\txlabel(\"x, player X location / meters\")\n\t\tylabel(\"y, player Y location / meters\")\n\t\ttitle('Player Y strategy, P(yield|y,x)')\n\t\tcolorbar()\n\t\tsavefig('S.png', bbox_inches='tight')\n\n\tif 1:\n\t\tfigure(6)\n\t\tclf()\n\t\timshow(S[:,:,1], interpolation='none', cmap='gray')\n\t\txlabel(\"x, player X location / meters\")\n\t\tylabel(\"y, player Y location / meters\")\n\t\ttitle('Player X strategy, P(yield|y,x)')\n\t\tcolorbar()\n\t\t#savefig('SU.png', bbox_inches='tight')\n\n\tshow()\n","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":6749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"652359515","text":"import requests\nimport time\nfrom bs4 import BeautifulSoup\nfrom fake_useragent import UserAgent\nfrom app import db\nfrom app.item.models import marketItem\nfrom lxml.html import fromstring\nfrom lxml.html.soupparser import fromstring\nfrom crawlerparts.urls import geturls\nfrom updatecrawlerparts.updatequantity import getquantity\nfrom updatecrawlerparts.updateprice import getprice\nfrom updatecrawlerparts.updatetitle import gettitle\nfrom updatecrawlerparts.updatecategory import getcategory\nfrom updatecrawlerparts.updatedescription import getdescription\nfrom lxml import html\n\n\nbannedproxy = []\nproxylist = []\ntest = 0\n\n\ndef get_proxies():\n    print(\"Getting new Pxoxies\")\n    url = 'https://free-proxy-list.net/'\n    response = requests.get(url)\n    print(response)\n    parser = fromstring(response.text)\n    for i in parser.xpath('//tbody/tr')[:250]:\n        if i.xpath('.//td[7][contains(text(),\"yes\")]'):\n            proxyifound = \":\".join([i.xpath('.//td[1]/text()')[0],\n                                    i.xpath('.//td[2]/text()')[0]])\n            proxylist.append(proxyifound)\n\n\n\ndef main():\n\n    if len(proxylist) < 5:\n        get_proxies()\n    print(\"Found \" + str(len(proxylist)) + \" proxies\")\n    for i in proxylist:\n        # Get a proxy from the pool\n        print(\"Using proxy:\", i)\n\n        # get all items\n        getcurrentitems = db.session.query(marketItem).filter(marketItem.amazonid != '0', marketItem.categoryid0 == 0).all()\n        for itemtocheck in getcurrentitems:\n\n\n            time.sleep(10)\n            urltogetforitems = \"http://www.amazon.com/dp/\" + str(itemtocheck.amazonid)\n\n            try:\n                ua = UserAgent()\n                header = {'User-Agent': str(ua.chrome)}\n\n                # read from amazon\n\n                page = requests.get(urltogetforitems,\n                                    headers=header,\n                                    proxies={\"http\": i, \"https\": i}, timeout=10)\n\n            except:\n                page = 0\n                print(\"error\")\n                bannedproxy.append(i)\n                proxylist.remove(i)\n                main()\n\n            if page.status_code == 504:\n                print(\"504 error: Timeout\")\n                bannedproxy.append(i)\n                proxylist.remove(i)\n                main()\n            elif page.status_code == 503:\n                print(\"503 error: Unavailable\")\n                bannedproxy.append(i)\n                proxylist.remove(i)\n                main()\n            elif page.status_code == 200:\n\n\n                doc = html.fromstring(page.content)\n                soup = BeautifulSoup(page.content, 'html.parser')\n\n                # check robot warnings\n                wrongtitle = '<title>Sorry! Something went wrong!</title>'\n                robottitle = '<title dir=\"ltr\">Robot Check</title>'\n                if str(soup.title) == str(wrongtitle):\n                    print(wrongtitle)\n                    bannedproxy.append(i)\n                    proxylist.remove(i)\n                    main()\n                elif str(soup.title) == str(robottitle):\n                    print(robottitle)\n                    bannedproxy.append(i)\n                    proxylist.remove(i)\n                    main()\n                else:\n                    print(\"\")\n                    print(\"\")\n                    print(\"\")\n                    print(\"\")\n                    # Get content urls for future search\n                    geturls(soup=soup)\n\n                    # manual content\n                    print(\"http://www.amazon.com/dp/\" + str(itemtocheck.amazonid))\n\n                    quantity_of_item = getquantity(soup=soup, doc=doc)\n                    price_of_item = getprice(soup=soup, doc=doc)\n                    title_of_item = gettitle(soup=soup, doc=doc)\n                    description_of_item = getdescription(soup=soup, doc=doc)\n                    print(\"Title: \", title_of_item)\n                    print(\"Description: \", description_of_item)\n                    print(\"\")\n                    print(\"Quantity: \", quantity_of_item)\n                    print(\"Price: \", price_of_item)\n\n                    catid0, catname0, \\\n                    catid1, catname1, \\\n                    catid2, catname2, \\\n                    catid3, catname3, \\\n                    catid4, catname4, \\\n                    catid5, catname5, \\\n                        = getcategory(soup=soup, doc=doc)\n\n                    # Double check\n                    if quantity_of_item is None:\n                        quantity_of_item = 0\n                    if price_of_item is None:\n                        price_of_item = 0\n                    if title_of_item is None:\n                        title_of_item = 0\n\n                    # if its got all information\n                    if quantity_of_item != 0\\\n                            and price_of_item != 0\\\n                            and title_of_item != 0\\\n                            and description_of_item != 0:\n\n\n                        print(\"Good item\")\n\n                        itemtocheck.itemtitlee = title_of_item\n                        itemtocheck.itemcount = quantity_of_item\n                        itemtocheck.itemdescription = description_of_item\n                        itemtocheck.categoryname0 = catname0\n                        itemtocheck.categoryid0 = catid0\n                        itemtocheck.categoryname1 = catname1\n                        itemtocheck.categoryid1 = catid1\n                        itemtocheck.categoryname2 = catname2\n                        itemtocheck.categoryid2 = catid2\n                        itemtocheck.categoryname3 = catname3\n                        itemtocheck.categoryid3 = catid3\n                        itemtocheck.categoryname4 = catname4\n                        itemtocheck.categoryid4 = catid4\n                        itemtocheck.categoryname5 = catname5\n                        itemtocheck.categoryid5 = catid5\n\n                        # categories\n                        db.session.add(itemtocheck)\n                        db.session.commit()\n                        print(\"Final Status\")\n                        print(\"Item Update\")\n\n\n                    else:\n                        itemtocheck.online = 1\n                        db.session.add(itemtocheck)\n                        db.session.commit()\n\n                        print(\"Final Status\")\n                        print(\"BAD ITEM\")\n                        print(\"Item Passed\")\n\n            # weird status code ignore request\n            else:\n                print(page.status_code)\n                main()\n\n\nmain()\n","sub_path":"updatecrawler.py","file_name":"updatecrawler.py","file_ext":"py","file_size_in_byte":6613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"171819537","text":"# -*- coding: utf-8 -*-\r\n\r\n\"\"\" contains classes \"\"\"\r\n\r\ntry:\r\n    from dl_law_codes.html_to_ascii_table import Table\r\nexcept:\r\n    from CreateYourLaws.dl_law_codes.html_to_ascii_table import Table\r\n\r\n\r\nclass Article:\r\n    \"\"\" Object containing all the paramaeters\r\n    concerning a law article\"\"\"\r\n\r\n    def __init__(self):\r\n        # /!\\ A remodifier avec la définition\r\n        # des bons types de champs dans le cadre de Django!!! /!\\\r\n        self.name = \"\"\r\n        self.html = \"\"\r\n        self.contents = []\r\n\r\n    def __repr__(self):\r\n        txt = \"=\" * 70 + \"\\n\"\r\n        txt += self.name + \"\\n\"\r\n        txt += \"=\" * 70 + \"\\n\"\r\n        for i in range(len(self.contents)):\r\n            if isinstance(self.contents[i], Table):\r\n                txt += self.contents[i].represent + \"\\n\\n\"\r\n            else:\r\n                txt += self.contents[i] + \"\\n\\n\"\r\n        return txt\r\n\r\n\r\nclass Titre:\r\n    \"\"\" containers of Articles or other containers\"\"\"\r\n\r\n    def __init__(self, rank, name):\r\n        self.rank = rank\r\n        self.name = name\r\n        self.idsql = 0\r\n        self.contents = []\r\n\r\n    def __repr__(self):\r\n        \"\"\"txt = \"*\" * 70 + \"\\n\"\r\n        txt += self.name + \"\\n\"\r\n        txt += \"RANK: \" + str(self.rank) + \"\\n\"\r\n        txt += \"*\" * 70 + \"\\n\\n\\n\" \"\"\"\r\n        return self.name  # txt\r\n\r\n    def in_contents(self):\r\n        \"\"\" maid for the __repr__ function.\r\n        recusivity must by improved/finished\"\"\"\r\n        txt = \"\"\r\n        if len(self.contents) != 0:\r\n            for i in range(len(self.contents)):\r\n                txt += self.contents[i].__repr__()\r\n                if isinstance(self.contents[i], Article) is not True:\r\n                    txt += self.in_contents(self.contents[i])\r\n        return txt\r\n\r\n\r\nclass Code_de_lois:\r\n    \"\"\" A Law code \"\"\"\r\n\r\n    def __init__(self):\r\n        self.name = \"\"\r\n        self.contents = []\r\n\r\n    def __repr__(self):\r\n        txt = \"-\" * 70 + \"\\n\"\r\n        txt = \"-\" * 70 + \"\\n\"\r\n        txt += \"||\" + self.name + \"||\\n\"\r\n        txt = \"-\" * 70 + \"\\n\"\r\n        txt += \"-\" * 70 + \"\\n\\n\\n\"\r\n        txt += self.in_contents()\r\n        return txt\r\n\r\n    def in_contents(self):\r\n        \"\"\" maid for the __repr__ function. must be completed to\r\n        have recurcivity with contents attribute from Titre class\"\"\"\r\n        txt = \"\"\r\n        if len(self.contents) != 0:\r\n            for i in range(len(self.contents)):\r\n                txt += self.contents[i].__repr__()\r\n                if isinstance(self.contents[i], Article) is not True:\r\n                    txt += self.in_contents(self.contents[i])\r\n        return txt\r\n","sub_path":"democratos/CreateYourLaws/dl_law_codes/classe_pdf.py","file_name":"classe_pdf.py","file_ext":"py","file_size_in_byte":2608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"539661005","text":"def parse_comment_page(page_url):\n    user_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'\n    request = urllib.request.Request(page_url,headers={'User-Agent': user_agent})\n    html = urllib.request.urlopen(request).read()\n    soup = BeautifulSoup(html,'html.parser')\n    main_post = soup.find('div',attrs={'id':'siteTable'})\n    title = main_post.find('a',attrs={'class':'title'}).text\n    upvotes = main_post.find('div',attrs={'class':'score unvoted'}).text\n    original_poster = main_post.find('a',attrs={'class':'author'}).text\n    comments_count = main_post.find('a',attrs={'class':'bylink comments may-blank'}).text\n    comment_area = soup.find('div',attrs={'class':'commentarea'})\n    comments = comment_area.find_all('div', attrs={'class':'entry unvoted'})\n    extracted_comments = []\n    for comment in comments:\n        if comment.find('form'):\n            commenter = comment.find(attrs={'class':'author'}).text\n            comment_text = comment.find('div',attrs={'class':'md'}).text.strip()\n            permalink = comment.find('a',attrs={'class':'bylink'})['href']\n            extracted_comments.append({'commenter':commenter,'comment_text':comment_text,'permalink':permalink})\n    post_data = {\n        'title':title,\n        'no_of_upvotes':upvotes,\n        'poster':original_poster,\n        'no_of_comments':comments_count,\n        'comments':extracted_comments\n    }\n    return post_data\n\n\n\n\n\nimport urllib.request\nfrom bs4 import BeautifulSoup\nimport json\nfrom time import sleep \nurl = \"https://old.reddit.com/top/\"\nuser_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'\nrequest = urllib.request.Request(url,headers={'User-Agent': user_agent})\nhtml = urllib.request.urlopen(request).read()\nsoup = BeautifulSoup(html,'html.parser')\nmain_table = soup.find(\"div\",attrs={'id':'siteTable'})\ncomment_a_tags = main_table.find_all('a',attrs={'class':'bylink comments may-blank'})\nextracted_data = []\nfor a_tag in comment_a_tags[:10]:\n    url = a_tag['href']\n    if not url.startswith('http'):\n        url = \"https://reddit.com\"+url\n    print('Extracting data from %s'%url)\n    extracted_data.append(parse_comment_page(url))\nwith open('comment_data.json', 'w') as outfile:\n    json.dump(extracted_data, outfile, indent=4)","sub_path":"reddit_comments.py","file_name":"reddit_comments.py","file_ext":"py","file_size_in_byte":2393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"351041872","text":"from django.core.urlresolvers import reverse\n\nfrom apps.photos.models import Location\nfrom apps.photos.tests import SuperuserTest\n\n\nclass LocationListView(SuperuserTest):\n    def test_list(self):\n        \"\"\"\n        Test that the location list view works properly.\n        \"\"\"\n        response = self.client.get(reverse('locations'))\n        self.assertTrue('location_list' in response.context)\n        self.assertTrue('paginator' in response.context)\n\n\nclass LocationCreateView(SuperuserTest):\n    def test_create(self):\n        \"\"\"\n        Test that location creation works properly.\n        \"\"\"\n        # invalid location, display form again\n        data = {'name': ''}\n        self.json_post_value(reverse('location_create'), 'html', data)\n        # valid location, redirect (via ajax)\n        data = {'name': 'location1'}\n        self.json_post_value(reverse('location_create'), 'url', data)\n\n\nclass LocationDetailView(SuperuserTest):\n    def test_detail(self):\n        \"\"\"\n        Test that the location detail view works properly.\n        \"\"\"\n        Location.objects.create(name='location1')\n        response = self.client.get(reverse('location', kwargs=dict(pk=1)))\n        self.assertEqual(response.status_code, 200)\n        self.assertTrue('location' in response.context)\n        self.assertTrue('paginator' in response.context)\n        self.assertTrue('album_list' in response.context)\n\n\nclass LocationRenameView(SuperuserTest):\n    def test_rename(self):\n        \"\"\"\n        Test that the location rename view works properly.\n        \"\"\"\n        Location.objects.create(name='location1')\n        self.assertEqual(Location.objects.get(pk=1).name, 'location1')\n        # invalid location, display form again\n        data = {'name': ''}\n        self.json_post_value(\n            reverse('location_rename', kwargs=dict(pk=1)), 'html', data)\n        # valid location, redirect (via ajax)\n        data = {'name': 'location2'}\n        self.json_post_value(\n            reverse('location_rename', kwargs=dict(pk=1)), 'url', data)\n        self.assertEqual(Location.objects.get(pk=1).name, 'location2')\n\n\nclass LocationDeleteView(SuperuserTest):\n    def test_delete(self):\n        Location.objects.create(name='location1')\n        self.json_get_value(\n            reverse('location_delete', kwargs=dict(pk=1)), 'html')\n        self.assertEqual(Location.objects.count(), 1)\n        data = {'submit': True}\n        self.json_post_value(\n            reverse('location_delete', kwargs=dict(pk=1)), 'url', data)\n        self.assertEqual(Location.objects.count(), 0)\n","sub_path":"apps/photos/tests/test_location.py","file_name":"test_location.py","file_ext":"py","file_size_in_byte":2564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"14388361","text":"def calc(l):\n    global ans\n    ans+=1\n    nl=[]\n    for a in l:\n        nl.append(a-1)\n        if a%3==0 and a>=3:\n            nl.append(a/3)\n        if a%2==0 and a>=2:\n            nl.append(a/2)\n    return nl\n\nn=int(input())\nl=[]\nl.append(n)\nans=0\nif n==1:\n    print(ans)\nelse:\n    while(1):\n        l=calc(l)\n        if min(l)==1:\n            break\n    print(ans)","sub_path":"백준/1로 만들기.py","file_name":"1로 만들기.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"83289228","text":"from copy import deepcopy\nimport itertools\nimport numpy as np\nimport torch\nfrom torch.optim import Adam\nimport gym\nimport time\nimport argparse\nfrom buffer import Buffer\nfrom models import ActorCritic\nfrom baselines.envs import TorchEnv\n\n\ndef sac(args):\n    #set seed if non default is entered\n    if args.seed != -1:\n        torch.manual_seed(args.seed)\n        np.random.seed(args.seed)\n\n    env, test_env = TorchEnv(args.env_name, args.max_ep_len), TorchEnv(args.env_name, args.max_ep_len)\n    state_dim = env.observation_space.shape[0]\n    action_dim = env.action_space.shape[0]\n    action_limit = env.action_space.high[0]\n    # Create actor-critic module and target networks\n    ac = ActorCritic(state_dim,action_dim,action_limit,args.hidden_size,args.gamma,args.alpha,device=args.device)\n    ac_targ = deepcopy(ac)\n    # Freeze target networks with respect to optimizers (only update via polyak averaging)\n    for p in ac_targ.parameters():\n        p.requires_grad = False\n    # List of parameters for both Q-networks (save this for convenience)\n    q_params = itertools.chain(ac.q1.parameters(), ac.q2.parameters())\n    # Experience buffer\n    buffer = Buffer(state_dim,action_dim, buffer_size=args.buffer_size,device=args.device)\n    # Set up optimizers for policy and q-function\n    pi_optimizer = Adam(ac.pi.parameters(), lr=args.lr)\n    q_optimizer = Adam(q_params, lr=args.lr)\n\n    def update(data):\n        # First run one gradient descent step for Q1 and Q2\n        q_optimizer.zero_grad()\n        loss_q = ac.compute_loss_q(data,ac_targ)\n        loss_q.backward()\n        q_optimizer.step()\n\n        # Freeze Q-networks so you don't waste computational effort computing gradients for them during the policy learning step.\n        for p in q_params:\n            p.requires_grad = False\n\n        # Next run one gradient descent step for pi.\n        pi_optimizer.zero_grad()\n        loss_pi = ac.compute_loss_pi(data)\n        loss_pi.backward()\n        pi_optimizer.step()\n\n        # Unfreeze Q-networks so you can optimize it at next DDPG step.\n        for p in q_params:\n            p.requires_grad = True\n\n        # Finally, update target networks by polyak averaging.\n        with torch.no_grad():\n            for p, p_targ in zip(ac.parameters(), ac_targ.parameters()):\n                # NB: We use an in-place operations \"mul_\", \"add_\" to update target params, as opposed to \"mul\" and \"add\", which would make new tensors.\n                p_targ.data.mul_(args.polyak)\n                p_targ.data.add_((1 - args.polyak) * p.data)\n\n    def test_agent(deterministic=True):\n        for j in range(args.num_test_episodes):\n            o, d, ep_ret, ep_len = test_env.reset(), False, 0, 0\n            while not(d or (ep_len == args.max_ep_len)):\n                # Take deterministic actions at test time\n                o, r, d = test_env.step(ac.act(torch.as_tensor(o, dtype=torch.float32).to(args.device), deterministic))\n                ep_ret += r\n                ep_len += 1\n\n    # Prepare for interaction with environment\n    total_steps = args.steps_per_epoch * args.epochs\n    start_time = time.time()\n    o, ep_ret, ep_len = env.reset(), 0, 0\n\n    # Main loop: collect experience in env and update/log each epoch\n    for t in range(total_steps):\n        # Until start_steps have elapsed, randomly sample actions from a uniform distribution for better exploration. Afterwards, use the learned policy.\n        if t > args.start_steps:\n            a = ac.act(torch.as_tensor(o, dtype=torch.float32).to(args.device))\n        else:\n            a = env.action_space.sample()\n        # Step the env\n        o2, r, d = env.step(a)\n        if args.render_env:\n            env.render()\n        ep_ret += r\n        ep_len += 1\n        # Ignore the \"done\" signal if it comes from hitting the time horizon (that is, when it's an artificial terminal signal that isn't based on the agent's state)\n        d = False if ep_len==args.max_ep_len else d\n        # Store experience to replay buffer\n        buffer.add(o,a,r,o2,d)\n        o = o2\n        # End of trajectory handling\n        if d or (ep_len == args.max_ep_len):\n            print(\"EPISODE REWARD: \", ep_ret)\n            o, ep_ret, ep_len = env.reset(), 0, 0\n\n        # Update handling\n        if t >= args.update_after and t % args.update_every == 0:\n            batch_generator = buffer.get_train_batches(args.batch_size)\n            for j in range(args.update_every):\n              #my_batch = my_buffer.get_train_batches(args.batch_size).__next__()\n              try:\n                    batch = batch_generator.__next__()\n              except:\n                    batch_generator = buffer.get_train_batches(args.batch_size)\n                    batch = batch_generator.__next__()\n              update(batch)\n\n        # End of epoch handling\n        if (t+1) % args.steps_per_epoch == 0:\n            epoch = (t+1) // args.steps_per_epoch\n            # Test the performance of the deterministic version of the agent.\n            test_agent()\n\n\ndef boolcheck(x):\n        return str(x).lower() in [\"true\", \"1\", \"yes\"]\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    DEVICE = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n    parser.add_argument(\"--env_name\", type=str, default=\"Pendulum-v0\")\n    parser.add_argument(\"--max_ep_len\", type=int, default=1000)\n    parser.add_argument(\"--seed\",type=int, default=-1)\n    parser.add_argument(\"--hidden_size\",type=int, default=256)\n    parser.add_argument(\"--steps_per_epoch\", type=int, default=4000)\n    parser.add_argument(\"--epochs\",type=int,default=100)\n    parser.add_argument(\"--buffer_size\",type=int,default=int(1e6))\n    parser.add_argument(\"--gamma\",type=float,default=0.99)\n    parser.add_argument(\"--polyak\",type=float,default=0.995)\n    parser.add_argument(\"--lr\",type=float,default=1e-3)\n    parser.add_argument(\"--alpha\",type=float,default=0.2)\n    parser.add_argument(\"--args.batch_size\",type=int,default=100)\n    parser.add_argument(\"--start_steps\",type=int,default=10000)\n    parser.add_argument(\"--update_after\",type=int,default=1000)\n    parser.add_argument(\"--update_every\",type=int,default=50)\n    parser.add_argument(\"--num_test_episodes\",type=int,default=10)\n    parser.add_argument(\"--batch_size\",type=int,default=100)\n    parser.add_argument(\"--render_env\",type=boolcheck,default=False)\n    args =parser.parse_args()\n    args.device = DEVICE\n    sac(args)\n","sub_path":"baselines/algos/sac/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"488819100","text":"from tkinter import *\nfrom tkinter import ttk\n\nclass App:\n    def __init__(self, master):\n        mainframe = ttk.Frame(master, padding=\"3 6 3 6\")\n        mainframe.grid(column=0, row=0, sticky=(N, W, S, E))\n        ttk.Button(mainframe, text=\"CLICK ME\", command=self.second_layer).grid(column=0, row=0)\n        self.master = master\n\n    def second_layer(self):\n        t = Toplevel()\n        ttk.Label(t, text=\"Huzzah!\").pack(side=LEFT)\n        ttk.Button(t, text=\"IT WORKED\", command=self.third_layer).pack(side=RIGHT)\n\n    def third_layer(self):\n        top = Toplevel()\n        ttk.Label(top, text=\"Python is awesome\").pack(side=RIGHT)\n        \nroot = Tk()\napp = App(root)\nroot.mainloop()\n\n","sub_path":"reddit_replies/tkinter_windows/test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"493446807","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul  9 16:05:39 2019\n\n@author: dlymhth\n\"\"\"\n\nimport sys\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\n\ndef miu_iter_tensor(miu, wt, wt_prime, c):\n    \"\"\"\n\n    :param miu:         shape=(timestamps)\n    :param wt:          w_t, shape=(timestamps, varieties)\n    :param wt_prime:    w_t prime, has same shape with w_t\n    :param c:           commission rate\n    :return:            Tensor of new miu, shape=(timestamps)\n    \"\"\"\n    miu = miu[:, None]\n    return 1 - (2 * c - c ** 2) * tf.reduce_sum(tf.nn.relu(wt_prime - miu * wt), axis=1)\n\n\ndef miu_iter_nd(miu, wt, wt_prime, c):\n    \"\"\"\n\n    :param miu:         numpy.ndarray of miu, shape=(timestamps)\n    :param wt:\n    :param wt_prime:\n    :param c:\n    :return:            numpy.ndarray of new miu, shape=(timestamps)\n    \"\"\"\n    miu = miu[:, None]\n    return 1 - (2 * c - c ** 2) * np.sum(np.maximum(0, wt_prime - miu * wt), axis=1)\n\n\ndef miu_iter_scalar(miu, wt, wt_prime, c):\n    \"\"\"\n\n    :param miu:         numpy.ndarray of miu, shape=(timestamps)\n    :param wt:\n    :param wt_prime:\n    :param c:\n    :return:            numpy.ndarray of new miu, shape=(timestamps)\n    \"\"\"\n    return 1 - (2 * c - c ** 2) * np.sum(np.maximum(0, wt_prime - miu * wt))\n\n\nclass NNAgent:\n\n    def __init__(self, parameters):\n\n        self.n_batch = parameters.n_batch\n        self.n_timesteps = parameters.n_timesteps\n        self.n_varieties = len(parameters.varieties)\n        self.n_features = len(parameters.features)\n\n        self.height_cov1 = parameters.height_cov1\n        self.n_cov1_core = parameters.n_cov1_core\n        self.height_cov2 = parameters.height_cov2\n        self.n_cov2_core = parameters.n_cov2_core\n        self.height_cov3 = parameters.height_cov3\n\n        self.n_epochs = parameters.n_epochs\n        self.display_step = parameters.display_step\n        self.checkpoint = parameters.checkpoint\n\n        self.start_learning_rate = parameters.start_learning_rate\n        self.decay_steps = parameters.decay_steps\n        self.decay_rate = parameters.decay_rate\n\n        self.commission_rate = parameters.commission_rate\n\n        self.model_file_location = parameters.model_file_location\n        self.figure_file_location = parameters.figure_file_location\n\n        self.logfile = parameters.logfile\n\n        # tf Graph\n        # input_x shape [n_batch, 50, 7, 3]\n        self.X = tf.placeholder('float32', [None, self.n_timesteps,\n                                            self.n_varieties, self.n_features])\n\n        # Cov 1 core: [height: 3, width: 1, in_channels: 3, out_channels: 2]\n        cov1_core = tf.Variable(tf.random_normal([self.height_cov1, 1,\n                                                  self.n_features, self.n_cov1_core]))\n        raw_cov_layer1 = tf.nn.conv2d(input=self.X, filter=cov1_core,\n                                      strides=[1, 1, 1, 1], padding='VALID')  # [n_batch, 48, 7, 2]\n        cov_layer1 = tf.nn.relu(raw_cov_layer1)\n\n        # Cov 2 core: [height: 48, width: 1, in_channels: 2, out_channels: 20]\n        cov2_core = tf.Variable(tf.random_normal([self.height_cov2, 1,\n                                                  self.n_cov1_core, self.n_cov2_core]))\n        raw_cov_layer2 = tf.nn.conv2d(input=cov_layer1, filter=cov2_core,\n                                      strides=[1, 1, 1, 1], padding='VALID')  # [n_batch, 1, 7, 20]\n        cov_layer2 = tf.nn.relu(raw_cov_layer2)\n\n        self.last_w = tf.placeholder('float32', [None, self.n_varieties])  # [n_batch, 7]\n        last_w_1 = tf.expand_dims(self.last_w, axis=1)  # [n_batch, 1, 7]\n        last_w_2 = tf.expand_dims(last_w_1, axis=3)  # [n_batch, 1, 7, 1]\n        concat_layer = tf.concat([cov_layer2, last_w_2], axis=3)  # [n_batch, 1, 7, 21]\n\n        # Cov 3 core: [height: 1, width: 1, in_channels: 21, out_channels: 1]\n        cov3_core = tf.Variable(tf.random_normal([1, 1, self.height_cov3, 1]))\n        raw_cov_layer3 = tf.nn.conv2d(input=concat_layer, filter=cov3_core,\n                                      strides=[1, 1, 1, 1], padding='VALID')  # [n_batch, 1, 7, 1]\n        self.output_w = tf.nn.softmax(tf.squeeze(raw_cov_layer3))  # [n_batch, 7]\n\n        # Define loss and optimizer\n        # input_y shape [n_batch, 7]\n        self.y = tf.placeholder('float32', [None, self.n_varieties])\n        omega_y = tf.reduce_sum(tf.multiply(self.y, self.output_w), axis=1)  # [n_batch]\n\n        future_omega = tf.multiply(self.y, self.output_w) / omega_y[:, None]  # [n_batch,7]\n        w_t_prime = future_omega[:-1, :]\n        w_t = self.output_w[1:, :]\n        self.miu = 1 - tf.reduce_sum(tf.abs(w_t_prime - w_t), axis=1) * self.commission_rate  # [n_batch-1]\n        # self.miu = miu_iter_tensor(self.mu0, w_t_prime, w_t, self.commission_rate)\n        # for i in range(20):\n        #     self.miu = miu_iter_tensor(self.miu, w_t_prime, w_t, self.commission_rate)\n\n        p_vec = tf.multiply(omega_y, tf.concat([tf.ones(1, dtype='float32'), self.miu], axis=0))  # [n_batch]\n        self.loss = -tf.reduce_mean(tf.log(p_vec))\n\n        self.global_step = tf.Variable(0, trainable=False)\n        self.learning_rate = tf.train.exponential_decay(learning_rate=self.start_learning_rate,\n                                                        global_step=self.global_step,\n                                                        decay_steps=self.decay_steps,\n                                                        decay_rate=self.decay_rate)\n        self.optimizer = tf.train.AdamOptimizer(self.learning_rate)\n        self.train_op = self.optimizer.minimize(self.loss, global_step=self.global_step)\n\n        self.config = tf.ConfigProto(log_device_placement=False,\n                                     allow_soft_placement=True)\n\n    def train(self, dataset):\n        with open(self.logfile, 'w') as logf:\n            print('\\nTraining start...')\n            init = tf.global_variables_initializer()\n            sess = tf.Session(config=self.config)\n\n            # Run the initializer\n            sess.run(init)\n\n            # Training\n            for epoch in range(self.n_epochs):\n                rand_i, input_x, input_y, last_w = dataset.next_batch()\n                # debug\n                logf.write('epoch: %d\\n' % epoch)\n                logf.write('last_w: %s\\n' % last_w)\n                logf.write('y: %s\\n' % input_y)\n\n                # calculate output_w\n                _, loss, output_w, miu = sess.run([self.train_op, self.loss, self.output_w, self.miu],\n                                                         feed_dict={self.X: input_x,\n                                                                    self.y: input_y,\n                                                                    self.last_w: last_w})\n                # Write output_w into train_matrix_w\n                dataset.set_w(rand_i, output_w)\n\n                # debug\n                logf.write('miu: %s\\n' % (miu))\n                logf.write('output_w: %s\\n' % output_w)\n\n                # Display\n                if epoch % self.display_step == 0:\n                    print('epoch %d/%d, loss=%.5f' % (epoch, self.n_epochs, loss))\n\n                if epoch % self.checkpoint == 0:\n                    cp_model = '%s_%d' % (self.model_file_location, epoch)\n                    tf.train.Saver().save(sess, cp_model)\n\n            # Save model\n            saver = tf.train.Saver()\n            saver.save(sess, self.model_file_location)\n\n            sess.close()\n            print('Training done.')\n\n    def test(self, dataset, model):\n        with open(self.logfile, 'w') as logf:\n            print('\\nTesting')\n            n_test = dataset.n_test\n            n_timesteps = dataset.n_timesteps\n\n            sess = tf.Session(config=self.config)\n            saver = tf.train.Saver()\n            saver.restore(sess, model)\n\n            dltymean = np.mean(np.abs(dataset.test_dataset[None, 1:, :, 0]/dataset.test_dataset[None, :-1, :, 0] - 1))\n            print('delta_y_mean = %f' % dltymean)\n\n            for i in range(0, n_test - n_timesteps):\n                input_data = dataset.test_dataset[None, i:n_timesteps + i + 1, :, :]  # [1, 51, 7, 3]\n                input_x = input_data[:, :-1, :, :] / input_data[:, -2, None, :, 0, None]  # [1, 50, 7, 3]\n                input_y = input_data[:, -1, :, 0] / input_data[:, -2, :, 0]  # [1, 7]\n                last_w = dataset.test_matrix_w[n_timesteps + i - 1, None, :]  # [1, 7]\n\n                logf.write('i:%d\\ninput_x.shape:%s\\n' % (i, input_x.shape))\n\n                output_w = sess.run(self.output_w, feed_dict={self.X: input_x,\n                                                              self.y: input_y,\n                                                              self.last_w: last_w})\n\n                # if miu < threshold, keep last_w as output_w\n                # last_y = input_data[:, -2, :, 0] / input_data[:, -3, :, 0]  # [1, 7]\n                # w_t = output_w\n                # w_t_prime = (last_w * last_y) / np.sum(last_w * last_y)\n                # miu0 = 1\n                # miu1 = 1 - np.sum(np.abs(w_t_prime - w_t)) * self.commission_rate\n                # while np.sum(np.abs(miu0 - miu1)) > 1e-9:\n                #     miu0 = miu1\n                #     miu1 = miu_iter_scalar(miu1, w_t, w_t_prime, self.commission_rate)\n                # print('i:%d, miu:%f\\n' % (i, miu1))\n                # if miu1 < 0.99999:\n                #     output_w = last_w\n\n                dataset.test_matrix_w[n_timesteps + i, :] = output_w\n\n                logf.write('i:%d\\nout_w:%s\\nlast_w:%s\\ny:%s\\n\\n' % (i, output_w, last_w, input_y))\n\n            sess.close()\n            print('Test done.')\n\n    def rr(self, y, w):\n        p_vec = np.sum(w[:-1, :] * y, axis=1)  # (n-1)\n        w_t_prime = (w[:-1, :] * y) / p_vec[:, None]  # (n-1, varieties)\n        w_t = w[1:, :]  # (n-1, v)\n\n        miu0 = 1\n        miu1 = 1 - np.sum(np.abs(w_t_prime - w_t), axis=1) * self.commission_rate  # (n-1)\n        while np.sum(np.abs(miu0 - miu1)) > 1e-6:\n            miu0 = miu1\n            miu1 = miu_iter_nd(miu1, w_t, w_t_prime, self.commission_rate)\n        print(miu1)\n        print(np.sum(np.log(miu1)))\n        rr_vec = np.log(p_vec) + np.log(miu1)  # (n-1)\n        result_list = [0]\n        for i in range(len(rr_vec)):\n            result_list.append(result_list[-1] + rr_vec[i])\n        return result_list\n\n    def plot_test_result(self, dataset, img_name):\n        with open(self.logfile, 'a') as logf:\n            matrix_y = dataset.test_dataset     # (timestamps, varieties, features)\n            matrix_w = dataset.test_matrix_w    # (timestamps, varieties)\n            y = matrix_y[1:, :, 0] / matrix_y[:-1, :, 0]    # (n-1, varieties)\n\n            for i in range(1, matrix_w.shape[0]):\n                if (i % 60) != 0:\n                    matrix_w[i, :] = matrix_w[i-1, :]\n            result_list = self.rr(y, matrix_w)\n\n\n            # Identity weights rebalanced\n            identity_matrix_w = np.ones((dataset.n_test, self.n_varieties)) / self.n_varieties\n            result_list_ucrp = self.rr(y, identity_matrix_w)\n\n\n            # buy and hold\n            rr_vec_control = np.sum(y, axis=1) / self.n_varieties\n            rr_vec_control = np.log(rr_vec_control)\n            result_list_control = [0]\n            for i in range(len(rr_vec_control)):\n                result_list_control.append(result_list_control[-1] + rr_vec_control[i])\n\n            fig = plt.figure(figsize=(16, 9))\n            plt.plot(result_list, label='test', color='blue')\n            plt.plot(result_list_control, label='control', color='red')\n            plt.plot(result_list_ucrp, label='ucrp', color='green')\n            plt.legend()\n            fig.savefig(self.figure_file_location + img_name + '.jpg')\n\n            return plt\n","sub_path":"train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":11807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"244419541","text":"import os\nimport board\nimport busio as io\nimport digitalio\nimport storage\nimport adafruit_sdcard\nimport microcontroller\nfrom time import sleep\n# Use any pin that is not taken by SPI\nSD_CS = board.GP13\n \n# Connect to the card and mount the filesystem.\nspi = io.SPI(board.GP10, board.GP11, board.GP12)\ncs = digitalio.DigitalInOut(SD_CS)\nsdcard = adafruit_sdcard.SDCard(spi, cs)\nvfs = storage.VfsFat(sdcard)\nstorage.mount(vfs, \"/sd\")\n\n\nwith open(\"/sd/testfile.txt\", \"w\") as writefile:\n    print(\"First Line\", file=writefile)\n    writefile.close()\n\nwith open(\"/sd/testfile.txt\", \"a\") as appendfile:\n    print(\"Second Line\", file=appendfile)\n    appendfile.close\n\nwith open(\"/sd/testfile.txt\", \"r\") as inputfile:\n    for line in inputfile:\n        print(line)\n    inputfile.close\n\nwhile True:\n    with open(\"/sd/temphistory.txt\", \"a\") as appendfile:        \n        print(microcontroller.cpu.temperature, file=appendfile)\n    appendfile.close\n    sleep(5)\n\n\n\n","sub_path":"SDCartTest.py","file_name":"SDCartTest.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"237962876","text":"import imageio\nfrom os import listdir\nimport os\nimport numpy as np\n\n\ndef image_sequence():\n    from skimage.color import rgb2gray\n    from skimage import img_as_float, img_as_ubyte\n    images = []\n    try:\n        while 1:\n            img = img_as_float(rgb2gray(video.get_next_data()))\n            dec_img = img[::10, ::10]\n            res = img_as_ubyte((dec_img - np.min(dec_img)) / (np.max(dec_img) - np.min(dec_img)))\n            images.append(res)\n    except:\n        imageio.mimsave(save_path + str(len(listdir(save_path)) + 1) + \".gif\", images)\n\n\nsave_path = \"искаженная последовательность/уменьшение в 10 раз/\"\nif not os.path.exists(save_path): os.makedirs(save_path)\nvideo_name = \"D:/my docs/stud/_ДИПЛОМНАЯ РАБОТА/Test Video/5.gif\"\nvideo = imageio.get_reader(video_name)\nimage_sequence()\n","sub_path":"Автоматизация/Невзвешенное комплексирование/automated_warp.py","file_name":"automated_warp.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"431691883","text":"from datetime import datetime\nfrom datetime import timedelta\nimport logging\nlog = logging.getLogger(__file__)\n\nfrom ott.controller.util import object_utils\nfrom ott.controller.util import json_utils\nfrom ott.controller.util import html_utils\nfrom ott.controller.util import config\n\nclass Adverts(object):\n\n    def __init__(self, domain):\n        '''\n        '''\n        self.domain = domain\n        self.last_update = datetime.now() - timedelta(minutes = 60)\n        self.safe_content = None\n        self.content = {'rail':{}, 'bus':{}}\n        log.info(\"create an instance of {0}\".format(self.__class__.__name__))\n\n    def update(self):\n        ''' update content...\n        '''\n        try:\n            #import pdb; pdb.set_trace()\n            timeout = config.get_int('adverts_timeout', 30)\n            if datetime.now() - self.last_update > timedelta(minutes = timeout):\n                log.debug(\"updating the advert content\")\n                self.last_update = datetime.now()\n                self.content['rail']['en'] = json_utils.stream_json(self.domain, extra_path='adverts_train.json')\n                self.content['rail']['es'] = json_utils.stream_json(self.domain, extra_path='adverts_train_es.json')\n                self.content['bus']['en'] = json_utils.stream_json(self.domain,  extra_path='adverts_bus.json')\n                self.content['bus']['es'] = json_utils.stream_json(self.domain,  extra_path='adverts_bus_es.json')\n                self.safe_content = self.content['rail']['en']\n        except:\n            log.warn(\"couldn't update the advert\")\n \n    def query(self, mode=\"rail\", lang=\"en\"):\n        ''' \n        '''\n        ret_val = self.safe_content\n        try:\n            self.update()\n            m = mode if object_utils.has_content(mode) else 'rail'\n            ret_val = self.content[m][lang]\n        except:\n            log.warn(\"no advert content for mode={0}, lang={1}\".format(mode, lang))\n        return ret_val \n\n    def query_by_request(self, request, mode=\"rail\", lang=\"en\"):\n        m = html_utils.get_first_param(request, 'mode',     mode)\n        l = html_utils.get_first_param(request, '_LOCALE_', lang)\n        log.debug(\"mode={0}, lang={1}\".format(m, l))\n        return self.query(m, l)\n\n","sub_path":"ott/controller/services/content/adverts.py","file_name":"adverts.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"103840621","text":"# _*_ coding: utf-8 _*_\n\"\"\"\n  Created by Allen7D on 2018/11/26.\n\"\"\"\nfrom app.libs.swagger_filed import BodyField\n\n__author__ = 'Allen7D'\n\nname = BodyField('name', 'string', '姓名', ['董冬伟'])\nmobile = BodyField('mobile', 'string', '手机号', ['13758787058'])\nemail = BodyField('email', 'string', '邮箱', ['462870781@qq.com'])\nprovince = BodyField('province', 'string', '省', ['浙江', '江苏', '上海'])\ncity = BodyField('city', 'string', '市', ['杭州', '温州'])\ncountry = BodyField('country', 'string', '县区', ['萧山', '滨江'])\ndetail = BodyField('detail', 'string', '详细地址', ['金地天逸三期2栋2单元701室'])","sub_path":"app/api_docs/v1/address.py","file_name":"address.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"348024532","text":"import json\nfrom math import ceil\nimport pandas as pd\nfrom requests import get\n\nHEADERS = {\n    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 \\\n    (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'\n}\npage, PER_PAGE = 1, 999\nWEBSITE = f'''https://www.sreality.cz/api/cs/v2/estates?category_main_cb=4&category_sub_cb=38&category_type_cb=1&locality_region_id=10%7C2%7C8%7C14%7C6&page={page}&per_page={PER_PAGE}&tms=1635964216059'''\nWEB_JSON = json.loads(get(WEBSITE, headers=HEADERS).text)\nCOUNT_OF_PAGE = ceil(WEB_JSON['result_size'] / PER_PAGE)\nXHR_PATTERN = \"https://www.sreality.cz/api\"\n\n# add a repeating piece of the offer link here\nLINK_PATTERN = \"https://www.sreality.cz/detail/prodej/komercni/cinzovni-dum/\"\ndata = {}\n\n\ndef get_page(number_of_actual_elem):\n    website = f'''https://www.sreality.cz/api/cs/v2/estates?category_main_cb=4&category_sub_cb=38&category_type_cb=1&locality_region_id=10%7C2%7C8%7C14%7C6&page={page}&per_page={PER_PAGE}&tms=1635964216059'''\n    page_json = json.loads(get(website, headers=HEADERS).text)\n\n    # the first element is advertising, so I trimmed it\n    for offer in page_json['_embedded']['estates'][1:]:\n        actual_offer = json.loads(get(XHR_PATTERN + offer['_links']['self']['href'], headers=HEADERS).text)\n\n        # link\n        data.setdefault(actual_offer['_links']['self']['title'], [])\n        data[actual_offer['_links']['self']['title']].append(XHR_PATTERN + actual_offer['_links']['self']['href'])\n\n        # I didn't find another way, but I really wanted to add a normal link to the offer :(\n        data.setdefault('Odkaz', [])\n        data['Odkaz'].append(LINK_PATTERN + actual_offer['seo']['locality'] + \"/\" +\n                             actual_offer['_links']['self']['href'].split('estates/')[1])\n\n        # name\n        data.setdefault(actual_offer['name']['name'], [])\n        data[actual_offer['name']['name']].append(actual_offer['name']['value'])\n\n        # description\n        data.setdefault(actual_offer['text']['name'], [])\n        data[actual_offer['text']['name']].append(actual_offer['text']['value'])\n\n        # locality\n        data.setdefault(actual_offer['locality']['name'], [])\n        data[actual_offer['locality']['name']].append(actual_offer['locality']['value'])\n\n        # coordinates\n        data.setdefault('lat', [])\n        data['lat'].append(actual_offer['map']['lat'])\n        data.setdefault('lon', [])\n        data['lon'].append(actual_offer['map']['lon'])\n\n        # parameters\n        for k in actual_offer['items']:\n            # for example, if this is the fifth house and the parameter has just appeared,\n            # then you need to add an empty parameter to the four previous houses\n            if k['name'] not in data:\n                new_parameter(k['name'], number_of_actual_elem)\n            # sometimes parameter is array of parameters\n            if type(k['value']) != list:\n                data[k['name']].append(k['value'])\n            else:\n                data[k['name']].append(\"\".join([j['value'] for j in k['value']]))\n\n        # near objects\n        for k in actual_offer['poi']:\n            if k['name'] not in data:\n                new_parameter(k['name'], number_of_actual_elem)\n            data[k['name']].append(k['distance'])\n\n        # if the house has no parameters that exist in the dictionary, add an empty one\n        for k in data.keys():\n            if len(data[k]) != number_of_actual_elem + 1:\n                data[k].append(\"\")\n\n        number_of_actual_elem += 1\n\n\ndef new_parameter(key, count):\n    data.setdefault(key, [])\n    for i in range(count):\n        data[key].append(\"\")\n\n\nfor _ in range(COUNT_OF_PAGE):\n    get_page((page - 1) * PER_PAGE)\n    page += 1\n\npd.DataFrame(data).to_excel('./result.xlsx')\n","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":3789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"643742492","text":"import os\nimport json\nimport datetime\nfrom shutil import rmtree\nimport string\nimport scipy.stats as stat\nimport numpy as np\nfrom dateutil.relativedelta import *\nfrom calendar import monthrange\nfrom geotiff import *\nfrom lmoments3 import distr\n\nimport matplotlib.pylab as plt\nimport matplotlib as mpl\n\ndef simple_plot_index(a,title=\"\", save_img=False,save_dir='.'):\n    fig, ax = plt.subplots()\n    #cmap='RdBu'\n    cmap = mpl.colors.LinearSegmentedColormap.from_list(\"\", [\"red\",\"orange\",\"yellow\",\"white\",\"pink\",\"violet\", \"#0f0f0f\"])\n    plt.title(title)\n    im = plt.imshow(a, interpolation='none',cmap=cmap)\n    plt.colormaps()\n    # cbar = fig.colorbar(cax,ticks=[-1, 0, 1, 2, 10],orientation='vertical')\n    cbar = fig.colorbar(im, orientation='vertical')\n    #plt.clim(-3,3)\n    # cbar.ax.set_yticklabels(['< -1', '0', 1, 2,'> 10'])# vertically oriented\n    # colorbar\n    #ax.format_coord = Formatter(im)\n    if save_img:\n        plt.savefig(os.path.join(save_dir,title+'.png'))\n    else:\n        plt.show()\n\ndef get_all_dates(foldAdd, nameIndex):\n    # remove path separator from end of folder address\n    if foldAdd[-1] == os.path.sep:\n        foldAdd = foldAdd[:-1]\n\n    # get list of all files\n    allFiles = [x[2] for x in os.walk(foldAdd)]\n\n    # get dates from file names\n    count = len(nameIndex)\n    result = []\n    for str in allFiles:\n        if len(str) > 0:\n            for stf in str:\n                if nameIndex in stf:\n                    date=stf.split('_')[1].split(\".\")[0]\n                    #date = stf[count+1:count+9]\n                     # print(date)\n                    result.append(date)\n\n    result.sort()\n    return result\n\n\ndef get_date(dateStr):\n    year = int(dateStr[0:4])\n    month = int(dateStr[4:6])\n    day = int(dateStr[6:8])\n    return year, month, day\n\ndef diff_date_str(curDate, refDate):\n    dateVal = datetime.datetime.strptime(curDate, \"%Y%m%d\").date()\n    refDateVal = datetime.datetime.strptime(refDate, \"%Y%m%d\").date()\n    return (dateVal - refDateVal).days\n\n\ndef convert_to_monthly_data(PrecFold, PrecNameIndex, PrecmonthlyFold, monthCount, maskFile):\n    newFold = os.path.join(PrecmonthlyFold,\"{:d}-Month-Files\".format(monthCount))\n    if os.path.isdir(newFold):\n        rmtree(newFold)\n    os.system (\"mkdir -p \" + newFold)\n    dateList = get_all_dates(PrecFold, PrecNameIndex)\n    dateList.sort()\n    #print ([int(s[0:6]) for s in dateList])\n\n    dateListMonths = [i[0:6] for i in dateList ]\n\n    oDateFrom = datetime.datetime.strptime(dateList[0],\"%Y%m%d\")\n    oDateTo =   datetime.datetime.strptime(dateList[-1],\"%Y%m%d\")\n\n    #oDateFrom = oDateFrom + relativedelta(months=monthCount)\n    oDate = oDateFrom\n    while (oDate <= oDateTo):\n\n        if oDate < oDateFrom + relativedelta(months=monthCount-1):\n            # remove the first monthCount from dateList\n            #dateList = [s for s in dateList if oDate.strftime(\"%Y%m\") not in s]\n            oDate = oDate +relativedelta(months=+1)\n            continue\n        year = int(oDate.strftime(\"%Y\"))\n        month = int(oDate.strftime(\"%m\"))\n\n        if oDate.strftime(\"%Y%m\")  in dateListMonths:\n            convert_to_month_average_data(PrecFold, PrecNameIndex, dateList, newFold, month, year, monthCount,maskFile)\n            #dateListMonths.append(oDate.strftime(\"%Y%m\"))\n        else:\n            print (\"Missing data for : \"+ oDate.strftime(\"%Y%m\"))\n\n        oDate = oDate +relativedelta(months=+1)\n\n\ndef convert_to_month_average_data(foldAdd, nameIndex, dateList, newFold,\n                                  month, year, monthCount,maskFile):\n    dateCountDic = dict()\n    for i in range(monthCount):\n        yy = year\n        mm = month - i\n        if (mm < 1):\n            mm += 12\n            yy -= 1\n        totalDays = monthrange(yy, mm)[1]\n\n        for dd in range(1, totalDays + 1):\n            dateStr = \"{:4d}{:02d}{:02d}\".format(yy, mm, dd)\n            if dateStr in dateList:\n                if dateStr in dateCountDic:\n                    dateCountDic[dateStr] += 1\n                else:\n                    dateCountDic[dateStr] = 1\n            else:\n                dateList.append(dateStr)\n                dateList.sort()\n                ind = dateList.index(dateStr)\n                topDate, bottDate = str(), str()\n                if ind == 0:\n                    topDate = dateList[1]\n                    bottDate = dateList[1]\n                elif ind == len(dateList) - 1:\n                    topDate = dateList[-2]\n                    bottDate = dateList[-2]\n                else:\n                    topDate = dateList[ind + 1]\n                    bottDate = dateList[ind - 1]\n                dateList.remove(dateStr)\n                refDate = bottDate\n                diff = diff_date_str(dateStr, bottDate)\n                if diff >= 8:\n                    newDiff = diff_date_str(dateStr, topDate)\n                    if newDiff < diff:\n                        refDate = topDate\n                if refDate in dateCountDic:\n                    dateCountDic[refDate] += 1\n                else:\n                    dateCountDic[refDate] = 1\n    calculate_weighted_averge_prec(foldAdd, nameIndex, dateCountDic, newFold, year, month, maskFile)\n    \ndef calculate_weighted_averge_prec(foldAdd, nameIndex, dateCountDic, newFold, year, month, maskFile):\n\n    data , col, row, geoTrans, geoproj = readGeotiff (maskFile)\n\n\n    data3d = np.zeros((row, col, len (dateCountDic)))\n    datasum = np.zeros((row, col ))\n    for i, key in enumerate(dateCountDic):\n\n        daFold = os.path.join(foldAdd, key[0:4], key[4:6], key[6:8])\n\n        fileName = os.path.join(daFold, nameIndex + \"_\" + key + \".tif\")\n\n        data , col, row, geoTrans, geoproj = readGeotiff (fileName)\n\n        #simple_plot_index(data)\n        data3d [:,:,i] = data\n\n    data3d[data3d<1]=0\n    data_mean = np.nanmean(data3d,axis=2)\n\n    outFileName= os.path.join(newFold, nameIndex + \"_\" +\"{:4d}{:02d}.tif\".format(year, month))\n\n    #simple_plot_index(data_mean)\n    print (outFileName)\n    writeGeotiffSingleBand(outFileName, geoTrans, geoproj, data_mean,nodata=np.nan, BandName=\"Prec_accumulation\")\n\n\ndef create_statistics(mainFoldAdd, newFold, nameIndex, monthCount, maskFile):\n    dateList = get_all_dates(mainFoldAdd, nameIndex)\n    dateList.sort()\n#    newFold = os.path.join(mainFoldAdd, \"Normal PDF\")\n    if not(os.path.isdir(newFold)):\n        os.system (\"mkdir -p \"+newFold)\n\n    for month in range(1, 13):\n        fileNames = []\n        for str in dateList:\n            yy = int(str[0:4])\n            mm = int(str[4:6])\n            if mm == month:\n                fileNames.append(nameIndex + \"_\" + str + \".tif\")\n        compute_statistics (mainFoldAdd, fileNames, newFold, monthCount, month,nameIndex, maskFile)\n\n\ndef compute_statistics(mainFoldAdd, fileNames, newFold, monthCount, month,nameIndex, maskFile):\n    print(\"Create statistics GeoTiff for {:d} months from month {:02d}\".\n          format(monthCount, month))\n\n\n    mask, col, row, geoTrans, geoproj=  readGeotiff(maskFile)\n\n    #data , col, row, geoTrans, geoproj = readGeotiff (\"GSMAP_20200501.tif\")\n\n\n\n    data3d = np.zeros((row,col,len(fileNames)))\n\n    for i, f in enumerate(fileNames):\n\n        data , col, row, geoTrans, geoproj = readGeotiff (os.path.join(mainFoldAdd,f))\n\n\n        data3d [:,:,i]= data\n\n    distr_param =np.zeros((row,col,4))*np.nan\n\n    invalid_pixels = 0\n\n    for i in range(row):\n        for j in range(col):\n\n            if sum(np.isnan(data3d[i,j,:])) ==len(fileNames):\n                continue\n\n            d1d = data3d[i,j,:]\n            dpd = d1d[np.where(d1d > 0)]  # only non null values\n            zeros = d1d[d1d==0]\n            if len(dpd) < 4:\n                print (\"Not enough valid values in time series, impossible fitting!\")\n                continue\n            \n            fit_dict = distr.gam.lmom_fit(dpd)\n            fit = (fit_dict['a'],fit_dict['loc'],fit_dict['scale'])\n\n            #max_distance, p_value = stat.kstest(dpd,\"gamma\",args=fit)\n            #if p_value < 0.5:\n            #    invalid_pixels += 1\n\n            #    continue\n                ##print(\"Kolmogorov-Smirnov test for goodness of fit: \"+str(round(p_value*100))+\"%, max distance: \"+str(max_distance))\n\n            distr_param[i,j,0] = fit[0]\n            distr_param[i,j,1] = fit[1]\n            distr_param[i,j,2] = fit[2]\n            #distr_param[i,j,3] = 1 - len(dpd) / len(d1d)    # zero probability\n            distr_param[i,j,3] = len(zeros) / len(d1d)    # zero probability\n\n    print (\"Invalid pixel: \" + str(round(invalid_pixels/(row*col)*100))+\"%\")\n    name = \"Statistics-\"+ nameIndex +\"-{:02d}months-Month{:02d}.tif\".format(monthCount, month)\n    fileStatsOut = os.path.join(newFold, name)\n\n    writeGeotiff(fileStatsOut,geoTrans, geoproj,distr_param, nodata=np.nan, BandNames=list(fit_dict.keys()).append(\"P0\")\n                 ,globalDescr = \"SPI_distr_param_c_loc_scale_P0\")\n\n\n\ndef main():\n    with open('SPI_config.json') as jf:\n        params = json.load(jf)\n\n        # get FAPARFold tif files\n\n        PrecFold = params[\"Prec_folder\"]\n        if PrecFold[-1] == os.path.sep:\n            PrecFold = PrecFold[:-1]\n\n        IndexFold = params[\"Index_folder\"]\n        if IndexFold[-1] == os.path.sep:\n            IndexFold = PrecFold[:-1]\n\n        PrecNameIndex = params[\"Prec_prefix\"]\n\n        maskFile = params[\"mask_file\"]\n\n       # monthlyFold = params[\"Monthly_folder\"]\n\n        statsFold = params[\"Stats_folder\"]\n\n        aggMonths = params[\"Agg_months\"]\n\n        PrecmonthlyFold = os.path.join(IndexFold,PrecNameIndex+\"-Monthly-Files\")\n\n        for nmonths in aggMonths:\n\n            # monthly averages\n\n            convert_to_monthly_data(PrecFold, PrecNameIndex, PrecmonthlyFold, nmonths, maskFile)\n\n            monthsFold = os.path.join(PrecmonthlyFold, \"{:d}-Month-Files\".format(nmonths))\n\n            create_statistics(monthsFold, statsFold, PrecNameIndex, nmonths, maskFile)\n\nif __name__ == '__main__':\n    main()\nelse:\n    pass","sub_path":"indexes/SPI-PERSIANN/SPI_stat_base_ks0.py","file_name":"SPI_stat_base_ks0.py","file_ext":"py","file_size_in_byte":9954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"284927346","text":"import sqlite3\n\n\nclass PersonCRUD:\n\n    def __init__(self, db_name: str):\n        self.db_name = db_name\n        self.connection = self.__get_connection()\n        self.cursor = self.__get_cursor()\n\n    def __get_connection(self):\n        try:\n            return sqlite3.connect(self.db_name)\n        except sqlite3.Error as e:\n            print(str(e))\n            return\n\n    def __get_cursor(self):\n        return self.connection.cursor()\n\n    def create_table(self):\n        create_table_query = \"\"\"CREATE TABLE IF NOT EXISTS person(\n                            person_id integer PRIMARY KEY AUTOINCREMENT,\n                            first_name VARCHAR,\n                            last_name VARCHAR,\n                            age INTEGER\n                    )\n        \"\"\"\n        self.cursor.execute(create_table_query)\n\n    def read_table(self):\n        self.cursor.execute('''SELECT * FROM person''')\n        for row in self.cursor.fetchall():\n            print(row)\n\n    def create_person(self, first_name: str, last_name: str, age: int) -> int:\n        create_person_query = f'INSERT INTO person(first_name, last_name, age) VALUES (?, ?, ?)'\n        self.cursor.execute(create_person_query, (first_name, last_name, age))\n        self.connection.commit()\n        print(f'Person {first_name} {last_name} {age} was successfully created!')\n\n    def delete_person(self, first_name: str):\n        delete_person_query = f'DELETE FROM person WHERE first_name = ?'\n        self.cursor.execute(delete_person_query, (first_name,))\n        self.connection.commit()\n\n    def update_person(self, age: int):\n        delete_person_query = f'UPDATE person SET age = 20 WHERE age = ?'\n        self.cursor.execute(delete_person_query, (age,))\n        self.connection.commit()\n\n    def get_persons_with_my_name(self, my_name: str):\n        my_name_query = f'SELECT last_name FROM person WHERE first_name = ?'\n        self.cursor.execute(my_name_query, (my_name,))\n        for row in self.cursor.fetchall():\n            print(row)\n        self.connection.commit()\n\n    def younger_than_25(self, appropriate_age: int):\n        my_name_query = f'SELECT first_name, last_name, age FROM person WHERE age < ?'\n        self.cursor.execute(my_name_query, (appropriate_age,))\n        for row in self.cursor.fetchall():\n            print(row)\n        self.connection.commit()\n\n    def from_15_to_18(self, bottom_border, upper_border: int):\n        my_name_query = f'SELECT first_name, last_name, age FROM person WHERE age BETWEEN ? AND ?'\n        self.cursor.execute(my_name_query, (bottom_border, upper_border,))\n        for row in self.cursor.fetchall():\n            print(row)\n        self.connection.commit()\n\n\nif __name__ == '__main__':\n\n    table = PersonCRUD(db_name='database1.db')\n    table.create_table()\n    table.get_persons_with_my_name('Andrey')\n    print('________________')\n    table.younger_than_25(25)\n    print('________________')\n    table.from_15_to_18(15, 18)\n    table.delete_person('Anton')\n    table.update_person(19)\n\n\n","sub_path":"HW16/task_16_1.py","file_name":"task_16_1.py","file_ext":"py","file_size_in_byte":3026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"455420023","text":"from member import MongoReplicaSetMember\nimport sys\n\n\nclass MongoDataNode(MongoReplicaSetMember):\n\n    NAME_TEMPLATE = '{envcl}-rs{replica_set}-{location}-{index}'\n    NAME_SEARCH_PREFIX = '{envcl}-rs{replica_set}-{location}-'\n    NAME_AUTO_INDEX = True\n\n    CHEF_RUNLIST = ['role[RoleMongo]']\n    CHEF_MONGODB_TYPE = 'data'\n\n    def __init__(self, group=None, server_type=None, instance_type=None,\n                 environment=None, ami=None, region=None, role=None,\n                 keypair=None, availability_zone=None,\n                 security_groups=None, block_devices=None,\n                 chef_path=None, subnet_id=None, dns_zones=None,\n                 ingress_groups_to_add=None, ports_to_authorize=None,\n                 classic_link=False, add_route53_dns=True, chef_server_url=None,\n                 replica_set=None, mongodb_version=None, data_volume_size=None,\n                 data_volume_iops=None, journal_volume_size=None,\n                 journal_volume_iops=None, log_volume_size=None,\n                 log_volume_iops=None):\n\n        super(MongoDataNode, self).__init__(group, server_type, instance_type,\n                                            environment, ami, region, role,\n                                            keypair, availability_zone,\n                                            security_groups, block_devices,\n                                            chef_path, subnet_id, dns_zones,\n                                            ingress_groups_to_add,\n                                            ports_to_authorize, classic_link,\n                                            add_route53_dns, chef_server_url,\n                                            replica_set, mongodb_version)\n\n        self.data_volume_size = data_volume_size\n        self.data_volume_iops = data_volume_iops\n        self.journal_volume_size = journal_volume_size\n        self.journal_volume_iops = journal_volume_iops\n        self.log_volume_size = log_volume_size\n        self.log_volume_iops = log_volume_iops\n\n    def validate_ebs_volume(self, volume_type):\n        volume_size = 0\n        volume_iops = 0\n\n        if volume_type == 'data':\n            volume_size = self.data_volume_size\n            volume_iops = self.data_volume_iops\n        elif volume_type == 'journal':\n            volume_size = self.journal_volume_size\n            volume_iops = self.journal_volume_iops\n        elif volume_type == 'log':\n            volume_size = self.log_volume_size\n            volume_iops = self.log_volume_iops\n        else:\n            msg = 'Unable to validate drive type: {volume_type}'.format(\n                volume_type=volume_type)\n            self.log.critical(msg)\n            sys.exit(1)\n\n        if volume_size is None:\n            msg = 'No {volume_type} volume size provided'.format(\n                volume_type=volume_type)\n            self.log.warn(msg)\n            volume_size = self.set_default_volume_size(volume_type)\n        elif volume_size < 1:\n            self.log.critical('The {volume_type} volume size is less than 1'.\n                              format(volume_type=volume_type))\n            sys.exit(1)\n\n        msg = 'Using {volume_type} volume size \"{size}\"'.format(\n            volume_type=volume_type, size=volume_size)\n        self.log.info(msg)\n\n        if volume_iops is None:\n            msg = 'No {volume_type} volume iops provided'.format(\n                volume_type=volume_type)\n            self.log.warn(msg)\n\n            volume_iops = self.set_default_volume_iops(volume_type)\n\n        msg = 'Using {volume_type} volume iops \"{iops}\"'.format(\n            volume_type=volume_type, iops=volume_iops)\n        self.log.info(msg)\n\n        iops_size_ratio = volume_iops / volume_size\n\n        self.log.info('The IOPS to Size ratio is \"{ratio}\"'.format(\n            ratio=iops_size_ratio))\n\n        if iops_size_ratio > 30:\n            self.log.critical('The IOPS to Size ratio is greater than 30')\n            sys.exit(1)\n\n    def set_default_volume_size(self, volume_type):\n        if volume_type == 'data':\n            self.data_volume_size = 400\n            size = 400\n        elif volume_type == 'journal':\n            self.journal_volume_size = 50\n            size = 50\n        elif volume_type == 'log':\n            self.log_volume_size = 10\n            size = 10\n\n        return size\n\n    def set_default_volume_iops(self, volume_type):\n        if volume_type == 'data':\n            if self.environment == 'prod':\n                self.data_volume_iops = 3000\n            else:\n                self.data_volume_iops = 0\n            default_volume_iops = self.data_volume_iops\n        elif volume_type == 'journal':\n            if self.environment == 'prod':\n                self.journal_volume_iops = 500\n            else:\n                self.journal_volume_iops = 0\n            default_volume_iops = self.journal_volume_iops\n        elif volume_type == 'log':\n            if self.environment == 'prod':\n                self.log_volume_iops = 200\n            else:\n                self.log_volume_iops = 0\n            default_volume_iops = self.log_volume_iops\n\n        return default_volume_iops\n\n    def set_chef_attributes(self):\n        super(MongoDataNode, self).set_chef_attributes()\n        ebs_volumes = [\n            {\n                'user': 'mongod',\n                'group': 'mongod',\n                'size': self.data_volume_size,\n                'iops': self.data_volume_iops,\n                'device': '/dev/xvdf',\n                'mount': '/volr'\n            },\n            {\n                'user': 'mongod',\n                'group': 'mongod',\n                'size': self.journal_volume_size,\n                'iops': self.journal_volume_iops,\n                'device': '/dev/xvdg',\n                'mount': '/volr/journal'\n            },\n            {\n                'user': 'mongod',\n                'group': 'mongod',\n                'size': self.log_volume_size,\n                'iops': self.log_volume_iops,\n                'device': '/dev/xvdh',\n                'mount': '/mongologs',\n            }\n        ]\n\n        if self.ephemeral_storage == []:\n            ebs_volumes.append({\n                'user': 'root',\n                'group': 'root',\n                'size': 8,\n                'iops': 24,\n                'device': '/dev/xvdc',\n                'mount': '/media/ephemeral0'\n            })\n\n            self.log.debug('No instance storage; including swap device')\n\n        self.CHEF_ATTRIBUTES['hudl_ebs'] = {'volumes': ebs_volumes}\n        self.log.info('Configured the hudl_ebs.volumes attribute')\n\n    def configure(self):\n\n        super(MongoDataNode, self).configure()\n        self.set_chef_attributes()\n\n        if self.environment == 'stage':\n            self.IAM_ROLE_POLICIES.append('allow-download-script'\n                                          '-s3-stage-updater')\n            self.resolve_iam_role()\n\n        self.validate_ebs_volume('data')\n        self.validate_ebs_volume('journal')\n        self.validate_ebs_volume('log')\n","sub_path":"tyr/servers/mongo/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":7039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"352473669","text":"#-*- coding: utf-8 -*-\n\"\"\"\nVAMP.py\nauthor: chernxh@tamu.edu\ndate  : 09/26/2017\n\nPython implementation of VAMP algorithm.\n\"\"\"\n\nimport numpy as np\nfrom scipy import linalg\n\ndef VAMP(A, X, Y, gamma1=0.001, gammaw=1.0, Tm=100):\n    \"\"\"\n\n    :A: np array, measurement matrix\n    :X: np array, ground truth sparse coding as column vectors\n    :Y: np array, measurements as column vectors\n    :Tm: int, maximum iteration steps\n    :gamma1: float, initial value of gamma1\n    :gammaw: float, assumed noise precision of X0\n    :returns:\n        :Xhat : np array, approximated sparse coding\n        :anmse: np array, averaged nmse respect to all samples\n\n    \"\"\"\n    m, n = A.shape\n    _, l = Y.shape\n\n    U, s, Vh = linalg.svd(A, full_matrices=False)\n    S = linalg.diagsvd(s, len(s), len(s))\n    Ytil = gammaw * np.dot(S.T, np.dot(U.T, Y))\n\n    nmse = np.concatenate(([np.ones(l)], np.zeros((Tm, l))), axis=0);\n        # nmse(t, k): nmse between xhat(:, j) and x(:, j) at time t\n    sum_square_X = np.sum(np.square(X.astype(np.float64)), 0)\n    sum_square_X = sum_square_X + ( sum_square_X == 0 )\n\n    gamma1 = gamma1 * np.ones(l).astype(np.float64)\n    R1 = np.dot(A.T, Y) # initialized R1\n    Xhat2 = np.zeros_like(X).astype(np.float64)\n    alf2  = np.zeros(l)\n    for t in range(Tm):\n        # Denoising step\n        Xhat1   = np.sign(R1) * np.maximum( np.abs(R1) - gamma1, 0 )\n        alf1    = np.sum(np.abs(Xhat1) != 0, axis=0) / n\n        eta1    = gamma1 / alf1\n        gamma2  = eta1 - gamma1\n        R2      = (eta1 * Xhat1 - gamma1 * R1) / gamma2\n\n        # LMMSE estimation\n        for i in range(l):\n            D = np.diag( 1./(gammaw * np.square(s) + gamma2[i]) )\n            Xhat2[:, i] = np.dot(np.dot(Vh.T, D), Ytil[:,i] + gamma2[i] * np.dot(Vh, R2[:, i]))\n            alf2[i]     = np.trace(D) * gamma2[i] / n\n        eta2    = gamma2 / alf2\n        gamma1  = eta2 - gamma2\n        R1      = ( eta2 * Xhat2 - gamma2 * R2 ) / gamma1\n\n        # print(\"max value in Xhat in %d step is %f\" % (t, np.max(Xhat)))\n        nmse[t+1,:] = np.sum(np.square(Xhat1-X), 0) / sum_square_X\n\n    return Xhat1, np.mean(nmse, 1)\n\n","sub_path":"VAMP.py","file_name":"VAMP.py","file_ext":"py","file_size_in_byte":2122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"383422531","text":"#  Copyright (c) 2021 GradsFlow. All rights reserved.\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.\nimport warnings\nfrom typing import Dict, List\n\nfrom rich import box\nfrom rich.table import Table\n\nfrom gradsflow.core.base import BaseTracker, TrackingValues\nfrom gradsflow.utility.common import AverageMeter, to_item\n\n\nclass Tracker(BaseTracker):\n    \"\"\"\n    Tracks loss, accuracy and model weights during model.fit()\n    \"\"\"\n\n    def __init__(self):\n        self.train.metrics = {}\n        self.val.metrics = {}\n        self.logs: List[Dict] = []\n\n    def mode(self, mode) -> TrackingValues:\n        if mode == \"train\":\n            return self.train\n        if mode == \"val\":\n            return self.val\n\n        raise NotImplementedError(f\"mode {mode} is not implemented!\")\n\n    def track(self, key, value, render=False):\n        \"\"\"Tracks values for each step\"\"\"\n        if render:\n            warnings.warn(\"render is deprecated!\")\n        epoch = self.current_epoch\n        step = self.current_step\n        data = {\"current_epoch\": epoch, \"current_step\": step, key: to_item(value)}\n        self.logs.append(data)\n\n    def track_loss(self, loss: float, mode: str):\n        \"\"\"Update `TrackingValues` loss. mode can be train or val\"\"\"\n        value_tracker = self.mode(mode)\n        value_tracker.loss.update(loss)\n        key = mode + \"/\" + \"loss\"\n        self.track(key, loss)\n\n    def track_metrics(self, metric: Dict[str, float], mode: str):\n        \"\"\"Update `TrackingValues` metrics. mode can be train or val and will update logs if render is True\"\"\"\n        value_tracker = self.mode(mode)\n        # Track values that averages with epoch\n        for key, value in metric.items():\n            try:\n                value_tracker.metrics[key].update(value)\n            except KeyError:\n                value_tracker.metrics[key] = AverageMeter(name=key)\n                value_tracker.metrics[key].update(value)\n\n        # track value for each step in a dict\n        for k, v in metric.items():\n            k = mode + \"/\" + k\n            self.track(k, v)\n\n    def get_metrics(self, mode: str):\n        value_tracker = self.mode(mode)\n        return value_tracker.metrics\n\n    def get_loss(self, mode: str):\n        value_tracker = self.mode(mode)\n        return value_tracker.loss.avg\n\n    def create_table(self) -> Table:\n        headings = [\"i\", \"train/loss\"]\n        row = [self.current_epoch, to_item(self.train_loss)]\n\n        if self.val.loss.computed:\n            headings.append(\"val/loss\")\n            row.append(to_item(self.val_loss))\n\n        for metric_name, value in self.train_metrics.items():\n            headings.append(\"train/\" + metric_name)\n            row.append(to_item(value.avg))\n\n        for metric_name, value in self.val_metrics.items():\n            headings.append(\"val/\" + metric_name)\n            row.append(to_item(value.avg))\n\n        row = list(map(lambda x: f\"{x: .3f}\" if isinstance(x, float) else str(x), row))\n        table = Table(*headings, expand=True, box=box.SIMPLE)\n        table.add_row(*row)\n        return table\n\n    def reset(self):\n        self.max_epochs = 0\n        self.current_epoch = 0\n        self.current_step = 0\n        self.steps_per_epoch = None\n        self.train = TrackingValues()\n        self.val = TrackingValues()\n        self.logs = []\n\n    @property\n    def train_loss(self):\n        return self.train.loss.avg\n\n    @property\n    def val_loss(self):\n        return self.val.loss.avg\n\n    @property\n    def train_metrics(self):\n        return self.train.metrics\n\n    @property\n    def val_metrics(self):\n        return self.val.metrics\n","sub_path":"gradsflow/models/tracker.py","file_name":"tracker.py","file_ext":"py","file_size_in_byte":4116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"642245710","text":"\"\"\"Classes that support Knuth TeX-style paragraph breaking.\"\"\"\n\nfrom texlib.wrap import ObjectList, Box, Glue, Penalty\nfrom .hyphenate import hyphenate_word\n\nSTRING_WIDTHS = {}\n\ndef wrap_paragraph(canvas, line, pp, indent):\n\n    olist = ObjectList()\n    # olist.debug = True\n\n    if indent:\n        olist.append(Glue(indent, 0, 0))\n\n    space_width = canvas.stringWidth(u' ')\n    hyphen_width = canvas.stringWidth(u'-')\n\n    for word in pp.text.split():\n        pieces = hyphenate_word(word)\n        for piece in pieces:\n            w = STRING_WIDTHS.get(piece)\n            if w is None:\n                w = STRING_WIDTHS[piece] = canvas.stringWidth(piece)\n            olist.append(Box(w, piece))\n            olist.append(Penalty(hyphen_width, 100))\n        olist.pop()\n        olist.append(Glue(space_width, space_width * .5, space_width * .25))\n    olist.pop()\n    olist.add_closing_penalty()\n\n    # for item in olist:\n    #     print item\n\n    line_lengths = [line.w]\n    for tolerance in 1, 2, 3, 4:\n        try:\n            breaks = olist.compute_breakpoints(\n                line_lengths, tolerance=tolerance)\n        except RuntimeError:\n            pass\n        else:\n            break\n\n    start = 0\n    n = 0\n    # if 'Confederate' in pp.text:\n    #     print breaks, len(olist)\n    # if breaks[-1] != len(olist) - 1:\n    #     breaks.append(len(olist) - 1)\n    for breakpoint in breaks[1:]:\n        keepers = []\n        r = olist.compute_adjustment_ratio(start, breakpoint, 0, (line.w,))\n        # if 'Confederate' in pp.text:\n        #     print breakpoint, r\n        n += 1\n        for i in range(start, breakpoint):\n            box = olist[i]\n            if box.is_glue():\n                box.glue_width = box.compute_width(r)\n                keepers.append(box)\n            elif box.is_box():\n                keepers.append(box)\n        bbox = olist[breakpoint]\n        if bbox.is_penalty() and bbox.width == hyphen_width:\n            b = Box(hyphen_width, u'-')\n            keepers.append(b)\n        graphic = KnuthLine()\n        graphic.things = keepers\n        line.graphics.append(graphic)\n        line = line.next()\n        start = breakpoint + 1\n\n    return line.previous\n\nclass KnuthLine(object):\n    \"\"\"A graphic that knows how to draw a justified line of text.\"\"\"\n\n    def __call__(self, line, canvas):\n        ww = 0.\n        ay = line.ay()\n        for thing in self.things:\n            if isinstance(thing, Box):\n                canvas.drawString(line.chase.x + ww, ay, thing.character)\n                ww += canvas.stringWidth(thing.character)\n            elif isinstance(thing, Glue):\n                ww += thing.glue_width\n","sub_path":"bookbinding/knuth.py","file_name":"knuth.py","file_ext":"py","file_size_in_byte":2652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"415628087","text":"import unittest\nimport subprocess as sp\nimport os\nimport glob\nimport shutil\nfrom frads import makesky, radutil\n\nclass TestGenmtx(unittest.TestCase):\n\n    def test_help(self):\n        cmd = [\"genmtx\", \"-h\"]\n        process = sp.run(cmd, check=True, stderr=sp.PIPE, stdout=sp.PIPE)\n        self.assertEqual(process.stderr, b'')\n\n    def test_sun_mtx(self):\n        window_path = os.path.join(\"Objects\", \"upper_glass.rad\")\n        window_normals = radutil.primitive_normal([window_path])\n        epw = makesky.getEPW(\"37\", \"-122\")\n        wea_metadata, wea_data = makesky.epw2wea(\n            epw.fname, dhour=True, shour=6, ehour=20,\n            remove_zero=True, window_normal=window_normals)\n        cmd, _ = makesky.gendaymtx(\n            wea_data, wea_metadata, mf=6, direct=True, onesun=True)\n        process = sp.run(cmd, stdout=sp.PIPE)\n        with open('test.smx', 'wb') as wtr:\n            wtr.write(process.stdout)\n        cmd = [\"genmtx\", \"-st\", \"v\", \"-s\", \"v1a.vf\", \"-r\", \"sun\", \"-rs\", \"r6\",\n               \"-o\", \"test_genmtx_sun_mtx\", \"-wpths\", window_path, \"-opt\", \"-ab 0\",\n               \"-res\", \"8\", \"8\", \"-smx\", 'test.smx']\n        process = sp.run(cmd, stderr=sp.PIPE)\n        print(process.stderr)\n        self.assertEqual(process.stderr, b'')\n        self.assertTrue(os.path.isdir(\"test_genmtx_sun_mtx\"))\n        self.assertEqual(len(glob.glob('test_genmtx_sun_mtx/*.hdr')), 555)\n        shutil.rmtree(\"test_genmtx_sun_mtx\")\n        os.remove('test.smx')\n\n    def test_sun_mtx2(self):\n        epw = makesky.getEPW(\"32.6056027\", \"-114.712143\")\n        wea_metadata, wea_data = makesky.epw2wea(\n            epw.fname, dhour=True, shour=6, ehour=20,\n            remove_zero=True)\n        cmd, _ = makesky.gendaymtx(\n            wea_data, wea_metadata, mf=6, direct=True, onesun=True)\n        process = sp.run(cmd, stdout=sp.PIPE)\n        with open('test.smx', 'wb') as wtr:\n            wtr.write(process.stdout)\n        cmd = [\"genmtx\", \"-st\", \"v\", \"-s\", \"v1a.vf\", \"-r\", \"sun\", \"-rs\", \"r6\",\n               \"-o\", \"test_genmtx_sun_mtx2\", \"-opt\", \"-ab 0\",\n               \"-res\", \"8\", \"8\", \"-smx\", 'test.smx']\n        process = sp.run(cmd, stderr=sp.PIPE)\n        print(process.stderr)\n        self.assertEqual(process.stderr, b'')\n        self.assertTrue(os.path.isdir(\"test_genmtx_sun_mtx2\"))\n        self.assertEqual(len(glob.glob('test_genmtx_sun_mtx2/*.hdr')), 657)\n        shutil.rmtree(\"test_genmtx_sun_mtx2\")\n        os.remove('test.smx')\n\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"test/test_genmtx.py","file_name":"test_genmtx.py","file_ext":"py","file_size_in_byte":2509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"603885869","text":"# -*- coding: utf-8 -*-\n\nimport urllib\nimport datetime\n\n\ndef create_csv(vals, header=None, delimiter=\",\", file_path=None):\n    \"\"\"\n    file_path + csv ex: abc/ef.csv\n\n    if file_path is not none -> save to file\n    else return bytes\n\n    header is an array\n    vals is an array of array\n    *Note all header and vals are string type\n    \"\"\"\n    vals_comma = [delimiter.join(val) for val in vals] # list of string val with comma\n    full_content = \"\\n\".join(vals_comma) # string full  vals with newline\n\n    if header is not None:\n        header = delimiter.join(header)\n        full_content = header + \"\\n\" + full_content\n\n    byte_full_content = full_content.encode()\n\n    if file_path:\n        with open(file_path, 'wb') as w:\n            w.write(byte_full_content)\n            return file_path\n    else:\n        return byte_full_content\n\n\ndef unquote_url(url, encoding='utf-8', errors='replace'):\n    \"\"\"\n    https://docs.python.org/3/library/urllib.parse.html\n    using unquote_plus\n    Example: unquote_plus('/El+Ni%C3%B1o/') yields '/El Niño/'.\n    \"\"\"\n    return urllib.parse.unquote_plus(url, encoding=encoding, errors=errors)\n\n\ndef mins_bw_two_times(time1, time2):\n    \"\"\"\n    time1 and time2 must be datetime.datetime type\n    Ex: datetime.datetime(2016, 6, 7, 23, 45)\n    \"\"\"\n    diff = time2 - time1\n    return diff.seconds / 60\n\n\ndef get_time_now(text=True, format=\"%Y-%m-%d\"):\n    if text is True:\n        return datetime.datetime.now().strftime(format)\n    else:\n        return datetime.datetime.now()\n\n\ndef check_like(value, pattern, like=True):\n    \"\"\"Check string match with a pattern\n    Check pattern match at beginning or ending of string,\n    or check pattern in value in any positions\n    Args:\n        value (string): string to check\n        pattern (string): pattern to check match\n        like (boolean): match LIKE or NOT LIKE, pattern in value or not\n    Returns:\n        type: boolean\n    Examples:\n        WHERE CustomerName LIKE 'a%'\tFinds any values that starts with \"a\"\n        WHERE CustomerName LIKE '%a'\tFinds any values that ends with \"a\"\n        WHERE CustomerName LIKE '%or%'\tFinds any values that have \"or\" in any position\n    \"\"\"\n    first = pattern[0]\n    end = pattern[-1]\n    # value = value.strip()\n    if first == \"%\" and end == \"%\":\n        return (pattern[1:-1] in value) == like\n    elif first == \"%\":\n        return value.endswith(pattern[1:]) == like\n    elif end == \"%\":\n        return value.startswith(pattern[:-1]) == like\n    else:\n        # check pattern in value\n        return (pattern in value) == like\n\n\ndef check_spmdf_pattern(file_name, pattern):\n    \"\"\"\n    rawdata*.csv\n    DOE rawdata*.csv\n    \"\"\"\n    patterns = pattern.split(\"*\")\n    return check_like(file_name, patterns[0]+\"%\") and check_like(file_name, \"%\"+patterns[-1])\n","sub_path":"meter-loader-nem/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":2793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"417029369","text":"from django.contrib import admin\nfrom .models import Post\n# Register your models here.\n\nadmin.site.site_header = 'ResLife Portal Admin'\n\n\nclass PostAdmin(admin.ModelAdmin):\n    list_display = ('title', 'author', 'date_posted')\n    list_filter = ('title', 'date_posted', 'author')\n\n\nadmin.site.register(Post, PostAdmin)\n","sub_path":"home/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"615940665","text":"# -*- coding: utf-8 -*-\ndef add(a,*b):\n    \"\"\"add any number of matrices of the same dimension \n        with respect to the indexes\n\n    Args:\n        a (list): first matrix\n        b : variable length of matrices\n\n    Raises:\n        ValueError: if input matrices doesn't have the same dimension\n    \"\"\"\n    if len(b) == 1:\n        return(add2(a,*b))\n    else :\n        return(add2(a,add(b[0],*b[1:])))\n\n\n\ndef add2(a,b):\n    \"\"\"add 2 matrices of the same dimension\n    \n    Args:\n        a (list): first matrix\n        b (list): second matrix\n\n    Return:\n        list: The addition of the 2 matrices\n\n    Raises:\n        ValueError: if a and b doesn't have the same dimension\n\n    \"\"\"\n    if len(a) != len(b):\n        raise ValueError\n\n    l = list()\n    for x,y in zip(a,b):\n        if type(x) == type(tuple()) or type(x) == type(list()):\n            l.append( add2(x,y))\n        else:\n            l.append(x+y)\n    return(l)\n\n\n\n","sub_path":"add/add.py","file_name":"add.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"329514187","text":"# https://pythonbasics.org/exercises/\n# Random numbers\nimport random\n\n# just a method to convert & print X\n\n\ndef printx(x):\n    x = str(x)\n    print(\"X is \" + x)\n\n0,100\n# Make a program that creates a random number and stores it into x.\nx = random.random()\nprintx(x)\n\n# Make a program that prints 3 random numbers.\niterator = 1\n\nprint(\"\\nbegin loop\")\n\nwhile iterator < 4:\n    x = random.randint(0, 10)\n    printx(x)\n    iterator += 1\n\nprint(\"end of loop\\n\")\n\n\n# Create a program that generates 100 random numbers and find the frequency of each number.\nlistOfNums = [random.randint(1,100) for _ in range(100)]\nfreqDict = {num : 0 for num in listOfNums if num not in freqDict.keys()} #This expression is not valid in 3.7.4\nfor num in listOfNums:\n    freqDict[num] += 1\n","sub_path":"Lecture 3 : Control Flow/Exercise_randomNumbers.py","file_name":"Exercise_randomNumbers.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"300598282","text":"\nclass Solution:\n    def uniquePathsWithObstacles(self,obstacleGrid) -> int:\n        row,cols = len(obstacleGrid),len(obstacleGrid[0])\n        \n        res = [[0]*cols for _ in range(row)]\n        \n        for i in range(0, row):\n            for j in range(0, cols):\n                if obstacleGrid[i][j] == 1:\n                    continue\n                if i== 0 or j == 0:\n                    res[i][j] = 1\n                else:\n                    res[i][j] = res[i-1][j] + res[i][j-1]\n        return res[row-1][cols-1]\n\nif __name__ == '__main__':\n    print(Solution().uniquePathsWithObstacles([[0,0]]))","sub_path":"Week_06/63.py","file_name":"63.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"24607545","text":"# Defines the building blocks and elements of a character\nimport math\n\nclass CH_AbilityScores:\n# Character Ability Score. Use getBonus to get Ability Score bonus\n#Adds Ability Scores + Methods\n\n    def __init__(self,Scores = [1,2,3,4,5,6]):\n    #Initialize Ability Scores (STR,DEX,CON,INT,WIS,CHA)\n    #Can Set Ability Score, and get Ability Bonus\n        self._AbilityScores = {\n            #Default Values\n            \"STR\": Scores[0],\n            \"DEX\": Scores[1],\n            \"CON\": Scores[2],\n            \"INT\": Scores[3],\n            \"WIS\": Scores[4],\n            \"CHA\": Scores[5]\n            }\n        for i in self._AbilityScores:\n            #Print to Check Success\n            print(\"Ability:\",i,self._AbilityScores.get(i))\n\n    def setAbilityScore(self, Target = \"Ability\",NewValue = 0):\n    #Set the Target Ability Score to the New Value \n        if Target in self._AbilityScores:\n        #Check Target key in AbilityScores dict. \n            self._AbilityScores[Target] = NewValue\n            print(\"Updated\",Target,\"to\",NewValue)\n        else:\n        #No Matching Key Found\n            print(Target,\"ability not found. No changes made.\")\n\n    def getAbilityBonus(self,Ability = \"Null\") -> int:\n    #Get the Ability Bonus of Target Score\n        if Ability in self._AbilityScores:\n        #Check Target key in AbliityScores dict, return int of: (Value - 10)/2\n            return int((self._AbilityScores.get(Ability) - 10) / 2)\n        else:\n        #No Matching Key Found, return 0 (just so that an int is always returned)\n            print(\"Ability\",Ability,\"Not Found\")\n            return 0\n\nclass CH_Class:\n#Character Class handles Character's Class(es) and Levels\n#Can Return Proficiency Bonus\n    def __init__(self,ClassName = \"Name\",Level = 0):\n        if ClassName == \"Name\":\n            self._CharacterClasses = dict()\n        else:\n            self._CharacterClasses = {ClassName: Level}\n        print(self._CharacterClasses)\n        \n    def addClass(self,ClassName = \"Name\",Level = 0):\n    #Add a new Character Class and Level\n        if ClassName == \"Name\":\n            print(\"No ClassName Given\")\n        else:\n            newclass = {ClassName: Level}\n            self._CharacterClasses.update(newclass)\n            print(self._CharacterClasses)\n    \n    def updateLevel(self,ClassName = \"Name\", NewLevel = 0):\n    #Change the level of an existing class\n        if ClassName == \"Name\":\n            print(\"No ClassName Given\")\n        elif ClassName in self._CharacterClasses:\n            self._CharacterClasses[ClassName] = NewLevel\n        else:\n            print(\"Class\",ClassName,\"not found.\")\n    \n    def getCharacterLevel(self) -> int:\n    #Return total of all Character Class Levels\n        Total = 0\n        for i in self._CharacterClasses:\n            Total = Total + int(self._CharacterClasses.get(i))\n        return Total\n    \n    def getProficiencyBonus(self) -> int:\n    #Return Proficiency Bonus (1+ 1/4 current level)\n        return math.ceil(1 + (self.getCharacterLevel() / 4))\n\nclass CH_Bonus:\n    def __init__(self,Trigger=\"Name\",Source=\"SourceName\",Bonus=0,Notes = \"\"):\n        self.Trigger = Trigger\n        self.Source = Source\n        self.Bonus = Bonus\n        self.Notes = Notes\n    def returnbonus(self) -> dict:\n        d = dict()\n        d.update({\"Source\":self.Source})\n        d.update({\"Trigger\":self.Trigger})\n        d.update({\"Bonus\":self.Bonus})\n        d.update({\"Notes\":self.Notes})\n        return d\n\n# Character Item\nclass CH_Item:\n#Handles Items a Character May get. For triggered bonuses, add a Bonus!\n    def __init__(self, Name=\"ItemName\", Description=\"Item Decription Here\"):\n        self.Name = dict({Name:Description})\n        self.Bonuses = set()\n\n    def addBonus(self, Trigger=\"SkillName\", Bonus=0):\n    #Add Bonus to Bonuses set\n        s = CH_Bonus(Trigger,self.Name,Bonus)\n        self.Bonuses.add(s)\n\n    def getBonus(self, Trigger):\n    #If Trigger key exist in Bonuses dict, return Bonus (int)\n        for i in self.Bonuses:\n            b = i.returnbonus()\n            t= b.get(\"Trigger\")\n            if Trigger == t:\n                return b\n    \nclass CH_Inventory:\n    def __init__(self):\n        self.Items = []\n    \n    def addItem(self,Name=\"\",Description=\"\"):\n        i = CH_Item(Name,Description)\n        self.Items.append(i)\n    \n    def addBonus(self,ItemName,Trigger,Bonus):\n        for i in self.Items:\n            if ItemName in i.Name:\n                i.addBonus(Trigger,Bonus)\n    \n    def getBonus(self,Trigger):\n        for i in self.Items:\n            for b in i.Bonuses:\n                l = b.returnbonus()\n                if l.get(\"Trigger\") == Trigger:\n                    return l\n    \nt = CH_Inventory\nt.addItem(\"Boots\",\"With the fur\")\nt.addItem(\"Helmet\",\"On yer head\")\nt.addBonus(\"Boots\",\"Attack\",1)\nt.addBonus(\"Helmet\",\"Attack\",5)\nprint(t.getbonus(\"Attack\"))\n\n            \n\nclass CH_Trait:\n#Handle Triggerable character traits (like bonus actions on attack). Add bonuses to dict to get triggers and whatever!\n    def __init__(self, TraitName=\"TraitName\", Description=\"Trait Decription Here\"):\n        self.Name = TraitName\n        self.Description = Description\n        self.Bonuses = set()\n    \n    def addBonus(self, Trigger=\"SkillName\", Bonus=0):\n    #Add Bonus to Bonuses set\n        s = CH_Bonus(Trigger,self.Name,Bonus)\n        self.Bonuses.add(s)\n\n    def getBonus(self, Trigger):\n    #If Trigger key exist in Bonuses dict, return Bonus (int)\n        for i in self.Bonuses:\n            b = i.returnbonus()\n            t= b.get(\"Trigger\")\n            if Trigger == t:\n                return b\n\n    def delBonus(self, Trigger):\n    #If Trigger key exist in Bonuses dict, remove Trigger + Bonus\n        if Trigger in self.Bonuses:\n            self.Bonuses.pop(Trigger)\n\n\n","sub_path":"CH_Blocks.py","file_name":"CH_Blocks.py","file_ext":"py","file_size_in_byte":5782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"437505329","text":"#!/usr/bin/env python3\nimport os\nimport sqlite3\nimport requests\n\nDB_PATH = os.environ.get(\"DB_PATH\", \"/var/www/ghost-search.db\")\nDOMAIN = os.environ.get(\"DOMAIN\", \"blakerain.com\")\nAPI_KEY = os.environ.get(\"API_KEY\")\nURL_BASE = f\"https://{DOMAIN}/ghost/api/v2/content/posts\"\n\nconn = sqlite3.connect(DB_PATH)\nconn.execute(\"\"\"\nCREATE VIRTUAL TABLE IF NOT EXISTS posts USING fts3(\n  post_id TEXT,\n  url TEXT,\n  date TEXT,\n  image TEXT,\n  title TEXT,\n  excerpt TEXT,\n  html TEXT\n);\n\"\"\")\nconn.commit()\n\nPARAMS = {\n    \"key\": API_KEY,\n    \"fields\": \"id,title,custom_excerpt,html,url,feature_image,published_at\",\n    \"page\": 1\n}\n\npage = 1\nwhile True:\n    print(f\"Fetching page {page} from Ghost content API\")\n    r = requests.get(url=URL_BASE, params=PARAMS)\n    data = r.json()\n    c = conn.cursor()\n    for post in data[\"posts\"]:\n        c.execute(\"DELETE FROM posts WHERE post_id = ?\", (post[\"id\"], ))\n        c.execute(\"INSERT INTO posts(post_id, url, date, image, title, excerpt, html) VALUES(?, ?, ?, ?, ?, ?, ?)\",\n                  (post[\"id\"], post[\"url\"], post[\"published_at\"], post[\"feature_image\"], post[\"title\"], post[\"custom_excerpt\"], post[\"html\"]))\n    if data[\"meta\"][\"pagination\"][\"pages\"] <= page:\n        print(f\"This is the last page\")\n        break\nconn.commit()\n","sub_path":"populate-db.py","file_name":"populate-db.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"226726956","text":"import datetime\nfrom dateutil import parser\n\nimport mockresponse\n\n\nPLOT_KINDS = (\n    'borehole',\n    'doas',\n    'edm',\n    'energy',\n    'gps',\n    'magnitude',\n    'platform',\n    'rsam',\n    'seismicity',\n    'thermal',\n)\n\ndef is_valid(*args, **kwargs):\n    try:\n        parser.parse(*args, **kwargs)\n        return True\n    except (ValueError, TypeError):\n        return False\n\n\ndef parse_baseline(baseline):\n    try:\n        stations = baseline.split('-')\n        if len(stations) != 2:\n            return None\n        for station in stations:\n            if not station:\n                return None\n        return stations\n    except AttributeError:\n        return None\n\n\ndef validate_kind(requested_data):\n    msg = {\n        \"message\": \"success\"\n    }\n\n    error_msg = {\n        \"error\": {\n            \"message\": \"\"\n        }\n    }\n\n    kind = requested_data.get('kind')\n    if kind is None:\n        error_msg['error']['message'] = \"Missing field name kind\"\n        return False, error_msg\n\n    if not kind:\n        error_msg['error']['message'] = \"Plot type must not be empty\"\n        return False, error_msg\n\n    if kind not in PLOT_KINDS:\n        error_msg['error']['message'] = \"Unrecognized plot type %s\" % kind\n        return False, error_msg\n\n    return True, msg\n\n\ndef validate(input_field, required_field):\n    msg = {\n        \"message\": \"success\"\n    }\n\n    error_msg = {\n        \"error\": {\n            \"message\": \"\"\n        }\n    }\n\n    # Check if any missing field\n    for req_field in required_field:\n        if req_field not in input_field:\n            error_msg['error']['message'] = \"Missing field name %s\" % req_field\n            return False, error_msg\n\n    kind = input_field.get('kind')\n    starttime = input_field.get('starttime')\n    endtime = input_field.get('endtime')\n    reflector = input_field.get('reflector')\n    event_type = input_field.get('event_type')\n    station = input_field.get('station')\n    baseline = input_field.get('baseline')\n\n    if not starttime:\n        error_msg['error']['message'] = \"starttime must not be empty\"\n        return False, error_msg\n\n    if not endtime:\n        error_msg['error']['message'] = \"endtime must not be empty\"\n        return False, error_msg\n\n    # Check if starttime is in ISO 8601 format\n    if not is_valid(starttime):\n        error_msg['error']['message'] = \"%s must be in ISO 8601 format\" % starttime\n        return False, error_msg\n\n    # Check if endtime is in ISO 8601 format\n    if not is_valid(endtime):\n        error_msg['error']['message'] = \"%s must be in ISO 8601 format\" % endtime\n        return False, error_msg\n\n    # Check if field value is properly defined\n    if kind == 'edm':\n        if not reflector:\n            error_msg['error']['message'] = \"reflector must not be empty\"\n            return False, error_msg\n\n        if reflector not in mockresponse.config.edm.edm_reflectors:\n            error_msg['error']['message'] = \"Unrecognized reflector value %s\" % reflector\n            return False, error_msg\n\n    elif kind == 'seismicity' or kind == 'magnitude':\n        if not event_type:\n            error_msg['error']['message'] = \"event_type must not be empty\"\n            return False, error_msg\n\n        if event_type not in mockresponse.config.seismicity.seismic_event_types:\n            error_msg['error']['message'] = \"Unrecognized seismic event type %s\" % event_type\n            return False, error_msg\n\n    elif kind == 'rsam':\n        if not station:\n            error_msg['error']['message'] = \"station must not be empty\"\n            return False, error_msg\n\n        if station not in mockresponse.config.rsam.rsam_stations:\n            error_msg['error']['message'] = \"Unrecognized RSAM station %s\" % station\n            return False, error_msg\n\n    elif kind == 'platform':\n        if not station:\n            error_msg['error']['message'] = \"station must not be empty\"\n            return False, error_msg\n\n        if station not in mockresponse.config.platform.platform_stations:\n            error_msg['error']['message'] = \"Unrecognized tiltmeter platform station %s\" % station\n            return False, error_msg\n\n    elif kind == 'borehole':\n        if not station:\n            error_msg['error']['message'] = \"station must not be empty\"\n            return False, error_msg\n\n        if station not in mockresponse.config.borehole.borehole_stations:\n            error_msg['error']['message'] = \"Unrecognized tiltmeter borehole station %s\" % station\n            return False, error_msg\n\n    elif kind == 'gps':\n        gps_stations = parse_baseline(baseline)\n        if gps_stations is None:\n            error_msg['error']['message'] = \"Invalid GPS baseline\"\n            return False, error_msg\n\n        for gps_station in gps_stations:\n            if not gps_station:\n                error_msg['error']['message'] = \"GPS station must not be empty\"\n                return False, error_msg\n\n            if gps_station not in mockresponse.config.gps.gps_stations:\n                error_msg['error']['message'] = \"Unrecognized GPS station %s\" % gps_station\n                return False, error_msg\n\n    return True, msg\n\n\ndef get_content_or_error(requested_data):\n    status, msg = validate_kind(requested_data)\n    if not status:\n        return msg\n\n    kind = requested_data.get('kind')\n    starttime = requested_data.get('starttime')\n    endtime = requested_data.get('endtime')\n    reflector = requested_data.get('reflector')\n    event_type = requested_data.get('event_type')\n    station = requested_data.get('station')\n    baseline = requested_data.get('baseline')\n\n    content = {}\n    if kind == 'doas':\n        status, msg = validate(requested_data, ('kind', 'starttime', 'endtime'))\n        if status:\n            content = mockresponse.get_response(kind, starttime, endtime)\n        else:\n            content = msg\n\n    elif kind == 'edm':\n        status, msg = validate(requested_data, ('kind', 'starttime', 'endtime', 'reflector'))\n        if status:\n            content = mockresponse.get_response(kind, starttime, endtime, reflector)\n        else:\n            content = msg\n\n    elif kind == 'energy':\n        status, msg = validate(requested_data, ('kind', 'starttime', 'endtime'))\n        if status:\n            content = mockresponse.get_response(kind, starttime, endtime)\n        else:\n            content = msg\n\n    elif kind == 'gps':\n        status, msg = validate(requested_data, ('kind', 'starttime', 'endtime', 'baseline'))\n        if status:\n            content = mockresponse.get_response(kind, starttime, endtime, baseline)\n        else:\n            content = msg\n        \n    elif kind == 'magnitude':\n        status, msg = validate(requested_data, ('kind', 'starttime', 'endtime', 'event_type'))\n        if status:\n            content = mockresponse.get_response(kind, starttime, endtime, event_type)\n        else:\n            content = msg\n\n    elif kind == 'rsam':\n        status, msg = validate(requested_data, ('kind', 'starttime', 'endtime', 'station'))\n        if status:\n            content = mockresponse.get_response(kind, starttime, endtime, station)\n        else:\n            content = msg\n\n    elif kind == 'seismicity':\n        status, msg = validate(requested_data, ('kind', 'starttime', 'endtime', 'event_type'))\n        if status:\n            content = mockresponse.get_response(kind, starttime, endtime, event_type)\n        else:\n            content = msg\n\n    elif kind == 'platform':\n        status, msg = validate(requested_data, ('kind', 'starttime', 'endtime', 'station'))\n        if status:\n            content = mockresponse.get_response(kind, starttime, endtime, station)\n        else:\n            content = msg\n\n    elif kind == 'borehole':\n        status, msg = validate(requested_data, ('kind', 'starttime', 'endtime', 'station'))\n        if status:\n            content = mockresponse.get_response(kind, starttime, endtime, station)\n        else:\n            content = msg\n\n    return content\n","sub_path":"mockserver/mockserver/mockapp/contents.py","file_name":"contents.py","file_ext":"py","file_size_in_byte":7965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"335688538","text":"import os\nimport sys\nimport clr\n\nsys.path.append(\"..\\\\B2R2\")\nclr.AddReference(\"MyUtils\")\nfrom MyUtils import *\n\nimport Utils\nfrom string import printable\nfrom unicorn import *\n\n\ndef hook_mem_invalid(uc, type, address, size, value, user_data):\n    if type == UC_MEM_READ_UNMAPPED:\n        print(\">>> Missing memory is being READ at 0x%x, data size = %u, data value = 0x%x\\n\" % (address, size, value))\n    return False\n\n\nclass Emulator:\n    def __init__(self, fileName):\n        self.binary = Common.Binary(fileName)\n        self.uc = None\n        self.assembler = None\n        self.disassmbler = None\n\n    def readData(self, address, number, size):\n        data = self.uc.mem_read(address, number * size)\n        return Utils.d2n(data, size)\n\n    def readString(self, address):\n        data = self.uc.mem_read(address, 1)[0]\n        address += 1\n        string = ''\n        while chr(data) in printable:\n            string += chr(data)\n            data = self.uc.mem_read(address, 1)[0]\n            address += 1\n            if data == 0:\n                break\n        return string\n\n    def writeData(self, address, data, size):\n        if type(data) != list:\n            data = [data]\n        for value in data:\n            data = Utils.n2d(value, size)\n            self.uc.mem_write(address, data)\n            address += size\n\n    def writeNumber(self, address, number, size):\n        data = Utils.n2d(number, size)\n        self.uc.mem_write(address, data)\n\n    def writeString(self, address, string):\n        self.writeData(address, string, 1)\n\n    def addHook(self, hookFunc):\n        self.uc.hook_add(UC_HOOK_CODE, hookFunc, user_data=self)\n\n    def getMaxAddress(self):\n        end = 0\n        for func in self.binary.Functions:\n            if end < func.getMaxAddr():\n                end = func.getMaxAddr()\n        return end\n\n    def initBeforeRun(self):\n        self.uc.hook_add(UC_HOOK_MEM_READ_UNMAPPED | UC_HOOK_MEM_WRITE_UNMAPPED, hook_mem_invalid)\n\n    def getInstrAt(self, address, size=16):\n        data = bytes(self.readData(address, size, 1))\n        instr = list(self.disassmbler.disasm(data, address))[0]\n        return \"0x%x: %s %s\" % (instr.address, instr.mnemonic, instr.op_str)\n","sub_path":"Unicorn/Emulator.py","file_name":"Emulator.py","file_ext":"py","file_size_in_byte":2202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"110027655","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport pandas as pd\nimport pandas_datareader.data as web\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom IPython.display import display\nimport datetime\nimport requests_cache\nfrom datetime import timedelta\nimport datetime\n\n\n# In[2]:\n\n\n# #загружаю данные с финама\n# s = pd.read_csv('AFLT_170605_210605.csv')\n# stock = s.iloc[:, 2:]\n# stock.rename({'<DATE>': 'date','<OPEN>': 'open', '<HIGH>': 'high', '<LOW>': 'low', '<CLOSE>': 'close',\n#           '<VOL>': 'vol'}, axis=1, inplace=True)\n# l=[]\n# #меняю даты на нужный формат\n# for i in range(len(stock.date)):\n#     p=str(stock.date[i])\n#     oldformat = p\n#     datetimeobject = datetime.strptime(oldformat,'%Y%m%d')\n#     q=datetimeobject.strftime('%Y-%m-%d')\n#     l.append(q)\n# ind=pd.DatetimeIndex(l)\n# stock.index = ind\n# df=stock\n# df=df.drop(df.columns[[0, 1]], axis=1)\n# df #все данные с акции\n\nstock_name = 'TSLA'\n\nstart = datetime.datetime(2017, 6, 1)\nend = datetime.datetime(2020, 12, 1)\n\ntry:\n    expire_after = datetime.timedelta(days=3)\n    session = requests_cache.CachedSession(cache_name='cache', backend='sqlite', expire_after=expire_after)\n    data_exchange =  web.DataReader(stock_name, 'yahoo', start, end, session=session)  \nexcept: \n    print('Этих акции на такие даты нет')\n    \ndata_exchange = data_exchange.drop(columns = 'Adj Close')\ndata_exchange.columns = ['open','high','low','close','vol']\ndf = data_exchange\ndf\n\n\n# In[3]:\n\n\ndef pivot_tikers(df,col):\n    item=[]\n    tikers=[]\n    data=df[col]\n    item.append(data)\n    combine=pd.concat(item, axis=1) \n    combine.columns=[stock_name]\n    return combine  \n\n\n# In[4]:\n\n\n#беру открытие\ndf_open = pivot_tikers(df,'open')\nma_200 = df_open.rolling(45).mean() #пробовала 150 и 200 на двух файлах\nma_50 = df_open.rolling(15).mean()\nresult=pd.concat([df_open,ma_200,ma_50],axis=1)\nresult.columns=[stock_name,'ma_200','ma_50']\nresult #таблица со скользящими и ценами акции по датам\n\n\n# In[5]:\n\n\n#заполняю NaN разными числами, чтобы при сравнении строчки не совпадали\nrrr = result.copy()\nrrr['ma_200'] = rrr['ma_200'].fillna(7)\nrrr['ma_50'] = rrr['ma_50'].fillna(9)\nrrr \n\n\n# In[6]:\n\n\n#ищу пересечение\nidx = np.argwhere(np.diff(np.sign(rrr[\"ma_200\"] - rrr[\"ma_50\"]))).flatten()\nif rrr.iloc[idx[0]][1]==7 or rrr.iloc[idx[0]][2]==9:\n    lk = idx[1:]\nelse:\n    lk = idx\nlk1 = lk+1 #чтобы покупать и продавать акции на следующий день после пересечения\n#разделяем даты на покупку и продажу\nlsa = []\nfor i in range(0, len(lk), 2):\n    o = lk[i]\n    lsa.append(o)\nlsd= []\nfor i in range(0, len(lk)):\n    if i % 2 != 0:\n        z =lk[i]\n        lsd.append(z)\n\n        \n        \nplt.figure(figsize=(15, 5), dpi=80)\nplt.plot(result.index[lsa], result.loc[result.index[lsa]]['ma_200'],'v', markersize=10, color='k')\nplt.plot(result.index[lsd], result.loc[result.index[lsd]]['ma_200'],'v', markersize=10, color='m')\nplt.plot(result.index, result.values)\nplt.xlabel('Дата')\nplt.ylabel('Цена')\nplt.title('Динамика цены акции')\nplt.legend([\"buy\", \"sell\", stock_name, \"ma_200\", \"ma_50\"])\nplt.show()\n\n\n# In[7]:\n\n\ndf_next = pd.DataFrame(df_open[stock_name][result.index[lk1]]) #датафрейм\nnextt = df_open[stock_name][result.index[lk1]] #Series\nnextt\n\n\n# In[8]:\n\n\n#работа со стоп-лоссом\nstoploss = 0.1\nseriestake = df_open[stock_name][result.index[:]] \nseriq = pd.Series(nextt.iloc[[0]])\nif len(nextt) % 2 ==0:\n    for j in range(len(lk1)):\n        if j % 2 ==0:\n            for i in range(len(seriestake)-1):\n                if (i > lk1[j] and df_open[stock_name][result.index[i]] < df_open[stock_name][result.index[lk1[j]]]*(1-stoploss) and i<lk1[j+1] and seriestake.iloc[[i]][0]>nextt[j+1]):\n                    seriq = seriq.append(nextt.iloc[[j]])\n                    seriq = seriq.append(seriestake.iloc[[i]])\n                    break\n                elif i == lk1[j+1]:\n                    seriq = seriq.append(nextt.iloc[[j]])\n                    seriq = seriq.append(seriestake.iloc[[lk1[j+1]]])\nelse:\n    for j in range(len(lk1)):\n        if (j % 2 ==0 and j != len(lk1)-1):\n            for i in range(len(seriestake)-1):\n                if (i > lk1[j] and df_open[stock_name][result.index[i]] < df_open[stock_name][result.index[lk1[j]]]*(1-stoploss) and i<lk1[j+1] and seriestake.iloc[[i]][0]>nextt[j+1]):\n                    seriq = seriq.append(nextt.iloc[[j]])\n                    seriq = seriq.append(seriestake.iloc[[i]])\n                    break\n                elif i == lk1[j+1]:\n                    seriq = seriq.append(nextt.iloc[[j]])\n                    seriq = seriq.append(seriestake.iloc[[lk1[j+1]]])\n        elif j == len(lk1)-1:\n            for i in range(len(seriestake)-1):\n                if (i > lk1[j] and df_open[stock_name][result.index[i]] < df_open[stock_name][result.index[lk1[j]]]*(1-stoploss)):\n                    seriq = seriq.append(nextt.iloc[[j]])\n                    seriq = seriq.append(seriestake.iloc[[i]])\n                    break  \n                elif i == (len(seriestake)-2):\n                    seriq = seriq.append(nextt.iloc[[j]])\n                    seriq = seriq.append(seriestake.iloc[[len(seriestake)-2]])\nseriq      \nendd = seriq.iloc[1:]\ndf_end = pd.DataFrame(endd)\nendd #получили новую талицу для дней покупки и продажи - учитывая стоп-ло��сы\n\n\n# In[9]:\n\n\n#считаем доходность с покупки и последующей продажи каждый раз\nlissq = []\nfor i in range(len(endd)-1):\n    lllq=(endd[i+1]-endd[i])/ endd[i]*100\n    lissq.append(lllq)\nfor i in range(len(lissq)):\n    if i % 2 !=0:\n        lissq[i] = lissq[i]*0\nlissq\nlissq.insert(0,0)\nlissq #массив с доходностью\n\n\n# In[10]:\n\n\n#итоговая таблица \ndf_end['Buy or Sell'] = ['Buy', 'Sell']*int(len(endd)/2)\ndf_end['Profit_pc'] = lissq\ndf_end\n\n\n# In[11]:\n\n\n#Вычисляем прибыль в денежных едеиницах, если каждую акцию покупаем на все деньги\nprofit_itog=0\nfor i in range(len(df_end)):\n    if i % 2==0:\n        get = df_end[stock_name][i+1] - df_end[stock_name][i] \n        profit_itog+=get\nprofit_itog\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"Example.py","file_name":"Example.py","file_ext":"py","file_size_in_byte":6574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"516537693","text":"import os\nimport logging\nimport cv2\n\n\ndef run_command(cmd, verbose=False):\n    \"\"\"\n    run commands , Linux only\n    :param cmd:\n    :return: string output if success otherwise False\n    \"\"\"\n    import subprocess\n    try:\n        output = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True).communicate()[0].decode(\"ascii\")\n        if verbose:\n            print(output)\n        return output\n    except Exception as e:\n        print(e)\n        return False\n\n\n# ====================================== gpu utilities =========================================================\ndef list_available_gpus(verbose=False):\n    \"\"\"\n    list available gpus and return id list by running cmd \"nvidia-smi\" in Linux\n    :param verbose:\n    :return:\n    \"\"\"\n    import re\n    output = run_command(\"nvidia-smi -L\")\n    # lines of the form GPU 0: TITAN X\n    gpu_regex = re.compile(r\"GPU (?P<gpu_id>\\d+):\")\n    result = []\n    for line in output.strip().split(\"\\n\"):\n        m = gpu_regex.match(line)\n        assert m, \"Couldnt parse \" + line\n        result.append(int(m.group(\"gpu_id\")))\n    if verbose:\n        print(result)\n    return result\n\n\ndef gpu_memory_map(verbose=False):\n    import re\n    \"\"\"Returns map of GPU id to memory allocated on that GPU.\n    working on ubuntu 16 with CUDA TOOLKIT10.2\n    \"\"\"\n    output = run_command(\"nvidia-smi\")\n    gpu_output = output[output.find(\"Memory-Usage\"):]\n    # lines of the form\n    # |    0      8734    C   python                                       11705MiB |\n    memory_regex = re.compile(r\"[|]\\s+?(?P<gpu_id>\\d+)\\D+?(?P<pid>\\d+).+[ ](?P<gpu_memory>\\d+)MiB\")\n    result = {gpu_id: 0 for gpu_id in list_available_gpus()}\n    gpu_id = 0\n    for row in gpu_output.split(\"\\n\"):\n        # m = memory_regex.search(row)\n        if '%' in row:\n            gpu_memory = int(row.split('MiB /')[0][-5:])\n            result[gpu_id] += gpu_memory\n            gpu_id += 1\n        # if not m:\n        #     continue\n        # gpu_id = int(m.group(\"gpu_id\"))\n        # gpu_memory = int(m.group(\"gpu_memory\"))\n        # result[gpu_id] += gpu_memory\n    if verbose:\n        print(result)\n    return result\n\n\ndef pick_n_gpu_lowest_memory(n=1):\n    \"\"\"Returns GPU with the least allocated memory\"\"\"\n    memory_gpu_map = [(memory, gpu_id) for (gpu_id, memory) in gpu_memory_map().items()]\n    best_gpu = []\n    for item in sorted(memory_gpu_map)[:n]:\n        if item[0] < 500:  # 500MB\n            best_gpu.append(item[1])\n    if len(best_gpu) == n:\n        return best_gpu\n    else:\n        print('not enough gpus available')\n        exit(0)\n\n\ndef set_gpus_visiable(gpu_num=1, verbose=True):\n    import os\n    best_gpu = pick_n_gpu_lowest_memory(gpu_num)\n    if isinstance(best_gpu, list):\n        assert len(best_gpu) >= gpu_num, 'not enough gpus found'\n        gpu_id_str = str(best_gpu[0])\n        for i in best_gpu[1:]:\n            gpu_id_str += ',{}'.format(i)\n            if (i + 2) >= gpu_num:\n                break\n    else:\n        gpu_id_str = str(best_gpu)\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_id_str\n    if verbose:\n        print('Using GPUs:{}'.format(gpu_id_str))\n    return\n\n\ndef say_hi_to_your_program():\n    \"\"\"\n    python program initialization, run before the main function in your python program\n    1. clean up console, filter warnings\n    :return:\n    \"\"\"\n    import warnings\n    warnings.filterwarnings(\"ignore\")\n    warnings.simplefilter(action='ignore',\n                          category=FutureWarning)  # disable nasty future warning in tensorflow and numpy\n\n\ndef disable_gpu():\n    import os\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = '-1'\n\n\ndef select_n_gpus(num=1, max_load=0.1, max_mem=0.1):\n    import os\n    try:\n        import GPUtil as gputil\n    except:\n        print('import GPUtil error, \"pip install gputil\" and try again')\n    available_gpu_ids = gputil.getAvailable(order='first', limit=num, maxLoad=max_load, maxMemory=max_mem,\n                                            includeNan=False,\n                                            excludeID=[], excludeUUID=[])\n    if len(available_gpu_ids) < num:\n        print('not enough gpus found!')\n        exit(0)\n    else:\n        if num == 0:\n            gpu_str = '-1'\n        else:\n            gpu_str = ''\n            for i in range(num):\n                gpu_str += available_gpu_ids[i]\n        os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_str\n\n\nclass EasyDict(dict):\n    from typing import Any\n    \"\"\"Convenience class that behaves like a dict but allows access with the attribute syntax.\"\"\"\n\n    def __getattr__(self, name: str) -> Any:\n        try:\n            return self[name]\n        except KeyError:\n            raise AttributeError(name)\n\n    def __setattr__(self, name: str, value: Any) -> None:\n        self[name] = value\n\n    def __delattr__(self, name: str) -> None:\n        del self[name]\n\n\n# =============================================others ========================================================\n\n\ndef flat_list(list_input):\n    \"\"\"Flatten nested list\"\"\"\n    list_flatten = []\n    for item in list_input:\n        if isinstance(item, list):\n            list_flatten += flat_list(item)\n        else:\n            list_flatten += [item]\n    return list_flatten\n\n\n# ============================================ file relative ===============================================\n# TODO: 异步模式\ndef video_reader(video_dir, loop=False, *args, **kwargs):\n    \"\"\"video reader generator, each run yields a img of RGB format\n    acapture is recommended over cv2, which yields a better streaming performance. however, if you don't bother to install it,\n     just stick with OpenCV.\n    \"\"\"\n    try:\n        import acapture\n    except ImportError:\n        pass\n    import cv2\n    try:\n        cap = acapture.open(video_dir, loop=loop)  # RGB\n        cvt_format = 'RGB'\n    except Exception as e:\n        cap = cv2.VideoCapture(video_dir)  # BGR\n        cvt_format = 'BGR'\n    error_count = 0\n    max_error_num = 5\n    while True:\n        ret, frame = cap.read()\n        if ret:\n            if cvt_format == 'BGR':\n                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n            yield frame\n        else:\n            error_count += 1\n            if error_count > max_error_num:\n                cap.release()\n                return\n            if loop and (type(video_dir) is str):\n                del cap\n                cap = cv2.VideoCapture(video_dir)\n                cap.set(cv2.CAP_PROP_FPS, 30)\n            else:\n                cap.release()\n                return\n\n\nclass VideoWriter:\n    \"\"\"\n    usage:\n    with VideoWriter('test.mp4') as w:\n        img = None # BRG\n        w.write_frame(img, verbose=False)\n    \"\"\"\n\n    def __init__(self, save_name, fps=30, *args, **kwargs):\n        self.save_name = save_name\n        self.fps = fps\n        self._out_size = None\n        self._video_writer = None\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        try:\n            self._video_writer.release()\n        finally:\n            pass\n\n    def __del__(self):\n        try:\n            self._video_writer.release()\n        finally:\n            pass\n\n    def reset(self):\n        self._video_writer.release()\n        self._video_writer = None\n\n    def _video_writer_init(self):\n        fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n        self._video_writer = cv2.VideoWriter(self.save_name, fourcc, self.fps, self._out_size)\n\n    def write_frame(self, image, verbose=False):\n        try:\n            if not self._video_writer:\n                self._out_size = (image.shape[1], image.shape[0])\n                self._video_writer_init()\n            assert (image.shape[0] == self._out_size[1]) & (\n                    image.shape[1] == self._out_size[0]), 'image shape not compilable with video saver shape'\n            self._video_writer.write(image)\n            if verbose:\n                cv2.namedWindow(\"video_writer\", cv2.WINDOW_NORMAL)\n                cv2.imshow('video_writer', image)\n                cv2.waitKey(1)\n        except Exception as e:\n            logging.error('write frame error:{}').format(e)\n            return False\n        return True\n\n    def release(self):\n        self._video_writer.release()\n\n\n# TODO: threading to save img to disk\nclass ImgSaver:\n    pass\n\n\ndef get_latest_file(folder):\n    \"\"\"get latest created file, helpful when finding your latest model checkpoint\n    \"\"\"\n    import os\n    file_list = os.listdir(folder)\n    latest_file = max(file_list, key=os.path.getctime)\n    latest_file = os.path.join(folder, latest_file)\n    return latest_file\n\n\n# TODO: more efficient implementation\ndef get_file_recursively(folder_dir):\n    \"\"\"\n    iteratively get file list under folder_dir\n    :param folder_dir: folder\n    :return: a list of files\n    \"\"\"\n    file_list = []\n    for root, dirs, files in os.walk(folder_dir, topdown=False):\n        for name in files:\n            sub_dir = os.path.join(root, name)\n            if os.path.isfile(sub_dir):\n                file_list.append(sub_dir)\n        for name in dirs:\n            sub_dir = os.path.join(root, name)\n            if os.path.isfile(sub_dir):\n                file_list.append(sub_dir)\n    return file_list\n\n\ndef read_textlines(txt_file):\n    with open(txt_file) as f:\n        provided_id_list = f.readlines()\n    val_list = [item.strip('\\n') for item in provided_id_list]\n    return val_list\n","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":9372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"206491490","text":"\"\"\"Module containing meta train & meta test methods.\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\nimport torch\nimport torch.optim as optim\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport higher\nfrom meta.simclr import simclr\n\n\nclass Meta(nn.Module):\n    \"\"\"Object containing slow weights and methods to meta train and meta test.\n    \"\"\"\n\n    def __init__(self, net, lamb=1, eps=1):\n        \"\"\"\n        Initialise meta model.\n\n        Args:\n            net (nn.Module) : meta model's slow weights.\n            lamb (float) : weight given to auto supervision loss during\n                           training.\n        \"\"\"\n        super(Meta, self).__init__()\n        self.net = net\n        self.lamb = lamb\n        self.eps = eps\n\n    def train(self, task_batch, inner_lr, n_inner_loop):\n        \"\"\"Meta train meta model. The meta model solves every tasks in\n        task_batch, then slow weights are updated according to meta loss.\n\n        Args:\n            task_batch (list) : list of tasks to solve.\n            inner_lr (float) : learning rate to use in inner optimization.\n            n_inner_loop (float) : number of inner loops.\n        Returns:\n            (float, float) : average classification & auto supervision accuracy\n                             of trained models on query images.\n        \"\"\"\n\n        self.net.train()\n\n        inner_opt = optim.Adam(self.net.parameters(), lr=inner_lr)\n\n        qry_accs = []\n        simclr_accs_src = []\n        simclr_accs_tgt = []\n\n        for task in task_batch:\n            x_spt, x_qry, y_spt, y_qry = task\n            x_spt, y_spt = x_spt.cuda(), y_spt.cuda()\n            x_qry, y_qry = x_qry.cuda(), y_qry.cuda()\n\n            with higher.innerloop_ctx(self.net, inner_opt, copy_initial_weights=False) as (fnet, diffopt):\n                super_meta_loss = 0\n                super_acc_src = 0\n                super_acc_tgt = 0\n                for _ in range(n_inner_loop):\n                    spt_logits, _ = fnet(x_spt)\n                    super_loss_src, acc_src = simclr(x_spt, fnet, self.eps)\n                    super_loss_tgt, acc_tgt = simclr(x_qry, fnet, self.eps)\n                    super_meta_loss += (1/n_inner_loop)*(super_loss_src+super_loss_tgt)\n                    super_acc_src += acc_src/n_inner_loop\n                    super_acc_tgt += acc_tgt/n_inner_loop\n                    clsf_loss = F.cross_entropy(spt_logits, y_spt)\n                    diffopt.step(clsf_loss)\n\n                qry_logits, _ = fnet(x_qry)\n                clsf_loss = F.cross_entropy(qry_logits, y_qry)\n                qry_loss = clsf_loss + self.lamb*super_meta_loss\n                qry_loss.backward()\n\n                qry_logits, _ = fnet(x_qry)\n                qry_logits = qry_logits.detach()\n                qry_acc = (qry_logits.argmax(dim=1) ==\n                           y_qry).sum().item()/y_qry.size(0)\n                qry_accs.append(qry_acc)\n                simclr_accs_src.append(super_acc_src)\n                simclr_accs_tgt.append(super_acc_tgt)\n\n        mean_qry_acc = np.average(qry_accs)\n        mean_simclr_acc_src = np.average(simclr_accs_src)\n        mean_simclr_acc_tgt = np.average(simclr_accs_tgt)\n        return mean_qry_acc, mean_simclr_acc_src, mean_simclr_acc_tgt\n\n    def test(self, task_batch, inner_lr, n_inner_loop):\n        \"\"\"Meta test meta model. The meta model solves every tasks in\n        task_batch.\n\n        Args:\n            task_batch (list) : list of tasks to solve.\n            inner_lr (float) : learning rate to use in inner optimization.\n            n_inner_loop (float) : number of inner loops.\n        Returns:\n            float : average classification accuracy of trained models on query\n                    images.\n        \"\"\"\n        self.net.train()\n\n        inner_opt = optim.Adam(self.net.parameters(), lr=inner_lr)\n\n        qry_accs = []\n        matrices = []\n\n        for task in task_batch:\n            x_spt, x_qry, y_spt, y_qry = task\n            x_spt, y_spt = x_spt.cuda(), y_spt.cuda()\n            x_qry, y_qry = x_qry.cuda(), y_qry.cuda()\n\n            with higher.innerloop_ctx(self.net, inner_opt, track_higher_grads=False) as (fnet, diffopt):\n                for _ in range(n_inner_loop):\n                    spt_logits, _ = fnet(x_spt)\n                    clsf_loss = F.cross_entropy(spt_logits, y_spt)\n                    diffopt.step(clsf_loss)\n\n                qry_logits = fnet(x_qry)[0].detach()\n                qry_acc = (qry_logits.argmax(dim=1) ==\n                           y_qry).sum().item()/y_qry.size(0)\n                matrix = confusion_matrix(\n                    y_qry.cpu(), qry_logits.cpu().argmax(dim=1))\n                qry_accs.append(qry_acc)\n                matrices.append(matrix)\n\n        return np.average(qry_accs)\n","sub_path":"visda/meta/meta.py","file_name":"meta.py","file_ext":"py","file_size_in_byte":4827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"287112366","text":"# Import the stuff\r\nimport tkinter as tk, os\r\nfrom PIL import Image\r\n\r\n\r\n# Set image path to the current directory wherever you are\r\nimageDirname = os.path.dirname(__file__)\r\nimagePath = os.path.join(imageDirname, 'images to change/')\r\n# Set asset path to the current directory wherever you are\r\nassetDirname = os.path.dirname(__file__)\r\nassetPath = os.path.join(imageDirname, 'assets/')\r\n\r\n\r\ndef change(endFormat, preserveAlpha):\r\n    \r\n\r\n    f = []\r\n\r\n    for (dirpath, dirnames, filenames) in os.walk(imagePath):\r\n    \r\n        f.extend(filenames)\r\n  \r\n        j = 0\r\n\r\n        for x in filenames:\r\n\r\n            # Get rid of the last . and whatever is past it\r\n            file = filenames[j]\r\n            splitat = file.rfind('.') + 1\r\n\r\n            r = file[splitat:]\r\n            file = file[:-len(r)]\r\n\r\n            # Opem that image\r\n            im = Image.open(imagePath + filenames[j])\r\n\r\n            if preserveAlpha == True:   \r\n                im.convert('RGBA').save(imagePath + file + endFormat.lower(), endFormat)\r\n\r\n            if preserveAlpha == False:\r\n                im.convert('RGB').save(imagePath + file + endFormat.lower(), endFormat)\r\n\r\n            j += 1\r\n\r\n        break\r\n\r\n\r\n# Make the window and set window name\r\nroot = tk.Tk(className = 'image changer')\r\n# Set background color\r\nroot.configure(bg ='#232323')\r\n# Set window size\r\nroot.geometry('200x100')\r\n# Make unable to be resized\r\nroot.resizable(False, False)\r\n# Set icon\r\nroot.iconphoto(False, tk.PhotoImage(file = assetPath + 'icon.png'))\r\n\r\n# Set the list of file types to transfer between\r\noptionsList = [\r\n    'PNG',\r\n    'JPEG',\r\n    'ICO',\r\n    'WEBP',\r\n    'TIFF',\r\n    'BMP'\r\n]\r\n\r\n# Make StringVar() for the label of the dropdown\r\noptionVariable = tk.StringVar()\r\noptionVariable.set(optionsList[0])\r\n\r\n\r\n# Make labels\r\nlblTo = tk.Label(text = 'to', bg = '#232323', fg = '#E16D00')\r\n\r\n\r\n# Make dropdown for all the different options\r\nopt = tk.OptionMenu(root, optionVariable, *optionsList)\r\nopt.config(bg = '#E16D00', fg = 'BLACK')\r\nopt['highlightthickness']=0\r\nopt['menu'].config(bg='#E16D00', fg = 'BLACK')\r\n\r\n\r\n# Make buttons\r\n#if optionVariable.get() == 'PNG' or optionVariable.get() == 'ICO' or optionVariable.get() == 'WEBP' or optionVariable.get() == 'TIFF' or optionVariable.get() == 'BMP':\r\nbtnConvert = tk.Button(text = 'Convert (preserve alpha)', bg = '#E16D00', fg = 'BLACK', command = lambda: change(optionVariable.get(), True))\r\nbtnConvertNoAlpha = tk.Button(text = \"Convert (don't preserve alpha)\", bg = '#E16D00', fg = 'BLACK', command = lambda: change(optionVariable.get(), False))\r\n\r\n\r\n# Stick all the stuff in the window\r\nlblTo.grid(row = 0, column = 0)\r\nopt.grid(row = 0, column = 1)\r\nbtnConvert.grid(row = 1, column = 1)\r\nbtnConvertNoAlpha.grid(row = 2, column = 1)\r\n\r\n# Run mainloop to actually have the window\r\nroot.mainloop()\r\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"534914725","text":"# Sebastian Raschka 2014-2018\n# mlxtend Machine Learning Library Extensions\n#\n# A function for plotting decision regions of classifiers.\n# Author: Sebastian Raschka <sebastianraschka.com>\n#\n# License: BSD 3 clause\n\nfrom itertools import cycle\nimport matplotlib.pyplot as plt\nimport numpy as np\n#from mlxtend.utils import check_Xy, format_kwarg_dictionaries\nimport warnings\nfrom math import floor\nfrom math import ceil\n\n\ndef check_Xy(X, y, y_int=True):\n\n    # check types\n    if not isinstance(X, np.ndarray):\n        raise ValueError('X must be a NumPy array. Found %s' % type(X))\n    if not isinstance(y, np.ndarray):\n        raise ValueError('y must be a NumPy array. Found %s' % type(y))\n\n    if 'int' not in str(y.dtype):\n        raise ValueError('y must be an integer array. Found %s. '\n                         'Try passing the array as y.astype(np.integer)'\n                         % y.dtype)\n\n    if not ('float' in str(X.dtype) or 'int' in str(X.dtype)):\n        raise ValueError('X must be an integer or float array. Found %s.'\n                         % X.dtype)\n\n    # check dim\n    if len(X.shape) != 2:\n        raise ValueError('X must be a 2D array. Found %s' % str(X.shape))\n    if len(y.shape) > 1:\n        raise ValueError('y must be a 1D array. Found %s' % str(y.shape))\n\n    # check other\n    if y.shape[0] != X.shape[0]:\n        raise ValueError('y and X must contain the same number of samples. '\n                         'Got y: %d, X: %d' % (y.shape[0], X.shape[0]))\n\n\ndef format_kwarg_dictionaries(default_kwargs=None, user_kwargs=None,\n                              protected_keys=None):\n    \"\"\"Function to combine default and user specified kwargs dictionaries\n    Parameters\n    ----------\n    default_kwargs : dict, optional\n        Default kwargs (default is None).\n    user_kwargs : dict, optional\n        User specified kwargs (default is None).\n    protected_keys : array_like, optional\n        Sequence of keys to be removed from the returned dictionary\n        (default is None).\n    Returns\n    -------\n    formatted_kwargs : dict\n        Formatted kwargs dictionary.\n    \"\"\"\n    formatted_kwargs = {}\n    for d in [default_kwargs, user_kwargs]:\n        if not isinstance(d, (dict, type(None))):\n            raise TypeError('d must be of type dict or None, but '\n                            'got {} instead'.format(type(d)))\n        if d is not None:\n            formatted_kwargs.update(d)\n    if protected_keys is not None:\n        for key in protected_keys:\n            formatted_kwargs.pop(key, None)\n\n    return formatted_kwargs\n\n\n\ndef get_feature_range_mask(X, filler_feature_values=None,\n                           filler_feature_ranges=None):\n    \"\"\"\n    Function that constucts a boolean array to get rid of samples\n    in X that are outside the feature range specified by filler_feature_values\n    and filler_feature_ranges\n    \"\"\"\n\n    if not isinstance(X, np.ndarray) or not len(X.shape) == 2:\n        raise ValueError('X must be a 2D array')\n    elif filler_feature_values is None:\n        raise ValueError('filler_feature_values must not be None')\n    elif filler_feature_ranges is None:\n        raise ValueError('filler_feature_ranges must not be None')\n\n    mask = np.ones(X.shape[0], dtype=bool)\n    for feature_idx in filler_feature_ranges:\n        feature_value = filler_feature_values[feature_idx]\n        feature_width = filler_feature_ranges[feature_idx]\n        upp_limit = feature_value + feature_width\n        low_limit = feature_value - feature_width\n        feature_mask = (X[:, feature_idx] > low_limit) & \\\n                       (X[:, feature_idx] < upp_limit)\n        mask = mask & feature_mask\n\n    return mask\n\n\ndef plot_decision_regions(X, y, clf,\n                          feature_index=None,\n                          filler_feature_values=None,\n                          filler_feature_ranges=None,\n                          ax=None,\n                          X_highlight=None,\n                          res=None,\n                          legend=1,\n                          hide_spines=True,\n                          markers='s^oxv<>',\n                          colors=('#1f77b4,#ff7f0e,#3ca02c,#d62728,'\n                                  '#9467bd,#8c564b,#e377c2,'\n                                  '#7f7f7f,#bcbd22,#17becf'),\n                          scatter_kwargs=None,\n                          contourf_kwargs=None,\n                          scatter_highlight_kwargs=None):\n    \"\"\"Plot decision regions of a classifier.\n\n    Please note that this functions assumes that class labels are\n    labeled consecutively, e.g,. 0, 1, 2, 3, 4, and 5. If you have class\n    labels with integer labels > 4, you may want to provide additional colors\n    and/or markers as `colors` and `markers` arguments.\n    See http://matplotlib.org/examples/color/named_colors.html for more\n    information.\n\n    Parameters\n    ----------\n    X : array-like, shape = [n_samples, n_features]\n        Feature Matrix.\n    y : array-like, shape = [n_samples]\n        True class labels.\n    clf : Classifier object.\n        Must have a .predict method.\n    feature_index : array-like (default: (0,) for 1D, (0, 1) otherwise)\n        Feature indices to use for plotting. The first index in\n        `feature_index` will be on the x-axis, the second index will be\n        on the y-axis.\n    filler_feature_values : dict (default: None)\n        Only needed for number features > 2. Dictionary of feature\n        index-value pairs for the features not being plotted.\n    filler_feature_ranges : dict (default: None)\n        Only needed for number features > 2. Dictionary of feature\n        index-value pairs for the features not being plotted. Will use the\n        ranges provided to select training samples for plotting.\n    ax : matplotlib.axes.Axes (default: None)\n        An existing matplotlib Axes. Creates\n        one if ax=None.\n    X_highlight : array-like, shape = [n_samples, n_features] (default: None)\n        An array with data points that are used to highlight samples in `X`.\n    res : float or array-like, shape = (2,) (default: None)\n        This parameter was used to define the grid width,\n        but it has been deprecated in favor of\n        determining the number of points given the figure DPI and size\n        automatically for optimal results and computational efficiency.\n        To increase the resolution, it's is recommended to use to provide\n        a `dpi argument via matplotlib, e.g., `plt.figure(dpi=600)`.\n    hide_spines : bool (default: True)\n        Hide axis spines if True.\n    legend : int (default: 1)\n        Integer to specify the legend location.\n        No legend if legend is 0.\n    markers : str (default: 's^oxv<>')\n        Scatterplot markers.\n    colors : str (default: 'red,blue,limegreen,gray,cyan')\n        Comma separated list of colors.\n    scatter_kwargs : dict (default: None)\n        Keyword arguments for underlying matplotlib scatter function.\n    contourf_kwargs : dict (default: None)\n        Keyword arguments for underlying matplotlib contourf function.\n    scatter_highlight_kwargs : dict (default: None)\n        Keyword arguments for underlying matplotlib scatter function.\n\n    Returns\n    ---------\n    ax : matplotlib.axes.Axes object\n\n    Examples\n    -----------\n    For usage examples, please see\n    http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions/\n\n    \"\"\"\n\n    check_Xy(X, y, y_int=True)  # Validate X and y arrays\n    dim = X.shape[1]\n\n    if ax is None:\n        ax = plt.gca()\n\n    if res is not None:\n        warnings.warn(\"The 'res' parameter has been deprecated.\"\n                      \"To increase the resolution, it's is recommended\"\n                      \"to use to provide a `dpi argument via matplotlib,\"\n                      \"e.g., `plt.figure(dpi=600)`.\",\n                      DeprecationWarning)\n\n    plot_testdata = True\n    if not isinstance(X_highlight, np.ndarray):\n        if X_highlight is not None:\n            raise ValueError('X_highlight must be a NumPy array or None')\n        else:\n            plot_testdata = False\n    elif len(X_highlight.shape) < 2:\n        raise ValueError('X_highlight must be a 2D array')\n\n    if feature_index is not None:\n        # Unpack and validate the feature_index values\n        if dim == 1:\n            raise ValueError(\n                'feature_index requires more than one training feature')\n        try:\n            x_index, y_index = feature_index\n        except ValueError:\n            raise ValueError(\n                'Unable to unpack feature_index. Make sure feature_index '\n                'only has two dimensions.')\n        try:\n            X[:, x_index], X[:, y_index]\n        except IndexError:\n            raise IndexError(\n                'feature_index values out of range. X.shape is {}, but '\n                'feature_index is {}'.format(X.shape, feature_index))\n    else:\n        feature_index = (0, 1)\n        x_index, y_index = feature_index\n\n    # Extra input validation for higher number of training features\n    if dim > 2:\n        if filler_feature_values is None:\n            raise ValueError('Filler values must be provided when '\n                             'X has more than 2 training features.')\n\n        if filler_feature_ranges is not None:\n            if not set(filler_feature_values) == set(filler_feature_ranges):\n                raise ValueError(\n                    'filler_feature_values and filler_feature_ranges must '\n                    'have the same keys')\n\n        # Check that all columns in X are accounted for\n        column_check = np.zeros(dim, dtype=bool)\n        for idx in filler_feature_values:\n            column_check[idx] = True\n        for idx in feature_index:\n            column_check[idx] = True\n        if not all(column_check):\n            missing_cols = np.argwhere(~column_check).flatten()\n            raise ValueError(\n                'Column(s) {} need to be accounted for in either '\n                'feature_index or filler_feature_values'.format(missing_cols))\n\n    marker_gen = cycle(list(markers))\n\n    n_classes = np.unique(y).shape[0]\n    colors = colors.split(',')\n    colors_gen = cycle(colors)\n    colors = [next(colors_gen) for c in range(n_classes)]\n\n    # Get minimum and maximum\n    x_min, x_max = X[:, x_index].min() - 1, X[:, x_index].max() + 1\n    if dim == 1:\n        y_min, y_max = -1, 1\n    else:\n        y_min, y_max = X[:, y_index].min() - 1, X[:, y_index].max() + 1\n\n    xnum, ynum = plt.gcf().dpi * plt.gcf().get_size_inches()\n    xnum, ynum = floor(xnum), ceil(ynum)\n    xx, yy = np.meshgrid(np.linspace(x_min, x_max, num=xnum),\n                         np.linspace(y_min, y_max, num=ynum))\n\n    if dim == 1:\n        X_predict = np.array([xx.ravel()]).T\n    else:\n        X_grid = np.array([xx.ravel(), yy.ravel()]).T\n        X_predict = np.zeros((X_grid.shape[0], dim))\n        X_predict[:, x_index] = X_grid[:, 0]\n        X_predict[:, y_index] = X_grid[:, 1]\n        if dim > 2:\n            for feature_idx in filler_feature_values:\n                X_predict[:, feature_idx] = filler_feature_values[feature_idx]\n    Z = clf.predict(X_predict.astype(X.dtype))\n    Z = Z.reshape(xx.shape)\n    # Plot decisoin region\n    # Make sure contourf_kwargs has backwards compatible defaults\n    contourf_kwargs_default = {'alpha': 0.45, 'antialiased': True}\n    contourf_kwargs = format_kwarg_dictionaries(\n                        default_kwargs=contourf_kwargs_default,\n                        user_kwargs=contourf_kwargs,\n                        protected_keys=['colors', 'levels'])\n    cset = ax.contourf(xx, yy, Z,\n                       colors=colors,\n                       levels=np.arange(Z.max() + 2) - 0.5,\n                       **contourf_kwargs)\n\n    ax.contour(xx, yy, Z, cset.levels,\n               colors='k',\n               linewidths=0.5,\n               antialiased=True)\n\n    ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n\n    # Scatter training data samples\n    # Make sure scatter_kwargs has backwards compatible defaults\n    scatter_kwargs_default = {'alpha': 0.8, 'edgecolor': 'black'}\n    scatter_kwargs = format_kwarg_dictionaries(\n                        default_kwargs=scatter_kwargs_default,\n                        user_kwargs=scatter_kwargs,\n                        protected_keys=['c', 'marker', 'label'])\n    for idx, c in enumerate(np.unique(y)):\n        if dim == 1:\n            y_data = [0 for i in X[y == c]]\n            x_data = X[y == c]\n        elif dim == 2:\n            y_data = X[y == c, y_index]\n            x_data = X[y == c, x_index]\n        elif dim > 2 and filler_feature_ranges is not None:\n            class_mask = y == c\n            feature_range_mask = get_feature_range_mask(\n                            X, filler_feature_values=filler_feature_values,\n                            filler_feature_ranges=filler_feature_ranges)\n            y_data = X[class_mask & feature_range_mask, y_index]\n            x_data = X[class_mask & feature_range_mask, x_index]\n        else:\n            continue\n\n        ax.scatter(x=x_data,\n                   y=y_data,\n                   c=colors[idx],\n                   marker=next(marker_gen),\n                   label=c,\n                   **scatter_kwargs)\n\n    if hide_spines:\n        ax.spines['right'].set_visible(False)\n        ax.spines['top'].set_visible(False)\n        ax.spines['left'].set_visible(False)\n        ax.spines['bottom'].set_visible(False)\n    ax.yaxis.set_ticks_position('left')\n    ax.xaxis.set_ticks_position('bottom')\n    if dim == 1:\n        ax.axes.get_yaxis().set_ticks([])\n\n    if plot_testdata:\n        if dim == 1:\n            x_data = X_highlight\n            y_data = [0 for i in X_highlight]\n        elif dim == 2:\n            x_data = X_highlight[:, x_index]\n            y_data = X_highlight[:, y_index]\n        else:\n            feature_range_mask = get_feature_range_mask(\n                    X_highlight, filler_feature_values=filler_feature_values,\n                    filler_feature_ranges=filler_feature_ranges)\n            y_data = X_highlight[feature_range_mask, y_index]\n            x_data = X_highlight[feature_range_mask, x_index]\n\n        # Make sure scatter_highlight_kwargs backwards compatible defaults\n        scatter_highlight_defaults = {'c': '',\n                                      'edgecolor': 'black',\n                                      'alpha': 1.0,\n                                      'linewidths': 1,\n                                      'marker': 'o',\n                                      's': 80}\n        scatter_highlight_kwargs = format_kwarg_dictionaries(\n                                    default_kwargs=scatter_highlight_defaults,\n                                    user_kwargs=scatter_highlight_kwargs)\n        ax.scatter(x_data,\n                   y_data,\n                   **scatter_highlight_kwargs)\n\n    if legend:\n        if dim > 2 and filler_feature_ranges is None:\n            pass\n        else:\n            handles, labels = ax.get_legend_handles_labels()\n            ax.legend(handles, labels,\n                      framealpha=0.3, scatterpoints=1, loc=legend)\n\n    return ax\n","sub_path":"src/decision_regions.py","file_name":"decision_regions.py","file_ext":"py","file_size_in_byte":15172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"86244707","text":"#!/usr/bin/env python3\n\n# The MIT License (MIT)\n# =====================\n#\n# Copyright © 2020 Azavea\n#\n# Permission is hereby granted, free of charge, to any person\n# obtaining a copy of this software and associated documentation\n# files (the “Software”), to deal in the Software without\n# restriction, including without limitation the rights to use,\n# copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the\n# Software is furnished to do so, subject to the following\n# 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\n# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\n# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\n# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\n# OTHER DEALINGS IN THE SOFTWARE.\n\nimport argparse\nimport copy\nimport json\n\n\ndef cli_parser() -> argparse.ArgumentParser:\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--input-json', required=True, type=str)\n    parser.add_argument('--input-geojson', required=True, type=str)\n    parser.add_argument('--output', required=True, type=str)\n    return parser\n\n\nif __name__ == '__main__':\n    args = cli_parser().parse_args()\n\n    with open(args.input_json, 'r') as f:\n        data_json = json.load(f)\n\n    with open(args.input_geojson, 'r') as f:\n        data_geojson = json.load(f)\n\n    for feature in data_geojson.get('features'):\n        properties = feature.get('properties')\n        index = int(properties.get('DN')) - 1\n        if index >= 0:\n            properties['id'] = copy.copy(\n                data_json.get('selections')[index].get('id'))\n            for (k, v) in copy.copy(data_json.get('selections')[index].get('sceneMetadata')).items():\n                properties[k] = v\n        del properties['DN']\n\n    with open(args.output, 'w') as f:\n        json.dump(data_geojson, f, sort_keys=True,\n                  indent=4, separators=(',', ': '))\n","sub_path":"python/postprocess/final-report.py","file_name":"final-report.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"243204867","text":"import numpy as np\nfrom hunterworld_v2 import HunterWorld\nfrom ple_v5 import PLE\nfrom logutils import *\nimport matplotlib.pyplot as plt\nfrom actionQnet_ import *\nfrom replay_memory import ReplayMemory\n\nroot = logging.getLogger()\nroot.setLevel(logging.DEBUG)\n\nch = logging.StreamHandler(sys.stdout)\nch.setLevel(logging.DEBUG)\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nch.setFormatter(formatter)\nroot.addHandler(ch)\nmx.random.seed(100)\n\n\n# kernprof -l hunterdqn-v3-single/asyn_dqn.py\n# python -m line_profiler asyn_dqn.py.lprof\n# @profile\ndef main():\n    rewards = {\n        \"positive\": 1.0,\n        \"negative\": -1.0,\n        \"tick\": -0.002,\n        \"loss\": -2.0,\n        \"win\": 2.0\n    }\n\n    testing = False\n    training_continue = False\n\n    q_ctx = mx.gpu(0)\n\n    game = HunterWorld(width=256, height=256, num_preys=10,\n                       num_hunters=2, num_toxins=5)\n\n    env = PLE(game, fps=30, force_fps=True, display_screen=False,\n              reward_values=rewards,\n              resized_rows=80, resized_cols=80, num_steps=2)\n\n    freeze_interval = 10000\n    epoch_num = 20\n    steps_per_epoch = 100000\n    update_interval = 5\n    replay_memory_size = 1000000\n    discount = 0.99\n    replay_start_size = 50000\n    history_length = 1\n    eps_start = 1.0\n    eps_min = 0.1\n    epoch_start = 0\n    a1_act_param_file = None\n    a1_com_param_file = None\n    a2_act_param_file = None\n    a2_com_param_file = None\n\n    if training_continue:\n        epoch_start = 0\n\n    if testing:\n        env.force_fps = False\n        env.display_screen = True\n        replay_start_size = 32\n        a1_act_param_file = 'a1_actQnet/network-dqn_mx0004.params'\n        a1_com_param_file = 'a1_comNet/network-dqn_mx0004.params'\n        a2_act_param_file = 'a2_actQnet/network-dqn_mx0004.params'\n        a2_com_param_file = 'a2_comNet/network-dqn_mx0004.params'\n        epoch_start = 19\n        eps_start = 0.1\n        eps_min = 0.1\n\n    if not testing:\n        logging_config(\"log\", \"single-output-custom\")\n\n    replay_memory1 = ReplayMemory(state_dim=(2, 74),\n                                  history_length=history_length,\n                                  memory_size=replay_memory_size,\n                                  replay_start_size=replay_start_size, state_dtype='float32')\n\n    replay_memory2 = ReplayMemory(state_dim=(2, 74),\n                                  history_length=history_length,\n                                  memory_size=replay_memory_size,\n                                  replay_start_size=replay_start_size, state_dtype='float32')\n\n    eps_decay = (eps_start - eps_min) / 1000000\n    eps_curr = eps_start\n    freeze_interval /= update_interval\n    minibatch_size = 32\n    soft_target_tau = 0.001\n    action_set = env.get_action_set()\n    action_map1 = []\n    for action1 in action_set[0].values():\n        action_map1.append(action1)\n\n    action_map2 = []\n    for action2 in action_set[1].values():\n        action_map2.append(action2)\n\n    # action_map1 = np.array(action_map1)\n    # action_map2 = np.array(action_map2)\n\n    action_num = 4\n    signal_num = 10\n    a1_comNet = comNet(signal_num=signal_num, ctx=q_ctx)\n    a1_comTarget = target_comNet(comNet=a1_comNet, ctx=q_ctx)\n    a1_actQnet = actQnet(actions_num=action_num, signal_num=signal_num, ctx=q_ctx)\n    a1_actTarget = target_actQnet(Qnet=a1_actQnet, signal_num=signal_num, ctx=q_ctx)\n\n    # a1_Qnet_grad_dict = dict(zip(\n    #     a1_actQnet.dqn.list_arguments(),\n    #     a1_actQnet.grad_arrays))\n\n    a2_comNet = comNet(signal_num=signal_num, ctx=q_ctx)\n    a2_comTarget = target_comNet(comNet=a2_comNet, ctx=q_ctx)\n    a2_actQnet = actQnet(actions_num=action_num, signal_num=signal_num, ctx=q_ctx)\n    a2_actTarget = target_actQnet(Qnet=a2_actQnet, signal_num=signal_num, ctx=q_ctx)\n\n    # a2_Qnet_grad_dict = dict(zip(\n    #     a2_actQnet.dqn.list_arguments(),\n    #     a2_actQnet.grad_arrays))\n\n    if a1_act_param_file is not None:\n        load_params(a1_actQnet, a1_act_param_file)\n        load_params(a1_comNet, a1_com_param_file)\n        load_params(a2_actQnet, a2_act_param_file)\n        load_params(a2_comNet, a2_com_param_file)\n\n    print_params1(a1_actQnet)\n    print_params1(a1_comNet)\n\n    training_steps = 0\n    total_steps = 0\n    for epoch in range(epoch_start, epoch_num):\n        steps_left = steps_per_epoch\n        episode = 0\n        epoch_reward = 0\n        start = time.time()\n        env.reset_game()\n        while steps_left > 0:\n            episode += 1\n            episode_loss = 0.0\n            episode_q_value = 0.0\n            episode_update_step = 0\n            episode_action_step = 0\n            episode_reward = 0\n            episode_step = 0\n            collisions = 0.0\n            time_episode_start = time.time()\n            env.reset_game()\n            # action_status[:] = 0\n            while not env.game_over():\n                if replay_memory1.size >= history_length and replay_memory1.size > replay_start_size:\n                    do_exploration = (np.random.rand() < eps_curr)\n                    eps_curr = max(eps_curr - eps_decay, eps_min)\n                    if do_exploration:\n                        action1 = np.random.randint(action_num)\n                        # signal1 = (np.random.random((1, signal_num)) - 0.5) * 2\n                        # signal2 = (np.random.random((1, signal_num)) - 0.5) * 2\n                        action2 = np.random.randint(action_num)\n                    else:\n                        current_state1 = replay_memory1.latest_slice()\n                        current_state2 = replay_memory2.latest_slice()\n                        state1 = nd.array(current_state1[:, 0], ctx=q_ctx)\n                        state2 = nd.array(current_state2[:, 1], ctx=q_ctx)\n                        signal1 = a2_comTarget.get_singal(nd.array(current_state1[:, 1], ctx=q_ctx))\n                        signal2 = a1_comTarget.get_singal(nd.array(current_state2[:, 0], ctx=q_ctx))\n                        q_value1 = a1_actTarget.get_qval(state1, signal1)[0].asnumpy()\n                        q_value2 = a1_actTarget.get_qval(state2, signal2)[0].asnumpy()\n                        action1 = numpy.argmax(q_value1)\n                        action2 = numpy.argmax(q_value2)\n                        episode_q_value += q_value1[action1]\n                        episode_q_value += q_value2[action2]\n                        episode_action_step += 1\n                else:\n                    # signal1 = (np.random.random((1, signal_num)) - 0.5) * 2\n                    # signal2 = (np.random.random((1, signal_num)) - 0.5) * 2\n                    action1 = np.random.randint(action_num)\n                    action2 = np.random.randint(action_num)\n\n                next_ob, reward, terminal_flag = env.act([action_map1[action1], action_map2[action2]])\n                replay_memory1.append(next_ob, action1, reward[0], terminal_flag)\n                replay_memory2.append(next_ob, action2, reward[1], terminal_flag)\n\n                total_steps += 1\n                sum_reward = sum(reward)\n                episode_reward += sum_reward\n                if sum_reward < 0:\n                    collisions += 1\n                episode_step += 1\n\n                if total_steps % update_interval == 0 and replay_memory1.size > replay_start_size:\n                    training_steps += 1\n\n                    state_batch1, actions1, rewards1, nextstate_batch1, terminate_flags1 = replay_memory1.sample(\n                        batch_size=minibatch_size)\n\n                    state_batch2, actions2, rewards2, nextstate_batch2, terminate_flags2 = replay_memory2.sample(\n                        batch_size=minibatch_size)\n\n                    a1_state_batch = nd.array(state_batch1[:, :, 0].reshape(32, 74), ctx=q_ctx)\n                    a1_signal_state = nd.array(state_batch1[:, :, 1].reshape(32, 74), ctx=q_ctx)\n                    a2_state_batch = nd.array(state_batch2[:, :, 1].reshape(32, 74), ctx=q_ctx)\n                    a2_signal_state = nd.array(state_batch2[:, :, 0].reshape(32, 74), ctx=q_ctx)\n\n                    a1_nextstate_batch = nd.array(nextstate_batch1[:, :, 0].reshape(32, 74), ctx=q_ctx)\n                    a1_signal_nextstate = nd.array(nextstate_batch2[:, :, 1].reshape(32, 74), ctx=q_ctx)\n                    a2_nextstate_batch = nd.array(nextstate_batch2[:, :, 1].reshape(32, 74), ctx=q_ctx)\n                    a2_signal_nextstate = nd.array(nextstate_batch2[:, :, 0].reshape(32, 74), ctx=q_ctx)\n\n                    actions_batch1 = nd.array(actions1, ctx=q_ctx)\n                    actions_batch2 = nd.array(actions2, ctx=q_ctx)\n\n                    reward_batch1 = nd.array(rewards1, ctx=q_ctx)\n                    reward_batch2 = nd.array(rewards2, ctx=q_ctx)\n\n                    terminate_flags1 = nd.array(terminate_flags1, ctx=q_ctx)\n                    terminate_flags2 = nd.array(terminate_flags2, ctx=q_ctx)\n\n                    a1_signal = a2_comTarget.get_singals(a1_signal_nextstate)\n                    a2_signal = a1_comTarget.get_singals(a2_signal_nextstate)\n\n                    a1_Qvalue = a1_actTarget.get_qvals(a1_nextstate_batch, a1_signal)\n                    a2_Qvalue = a2_actTarget.get_qvals(a2_nextstate_batch, a2_signal)\n\n                    y_batch1 = reward_batch1 + nd.choose_element_0index(a1_Qvalue, nd.argmax_channel(a1_Qvalue)) * (\n                        1.0 - terminate_flags1) * discount\n\n                    y_batch2 = reward_batch2 + nd.choose_element_0index(a2_Qvalue, nd.argmax_channel(a2_Qvalue)) * (\n                        1.0 - terminate_flags2) * discount\n\n                    a1_signal = a2_comNet.get_signals(a1_signal_state)\n                    a2_signal = a1_comNet.get_signals(a2_signal_state)\n\n                    Qnet1 = a1_actQnet.update_params(a1_state_batch, a1_signal, actions_batch1, y_batch1)\n                    Qnet2 = a2_actQnet.update_params(a2_state_batch, a2_signal, actions_batch2, y_batch2)\n\n                    a1_comNet.update_params(a2_actQnet.exe.grad_dict['com'])\n                    a2_comNet.update_params(a1_actQnet.exe.grad_dict['com'])\n\n                    # sys.exit(0)\n                    # print \"before fc1_weight\"\n                    # print a2_comNet.exe.grad_dict['fc1_weight'][:1].asnumpy()\n                    # print \"com grad\"\n                    # print(a2_actQnet.exe.grad_dict['com'][:1].asnumpy())\n                    #\n                    # print \"updated fc1_weight\"\n                    # print a2_comNet.exe.grad_dict['fc1_weight'][:1].asnumpy()\n\n                    if training_steps % 10 == 0:\n                        loss1 = 0.5 * nd.square(nd.choose_element_0index(Qnet1, actions_batch1) - y_batch1)\n                        loss2 = 0.5 * nd.square(nd.choose_element_0index(Qnet2, actions_batch2) - y_batch2)\n                        episode_loss += nd.sum(loss1).asnumpy()\n                        episode_loss += nd.sum(loss2).asnumpy()\n                        episode_update_step += 1\n\n                    if training_steps % freeze_interval == 0:\n                        copy_params_to(a1_actQnet, a1_actTarget)\n                        copy_params_to(a1_comNet, a1_comTarget)\n                        copy_params_to(a2_actQnet, a2_actTarget)\n                        copy_params_to(a2_comNet, a2_comTarget)\n\n            steps_left -= episode_step\n            time_episode_end = time.time()\n            epoch_reward += episode_reward\n            info_str = \"Epoch:%d, Episode:%d, Steps Left:%d/%d/%d, Reward:%f, fps:%f, Exploration:%f\" \\\n                       % (epoch, episode, steps_left, episode_step, steps_per_epoch, episode_reward,\n                          episode_step / (time_episode_end - time_episode_start), eps_curr)\n\n            info_str += \", Collision:%f/%d \" % (collisions / episode_step,\n                                                collisions)\n\n            if episode_update_step > 0:\n                info_str += \", Avg Loss:%f/%d\" % (episode_loss / episode_update_step,\n                                                  episode_update_step * 10)\n            if episode_action_step > 0:\n                info_str += \", Avg Q Value:%f/%d \" % (episode_q_value / episode_action_step,\n                                                      episode_action_step)\n\n            if episode % 1 == 0:\n                logging.info(info_str)\n\n        end = time.time()\n        fps = steps_per_epoch / (end - start)\n        save_params(a1_actQnet, 'a1_actQnet/network-dqn_mx%04d.params' % epoch, q_ctx)\n        save_params(a1_comNet, 'a1_comNet/network-dqn_mx%04d.params' % epoch, q_ctx)\n        save_params(a2_actQnet, 'a2_actQnet/network-dqn_mx%04d.params' % epoch, q_ctx)\n        save_params(a2_comNet, 'a2_comNet/network-dqn_mx%04d.params' % epoch, q_ctx)\n\n        logging.info(\"Epoch:%d, FPS:%f, Avg Reward: %f/%d\"\n                     % (epoch, fps, epoch_reward / float(episode), episode))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"for_server/dqn_2a_com_v1.py","file_name":"dqn_2a_com_v1.py","file_ext":"py","file_size_in_byte":12949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"152196032","text":"#!/usr/bin/env python\nimport rospy\nfrom rospkg.rospack import RosPack\nimport os\nimport json\nfrom os.path import join\nfrom pobax_playground.srv import *\nfrom pobax_playground.msg import SoundTrajectory\nfrom pobax_playground.voice.voice import Voice\n\n\nclass VoiceNode(object):\n    def __init__(self):\n        self.rospack = RosPack()\n\n        with open(join(self.rospack.get_path('pobax_playground'), 'config', 'voice.json')) as f:\n            self.params = json.load(f)\n\n        # Serving these services\n        self.service_name_execute_analyse = \"/pobax_playground/voice/execute_analyse\"\n        self.service_name_baxter_analyse = \"/pobax_playground/voice/baxter_analyse\"\n\n        # Init DIVA vocal tract simulator\n        self.voice = Voice(self.params[\"tau\"],\n                         self.params[\"pa\"],\n                         self.params[\"pc\"],\n                         gui=self.params[\"gui\"],\n                         audio=self.params[\"audio\"])\n\n    def run(self):\n        rospy.Service(self.service_name_execute_analyse, ExecuteAnalyseVocalTrajectory, self.cb_execute_analyse)\n        rospy.Service(self.service_name_baxter_analyse, BaxterAnalyseVocalTrajectory, self.cb_baxter_analyse)\n\n    def cb_baxter_analyse(self,request):\n        rospy.loginfo('Voice node analysing baxter sound response to torso movement')\n        baxter_sound_traj = self.voice.baxter_analyse(request.is_culbuto_touched)\n        return BaxterAnalyseVocalTrajectoryResponse(baxter_sound_trajectory=SoundTrajectory(data=baxter_sound_traj))\n\n    def cb_execute_analyse(self,request):\n        rospy.loginfo('Voice node producing sound...')\n        torso_sound_traj, baxter_sound_traj, is_culb_name, produced_name, raw_torso_sound_traj\\\n        = self.voice.execute_analyse(request.vocal_trajectory.data)\n        return ExecuteAnalyseVocalTrajectoryResponse(torso_sound_trajectory=SoundTrajectory(data=torso_sound_traj),\n                                                     baxter_sound_trajectory=SoundTrajectory(data=baxter_sound_traj),\n                                                     is_culbuto_name=is_culb_name,\n                                                     produced_name=produced_name,\n                                                     raw_torso_sound=SoundTrajectory(data=raw_torso_sound_traj))\n\nrospy.init_node('voice')\nVoiceNode().run()\nrospy.loginfo('%s node up and running' % rospy.get_name())\nrospy.spin()\n\t\t","sub_path":"nodes/voice_node.py","file_name":"voice_node.py","file_ext":"py","file_size_in_byte":2415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"183068810","text":"import os\n\ndef get_vocabulary(path):\n    char2id = {}\n    id2char = {}\n    with open(path,\"r\",encoding=\"utf-8-sig\") as rf:\n        lines = rf.readlines()\n        for line in lines:\n            char,id = line.strip(\"\\n\").split(\"\\t\")\n            id2char[int(id)] = char\n            char2id[char] = int(id)\n    return char2id, id2char\n","sub_path":"tumbocr/data/data_utils.py","file_name":"data_utils.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"498179730","text":"# --------------\n# import the libraries\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\n# Code starts here\n#Read data from json file\ndf = pd.read_json(path, lines=True)\n#Rename column name\ndf.rename(columns=lambda x: x.replace(' ', '_'), inplace=True)\n#Check missing values\nmissing_data = df.isnull().sum()*100/len(df)\n#Drop columns\ndf.drop(['waist', 'bust', 'user_name','review_text','review_summary','shoe_size','shoe_width'], axis=1, inplace=True)\n#Store indenpendent and target variables\nX = df.drop(['fit'], axis=1)\ny = df['fit']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=6)\n\n\n\n\n\n# Code ends here\n\n\n# --------------\ndef plot_barh(df,col, cmap = None, stacked=False, norm = None):\n    df.plot(kind='barh', colormap=cmap, stacked=stacked)\n    fig = plt.gcf()\n    fig.set_size_inches(24,12)\n    plt.title(\"Category vs {}-feedback -  cloth {}\".format(col, '(Normalized)' if norm else ''), fontsize= 20)\n    plt.ylabel('Category', fontsize = 18)\n    plot = plt.xlabel('Frequency', fontsize=18)\n\n\n# Code starts here\ng_by_category = df.groupby('category')\ncat_fit = df.groupby('category')['fit'].value_counts().unstack()\n\ncat_fit.plot(kind='barh')\n\n# Code ends here\n\n\n# --------------\n# Code starts here\ncat_len = df.groupby('category')['length'].value_counts().unstack()\n#plot_barh(df, cat_len)\ncat_len.plot(kind=\"barh\")\n# Code ends here\n\n\n# --------------\n# Code starts here\ndef get_cms(x) :\n    if type(x) == float :\n        return x\n    try :\n        return (int(x[0])*30.48) + (int(x[4:-2])*2.54)\n    except :\n        return (int(x[0])*30.48)\n\nX_train.height = X_train.height.apply(get_cms)\nX_test.height = X_test.height.apply(get_cms)\n\n\n# Code ends here\n\n\n# --------------\n# Code starts here\nX_train.dropna(subset=['height','length','quality'], inplace=True)\nX_test.dropna(subset=['height','length','quality'], inplace=True)\ny_train = y_train[X_train.index]\ny_test = y_test[X_test.index]\n\nX_train['bra_size'].fillna(X_train['bra_size'].mean(), inplace=True)\nX_train['hips'].fillna(X_train['hips'].mean(), inplace=True)\n\nX_test['bra_size'].fillna(X_test['bra_size'].mean(), inplace=True)\nX_test['hips'].fillna(X_test['hips'].mean(), inplace=True)\nmode_1 = X_train['cup_size'].mode()[0]\nmode_2 = X_test['cup_size'].mode()[0]\nX_train['cup_size'].fillna(mode_1, inplace=True)\nX_test['cup_size'].fillna(mode_2, inplace=True)\n\n\n# Code ends here\n\n\n# --------------\n# Code starts here\n\n\n\n\n\nX_train = pd.get_dummies(data=X_train, columns=['category', 'cup_size', 'length'], prefix = ['category', 'cup_size', 'length'])\nX_test = pd.get_dummies(data=X_test, columns=['category', 'cup_size', 'length'], prefix = ['category', 'cup_size', 'length'])\n\nprint(X_train.columns)\nprint(X_test.columns)\n\n\n# Code ends here\n\n\n# --------------\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import accuracy_score\n\n\n# Code starts here\nmodel = DecisionTreeClassifier(random_state=6)\n\nmodel.fit(X_train, y_train)\ny_pred = model.predict(X_test)\n\nscore = accuracy_score(y_test, y_pred)\nprecision = precision_score(y_test, y_pred, average=None)\nprint(score)\nprint(precision)\n\n# Code ends here\n\n\n# --------------\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.tree import DecisionTreeClassifier\n\n\n# parameters for grid search\nparameters = {'max_depth':[5,10],'criterion':['gini','entropy'],'min_samples_leaf':[0.5,1]}\n\n# Code starts here\nmodel = DecisionTreeClassifier(random_state=6)\n\ngrid = GridSearchCV(estimator=model, param_grid=parameters)\ngrid.fit(X_train, y_train)\ny_pred = grid.predict(X_test)\n\naccuracy = accuracy_score(y_test, y_pred)\nprint(accuracy)\n\n\n# Code ends here\n\n\n","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":3833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"13438443","text":"from django.shortcuts import render, redirect, reverse, get_object_or_404\nfrom .forms import SubForm\nfrom django.contrib.auth.models import User\nfrom .models import Cards, Subscription\nimport stripe\nfrom django.conf import settings\nimport datetime\n\n\n# Stripe public key\nstripe_public_key = settings.STRIPE_PUBLIC_KEY\n# Stripe secret key\nstripe_secret_key = settings.STRIPE_SECRET_KEY\n\n\n# Subscription plans view\ndef cards(request):\n\n    # Requiring a all subscription cards\n    cards = Cards.objects.all()\n\n    context = {\n        'cards': cards,\n    }\n\n    # If a user is not autheticated so\n    # displaying subscription plan\n    if not request.user.is_authenticated:\n        return render(request, 'cards.html', context)\n\n    if request.user.is_authenticated:\n        # If a user has a subscription so\n        # redirectiing to the video's page\n        if check_users_subscription(request.user):\n            return redirect(reverse('home'))\n\n        # If not so displaying a subscription plans\n        else:\n            return render(request, 'cards.html', context)\n\n\n# Cheking if a user has subscriptions and\ndef check_users_subscription(user):\n    subscribed = None\n    subscriptions = Subscription.objects.filter(user=user)\n    for subscription in subscriptions:\n        subscribed = subscription.subscribed\n    return subscribed\n\n\n# Checkout page view\ndef checkout(request):\n\n    if request.user.is_authenticated:\n\n        # Checking a POST request\n        if request.POST:\n\n            # Declairing variables\n            period = None\n            saving = None\n            price = None\n\n            # Requesting a Subscription's payment form\n            sub_form = SubForm()\n\n            # Requesting all cards\n            cards = Cards.objects.all()\n\n            # Current Plan's Price\n            current_price = request.POST['price']\n\n            # Stripe price ID\n            plan_id = request.POST['stripe_plan_id']\n\n            # Requaring current user's email address\n            user = User.objects.get(id=request.user.id)\n            customer_email_address = user.email\n\n            # Checking which card corresponds to the request\n            for card in cards:\n                if card.price == int(current_price):\n                    period = card.duration\n                    saving = card.saving\n                    price = card.price\n\n            # Converting a price for stripe\n            stripe_total = int(current_price) * 100\n\n            # Setting a buplic key to stripe\n            stripe.api_key = stripe_secret_key\n\n            # Creating a stripe payment intent\n            intent = stripe.PaymentIntent.create(\n                amount=stripe_total,\n                currency='EUR',\n                payment_method_types=['card'],\n            )\n\n            # Page context\n            context = {\n                'sub_form': sub_form,\n                'cards': cards,\n                'stripe_public_key': stripe_public_key,\n                'client_secret': intent.client_secret,\n                'customer_email_address': customer_email_address,\n                'payment_intent_id': intent.id,\n                'plan_id': plan_id,\n                'current_card': {\n                    'period': period,\n                    'saving': saving,\n                    'price': price,\n                },\n            }\n\n            # Rendering a checkout page\n            return render(request, 'checkout.html', context)\n\n        # If there is not POST request so\n        # redirecting to the subscription plans page\n        else:\n            # Rendering a plans page\n            return redirect(reverse('cards'))\n\n    # If a user is not authenticated so\n    # redirecting to the login page\n    else:\n        return redirect(reverse('account_login'))\n\n\n# Subscription Success Function\ndef sub_success(request):\n\n    # Checking a POST request\n    if request.POST:\n\n        # Storing a stripe intent ID\n        payment_intent_id = request.POST['payment_intent_id']\n\n        # Storing a payment method ID\n        payment_method_id = request.POST['payment_method_id']\n\n        # Storing a email address\n        customer_email_address = request.POST['customer_email_address']\n\n        # Storing a stripe price ID\n        stripe_plan_id = request.POST['plan_id']\n\n        # Requiring current subscription card\n        sub_card = get_object_or_404(Cards, stripe_plan_id=stripe_plan_id)\n\n        # Getting current plan's Price\n        current_plan_price = sub_card.price\n\n        # Initiating a stripe\n        stripe.api_key = stripe_secret_key\n\n        # Creating a stripe customer\n        customer = stripe.Customer.create(\n            email=customer_email_address,\n            payment_method=payment_method_id,\n            invoice_settings={\n                'default_payment_method': payment_method_id\n            }\n        )\n\n        # Creating a subscription\n        subs = stripe.Subscription.create(\n            customer=customer.id,\n            items=[\n                {\n                    'plan': stripe_plan_id\n                },\n            ]\n        )\n\n        # Modifying a stripe intent\n        stripe.PaymentIntent.modify(\n            payment_intent_id,\n            payment_method=payment_method_id,\n            customer=customer.id\n        )\n\n        # Confirming a stripe intent\n        stripe.PaymentIntent.confirm(\n            payment_intent_id\n        )\n\n        # Inserting a data in to the Subscription's module\n        if subs:\n            subscription = Subscription(\n                sub_number=subs.id,\n                user=request.user,\n                card=sub_card,\n                full_name=request.POST['full_name'],\n                email=request.POST['email'],\n                phone_number=request.POST['phone_number'],\n                start_date=datetime.datetime.fromtimestamp(float(subs.current_period_start)),\n                end_date=datetime.datetime.fromtimestamp(float(subs.current_period_end)),\n                subscribed=True,\n                price=current_plan_price,\n            )\n\n            subscription.save()\n\n        # Rendering a success page\n        return render(request, 'sub_success.html')\n\n    else:\n\n        # Rendering a plans page\n        return redirect(reverse('cards'))\n\n\n# Subscription Cancel Function\ndef sub_cancel(request):\n    return render(request, 'sub_cancel.html')\n\n","sub_path":"subscription/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"638058193","text":"from wagtail.core import blocks\r\nfrom wagtail.images.blocks import ImageChooserBlock\r\nfrom wagtail.documents.blocks import DocumentChooserBlock\r\n\r\n\r\n# Home Page Blocks\r\nclass NewsBlock(blocks.StructBlock):\r\n    \"\"\"Text and date and nothing else.\"\"\"\r\n\r\n    text = blocks.CharBlock(required=True, help_text=\"Add news main line\")\r\n    date_of_event = blocks.DateBlock(blank=False, null=True)\r\n\r\n    class Meta:  # noqa\r\n        template = \"streams/news_and_date_block.html\"\r\n        icon = \"edit\"\r\n        label = \"Text & Date\"\r\n\r\n\r\nclass GalleryBlock(blocks.StructBlock):\r\n    \"\"\"Gallery Images block\"\"\"\r\n    \r\n    image = ImageChooserBlock(icon=\"image\")\r\n       \r\n    class Meta:  #noqa\r\n        template = \"streams/gallery.html\"\r\n        icon=\"edit\"\r\n        label=\"Image\"\r\n\r\n# About Page Blocks\r\n\r\n\r\nclass RichtextBackgroundBlock(blocks.StructBlock):\r\n    \"\"\"Richtext with all the features.\"\"\"\r\n\r\n    text1 = blocks.RichTextBlock(icon=\"text\")\r\n    text2 = blocks.RichTextBlock(icon=\"text\")\r\n\r\n    class Meta:  # noqa\r\n        template = \"streams/richtext_bg_block.html\"\r\n        icon = \"doc-full\"\r\n        label = \"Full RichText\"\r\n\r\nclass RichtextProposalBlock(blocks.StructBlock):\r\n    \"\"\"Richtext with all the features.\"\"\"\r\n\r\n    text = blocks.RichTextBlock(icon='text')\r\n\r\n    class Meta:  # noqa\r\n        template = \"streams/richtext_proposal_block.html\"\r\n        icon = \"doc-full\"\r\n        label = \"Full RichText\"\r\n\r\n# Services Page\r\nclass ServicesInfoBlock(blocks.StructBlock):\r\n\r\n    point = blocks.CharBlock(required=True, help_text=\"Add a new service point\")\r\n    info = blocks.RichTextBlock(icon='text')\r\n\r\n    class Meta: #noqa\r\n        template = 'streams/services_info_block.html'\r\n        icon='doc-full'\r\n        label = \"Text and Information\"\r\n\r\n# Projects page\r\nclass ProjectsIntendedOutcomesBlock(blocks.StructBlock):\r\n\r\n    intended_outcomes = blocks.RichTextBlock(icon='text')\r\n    \r\n    class Meta: #noqa\r\n        template = 'streams/projects_intended_outcomes_block.html'\r\n        icon='doc-full'\r\n        label = \"Intended Outcomes\"\r\n\r\nclass ProjectsDocumentsBlock(blocks.StructBlock):\r\n\r\n    project_documents = blocks.RichTextBlock(icon='text') \r\n\r\n    class Meta: #noqa\r\n        template = 'streams/projects_documents_block.html'\r\n        icon='doc-full'\r\n        label = \"Project Documents\"\r\n\r\nclass ProjectsOutputsBlock(blocks.StructBlock):\r\n\r\n    project_outputs = blocks.RichTextBlock(icon='text')\r\n\r\n    class Meta: #noqa\r\n        template = 'streams/projects_outputs_block.html'\r\n        icon='doc-full'\r\n        label = \"Project Outputs\"\r\n\r\n# Resources Page Blocks\r\n\r\nclass PointAndPdfBlock(blocks.StructBlock):\r\n    \"\"\"List and buttons of main\"\"\"\r\n\r\n    heading = blocks.CharBlock(required=True)\r\n\r\n    points = blocks.ListBlock(\r\n            blocks.StructBlock(\r\n                    [\r\n                        (\"title\", blocks.CharBlock(required=True, max_length=200)),\r\n                        (\"info\", blocks.ListBlock(\r\n                            blocks.StructBlock(\r\n                            [\r\n                                (\"point\", blocks.CharBlock(required=True, max_length=200)),\r\n                                (\"pdf\", DocumentChooserBlock(required=False)),\r\n                                    \r\n                                \r\n                            ]\r\n                        )\r\n                    )\r\n                )\r\n            ]        \r\n        )\r\n    )\r\n\r\n    class Meta:  #noqa\r\n        template = \"streams/point_and_pdf_page.html\"\r\n        icon=\"edit\"\r\n        label=\"TDSC Documents\"\r\n\r\nclass PointAndPdfsBlock(blocks.StructBlock):\r\n    \"\"\"List and buttons of main\"\"\"\r\n\r\n    heading = blocks.CharBlock(required=True)\r\n\r\n    points = blocks.ListBlock(\r\n            blocks.StructBlock(\r\n                    [\r\n                        (\"title\", blocks.CharBlock(required=True, max_length=200)),\r\n                        (\"info\", blocks.ListBlock(\r\n                            blocks.StructBlock(\r\n                            [\r\n                                (\"point\", blocks.CharBlock(required=True, max_length=200)),\r\n                                (\"pdf\", DocumentChooserBlock(required=False)),\r\n                                    \r\n                                \r\n                            ]\r\n                        )\r\n                    )\r\n                )\r\n            ]        \r\n        )\r\n    )\r\n\r\n    class Meta:  #noqa\r\n        template = \"streams/point_and_pdf_page.html\"\r\n        icon=\"edit\"\r\n        label=\"TDSC Talks\"\r\n\r\n\r\nclass PointAndURLBlock(blocks.StructBlock):\r\n    \"\"\"List and buttons of main\"\"\"\r\n    heading = blocks.CharBlock(required=True)\r\n    tools_and_resources = blocks.RichTextBlock(icon='text')\r\n\r\n    class Meta:  #noqa\r\n        template = \"streams/point_and_url_page.html\"\r\n        icon=\"edit\"\r\n        label=\"TDSC Tools & Resources\"\r\n\r\n# Outreach Page Blocks\r\nclass TableRowHeadingBlock(blocks.StructBlock):\r\n\r\n    heading = blocks.CharBlock(required=True)\r\n\r\n    points = blocks.ListBlock(\r\n            blocks.StructBlock(\r\n                    [\r\n                        (\"SNo\", blocks.IntegerBlock(required=True)),\r\n                        (\"title\", blocks.RichTextBlock(icon='text')),\r\n                        (\"venue\", blocks.RichTextBlock(icon='text')),\r\n                        (\"dates\", blocks.RichTextBlock(icon='text')),\r\n                        (\"attendees\", blocks.RichTextBlock(icon='text')),\r\n                        (\"host\", blocks.RichTextBlock(icon='text')),\r\n                    ]        \r\n            )\r\n    )\r\n\r\n    class Meta:  #noqa\r\n        template = \"streams/table_row_page.html\"\r\n        icon=\"edit\"\r\n        label=\"Outreach Table\"\r\n\r\n# Contact Page Blocks\r\nclass SupervisorBlock(blocks.StructBlock):\r\n\r\n    info = blocks.RichTextBlock(icon=\"text\")\r\n\r\n    cards = blocks.ListBlock(\r\n            blocks.StructBlock(\r\n                    [\r\n                        (\"photo\", ImageChooserBlock(required=False)),\r\n                        (\"name\", blocks.RichTextBlock(icon='text')),\r\n                        (\"Dept\", blocks.RichTextBlock(icon='text')),\r\n                        (\"contact\", blocks.RichTextBlock(icon='text')),\r\n                        (\"website\", blocks.URLBlock(required=True)),\r\n                    ]        \r\n            )\r\n    )\r\n\r\n    class Meta:  #noqa\r\n        template = \"streams/supervisor_block.html\"\r\n        icon=\"edit\"\r\n        label=\"Supervisor Cards\"\r\n\r\nclass ProjectEngineersBlock(blocks.StructBlock):\r\n    \"\"\"List and buttons of main\"\"\"\r\n\r\n    info = blocks.RichTextBlock(icon=\"text\")\r\n\r\n    points = blocks.ListBlock(\r\n            blocks.StructBlock(\r\n                    [\r\n                        (\"title\", blocks.CharBlock(required=True, max_length=200)),\r\n                        (\"members\", blocks.ListBlock(\r\n                            blocks.StructBlock(\r\n                            [\r\n                                (\"name\", blocks.RichTextBlock(icon='text')),\r\n                                (\"designation\", blocks.RichTextBlock(icon='text')),\r\n                                (\"email\", blocks.EmailBlock()),\r\n                                (\"contact_no\", blocks.RichTextBlock(icon='number')),\r\n                            ]\r\n                        )\r\n                    )\r\n                )\r\n            ]        \r\n        )\r\n    )\r\n\r\n    class Meta:  #noqa\r\n        template = \"streams/project_engineers_block.html\"\r\n        icon=\"edit\"\r\n        label=\"Project Engineer and Members\"\r\n","sub_path":"streams/blocks.py","file_name":"blocks.py","file_ext":"py","file_size_in_byte":7470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"313234735","text":"#! /usr/bin/env python\n\nimport random\nimport math\nfrom shapely.geometry import Polygon, Point\nfrom descartes import PolygonPatch\nimport matplotlib.pyplot as plt\n\nfrom utils import check_intersection\nfrom utils import adjustable_random_sampler as sampler\n\n\nclass Node():\n    \"\"\"Node for RRT.\n\n    Attributes:\n        x: x-coordinate of the node.\n        y: y-coordinate of the node.\n        parent: parent node of current node.\n    \"\"\"\n\n    def __init__(self, x, y):\n        \"\"\"\n        Init node with x and y coordinates.\n        \"\"\"\n        self.x = x\n        self.y = y\n        self.parent = None\n\n    def __str__(self):\n        return (\"(\"+str(self.x)+','+str(self.y)+\")\")\n\n    @classmethod\n    def from_coordinates(cls, coordinate):\n        \"\"\"Create Node from tuple with coordinates.\"\"\"\n        return cls(x=coordinate[0], y=coordinate[1])\n\n    def to_tuple(self):\n        return self.x, self.y   \n\nclass RRT():\n    \"\"\"RRT Planner Class.\n\n    Attributes:\n        sample_area: area for sampling random points (min,max)\n        sampler: function to sample random points in sample_area\n        expand_dis: distance to expand tree by at each step\n        goal_sample_rate: rate at which to sample goal during random sampling\n    \"\"\"\n\n    def __init__(self, sample_area, sampler, expand_dis=0.1, goal_sample_rate=0.15):\n        \"\"\"Init RRT Parameters.\"\"\"\n\n        self.sample_area = sample_area\n        self.sampler = sampler\n        self.expand_dis = expand_dis\n        self.goal_sample_rate = goal_sample_rate\n\n\n    def __call__(self, start_point, goal_point, obstacle_list, animation=False):\n        \"\"\"Plans path from start to goal avoiding obstacles.\n\n        Args:\n            start_point: tuple with start point coordinates.\n            end_point: tuple with end point coordinates.\n            obstacle_list: list of obstacles which themselves are list of points\n            animation: flag for showing planning visualization (default False)\n\n        Returns:\n            A list of points representing the path determined from\n            start to goal while avoiding obstacles.\n            An list containing just the start point means path could not be planned.\n        \"\"\"\n\n        # Initialize start and goal nodes\n        start = Node.from_coordinates(start_point)\n        goal_node = Node.from_coordinates(goal_point)\n\n        # Initialize node_list with start\n        node_list = [start]\n\n        # Calculate distances between start and goal\n        del_x, del_y = start.x - goal_node.x, start.y - goal_node.y\n        distance_to_goal = math.sqrt(del_x**2+ del_y**2)\n\n        # Loop to keep expanding the tree towards goal if there is no direct connection\n        if check_intersection([start_point, goal_point], obstacle_list):\n            while True:\n                # Sample random point in specified area\n                rnd_point = sampler(self.sample_area, goal_point, self.goal_sample_rate)\n\n                # Find nearest node to the sampled point\n                distance_list = [(node.x - rnd_point[0])**2 + (node.y - rnd_point[1])**2 for node in node_list]\n                nearest_node_index = min( xrange(len(distance_list)), key=distance_list.__getitem__)\n                nearest_node = node_list[nearest_node_index]\n\n                # Create new point in the direction of sampled point\n                theta = math.atan2(rnd_point[1] - nearest_node.y, rnd_point[0] - nearest_node.x)  \n                new_point = nearest_node.x + self.expand_dis*math.cos(theta), \\\n                                nearest_node.y + self.expand_dis*math.sin(theta)\n\n                # Check whether new point is inside an obstacles\n                for obstacle in obstacle_list:\n                    if Point(new_point).within(Polygon(obstacle)):\n                        new_point = float('nan'), float('nan')\n                        continue\n\n                # Expand tree\n                if math.isnan(new_point[0]):\n                    continue\n                else:\n                    new_node = Node.from_coordinates(new_point)\n                    new_node.parent = nearest_node\n                    node_list.append(new_node)\n\n                # Check if goal has been reached or if there is direct connection to goal\n                del_x, del_y = new_node.x - goal_node.x, new_node.y - goal_node.y\n                distance_to_goal = math.sqrt(del_x**2+ del_y**2)\n                \n                if distance_to_goal < self.expand_dis or not check_intersection(\\\n                        [new_node.to_tuple(), goal_node.to_tuple()], obstacle_list):\n                    goal_node.parent = node_list[-1]\n                    node_list.append(goal_node)\n                    print(\"Goal reached!\")\n                    break\n\n        else:\n            goal_node.parent = start\n            node_list = [start, goal_node]\n\n\n        # Construct path by traversing backwards through the tree\n        path = []\n        last_node = node_list[-1]\n\n        while node_list[node_list.index(last_node)].parent is not None:\n            node = node_list[node_list.index(last_node)]\n            path.append(node.to_tuple())\n            last_node = node.parent\n        path.append(start.to_tuple())\n\n        if animation == True:\n            RRT.visualize_tree(node_list, obstacle_list)\n\n        return list(reversed(path)), node_list\n\n    @staticmethod\n    def visualize_tree(node_list, obstacle_list, rnd_point=None):\n        \"\"\"Draw the tree along with randomly sampled point.\n        \n            Args:\n                node_list: list of nodes in the tree.\n                obstacle_list: list of obstactles.\n                rnd_point: randomly sampled point.\n\n            Returns:\n                Nothing. Function is used to draw the tree.\n        \"\"\"\n\n        # Clear the figure\n        plt.clf()\n\n        # Plot randomly sampled point\n        if rnd_point is not None:\n            plt.plot(rnd_point[0], rnd_point[1], \"^k\")\n\n        # Plot each edge of the tree\n        for node in node_list:\n            if node.parent is not None:\n                plt.plot([node.x, node_list[node_list.index(node.parent)].x], \n                         [node.y, node_list[node_list.index(node.parent)].y], \"-g\")\n\n        # Draw the obstacles in the environment\n        for obstacle in obstacle_list:\n            obstacle_polygon = Polygon(obstacle)\n            fig = plt.figure(1, figsize=(5, 5), dpi=90)\n            ax = fig.add_subplot(111)\n            poly_patch = PolygonPatch(obstacle_polygon)\n            ax.add_patch(poly_patch)\n\n        plt.show()\n","sub_path":"planner/scripts/local/RRT.py","file_name":"RRT.py","file_ext":"py","file_size_in_byte":6539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"402541781","text":"\"\"\"Function that returns data from field AWS\n\"\"\"\n\n# External modules\nimport sys, os\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime, timedelta\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom pandas.plotting import register_matplotlib_converters\nimport math\nimport time\nfrom pathlib import Path\nfrom tqdm import tqdm\nimport logging\nimport coloredlogs\n\n# Locals\ndirname = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))\nsys.path.append(dirname)\nfrom src.utils.settings import config\n\n\ndef get_field(location=\"schwarzsee19\"):\n    SITE, FOLDER = config(location)\n    if location == \"gangles21\":\n        col_list = [\n            \"TIMESTAMP\",\n            \"AirTC_Avg\",\n            \"RH\",\n            \"WS\",\n        ]\n        cols = [\"temp\", \"RH\", \"wind\"]\n\n        df_in = pd.read_csv(\n            FOLDER[\"raw\"] + \"/Gangles_Table15Min.dat\",\n            sep=\",\",\n            skiprows=[0, 2, 3, 4],\n            parse_dates=[\"TIMESTAMP\"],\n        )\n        df_in = df_in[col_list]\n\n        df_in.rename(\n            columns={\n                \"TIMESTAMP\": \"time\",\n                \"AirTC_Avg\": \"temp\",\n                \"RH_probe_Avg\": \"RH\",\n                \"WS\": \"wind\",\n            },\n            inplace=True,\n        )\n\n        df_in1 = pd.read_csv(\n            FOLDER[\"raw\"] + \"/Gangles_Table60Min.dat\",\n            sep=\",\",\n            skiprows=[0, 2, 3],\n            parse_dates=[\"TIMESTAMP\"],\n        )\n        df_in1.rename(\n            columns={\n                \"TIMESTAMP\": \"time\",\n                \"BP_mbar\": \"press\",  # mbar same as hPa\n            },\n            inplace=True,\n        )\n\n        for col in df_in1:\n            if col != \"time\":\n                df_in1[col] = pd.to_numeric(df_in1[col], errors=\"coerce\")\n\n        df_in = df_in.set_index(\"time\")\n        df_in1 = df_in1.set_index(\"time\")\n\n        df_in1 = df_in1.reindex(\n            pd.date_range(df_in1.index[0], df_in1.index[-1], freq=\"15Min\"),\n            fill_value=np.NaN,\n        )\n\n        df_in = df_in.replace(\"NAN\", np.NaN)\n        df_in1 = df_in1.replace(\"NAN\", np.NaN)\n        df_in1 = df_in1.resample(\"15Min\").interpolate(\"linear\")\n        df_in.loc[:, \"press\"] = df_in1[\"press\"]\n\n        df_in = df_in.replace(\"NAN\", np.NaN)\n        if df_in.isnull().values.any():\n            print(\"Warning: Null values present\")\n            print(df_in[cols].isnull().sum())\n        df_in = df_in.round(3)\n        df_in = df_in.reset_index()\n        df_in.rename(\n            columns={\n                \"index\": \"time\",\n            },\n            inplace=True,\n        )\n\n        start_date = datetime(2020, 12, 14)\n        df_in = df_in.set_index(\"time\")\n        df_in = df_in[start_date:]\n\n        df1 = pd.read_csv(\n            FOLDER[\"raw\"] + \"/HIAL_input_field.csv\",\n            sep=\",\",\n            parse_dates=[\"When\"],\n        )\n        df1 = df1.rename(columns={\"When\": \"time\"})\n\n        df = df_in\n        df1 = df1.set_index(\"time\")\n        cols = [\"SW_global\"]\n        for col in cols:\n            df.loc[:, col] = df1[col]\n\n        df = df.reset_index()\n        df = df[df.columns.drop(list(df.filter(regex=\"Unnamed\")))]\n        df = df.dropna()\n        # df.to_csv(\"outputs/\" + location + \"_input_field.csv\")\n\n        mask = df[\"SW_global\"] < 0\n        mask_index = df[mask].index\n        df.loc[mask_index, \"SW_global\"] = 0\n        # diffuse_fraction = 0\n        # df[\"SW_diffuse\"] = diffuse_fraction * df.SW_global\n        # df[\"SW_direct\"] = (1-diffuse_fraction)* df.SW_global\n        df = df.set_index(\"time\").resample(\"H\").mean().reset_index()\n\n        df[\"ppt\"] = 0\n        df[\"missing_type\"] = \"-\"\n        # df[\"cld\"] = 0\n\n        df.to_csv(FOLDER[\"input\"] + location + \"_input_model.csv\")\n        return df\n\n    if location == \"guttannen20\":\n        df_in = pd.read_csv(\n            FOLDER[\"raw\"] + SITE[\"name\"] + \"_field.txt\",\n            header=None,\n            encoding=\"latin-1\",\n            skiprows=7,\n            sep=\"\\\\s+\",\n            index_col=False,\n            names=[\n                \"Date\",\n                \"Time\",\n                \"Discharge\",\n                \"Wind Direction\",\n                \"Wind Speed\",\n                \"Maximum Wind Speed\",\n                \"Temperature\",\n                \"Humidity\",\n                \"Pressure\",\n                \"Pluviometer\",\n            ],\n        )\n        types_dict = {\n            \"Date\": str,\n            \"Time\": str,\n            \"Discharge\": float,\n            \"Wind Direction\": float,\n            \"Wind Speed\": float,\n            \"Temperature\": float,\n            \"Humidity\": float,\n            \"Pressure\": float,\n            \"Pluviometer\": float,\n        }\n        for col, col_type in types_dict.items():\n            df_in[col] = df_in[col].astype(col_type)\n        df_in[\"time\"] = pd.to_datetime(df_in[\"Date\"] + \" \" + df_in[\"Time\"])\n        df_in[\"time\"] = pd.to_datetime(df_in[\"time\"], format=\"%Y.%m.%d %H:%M:%S\")\n        df_in = df_in.drop([\"Pluviometer\", \"Date\", \"Time\"], axis=1)\n        df_in = df_in.set_index(\"time\").resample(\"H\").mean().reset_index()\n\n        mask = (df_in[\"time\"] >= SITE[\"start_date\"]) & (\n            df_in[\"time\"] <= SITE[\"end_date\"]\n        )\n        df_in = df_in.loc[mask]\n        df_in = df_in.reset_index()\n        days = pd.date_range(start=SITE[\"start_date\"], end=SITE[\"end_date\"], freq=\"H\")\n        days = pd.DataFrame({\"time\": days})\n\n        df = pd.merge(\n            df_in[\n                [\n                    \"time\",\n                    \"Discharge\",\n                    \"Wind Speed\",\n                    \"Temperature\",\n                    \"Humidity\",\n                    \"Pressure\",\n                ]\n            ],\n            days,\n            on=\"time\",\n        )\n\n        df = df.round(3)\n        # CSV output\n        df.rename(\n            columns={\n                \"Wind Speed\": \"wind\",\n                \"Temperature\": \"temp\",\n                \"Humidity\": \"RH\",\n                \"Pressure\": \"press\",\n            },\n            inplace=True,\n        )\n        logger.info(df_in.head())\n        logger.info(df_in.tail())\n        df.to_csv(FOLDER[\"input\"] + SITE[\"name\"] + \"_input_field.csv\")\n\n    if location == \"guttannen21\":\n        df_in = pd.read_csv(\n            FOLDER[\"raw\"] + SITE[\"name\"] + \"_field.txt\",\n            header=None,\n            encoding=\"latin-1\",\n            skiprows=7,\n            sep=\"\\\\s+\",\n            names=[\n                \"Date\",\n                \"Time\",\n                \"Wind Direction\",\n                \"Wind Speed\",\n                \"Maximum Wind Speed\",\n                \"Temperature\",\n                \"Humidity\",\n                \"Pressure\",\n                \"Pluviometer\",\n            ],\n        )\n        types_dict = {\n            \"Date\": str,\n            \"Time\": str,\n            \"Wind Direction\": float,\n            \"Wind Speed\": float,\n            \"Temperature\": float,\n            \"Humidity\": float,\n            \"Pressure\": float,\n            \"Pluviometer\": float,\n        }\n        for col, col_type in types_dict.items():\n            df_in[col] = df_in[col].astype(col_type)\n        df_in[\"time\"] = pd.to_datetime(df_in[\"Date\"] + \" \" + df_in[\"Time\"])\n        df_in[\"time\"] = pd.to_datetime(df_in[\"time\"], format=\"%Y.%m.%d %H:%M:%S\")\n        df_in = df_in.drop([\"Pluviometer\", \"Date\", \"Time\"], axis=1)\n        logger.debug(df_in.head())\n        logger.debug(df_in.tail())\n        df_in = df_in.set_index(\"time\").resample(\"H\").mean().reset_index()\n\n        mask = (df_in[\"time\"] >= SITE[\"start_date\"]) & (\n            df_in[\"time\"] <= SITE[\"end_date\"]\n        )\n        df_in = df_in.loc[mask]\n        df_in = df_in.reset_index()\n        days = pd.date_range(start=SITE[\"start_date\"], end=SITE[\"end_date\"], freq=\"H\")\n        days = pd.DataFrame({\"time\": days})\n\n        df = pd.merge(\n            df_in[\n                [\n                    \"time\",\n                    \"Wind Speed\",\n                    \"Temperature\",\n                    \"Humidity\",\n                    \"Pressure\",\n                ]\n            ],\n            days,\n            on=\"time\",\n        )\n\n        df = df.round(3)\n        # CSV output\n        df.rename(\n            columns={\n                \"Wind Speed\": \"wind\",\n                \"Temperature\": \"temp\",\n                \"Humidity\": \"RH\",\n                \"Pressure\": \"press\",\n            },\n            inplace=True,\n        )\n        df.to_csv(FOLDER[\"input\"] + SITE[\"name\"] + \"_input_field.csv\")\n\n    if location == \"schwarzsee19\":\n        df_in = pd.read_csv(\n            FOLDER[\"raw\"] + SITE[\"name\"][:-2] + \"_aws.txt\",\n            header=None,\n            encoding=\"latin-1\",\n            skiprows=7,\n            sep=\"\\\\s+\",\n            names=[\n                \"Date\",\n                \"Time\",\n                \"Discharge\",\n                \"Wind Direction\",\n                \"Wind Speed\",\n                \"Maximum Wind Speed\",\n                \"Temperature\",\n                \"Humidity\",\n                \"Pressure\",\n                \"Pluviometer\",\n            ],\n        )\n\n        df_in = df_in.drop([\"Pluviometer\"], axis=1)\n\n        df_in[\"time\"] = pd.to_datetime(df_in[\"Date\"] + \" \" + df_in[\"Time\"])\n        df_in[\"time\"] = pd.to_datetime(df_in[\"time\"], format=\"%Y.%m.%d %H:%M:%S\")\n\n        # Correct datetime errors\n        for i in tqdm(range(1, df_in.shape[0])):\n            if str(df_in.loc[i, \"time\"].year) != \"2019\":\n                df_in.loc[i, \"time\"] = df_in.loc[i - 1, \"time\"] + pd.Timedelta(\n                    minutes=5\n                )\n\n        df_in = df_in.set_index(\"time\").resample(\"H\").last().reset_index()\n\n        mask = (df_in[\"time\"] >= SITE[\"start_date\"]) & (\n            df_in[\"time\"] <= SITE[\"end_date\"]\n        )\n        df_in = df_in.loc[mask]\n        df_in = df_in.reset_index()\n\n        days = pd.date_range(start=SITE[\"start_date\"], end=SITE[\"end_date\"], freq=\"H\")\n        days = pd.DataFrame({\"time\": days})\n\n        df = pd.merge(\n            days,\n            df_in[\n                [\n                    \"time\",\n                    \"Discharge\",\n                    \"Wind Speed\",\n                    \"Maximum Wind Speed\",\n                    \"Wind Direction\",\n                    \"Temperature\",\n                    \"Humidity\",\n                    \"Pressure\",\n                ]\n            ],\n            on=\"time\",\n        )\n\n        # Include Spray time\n        df_nights = pd.read_csv(\n            FOLDER[\"raw\"] + \"schwarzsee_fountain_time.txt\",\n            sep=\"\\\\s+\",\n        )\n\n        df_nights[\"Start\"] = pd.to_datetime(\n            df_nights[\"Date\"] + \" \" + df_nights[\"start\"]\n        )\n        df_nights[\"End\"] = pd.to_datetime(df_nights[\"Date\"] + \" \" + df_nights[\"end\"])\n        df_nights[\"Start\"] = pd.to_datetime(\n            df_nights[\"Start\"], format=\"%Y-%m-%d %H:%M:%S\"\n        )\n        df_nights[\"End\"] = pd.to_datetime(df_nights[\"End\"], format=\"%Y-%m-%d %H:%M:%S\")\n\n        df_nights[\"Date\"] = pd.to_datetime(df_nights[\"Date\"], format=\"%Y-%m-%d\")\n\n        df[\"Fountain\"] = 0\n\n        for i in range(0, df_nights.shape[0]):\n            df_nights.loc[i, \"Start\"] = df_nights.loc[i, \"Start\"] - pd.Timedelta(days=1)\n            df.loc[\n                (df[\"time\"] >= df_nights.loc[i, \"Start\"])\n                & (df[\"time\"] <= df_nights.loc[i, \"End\"]),\n                \"Fountain\",\n            ] = 1\n\n        # CSV output\n        df.rename(\n            columns={\n                \"Wind Speed\": \"wind\",\n                \"Temperature\": \"temp\",\n                \"Humidity\": \"RH\",\n                \"Pressure\": \"press\",\n            },\n            inplace=True,\n        )\n\n        df.Discharge = df.Fountain * df.Discharge\n        df.to_csv(FOLDER[\"input\"] + SITE[\"name\"] + \"_input_field.csv\")\n    df = df.set_index(\"time\").resample(\"H\").mean().reset_index()\n    return df\n","sub_path":"src/data/field.py","file_name":"field.py","file_ext":"py","file_size_in_byte":11794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"517473127","text":"import numbers\n\nfrom pyrsistent import pmap\nimport attr\n\nfrom jsonschema.compat import int_types, str_types\nfrom jsonschema.exceptions import UndefinedTypeCheck\n\n\ndef is_array(checker, instance):\n    return isinstance(instance, list)\n\n\ndef is_bool(checker, instance):\n    return isinstance(instance, bool)\n\n\ndef is_integer(checker, instance):\n    # bool inherits from int, so ensure bools aren't reported as ints\n    if isinstance(instance, bool):\n        return False\n    return isinstance(instance, int_types)\n\n\ndef is_null(checker, instance):\n    return instance is None\n\n\ndef is_number(checker, instance):\n    # bool inherits from int, so ensure bools aren't reported as ints\n    if isinstance(instance, bool):\n        return False\n    return isinstance(instance, numbers.Number)\n\n\ndef is_object(checker, instance):\n    return isinstance(instance, dict)\n\n\ndef is_string(checker, instance):\n    return isinstance(instance, str_types)\n\n\ndef is_any(checker, instance):\n    return True\n\n\n@attr.s(frozen=True)\nclass TypeChecker(object):\n    \"\"\"\n    A ``type`` property checker.\n\n    A `TypeChecker` performs type checking for an `IValidator`. Type\n    checks to perform are updated using `TypeChecker.redefine` or\n    `TypeChecker.redefine_many` and removed via `TypeChecker.remove`.\n    Each of these return a new `TypeChecker` object.\n\n    Arguments:\n\n        type_checkers (dict):\n\n            The initial mapping of types to their checking functions.\n    \"\"\"\n    _type_checkers = attr.ib(default=pmap(), converter=pmap)\n\n    def is_type(self, instance, type):\n        \"\"\"\n        Check if the instance is of the appropriate type.\n\n        Arguments:\n\n            instance (object):\n\n                The instance to check\n\n            type (str):\n\n                The name of the type that is expected.\n\n        Returns:\n\n            bool: Whether it conformed.\n\n\n        Raises:\n\n            `jsonschema.exceptions.UndefinedTypeCheck`:\n                if type is unknown to this object.\n        \"\"\"\n        try:\n            fn = self._type_checkers[type]\n        except KeyError:\n            raise UndefinedTypeCheck(type)\n\n        return fn(self, instance)\n\n    def redefine(self, type, fn):\n        \"\"\"\n        Produce a new checker with the given type redefined.\n\n        Arguments:\n\n            type (str):\n\n                The name of the type to check.\n\n            fn (collections.Callable):\n\n                A function taking exactly two parameters - the type\n                checker calling the function and the instance to check.\n                The function should return true if instance is of this\n                type and false otherwise.\n\n        Returns:\n\n            A new `TypeChecker` instance.\n        \"\"\"\n        return self.redefine_many({type: fn})\n\n    def redefine_many(self, definitions=()):\n        \"\"\"\n        Produce a new checker with the given types redefined.\n\n        Arguments:\n\n            definitions (dict):\n\n                A dictionary mapping types to their checking functions.\n\n        Returns:\n\n            A new `TypeChecker` instance.\n        \"\"\"\n        return attr.evolve(\n            self, type_checkers=self._type_checkers.update(definitions),\n        )\n\n    def remove(self, *types):\n        \"\"\"\n        Produce a new checker with the given types forgotten.\n\n        Arguments:\n\n            types (~collections.Iterable):\n\n                the names of the types to remove.\n\n        Returns:\n\n            A new `TypeChecker` instance\n\n        Raises:\n\n            `jsonschema.exceptions.UndefinedTypeCheck`:\n\n                if any given type is unknown to this object\n        \"\"\"\n\n        checkers = self._type_checkers\n        for each in types:\n            try:\n                checkers = checkers.remove(each)\n            except KeyError:\n                raise UndefinedTypeCheck(each)\n        return attr.evolve(self, type_checkers=checkers)\n\n\ndraft3_type_checker = TypeChecker(\n    {\n        u\"any\": is_any,\n        u\"array\": is_array,\n        u\"boolean\": is_bool,\n        u\"integer\": is_integer,\n        u\"object\": is_object,\n        u\"null\": is_null,\n        u\"number\": is_number,\n        u\"string\": is_string,\n    },\n)\ndraft4_type_checker = draft3_type_checker.remove(u\"any\")\ndraft6_type_checker = draft4_type_checker.redefine(\n    u\"integer\",\n    lambda checker, instance: (\n        is_integer(checker, instance) or\n        isinstance(instance, float) and instance.is_integer()\n    ),\n)\ndraft7_type_checker = draft6_type_checker\n","sub_path":"contrib/python/jsonschema/py3/jsonschema/_types.py","file_name":"_types.py","file_ext":"py","file_size_in_byte":4490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"535869093","text":"\nimport math\n\nimport numpy as np\nimport scipy.io\n\nimport random\nimport tensorflow as tf \nfrom glob import glob as glb\n\nimport bouncing_balls as b\n\nfrom tqdm import *\n\n\nFLAGS = tf.app.flags.FLAGS\n\n# helper function\ndef _bytes_feature(value):\n  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\ndef get_converted_frame(cap, shape):\n  ret, frame = cap.read()\n  frame = cv2.resize(frame, shape, interpolation = cv2.INTER_CUBIC)\n  frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n  return frame\n\ndef tryint(s):\n  try:\n    return int(s)\n  except:\n    return s\n\ndef alphanum_key(s):\n  return [ tryint(c) for c in re.split('([0-9]+)', s) ]\n\ndef generate_tfrecords(seq_length, num_runs, dir_name):\n\n  frame_num = 1\n  shape = (32,32)\n\n  if not tf.gfile.Exists(FLAGS.data_dir + 'tfrecords/' + dir_name):\n    tf.gfile.MakeDirs(FLAGS.data_dir + 'tfrecords/' + dir_name)\n\n  print('generating records')\n  for run in tqdm(xrange(num_runs)):\n    filename = FLAGS.data_dir + 'tfrecords/' + dir_name + '/run_' + str(run) + '_seq_length_' + str(seq_length) + '.tfrecords'\n  \n    tfrecord_filename = glb(FLAGS.data_dir + 'tfrecords/' + dir_name + '/*')  \n    if filename not in tfrecord_filename:\n   \n      writer = tf.python_io.TFRecordWriter(filename)\n  \n      mat_filenames = glb('../systems/store_' + dir_name + '/run_' + str(run) + '/state_step_*')\n      #mat_filenames = glb('../systems/store_' + dir_name + '/run_' + str(run) + '/state_step_*')\n      #mat_filenames = nat_filenames.sort(key=alphanum_key)\n      num_samples = len(mat_filenames)\n      \n      ind_dat = 0\n      print(mat_filenames)\n      while ind_dat < (num_samples - seq_length):\n        seq_frames = np.zeros((seq_length,shape[0],shape[1],frame_num))\n        for i in xrange(seq_length):\n          diff_state = scipy.io.loadmat('../systems/store_' + dir_name + '/run_' + str(run) + '/state_step_' + str(i+ind_dat+1) + '.mat')\n          diff_state = np.array(diff_state['u'])\n          diff_state = np.float32(diff_state)\n          diff_state = diff_state.reshape([32,32,1])\n          seq_frames[i, :, :, :] = diff_state \n        ind_dat = ind_dat + 1\n        seq_frames = np.float32(seq_frames)\n        seq_frames_flat = seq_frames.reshape([1,seq_length*shape[0]*shape[1]*frame_num])\n        seq_frame_raw = seq_frames_flat.tostring()\n        # create example and write it\n        example = tf.train.Example(features=tf.train.Features(feature={\n          'image': _bytes_feature(seq_frame_raw)})) \n        writer.write(example.SerializeToString()) \n    \n    \n","sub_path":"systems/diffusion_createTFRecords.py","file_name":"diffusion_createTFRecords.py","file_ext":"py","file_size_in_byte":2532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"463998400","text":"from django.shortcuts import render,redirect\nfrom . import forms as f\nfrom django.http import HttpResponse\nfrom django.views.decorators.csrf import csrf_protect\nfrom .models import Sale,Creditos,Tarjeta\nfrom datetime import date,datetime\nfrom . import models as m\nfrom django.contrib import messages\nfrom django.conf import settings\nfrom django.contrib.auth.decorators import login_required\nfrom sesion1 import models as ms1\nfrom cliente import models as mc\nfrom login import models as mL\nfrom django.contrib.auth.models import User\nfrom django.contrib.admin.views.decorators import staff_member_required\nfrom django.db.models import Q\nfrom django.http import JsonResponse\n\n\n@csrf_protect\n@login_required(redirect_field_name='login:login')\ndef orden(request):\n    if request.user.persona.idRol.pk == 2 or request.user.persona.idRol.pk == 3:\n        if request.method == 'GET':\n            tarjetas = Tarjeta.objects.filter(asesor=request.user)\n            context = {\n                'tarjetas':tarjetas,\n            }\n            return render(request, 'conekta/main.html',context)\n        else:\n            token_id = request.POST.get(\"conektaTokenId\",False)\n            tarjeta = request.POST.get(\"nombre\",False)\n            numero = request.POST.get(\"numero\",False)\n            Tarjeta(numero=numero,asesor=request.user,tarjeta_habiente=tarjeta).save()\n            sale = Sale()\n            if token_id:\n                cantidad = int(request.POST.get(\"cantidad\",None))\n                customer = request.user.first_name + \" \" + request.user.last_name\n                sale.charge(token_id,cantidad,customer)\n                sale.save()\n                for x in range(0,cantidad):\n                    credito = Creditos(\n                        idAsesor=request.user,\n                        activo = True,\n                        fechaActualizacion = date.today(),\n                    )\n                    credito.save()\n                messages.add_message(request, messages.SUCCESS, 'Se han agregado tus creditos.')\n            else:\n                messages.add_message(request, messages.ERROR,'Tu pago no pudo ser procesado, por favor, consulta con tu banco.')\n            return redirect('conekta:inicio')\n    else:\n        return render(request,'error/404.html')\n\n# Vista de general del asesor\n\n@login_required(redirect_field_name='login:login')\ndef index(request):\n    if request.user.persona.idRol.pk == 2 or request.user.persona.idRol.pk == 3:\n        no_usados = m.Creditos.objects.filter(idAsesor=request.user,activo=True)\n        usados = m.Creditos.objects.filter(idAsesor=request.user,activo=False)\n        creditos = m.Creditos.objects.filter(idAsesor=request.user)\n        asesorcliente = mc.AsesorCliente.objects.filter(idAsesor=request.user,activo=True)\n        configuracionSesion1 = []\n        sesion1 = []\n        calificacion = 0\n        div = 0\n        calificacionA = 0\n        contador = 0\n        comentarios = []\n        for s in asesorcliente:\n            if(s.Origen.pk==1 or s.Origen.pk==2):\n                configuracionSesion1.append(s)\n            if(s.ranking):\n                contador += 1\n                calificacionA = calificacionA + s.ranking\n            try:\n                sesion = ms1.Sesion1.objects.get(idAsesorCliente=s)\n                calificacion = calificacion + sesion.calificacion\n                comentarios.append(sesion.opinion)\n                div += 1\n                sesion1.append(sesion)\n            except:\n                pass\n                \n        if(div != 0):\n            calificacion = calificacion/div\n        if(contador != 0):\n            calificacionA = calificacionA/contador\n        context = {\n            \"usados\":len(usados),\n            \"no_usados\":len(no_usados),\n            \"creditos\":creditos,\n            \"sesion\":sesion1,\n            \"configuracionSesion\":configuracionSesion1,\n            \"comentarios\":comentarios,\n            \"calificacion\":calificacion,\n            \"calificacionA\":calificacionA,\n        }\n        return render(request,'conekta/creditos.html',context)\n\n@staff_member_required\ndef creditos(request):\n    if request.method == 'GET':\n        asesores = mL.Persona.objects.filter(Q(idRol=2)|Q(idRol=3))\n        creditos = {}\n        for i in asesores:\n            creditos[i.user.username] = m.Creditos.objects.filter(idAsesor=i.user,activo=True)\n\n        context = {\n            \"asesores\":asesores,\n            \"creditos\":creditos,\n        }\n    else:\n        email = request.POST.get(\"mail\",None)\n        cantidad = request.POST.get(\"cantidad\",None)\n        user = User.objects.get(username=email)\n        creditos = m.Creditos.objects.filter(idAsesor=user,activo=True)\n        if(request.POST.get(\"bandera\") == \"0\"):\n            if(len(creditos) < abs(int(cantidad))):\n                messages.add_message(request, messages.ERROR, 'No puedes eliminar más créditos de los que tiene este usuario.')\n            else:\n                for c in range(0,abs(int(cantidad))):\n                    creditos[c].delete()\n                messages.add_message(request, messages.SUCCESS, 'Se han eliminado los creditos.')\n        else:\n            for c in range(0,int(cantidad)):\n                credito = Creditos(\n                    idAsesor=user,\n                    activo = True,\n                    fechaActualizacion = date.today(),\n                )\n                credito.save()\n            messages.add_message(request, messages.SUCCESS, 'Se han agregado creditos.')\n        response = {\n            \"messages\":\"ok\",\n        }\n        return JsonResponse(response)\n        \n    return render(request,'conekta/gestionarCreditos.html',context)\n\n","sub_path":"creditos/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"59619451","text":"import rollbar\nfrom flask import jsonify, request\nfrom flask_login import current_user, login_required\n\nfrom app.api import api\nfrom app.api.business import team_business\nfrom app.api.business.errors import NotFoundError, TeamError, ValidationError, UnauthorisedError\nfrom app.api.helpers import (\n    abort,\n    forbidden,\n    role_required,\n    not_found,\n    exception_logger\n)\nfrom app.api.services import team_member_service\nfrom ...utils import get_json_from_request\n\n\n@api.route('/teams', methods=['GET'])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef get_teams_overview():\n    team = team_business.get_teams_overview()\n\n    return jsonify(team)\n\n\n@api.route('/people', methods=['GET'])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef get_people_overview():\n    team = team_business.get_people_overview()\n\n    return jsonify(team)\n\n\n@api.route('/team/create', methods=['POST'])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef create_team():\n    try:\n        team = team_business.create_team()\n    except TeamError as e:\n        abort(str(e))\n    except NotFoundError as e:\n        return not_found(str(e))\n    except UnauthorisedError as e:\n        return forbidden(str(e))\n\n    return jsonify(team)\n\n\n@api.route('/team/<int:team_id>', methods=[\"GET\"])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef get_team(team_id):\n    team = None\n    try:\n        team = team_business.get_team(team_id)\n    except NotFoundError as e:\n        return not_found(str(e))\n    except UnauthorisedError as e:\n        return forbidden(str(e))\n\n    return jsonify(team)\n\n\n@api.route('/team/<int:team_id>/update', methods=['POST'])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef update_team(team_id):\n    data = get_json_from_request()\n    try:\n        team = team_business.update_team(team_id, data)\n    except ValidationError as e:\n        return abort(str(e))\n    except NotFoundError as e:\n        return not_found(str(e))\n    except UnauthorisedError as e:\n        return forbidden(str(e))\n\n    return jsonify(team)\n\n\n@api.route('/team/members/search', methods=['GET'])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef find_team_members():\n    keywords = request.args.get('keywords') or ''\n    exclude = request.args.get('exclude') or ''\n\n    if keywords:\n        results = team_business.search_team_members(\n            current_user,\n            current_user.agency_id,\n            keywords=keywords,\n            exclude=exclude.split(',')\n        )\n\n        return jsonify(users=results), 200\n    else:\n        abort('You must provide a keywords param.')\n\n\n@api.route('/team/request-access', methods=['POST'])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef request_access():\n    data = get_json_from_request()\n\n    try:\n        team_business.request_access(data)\n    except ValidationError as e:\n        abort(str(e))\n\n    return jsonify(success=True)\n\n\n@api.route('/team/<int:team_id>/request-join', methods=['POST'])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef request_to_join(team_id):\n    try:\n        team_business.request_to_join(current_user.email_address, team_id, current_user.agency_id)\n    except NotFoundError as e:\n        return not_found(str(e))\n    except UnauthorisedError as e:\n        return forbidden(str(e))\n    except ValidationError as e:\n        abort(str(e))\n\n    return jsonify(success=True)\n\n\n@api.route('/team/join-requests', methods=['GET'])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef get_join_requests():\n    join_requests = team_business.get_join_requests(current_user.email_address)\n    results = {}\n    if join_requests:\n        for request in join_requests:\n            if request.data['team_id'] not in results:\n                results[request.data['team_id']] = []\n            results[request.data['team_id']].append({\n                \"id\": request.id,\n                \"team_id\": request.data['team_id'],\n                \"created_at\": request.created_at\n            })\n    return jsonify(join_requests=results)\n\n\n@api.route('/team/join-request/<int:team_id>/<string:token>', methods=['GET'])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef get_join_request(team_id, token):\n    try:\n        team = team_business.get_team(team_id)\n    except NotFoundError as e:\n        return not_found(str(e))\n    except UnauthorisedError as e:\n        return forbidden(str(e))\n\n    join_request = team_business.get_join_request(token)\n    if not join_request or int(join_request.data['team_id']) != team_id:\n        return not_found('The token is invalid, or this request has already been declined or accepted')\n\n    return jsonify(join_request=join_request)\n\n\n@api.route('/team/decline-join-request/<int:team_id>/<string:token>', methods=['POST'])\n@exception_logger\n@login_required\n@role_required('buyer')\ndef decline_join_request(team_id, token):\n    data = get_json_from_request()\n\n    if 'reason' not in data or not data['reason']:\n        abort('Must provide reason for decline')\n\n    try:\n        team = team_business.get_team(team_id)\n    except NotFoundError as e:\n        return not_found(str(e))\n    except UnauthorisedError as e:\n        return forbidden(str(e))\n\n    join_request = team_business.get_join_request(token)\n    if not join_request or int(join_request.data['team_id']) != team_id:\n        return not_found('The token is invalid, or this request has already been declined or accepted')\n\n    if (\n        'user_id' in join_request.data and\n        (\n            team_member_service.get_team_members_by_user_id(team_id, [int(join_request.data['user_id'])]) or\n            team_member_service.get_team_leads_by_user_id(team_id, [int(join_request.data['user_id'])])\n        )\n    ):\n        abort('This user is already a member of the team')\n\n    team_business.decline_join_request(join_request, data['reason'], team_id)\n\n    return jsonify(success=True)\n","sub_path":"app/api/views/teams.py","file_name":"teams.py","file_ext":"py","file_size_in_byte":5940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"562148799","text":"from sklearn.linear_model import LogisticRegression\nimport pandas as pd\nimport pickle\nimport numpy as np\n\n# モデルのロード\nfilename = \"/work/chapter5/model/model.sav\"\nmodel = pickle.load(open(filename, 'rb'))\n\n# データのロード\nx_train = pd.read_csv(\"/work/chapter5/data/train.feature.txt\", sep='\\t', header=None)\nx_test = pd.read_csv('/work/chapter5/data/test.feature.txt', sep='\\t', header=None)\ny_train = pd.read_csv('/work/chapter5/data/train.txt', sep='\\t', header=None)[0]\ny_test = pd.read_csv('/work/chapter5/data/test.txt', sep='\\t', header=None)[0]\n\n# 予測結果とその確率を表示\ndef predict(model, data):\n    prob = pd.DataFrame(np.max(model.predict_proba(data), axis=1))\n    prob.columns = [\"Probability\"]\n    pred = pd.DataFrame(model.predict(data))\n    pred.columns = [\"Predict\"]\n    result = pd.concat([prob, pred], axis = 1)\n    return result\n\n# 正解率を計算\ndef cal_correct(pred, ref):\n    correct = 0\n    for p, r in zip(pred, ref):\n        if p == r:\n            correct += 1\n    return correct/len(pred)\n\ndef main():\n    category = {0:\"b\", 1:\"t\", 2:\"e\", 3:\"m\"}\n    # 予測\n    result_train = predict(model, x_train)\n    result_test = predict(model, x_test)\n    result_train.replace(category, inplace=True)\n    result_test.replace(category, inplace=True)\n\n    # 正解率を計算\n    correct_train = cal_correct(result_train[\"Predict\"],y_train)\n    correct_test = cal_correct(result_test[\"Predict\"],y_test)\n\n    print(correct_train)\n    print(correct_test)\n\nmain()\n\n\n","sub_path":"chapter6/scripts/54.py","file_name":"54.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"49424412","text":"'''\nPython mapping for the LaunchServices framework.\n\nThis module does not contain docstrings for the wrapped code, check Apple's\ndocumentation for details on how to use these functions and classes. \n'''\n\nimport objc as _objc\nfrom Foundation import *\n#import AE\n#import CoreServices\n\ntry:\n    __bundle__ = _objc.initFrameworkWrapper(\"LaunchServices\",\n        frameworkIdentifier=\"com.apple.LaunchServices\",\n        frameworkPath=_objc.pathForFramework(\n            \"/System/Library/Frameworks/CoreServices.framework/Frameworks/LaunchServices.framework\"),\n        globals=globals())\n\nexcept ImportError:\n    __bundle__ = _objc.initFrameworkWrapper(\"LaunchServices\",\n        frameworkIdentifier=\"com.apple.LaunchServices\",\n        frameworkPath=_objc.pathForFramework(\n            \"/System/Library/Frameworks/ApplicationServices.framework/Frameworks/LaunchServices.framework\"),\n        globals=globals())\n\n# \n# Load an undocumented, yet announced function. This function was announced\n# in TN2029 (http://developer.apple.com/technotes/tn/tn2029.html)\n_objc.loadBundleFunctions(\n        __bundle__, globals(),\n        [\n            ('_LSCopyAllApplicationURLs', 'v^@', '', { 'arguments': { 0: { 'already_retained': True, 'type_modifier': 'o',  } } } ),\n        ])\n","sub_path":"pyobjc/pyobjc-framework-LaunchServices/Lib/LaunchServices/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"484457855","text":"# fromfile.py\n# Tom Wexler\n# 4/1/13\n# Creates a soundwave from a .wav file.\n# Testing your soundwave constructor and play function.\n\nimport soundwave\n\ndef main() :\n \n    snippet = soundwave.Soundwave(\"../Mfiles/M55.wav\")\n    snippet.play()\n    \nmain()","sub_path":"lab08/fromfile.py","file_name":"fromfile.py","file_ext":"py","file_size_in_byte":251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"301402698","text":"\nfrom sklearn.metrics import jaccard_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import accuracy_score\n\nfrom sklearn.linear_model import LogisticRegression\nimport numpy as np\n\n\ndef _logisticregression(*, train, test, x_predict=None, metrics, penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True,\nintercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0,\n warm_start=False, n_jobs=None, l1_ratio=None):\n    \"\"\"For for info visit :\n        https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression\n    \"\"\"\n\n    model = LogisticRegression(penalty=penalty, dual=dual, tol=tol, C=C, fit_intercept=fit_intercept, intercept_scaling=intercept_scaling,\nclass_weight=class_weight, random_state=random_state, solver=solver, max_iter=max_iter, multi_class=multi_class, verbose=verbose,\nwarm_start=warm_start, n_jobs=n_jobs, l1_ratio=l1_ratio)\n    model.fit(train[0], train[1])\n    model_name = 'Logistic Regression'\n    y_hat = model.predict(test[0])\n\n    if metrics == 'f1_score':\n        accuracy = f1_score(test[1], y_hat)\n    if metrics == 'jaccard_score':\n        accuracy = jaccard_score(test[1], y_hat)\n    if metrics == 'accuracy_score':\n        accuracy = accuracy_score(test[1], y_hat)\n\n    if x_predict is None:\n        return (model_name, accuracy, None)\n\n    y_predict = model.predict(x_predict)\n    return (model_name, accuracy, y_predict)\n","sub_path":"datascientist/model/classification/skl/linear_model/logisticregression.py","file_name":"logisticregression.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"490444608","text":"#!/usr/bin/python\n\n# Script to split up data by occupancy\n# using raw size of SciFi raw banks as measure\n# output: files lists for the different occupancy bins\n\nimport subprocess\nimport os\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import FormatStrFormatter\nfrom matplotlib import font_manager\nimport numpy as np\n\ndef file_len(fname):\n    if (os.stat(fname).st_size == 0):\n        return 0\n    else:\n        with open(fname) as f:\n            for i, l in enumerate(f):\n                pass\n            return i + 1\n\ninput_directory = \"/data/dvombruc/2019-07/minbias/mag_down/\"\n#input_directory = \"/home/dvombruc/Allen/input/minbias/\"\nbanks_directory = input_directory + \"banks/FTCluster/\"\nallen_dir = \"/home/dvombruc/Allen/\"\nn_bins = 13\nsize_bins = [5000, 7000, 9000, 11000, 13000, 15000, 17000, 19000, 21000, 23000, 25000, 27000]\nevent_lists = []\nfor ibin in range(n_bins):\n    event_lists.append([])\n\nproc = subprocess.Popen(['ls','-Sl', banks_directory], stdout=subprocess.PIPE)\nfor line in proc.stdout:\n    if \"total\" in str(line):\n        continue\n    split = line.split()\n    size = int(split[4])\n    name = split[8]\n    #print('name = ',name, ', size = ', size)\n    # Sort events into bins by size\n    for ibin in range(n_bins):\n        # underflow bin\n        if (ibin == 0):\n            if (size < size_bins[ibin]):\n                event_lists[ibin].append(name)\n        # all actual bins (no underflow or overflow)\n        elif (ibin < n_bins-1):\n            if (size_bins[ibin-1] <= size <= size_bins[ibin]):\n                event_lists[ibin].append(name)\n        # overflow bin\n        elif (ibin == n_bins-1):\n            if (size > size_bins[ibin-1]):\n                event_lists[ibin].append(name)\n \n# write event lists to files\nfile_lists_dir = allen_dir + \"checker/perfscans/file_lists\"\nif not os.path.exists(file_lists_dir):\n    os.makedirs(file_lists_dir)\nfor ibin in range(n_bins):\n    file_name = file_lists_dir + \"/file_list_bin_\" + str(ibin) + \".txt\"\n    f = open(file_name, \"w\")\n    for event in event_lists[ibin]:\n        f.write(event.decode(\"utf-8\")+'\\n')\n \n\n# Run Allen on the different occupancy events\n# save throughput for every run\nallen_build_dir = allen_dir + \"build\"\noutput_dir = allen_dir + \"checker/perfscans/output\"\nif not os.path.exists(output_dir):\n    os.makedirs(output_dir)\nfilled_bins = []\nbin_errs = []\nthroughput = []\nn_events_array = []\nfor ibin in range(n_bins):\n    file_name = file_lists_dir + \"/file_list_bin_\" + str(ibin) + \".txt\"\n    # only process bins with events\n    n_events = file_len(file_name)\n    print(n_events, 'in bin ', ibin)\n    if (n_events > 1000):\n        os.chdir(allen_build_dir)\n        #n_events_to_process = n_events\n        n_events_to_process = 1000\n        stdout_file = output_dir + \"/stdout_bin\" + str(ibin) + \".stdout\"\n        command = \"./Allen -f \" + input_directory + \" -n \" + str(n_events_to_process) + \" --file-list \" + file_name + \" -t 12 -c 0 -r 100\" + \" >& \" + stdout_file\n        print('executing Allen: ', command)\n        os.system(command)\n        # get throughput\n        proc = subprocess.Popen(['tail','-n', '2', stdout_file], stdout=subprocess.PIPE)\n        for line in proc.stdout:\n            if (\"events\" in str(line)):\n                split = line.split()\n                throughput.append(float(split[0]) / 1000) # convert to kHz\n                half_bin_diff = (size_bins[ibin]-size_bins[ibin-1])/2\n                central_val = (size_bins[ibin] - half_bin_diff) / 1000\n                print(\"central val = \", central_val)\n                filled_bins.append(central_val)\n                n_events_array.append(n_events)\n                bin_errs.append(half_bin_diff/1000)\n                print('added throughput measurement: ', split[0])\n\n# Plot the result\nfont = {'family': 'serif',\n        'color':  'black',\n        'weight': 'normal',\n        'size': 16,\n        }\nif (len(filled_bins) != len(throughput) ):\n    print('Filled bins and throughput measurements do not have the same length!')\n    exit()\nmax_throughput = max(throughput)\nmax_n_events = max(n_events_array)\nscale = max_throughput / max_n_events\nn_events_array_scaled = [i * scale for i in n_events_array]\nprint('max throughput = ', max_throughput)\nfig, ax = plt.subplots()\nthroughput_plot = plt.errorbar(filled_bins, throughput, xerr=bin_errs, fmt=\"o\")\n\n# histogram of data volume distribution\nbin_edges = []\nbin_edges.append(filled_bins[0]-1)\nfor ibin in filled_bins:\n    bin_edges.append(ibin+1)\nprint('# of filled bins = ', len(filled_bins), ', # of bin edges = ', len(bin_edges))\nbins = np.array(bin_edges)\nvals = np.array(n_events_array_scaled)\nhistogram = plt.fill_between(bins,np.concatenate(([0],vals)), step=\"pre\", alpha=0.2)\n\nplt.xlabel(\"SciFi raw data volume [kB]\", fontdict=font)\nplt.ylabel(\"Throughput on Tesla V100 16GB [kHz]\", fontdict=font)\n\n# Second axis\nsecaxy = ax.secondary_yaxis('right')\nsecaxy.set_ylabel(r'Number of events [a.u.]', fontdict=font)\nticks_font = font_manager.FontProperties(family='serif', style='normal',\n    size=12, weight='normal', stretch='normal')\nfor labelx, labely, labelsecy in zip(ax.get_xticklabels(), ax.get_yticklabels(), secaxy.get_yticklabels()):\n    labelx.set_fontproperties(ticks_font)\n    labely.set_fontproperties(ticks_font)\n    labelsecy.set_fontproperties(ticks_font)\n    \nplt.ylim(0, 160)\nplt.xlim(5, 17)\n\n# Legend\nlegend_font = font_manager.FontProperties(family='serif', style='normal',\n    size=16, weight='normal', stretch='normal')\nlegend = plt.legend(['Data volume distribution','Throughput'], frameon=False, prop=legend_font, loc='lower left')\n\n\n\nplt.show()\n","sub_path":"checker/perfscans/throughput_vs_occupancy.py","file_name":"throughput_vs_occupancy.py","file_ext":"py","file_size_in_byte":5605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"477201260","text":"import shutil, os, pathlib, pickle, sys, math, importlib, json.tool\nimport numpy as np\nfrom os.path import join, exists\nfrom tqdm import tqdm\nfrom itertools import product\nimport glob\nimport webrtcvad\nfrom timeit import default_timer as timer\nimport collections\nimport contextlib\nimport wave\nimport librosa\nimport soundfile as sf\n\ndef read_wave(path):\n    \"\"\"Reads a .wav file.\n    Takes the path, and returns (PCM audio data, sample rate).\n    \"\"\"\n    with contextlib.closing(wave.open(path, 'rb')) as wf:\n        num_channels = wf.getnchannels()\n        assert num_channels == 1\n        sample_width = wf.getsampwidth()\n        assert sample_width == 2, 'sample width is '+str(sample_width) + path\n        sample_rate = wf.getframerate()\n        if sample_rate != 16000:\n            print('Rewriting b/c sample rate is', sample_rate)\n            x, sr = librosa.load(path)\n            target_sr = 16000\n            x = librosa.resample(x,orig_sr=sr,target_sr=target_sr)\n            sf.write(path, x, target_sr)\n            return read_wave(path)\n\n        assert sample_rate in (8000, 16000, 32000), f'sample_rate is {sample_rate} which is not supported'\n        frames = wf.getnframes()\n        pcm_data = wf.readframes(frames)\n        duration = frames / sample_rate\n        return pcm_data, sample_rate, duration\n\n\ndef write_wave(path, audio, sample_rate):\n    \"\"\"Writes a .wav file.\n    Takes path, PCM audio data, and sample rate.\n    \"\"\"\n    with contextlib.closing(wave.open(path, 'wb')) as wf:\n        wf.setnchannels(1)\n        wf.setsampwidth(2)\n        wf.setframerate(sample_rate)\n        wf.writeframes(audio)\n\n\nclass Frame(object):\n    \"\"\"Represents a \"frame\" of audio data.\"\"\"\n    def __init__(self, bytes, timestamp, duration):\n        self.bytes = bytes\n        self.timestamp = timestamp\n        self.duration = duration\n\n\ndef frame_generator(frame_duration_ms, audio, sample_rate):\n    \"\"\"Generates audio frames from PCM audio data.\n    Takes the desired frame duration in milliseconds, the PCM data, and\n    the sample rate.\n    Yields Frames of the requested duration.\n    \"\"\"\n    n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)\n    offset = 0\n    timestamp = 0.0\n    duration = (float(n) / sample_rate) / 2.0\n    while offset + n < len(audio):\n        yield Frame(audio[offset:offset + n], timestamp, duration)\n        timestamp += duration\n        offset += n\n\n\ndef vad_collector(sample_rate, frame_duration_ms,\n                  padding_duration_ms, vad, frames):\n    \"\"\"Filters out non-voiced audio frames.\n    Given a webrtcvad.Vad and a source of audio frames, yields only\n    the voiced audio.\n    Uses a padded, sliding window algorithm over the audio frames.\n    When more than 90% of the frames in the window are voiced (as\n    reported by the VAD), the collector triggers and begins yielding\n    audio frames. Then the collector waits until 90% of the frames in\n    the window are unvoiced to detrigger.\n    The window is padded at the front and back to provide a small\n    amount of silence or the beginnings/endings of speech around the\n    voiced frames.\n    Arguments:\n    sample_rate - The audio sample rate, in Hz.\n    frame_duration_ms - The frame duration in milliseconds.\n    padding_duration_ms - The amount to pad the window, in milliseconds.\n    vad - An instance of webrtcvad.Vad.\n    frames - a source of audio frames (sequence or generator).\n    Returns: A generator that yields PCM audio data.\n    \"\"\"\n    num_padding_frames = int(padding_duration_ms / frame_duration_ms)\n    # We use a deque for our sliding window/ring buffer.\n    ring_buffer = collections.deque(maxlen=num_padding_frames)\n    # We have two states: TRIGGERED and NOTTRIGGERED. We start in the\n    # NOTTRIGGERED state.\n    triggered = False\n\n    voiced_frames = []\n    for frame in frames:\n        is_speech = vad.is_speech(frame.bytes, sample_rate)\n\n        if not triggered:\n            ring_buffer.append((frame, is_speech))\n            num_voiced = len([f for f, speech in ring_buffer if speech])\n            # If we're NOTTRIGGERED and more than 90% of the frames in\n            # the ring buffer are voiced frames, then enter the\n            # TRIGGERED state.\n            if num_voiced > 0.9 * ring_buffer.maxlen:\n                triggered = True\n                # We want to yield all the audio we see from now until\n                # we are NOTTRIGGERED, but we have to start with the\n                # audio that's already in the ring buffer.\n                for f, s in ring_buffer:\n                    voiced_frames.append(f)\n                ring_buffer.clear()\n        else:\n            # We're in the TRIGGERED state, so collect the audio data\n            # and add it to the ring buffer.\n            voiced_frames.append(frame)\n            ring_buffer.append((frame, is_speech))\n            num_unvoiced = len([f for f, speech in ring_buffer if not speech])\n            # If more than 90% of the frames in the ring buffer are\n            # unvoiced, then enter NOTTRIGGERED and yield whatever\n            # audio we've collected.\n            if num_unvoiced > 0.9 * ring_buffer.maxlen:\n                triggered = False\n                yield b''.join([f.bytes for f in voiced_frames])\n                ring_buffer.clear()\n                voiced_frames = []\n    if triggered:\n        pass\n    # If we have any leftover voiced audio when we run out of input,\n    # yield it.\n    if voiced_frames:\n        yield b''.join([f.bytes for f in voiced_frames])\n\n'''\nGenerate VAD segments. Filters out non-voiced audio frames.\n@param waveFile: Input wav file to run VAD on.0\n@Retval:\nReturns tuple of\n    segments: a bytearray of multiple smaller audio frames\n              (The longer audio split into mutiple smaller one's)\n    sample_rate: Sample rate of the input audio file\n    audio_length: Duraton of the input audio file\n'''\ndef vad_segment_generator(wavFile, aggressiveness):\n    audio, sample_rate, audio_length = read_wave(wavFile)\n    assert sample_rate == 16000, \"Only 16000Hz input WAV files are supported for now!\"\n    vad = webrtcvad.Vad(int(aggressiveness))\n    frames = frame_generator(30, audio, sample_rate)\n    frames = list(frames)\n    segments = vad_collector(sample_rate, 30, 300, vad, frames)\n\n    return segments, sample_rate, audio_length\n\n\ndef obj_to_grid(a):\n    '''get all objects corresponding to hyperparamter grid search\n    a = {\n        'hey': [1,2],\n        'there': [3,4],\n        'people': 5\n    }\n    ->\n    {'hey': 1, 'there': 3, 'people': 5}\n    {'hey': 1, 'there': 4, 'people': 5}\n    {'hey': 2, 'there': 3, 'people': 5}\n    {'hey': 2, 'there': 4, 'people': 5}\n    '''\n\n    for k,v in list(a.items()):\n        if type(v) != list:\n            a[k] = [v]\n\n    to_ret = []\n    for values in list(product(*list(a.values()))):\n        to_ret.append({k:v for k,v in zip(a.keys(), values)})\n    return to_ret\n\ndef ar(a):\n    return np.array(a)\n\ndef rmtree(dir_path):\n    # print(f'Removing {dir_path}')\n    if os.path.isdir(dir_path):\n        shutil.rmtree(dir_path)\n    # else:\n        # print(f'{dir_path} is not a directory, so cannot remove')\n\ndef npr(x, decimals=2):\n    return np.round(x, decimals=decimals)\n    \ndef int_to_str(*keys):\n    return [list(map(lambda elt: str(elt), key)) for key in keys]\n    \ndef rm_mkdirp(dir_path, overwrite, quiet=False):\n    if os.path.isdir(dir_path):\n        if overwrite:\n            # if not quiet:\n                # print('Removing ' + dir_path)\n            shutil.rmtree(dir_path, ignore_errors=True)\n\n        else:\n            print('Directory ' + dir_path + ' exists and overwrite flag not set to true.  Exiting.')\n            exit(1)\n    if not quiet:\n        print('Creating ' + dir_path)\n    pathlib.Path(dir_path).mkdir(parents=True)\n\ndef lists_to_2d_arr(list_in, max_len=None):\n    '''2d list in, but where sub lists may have differing lengths, one big padded 2d arr out'''\n    max_len = max([len(elt) for elt in list_in]) if max_len is None else max_len\n    new_arr = np.zeros((len(list_in), max_len))\n    for i,elt in enumerate(list_in):\n        if len(elt) < max_len:\n            new_arr[i,:len(elt)] = elt\n        else:\n            new_arr[i,:] = elt[:max_len]\n    return new_arr\n\n\ndef mkdirp(dir_path):\n    if not os.path.isdir(dir_path):\n        pathlib.Path(dir_path).mkdir(parents=True)\n\ndef rm_file(file_path):\n    if os.path.exists(file_path):\n        os.remove(file_path)\n    \ndef rglob(dir_path, pattern):\n    return list(map(lambda elt: str(elt), pathlib.Path(dir_path).rglob(pattern)))\n\ndef move_matching_files(dir_path, pattern, new_dir, overwrite):\n    rm_mkdirp(new_dir, True, overwrite)\n    for elt in rglob(dir_path, pattern):\n        shutil.move(elt, new_dir)\n    \ndef subset(a, b):\n    return np.min([elt in b for elt in a]) > 0\n\ndef list_gpus():\n    return tf.config.experimental.list_physical_devices('GPU')\n\n\ndef save_pk(file_stub, pk, protocol=None):\n    filename = file_stub if '.pk' in file_stub else f'{file_stub}.pk'\n    with open(filename, 'wb') as f:\n        pickle.dump(pk, f, protocol=protocol)\n    \ndef load_pk(file_stub):\n    filename = file_stub\n    if not os.path.exists(filename):\n        return {}\n    \n    with open(filename, 'rb') as f:\n        obj = pickle.load(f)\n        return obj\n\ndef load_pk_old(filename):\n    with open(filename, 'rb') as f:\n        u = pickle._Unpickler(f)\n        u.encoding = 'latin1'\n        p = u.load()\n        return p\n\ndef get_ints(*keys):\n    return [int(key) for key in keys]\n\nclass NumpyEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, np.ndarray):\n            return obj.tolist()\n        return json.JSONEncoder.default(self, obj)\n\ndef save_json(file_stub, obj):\n    filename = file_stub\n    with open(filename, 'w') as f:\n        json.dump(obj, f, cls=NumpyEncoder, indent=4)\n\ndef load_json(file_stub):\n    filename = file_stub\n    with open(filename) as json_file:\n        return json.load(json_file)\n\ndef lfilter(fn, iterable):\n    return list(filter(fn, iterable))\n\ndef lkeys(obj):\n    return list(obj.keys())\n\ndef lvals(obj):\n    return list(obj.values())\n\ndef lmap(fn, iterable):\n    return list(map(fn, iterable))\n\ndef sort_dict(d, reverse=False):\n    return {k: v for k,v in sorted(d.items(), key=lambda elt: elt[1], reverse=reverse)}\n\ndef csv_path(sym):\n    return join('csvs', f'{sym}.csv')\n\ndef is_unique(a):\n    return len(np.unique(a)) == len(a)\n\ndef lists_equal(a,b):\n    return np.all([elt in b for elt in a]) and np.all([elt in a for elt in b])\n    \ndef split_arr(cond, arr):\n    return lfilter(cond, arr), lfilter(lambda elt: not cond(elt), arr)\n\ndef lzip(*keys):\n    return list(zip(*keys))\n\ndef arzip(*keys):\n    return [ar(elt) for elt in lzip(*keys)]\n    \ndef dilation_pad(max_len, max_dilation_rate):\n    to_ret = math.ceil(max_len/max_dilation_rate)*max_dilation_rate\n    assert (to_ret % max_dilation_rate) == 0\n    return to_ret\n\ndef zero_pad_to_length(data, length):\n    padAm = length - data.shape[0]\n    if padAm == 0:\n        return data\n    else:\n        return np.pad(data, ((0,padAm), (0,0)), 'constant')\n\ndef paths_to_mfbs(paths, max_len):\n    '''Get normalized & padded mfbs from paths'''\n    # normalize\n    mfbs = None\n    for file_name in paths:\n        if mfbs is None:\n            mfbs = np.array(np.load(file_name))\n        else:\n            mfbs = np.concatenate([mfbs, np.load(file_name)], axis=0)\n    mean_vec = np.mean(mfbs, axis=0)\n    std_vec  = np.std(mfbs, axis=0)\n\n    # concat & pad\n    mfbs = None\n    for file_name in paths:\n        mfb = (np.load(file_name) - mean_vec) / (std_vec + np.ones_like(std_vec)*1e-3)\n        if mfbs is None:\n            mfbs = np.array([zero_pad_to_length(mfb, max_len)])\n        else:\n            mfbs = np.concatenate([mfbs, [zero_pad_to_length(mfb, max_len)]], axis=0)\n    return tf.cast(mfbs, tf.float64)\n\ndef str2bool(v):\n    if isinstance(v, bool):\n       return v\n    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n        return True\n    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n        return False\n    else:\n        raise argparse.ArgumentTypeError('Boolean value expected.')\n\ndef destring(y, width=3):\n    ''' y is an array with elements in the format '.5;.5;0.'.  Need to turn into nx3 arr'''\n    y = np.array(y)\n    y_new = np.zeros((len(y), width))\n    for i in range(len(y)):\n        if '[0.333' in y[i]:\n            y_new[i] = [.333, .333, .333]\n            continue\n        assert ';' in y[i] or ' ' in y[i]\n        char = ';' if ';' in y[i] else None\n        y_new[i] = list(map(lambda elt: float(elt), y[i].split(char)))\n    return y_new\n\ndef get_batch(arr, batch_idx, batch_size):\n    return arr[batch_idx * batch_size:(batch_idx + 1) * batch_size]\n\ndef sample_batch(x, y, batch_size):\n    start = np.random.randint(x.shape[0]-batch_size)\n    x_batch = x[start:start+batch_size]\n    y_batch = y[start:start+batch_size]\n    return x_batch, y_batch\n\ndef get_mfbs(paths, lengths_dict, max_dilation_rate):\n    max_len = max([lengths_dict[file_name] for file_name in paths])\n    max_len = dilation_pad(max_len, max_dilation_rate)\n    return paths_to_mfbs(paths, max_len)\n\ndef shuffle_data(*arrs):\n    rnd_state = np.random.get_state()\n    for arr in arrs:\n        np.random.shuffle(arr)\n        np.random.set_state(rnd_state)\n\ndef get_class_weights(arr):\n    '''pass in dummies'''\n    class_weights = np.nansum(arr, axis=0)\n    return np.sum(class_weights) / (class_weights*len(class_weights))\n\ndef get_class_weights_ds(arr):\n    '''do not pass in dummies'''\n    arr = np.stack(np.unique(np.array(arr), return_counts=True), axis=1)\n    return (np.sum(arr[:,1]) - arr[:,1]) / arr[:,1]\n\ndef trunc(values, decs=0):\n    return np.trunc(values*10**decs)/(10**decs)\n\ndef path_to_func(path, func_name):\n    '''From path, import function from module at that path'''\n    import importlib.util\n    spec = importlib.util.spec_from_file_location(\"module.name\", path)\n    foo = importlib.util.module_from_spec(spec)\n    spec.loader.exec_module(foo)\n    return getattr(foo, func_name)\n\n    \ndef batch_split(arr, batch_size):\n    num_batches = np.ceil(arr.shape[0] / batch_size)\n    return [arr[batch_idx*batch_size:(batch_idx+1)*batch_size] for batch_idx in range(int(num_batches))]\n","sub_path":"ds_utils.py","file_name":"ds_utils.py","file_ext":"py","file_size_in_byte":14204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"199071228","text":"from computations.PredictorPhysics import *\nfrom computations.comp_utils.Measures import AngularMeasure\nfrom database.DatabaseAccessor import *\n# from simulations.data_generation_8_b_w_fake_do_not_use_easier_problem import next_batch\nfrom simulations.data_generation_10_b_w import next_batch\n\n\ndef test():\n    DEBUG = True\n    try:\n        os.remove('roulette-experiment.db')\n    except FileNotFoundError:\n        pass\n    da = DatabaseAccessor.get_instance()\n    np.random.seed(126)\n    nb = next_batch(debug=DEBUG)\n    session_id = da.increment_and_get_session_id()\n    bs_list = np.append(nb['abs_ball_times'], nb['abs_cutoff_time']) * 1000\n    ws_list = np.array(nb['abs_wheel_times']) * 1000\n\n    print(bs_list)\n    print(ws_list)\n\n    for bs in bs_list:\n        da.insert_ball_lap_times(session_id, bs)\n    for ws in ws_list:\n        da.insert_wheel_lap_times(session_id, ws)\n\n    PredictorPhysics.load_cache(da)\n    num_cutoff, _ = PredictorPhysics.predict_most_probable_number(bs_list[:-3], ws_list, debug=DEBUG)\n    e = AngularMeasure(nb['number_cutoff'], num_cutoff).error()\n    print(e)\n\n\nif __name__ == '__main__':\n    test()\n","sub_path":"simulations/physics/main_2.py","file_name":"main_2.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"411904001","text":"#!/usr/bin/python3\n# -*- coding: UTF-8 -*-\n'''\n前段测试环境自动化部署\n放在前段项目根目录下\ngwtph-test项目标识 yaml文件配置\n使用python deploy.py <gwtph-web> --branch <branch>\n'''\n\nimport os\n# import sys\nimport argparse\nimport yaml\nimport subprocess\nfrom datetime import datetime\nimport platform\n\n\nBASE_PATH = os.path.dirname(__file__)\n# 操作系统\nOS = platform.platform().split('-')[0]\nprint(BASE_PATH)\nprint(OS)\n\ndef parser_yaml():\n    yaml_path = os.path.join(BASE_PATH, 'deploy.yaml')\n    with open(yaml_path, 'r', encoding='utf8') as f:\n        return yaml.safe_load(f.read())\n\n\n# 将本地的~/.ssh/id_rsa.pub 内容追加到服务器 /root/.ssh/authorized_keys文件中（服务器运行echo \"公钥内容\" >>/root/.ssh/authorized_keys ）\ndef test_ssh_connect(server_ip='', **kwargs):\n    print('test connect {} ...'.format(server_ip))\n    command_args = ['ssh', 'root@{}'.format(server_ip), \"echo 'hello'\"]\n    res = subprocess.check_output(command_args, encoding='utf8')\n    return True if 'hello\\n' == res else False\n\n\ndef build_package(branch, project_path='', package_name='', build_command='yarn build', **kwargs):\n    os.chdir(project_path)\n    os.system('git checkout ' + branch)\n    os.system('git pull')\n    print('local executing: {0}'.format(build_command))\n    os.system(build_command)\n    if not os.path.exists('dist'):\n        print('build failed')\n        return False\n    os.system('mv dist ' + package_name)\n    return True\n\n\n# 最多保存5份\ndef backup_dist(server_ip, server_deploy_path, package_dir,package_name, back_nums=5):\n    backup_name = package_name + '_' + datetime.today().strftime('%m%d%H%M')\n    # suf = re.sub(u'([^\\u0041-\\u007a])', '', file)\n    # name = re.sub(u\"([^\\u0030-\\u0039])\", \"\", file)\n    command_args = ['ssh', 'root@{}'.format(server_ip), 'ls {}'.format(server_deploy_path)]\n    result_ls = subprocess.check_output(command_args, encoding='utf8')\n    file_list = result_ls.split('\\n')\n    backup_name_list = list(filter(lambda i:i.startswith(package_name + '_'), file_list))\n    # print(list(backup_name_list))\n    if (len(backup_name_list)>=back_nums-1):\n        # print(sorted(backup_name_list))\n        rm_file = sorted(backup_name_list)[0]\n        rm_file_path = os.path.join(server_deploy_path, rm_file)\n        if(OS == 'Windows'):\n          rm_file_path = rm_file_path.replace('\\\\', '/')\n        rm_command = \"rm -rf {0}\".format(rm_file_path)\n        os.system('ssh root@' + server_ip + ' ' + rm_command)\n        print(\"executing ssh backup command: \" + rm_command)\n    backup_name_path = os.path.join(server_deploy_path, backup_name)\n    if(OS == 'Windows'):\n          backup_name_path = backup_name_path.replace('\\\\', '/')\n    backup_command = \"mv {0} {1}\".format(package_dir, backup_name_path)\n    os.system('ssh root@' + server_ip + ' ' + backup_command)\n    print(\"executing ssh backup command: \" + backup_command)\n\n\ndef ssh_deploy(server_ip='', server_deploy_path='', package_name='', project_path='', **kwargs):\n    # backup\n    if(not os.path.exists(project_path)):\n      return print('project_path {} not exists'.format(project_path))\n    package_dir = os.path.join(server_deploy_path, package_name)\n    if(OS == 'Windows'):\n      package_dir = package_dir.replace('\\\\','/')\n    # 备份\n    backup_dist(server_ip, server_deploy_path, package_dir,package_name)\n\n    # scp translate\n    package_dir_local = os.path.join(project_path, package_name)\n    scp_command = \"scp -r {0} root@{1}:{2}\".format(package_dir_local, server_ip,server_deploy_path)\n    print('executing scp command: ' + scp_command)\n\n    os.system(scp_command)\n\n    print('deploy success')\n    # delete local dist\n    os.system('rm -rf' + ' ' + os.path.join(BASE_PATH, package_dir_local))\n\ndef deploy(project, branch):\n    server_dict = parser_yaml()\n    # print(server_dict)\n    if (project not in server_dict):\n        return print('%s not config, please add to deploy.yaml'%(project))\n    deploy_info = server_dict.get(project)\n    if(not test_ssh_connect(**deploy_info)):\n        return print('unable to connect {server_ip}'.format(**deploy_info))\n    else:\n      print('connected {}'.format(deploy_info['server_ip']))\n    if(build_package(branch, **deploy_info)):\n    # if(1):\n        ssh_deploy(**deploy_info)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('project', help='项目标识必填yaml文件配置')\n    parser.add_argument('--branch', help='发布分支名称，默认使用dev')\n    args = parser.parse_args()\n    project = args.project\n    branch = args.branch or 'dev'\n    deploy(project, branch)\n    # print(test_ssh_connect(server_ip='192.168.10.8'))\n","sub_path":"python_script/deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":4688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"650524455","text":"class Weapon:\n    def __init__(self,nm,strength):\n        self.name = nm\n        self.strength = strength\n\n\nclass Role:\n    def __init__(self, nm, wp):\n        '一般用于绑定属性到对象,  self 不是关键字 只是变量 任何都可以 非必须'\n        self.name = nm\n        self.wepon = wp\n\n\n    def show_me(self):\n        print('I am %s and my wepon is %s' % (self.name, self.wepon))\n\n\n\n\nif __name__ == '__main__':\n    # 实例将会自动作为第一个参数传给方法  lb-->self  吕布---> nm 方天画戟-->wp\n    wp = Weapon('方天画戟',100)\n    lb = Role('吕布',wp)  # 自动调用__init__方法\n    print(wp.name,wp.strength)\n    print(lb.wepon.name)  #wepon 是Role 的定义全局变量 但是又是另一个类Weapon的实例\n    #wepon-->wp--->Weapon\n","sub_path":"python/oop/class_join.py","file_name":"class_join.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"582849862","text":"\"\"\"Monitoring app celery tasks.\"\"\"\nimport csv\nfrom datetime import datetime\n\nfrom celery.utils.log import get_task_logger\n\nfrom servers.celery import app\n\nlogger = get_task_logger(__name__)\n\n\n@app.task\ndef process_csv_task(file_id):\n    \"\"\"Celery task to process the imported csv files.\"\"\"\n    from monitoring.models import CSVUpload, Login\n    file = CSVUpload.objects.get(pk=file_id)\n\n    logger.info(\"Processing file {}\".format(file))\n\n    with file.csv.open(mode=\"rt\") as f:\n        reader = csv.reader(f)\n        next(reader)  # skip the first row containing the headers\n\n        for row in reader:\n            logger.debug(row)\n\n            # define the csv rows layout:\n            server_name = row[0]\n            server_ip = row[1]\n            username = row[2]\n            full_name = row[3]\n            contact = row[4]\n            login_time = row[5]\n\n            # TODO: Current Limitations to be addressed\n            \"\"\"\n            Currently there is no check to check every single date both ways assumptions are made that:\n            if there is a back slash we expect the date to be in dd-mm-yy or dd-mm-YYYY\n            if there is a pipe divider we expect the same as a backslash\n            if there is a forward slash then we expect the date to be in YYYY/mm/dd or yy/mm/dd\n            if there is a dash then we expect to be standar YYYY-mm-dd format only\n\n            Note: All of these could be swapped around and there could be further ambiguity in reading date.\n            Some dates could be in the future as a result of this issue.\n\n            For any date not confined to any of the above we try to keep the entry but roll the date back to\n            Unix start date i.e. 01-01-1970 00:00:00\n            \"\"\"\n\n            try:\n                # 12\\10\\2016\n                if \"\\\"\" in login_time:\n                    if len(login_time) < 10:\n                        login_time = datetime.strptime(login_time, '%d\\%m\\%y')\n                    else:\n                        login_time = datetime.strptime(login_time, '%d\\%m\\%Y')\n\n                # 10|12|2016\n                elif \"|\" in login_time:\n                    if len(login_time) < 10:\n                        login_time = datetime.strptime(login_time, '%d|%m|%y')\n                    else:\n                        login_time = datetime.strptime(login_time, '%d|%m|%Y')\n\n                # 2016/10/12\n                elif \"/\" in login_time:\n                    if len(login_time) < 10:\n                        login_time = datetime.strptime(login_time, '%y/%m/%d')\n                    else:\n                        login_time = datetime.strptime(login_time, '%Y/%m/%d')\n\n                # 2016-10-12 12:00:00.000000\n                elif \"-\" and \":\" in login_time:\n                    login_time = datetime.strptime(login_time, '%Y-%m-%d %H:%M:%S.%f')\n\n                # 2016-10-12\n                elif \"-\" in login_time and login_time != \"login-time\":\n                    login_time = datetime.strptime(login_time, '%Y-%m-%d')\n\n                else:\n                    login_time = datetime.strptime('2017-12-12', '%Y-%m-%d')\n\n            # dates such as 17\\19\\06 or 17/19/06 don't make sense and will be defaulted back to start of unix time.\n            except ValueError:\n                login_time = datetime.strptime('1970-01-01', '%Y-%m-%d')\n                pass\n\n            # figure out if the contact detail is an email or number\n            # assuming that if its not an email then it must be a number\n            contact_email = \"\"\n            contact_number = \"\"\n            if \"@\" in contact:\n                contact_email = contact\n            else:\n                contact_number = contact\n\n            # process the data into the Login model\n            _, created = Login.objects.get_or_create(\n                server_ip=server_ip,\n                username=username,\n                full_name=full_name,\n                login_time=login_time,\n                defaults={\n                    'server_name': server_name,\n                    'contact_email': contact_email,\n                    'contact_number': contact_number,\n                }\n            )\n\n    # end of file parsing\n    file.csv.close()\n","sub_path":"monitoring/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":4206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"52522287","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport re\n\nMIPSSET = {\n    \"HLT\": {\"cycle\": 0, \"func\": \"halt\"},\n    \"J\": {\"cycle\": 0, \"func\": \"jump\"},\n    \"BEQ\": {\"cycle\": 0, \"func\": \"branch_eq\"},\n    \"BNE\": {\"cycle\": 0, \"func\": \"branch_neq\"},\n    \"LI\": {\"cycle\": 0, \"func\": \"load_imm\"},\n    \"LUI\": {\"cycle\": 0, \"func\": \"load_up_imm\"},\n    \"AND\": {\"cycle\": 1, \"func\": \"and_reg\"},\n    \"ANDI\": {\"cycle\": 1, \"func\": \"and_imm\"},\n    \"OR\": {\"cycle\": 1, \"func\": \"or_reg\"},\n    \"ORI\": {\"cycle\": 1, \"func\": \"or_imm\"},\n    \"LW\": {\"cycle\": 1, \"func\": \"load_word\"},\n    \"SW\": {\"cycle\": 1, \"func\": \"store_word\"},\n    \"ADD\": {\"cycle\": 2, \"func\": \"add_reg\"},\n    \"ADDI\": {\"cycle\": 2, \"func\": \"add_imm\"},\n    \"SUB\": {\"cycle\": 2, \"func\": \"sub_reg\"},\n    \"SUBI\": {\"cycle\": 2, \"func\": \"sub_imm\"},\n    \"MULT\": {\"cycle\": 4, \"func\": \"mult_reg\"},\n    \"MULTI\": {\"cycle\": 4, \"func\": \"mult_imm\"},\n}\n\nreg_pat = re.compile(\"(?<=R)\\d{,2}\")\noffset_pat = re.compile(\"\\d{,2}(?=\\()\")\n\n\ndef parse_reg(rt):\n\n    return to_dec(reg_pat.search(rt).group())\n\n\ndef parse_offset(offset):\n\n    return int(offset_pat.search(offset).group())\n\n\ndef to_dec(num):\n\n    return int(num.replace(\"H\", \"\"), 16) if \"H\" in num else int(num)\n\n\ndef hex_to_dec_to_hex(num):\n\n    return int(hex(num)[2:])\n\n\nclass MIPS(object):\n\n    def __init__(self, datamem=[], registers=[]):\n\n        self.DATAMEM = datamem\n        self.REG = registers\n\n    def parse_addr(self, addr):\n\n        # if there is an offset in the immediate value\n        if \"(\" in addr:\n            # 100/4 = 25 -> 32 - 25 = 7\n            return (\n                parse_offset(addr) +\n                hex_to_dec_to_hex(self.REG[parse_reg(addr)]) / 4 + 7\n            )\n\n        return to_dec(addr)\n\n    def and_reg(self, ops, prog_ctr):\n\n        rd = parse_reg(ops[0])\n        rs = parse_reg(ops[1])\n        rt = parse_reg(ops[2])\n\n        self.REG[rd] = self.REG[rs] & self.REG[rt]\n\n    def and_imm(self, ops, prog_ctr):\n\n        rd = parse_reg(ops[0])\n        rs = parse_reg(ops[1])\n        imm = self.parse_addr(ops[2])\n\n        self.REG[rd] = self.REG[rs] & imm\n\n    def or_reg(self, ops, prog_ctr):\n\n        rd = parse_reg(ops[0])\n        rs = parse_reg(ops[1])\n        rt = parse_reg(ops[2])\n\n        self.REG[rd] = self.REG[rs] | self.REG[rt]\n\n    def or_imm(self, ops, prog_ctr):\n\n        rd = parse_reg(ops[0])\n        rs = parse_reg(ops[1])\n        imm = self.parse_addr(ops[2])\n\n        self.REG[rd] = self.REG[rs] | imm\n\n    def add_reg(self, ops, prog_ctr):\n\n        rd = parse_reg(ops[0])\n        rs = parse_reg(ops[1])\n        rt = parse_reg(ops[2])\n\n        self.REG[rd] = self.REG[rs] + self.REG[rt]\n\n    def add_imm(self, ops, prog_ctr):\n\n        rd = parse_reg(ops[0])\n        rs = parse_reg(ops[1])\n        imm = self.parse_addr(ops[2])\n\n        self.REG[rd] = self.REG[rs] + imm\n\n    def sub_reg(self, ops, prog_ctr):\n\n        rd = parse_reg(ops[0])\n        rs = parse_reg(ops[1])\n        rt = parse_reg(ops[2])\n\n        self.REG[rd] = self.REG[rs] - self.REG[rt]\n\n    def sub_imm(self, ops, prog_ctr):\n\n        rd = parse_reg(ops[0])\n        rs = parse_reg(ops[1])\n        imm = self.parse_addr(ops[2])\n\n        self.REG[rd] = self.REG[rs] - imm\n\n    def mult_reg(self, ops, prog_ctr):\n\n        rd = parse_reg(ops[0])\n        rs = parse_reg(ops[1])\n        rt = parse_reg(ops[2])\n\n        self.REG[rd] = self.REG[rs] * self.REG[rt]\n\n    def mult_imm(self, ops, prog_ctr):\n\n        rd = parse_reg(ops[0])\n        rs = parse_reg(ops[1])\n        imm = self.parse_addr(ops[2])\n\n        self.REG[rd] = self.REG[rs] * imm\n\n    def load_imm(self, ops, prog_ctr):\n\n        rt = parse_reg(ops[0])\n        addr = self.parse_addr(ops[-1])\n        self.REG[rt] = addr\n\n    def load_up_imm(self, ops, prog_ctr):\n\n        rt = parse_reg(ops[0])\n        addr = self.parse_addr(ops[-1]) << 16\n        self.REG[rt] = addr\n\n    def load_word(self, ops, prog_ctr):\n\n        rt = parse_reg(ops[0])\n        self.REG[rt] = int(self.DATAMEM[self.parse_addr(ops[-1])], 2)\n\n    def store_word(self, ops, prog_ctr):\n\n        rt = parse_reg(ops[0])\n        rs = self.parse_addr(ops[-1])\n        self.DATAMEM[rs] = '{:032b}'.format(self.REG[rt])\n\n    def branch_eq(self, ops, prog_ctr):\n\n        rs = parse_reg(ops[0])\n        rt = parse_reg(ops[1])\n        return self.REG[rs] == self.REG[rt]\n\n    def branch_neq(self, ops, prog_ctr):\n\n        rs = parse_reg(ops[0])\n        rt = parse_reg(ops[1])\n        return self.REG[rs] != self.REG[rt]\n","sub_path":"CMSC411-Computer-Architecture/proj/src/architecture/mips.py","file_name":"mips.py","file_ext":"py","file_size_in_byte":4443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"506299085","text":"#! usr/bin/env python3\r\n# -*- Coding: UTF-8 -*-\r\n\r\nfrom django.core.management.base import BaseCommand, CommandError\r\nfrom django.db.utils import IntegrityError\r\nimport requests\r\nfrom purbeurre.models.products import Products\r\nfrom purbeurre.models.categories import Categories\r\n\r\n\r\nclass APIInformation:\r\n    \"\"\"\r\n    Parameters needed in order to use the OpenFoodFacts's API\r\n    \"\"\"\r\n    PRODUCTS_LINK = \"https://fr.openfoodfacts.org/cgi/search.pl?\"\r\n    PARAMETERS = {\r\n        \"search_simple\": '1',\r\n        \"action\": 'process',\r\n        \"tagtype_0\": 'categories',\r\n        \"tagtype_1\": \"nutrition_grades\",\r\n        \"tag_contains_0\": 'contains',\r\n        \"page\": 1,\r\n        \"page_size\": 100,\r\n        \"json\": '1',\r\n    }\r\n    NUTRISCORE = ['A', 'B', 'C', 'D', 'E']\r\n    CATEGORIES = [\r\n        \"Snacks salés\",\r\n        \"Produits à tartiner salés\",\r\n        \"Pâtes à tartiner aux noisettes\",\r\n        \"Biscuits au chocolat\",\r\n        \"Biscuits apéritifs\",\r\n        \"Biscuits sablés\",\r\n        \"Biscuits fourrés\",\r\n        \"Biscuits secs\",\r\n        \"Biscuits au chocolat au lait\",\r\n        \"Fromages\",\r\n        \"Confitures\",\r\n        \"Chocolats\",\r\n        \"Viennoiserie\",\r\n        \"Dessert glacés\",\r\n        \"Sirop\",\r\n        \"Jus de fruits\",\r\n        \"Sodas\"\r\n    ]\r\n\r\n\r\nclass Command(BaseCommand):\r\n    \"\"\"\r\n    Get Openfoodfacts products in project database\r\n    \"\"\"\r\n\r\n    def get_categories(self, categories):\r\n        \"\"\"\r\n        Get categories by name inserted in parameter\r\n        \"\"\"\r\n        category, created = Categories.objects.get_or_create(name=categories)\r\n        category.save()\r\n\r\n    def get_products(self, data, category):\r\n        \"\"\"\r\n        Get products filtering needed product information and\r\n        tie it to a category\r\n        \"\"\"\r\n        for product_information in data['products']:\r\n            name = product_information.get('product_name', None)\r\n            # in order to remove linebreak from product name\r\n            # print(\"WITH LINEBREAK : \", repr(name))\r\n            if name:\r\n                name = name.replace('\\n', '')\r\n                # print(\"WITHOUT LINEBREAK : \", repr(name))\r\n            category = Categories.objects.get(name=category)\r\n            nutriscore = product_information.get('nutrition_grades', None)\r\n            link = product_information.get('url', None)\r\n            image = product_information.get('image_url', None)\r\n            nutrition_image = product_information.get\\\r\n                ('image_nutrition_url', None)\r\n            if category is None \\\r\n                    or name is None \\\r\n                    or len(name) > 75 \\\r\n                    or nutriscore is None \\\r\n                    or link is None \\\r\n                    or image is None \\\r\n                    or nutrition_image is None:\r\n                continue\r\n            else:\r\n                try:\r\n                    product, created = Products.objects.get_or_create(\r\n                        name=str(name),\r\n                        category=category,\r\n                        nutriscore=nutriscore,\r\n                        link=link,\r\n                        image=image,\r\n                        nutrition_image=nutrition_image,\r\n                    )\r\n                    if created:\r\n                        product.save()\r\n                        print(product.name)\r\n\r\n                except Products.DoesNotExist:\r\n                    raise CommandError(\"Products %s could not been reached\"\r\n                                       % name)\r\n                except IntegrityError:\r\n                    continue\r\n\r\n    def handle(self, *args, **options):\r\n        \"\"\"\r\n        Loop open list of categories and create it,\r\n        then get products through request and return json response\r\n        which will be parsed\r\n        \"\"\"\r\n        for category in APIInformation.CATEGORIES:\r\n            self.get_categories(category)\r\n            APIInformation.PARAMETERS['tag_0'] = category\r\n            for tag in APIInformation.NUTRISCORE:\r\n                APIInformation.PARAMETERS['tag_1'] = tag\r\n                response = requests.get(APIInformation.PRODUCTS_LINK,\r\n                                        params=APIInformation.PARAMETERS)\r\n                products = response.json()\r\n\r\n                self.get_products(products, category)\r\n","sub_path":"purbeurre/management/commands/api_openfoodfacts.py","file_name":"api_openfoodfacts.py","file_ext":"py","file_size_in_byte":4314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"314267220","text":"# coding=utf-8\n#\n# ─── MAINMENU CLASS ─────────────────────────────────────────────────────────────\n#\nfrom tkinter import *\n# from Configuration import Configuration\n\nclass MainMenu:\n    def __init__(self, master, settings):\n        self.config = {}\n        self.config['bg_colour'] = \"#F19C79\"\n        self.settings = settings\n        self.state = 0\n        \n        #\n        # ─── DEFINE THE FRAME ────────────────���───────────────────────────\n        #\n        self.frame = Frame(master, background=\"#F19C79\", borderwidth=10, height=720, width=720)\n        self.frame.pack()\n\n        #\n        # ─── DEFINE THE IMAGES ───────────────────────────────────────────\n        #\n        # Choose Mode Btn\n        self.square_btn_img = PhotoImage(file=\"assets/stdBtn.png\")\n        self.hex_btn_img    = PhotoImage(file=\"assets/hexBtn.png\")\n\n        # Choose Size Btn\n        self.small_btn_img  = PhotoImage(file=\"assets/small_btn.png\")\n        self.medium_btn_img = PhotoImage(file=\"assets/medium_btn.png\")\n        self.large_btn_img  = PhotoImage(file=\"assets/large_btn.png\")\n\n        self.normal_btn_img  = PhotoImage(file=\"assets/normal_btn.png\")\n        self.hard_btn_img    = PhotoImage(file=\"assets/hard_btn.png\")\n        self.extreme_btn_img = PhotoImage(file=\"assets/extreme_btn.png\")\n        \n        # Labels\n        self.title_img             = PhotoImage(file=\"assets/title.png\")\n        self.select_size_img       = PhotoImage(file=\"assets/select_size.png\")\n        self.select_difficulty_img = PhotoImage(file=\"assets/select_difficulty.png\")\n\n        #highscore_btn_img = PhotoImage(file=\"assets/highscoreBtn.png\")\n\n\n        #\n        # ─── CREATE COMMON COMPONENTS ────────────────────────────────────\n        #\n        self.title_label = Label(\n                                image=self.title_img,\n                                bg=self.config['bg_colour']\n                                )\n            \n        #\n        # ─── CREATE SELECT MODE COMPONENTS ───────────────────────────────\n        #\n        self.square_button = self.create_button(self.square_btn_img, \"game_mode\", \"SQUARE\")\n        self.hex_button = self.create_button(self.hex_btn_img, \"game_mode\", \"HEX\")\n        \n        #\n        # ─── CREATE SELECT SIZE COMPONENTS ───────────────────────────────\n        #\n        self.select_size_label = Label(\n                                image=self.select_size_img,\n                                bg=self.config['bg_colour']\n                                )\n        self.small_button  = self.create_button(self.small_btn_img, \"game_size\", 8)\n        self.medium_button = self.create_button(self.medium_btn_img, \"game_size\", 12)\n        self.large_button  = self.create_button(self.large_btn_img, \"game_size\", 16)\n        #\n        # ─── CREATE SELECT DIFFICULTY COMPONENTS ─────────────────────────\n        #\n        self.select_difficulty_label = Label(\n                                image=self.select_difficulty_img,\n                                bg=self.config['bg_colour']\n                                )\n\n        self.normal_button  = self.create_button(self.normal_btn_img, \"game_difficulty\", 0.05)\n        self.hard_button    = self.create_button(self.hard_btn_img, \"game_difficulty\", 0.2)\n        self.extreme_button = self.create_button(self.extreme_btn_img, \"game_difficulty\", 0.3)\n            \n        #\n        # ─── PACK AND PLACE COMPONENTS ───────────────────────────────────\n        #\n        self.square_button.pack()\n        self.hex_button.pack()\n        self.title_label.pack()\n        self.square_button.place(bordermode=OUTSIDE, x=280, y=300)\n        self.hex_button.place(bordermode=OUTSIDE, x=280, y=400)\n        self.title_label.place(bordermode=OUTSIDE, x=60, y=80)\n\n\n    #\n    # ─── FUNCTION TO CREATE A BUTTON OBJECT ─────────────────────────────────────────\n    #\n    def create_button(self, display_image, spec_key, spec_value):\n        return Button(\n                self.frame,\n                image=display_image,\n                command=lambda key=spec_key, value=spec_value: self.save_setting(key, value),\n                width=149,\n                height=47,\n                bg=self.config['bg_colour'],\n                relief=FLAT,\n                borderwidth=0,\n                highlightthickness=0,\n                bd=0\n             )\n\n\n    #\n    # ─── FUNCTION FOR STATE SWITCHING ───────────────────────────────────────────────\n    #\n    def switch_state(self):\n        self.state += 1\n        if self.state == 1:\n            self.clean_components(\"MODE\")\n            self.display(\"SIZE\");\n        elif self.state == 2:\n            self.clean_components(\"SIZE\")\n            self.display(\"DIFFICULTY\")\n        else:\n            self.clean_components(\"DIFFICULTY\")\n            self.stop_menu()\n            \n\n    #\n    # ─── DISPLAY GROUPS OF COMPONENTS ───────────────────────────────────────────────\n    #\n    def display(self, group):\n        if group == \"SIZE\":\n            # ─── PACK AND PLACE SIZE SELECTION COMPONENTS ────────────────────\n            self.select_size_label.pack()\n            self.small_button.pack()\n            self.medium_button.pack()\n            self.large_button.pack()\n\n            self.small_button.place(bordermode=OUTSIDE, x=280, y=350)\n            self.medium_button.place(bordermode=OUTSIDE, x=280, y=450)\n            self.large_button.place(bordermode=OUTSIDE, x=280, y=550)\n            self.select_size_label.place(bordermode=OUTSIDE, x=165, y=250)\n        elif group == \"DIFFICULTY\":\n            # ─── PACK AND PLACE DIFFICULTY SELECTION COMPONENTS ──────────────\n            self.select_difficulty_label.pack()\n            self.normal_button.pack()\n            self.hard_button.pack()\n            self.extreme_button.pack()\n\n            self.normal_button.place(bordermode=OUTSIDE, x=280, y=350)\n            self.hard_button.place(bordermode=OUTSIDE, x=280, y=450)\n            self.extreme_button.place(bordermode=OUTSIDE, x=280, y=550)\n            self.select_difficulty_label.place(bordermode=OUTSIDE, x=165, y=250)\n\n\n    #\n    # ─── FUNCTION TO SAVE A SETTING ─────────────────────────────────────────────────\n    #\n    def save_setting(self, setting_key, setting_value, quit=False):\n        self.settings[setting_key] = setting_value\n        print(\"Setting Key:\", setting_key, \"Setting Value:\", setting_value)\n        self.switch_state();\n\n\n    #\n    # ─── FUNCTION TO EXIT THE MAIN MENU ─────────────────────────────────────────────\n    #\n    def stop_menu(self):\n        self.frame.destroy()\n        self.frame.quit()\n    \n\n    #\n    # ─── FUNCTION TO DESTROY ALL COMPONENTS [CLEAN UP] ──────────────────────────\n    #\n    def clean_components(self, group):\n        if group == \"MODE\":\n            self.square_button.destroy()\n            self.hex_button.destroy()\n        elif group == \"SIZE\":\n            self.small_button.destroy()\n            self.medium_button.destroy()\n            self.large_button.destroy()\n            self.select_size_label.destroy()\n        elif group == \"DIFFICULTY\":\n            self.title_label.destroy()\n            self.normal_button.destroy()\n            self.hard_button.destroy()\n            self.extreme_button.destroy()\n            self.select_difficulty_label.destroy()\n        ","sub_path":"MainMenu.py","file_name":"MainMenu.py","file_ext":"py","file_size_in_byte":8523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"47948031","text":"linea=str(input())\nlinesplit=linea.split()\na=float(linesplit[0])\nb=float(linesplit[1])\nc=float(linesplit[2])\npi=3.14159\ntri=(1/2)*a*c\ncir=pi*pow(c,2)\ntrap=((a+b)/2)*c\nsq=pow(b,2)\nrec=a*b\nprint('TRIANGULO: %0.3f'%tri)\nprint('CIRCULO: %0.3f'%cir)\nprint('TRAPEZIO: %0.3f'%trap)\nprint('QUADRADO: %0.3f'%sq)\nprint('RETANGULO: %0.3f'%rec)","sub_path":"1012.py","file_name":"1012.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"303102807","text":"import logging\n\ndef get_logger(moduleName, logFilePath):\n\n    logger = logging.getLogger(moduleName)\n    logger.setLevel(logging.DEBUG)\n\n    formatter = logging.Formatter('[%(asctime)s] : %(name)s - %(levelname)s - %(message)s')\n\n    cnsl_handler = logging.StreamHandler()\n    cnsl_handler.setLevel(logging.INFO)\n    cnsl_handler.setFormatter(formatter)\n\n    file_handler = logging.FileHandler(logFilePath)\n    file_handler.setLevel(logging.INFO)\n    file_handler.setFormatter(formatter)\n\n    logger.addHandler(cnsl_handler)\n    logger.addHandler(file_handler)\n\n    return logger\n","sub_path":"src/loggerSetup.py","file_name":"loggerSetup.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"175880002","text":"def roadsAndLibraries(n, c_lib, c_road, cities):\n    adj = {i: [] for i in range(1, n + 1)}\n    for v1, v2 in cities:\n        adj[v1].append(v2)\n        adj[v2].append(v1)\n    if c_road >= c_lib:\n        return c_lib * n\n    visited = set()\n\n    def dfs(u):\n        visited.add(u)\n        ans = 1\n        for v in adj[u]:\n            if v not in visited:\n                ans += dfs(v)\n        return ans\n\n    res = 0\n    for i in range(1, n + 1):\n        if i not in visited:\n            count = dfs(i)\n            res += c_lib + (count - 1) * c_road\n    return res\n\n\nprint(roadsAndLibraries(3, 2, 1, [[1, 2], [3, 1], [2, 3]]))\nprint(roadsAndLibraries(8, 2, 3, [[1, 7], [1, 3], [1, 2], [2, 3], [5, 6], [6, 8]]))\n","sub_path":"hackerank/2022/medium/road_and_libraries.py","file_name":"road_and_libraries.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"8440231","text":"\n# coding: utf-8\n\n# In[30]:\n\nfrom math import factorial\ncn=0\nn=0\nwhile cn <= 1000000:\n    cn = (factorial(2*n))/(factorial(n+1)*factorial(n))\n    if cn <= 1000000:\n        print(cn)\n    n += 1\n    \n\n\n# In[ ]:\n\n\n\n\n# In[ ]:\n\n\n\n\n# In[ ]:\n\n\n\n","sub_path":"Actividad2/Problema5.py","file_name":"Problema5.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"505629392","text":"# The MIT License (MIT)\n#\n# Copyright (c) 2021 Jonah Yolles-Murphy (TG-Techie)\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# 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 FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\nfrom __future__ import annotations\n\nfrom tg_gui_core import State, DerivedState, uid, themedwidget, _Screen_\nfrom tg_gui_platform.label import Label\n\nimport time\nfrom time import struct_time\n\n__all__ = (\n    \"Date\",\n    \"refresh_time\",\n    \"refresh_date\",\n)\n\ntry:\n    from typing import ClassVar, Type\nexcept:\n    pass\n\n_MONTHS = (\n    \"<INVALID MONTH>\",\n    \"January\",\n    \"February\",\n    \"March\",\n    \"April\",\n    \"May\",\n    \"June\",\n    \"July\",\n    \"August\",\n    \"September\",\n    \"October\",\n    \"Novemer\",\n    \"December\",\n)\n\n_WEEKDAYS = (\n    \"Monday\",\n    \"Tuesday\",\n    \"Wednesday\",\n    \"Thursday\",\n    \"Friday\",\n    \"Saturday\",\n    \"Sunday\",\n)\n_SHORTWEEKDAYS = (\"mon\", \"tue\", \"wed\", \"thu\", \"fri\", \"sat\", \"sun\")\n\n\n@themedwidget\nclass Date(Label):\n\n    # _default_styling_ = {}\n    # _default_styling_.update(Label._default_styling_)\n    # _default_styling_.update(fit_to_text=True)\n\n    _prev_refresh: ClassVar[struct_time] = time.localtime()\n\n    year, month, weekday = State(0000), State(00), State(0)\n    monthday, yearday = State(00), State(000)\n    hours, mins, secs = State(0), State(0), State(0)\n\n    _date_components: ClassVar[dict[str, State]] = {\n        # TODO: change the names of the date component dict keys to something a bit clearer\n        \"year\": year,\n        \"month\": month,\n        \"weekday\": weekday,\n        \"monthday\": monthday,\n        \"yearday\": yearday,\n        \"hours\": hours,\n        \"mins\": mins,\n        \"secs\": secs,\n        \"monthname\": DerivedState(month, lambda m: _MONTHS[((m + 1) % 13) - 1]),\n        \"shortmonth\": DerivedState(month, lambda m: _MONTHS[((m + 1) % 13) - 1][0:3]),\n        \"dayname\": DerivedState(weekday, lambda w: _WEEKDAYS[w]),\n        \"shortyear\": DerivedState(year, lambda y: y % 100),\n    }\n\n    def __init__(self, format: str, **kwargs) -> None:\n\n        self._format_src = format\n\n        # # find which date components this\n        # self._components = comps = {\n        #     name: cmp for name, cmp in self._date_components.items() if name in format\n        # }\n        deps = tuple(\n            cmp for name, cmp in self._date_components.items() if name in format\n        )\n\n        self._inst_state = state = (\n            DerivedState(deps, self._derive_new_str) if len(deps) else None\n        )\n\n        if format == \"\":\n            self._id_ = uid()  # hack in identiable for setting purposes\n        else:\n            super().__init__(\n                format if state is None else state,\n                **kwargs,\n            )\n\n    @classmethod\n    def dateshort(cls: \"Type[Date]\", **kwargs) -> \"Date\":\n        return cls(\"{monthday:02}{shortmonth:02}{year}\", **kwargs)\n\n    @classmethod\n    def american_date(cls: \"Type[Date]\", **kwargs) -> \"Date\":\n        return cls(\"{month:02}/{monthday:02}/{year}\", **kwargs)\n\n    @classmethod\n    def international_date(cls: \"Type[Date]\", **kwargs) -> \"Date\":\n        return cls(\"{monthday:02}/{month:02}/{year}\", **kwargs)\n\n    @classmethod\n    def time(cls: \"Type[Date]\", secs=False, **kwargs) -> \"Date\":\n        return cls(\n            \"{hours:02}:{mins:02}:{secs:02}\" if secs else \"{hours:02}:{mins:02}\",\n            **kwargs,\n        )\n\n    def _derive_new_str(self, *_) -> str:\n        # takes any number of args b/c the order is not guaranteed\n        format = self._format_src\n        return format.format(\n            **{\n                name: state.value(self)\n                for name, state in self._date_components.items()\n                if name in format\n            }\n        )\n\n    def _refresh_time(self, now: struct_time) -> None:\n\n        # if is 24 hour time:\n        self.hours = now.tm_hour\n        self.mins = now.tm_min\n        self.secs = now.tm_sec\n\n        if type(self)._prev_refresh.tm_hour > now.tm_hour:\n            self._refresh_date(now)\n\n    def _refresh_date(self, now: struct_time) -> None:\n\n        self.year = now.tm_year\n        self.month = now.tm_mon\n        self.monthday = now.tm_mday\n        self.weekday = now.tm_wday\n        self.yearday = now.tm_yday\n\n        type(self)._prev_refresh = now\n\n\n_date_inst = Date(\"\")\n_date_inst._refresh_date(time.localtime())\n_date_inst._refresh_time(time.localtime())\n\n# --- interface ---\n# these should be registed into the event loop for the gui\nrefresh_time = _date_inst._refresh_time\nrefresh_date = _date_inst._refresh_date\n\n_Screen_._register_recurring_update_(\n    _date_inst,\n    lambda: refresh_time(time.localtime()),\n    1.0,\n)\n","sub_path":"tg_gui/date.py","file_name":"date.py","file_ext":"py","file_size_in_byte":5569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"7818887","text":"#!/usr/bin/python3\n#Community and school network\n#Author :   Ravisutha Sakrepatna Srinivasamurthy\n#Project:   Analysis of Chicago Crime Data\n\nimport datetime\nimport csv\nimport numpy as np\n\nclass SchoolNetwork:\n    def __init__(self, path, code=110000, debug=False):\n        \"\"\" Accepts the path of school data and community data. \"\"\"\n\n        #Get the path for the required datasets\n        self.school_path = path[0];\n\n        #Code number for this network\n        self.code = code\n\n        #Debug mode ON/OFF\n        self.debug = debug\n\n        #Build school network\n        self._school_network ()\n\n    def get_network (self):\n        \"\"\"Returns a dict containing edge list from community to school station. \"\"\"\n\n        return (self.community)\n\n    #Build school network\n    def _school_network (self):\n        \"\"\"Connect schools and community. \"\"\"\n\n        self.community = {}\n\n        #Initialize the community dictionary\n        for i in range (1, 78):\n            self.community[i] = {}\n\n        #Open School dataset\n        with open (self.school_path, \"rt\") as f:\n\n            reader = csv.reader (f)\n            i = 0\n\n            #Go through all rows\n            for row in reader:\n\n                #Skip first line\n                if (i == 0):\n                    i += 1\n                    continue\n\n                #Read last column\n                communities = row[-1].replace (')', '')\n                communities = communities.replace ('(', '')\n                communities = communities.replace (' ', '')\n\n                #For multiple communities, assign the school data \n                for community in communities.split (','):\n                    if (community != '1.1'):\n                        community = int (float (community))\n\n                    if (community in self.community):\n                        try: \n                            self.community[community][i + self.code] = float (row[12])\n                        except ValueError:\n                            self._my_print (\"Bad Data\")\n                            continue\n                    else:\n                        self._my_print (\"Bad Data: School with community {}\".format (community))\n                i += 1\n\n    def _my_print (self, argv):\n        if (self.debug == True):\n            print (argv)\n\ndef main ():\n    \"\"\" Main function.\"\"\"\n\n    path = []\n    path.append (\"../../Data/2015/school_2015.csv\")\n\n    a = SchoolNetwork (path, debug=True)\n    print (a.get_network ())\n\nif (__name__=='__main__'):\n    main ()\n","sub_path":"Code/Network/school_network.py","file_name":"school_network.py","file_ext":"py","file_size_in_byte":2518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"182857203","text":"# Zad3. Napisz skrypt, w którym stworzysz operatory przyrostkowe dla operacji: +, -, *, /, **, %\n\n#MACIEJ SZULIA 154733\n\na,b,c,d,e,d,f,g,h = [1,2,3,4,5,6,7,8,9]\n\n\na = ++a\nb = --b\nc = c * c\nd = d / d\ne = e ** e\nf = f % f\n\nzmienne = [a,b,c,d,e,d,f,g,h]\n\nfor x in range(6):\n    print(zmienne[x])","sub_path":"praca-domowa-1-wstęp/zad_lab_3.py","file_name":"zad_lab_3.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"365388655","text":"from django.utils.version import get_version\nfrom django.views.generic import View, TemplateView, ListView, DetailView\nfrom django.shortcuts import render\nfrom django.core.cache import caches\nfrom django.db.models import Q\nfrom django.conf import settings\nfrom freezingsimulation.models import ArticleFreezingsimu, CategoryFreezingsimu\nfrom django.http import HttpResponse, JsonResponse\nimport os\nimport json\nimport logging\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom numpy import linspace, sin, shape, argmax, argmin\nimport freezingsimulation.fd_nopac as fd\nfrom django.utils.decorators import classonlymethod\n# Create your views here.\n\n# 缓存\ntry:\n    cache = caches['memcache']\nexcept ImportError as e:\n    cache = caches['default']\n\nlogger = logging.getLogger(__name__)\n\n\n# * Base_Mixin\nclass Base_Mixin(object):\n    \"\"\"Basic mix class.\"\"\"\n\n    def get_context_data(self, *args, **kwargs):\n        context = super(Base_Mixin, self).get_context_data(**kwargs)\n        try:\n            # 网站标题等内容\n            context['website_title'] = settings.WEBSITE_TITLE\n            context['website_welcome'] = settings.WEBSITE_WELCOME\n            context['django_version'] = get_version()\n            user = self.request.user\n            if user.is_authenticated():\n                context[\n                    'notification_count'] = user.to_user_notification_set.filter(\n                        is_read=0).count()\n        except Exception:\n            logger.error(u'[BaseMixin]加载基本信息出错')\n\n        return context\n\n\n# * Index_View\nclass Index_View(Base_Mixin, ListView):\n    \"\"\"view for index.html\"\"\"\n\n    template_name = 'freezingsimulation/index.html'\n    context_object_name = 'article_list'\n    paginate_by = settings.PAGE_NUM  # 分页--每页的数目\n\n    def get_queryset(self):\n        article_list = ArticleFreezingsimu.objects.filter(status=0)\n        return article_list\n\n\n# * Freezingsimu\nclass Freezingsimu_View(Base_Mixin, ListView):\n    \"\"\"view for freezingsimu.html\"\"\"\n\n    template_name = 'freezingsimulation/freezingsimu.html'\n    context_object_name = 'article_list'\n    paginate_by = settings.PAGE_NUM  # 分页--每页的数目\n\n    def get_queryset(self):\n        article_list = ArticleFreezingsimu.objects.filter(status=0)\n        return article_list\n\n# ** Freezingsimu-DoingSimulation.\n\n    @classonlymethod\n    def simu(self, request):\n\n        # Acquire data from request.GET.\n        food_product = request.GET.get('food_product')\n        temperature = request.GET.get('temperature')\n        heattr = request.GET.get('heattr')\n        shelfthickness = request.GET.get('shelfthickness')\n        food_product = float(food_product)\n        temperature = float(temperature)\n        heattr = float(heattr)\n        shelfthickness = float(shelfthickness)\n\n        file_dir = 'media/freezingsimulation/'\n        plot_file_base_name = 'simu_{}_{}_{}_{}'.format(\n            int(food_product),\n            int(temperature), int(heattr), int(shelfthickness))\n\n        # Doing simulation actually.\n        Tc, Ts_top, Ts_bottom, qq_top, qq_bottom, time, T_all, H_all = fd.freezing_simu(\n            Ta=temperature, Tp=food_product, ha=heattr,\n            dp=shelfthickness).simu()\n        T_all = T_all[0:T_all.shape[0]:100]\n        H_all = H_all[0:H_all.shape[0]:100]\n\n        # Plot on temperature change of thermal center.\n        fig1, ax1 = plt.subplots(figsize=(5, 5), dpi=98)\n        ax1.set_title(u'食品温度变化', fontproperties='KaiTi')\n        ax1.set_xlabel(u'冷冻时间(小时)', fontproperties='KaiTi')\n        ax1.set_ylabel(u'食品温度(\\u2103)', fontproperties='KaiTi')\n        ax1.plot(time[0:len(time):100], Tc[0:len(Tc):100], 'yo',\n                 time[0:len(time):100], Ts_bottom[0:len(Ts_bottom):100], 'ms',\n                 time[0:len(time):100], Ts_top[0:len(Ts_top):100], 'cs')\n        fig1.savefig(\n            os.path.join(file_dir, ''.join([plot_file_base_name, '_1.png'])))\n\n        # Plot on heat load change.\n        fig2 = plt.figure(figsize=(5, 5), dpi=98)\n        p2 = fig2.add_subplot(111)\n        plt.title(u'食品负荷变化', fontproperties='KaiTi')\n        plt.xlabel(u'冷冻时间(小时)', fontproperties='KaiTi')\n        plt.ylabel(u'食品负荷(W)', fontproperties='KaiTi')\n        p2.plot(time[0:len(time):100], qq_top[0:len(qq_top):100], 'mo',\n                time[0:len(time):100], qq_bottom[0:len(qq_bottom):100], 'cs')\n        fig2.savefig(\n            os.path.join(file_dir, ''.join([plot_file_base_name, '_2.png'])))\n\n        # Animation on temperature change of food product.\n        fig3, ax3 = plt.subplots(figsize=(5, 5))\n        ax3.set_xlim(-1, 24)\n        ax3.set_ylim(-30, 30)\n        ax3.set_title(u'食品离散点温度', fontproperties='KaiTi')\n        ax3.set_xlabel(u'离散点', fontproperties='KaiTi')\n        ax3.set_ylabel(u'温度(\\u2103)', fontproperties='KaiTi')\n        line3, = ax3.plot(\n            range(T_all[1, ].shape[0]), T_all[1, ], 'r--o', linewidth=2)\n        n3 = argmax(T_all[1, ])\n        line3_center, = ax3.plot(\n            (range(T_all[1, ].shape[0])[n3], range(T_all[1, ].shape[0])[n3]),\n            (T_all[1, ][n3] - 5, T_all[1, ][n3] + 5),\n            'g-o',\n            linewidth=2)\n\n        def update3(data):\n            line3.set_ydata(data)\n            line3_center.set_xdata((range(data.shape[0])[argmax(data)],\n                                    range(data.shape[0])[argmax(data)]))\n            line3_center.set_ydata((data[argmax(data)] - 5,\n                                    data[argmax(data)] + 5))\n            return line3, line3_center\n\n        ani3 = animation.FuncAnimation(fig3, update3, T_all, interval=1 * 800)\n        ani3.save(\n            os.path.join(file_dir, ''.join([plot_file_base_name, '_1.gif'])),\n            dpi=80,\n            writer='imagemagick')\n\n        # Animation on enthalpy change of food product.\n        fig4, ax4 = plt.subplots(figsize=(5, 5))\n        ax4.set_xlim(-1, 24)\n        ax4.set_ylim(0, 4e8)\n        ax4.set_title(u'食品离散点焓值', fontproperties='KaiTi')\n        ax4.set_xlabel(u'离散点', fontproperties='KaiTi')\n        ax4.set_ylabel(u'焓值(\\u2103)', fontproperties='KaiTi')\n        line4, = ax4.plot(\n            range(H_all[1, ].shape[0]), H_all[1, ], 'g-.s', linewidth=2)\n        n4 = argmax(H_all[1, ])\n        line4_center, = ax4.plot(\n            (range(H_all[1, ].shape[0])[n4], range(H_all[1, ].shape[0])[n4]),\n            (H_all[1, ][n4] - 0.3e8, H_all[1, ][n4] + 0.3e8),\n            'r-s',\n            linewidth=2)\n\n        def update4(data):\n            line4.set_ydata(data)\n            line4_center.set_xdata((range(data.shape[0])[argmax(data)],\n                                    range(data.shape[0])[argmax(data)]))\n            line4_center.set_ydata((data[argmax(data)] - 0.3e8,\n                                    data[argmax(data)] + 0.3e8))\n            return line4, line4_center\n\n        ani4 = animation.FuncAnimation(fig4, update4, H_all, interval=1 * 800)\n        ani4.save(\n            os.path.join(file_dir, ''.join([plot_file_base_name, '_2.gif'])),\n            dpi=80,\n            writer='imagemagick')\n\n        plt.close('all')\n\n        # Filter figs and append numbers.\n        freezing_time = '{} 小时'.format(round(time[-1], 2))\n        freezing_rate = '{} cm/h'.format(\n            round(0.005 * 23 * 100 / (time[argmin(abs(Tc - (-0.78 - 5)))] -\n                                      time[argmin(abs(Ts_top))]), 2))\n        result_list = list(\n            filter(lambda x: x.startswith(plot_file_base_name),\n                   os.listdir(file_dir)))\n        result_list.append(freezing_time)\n        result_list.append(freezing_rate)\n        return JsonResponse(result_list, safe=False)\n\n\n# * Category_View\nclass Category_View(Base_Mixin, ListView):\n    \"\"\"view for category.html\"\"\"\n    template_name = 'freezingsimulation/category.html'\n    context_object_name = 'article_list'\n    paginate_by = settings.PAGE_NUM\n\n    def get_queryset(self):\n        category = self.kwargs.get('category', '')\n        try:\n            article_list = \\\n                CategoryFreezingsimu.objects.get(name=category).articlefreezingsimulation_set.all()\n        except CategoryFreezingsimu.DoesNotExist:\n            logger.error(u'[Category_View]此分类不存在:[%s]' % category)\n            raise Http404\n\n        return article_list\n\n\n# * User_View\nclass User_View(Base_Mixin, TemplateView):\n    template_name = 'freezingsimulation/user.html'\n\n    def get(self, request, *args, **kwargs):\n\n        if not request.user.is_authenticated():\n            logger.error(u'[User_View]用户未登陆')\n            return render(request, 'freezingsimulation/login.html')\n\n        slug = self.kwargs.get('slug')\n\n        if slug == 'changetx':\n            self.template_name = 'freezingsimulation/user_changetx.html'\n        elif slug == 'changepassword':\n            self.template_name = 'freezingsimulation/user_changepassword.html'\n        elif slug == 'changeinfo':\n            self.template_name = 'freezingsimulation/user_changeinfo.html'\n        elif slug == 'message':\n            self.template_name = 'freezingsimulation/user_message.html'\n        elif slug == 'notification':\n            self.template_name = 'freezingsimulation/user_notification.html'\n\n        return super(User_View, self).get(request, *args, **kwargs)\n\n        logger.error(u'[User_View]不存在此接口')\n        raise Http404\n\n    def get_context_data(self, **kwargs):\n        context = super(User_View, self).get_context_data(**kwargs)\n\n        slug = self.kwargs.get('slug')\n\n        if slug == 'notification':\n            context['notifications'] = \\\n                self.request.user.to_user_notification_set.order_by(\n                    '-create_time'\n                ).all()\n\n        return context\n\n\n# * Article_View\nclass Article_View(Base_Mixin, DetailView):\n    queryset = ArticleFreezingsimu.objects.filter(Q(status=0) | Q(status=1))\n    template_name = 'freezingsimulation/article.html'\n    context_object_name = 'article'\n    # 从url中获得id值为slug_url_kwarg，并在objects中使用slug_field过滤。\n    slug_field = 'id'\n    slug_url_kwarg = 'id'\n\n    def get(self, request, *args, **kwargs):\n        # 获取访问网站的用户的ip地址。\n        if 'HTTP_X_FORWARDED_FOR' in request.META:\n            ip = request.META['HTTP_X_FORWARDED_FOR']\n        else:\n            ip = request.META['REMOTE_ADDR']\n            self.cur_user_ip = ip\n\n        article_id = self.kwargs.get('id')\n        # 获取15*60s时间内访问过这篇文章的所有ip\n        visited_ips = cache.get(article_id, [])\n\n        # 如果ip不存在就把文章的浏览次数+1。\n        if ip not in visited_ips:\n            try:\n                article = self.queryset.get(id=article_id)\n            except ArticleFreezingsimu.DoesNotExist:\n                logger.error(u'[ArticleView]访问不存在的文章:[%s]' % article_id)\n                raise Http404\n            else:\n                article.view_times += 1\n                article.save()\n                visited_ips.append(ip)\n\n            # 更新缓存\n            cache.set(article_id, visited_ips, 15 * 60)\n\n        return super(Article_View, self).get(request, *args, **kwargs)\n\n    def get_context_data(self, **kwargs):\n        # 评论\n        # en_title = self.kwargs.get('slug', '')\n        # kwargs['comment_list'] = \\\n        #     self.queryset.get(en_title=en_title).comment_set.all()\n        return super(Article_View, self).get_context_data(**kwargs)\n","sub_path":"freezingsimulation/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"278967041","text":"# General imports\nimport numpy as np\nimport pandas as pd\nimport os, sys, gc, re, warnings, pickle, itertools, emoji, psutil, random, unicodedata\n\n# custom imports\nfrom gensim.utils import deaccent\nfrom collections import Counter\nfrom bs4 import BeautifulSoup\nfrom multiprocessing import Pool\n\nwarnings.filterwarnings('ignore')\npd.options.display.max_columns = 10\npd.options.display.max_colwidth = 200\n\n########################### Helpers\n#################################################################################\n## Multiprocessing Run.\n# :df - DataFrame to split                      # type: pandas DataFrame\n# :func - Function to apply on each split       # type: python function\n# This function is NOT 'bulletproof', be carefull and pass only correct types of variables.\ndef df_parallelize_run(df, func):\n    num_partitions, num_cores = 16, psutil.cpu_count()  # number of partitions and cores\n    df_split = np.array_split(df, num_partitions)\n    pool = Pool(num_cores)\n    df = pd.concat(pool.map(func, df_split))\n    pool.close()\n    pool.join()\n    return df\n\n## Build of vocabulary from file - reading data line by line\n## Line splited by 'space' and we store just first argument - Word\n# :path - txt/vec/csv absolute file path        # type: str\ndef get_vocabulary(path):\n    with open(path) as f:\n        return [line.strip().split()[0] for line in f][0:]\n\n## Check how many words are in Vocabulary\n# :c_list - 1d array with 'comment_text'        # type: pandas Series\n# :vocabulary - words in vocabulary to check    # type: list of str\n# :response - type of response                  # type: str\ndef check_vocab(c_list, vocabulary, response='default'):\n    try:\n        words = set([w for line in c_list for w in line.split()])\n        u_list = words.difference(set(vocabulary))\n        k_list = words.difference(u_list)\n    \n        if response=='default':\n            print('Unknown words:', len(u_list), '| Known words:', len(k_list))\n        elif response=='unknown_list':\n            return list(u_list)\n        elif response=='known_list':\n            return list(k_list)\n    except:\n        return []\n        \n## Seeder\n# :seed to make all processes deterministic     # type: int\ndef seed_everything(seed=0):\n    random.seed(seed)\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    np.random.seed(seed)\n\n    if 'torch' in sys.modules:\n        torch.manual_seed(seed)\n        torch.cuda.manual_seed(seed)\n        torch.backends.cudnn.deterministic = True\n \n## Simple \"Memory profilers\" to see memory usage\ndef get_memory_usage():\n    return np.round(psutil.Process(os.getpid()).memory_info()[0]/2.**30, 2) \n        \ndef sizeof_fmt(num, suffix='B'):\n    for unit in ['','Ki','Mi','Gi','Ti','Pi','Ei','Zi']:\n        if abs(num) < 1024.0:\n            return \"%3.1f%s%s\" % (num, unit, suffix)\n        num /= 1024.0\n    return \"%.1f%s%s\" % (num, 'Yi', suffix)\n    \n## Export pickle\ndef make_export(tr, tt, file_name):\n    train_export = train[['id']]\n    test_export = test[['id']]\n\n    try:\n        cur_shape = tr.shape[1]>1\n        train_export = pd.concat([train_export, tr], axis=1)\n        test_export = pd.concat([test_export, tt], axis=1)        \n    except:\n        train_export['p_comment'] = tr\n        test_export['p_comment'] = tt\n    \n    train_export.to_pickle(file_name + '_x_train.pkl')\n    test_export.to_pickle(file_name + '_x_test.pkl')\n\n## Domain Search\nre_3986_enhanced = re.compile(r\"\"\"\n        # Parse and capture RFC-3986 Generic URI components.\n        ^                                    # anchor to beginning of string\n        (?:  (?P<scheme>    [^:/?#\\s]+):// )?  # capture optional scheme\n        (?:(?P<authority>  [^/?#\\s]*)  )?  # capture optional authority\n             (?P<path>        [^?#\\s]*)      # capture required path\n        (?:\\?(?P<query>        [^#\\s]*)  )?  # capture optional query\n        (?:\\#(?P<fragment>      [^\\s]*)  )?  # capture optional fragment\n        $                                    # anchor to end of string\n        \"\"\", re.MULTILINE | re.VERBOSE)\n\nre_domain =  re.compile(r\"\"\"\n        # Pick out top two levels of DNS domain from authority.\n        (?P<domain>[^.]+\\.[A-Za-z]{2,6})  # $domain: top two domain levels.\n        (?::[0-9]*)?                      # Optional port number.\n        $                                 # Anchor to end of string.\n        \"\"\", \n        re.MULTILINE | re.VERBOSE)\n\ndef domain_search(text):\n    try:\n        return re_domain.search(re_3986_enhanced.match(text).group('authority')).group('domain')\n    except:\n        return 'unkownwebsite.com'\n\n## Load helper helper))\ndef load_helper_file(filename):\n    with open(HELPER_PATH+filename+'.pickle', 'rb') as f:\n        temp_obj = pickle.load(f)\n    return temp_obj\n        \n## Preprocess helpers\ndef place_hold(w):\n    return WPLACEHOLDER + '['+re.sub(' ', '___', w)+']'\n\ndef check_replace(w):\n    return not bool(re.search(WPLACEHOLDER, w))\n\ndef make_cleaning(s, c_dict):\n    if check_replace(s):\n        s = s.translate(c_dict)\n    return s\n  \ndef make_dict_cleaning(s, w_dict):\n    if check_replace(s):\n        s = w_dict.get(s, s)\n    return s\n\ndef export_dict(temp_dict, serial_num):\n    pd.DataFrame.from_dict(temp_dict, orient='index').to_csv('dict_'+str(serial_num)+'.csv')\n## ----------------------------------------------------------------------------------------------------\n\n\n\n\n\n\n\n########################### Initial vars\n#################################################################################\nCRAWL_EMBEDDING_PATH    = '../input/fasttext-crawl-300d-2m/crawl-300d-2M.vec'\nGLOVE_EMBEDDING_PATH    = '../input/glove840b300dtxt/glove.840B.300d.txt'\nHELPER_PATH             = '../input/jigsaw-general-helper/'\n\nLOCAL_TEST = False      ## Local test - for test performance on train set only\nSEED = 42               ## Seed for enviroment\nseed_everything(SEED)   ## Seed everything\n\nWPLACEHOLDER = 'word_placeholder'\n\n########################### DATA LOAD\n#################################################################################\nprint('1.1. Load Data')\ngood_cols       = ['id', 'comment_text']\nif LOCAL_TEST:\n    tt          = pd.read_csv('../input/jigsaw-unintended-bias-in-toxicity-classification/train.csv', nrows=200000)\n    train       = tt.iloc[:-100000,:]\n    test        = tt.iloc[-100000:,:]\n    del tt\n    train, test = train[good_cols+['target']], test[good_cols]\nelse:\n    train       = pd.read_csv('../input/jigsaw-unintended-bias-in-toxicity-classification/train.csv')\n    test        = pd.read_csv('../input/jigsaw-unintended-bias-in-toxicity-classification/test.csv')    \n    augment     = pd.read_csv('../input/jigsaw-toxic-comment-classification-challenge/train.csv') \n    train, test = train[good_cols+['target', 'created_date']], test[good_cols]\n\n########################### Get basic helpers\n#################################################################################\nprint('1.2. Basic helpers')\ncrawl_vocab             = get_vocabulary(CRAWL_EMBEDDING_PATH)\nglove_vocab             = get_vocabulary(GLOVE_EMBEDDING_PATH)\nknown_char_list         = ''.join([c for c in glove_vocab if len(c) == 1]) + ''.join([c for c in crawl_vocab if len(c) == 1])\n\nbert_uncased_vocabulary = load_helper_file('helper_bert_uncased_vocabulary')\nbert_cased_vocabulary   = load_helper_file('helper_bert_cased_vocabulary')\nbert_char_list          = list(set([c for line in bert_uncased_vocabulary+bert_cased_vocabulary for c in line]))\n\nurl_extensions          = load_helper_file('helper_url_extensions')\nhtml_tags               = load_helper_file('helper_html_tags')\ngood_chars_dieter       = load_helper_file('helper_good_chars_dieter')\nbad_chars_dieter        = load_helper_file('helper_bad_chars_dieter')\nhelper_contractions     = load_helper_file('helper_contractions')\nglobal_vocabulary       = load_helper_file('helper_global_vocabulary')\nglobal_vocabulary_chars = load_helper_file('helper_global_vocabulary_chars')\nnormalized_chars        = load_helper_file('helper_normalized_chars')\nwhite_list_chars        = load_helper_file('helper_white_list_chars')\nwhite_list_punct        = \" '*-.,?!/:;_()[]{}<>=\" + '\"'\npictograms_to_emoji     = load_helper_file('helper_pictograms_to_emoji')\ntoxic_misspell_dict     = load_helper_file('helper_toxic_misspell_dict')\n\n\n\n\n\n\n\n#################################################################################\n#################################################################################\n#################################################################################\n#################################################################################\n#################################################################################\n########################### Experimental preprocess for BERT\n#################################################################################\n#################################################################################\n#################################################################################\n#################################################################################\n#################################################################################\nprint('1.x. Experimental preprocess for BERT (with ideas from \"Modified classic\")')\n\ndef mod_bert(data, local_vocab=bert_uncased_vocabulary, verbose=False, global_lower=True):\n    \n    #data = train['comment_text']\n    #local_vocab = bert_uncased_vocabulary\n    #verbose = True\n    #global_lower=True\n    data = data.astype(str)\n    \n    if verbose: print('#' *20 ,'Initial State:'); check_vocab(data, local_vocab)\n\n    if global_lower:\n        data = data.apply(lambda x: x.lower())\n        if verbose: print('#'*10 ,'Step - Lowering everything:'); check_vocab(data, local_vocab)\n        \n    # Normalize chars and dots - SEE HELPER FOR DETAILS\n    # Global\n    data = data.apply(lambda x: ' '.join([make_cleaning(i,normalized_chars) for i in x.split()]))\n    data = data.apply(lambda x: re.sub('\\(dot\\)', '.', x))\n    data = data.apply(lambda x: deaccent(x))\n    if verbose: print('#'*10 ,'Step - Normalize chars and dots:'); check_vocab(data, local_vocab)\n\n    # Remove 'control' chars\n    # Global    \n    global_chars_list = list(set([c for line in data for c in line]))\n    chars_dict = {c:'' for c in global_chars_list if unicodedata.category(c)[0]=='C'}\n    data = data.apply(lambda x: ' '.join([make_cleaning(i,chars_dict) for i in x.split()]))\n    if verbose: print('#'*10 ,'Step - Control Chars:'); check_vocab(data, local_vocab)\n\n    # Remove hrefs\n    # Global    \n    data = data.apply(lambda x: re.sub(re.findall(r'\\<a(.*?)\\>', x)[0], '', x) if (len(re.findall(r'\\<a (.*?)\\>', x))>0) and ('href' in re.findall(r'\\<a (.*?)\\>', x)[0]) else x)\n    if verbose: print('#'*10 ,'Step - Remove hrefs:'); check_vocab(data, local_vocab)\n\n    # Convert or remove Bad Symbols\n    # Global\n    global_chars_list = list(set([c for line in data for c in line]))\n    chars = ''.join([c for c in global_chars_list if (c not in bert_char_list) and (c not in emoji.UNICODE_EMOJI) and (c not in white_list_chars)])\n    chars_dict = {}\n    for char in chars:\n        try:\n            new_char = unicodedata.name(char).split()[-1:][0].lower()\n            if len(new_char)==1:\n                chars_dict[ord(char)] = new_char\n            else:\n                chars_dict[ord(char)] = ''\n        except:\n            chars_dict[ord(char)] = ''\n    data = data.apply(lambda x: ' '.join([make_cleaning(i,chars_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Remove Bad Symbols:'); check_vocab(data, local_vocab)\n    \n    # Remove Bad Symbols PART 2\n    # Global\n    global_chars_list = list(set([c for line in data for c in line]))\n    chars = '·' + ''.join([c for c in global_chars_list if (c not in white_list_chars) and (c not in emoji.UNICODE_EMOJI) and (c not in white_list_punct) and (ord(c)>256)])\n    chars_dict = {}\n    for char in chars:\n        try:\n            new_char = unicodedata.name(char).split()[-1:][0].lower()\n            if len(new_char)==1:\n                chars_dict[ord(char)] = new_char\n            else:\n                chars_dict[ord(char)] = ''\n        except:\n            chars_dict[ord(char)] = ''\n    data = data.apply(lambda x: ' '.join([make_cleaning(i,chars_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Remove Bad Symbols PART 2:'); check_vocab(data, local_vocab)\n\n    # Remove html tags\n    # Global\n    temp_vocab = list(set([c for line in data for c in line.split()]))\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        if ('<' in word) and ('>' in word):\n            for tag in html_tags:\n                if ('<'+tag+'>' in word) or ('</'+tag+'>' in word):\n                    temp_dict[word] = BeautifulSoup(word, 'html5lib').text  \n    data = data.apply(lambda x: ' '.join([temp_dict.get(i, i) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - HTML tags:'); check_vocab(data, local_vocab);\n    \n    # Remove links (There is valuable information in links (probably you will find a way to use it)) \n    # Global\n    temp_vocab = list(set([c for line in data for c in line.split()]))\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    url_rule = r'(?P<url>https?://[^\\s]+)'\n    temp_dict = {k:domain_search(k) for k in temp_vocab if k!= re.compile(url_rule).sub('url', k)}\n    \n    for word in temp_dict:\n        new_value = temp_dict[word]\n        if word.find('http')>2:\n            temp_dict[word] =  word[:word.find('http')] + ' ' + place_hold(new_value)\n        else:\n            temp_dict[word] = new_value  #place_hold(new_value)\n            \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Convert urls part 1:'); check_vocab(data, local_vocab); \n\n    # Convert urls part 2\n    # Global\n    temp_vocab = list(set([c for line in data for c in line.split()]))\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n\n    for word in temp_vocab:\n        url_check = False\n        if 'file:' in word:\n            url_check = True\n        elif ('http' in word) or ('ww.' in word) or ('.htm' in word) or ('ftp' in word) or ('.php' in word) or ('.aspx' in word):\n            if 'Aww' not in word:\n                for d_zone in url_extensions:\n                    if '.' + d_zone in word:\n                        url_check = True\n                        break            \n        elif ('/' in word) and ('.' in word):\n            for d_zone in url_extensions:\n                if '.' + d_zone + '/' in word:\n                    url_check = True\n                    break\n\n        if url_check:\n            temp_dict[word] =  place_hold(domain_search(word))\n        \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Convert urls part 2:'); check_vocab(data, local_vocab); \n\n    # Normalize pictograms\n    # Local (only unknown words)\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        if len(re.compile('[a-zA-Z0-9]').sub('', word))>2:\n            for pict in pictograms_to_emoji:\n                if (pict in word) and (len(pict)>2):\n                    temp_dict[word] = word.replace(pict, pictograms_to_emoji[pict])\n                elif pict==word:  \n                    temp_dict[word] = pictograms_to_emoji[pict]\n\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Normalize pictograms:'); check_vocab(data, local_vocab); \n\n    # Isolate emoji\n    # Global\n    global_chars_list = list(set([c for line in data for c in line]))\n    chars = ''.join([c for c in global_chars_list if c in emoji.UNICODE_EMOJI])\n    chars_dict = {ord(c):f' {c} ' for c in chars}\n    data = data.apply(lambda x: ' '.join([make_cleaning(i,chars_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Isolate emoji:'); check_vocab(data, local_vocab)\n\n    # Duplicated dots, question marks and exclamations\n    # Local\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        new_word = word\n        if (Counter(word)['.']>1) or (Counter(word)['!']>1) or (Counter(word)['?']>1) or (Counter(word)[',']>1):\n            if (Counter(word)['.']>1):\n                new_word = re.sub('\\.\\.+', ' . . . ', new_word)\n            if (Counter(word)['!']>1):\n                new_word = re.sub('\\!\\!+', ' ! ! ! ', new_word)\n            if (Counter(word)['?']>1):\n                new_word = re.sub('\\?\\?+', ' ? ? ? ', new_word)\n            if (Counter(word)[',']>1):\n                new_word = re.sub('\\,\\,+', ' , , , ', new_word)\n            temp_dict[word] = new_word\n    temp_dict = {k: v for k, v in temp_dict.items() if k != v}\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Duplicated Chars:'); check_vocab(data, local_vocab);\n\n    # Remove underscore for spam words\n    # Local\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        if (len(re.compile('[a-zA-Z0-9\\-\\.\\,\\/\\']').sub('', word))/len(word) > 0.6) and ('_' in word):\n            temp_dict[word] = re.sub('_', '', word)       \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Remove underscore:'); check_vocab(data, local_vocab);\n\n    # Isolate spam chars repetition\n    # Local\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        if (len(re.compile('[a-zA-Z0-9\\-\\.\\,\\/\\']').sub('', word))/len(word) > 0.6) and (len(Counter(word))==1) and (len(word)>2):\n            temp_dict[word] = ' '.join([' ' + next(iter(Counter(word).keys())) + ' ' for i in range(3)])\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Spam chars repetition:'); check_vocab(data, local_vocab);\n\n    # Normalize pictograms part 2\n    # Local (only unknown words)\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        if len(re.compile('[a-zA-Z0-9]').sub('', word))>1:\n            for pict in pictograms_to_emoji:\n                if pict==word:  \n                    temp_dict[word] = pictograms_to_emoji[pict]\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Normalize pictograms part 2:'); check_vocab(data, local_vocab); \n                \n    # Isolate brakets and quotes\n    # Global\n    chars = '()[]{}<>\"'\n    chars_dict = {ord(c):f' {c} ' for c in chars}\n    data = data.apply(lambda x: ' '.join([make_cleaning(i,chars_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Brackets and quotes:'); check_vocab(data, local_vocab)\n\n    # Break short words\n    # Global\n    temp_vocab = list(set([c for line in data for c in line.split()]))\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_vocab = [k for k in temp_vocab if len(k)<=20]\n    \n    temp_dict = {}\n    for word in temp_vocab:\n        if '/' in word:\n            temp_dict[word] = re.sub('/', ' / ', word)\n    \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Break long words:'); check_vocab(data, local_vocab); \n    \n    # Break long words\n    # Global\n    temp_vocab = list(set([c for line in data for c in line.split()]))\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_vocab = [k for k in temp_vocab if len(k)>20]\n    \n    temp_dict = {}\n    for word in temp_vocab:\n        if '_' in word:\n            temp_dict[word] = re.sub('_', ' ', word)\n        elif '/' in word:\n            temp_dict[word] = re.sub('/', ' / ', word)\n        elif len(' '.join(word.split('-')).split())>2:\n            temp_dict[word] = re.sub('-', ' ', word)\n    \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Break long words:'); check_vocab(data, local_vocab); \n    \n    # Remove/Convert usernames and hashtags (add username/hashtag word?????)\n    # Global\n    temp_vocab = list(set([c for line in data for c in line.split()]))\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        new_word = word\n        if (len(word) > 3) and (word[1:len(word)-1].isalnum()) and (not re.compile('[#@,.:;]').sub('', word).isnumeric()):\n            if word[len(word)-1].isalnum():\n                if (word.startswith('@')) or (word.startswith('#')):\n                    new_word = place_hold(new_word[0] + ' ' + new_word[1:]) \n            else:\n                if (word.startswith('@')) or (word.startswith('#')):\n                    new_word = place_hold(new_word[0] + ' ' + new_word[1:len(word)-1]) + ' ' + word[len(word)-1]\n\n        temp_dict[word] = new_word\n    temp_dict = {k: v for k, v in temp_dict.items() if k != v}\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - UserName and Hashtag:'); check_vocab(data, local_vocab);\n\n    # Remove ending underscore (or add quotation marks???)\n    # Local\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if (check_replace(k)) and ('_' in k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        new_word = word\n        if word[len(word)-1]=='_':\n            for i in range(len(word),0,-1):\n                if word[i-1]!='_':\n                    new_word = word[:i]\n                    temp_dict[word] = new_word   \n                    break\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Remove ending underscore:'); check_vocab(data, local_vocab);\n    \n    # Remove starting underscore \n    # Local\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if (check_replace(k)) and ('_' in k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        new_word = word\n        if word[0]=='_':\n            for i in range(len(word)):\n                if word[i]!='_':\n                    new_word = word[i:]\n                    temp_dict[word] = new_word   \n                    break\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Remove starting underscore:'); check_vocab(data, local_vocab);\n        \n    # End word punctuations\n    # Global\n    temp_vocab = list(set([c for line in data for c in line.split()]))\n    temp_vocab = [k for k in temp_vocab if (check_replace(k)) and (not k[len(k)-1].isalnum())]\n    temp_dict = {}\n    for word in temp_vocab:\n        new_word = word\n        for i in range(len(word),0,-1):\n            if word[i-1].isalnum():\n                new_word = word[:i] + ' ' + word[i:]\n                break\n        temp_dict[word] = new_word     \n    temp_dict = {k: v for k, v in temp_dict.items() if k != v}\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - End word punctuations:'); check_vocab(data, local_vocab);\n       \n    # Start word punctuations\n    # Global\n    temp_vocab = list(set([c for line in data for c in line.split()]))\n    temp_vocab = [k for k in temp_vocab if (check_replace(k)) and (not k[0].isalnum())]\n    temp_dict = {}\n    for word in temp_vocab:\n        new_word = word\n        for i in range(len(word)):\n            if word[i].isalnum():\n                new_word = word[:i] + ' ' + word[i:]\n                break\n        temp_dict[word] = new_word     \n    temp_dict = {k: v for k, v in temp_dict.items() if k != v}\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Start word punctuations:'); check_vocab(data, local_vocab);\n\n    # Find and replace acronims\n    # Local (only unknown words)\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        if (Counter(word)['.']>1) and (check_replace(word)):\n            if (domain_search(word)!='') and (('www' in word) or (Counter(word)['/']>3)):\n                temp_dict[word] = place_hold('url ' + domain_search(word))\n            else: \n                if (re.compile('[\\.\\,]').sub('', word) in local_vocab) and (len(re.compile('[0-9\\.\\,\\-\\/\\:]').sub('', word))>0):\n                    temp_dict[word] =  place_hold(re.compile('[\\.\\,]').sub('', word))\n    temp_dict = {k: v for k, v in temp_dict.items() if k != v}\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Find and replace acronims:'); check_vocab(data, local_vocab);\n\n    # Apply spellchecker for contractions\n    # Local (only unknown words)\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if (check_replace(k)) and (\"'\" in k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        if word in helper_contractions:\n            temp_dict[word] = place_hold(helper_contractions[word])\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Contractions:'); check_vocab(data, local_vocab)\n        \n    # Isolate obscene (or just keep the word????)\n    # Global\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        new_word = re.compile('[a-zA-Z0-9\\-\\.\\,\\/\\']').sub('', word)\n        if len(Counter(new_word))>2:\n            temp_dict[word] = place_hold('fuck')\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Possible obscene:'); check_vocab(data, local_vocab);\n             \n    # Remove 's (DO WE NEED TO REMOVE IT???)\n    # Global\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {k:k[:-2] for k in temp_vocab if (check_replace(k)) and (k.lower()[-2:]==\"'s\")}\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Remove \"s:'); check_vocab(data, local_vocab);\n     \n    # Convert backslash\n    # Global\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if (check_replace(k)) and ('\\\\' in k)]    \n    temp_dict = {k:re.sub('\\\\\\\\+', ' / ', k) for k in temp_vocab}\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Convert backslash:'); check_vocab(data, local_vocab)\n     \n    # Try remove duplicated chars (not sure about this!!!!!)\n    # Local (only unknown words)\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    \n    temp_dict = {}\n    temp_vocab_dup = []\n    \n    for word in temp_vocab:\n        temp_vocab_dup.append(''.join(ch for ch, _ in itertools.groupby(word)))\n    temp_vocab_dup = set(temp_vocab_dup)\n    temp_vocab_dup = temp_vocab_dup.difference(temp_vocab_dup.difference(set(local_vocab)))\n            \n    for word in temp_vocab:\n        new_word = ''.join(ch for ch, _ in itertools.groupby(word))\n        if new_word in temp_vocab_dup:\n            temp_dict[word] = new_word\n    temp_dict = {k: v for k, v in temp_dict.items() if (k != v) and (v in local_vocab)}\n    \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Dup chars (with vocab check):'); check_vocab(data, local_vocab);\n\n    # Isolate numbers\n    # Local (only unknown words)\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    temp_dict = {}\n    for word in temp_vocab:\n        if re.compile('[a-zA-Z]').sub('', word) == word:\n            if re.compile('[0-9]').sub('', word) != word:\n                temp_dict[word] = word\n\n    global_chars_list = list(set([c for line in temp_dict for c in line]))\n    chars = ''.join([c for c in global_chars_list if not c.isdigit()])\n    chars_dict = {ord(c):f' {c} ' for c in chars}                \n    temp_dict = {k:place_hold(make_cleaning(k,chars_dict)) for k in temp_dict}\n    \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Isolate numbers:'); check_vocab(data, local_vocab);\n    \n    # Join dashes\n    # Local (only unknown words)\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    \n    temp_dict = {}\n    for word in temp_vocab:\n        temp_dict[word] = re.sub('\\-\\-+', '-', word)\n    temp_dict = {k: v for k, v in temp_dict.items() if k != v}\n    \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Join dashes:'); check_vocab(data, local_vocab);\n    \n    # Try join word (Sloooow)\n    # Local (only unknown words)\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if (check_replace(k)) and (Counter(k)['-']>1)]\n    \n    temp_dict = {}\n    for word in temp_vocab:\n        new_word = ''.join(['' if c in '-' else c for c in word])\n        if (new_word in local_vocab) and (len(new_word)>3):\n            temp_dict[word] = new_word    \n    \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Try Split word:'); check_vocab(data, local_vocab);\n     \n    # Try Split word\n    # Local (only unknown words)\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    \n    temp_dict = {}\n    for word in temp_vocab:\n        if len(re.compile('[a-zA-Z0-9\\*]').sub('', word))>0:\n            chars = re.compile('[a-zA-Z0-9\\*]').sub('', word)\n            temp_dict[word] = ''.join([' ' + c + ' ' if c in chars else c for c in word])\n    \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Try Split word:'); check_vocab(data, local_vocab);\n   \n    # L33T vocabulary (SLOW)\n    # https://simple.wikipedia.org/wiki/Leet\n    # Local (only unknown words)\n    def convert_leet(word):\n        # basic conversion \n        word = re.sub('0', 'o', word)\n        word = re.sub('1', 'i', word)\n        word = re.sub('3', 'e', word)\n        word = re.sub('\\$', 's', word)\n        word = re.sub('\\@', 'a', word)\n        return word\n            \n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if check_replace(k)]\n    \n    temp_dict = {}\n    for word in temp_vocab:\n        new_word = convert_leet(word)\n        if (new_word!=word): \n            if (len(word)>2) and (new_word in local_vocab):\n                temp_dict[word] = new_word\n    \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - L33T (with vocab check):'); check_vocab(data, local_vocab);\n    \n    # Search \"fuck\"\n    # Local (only unknown words)\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if (check_replace(k))]\n    temp_dict = {}\n    \n    for word in temp_vocab:\n        if ('*' in word.lower()):\n            if (word.lower()[0]=='n') and ('er' in word.lower()):\n                temp_dict[word] = 'nigger'\n            elif (('fuck' in word.lower()) or (word.lower()[0]=='f')) and ('k' in word.lower()):\n                temp_dict[word] = 'fuck'\n            elif (word.lower()[0]=='a') and ('le' in word.lower()):\n                temp_dict[word] = 'asshole'\n            elif (word.lower()[0]=='s') and (word.lower()[len(word)-1]=='t'):\n                temp_dict[word] = 'shit'\n            else:\n                temp_dict[word] = 'fuck'   \n\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Bad words:'); check_vocab(data, local_vocab);\n        \n    # Open Holded words\n    # Global\n    temp_vocab = list(set([c for line in data for c in line.split()]))\n    temp_vocab = [k for k in temp_vocab if (not check_replace(k))]\n    temp_dict = {}\n    for word in temp_vocab:\n        temp_dict[word] = re.sub('___', ' ', word[17:-1])\n    data = data.apply(lambda x: ' '.join([temp_dict.get(i, i) for i in x.split()]))\n    data = data.apply(lambda x: ' '.join([i for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Open Holded words:'); check_vocab(data, local_vocab)\n\n    # Search multiple form\n    # Local | example -> flashlights / flashlight -> False / True\n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if (k[-1:]=='s') and (len(k)>4)]\n    temp_dict = {k:k[:-1] for k in temp_vocab if (k[:-1] in local_vocab)}\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Multiple form:'); check_vocab(data, local_vocab);\n\n    # Convert emoji to text\n    # Local \n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab if (k in emoji.UNICODE_EMOJI)]\n    temp_dict = {}\n    for word in temp_vocab:\n        temp_dict[word] = re.compile('[:_]').sub(' ', emoji.UNICODE_EMOJI.get(word)) \n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Convert emoji to text:'); check_vocab(data, local_vocab);\n\n    # Isolate Punctuation\n    # Local \n    temp_vocab = check_vocab(data, local_vocab, response='unknown_list')\n    temp_vocab = [k for k in temp_vocab]\n    temp_dict = {}\n    for word in temp_vocab:\n        new_word = re.compile('[a-zA-Z0-9]').sub('', word)\n        chars_dif = set(word).difference(set(word).difference(set(new_word)))\n        if len(chars_dif)>0:\n            temp_dict[word] = ''.join([' ' + c + ' ' if c in chars_dif else c for c in word])\n    data = data.apply(lambda x: ' '.join([make_dict_cleaning(i,temp_dict) for i in x.split()]))\n    if verbose: print('#' * 10, 'Step - Isolate Punctuation:'); check_vocab(data, local_vocab);\n\n    return data\n    \naugment['comment_text'] = df_parallelize_run(augment['comment_text'], mod_bert)\naugment.to_pickle('augment_data.pkl')\n\n\n\n","sub_path":"preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":35830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"43715278","text":"import re\nimport requests\nimport json\nimport urllib\nimport time\nimport timeit\nimport math\nimport sys\nfrom datetime import datetime\nfrom dateutil import tz\nimport os\n''''微信小程序入口：\n来客有礼 - > 首页 -> 东东小窝\n网页入口（注：进入后不能再此刷新，否则会有问题，需重新输入此链接进入）\nhttps://h5.m.jd.com/babelDiy/Zeus/2HFSytEAN99VPmMGZ6V4EYWus1x/index.html'''\n\n\n\ndjj_bark_cookie=''\ndjj_sever_jiang=''\ndjj_small_headers=''\nDefalt_ShareCode=[]\n\n\n\n\n\n\n    \n    \n    \n    \ncookiesList=[]\nresult=''\nmypin=''\nmytoken=''\nmywoB=''\nnumm=0\ncreateAssistUserID=''\nisPurchaseShops=True\n\ndef JD_homesmall():\n   ssjj_rooms()\n   queryByUserId()\n   getTaskList()\n   createInviteUser()\n   queryDraw()\n   queryByUserId()\n   queryFurnituresCenterList()\n   helpFriends()\ndef ssjj_rooms():\n   print('\\n ssjj-rooms')\n   try:\n      url='https://lkyl.dianpusoft.cn/api/ssjj-rooms/info/%E5%AE%A2%E5%8E%85?body=%7B%7D'\n      data=json.loads(requests.get(url,headers=headers).text)\n      #print(data)\n   except Exception as e:\n      msg=str(e)\n      print(msg)\n\ndef queryByUserId():\n   print('\\n queryByUserId')\n   try:\n      global mywoB\n      url='https://lkyl.dianpusoft.cn/api/ssjj-wo-home-info/queryByUserId/2?body=%7B%7D'\n      data=json.loads(requests.get(url,headers=headers).text)\n      #print(data)\n      msg=f'''{data['body']['name']}|{data['body']['name']}|{data['body']['woB']}|{data['body']['nick']}|{data['body']['userId']}'''\n      mywoB=data['body']['woB']\n      loger(msg)\n   except Exception as e:\n      msg=str(e)\n      print(msg)\n\ndef createInviteUser():\n   print('\\n createInviteUser')\n   if numm>len(cookiesList)+1:\n         print('收集助力id完毕✌🏻️✌🏻️✌🏻️✌🏻️....') \n         return \n   global Defalt_ShareCode\n   try:\n      url='https://lkyl.dianpusoft.cn/api/ssjj-task-record/createInviteUser?body=%7B%7D'\n      data=json.loads(requests.get(url,headers=headers).text)\n      #print(data)\n      msg=data['body']['id']\n      print('Sharecode='+str(msg))\n      Defalt_ShareCode.append(msg)\n      print(Defalt_ShareCode)\n   except Exception as e:\n      msg=str(e)\n      print(msg)\n      \ndef createAssistUser(inviteId, taskId):\n   print('\\n createAssistUser')\n   global Defalt_ShareCode\n   if numm<=len(cookiesList):\n         print('收集助力id中....')\n         return \n   try:\n      url='https://lkyl.dianpusoft.cn/api/ssjj-task-record/createAssistUser/'+inviteId+'/'+taskId+'?body=%7B%7D'\n      data=json.loads(requests.get(url,headers=headers).text)\n      print(data)\n    \n   except Exception as e:\n      msg=str(e)\n      print(msg)\n      \n      \ndef helpFriends():\n   try:\n      if numm<=len(cookiesList):\n         print('收集助力id中....')\n         return \n      print('\\n helpFriends')\n      print(Defalt_ShareCode)\n      for id in Defalt_ShareCode:\n        createAssistUser(id, createAssistUserID)\n\n   except Exception as e:\n      msg=str(e)\n      print(msg)\n      \n\n\n\n\ndef getTaskList():\n   try:\n     global createAssistUserID\n     print('💎getTaskList')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-task-info/queryAllTaskInfo/2'\n     m=''\n     data=json.loads(requests.get(url,headers=headers).text)\n     tasklist=data['body']\n     for item in tasklist:\n       if not item['ssjjTaskInfo']['type']==1 and not item['ssjjTaskInfo']['type']==2:\n         m=item['ssjjTaskInfo']['awardOfDayNum']\n       else:\n         m=1\n       print(f'''{item['ssjjTaskInfo']['name']}----{item['doneNum']}/{m}---{item['ssjjTaskInfo']['type']}''')\n     for item in tasklist:\n       if (item['ssjjTaskInfo']['type'] == 1):\n         createAssistUserID = item['ssjjTaskInfo']['id']\n         \n         print(f'''createAssistUserID:{item['ssjjTaskInfo']['id']}''')\n         print(f'''\\n\\n助力您的好友:{item['doneNum']}人''')\n       if (item['ssjjTaskInfo']['type'] ==2):\n         \n         if (item['doneNum'] ==1):\n            print(f'''{item['ssjjTaskInfo']['name']}已完成[{item['doneNum']}/1]''')\n            continue\n         print(item['ssjjTaskInfo']['id'])\n         signclock(item['ssjjTaskInfo']['id'])\n\n       if (item['ssjjTaskInfo']['type'] ==3):\n        if (item['doneNum'] ==item['ssjjTaskInfo']['awardOfDayNum']):\n           print(f'''{item['ssjjTaskInfo']['name']}已完成[{item['doneNum']}/{item['ssjjTaskInfo']['awardOfDayNum']}]''')\n           continue\n          \n        for ii in range(item['ssjjTaskInfo']['awardOfDayNum']):\n           game(item['ssjjTaskInfo']['id'], item['doneNum'],ii)\n\n       if (item['ssjjTaskInfo']['type'] == 4):\n      #关注店铺\n         if (item['doneNum'] ==item['ssjjTaskInfo']['awardOfDayNum']):\n           print(f'''{item['ssjjTaskInfo']['name']}已完成[{item['doneNum']}/{item['ssjjTaskInfo']['awardOfDayNum']}]''')\n           continue\n         for ii in range(item['ssjjTaskInfo']['awardOfDayNum']):\n           followShops('followShops', item['ssjjTaskInfo']['id'])#一键关注店铺\n           queryDoneTaskRecord(item['ssjjTaskInfo']['id'], item['ssjjTaskInfo']['type'])\n     \n   \n       if (item['ssjjTaskInfo']['type'] == 5):\n      #浏览店铺\n         if (item['doneNum'] ==item['ssjjTaskInfo']['awardOfDayNum']):\n           print(f'''{item['ssjjTaskInfo']['name']}已完成[{item['doneNum']}/{item['ssjjTaskInfo']['awardOfDayNum']}]''')\n           continue\n         for ii in range(item['ssjjTaskInfo']['awardOfDayNum']):\n           browseChannels('browseShops', item['ssjjTaskInfo']['id'], item['browseId'])\n     \n   \n       if (item['ssjjTaskInfo']['type'] == 6):\n      #关注4个频道\n         if (item['doneNum'] ==item['ssjjTaskInfo']['awardOfDayNum']):\n            print(f'''{item['ssjjTaskInfo']['name']}已完成[{item['doneNum']}/{item['ssjjTaskInfo']['awardOfDayNum']}]''')\n            continue\n         doChannelsListTask(item['ssjjTaskInfo']['id'], item['ssjjTaskInfo']['type'])\n        \n        \n       if (item['ssjjTaskInfo']['type'] == 7):\n     #浏览3个频道\n         if (item['doneNum'] ==item['ssjjTaskInfo']['awardOfDayNum']):\n           print(f'''{item['ssjjTaskInfo']['name']}已完成[{item['doneNum']}/{item['ssjjTaskInfo']['awardOfDayNum']}]''')\n           continue\n         for ii in range(item['ssjjTaskInfo']['awardOfDayNum']):\n           browseChannels('browseChannels', item['ssjjTaskInfo']['id'], item['browseId'])\n       if (isPurchaseShops and item['ssjjTaskInfo']['type'] == 9):\n      #加购商品\n          if (item['doneNum'] ==item['ssjjTaskInfo']['awardOfDayNum']):\n             print(f'''{item['ssjjTaskInfo']['name']}已完成[{item['doneNum']}/{item['ssjjTaskInfo']['awardOfDayNum']}]''')\n             continue\n          for ii in range(item['ssjjTaskInfo']['awardOfDayNum']):\n            followShops('purchaseCommodities', item['ssjjTaskInfo']['id'])#一键加购商品\n            queryDoneTaskRecord(item['ssjjTaskInfo']['id'], item['ssjjTaskInfo']['type'])\n     \n    \n       if (item['ssjjTaskInfo']['type'] ==10):\n      #浏览商品\n          if (item['doneNum'] ==item['ssjjTaskInfo']['awardOfDayNum']):\n             print(f'''{item['ssjjTaskInfo']['name']}已完成[{item['doneNum']}/{item['ssjjTaskInfo']['awardOfDayNum']}]''')\n             continue\n          for ii in range(item['ssjjTaskInfo']['awardOfDayNum']):\n           browseChannels('browseCommodities', item['ssjjTaskInfo']['id'], item['browseId']);\n  \n       if (item['ssjjTaskInfo']['type'] == 11):\n      #浏览会场\n          if (item['doneNum'] ==item['ssjjTaskInfo']['awardOfDayNum']):\n              print(f'''{item['ssjjTaskInfo']['name']}已完成[{item['doneNum']}/{item['ssjjTaskInfo']['awardOfDayNum']}]''')\n              continue\n          for ii in range(item['ssjjTaskInfo']['awardOfDayNum']):\n              browseChannels('browseMeetings',item['ssjjTaskInfo']['id'], item['browseId'])\n      \n      \n\n          \n          \n          \n   except Exception as e:\n      msg=str(e)\n      print(msg)\n      \n      \n      \n      \n      \n      \n      \n      \ndef signclock(taskId):\n   try:\n     print('💎signclock')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-task-record/clock/'+taskId+'?body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)\n     print(data)\n   \n   except Exception as e:\n      msg=str(e)\n      print(msg)\ndef game(taskId,index,k):\n   try:\n     print('💎game======'+str(k))\n     url='https://lkyl.dianpusoft.cn/api/ssjj-task-record/game/'+str(index)+'/'+taskId+'?body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)\n     print(data)\n   \n   except Exception as e:\n      msg=str(e)\n      print(msg)\ndef followShops(functionID,taskId):\n   try:\n     print('💎followShops======')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-task-record/'+functionID+'/'+taskId+'?body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)\n     print(data)\n   \n   except Exception as e:\n      msg=str(e)\n      print(msg)\ndef queryDoneTaskRecord(taskId, taskType):\n   try:\n     print('💎queryDoneTaskRecord======')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-task-record/queryDoneTaskRecord/'+str(taskType)+'/'+taskId+'?body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)\n     print(data['head']['msg'])\n   except Exception as e:\n      msg=str(e)\n      print(msg)\n      \n      \n      \ndef browseChannels(functionID,taskId, browseId):\n   try:\n     print('💎browseChannels======')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-task-record/'+functionID+'/'+taskId+'/'+str(browseId)+'?body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)\n     print(data)\n   except Exception as e:\n      msg=str(e)\n      print(msg)\ndef doChannelsListTask(taskId,taskType):\n   try:\n     print('💎doChannelsListTask======')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-task-channels/queryChannelsList/'+taskId+'?body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)['body']\n     for item in data:\n          time.sleep(1)\n          followChannel(taskId, item['id'])\n          queryDoneTaskRecord(taskId, taskType)\n   except Exception as e:\n      msg=str(e)\n      print(msg)\ndef followChannel(taskId, channelId):\n   try:\n     print('💎followChannel======')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-task-record/followChannel/'+channelId+'/'+taskId+'?body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)\n     print(data)\n   except Exception as e:\n      msg=str(e)\n      print(msg)\n\ndef queryDraw():\n   try:\n     print('💎queryDraw======')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-draw-center/queryDraw?body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)\n     print(data['head']['msg'])\n     freeDrawCount = data['body']['freeDrawCount']\n     lotteryId = data['body']['center']['id']\n     if(freeDrawCount>0):\n        drawRecord(lotteryId)\n     else:\n        print('抽奖次数用完......')\n   except Exception as e:\n      msg=str(e)\n      print(msg)\ndef drawRecord(Id):\n   try:\n     print('💎drawRecord======')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-draw-record/draw/'+Id+'?body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)\n     print(data['head']['msg'])\n   except Exception as e:\n      msg=str(e)\n      print(msg)\n   \n   \ndef queryFurnituresCenterList():\n   try:\n     print('💎queryFurnituresCenterList======')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-furnitures-center/queryFurnituresCenterList?body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)\n     print(data['head']['msg'])\n     #print(data['body'])\n     canBuyList = []\n     l={}\n     for item in data['body']['list']:\n       l.update({'id':item['id'],'needWoB':item['needWoB'],'jdBeanNum':item['jdBeanNum']})\n       canBuyList.append(l)\n     print(canBuyList)\n     for item in canBuyList:\n       if(item['needWoB']<=mywoB):\n         furnituresCenterPurchase(item['id'],item['jdBeanNum'])\n        \n   except Exception as e:\n      msg=str(e)\n      print(msg)\n   \ndef furnituresCenterPurchase(id,bnum):\n   try:\n     print('💎furnituresCenterPurchase======')\n     url='https://lkyl.dianpusoft.cn/api/ssjj-furnitures-center/furnituresCenterPurchase/'+id++'body=%7B%7D'\n     data=json.loads(requests.get(url,headers=headers).text)\n     print(data['head']['msg'])\n   except Exception as e:\n      msg=str(e)\n      print(msg)\n   \n   \n   \n   \n\ndef TotalBean(cookies,checkck):\n   print('检验过期')\n   signmd5=False\n   headers= {\n        \"Cookie\": cookies,\n        \"Referer\": 'https://home.m.jd.com/myJd/newhome.action?',\n        \"User-Agent\": 'Mozilla/5.0 (iPhone; CPU iPhone OS 13_5_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.1 Mobile/15E148 Safari/604.1'\n      }\n   try:\n       ckresult= requests.get('https://wq.jd.com/user_new/info/GetJDUserInfoUnion?orgFlag=JD_PinGou_New',headers=headers,timeout=10).json()\n       if json.dumps(ckresult).find(checkck)>0:\n           signmd5=True\n           loger(f'''【京东{checkck}】''')\n       else:\n       \t  signmd5=False\n       \t  msg=f'''【京东账号{checkck}】cookie已失效,请重新登录京东获取'''\n       \t  print(msg)\n          pushmsg(msg)\n   except Exception as e:\n      signmd5=False\n      msg=str(e)\n      print(msg)\n      pushmsg('京东cookie',msg)\n   return signmd5\n\n\n\n\n\ndef check():\n   print('Localtime',datetime.now(tz=tz.gettz('Asia/Shanghai')).strftime(\"%Y-%m-%d %H:%M:%S\", ))\n   global djj_small_headers\n   global djj_bark_cookie\n   global djj_sever_jiang\n   if \"DJJ_BARK_COOKIE\" in os.environ:\n     djj_bark_cookie = os.environ[\"DJJ_BARK_COOKIE\"]\n   if \"DJJ_SEVER_JIANG\" in os.environ:\n      djj_sever_jiang = os.environ[\"DJJ_SEVER_JIANG\"]\n   if 'DJJ_SMALL_HEADERS' in os.environ:\n      djj_small_headers =os.environ[\"DJJ_SMALL_HEADERS\"]\n      for line in djj_small_headers.split('\\n'):\n        if not line:\n          continue \n        cookiesList.append(line.strip())\n   elif djj_small_headers:\n       for line in djj_small_headers.split('\\n'):\n         if not line:\n            continue \n         cookiesList.append(line.strip())\n   else:\n     print('DTask is over.')\n     exit()\n\ndef pushmsg(title,txt,bflag=1,wflag=1):\n   txt=urllib.parse.quote(txt)\n   title=urllib.parse.quote(title)\n   if bflag==1 and djj_bark_cookie.strip():\n      print(\"\\n【通知汇总】\")\n      purl = f'''https://api.day.app/{djj_bark_cookie}/{title}/{txt}'''\n      response = requests.post(purl)\n      #print(response.text)\n   if wflag==1 and djj_sever_jiang.strip():\n      print(\"\\n【微信消息】\")\n      purl = f'''http://sc.ftqq.com/{djj_sever_jiang}.send'''\n      headers={\n    'Content-Type':'application/x-www-form-urlencoded; charset=UTF-8'\n    }\n      body=f'''text={txt})&desp={title}'''\n      response = requests.post(purl,headers=headers,data=body)\n   global result\n   print(result)\n   result =''\n    \ndef loger(m):\n   print(m)\n   global result\n   result +=m+'\\n'\ndef gettoken(hd):\n   hd=json.dumps(hd)\n   t=hd.split(',')\n   for i in t:\n      ii=i.find('&token=')\n      if ii>0:\n        return i[ii+8:len(i)-1]\n        \n        \ndef islogon(j,count):\n    JD_islogn=False\n    global jd_name\n    for i in count.split(';'):\n       if i.find('pin=')>=0:\n          jd_name=i.strip()[i.find('pin=')+4:len(i)]\n          print(f'''>>>>>>>>>【账号{str(j)}开始】{jd_name}''')\n    if(TotalBean(count,jd_name)):\n        JD_islogn=True\n    return JD_islogn\n   \ndef clock(func):\n    def clocked(*args, **kwargs):\n        t0 = timeit.default_timer()\n        result = func(*args, **kwargs)\n        elapsed = timeit.default_timer() - t0\n        name = func.__name__\n        arg_str = ', '.join(repr(arg) for arg in args)\n        print('[🔔运行完毕用时%0.8fs] %s(%s) -> %r' % (elapsed, name, arg_str, result))\n        return result\n    return clocked\n    \n@clock\ndef start():\n   global headers,mypin,mytoken,numm\n   check()\n   numm=0\n   iii=0\n   for i in range(2):\n     for count in cookiesList:\n       numm+=1\n       iii+=1\n       print('【账号'+str(iii)+'】运行中'+'🔔'*iii)\n     #if j!=1:\n       #continue\n       headers=eval(count)\n       mytoken=gettoken(headers)\n     #headers['Cookie']=count\n     #if(islogon(j,count)):\n       JD_homesmall()\n     print('=====:💎:第'+str(i+1)+'💎次::::=::::::')\n     if(i<1):\n       time.sleep(10)\nif __name__ == '__main__':\n       start()\n","sub_path":"djj/DJ_smallhome.py","file_name":"DJ_smallhome.py","file_ext":"py","file_size_in_byte":16247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"170570831","text":"# extend arr to be divisible by n (extend using ch\ndef extend(arr, n, ch):\n\tdiff = 4 - (len(arr) % 4)\n\tarr.extend([ch] * diff)\n\treturn arr\n\n# convert string to integer in little endian in groups of 4 bytes\ndef string2int(st):\n\tdef change(ch): return hex(ord(ch))[2:]\n\tarr = list(map(change, list(st)))\n\tarr = extend(arr, 4, '00')\n\n\tres = []\n\tfor i in range(0, len(arr), 4):\n\t\tres.append(arr[i:i+4])\n\n\tfor i in range(len(res)):\n\t\teach = res[i]\n\t\teach.reverse()\n\t\tfin = ''.join(each)\n\t\tres[i] = int(fin, 16)\n\t\n\treturn res\n\n# takes string and bad characters and returns a good string\n# with the number to XOR chars with as a tuple\n# or None if no solution\ndef get_good_string(string, badchars):\n\n\tfor i in range(128):\n\t\ttemp = []\n\t\tcount = 0\n\t\tfor ch in string:\n\t\t\tnew = chr(ord(ch) ^ i)\n\t\t\ttemp.append(new)\n\n\t\t\tif new not in badchars:\n\t\t\t\tcount += 1\n\t\t\t\n\t\t\tif count == len(string):\n\t\t\t\treturn (i, ''.join(temp))\n\t\n\treturn None\n\n# takes string and bad characters as input and returns 3 values\n# a good string with bad characters replaced, a list of positions of bad characters\n# and the number used to XOR the bad characters\ndef get_good_string(string, badchars):\n\tbad = []\n\tpos = []\n\tfinal = list(string)\n\n\tfor i in range(len(string)):\n\t\tif string[i] in badchars:\n\t\t\tbad.append(string[i])\n\t\t\tpos.append(i)\n\n\tres = None\n\toff = -1\n\n\tfor i in range(128):\n\t\ttemp = []\n\n\t\tfor ch in bad:\n\t\t\tnew = chr(ord(ch) ^ i)\n\t\t\tif new not in badchars:\n\t\t\t\ttemp.append(new)\n\t\t\n\t\tif len(temp) == len(bad):\n\t\t\tres = temp\n\t\t\toff = i\n\t\t\tbreak\n\t\n\tfor i in range(len(res)):\n\t\tfinal[pos[i]] = res[i]\n\t\n\treturn ''.join(final), pos, off","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"209428606","text":"import numpy as np\nimport pandas as pd\nimport pytest\n\nfrom cirrocumulus.data_processing import handle_data\nfrom cirrocumulus.embedding_aggregator import EmbeddingAggregator, get_basis\n\n\ndef create_df(test_data, measures, dimensions, basis):\n    df = pd.DataFrame(test_data[:, measures].X.toarray(), columns=measures)\n    df = df.join(test_data.obs[dimensions].reset_index())\n    return df.join(pd.DataFrame(test_data.obsm['X_umap'][:, 0:2], columns=basis['coordinate_columns']))\n\n\ndef group_df(test_data, measures, dimensions, continuous_obs, basis):\n    df = create_df(test_data, measures, dimensions + continuous_obs, basis)\n    agg_dict = {}\n    for key in basis['coordinate_columns']:\n        agg_dict[key] = 'max'\n    for key in measures + continuous_obs:\n        agg_dict[key] = basis['agg']\n    for key in dimensions:\n        agg_dict[key] = lambda x: x.mode()[0]\n    EmbeddingAggregator.convert_coords_to_bin(df, nbins=100,\n                                              coordinate_columns=basis['coordinate_columns'],\n                                              bin_name=basis['full_name'])\n    # group by bin then agg\n    return df.groupby(basis['full_name']).agg(agg_dict)\n\n\ndef diff_binning(grouped_df, measures, dimensions, continuous_obs, basis, results):\n    for key in basis['coordinate_columns']:\n        np.testing.assert_array_equal(results['coordinates'][key], grouped_df[key].values, err_msg=key)\n    for key in measures:\n        v = results['values'][key]  # dict of indices, values\n        if isinstance(v, dict):\n            values = np.zeros(len(grouped_df))\n            values[v['indices']] = v['values']\n        else:\n            values = v\n        np.testing.assert_allclose(values, grouped_df[key].values, err_msg=key, rtol=1e-05, atol=1e-06)\n    for key in continuous_obs:\n        np.testing.assert_allclose(results['values'][key], grouped_df[key].values, err_msg=key, rtol=1e-05,\n                                   atol=1e-08)\n    for key in dimensions:\n        np.testing.assert_array_equal(results['values'][key]['value'], grouped_df[key].values, err_msg=key)\n\n\ndef test_no_binning(dataset_api, input_dataset, test_data, measures, dimensions, continuous_obs, basis):\n    embedding_list = [dict(ndim=2, basis=basis)]\n    basis = get_basis(basis)\n    values = dict(dimensions=dimensions, measures=measures + list(map(lambda x: 'obs/' + x, continuous_obs)))\n    results = handle_data(dataset_api=dataset_api, dataset=input_dataset, values=values, embedding_list=embedding_list)\n    df = create_df(test_data, measures, dimensions + continuous_obs, basis)\n\n    for key in measures:\n        v = results['values'][key]  # dict of indices, values\n        if isinstance(v, dict):\n            values = np.zeros(len(df))\n            values[v['indices']] = v['values']\n        else:\n            values = v\n        np.testing.assert_array_equal(values, df[key].values, err_msg=key)\n    for key in dimensions:\n        np.testing.assert_array_equal(results['values'][key], test_data.obs[key], err_msg=key)\n    for key in basis['coordinate_columns']:\n        np.testing.assert_array_equal(results['embeddings'][0]['coordinates'][key], df[key].values, err_msg=key)\n\n\n@pytest.fixture(autouse=True, params=['sum', 'mean', 'max'])\ndef agg_function(request):\n    return request.param\n\n\ndef test_binning(dataset_api, input_dataset, test_data, measures, dimensions, continuous_obs, basis,\n                 agg_function):\n    nbins = 100\n    basis_dict = get_basis(basis, nbins, agg_function)\n    grouped_df = group_df(test_data, measures, dimensions, continuous_obs, basis_dict)\n    embedding_list = [dict(nbins=nbins, ndim=2, basis=basis, dimensions=dimensions, agg=agg_function)]\n    embedding_list[0]['measures'] = measures + list(map(lambda x: 'obs/' + x, continuous_obs))\n    results = handle_data(dataset_api=dataset_api, dataset=input_dataset, embedding_list=embedding_list)\n    diff_binning(grouped_df, measures, dimensions, continuous_obs, basis_dict, results['embeddings'][0])\n","sub_path":"tests/test_embedding.py","file_name":"test_embedding.py","file_ext":"py","file_size_in_byte":3998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"204260024","text":"import os\nos.environ.setdefault('DJANGO_SETTINGS_MODULE','tango_with_django_project.settings')\n\nimport django\ndjango.setup()\n\nfrom rango.models import Category,Page\n\n#create list of Pages\ndef populate():\n\tpython_pages = [\n\t {\"title\": \"Official Python Tutorial\",\n\t \"url\":\"http://docs.python.org/2/tutorial/\"},\n\t {\"title\":\"How to Think like a Computer Scientist\",\n\t \"url\":\"http://www.greenteapress.com/thinkpython/\"},\n\t {\"title\":\"Learn Python in 10 Minutes\",\n\t \"url\":\"http://www.korokithakis.net/tutorials/python/\"} ]\n\n\tdjango_pages = [\n\t {\"title\":\"Official Django Tutorial\",\n\t \"url\":\"https://docs.djangoproject.com/en/1.9/intro/tutorial01/\"},\n\t {\"title\":\"Django Rocks\",\n\t \"url\":\"http://www.djangorocks.com/\"},\n\t {\"title\":\"How to Tango with Django\",\n\t \"url\":\"http://www.tangowithdjango.com/\"} ]\n\n\n\tother_pages = [\n\t {\"title\":\"Bottle\",\n\t \"url\":\"http://bottlepy.org/docs/dev/\"},\n\t {\"title\":\"Flask\",\n\t \"url\":\"http://flask.pocoo.org\"} ]\n\n\tcats = {\"Python\": {\"pages\": python_pages},\n\t \"Django\": {\"pages\": django_pages},\n\t \"Other Frameworks\": {\"pages\": other_pages} }\n\n\tfor cat,catsPages in cats.items():\n\t\tc=add_cat(cat)\n\t\tfor page in catsPages['pages']:\n\t\t\tadd_page(c,page['title'],page['url'])\n\ndef add_cat(category):\n\tc=Category.objects.get_or_create(name=category)[0]\n\tc.save()\n\treturn c \n\ndef add_page(category_name,title,url_str,views=0):\n\tp=Page.objects.get_or_create(category=category_name,title=title)[0]\n\tp.url=url_str\n\tp.views=views\n\tp.save()\n\treturn p\n\nif __name__ == '__main__':\n\tprint('script starts')\n\tpopulate()\n\tprint('script ends')\n\n","sub_path":"tango_with_django_project/populateDB.py","file_name":"populateDB.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"16229433","text":"from datetime import datetime\nfrom github import Github\nimport os\nimport requests\nimport schedule\nimport sys\nimport time\n\nignore_labels = os.environ.get('GITHUB_IGNORE_LABELS')\nGITHUB_IGNORE_LABELS = ignore_labels.split(',') if ignore_labels else []\n\nignore_title_words = os.environ.get('GITHUB_IGNORE_TITLE_WORDS')\nGITHUB_IGNORE_TITLE_WORDS = ignore_title_words.split(',') if ignore_title_words else []\n\nSLACK_CHANNEL = os.environ.get('SLACK_CHANNEL', '#general')\n\ntry:\n    SLACK_API_TOKEN = os.environ['SLACK_API_TOKEN']\n    GITHUB_API_TOKEN = os.environ['GITHUB_API_TOKEN']\n    GITHUB_ORGANIZATION = os.environ['GITHUB_ORGANIZATION']\nexcept KeyError as error:\n    sys.stderr.write('Please set the environment variable {0}'.format(error))\n    sys.exit(1)\n\nclient = Github(GITHUB_API_TOKEN)\n\ndef is_ignored(pull_request):\n    lowercase_title = pull_request.title.lower()\n    ignored_title = False\n    for ignored_word in GITHUB_IGNORE_TITLE_WORDS:\n        if ignored_word.lower() in lowercase_title:\n            ignored_title = True\n\n    ignored_label = False\n    # TODO the python library doesn't fetch the labels, even though Githubs Api returns them\n    # https://github.com/PyGithub/PyGithub/blob/v1.39/github/PullRequest.py\n    # for label in (pull_request.labels if pull_request.labels else []):\n    #     if label in GITHUB_IGNORE_LABELS:\n    #         ignored_label = True\n\n    return ignored_title or ignored_label\n\n\ndef format_pull_request(pull_request, repository):\n    assignees = []\n    for assignee in pull_request.assignees:\n        assignees.append(assignee.login)\n\n    # TODO the python library doesn't fetch the reviewers, even though GitHub's Api returns them\n    # https://github.com/PyGithub/PyGithub/blob/v1.39/github/PullRequest.py\n    # for reviewer in pull_request.reviewers:\n    #     assignees.append(reviewer.login)\n\n    assignee_text = ', '.join(assignees) if assignees else \"no one\"\n    creator = pull_request.user.login\n    created_days_ago = (datetime.now() - pull_request.created_at).days\n\n    return f\"<{pull_request.html_url}|{repository}/{pull_request.title}> - by {creator} - assigned to *{assignee_text}* - created {created_days_ago} days ago\"\n\n\ndef fetch_open_pull_requests(organization_name):\n    formatted_pull_requests = []\n\n    for repository in client.get_organization(organization_name).get_repos('all'):\n        pull_requests = [pull_request for pull_request in repository.get_pulls(state='open')]\n        pull_requests.sort(key=lambda pr: pr.created_at)\n        for pull_request in pull_requests:\n            if not is_ignored(pull_request):\n                formatted_pull_requests.append(format_pull_request(pull_request, repository.name))\n\n    return formatted_pull_requests\n\n\ndef send_to_slack(text):\n    payload = {\n        'token': SLACK_API_TOKEN,\n        'channel': SLACK_CHANNEL,\n        'username': 'Pull Request Reminder',\n        'icon_emoji': ':bell:',\n        'text': text\n    }\n    response = requests.post('https://slack.com/api/chat.postMessage', data=payload)\n    answer = response.json()\n    if not answer['ok']:\n        raise Exception(answer['error'])\n\n\ndef update_slack():\n    print('Checking for pull requests')\n    pull_requests = fetch_open_pull_requests(GITHUB_ORGANIZATION)\n    text = \"No open pull requests today :partyparrot:\" if not pull_requests else \"Hi! There's a few open pull requests you should take a look at:\\n\" + '\\n'.join(pull_requests)\n    print(text)\n    send_to_slack(text)\n\n\nif __name__ == '__main__':\n    print('beep boop')\n    schedule.every().day.at(\"09:30\").do(update_slack)\n    while True:\n        schedule.run_pending()\n        time.sleep(1)\n","sub_path":"pull_reminder.py","file_name":"pull_reminder.py","file_ext":"py","file_size_in_byte":3639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"617877005","text":"from building import *\nimport rtconfig\n\n# get current directory\ncwd     = GetCurrentDir()\n# The set of source files associated with this SConscript file.\nsrc     = Glob('src/*.c')\n\nif GetDepend('VT100_USING_MONITOR'):\n\tsrc    += Glob('examples/vt_monitor.c')\n\nif GetDepend('VT100_USING_COLOR'):\n\tsrc    += Glob('examples/vt_color.c')\n\nif GetDepend('VT100_USING_SL'):\n\tsrc    += Glob('examples/vt_sl.c')\n\npath   =  [cwd + '/src']\npath   += [cwd + '/examples']\n\nLOCAL_CCFLAGS = ''\n\ngroup = DefineGroup('vt100', src, depend = ['PKG_USING_VT100'], CPPPATH = path, LOCAL_CCFLAGS = LOCAL_CCFLAGS)\n\nReturn('group')\n","sub_path":"SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"360159928","text":"import pdfkit\nimport mongoengine as mongo\nimport celery.decorators\nimport logging\nfrom jinja2 import Template\nimport os\n\nfrom ccl_scratch_tools import Scraper\n\nfrom . import common as common\nfrom . import scrape, settings\n\nconnect_db = common.connect_db\n\n\ndef convert_cert(template,\n                 username,\n                 projectnum,\n                 cache_directory=settings.CACHE_DIRECTORY):\n    \"\"\"Converts template to the certificate pdf with inputs.\n\n    Args:\n        template (str): the certificate template provided as a string directed to a html page.\n        username (str): username to add to the certificate.\n        projectnum (int): the number of projects completed by the user, should be 10 or fewer.\n        cache_directory (str): will save this certificate into the cache directory specified.\n            Defaults to settings.CACHE_DIRECTORY.\n\n    Returns:\n        True or False depending on the success of the certificate conversion.\n    \"\"\"\n\n    # Set formatting parameters for the look of the certificate\n    options = {\n        \"orientation\": \"Landscape\",\n        \"page-size\": \"Letter\",\n        \"margin-top\": \"0\",\n        \"margin-right\": \"0\",\n        \"margin-bottom\": \"0\",\n        \"margin-left\": \"0\",\n        \"encoding\": \"UTF-8\",\n        \"no-outline\": None\n    }\n\n    # Location of the css file\n    css = \"{}/lib/assets/certificate.css\".format(settings.PROJECT_DIRECTORY)\n\n    try:\n        # Use of jinja and pdfkit to build the certificate pdf\n        jinja2_template_string = open(\"{}/lib/assets/{}\".format(settings.PROJECT_DIRECTORY,template), \"rb\").read()  # yapf: disable\n        template = Template(jinja2_template_string.decode(\"utf-8\"))\n\n        html_template_string = template.render(name=username,\n                                               projectnum=projectnum)\n\n        config = pdfkit.configuration(wkhtmltopdf=settings.WKPDF_LOCATION)\n        pdfkit.from_string(html_template_string,\n                           f\"{cache_directory}/certificates/{username}.pdf\",\n                           options=options,\n                           css=css,\n                           configuration=config)\n\n        return True\n    except:\n        return False\n\n\n@celery.decorators.task\ndef generate_certs(usernames,\n                   credentials_file=settings.DEFAULT_CREDENTIALS_FILE,\n                   cache_directory=settings.CACHE_DIRECTORY):\n    \"\"\"Initiates the generation of all Getting Unstuck certificates.\n\n    Args:\n        usernames (array-like): list of usernames to create and scrape certificates for.\n        credentials_file (str): path to the database credentials file.   \n        cache_directory (str): if set, will save this certificate into the cache directory specified.\n\n    Returns: \n        None.\n    \"\"\"\n\n    Scraper().make_dir(f\"{cache_directory}/certificates\")\n\n    logging.info(\"attempting to generate certificates\")\n    connect_db(credentials_file=credentials_file)\n\n    # Get schema IDs, and add to a reusable query that will get all the projects that have one of the schemas\n    schema_ids = scrape.Studio.objects(\n        public_show=True).values_list(\"challenge_id\")\n    query = []\n    for schema_id in schema_ids:\n        query.append({f\"validation.{schema_id}\": {\"$exists\": True}})\n    projects = scrape.Project.objects(__raw__={\"$or\": query})\n\n    # Loop through each username to generate certificate\n    for username in usernames:\n        # Get number of projects completed\n        author_count = projects.filter(author=username).count()\n        if author_count > 10:\n            logging.info(\n                \"certificate for {} has more than 10 projects! reset to 10\"\n                .format(username))\n            author_count = 10\n\n        # Generate certificate\n        cert_download = convert_cert(\"pdf.html\",\n                                     username,\n                                     author_count,\n                                     cache_directory)\n\n        if not cert_download:\n            logging.info(\"certificate download failed for {}\".format(username))\n\n    logging.info(\"certificate generation completed!\")\n","sub_path":"lib/certificate.py","file_name":"certificate.py","file_ext":"py","file_size_in_byte":4102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"370902947","text":"#!/usr/bin/env python3\r\nimport json\r\nimport collections\r\n\r\nfilepath=\"data/\"\r\nfilepath2=\"images/\" #B:\\Projects\\Python\\\r\nfilename=\"stats\"\r\nfileext=[\".json\",\".png\"]\r\nname=[\"garth\",\"bull\",\"bold\",\"zamby\",\"level\",\"maxi\",\"becca\",\"living\",\"zeph\",\"luna\",\"moggo\",\"money\",\"mel\"]\r\nstat=[\"strength\",\"vitality\",\"magic\",\"agility\",\"technique\",\"charisma\",\"focus\"]\r\n\r\ndata = {}  \r\ndata['character'] = {}\r\nfor n in name:\r\n\tchardict1 = {}\r\n\tchardict3 = {}\r\n\tchardict1[n] = {}\r\n\tpath2 = \"\"\r\n\tpsq2=(filepath2,n,fileext[1])\r\n\tpath2=(path2.join(psq2))\r\n\tchardict2={\r\n\t\t\"name\": n,\r\n\t\t\"image\": path2,\r\n\t\t\"stats\":{}}\r\n\tfor s in stat:\r\n\t\tchardict4={s:1}\r\n\t\tchardict3.update(chardict4)\r\n\tchardict2[\"stats\"]=(chardict3)\r\n\tchardict1[n] = chardict2\r\n\tchardata[\"character\"].update(chardict1)\r\n\r\npath = \"\"\r\npsq=(filepath,filename,fileext[0])\r\npath=(path.join(psq))\r\nwith open(path, 'w') as outfile:\r\n    json.dump(chardata,outfile, indent=4)","sub_path":"Python/jsonsave.py","file_name":"jsonsave.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"586503991","text":"#import init as init #has file paths\r\nimport os\r\nimport json\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.patches as patches\r\nfrom scipy import misc\r\nimport scipy.io as sio\r\nimport cv2\r\nimport numpy as np\r\nimport argparse\r\n\r\n\r\ndef vis_boxes_and_move(scene_path):\r\n    \"\"\" Visualizes bounding boxes and images in the scene.\r\n    Allows user to navigate the scene via the movement \r\n    pointers using the keyboard\r\n    ARGUMENTS:\r\n        scene_path: the string full path of the scene to view\r\n            Ex) vis_camera_pos_dirs('/path/to/data/Home_01_1')\r\n    \"\"\"\r\n\r\n\r\n    #set up scene specfic paths\r\n    images_path = os.path.join(scene_path,'jpg_rgb')\r\n    annotations_path = os.path.join(scene_path,'annotations.json')\r\n\r\n    #load data\r\n    image_names = os.listdir(images_path)\r\n    image_names.sort()\r\n    ann_file = open(annotations_path)\r\n    annotations = json.load(ann_file)\r\n\r\n\r\n    #set up for first image\r\n    cur_image_name = image_names[0]\r\n    next_image_name = ''\r\n    move_command = ''\r\n    #fig,ax = plt.subplots(1)\r\n    \r\n    key = 0\r\n    while True:\r\n\r\n        #load the current image and annotations \r\n        rgb_image = cv2.imread(os.path.join(images_path,cur_image_name))\r\n        boxes = annotations[cur_image_name]['bounding_boxes']\r\n        \r\n        \r\n        \r\n        #Setting up feature detection\r\n        #Parameters for ShiTomasi Corder detection\r\n        shito_params = dict( maxCorners = 100,\r\n                             qualityLevel = 0.01,\r\n                             minDistance = 7,\r\n                             blockSize = 7 )\r\n\r\n        #Parameters for Lucas Kanade Optical Flow\r\n        lk_params = dict( winSize = (15,15),\r\n                          maxLevel = 2,\r\n                          criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))\r\n\r\n        \r\n        #Create random colours\r\n        #color = np.random.randint(0,255,(100,3))\r\n        #print(color)\r\n        \r\n        #red colour list\r\n        color = (255, 0 ,0)\r\n        \r\n        #Find corner of first frame\r\n        #ret, prev_frame = cap\r\n        cur_frame = 0\r\n        prev_frame = rgb_image\r\n        prev_frame_grey = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)\r\n        #detecing standout corner of image\r\n        p0 = cv2.goodFeaturesToTrack(prev_frame_grey, mask = None, **shito_params)\r\n\r\n        \r\n        #create mask for line drawing\r\n        mask = np.zeros_like(prev_frame_grey)  #return array of zeroes similar to the image\r\n\r\n        \r\n        #plot the image and draw the boxes\r\n        cv2.namedWindow('image', cv2.WINDOW_NORMAL)\r\n        cv2.resizeWindow('image', 960, 540)\r\n        cv2.imshow('image', rgb_image)\r\n\r\n        key = cv2.waitKey(-1)\r\n        \r\n        '''\r\n        plt.cla()\r\n        ax.imshow(rgb_image)\r\n        plt.title(cur_image_name)\r\n        \r\n        for box in boxes:\r\n            # Create a Rectangle patch\r\n            rect =patches.Rectangle((box[0],box[1]),box[2]-box[0],box[3]-box[1],\r\n                                     linewidth=2,edgecolor='r',facecolor='none')\r\n            # Add the patch to the Axes\r\n            ax.add_patch(rect)\r\n        #draw the plot on the figure\r\n        plt.draw()\r\n        plt.pause(.001)\r\n        '''\r\n        \r\n        #---------USER COMMAND FOR DEBUG-----------#\r\n\r\n        #get input from user \r\n        #move_command = input('Enter command: ')\r\n\r\n\r\n        #get the next image name to display based on the \r\n        #user input, and the annotation.\r\n        if key == 119:\r\n            next_image_name = annotations[cur_image_name]['forward']\r\n        elif key == 97:\r\n            next_image_name = annotations[cur_image_name]['rotate_ccw']\r\n        elif key == 115:\r\n            next_image_name = annotations[cur_image_name]['backward']\r\n        elif key == 100:\r\n            next_image_name = annotations[cur_image_name]['rotate_cw']\r\n        elif key == 101:\r\n            next_image_name = annotations[cur_image_name]['left']\r\n        elif key == 114:\r\n            next_image_name = annotations[cur_image_name]['right']\r\n        elif key == 104:\r\n            next_image_name = cur_image_name\r\n            print(\"{}\\n{}\\n{}\\n{}\\n{}\\n{}\\n{}\\n{}\\n\".format(\r\n                  \"Enter a character to move around the scene:\",\r\n                  \"'w' - forward\", \r\n                  \"'a' - rotate counter clockwise\", \r\n                  \"'s' - backward\", \r\n                  \"'d' - rotate clockwise\", \r\n                  \"'e' - left\", \r\n                  \"'r' - right\", \r\n                  \"'q' - quit\", \r\n                  \"'h' - print this help menu\"))\r\n        elif key == 113:\r\n            cv2.destroyAllWindows\r\n            break\r\n\r\n\r\n        #if the user inputted move is valid (there is an image there) \r\n        #then update the image to display. If the move was not valid, \r\n        #the current image will be displayed again\r\n        if next_image_name != '':\r\n            cur_image_name = next_image_name\r\n            \r\n            \r\n        #Optical flow tracking when transition into the next frame    \r\n\r\n        if True:\r\n            print('checkpoint')\r\n            #ret, cur_frame = cap\r\n            cur_frame = rgb_image\r\n            cur_frame_grey = cv2.cvtColor(cur_frame, cv2.COLOR_BGR2GRAY)\r\n            \r\n            #Calculate Optical Flow\r\n            p1, st, err = cv2.calcOpticalFlowPyrLK(prev_frame_grey, cur_frame_grey, p0, None, **lk_params)\r\n            \r\n            #Select good points\r\n            good_cur = p1[st==1]\r\n            good_prev = p0[st==1]\r\n            \r\n            #draw tracks\r\n            for i,(cur,prev) in enumerate(zip(good_cur, good_prev)):\r\n                a,b = cur.ravel()\r\n                c,d = prev.ravel()\r\n                mask = cv2.line(mask, (a,b),(c,d), color, 2)\r\n                cur_frame = cv2.circle(cur_frame, (a,b),5,color,-1)\r\n                \r\n        #print(cur_frame.shape)\r\n        #print(mask.shape)\r\n\r\n        #img = cv2.addWeighted(cur_frame_list[0:1], 0.5, mask, 0.5, 0)\r\n        cv2.namedWindow('optical tracking', cv2.WINDOW_NORMAL)\r\n        cv2.resizeWindow('optical tracking', 640, 360)\r\n        cv2.imshow('optical tracking', mask)\r\n        \r\n        #img = cv2.addWeighted(cur_frame_list[0:1], 0.5, mask, 0.5, 0)\r\n        cv2.namedWindow('point detection', cv2.WINDOW_NORMAL)\r\n        cv2.resizeWindow('point detection', 640, 360)\r\n        cv2.imshow('point detection', cv2.cvtColor(cur_frame, cv2.COLOR_BGR2GRAY))\r\n                        \r\n        #update previous frame and previous points\r\n        prev_frame_grey = cur_frame_grey.copy()\r\n        p0 = good_cur.reshape(-1,1,2)\r\n    \r\n        \r\n\r\n\r\n\r\ndef vis_camera_pos_dirs(scene_path, plot_directions=True, scale_positions=True):\r\n    \"\"\" Visualizes camera positions and directions in the scene.\r\n    ARGUMENTS:\r\n        scene_path: the string full path of the scene to view\r\n            Ex) vis_camera_pos_dirs('/path/to/data/Home_01_1')\r\n    KEYWORD ARGUMENTS:\r\n        plot_directions: bool, whether or not to plot camera directions\r\n                         defaults to True\r\n            Ex) vis_camera_pos_dirs('Home_01_1', plot_directions=False)\r\n        scale_positions: bool, whether or not to scale camera positions \r\n                         to be in millimeters. Defaults to True\r\n            Ex) vis_camera_pos_dirs('Home_01_1', scale_positions=False)\r\n    \"\"\"\r\n\r\n    #TODO - make faster - organize all positions/directions, then plot\r\n\r\n    #set up scene specfic paths\r\n    images_path = os.path.join(scene_path,'jpg_rgb')\r\n    image_structs_path = os.path.join(scene_path,'image_structs.mat')\r\n\r\n    #load data.\r\n    #the transition from matlab to python is not pretty\r\n    image_structs = sio.loadmat(image_structs_path)\r\n    scale = image_structs['scale'][0][0]\r\n    image_structs = image_structs['image_structs']\r\n    image_structs = image_structs[0]\r\n\r\n    #make plot\r\n    fig,ax = plt.subplots(1)\r\n\r\n    for camera in image_structs:\r\n        #get 3D camera position in the reconstruction\r\n        #coordinate frame. The scale is arbitrary\r\n        world_pos = camera[3] \r\n        if scale_positions:\r\n            world_pos *= scale\r\n\r\n        #get 3D vector that indicates camera viewing direction\r\n        #Add the world_pos to translate the vector from the origin\r\n        #to the camera location.\r\n        camera[4] /= 2;#to make plot look nicer\r\n        if scale_positions:\r\n            direction = world_pos + camera[4]*scale\r\n        else:\r\n            direction = world_pos + camera[4]\r\n            \r\n        #plot only 2D, as all camera heights are the same\r\n\r\n        #draw the position\r\n        plt.plot(world_pos[0], world_pos[2],'ro')    \r\n        #draw the direction if user sets option \r\n        if plot_directions:\r\n            plt.plot([world_pos[0], direction[0]], \r\n                             [world_pos[2], direction[2]], 'b-')    \r\n\r\n\r\n    #for camera in image_structs \r\n    plt.axis('equal')\r\n    plt.show()    ","sub_path":"foong_POF.py","file_name":"foong_POF.py","file_ext":"py","file_size_in_byte":8921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"592600485","text":"#! /usr/local/bin/python3\n\n'''\nDrawn from examples:\n- PyQt 5.3.2 Reference Quide \"Integrating Python and QML\"\n- PySide wiki\n- Qt gallery example\n- Qt QML reference guide\n-- \"Interacting with QML Objects from C++\"\n'''\n\nfrom qmlApp.qmlApp import QmlApp\nfrom widgetApp.widgetApp import WidgetApp\n\n\n'''\nPure QML apps (invokes QmlApp)\n\nCreate and init app enters event loop, doesn't return\n'''\n'''\nCruft\n# \"person.qml\"\n# \"qrc:/resources/helloWorld.qml\"\n# qrc:/weatherapp/qml/main.qml\"))\n\n# Qt's gallery exampe\n# Half working, imports a contents dir of qml? Much commented out\n# app = QmlApp(qml=\"qml/gallery/gallery.qml\")\n'''\n\n'''\nThese seem to work.\nUncomment one to test.\n'''\n#app = QmlApp(qml=\"qml/pureQMLApps/helloWorld.qml\")\n'''\nNot working OSX in Python in terminal because app menubar hidden?\n'''\n#app = QmlApp(qml=\"qml/pureQMLApps/testDialogs.qml\")\n#app = QmlApp(qml=\"qml/pureQMLApps/testViews.qml\")\n#app = QmlApp(qml=\"qml/pureQMLApps/testBars.qml\")\n#app = QmlApp(qml=\"qml/pureQMLApps/testMenu.qml\")\n#app = QmlApp(qml=\"qml/pureQMLApps/testComboBox.qml\")\n\n\n\" Test signals from QML to Python slot\"\n#app = QmlApp(qml=\"qml/pureQMLApps/testSignals.qml\")\n\" Test signal from Python to QML\"\n#app = QmlApp(qml=\"qml/pureQMLApps/testSignalsUp.qml\")\n\" Test model with nested properties accessible in QML\"\n#app = QmlApp(qml=\"qml/pureQMLApps/testNestedProperties.qml\")\n\" Test model returning a type via a method\"\n#app = QmlApp(qml=\"qml/pureQMLApps/testModelSlot.qml\")\n\n\n\n'''\nQWidget app embedding QML\n'''\n\n# menuBar does NOT seem to work\n#app = WidgetApp(embeddedQml=\"qml/bars/menuBar.qml\")\n\n# This doesn't work: 'QQuickView only supports loading of root objects that derive from QQuickItem.'\n#app = WidgetApp(embeddedQml=\"qml/helloWorld.qml\")\n\n\"These work\"\n\n# Popup menu\n#app = WidgetApp(embeddedQml=\"qml/menu.qml\")\n\n# dialog not working?\n#app = WidgetApp(embeddedQml=\"qml/dialogs/fontDialog.qml\")\n\n# Toolbar works.  toolbar are NOT exclusive to ApplicationWindow?\n#app = WidgetApp(embeddedQml=\"qml/bars/toolBar.qml\")\n\n\" Test round trip to modal context menu \"\n# Toolbar, context menu, and dialog\napp = WidgetApp(embeddedQml=\"qml/bars/toolBar.qml\", secondEmbeddedQml=\"qml/embeddedQML/MenuAndDialog.qml\")\n#\"qml/embeddedQML/PickMenu.qml\"\n#\"qml/dialogs/dialog.qml\"\n\n# Menu with delegate.  Doesn't work unless Menu embedded in Item\n#app = WidgetApp(embeddedQml=\"qml/bars/toolBar.qml\", secondEmbeddedQml=\"qml/embeddedQML/MenuWDelegate.qml\")\n\n# Dialog with controls.  Slider owning model\n# Works Linux, OSX\n#app = WidgetApp(embeddedQml=\"qml/bars/toolBar.qml\", secondEmbeddedQml=\"qml/embeddedQML/DialogWControls.qml\")\n\n# Dialog with calendar\n#app = WidgetApp(embeddedQml=\"qml/bars/toolBar.qml\", secondEmbeddedQml=\"qml/embeddedQML/DialogWCalendar.qml\")\n\n# Dialog implemented using Window with Qt.Dialog flag\n#app = WidgetApp(embeddedQml=\"qml/bars/toolBar.qml\", secondEmbeddedQml=\"qml/embeddedQML/WindowAsDialog.qml\")\n\n\n# Test toolbar and popupmenu \n#app = WidgetApp(embeddedQml=\"qml/bars/toolBar.qml\")\n\n\n# Not work? QmlApp requires a QWindow, which this does not have\n#app = QmlApp(qml=\"qml/pureQMLApps/helloWorldRedRect.qml\")\n\n\n  \n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"328351703","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Ed Mountjoy\n#\n\nimport sys\nimport os\nimport argparse\nimport pandas as pd\nimport numpy as np\nimport re\n\ndef main():\n\n    # Parse args\n    args = parse_args()\n\n    #\n    # Load GWAS Catalog data\n    #\n\n    # Load gwas catalog data\n    gwas_raw = pd.read_csv(args.gwas, sep='\\t', header=0, dtype={'P-VALUE':str})\n\n    # Set a row (association) id\n    gwas_raw['assoc_id'] = list(range(1, gwas_raw.shape[0] + 1))\n    gwas = gwas_raw.copy()\n\n    # Split rows by multiple variants\n    gwas['CHR_ID'] = gwas['CHR_ID'].astype(str).str.split(';')\n    gwas['CHR_POS'] = gwas['CHR_POS'].astype(str).str.split(';')\n    gwas['SNPS'] = gwas['SNPS'].astype(str).str.split('; ')\n    gwas['STRONGEST SNP-RISK ALLELE'] = gwas['STRONGEST SNP-RISK ALLELE'].astype(str).str.split('; ')\n\n    # Assert that same number of variants exist\n    assert(((gwas['CHR_ID'].apply(len)) == (gwas['CHR_POS'].apply(len))).all())\n    assert(((gwas['SNPS'].apply(len)) == (gwas['STRONGEST SNP-RISK ALLELE'].apply(len))).all())\n    # Filter rows (2 exist) whether CHR_ID != SNPS length\n    to_keep = ((gwas['CHR_ID'].apply(len)) == (gwas['SNPS'].apply(len)))\n    gwas = gwas.loc[to_keep, :]\n    # Add column to flag that multiple variants were reported\n    gwas['multi_SNPs_reported'] = np.where(gwas['CHR_ID'].apply(len) > 1, 1, 0)\n\n    # Explode multivars into different rows\n    gwas = explode(gwas, ['CHR_ID', 'CHR_POS', 'SNPS', 'STRONGEST SNP-RISK ALLELE'])\n\n    #\n    # Load HRC VCF data\n    #\n\n    # Load\n    vcf_hrc = pd.read_csv(args.vcf_hrc, sep='\\t', header=None).iloc[:, 0:5]\n    vcf_hrc.columns = ['chrom', 'pos', 'rsid', 'ref', 'alt']\n\n    # Assert only 1 rsid per row\n    assert((vcf_hrc.rsid.str.split(',').apply(len) == 1).all())\n\n    # Split multiallelic\n    vcf_hrc['alt'] = vcf_hrc['alt'].str.split(',')\n    vcf_hrc['is_multiallelic'] = np.where(vcf_hrc['alt'].apply(len) > 1, 1, 0)\n    vcf_hrc = explode(vcf_hrc, ['alt'])\n\n    #\n    # Merge to HRC. First on rsids, then on chr:pos\n    #\n\n    # Make sure types match\n    vcf_hrc['chrom'] = vcf_hrc['chrom'].astype(str)\n    vcf_hrc['pos'] = vcf_hrc['pos'].astype(int)\n\n    # Make new rsid column based on either SNP_ID_CURRENT or SNPS\n    gwas['best_rsid'] = gwas.apply(get_best_rsid, axis=1)\n\n    # Merge on rsids\n    gwas_rs = gwas.loc[gwas.best_rsid.str.startswith('rs'), :]\n    merged_rs = pd.merge(gwas_rs, vcf_hrc,\n                         how='left',\n                         left_on='best_rsid', right_on='rsid')\n\n    # Merge on chr:pos\n    gwas_chr = gwas.loc[gwas.best_rsid.str.startswith('chr'), :]\n    gwas_chr[['chrom_37', 'pos_37']] = ( gwas_chr.best_rsid.apply(str_to_chrompos)\n                                                           .apply(pd.Series) )\n    merged_chr = pd.merge(gwas_chr, vcf_hrc,\n                          how='left',\n                          left_on=['chrom_37', 'pos_37'],\n                          right_on=['chrom', 'pos'])\n\n    # Combine\n    merged = pd.concat([merged_rs, merged_chr])\n\n\n    # Split these into those with HRC mapping and those without\n    has_hrc = ~pd.isnull(merged.rsid)\n    merged_w_hrc = merged.loc[has_hrc, :]\n    merged_wo_hrc = merged.loc[~has_hrc, gwas.columns]\n\n    #\n    # Load Ensembl VCF data\n    #\n\n    # Load\n    vcf_ensembl = pd.read_csv(args.vcf_ensembl, sep='\\t', header=None).iloc[:, 0:5]\n    vcf_ensembl.columns = ['chrom', 'pos', 'rsid', 'ref', 'alt']\n\n    # Assert only 1 rsid per row\n    assert((vcf_ensembl.rsid.str.split(',').apply(len) == 1).all())\n\n    # Split multiallelic\n    vcf_ensembl['alt'] = vcf_ensembl['alt'].str.split(',')\n    vcf_ensembl['is_multiallelic'] = np.where(vcf_ensembl['alt'].apply(len) > 1, 1, 0)\n    vcf_ensembl = explode(vcf_ensembl, ['alt'])\n\n    #\n    # Merge to Ensembl. First on rsids, then on chr:pos\n    #\n\n    # Make sure types match\n    vcf_ensembl['chrom'] = vcf_ensembl['chrom'].astype(str)\n    vcf_ensembl['pos'] = vcf_ensembl['pos'].astype(int)\n\n    # Make new rsid column based on either SNP_ID_CURRENT or SNPS\n    merged_wo_hrc['best_rsid'] = merged_wo_hrc.apply(get_best_rsid, axis=1)\n\n    # Merge on rsids\n    merged_wo_hrc_rs = merged_wo_hrc.loc[merged_wo_hrc.best_rsid.str.startswith('rs'), :]\n    merged_rs = pd.merge(merged_wo_hrc_rs, vcf_ensembl,\n                         how='left',\n                         left_on='best_rsid', right_on='rsid')\n\n    # Merge on chr:pos\n    merged_wo_hrc_chr = merged_wo_hrc.loc[merged_wo_hrc.best_rsid.str.startswith('chr'), :]\n    merged_wo_hrc_chr[['chrom_37', 'pos_37']] = ( merged_wo_hrc_chr.best_rsid.apply(str_to_chrompos)\n                                                           .apply(pd.Series) )\n    merged_chr = pd.merge(merged_wo_hrc_chr, vcf_ensembl,\n                          how='left',\n                          left_on=['chrom_37', 'pos_37'],\n                          right_on=['chrom', 'pos'])\n\n    # Combine\n    ens_merged = pd.concat([merged_rs, merged_chr])\n\n    #\n    # Combine HRC and Ensembl merges\n    #\n\n    hrc_ens_merged = pd.concat([merged_w_hrc, ens_merged])\n\n\n    # Drop rows without mapping in VCF\n    hrc_ens_merged = hrc_ens_merged.dropna(subset=['rsid'])\n    hrc_ens_merged['pos'] = hrc_ens_merged['pos'].astype(int)\n\n    #\n    # Create variant IDs\n    #\n\n    # Make a variant_id\n    hrc_ens_merged['variant_id_b37'] = hrc_ens_merged.apply(make_var_id, axis=1)\n\n    #\n    # For rows with a risk allele reported, see if concordant with ref or alt\n    # alleles on forward or reverse strands\n    #\n\n    # Extract risk allele\n    hrc_ens_merged['risk_allele'] = hrc_ens_merged['STRONGEST SNP-RISK ALLELE'].apply(extract_risk_allele)\n    # Check concordancy\n    hrc_ens_merged['risk_allele_concordant'] = hrc_ens_merged[['risk_allele', 'ref', 'alt']].apply(check_concordancy, axis=1)\n    # Remove rows where not concordant\n    hrc_ens_merged = hrc_ens_merged.loc[hrc_ens_merged['risk_allele_concordant'] != 'no', :]\n\n    #\n    # Tidy\n    #\n\n    # Drop unneeded columns\n    hrc_ens_merged = hrc_ens_merged.drop(labels=[\n        'alt',\n        'best_rsid',\n        'chrom',\n        'chrom_37',\n        'pos',\n        'pos_37',\n        'risk_allele',\n        'risk_allele_concordant',\n        'ref'], axis=1)\n    hrc_ens_merged = hrc_ens_merged.drop_duplicates()\n\n\n    #\n    # Collapse multiple variants into one row per locus\n    #\n\n    # Collapse multiple variants into a single row separated by ';'\n    multivar = hrc_ens_merged.loc[:, ['assoc_id', 'rsid', 'variant_id_b37']]\n    multivar_collapsed = (\n        multivar.groupby('assoc_id')\n                .agg({'rsid':combine_rows, 'variant_id_b37':combine_rows})\n                .reset_index() )\n\n    # Merge collapsed variant IDs back to the raw gwas data\n    out_df = pd.merge(gwas_raw, multivar_collapsed,\n                      how='left',\n                      on='assoc_id')\n\n    out_df.to_csv(args.out, sep='\\t', index=None)\n\n    return 0\n\ndef check_concordancy(row):\n    ''' Check whether risk allele is concordant with ref or alt on wither forward or reverse.\n    Args:\n        row (pd.series)\n    Returns:\n        str of yes, no or ambiguous\n    '''\n    if pd.isnull(row['risk_allele']):\n        ret = 'ambiguous'\n    elif any( [row['risk_allele'] == row['alt'],\n               row['risk_allele'] == row['ref'],\n               row['risk_allele'] == revcomp(row['alt']),\n               row['risk_allele'] == revcomp(row['ref'])] ):\n        ret = 'yes'\n    else:\n        ret = 'no'\n    return ret\n\ndef comp(s):\n    \"\"\" Complementary sequence \"\"\"\n    com_dict = {\"A\":\"T\", \"T\":\"A\", \"G\":\"C\", \"C\":\"G\"}\n    com_seq = \"\".join([com_dict.get(nucl, \"N\") for nucl in s])\n    return com_seq\n\ndef revcomp(s):\n    \"\"\" Reverse complement sequence \"\"\"\n    return comp(s[::-1])\n\ndef extract_risk_allele(s):\n    ''' Takes a string from GWAS Catalog STRONGEST SNP-RISK ALLELE field and\n        returns the risk allele, if any.\n    Args:\n        row (pd.series)\n    Returns:\n        str\n    '''\n    mtch = re.match(r'.*-([A|T|G|C]+)', s)\n    if mtch:\n        allele = mtch.group(1)\n    else:\n        allele = None\n    return allele\n\ndef combine_rows(items):\n    return ';'.join(items)\n\ndef make_var_id(row):\n    ''' Make variant ID\n    Args:\n        row (pd.series)\n    Returns:\n        str\n    '''\n    # print(row)\n    return '{chrom}_{pos}_{ref}_{alt}'.format(**row)\n\ndef get_best_rsid(row):\n    ''' Returns the best rsid from:\n            1. SNP_ID_CURRENT\n            2. SNPS\n            3. None\n    Args:\n        row (pd.series)\n    Returns:\n        str\n    '''\n    if not pd.isnull(row['SNP_ID_CURRENT']):\n        try:\n            return 'rs{0}'.format(int(row['SNP_ID_CURRENT']))\n        except ValueError:\n            numeric_only = re.sub('[^0-9]','', row['SNP_ID_CURRENT'])\n            return 'rs{0}'.format(int(numeric_only))\n    elif not pd.isnull(row['SNPS']):\n        return row['SNPS']\n    return None\n\ndef str_to_chrompos(s):\n    ''' Converts gwas catalog snp str to chrom and pos\n    Args:\n        s (str)\n    Returns:\n        str, int\n    '''\n\n    chrom, pos = ( s.replace('chr', '')\n                    .replace('_', ':')\n                    .replace('.', ':')\n                    .replace('-', ':')\n                    .split(':')[:2] )\n    return str(chrom), int(pos)\n\ndef explode(df, columns):\n    ''' Explodes multiple columns\n    '''\n    idx = np.repeat(df.index, df[columns[0]].str.len())\n    a = df.T.reindex_axis(columns).values\n    concat = np.concatenate([np.concatenate(a[i]) for i in range(a.shape[0])])\n    p = pd.DataFrame(concat.reshape(a.shape[0], -1).T, idx, columns)\n    return pd.concat([df.drop(columns, axis=1), p], axis=1).reset_index(drop=True)\n\ndef parse_args():\n    \"\"\" Load command line args \"\"\"\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--gwas', metavar=\"<file>\", help=('GWAS Catalog input'), type=str, required=True)\n    parser.add_argument('--vcf_hrc', metavar=\"<file>\", help=(\"HRC VCF input\"), type=str, required=True)\n    parser.add_argument('--vcf_ensembl', metavar=\"<file>\", help=(\"Ensembl VCF input\"), type=str, required=True)\n    parser.add_argument('--out', metavar=\"<str>\", help=(\"Output\"), type=str, required=True)\n    args = parser.parse_args()\n    return args\n\nif __name__ == '__main__':\n\n    main()\n","sub_path":"scripts/annotate_gwascat_varaintids.py","file_name":"annotate_gwascat_varaintids.py","file_ext":"py","file_size_in_byte":10202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"542361221","text":"### 전력량 예측하기\n\n### 함수\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport datetime\nimport matplotlib.pyplot as plt\n\n### seed 설정\n# 랜덤에 의해 똑같은 결과를 재현하도록 시드 설정\n# 하이퍼파라미터 튜닝하기 위한 용도(흔들리면 무엇때문에 좋아졌는지 알기 어려움)\ntf.set_random_seed(777)\n\n### Standardization (정규분포를 따르는 데이터의 표준정규분포로의 표준화: 평균과 표준편차 이용)\n# 각 observation이 평균을 기준으로 어느 정도 떨어져 있는지를 나타낼때 사용한다\n# 서로 다른 통계 데이터들을 비교하기 용이하게 하기 위해\n# 어떤 변수를 어떤 표본에 대해 통계를 구하였는가에 따라 평균과 분산값은 제각각이기 때문에, 서로 비교하기가 불편하다\n# 표준화를 하면 평균은 0, 분산과 표준편차는 1이 되므로 비교하기가 용이하다\n# 각 값에서 평균을 빼고, 표준편차로 나눠줌\ndef data_standardization(x):\n    x_np = np.asarray(x)\n    return (x_np - x_np.mean()) / x.np.std()\n\n# a = [80, 90, 100, 110, 120]\n# a = pd.DataFrame(a)\n# a.mean()\n# a.std()\n# print(20 / a.std())\n# print(10 / a.std())\n# print(0 / a.std())\n# print(-10 / a.std())\n# print(-20 / a.std())\n\n\n### Min-Max soaling (이상치/특이값이 포함되어 있는 데이터의 표준화: 중앙값과 IQR(InterQuarile Range) 이용)\n# 사이킷런에서 sklearn.preprocessing.MinMaxScaler(), sklearn.preprocessing.minmax_scale()로 제공\n# StandardScaler(X): 평균이 0과 표준편차가 1이 되도록 변환.\n# RobustScaler(X)  : 중앙값(median)이 0, IQR(interquartile range)이 1이 되도록 변환.\n# MinMaxScaler(X)  : 최대값이 각각 1, 최소값이 0이 되도록 변환\n# MaxAbsScaler(X)  : 0을 기준으로 절대값이 가장 큰 수가 1또는 -1이 되도록 변환\n# 너무 작거나 너무 큰 값이 학습을 방해하는 것을 방지하고자 정규화한다.\n# x가 양수라는 가정하에 최소값과 최대값을 이용하여 0-1사이의 값으로 변환\n# (값 - 최소값) / (최대값 - 최소값)\n\ndef min_max_scaling(x):\n    x_np = np.asarray(x)\n    return (x_np - x_np.min()) / (x_np.max() - x_np.min() + 1e-7) # 1e-7 0으로 나누는 오류 예방차원\n\n######\n# from sklearn.preprocessing import MinMaxScaler\n# a = [80, 90, 100, 110, 120]\n# a = pd.DataFrame(a)\n# a.min()\n# a.max()\n# min_max_scaler = MinMaxScaler()\n# trainA = min_max_scaler.fit_transform(a)\n#\n# from sklearn.preprocessing import minmax_scale\n# a_MinMax_scaled = minmax_scale(a, axis=0, copy=True)\n# a_MinMax_scaled\n\n\n### 정규화된 값을 원래의 값으로 되돌린다\n# 정규화하기 이전의 org_x값과 되돌리고 싶은 x를 입력하면 역정규화된 값을 리턴한다\ndef reverse_min_max_scaling(org_x, x):\n    org_x_np = np.asarray(org_x)\n    x_np = np.asarray(x)\n    return (x_np * (org_x_np.max() - org_x_np.min() + 1e-7)) + org_x_np.min()\n\n\n### 하이퍼파라미터\ninput_data_column_cnt = 2     # 입력데이터의 컬럼 개수\noutput_data_column_cnt = 1    # 결과데이터의 컬럼 개수\n\nseq_length = 60               # 1개 시퀀스의 길이)시계열데이터 입력 개수)\nrnn_cell_hidden_dim = 20      # 각 셀의 (hidden)출력 크기\nforget_bias = 1.0             # 망각편향 (기본값 1.0) - 셀 스테이트에서 어떤 정보를 버릴지 선택하는 과정\nnum_stacked_layers = 1        # stacked LSTM layers 개수\nkeep_prob = 1.0               # dropout 할 때 keep 할 비율\n\nepoch_num = 1000              # 에폭 횟수\nlearning_rate = 0.01          # 학습률\n\n# 데이터 불러오기\nraw_data = pd.read_csv('dataset/통신구/water_level.csv',\n                       encoding = 'euc-kr')\nraw_data.info()\n# data_des = raw_data.describe()\n# data_des.to_csv('Data/정보.csv',\n#                 encoding = 'euc-kr')\n\nraw_data['time'] = raw_data['time'].astype(str)\nraw_data['time'] = pd.to_datetime(raw_data['time'])\nraw_data.set_index('time', inplace = True)\n# del raw_data['date']\nraw_data.value.plot()\nraw_data.label.plot()\n\n#\n# import seaborn as sns\n# sns.heatmap(data = raw_data.corr(), annot=True, fmt = '.2f', linewidths = 0.5)\n\n\n## 데이터를 array로 바꿈\nwater = raw_data.values.astype(np.float)\nprint('watt_info.shape: ', water.shape)\nprint('watt_info[0]: ', water[0])\n\n\n### 데이터 전처리\n\n# water value 컬럼 정규화\nwater_value = water[:, :-1]\nnorm_water_value = min_max_scaling(water_value)  # 전력량 데이터 정규화 처리\nprint('water_value.shape: ', norm_water_value.shape)\nprint('water_value[0]: ', norm_water_value[0])\nprint('norm_water_value.shape: ', norm_water_value.shape)\nprint('norm_water_value[0]: ', norm_water_value[0])\nprint('-'*100)   # 화면상 구분용\n\n# from sklearn.preprocessing import MinMaxScaler\n# min_max_scaler = MinMaxScaler()\n# trainA = min_max_scaler.fit_transform(weather)\n# trainA.min()\n# trainA.max()\n\n# water level 컬럼 정규화\nwater_level = water[:, -1:]\n# elec_norm = min_max_scaling(elec)\n# print('elec.shape: ', elec.shape)\n# print('elec[0]: ', elec[0])\n# print('elec_norm.shape: ', elec_norm.shape)\n# print('elec_norm[0]: ', elec_norm[0])\n# print('-'*100)   # 화면상 구분용\n\n# from sklearn.preprocessing import MinMaxScaler\n# min_max_scaler = MinMaxScaler()\n# trainB = min_max_scaler.fit_transform(elec)\n# trainB.min()\n# trainB.max()\n\n# 배열 결합\nx = np.concatenate((norm_water_value, water_level), axis = 1)\nprint('x.shape: ', x.shape)\nprint('x[0]: ', x[0])\nprint('x[-1]: ', x[-1])\nprint('-'*100)   # 화면상 구분용\n\n# AB = np.concatenate((trainA, trainB), axis = 1)\n# print('AB.shape: ', AB.shape)\n# print('AB[0]: ', AB[0])\n# print('AB[-1]: ', AB[-1])\n# print('-'*100)   # 화면상 구분용\n\n\n### 왜 날씨데이터와 전력량데이터 정규화를 따로하는지 의문이다\n### 하지만 따로 정규화를 하고 결합을 했을 때와 값이랑 통채로 정규화를 했을 때의 값은 달랐다\n### sklearn.preprocessing을 이용하면 정규화할때 각 컬럼별로 정규화를 하지만\n### 위에서 만든 min_max_scaling은 전체 데이터에서 최소값, 최대값으로 계산을 하기 때문에 값이 달라진다\n# a = min_max_scaling(watt)\n# a.min()\n# a.max()\n# print('a.shape: ', a.shape)\n# print('a[0]: ', a[0])\n# print('a[-1]: ', a[-1])\n\n# from sklearn.preprocessing import MinMaxScaler\n# min_max_scaler = MinMaxScaler()\n# c = min_max_scaler.fit_transform(watt)\n# c.min()\n# c.max()\n\n# d = watt[:, 0:1]\n# from sklearn.preprocessing import MinMaxScaler\n# min_max_scaler = MinMaxScaler()\n# d = min_max_scaler.fit_transform(d)\n# d.min()\n# d.max()\n\n\n\n\ny = x[:, [-1]]       # 타겟은 전력량\nprint('y[0]: ', y[0])\nprint('y[-1]: ', y[-1])\n\n\ndataX = []  # 입력으로 사용될 Sequence Data\ndataY = []  # 출력(타켓)으로 사용\n\nfor i in range(0, len(y) - (seq_length)):          # 10741\n    _x = x[i: i + seq_length]                      # [0 : 0 + 60]\n    _y = y[i + seq_length]                         # [0 + 60] 다음에 나타날 전력량(정답)\n    if i is 0:\n        print(_x, \"->\", _y)        # 첫번째 행만 출력해 봄\n    dataX.append(_x)               # dataX 리스트에 추가\n    dataY.append(_y)               # dataY 리스트에 추가\n\nlen(dataX)\n### 학습용/테스트용 데이터 생성\n# 전체 70%를 학습용 데이터로 사용\ntrain_size = int(len(dataY) * 0.7)\n\n# 나머지(30%)를 테스트용 데이터로 사용\ntest_size = len(dataY) - train_size\n\n# 데이터를 잘라 학습용 데이터 생성\ntrainX = np.array(dataX[0:train_size])\ntrainY = np.array(dataY[0:train_size])\n\n# 데이터를 잘라 테스트용 데이터 생성\ntestX = np.array(dataX[train_size:len(dataX)])\ntestY = np.array(dataY[train_size:len(dataY)])\n\n########################################################################################################################\n\n### 텐서플로우 플레이스홀더 생성\n# tf.placeholder(dtype, [shape], name)\n# dtype : 데이터 타입을 의미하며 반드시 적어주어야 한다.\n# shape : 입력 데이터의 형태를 의미한다. 상��� 값이 될 수도 있고 다차원 배열의 정보가 들어올 수도 있다. ( 디폴트 파라미터로 None 지정 )\n# name  : 해당 placeholder의 이름을 부여하는 것으로 적지 않아도 된다.  ( 디폴트 파라미터로 None 지정 )\n# 입력 X, 출력 Y를 생성한다\nX = tf.placeholder(tf.float32, [None, seq_length, input_data_column_cnt])\nprint(\"X: \", X)\nY = tf.placeholder(tf.float32, [None, 1])\nprint(\"Y: \", Y)\n\n# 검증용 측정지표를 산출하기 위한 targets, predictions를 생성한다\ntargets = tf.placeholder(tf.float32, [None, 1])\nprint(\"targets: \", targets)\n\npredictions = tf.placeholder(tf.float32, [None, 1])\nprint(\"predictions: \", predictions)\n\n\n# 모델(LSTM 네트워크) 생성\ndef lstm_cell():\n    # LSTM셀을 생성\n    # num_units: 각 Cell 출력 크기\n    # forget_bias:  셀 스테이트에서 어떤 정보를 버릴지 선택하는 과정\n    #               이 결정은 'forget gate laye'라고 불리는 시그모이드 레이어로 만들어짐\n    #               이 게이트에서는 0과 1의 입력값을 받는다(0: 완전히 이 값을 버려라, 1: 완전히 이 값을 유지해라)\n    # state_is_tuple: True ==> accepted and returned states are 2-tuples of the c_state and m_state.\n    # state_is_tuple: False ==> they are concatenated along the column axis.\n    cell = tf.contrib.rnn.BasicLSTMCell(num_units=rnn_cell_hidden_dim,\n                                        forget_bias=forget_bias, state_is_tuple=True, activation=tf.nn.softsign)\n    if keep_prob < 1.0:\n        cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=keep_prob)\n    return cell\n\n\n# Multi Layer Perceptron RNN: layer 연결\n# 입력층과 출력층 사이에 하나 이상의 중간층(hidden layer: 은닉층)이 존재하는 신경망\n# 네트워크는 입력층, 은닉층, 출력층 방향으로 연결\n# 각 층내의 연결과 출력층에서 입력층으로의 직접적인 연결은 존재하지 않은 전방향 (Feedforward) 네트워크\n# num_stacked_layers개의 층으로 쌓인 Stacked RNNs 생성\nstackedRNNs = [lstm_cell() for _ in range(num_stacked_layers)]\nmulti_cells = tf.contrib.rnn.MultiRNNCell(stackedRNNs, state_is_tuple=True) if num_stacked_layers > 1 else lstm_cell()\n\n# RNN Cell(여기서는 LSTM셀임)들을 연결\nhypothesis, _states = tf.nn.dynamic_rnn(multi_cells, X, dtype=tf.float32)\nprint(\"hypothesis: \", hypothesis)\n\n# [:, -1]를 잘 살펴보자. LSTM RNN의 마지막 (hidden)출력만을 사용했다.\n# 과거 여러일수의 전력량을 이용해서 다음날의 전력량 1개를 에측하기때문에 many-to-one 형태이다\n# one to one: Vanilla Neural Networks\n# one to many: Image Captioning - 한장의 이미지에 대해 여러개의 문장으로 해석하는 형태, '소년이 사과를 고르고 있다'\n# many to one: Sentiment Classification - 여러개의 문장으로 구성된 글을 해석하여, 감정상태를 나타내는 형태, '긍정', '부정'\n# many to many: Machine Translation - 여러개의 문장에서 각각의 문장들을 다른 언어로 해석해주는 형태, 'Hello' -> '안녕'\n# many to many: Video classification - 여러개의 이미지에 대해 여러개의 설병, 번역을 하는 형태\n\nhypothesis = tf.contrib.layers.fully_connected(hypothesis[:, -1], output_data_column_cnt, activation_fn=tf.identity)\n\n# 손실함수로 평균제곱오차를 사용한다\nloss = tf.reduce_sum(tf.square(hypothesis - Y))\n\n# 최적화함수로 AdamOptimizer를 사용한다\n# optimizer = tf.train.RMSPropOptimizer(learning_rate) # LSTM과 궁합 별로임\noptimizer = tf.train.AdamOptimizer(learning_rate)\n\ntrain = optimizer.minimize(loss)\n\n# RMSE(Root Mean Square Error)\n# 제곱오차의 평균을 구하고 다시 제곱근을 구하면 평균 오차가 나온다\n# rmse = tf.sqrt(tf.reduce_mean(tf.square(targets-predictions))) # 아래 코드와 같다\nrmse = tf.sqrt(tf.reduce_mean(tf.squared_difference(targets, predictions)))\n\ntrain_error_summary = []  # 학습용 데이터의 오류를 중간 중간 기록한다\ntest_error_summary = []  # 테스트용 데이터의 오류를 중간 중간 기록한다\ntest_predict = ''  # 테스트용데이터로 예측한 결과\n\n### 세션 정의\n# 세션 생성: Session 객체 생성. 분산 환경에서는 계산 노드와의 연결을 만든다.\n# 세션 사용: run 메서드에 그래프를 입력하면 출력 값을 계산하여 반환한다. 분산 환경에서는 계산 노드로 그래프를 보내 계산을 수행한다.\n# 세션 종료: close 메서드. with 문을 사용하면 명시적으로 호출하지 않아도 된다.\nsess = tf.Session()\nsess.run(tf.global_variables_initializer())\n\n\ndef build_accuracy(predictions, labels_):\n    \"\"\"\n    Create accuracy\n    \"\"\"\n    correct_pred = tf.equal(tf.cast(tf.round(predictions), tf.int32), labels_)\n\n    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n    return accuracy\n\n# # 학습\nstart_time = datetime.datetime.now()  # 시작시간을 기록���다\nprint('학습을 시작합니다...')\n\nfor epoch in range(epoch_num):\n    _, _loss = sess.run([train, loss], feed_dict={X: trainX, Y: trainY})\n    if ((epoch + 1) % 100 == 0) or (epoch == epoch_num - 1):  # 100번째마다 또는 마지막 epoch인 경우\n        # 학습용데이터로 rmse오차를 구한다\n        train_predict = sess.run(hypothesis, feed_dict={X: trainX})\n        train_error = sess.run(rmse, feed_dict={targets: trainY, predictions: train_predict})\n        train_error_summary.append(train_error)\n\n        # 테스트용데이터로 rmse오차를 구한다\n        test_predict = sess.run(hypothesis, feed_dict={X: testX})\n        test_error = sess.run(rmse, feed_dict={targets: testY, predictions: test_predict})\n        test_error_summary.append(test_error)\n\n        # 현재 오류를 출력한다\n        print(\"epoch: {}, train_error(A): {}, test_error(B): {}, B-A: {}\".format(epoch + 1,\n                                                                                 train_error,\n                                                                                 test_error,\n                                                                                 test_error - train_error))\n\n\n\nprint(build_accuracy(test_predict, testY))\n\n# 하이퍼파라미터 출력\nprint('input_data_column_cnt:', input_data_column_cnt, end='')\nprint(',output_data_column_cnt:', output_data_column_cnt, end='')\n\nprint(',seq_length:', seq_length, end='')\nprint(',rnn_cell_hidden_dim:', rnn_cell_hidden_dim, end='')\nprint(',forget_bias:', forget_bias, end='')\nprint(',num_stacked_layers:', num_stacked_layers, end='')\nprint(',keep_prob:', keep_prob, end='')\n\nprint(',epoch_num:', epoch_num, end='')\nprint(',learning_rate:', learning_rate, end='')\n\nprint(',train_error:', train_error_summary[-1], end='')\nprint(',test_error:', test_error_summary[-1], end='')\nprint(',min_test_error:', np.min(test_error_summary))\n\n# 결과 그래프 출력\nplt.figure(1)\nplt.plot(train_error_summary, 'red')\nplt.plot(test_error_summary, 'blue')\nplt.xlabel('Epoch(x1000)')\nplt.ylabel('Root Mean Square Error')\n\nplt.figure(2)\nplt.plot(testY, 'red')\nplt.plot(test_predict, 'blue')\nplt.xlabel('Time Period')\nplt.ylabel('elec')\nplt.show()\n\n\n\n\n# sequence length만큼의 가장 최근 데이터를 슬라이싱한다\nrecent_data = np.array([x[len(x) - seq_length-1:-1]])\nprint(\"recent_data.shape: \", recent_data.shape)\nprint(\"recent_data: \", recent_data)\n\n# 내일 전력량을 예측해본다\ntest_predict = sess.run(hypothesis, feed_dict={X: recent_data})\n\nprint(\"test_predict: \", test_predict[0])\n# test_predict = reverse_min_max_scaling(water_level, test_predict)  # 전력량 데이터를 다시 역정규화\n# print(\"Tomorrow's elec price: \", test_predict[0])             # 내일 전력량 데이터\n\n# test_predict:  [0.5484068]\n# Tomorrow's elec price:  [788970.2]\n\n# test_predict [0.5380935]\n# Tomorrow's elec price [787583.9]\n\n","sub_path":"20200228/water level 예측.py","file_name":"water level 예측.py","file_ext":"py","file_size_in_byte":16066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"141158043","text":"# -*- coding: utf-8 -*-\n# @Author  : Jing\n# @FileName: 14.剪绳子.py\n# @IDE: PyCharm\n# Solution 1: DP O(n^2)Time O(n)Space\n# Solution 2: greed algorithm O(1)Time, O(1)Space\n\n\ndef cut_rop1(n):\n    # 边界条件\n    if n < 2:\n        return 0\n    if n <= 3:\n        return n-1\n    dp = [0 for _ in range(n+1)]\n\n    # 初始化条件，当绳子的长度小于4时，将其当作独立的一部分不再裁剪\n    for i in range(4):\n        dp[i] = i\n\n    # dp[i]表示绳子长为i时剪成若干段后，各段长度乘积的最大值\n    # 状态转移方程：dp[i] = max(dp[i], dp[j]*dp[j-i]), 1<=j<=(i//2)+1\n    for i in range(4, n+1):\n        for j in range(1, (i//2)+1):\n            dp[i] = max(dp[i], dp[j]*dp[i-j])\n    return dp[n]\n\ndef cut_rop2(n):\n    # 尽可能多剪出长度为3或2的绳子\n    if n < 2:\n        return 0\n    if n <= 3:\n        return n-1\n    times_of_3 = n // 3\n    if n - times_of_3 * 3 == 1:\n        times_of_3 -= 1\n    times_of_2 = (n-times_of_3*3)//2\n    return 3**times_of_3*2**times_of_2\n\n\nif __name__ == '__main__':\n    n = 0\n    print(cut_rop2(n))\n","sub_path":"剑指offer/14.剪绳子.py","file_name":"14.剪绳子.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"37020142","text":"import unittest\r\n\r\nfrom mlscripts.ml.cluster_validation import *\r\nfrom scipy.spatial.distance import pdist, squareform\r\n\r\nfrom pylab import *\r\nfrom numpy import *\r\nfrom numpy.testing import *\r\nimport logging\r\n\r\nclass TestClusterValidation(unittest.TestCase):\r\n    def setUp(self):\r\n        pass\r\n\r\n    def test_jaccard_index(self):\r\n        index = jaccard_index([1, 3, 5, 6], [3, 2, 6, 1, 8])\r\n        self.assertEquals(index, 3.0 / 6)\r\n        index = jaccard_index([0, 1, 5], [5, 1, 5, 5, 5])\r\n        self.assertEquals(index, 2.0 / 3)\r\n\r\n    def test_confusion_matrix(self):\r\n        conf_matrix = confusion_matrix([[1, 3, 5, 6], [8]], [[3, 2, 6, 1, 8]])\r\n        correct = array([[3.0 / 6], [1.0 / 5]])\r\n        self.assertTrue(array_equal(conf_matrix, correct))\r\n        conf_matrix = confusion_matrix([[1, 3, 5, 6], [8]], [[3, 2, 6, 1, 8], [1, 3, 8]])\r\n        correct = array([[3.0 / 6, 2.0 / 5], [1.0 / 5, 1.0 / 3]])\r\n        self.assertTrue(array_equal(conf_matrix, correct))\r\n\r\nif __name__ == '__main__':\r\n    logging.basicConfig()\r\n    unittest.main()\r\n","sub_path":"mlscripts/ml/test_cluster_validation.py","file_name":"test_cluster_validation.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"635616211","text":"from sciwing.models.science_ie_tagger import ScienceIETagger\nfrom sciwing.modules.lstm2seqencoder import Lstm2SeqEncoder\nfrom sciwing.modules.charlstm_encoder import CharLSTMEncoder\nfrom sciwing.modules.embedders.vanilla_embedder import VanillaEmbedder\nfrom sciwing.modules.embedders.concat_embedders import ConcatEmbedders\nfrom sciwing.datasets.seq_labeling.science_ie_dataset import ScienceIEDataset\nfrom sciwing.metrics.token_cls_accuracy import TokenClassificationAccuracy\nimport sciwing.constants as constants\nfrom allennlp.modules.conditional_random_field import allowed_transitions\nimport os\nimport torch\nimport torch.optim as optim\nfrom sciwing.engine.engine import Engine\nimport json\nimport argparse\nimport pathlib\nimport torch.nn as nn\nimport wasabi\n\nDATA_DIR = constants.PATHS[\"DATA_DIR\"]\n\nif __name__ == \"__main__\":\n    # read the hyperparams from config file\n    parser = argparse.ArgumentParser(\n        description=\"LSTM CRF Parscit tagger for reference string parsing\"\n    )\n\n    parser.add_argument(\"--exp_name\", help=\"Specify an experiment name\", type=str)\n    parser.add_argument(\n        \"--max_num_words\",\n        help=\"Maximum number of words to be considered \" \"in the vocab\",\n        type=int,\n    )\n    parser.add_argument(\n        \"--max_len\", help=\"Maximum length of sentences to be considered\", type=int\n    )\n    parser.add_argument(\n        \"--max_char_len\", help=\"Maximum length of sentences to be considered\", type=int\n    )\n\n    parser.add_argument(\n        \"--debug\",\n        help=\"Specify whether this is run on a debug options. The \"\n        \"dataset considered will be small\",\n        action=\"store_true\",\n    )\n    parser.add_argument(\n        \"--debug_dataset_proportion\",\n        help=\"The proportion of the dataset \" \"that will be used if debug is true\",\n        type=float,\n    )\n    parser.add_argument(\"--bs\", help=\"batch size\", type=int)\n    parser.add_argument(\"--lr\", help=\"learning rate\", type=float)\n    parser.add_argument(\"--epochs\", help=\"number of epochs\", type=int)\n    parser.add_argument(\n        \"--save_every\", help=\"Save the model every few epochs\", type=int\n    )\n    parser.add_argument(\n        \"--log_train_metrics_every\",\n        help=\"Log training metrics every few iterations\",\n        type=int,\n    )\n    parser.add_argument(\"--emb_dim\", help=\"embedding dimension\", type=int)\n    parser.add_argument(\n        \"--char_emb_dim\", help=\"character embedding dimension\", type=int\n    )\n    parser.add_argument(\n        \"--emb_type\",\n        help=\"The type of glove embedding you want. The allowed types are glove_6B_50, glove_6B_100, \"\n        \"glove_6B_200, glove_6B_300\",\n    )\n    parser.add_argument(\n        \"--hidden_dim\", help=\"Hidden dimension of the lstm encoder\", type=int\n    )\n    parser.add_argument(\n        \"--bidirectional\",\n        help=\"Specify Whether the lstm is bidirectional or uni-directional\",\n        action=\"store_true\",\n    )\n    parser.add_argument(\n        \"--use_char_encoder\",\n        help=\"Specify whether to use character encoder with neural-parscit\",\n        action=\"store_true\",\n    )\n    parser.add_argument(\n        \"--char_encoder_hidden_dim\",\n        help=\"Character encoder hidden dimension.\",\n        type=int,\n    )\n    parser.add_argument(\n        \"--combine_strategy\",\n        help=\"How do you want to combine the hidden dimensions of the two \"\n        \"combinations\",\n    )\n\n    parser.add_argument(\"--device\", help=\"Device on which the model is run\", type=str)\n\n    parser.add_argument(\"--reg\", help=\"Regularization strength\", type=float)\n    parser.add_argument(\n        \"--dropout\", help=\"Dropout added to multiple layer lstm\", type=float\n    )\n\n    parser.add_argument(\n        \"--seq_num_layers\", help=\"Number of layers in the Seq2Seq encoder\", type=int\n    )\n    parser.add_argument(\n        \"--exp_dir_path\", help=\"Directory to store all experiment related information\"\n    )\n    parser.add_argument(\n        \"--model_save_dir\",\n        help=\"Directory where the checkpoints during model training are stored.\",\n    )\n    parser.add_argument(\n        \"--vocab_store_location\", help=\"File in which the vocab is stored\"\n    )\n    parser.add_argument(\n        \"--char_vocab_store_location\",\n        help=\"File in which the character vocab  is stored\",\n    )\n\n    args = parser.parse_args()\n    msg_printer = wasabi.Printer()\n\n    config = {\n        \"EXP_NAME\": args.exp_name,\n        \"DEBUG\": args.debug,\n        \"DEBUG_DATASET_PROPORTION\": args.debug_dataset_proportion,\n        \"BATCH_SIZE\": args.bs,\n        \"EMBEDDING_DIMENSION\": args.emb_dim,\n        \"CHAR_EMBEDDING_DIMENSION\": args.char_emb_dim,\n        \"LEARNING_RATE\": args.lr,\n        \"NUM_EPOCHS\": args.epochs,\n        \"SAVE_EVERY\": args.save_every,\n        \"LOG_TRAIN_METRICS_EVERY\": args.log_train_metrics_every,\n        \"EMBEDDING_TYPE\": args.emb_type,\n        \"MAX_NUM_WORDS\": args.max_num_words,\n        \"MAX_LENGTH\": args.max_len,\n        \"MAX_CHAR_LENGTH\": args.max_char_len,\n        \"DEVICE\": args.device,\n        \"HIDDEN_DIM\": args.hidden_dim,\n        \"BIDIRECTIONAL\": args.bidirectional,\n        \"COMBINE_STRATEGY\": args.combine_strategy,\n        \"USE_CHAR_ENCODER\": args.use_char_encoder,\n        \"CHAR_ENCODER_HIDDEN_DIM\": args.char_encoder_hidden_dim,\n        \"REGULARIZATION_STRENGTH\": args.reg,\n        \"DROPOUT\": args.dropout,\n        \"NUM_LAYERS\": args.seq_num_layers,\n        \"EXP_DIR_PATH\": args.exp_dir_path,\n        \"MODEL_SAVE_DIR\": args.model_save_dir,\n        \"VOCAB_STORE_LOCATION\": args.vocab_store_location,\n        \"CHAR_VOCAB_STORE_LOCATION\": args.char_vocab_store_location,\n    }\n\n    EXP_NAME = config[\"EXP_NAME\"]\n    VOCAB_STORE_LOCATION = config[\"VOCAB_STORE_LOCATION\"]\n    DEBUG = config[\"DEBUG\"]\n    DEBUG_DATASET_PROPORTION = config[\"DEBUG_DATASET_PROPORTION\"]\n    BATCH_SIZE = config[\"BATCH_SIZE\"]\n    LEARNING_RATE = config[\"LEARNING_RATE\"]\n    NUM_EPOCHS = config[\"NUM_EPOCHS\"]\n    SAVE_EVERY = config[\"SAVE_EVERY\"]\n    LOG_TRAIN_METRICS_EVERY = config[\"LOG_TRAIN_METRICS_EVERY\"]\n    EMBEDDING_DIMENSION = config[\"EMBEDDING_DIMENSION\"]\n    CHAR_EMBEDDING_DIMENSION = config[\"CHAR_EMBEDDING_DIMENSION\"]\n    EMBEDDING_TYPE = config[\"EMBEDDING_TYPE\"]\n    MAX_NUM_WORDS = config[\"MAX_NUM_WORDS\"]\n    MAX_LENGTH = config[\"MAX_LENGTH\"]\n    DEVICE = config[\"DEVICE\"]\n    HIDDEN_DIM = config[\"HIDDEN_DIM\"]\n    BIDIRECTIONAL = config[\"BIDIRECTIONAL\"]\n    COMBINE_STRATEGY = config[\"COMBINE_STRATEGY\"]\n    MAX_CHAR_LENGTH = config[\"MAX_CHAR_LENGTH\"]\n    USE_CHAR_ENCODER = config[\"USE_CHAR_ENCODER\"]\n    CHAR_ENCODER_HIDDEN_DIM = config[\"CHAR_ENCODER_HIDDEN_DIM\"]\n    REGULARIZATION_STRENGTH = config[\"REGULARIZATION_STRENGTH\"]\n    DROPOUT = config[\"DROPOUT\"]\n    NUM_LAYERS = config[\"NUM_LAYERS\"]\n    EXP_DIR_PATH = config[\"EXP_DIR_PATH\"]\n    EXP_DIR_PATH = pathlib.Path(EXP_DIR_PATH)\n    MODEL_SAVE_DIR = config[\"MODEL_SAVE_DIR\"]\n    CHAR_VOCAB_STORE_LOCATION = config[\"CHAR_VOCAB_STORE_LOCATION\"]\n\n    train_dataset = ScienceIEDataset(\n        filename=pathlib.Path(\".\", \"train_science_ie_conll.txt\"),\n        dataset_type=\"train\",\n        max_num_words=MAX_NUM_WORDS,\n        max_instance_length=MAX_LENGTH,\n        max_char_length=MAX_CHAR_LENGTH,\n        word_vocab_store_location=VOCAB_STORE_LOCATION,\n        char_vocab_store_location=CHAR_VOCAB_STORE_LOCATION,\n        debug=DEBUG,\n        debug_dataset_proportion=DEBUG_DATASET_PROPORTION,\n        word_embedding_type=EMBEDDING_TYPE,\n        word_embedding_dimension=EMBEDDING_DIMENSION,\n        char_embedding_dimension=CHAR_EMBEDDING_DIMENSION,\n        word_start_token=\"<SOS>\",\n        word_end_token=\"<EOS>\",\n        word_pad_token=\"<PAD>\",\n        word_unk_token=\"<UNK>\",\n        word_add_start_end_token=False,\n    )\n\n    validation_dataset = ScienceIEDataset(\n        filename=pathlib.Path(\".\", \"dev_science_ie_conll.txt\"),\n        dataset_type=\"valid\",\n        max_num_words=MAX_NUM_WORDS,\n        max_instance_length=MAX_LENGTH,\n        max_char_length=MAX_CHAR_LENGTH,\n        word_vocab_store_location=VOCAB_STORE_LOCATION,\n        char_vocab_store_location=CHAR_VOCAB_STORE_LOCATION,\n        debug=DEBUG,\n        debug_dataset_proportion=DEBUG_DATASET_PROPORTION,\n        word_embedding_type=EMBEDDING_TYPE,\n        word_embedding_dimension=EMBEDDING_DIMENSION,\n        char_embedding_dimension=CHAR_EMBEDDING_DIMENSION,\n        word_start_token=\"<SOS>\",\n        word_end_token=\"<EOS>\",\n        word_pad_token=\"<PAD>\",\n        word_unk_token=\"<UNK>\",\n        word_add_start_end_token=False,\n    )\n\n    test_dataset = ScienceIEDataset(\n        filename=pathlib.Path(\".\", \"dev_science_ie_conll.txt\"),\n        dataset_type=\"test\",\n        max_num_words=MAX_NUM_WORDS,\n        max_instance_length=MAX_LENGTH,\n        max_char_length=MAX_CHAR_LENGTH,\n        word_vocab_store_location=VOCAB_STORE_LOCATION,\n        char_vocab_store_location=CHAR_VOCAB_STORE_LOCATION,\n        debug=DEBUG,\n        debug_dataset_proportion=DEBUG_DATASET_PROPORTION,\n        word_embedding_type=EMBEDDING_TYPE,\n        word_embedding_dimension=EMBEDDING_DIMENSION,\n        char_embedding_dimension=CHAR_EMBEDDING_DIMENSION,\n        word_start_token=\"<SOS>\",\n        word_end_token=\"<EOS>\",\n        word_pad_token=\"<PAD>\",\n        word_unk_token=\"<UNK>\",\n        word_add_start_end_token=False,\n    )\n\n    train_dataset.print_stats()\n    validation_dataset.print_stats()\n    test_dataset.print_stats()\n\n    if not EXP_DIR_PATH.is_dir():\n        EXP_DIR_PATH.mkdir()\n\n    VOCAB_SIZE = train_dataset.word_vocab.get_vocab_len()\n    NUM_CLASSES = train_dataset.get_num_classes()\n    config[\"VOCAB_SIZE\"] = VOCAB_SIZE\n    config[\"NUM_CLASSES\"] = NUM_CLASSES\n\n    with open(os.path.join(f\"{EXP_DIR_PATH}\", \"config.json\"), \"w\") as fp:\n        json.dump(config, fp)\n\n    embedding = train_dataset.word_vocab.load_embedding()\n    embedding = nn.Embedding.from_pretrained(embedding, freeze=False)\n    char_embedding = train_dataset.char_vocab.load_embedding()\n    char_embedding = nn.Embedding.from_pretrained(char_embedding, freeze=False)\n\n    classnames2idx = train_dataset.classnames2idx\n    idx2classnames = {idx: classname for classname, idx in classnames2idx.items()}\n\n    task_idx2classnames = {\n        idx: classname\n        for idx, classname in idx2classnames.items()\n        if idx in range(0, 8)\n    }\n    process_idx2classnames = {\n        idx - 8: classname\n        for idx, classname in idx2classnames.items()\n        if idx in range(8, 16)\n    }\n    material_idx2classnames = {\n        idx - 16: classname\n        for idx, classname in idx2classnames.items()\n        if idx in range(16, 24)\n    }\n\n    task_constraints = allowed_transitions(\n        constraint_type=\"BIOUL\", labels=task_idx2classnames\n    )\n    process_constraints = allowed_transitions(\n        constraint_type=\"BIOUL\", labels=process_idx2classnames\n    )\n    material_constraints = allowed_transitions(\n        constraint_type=\"BIOUL\", labels=material_idx2classnames\n    )\n\n    embedder = VanillaEmbedder(embedding=embedding, embedding_dim=EMBEDDING_DIMENSION)\n\n    if USE_CHAR_ENCODER:\n        char_embedder = VanillaEmbedder(\n            embedding=char_embedding, embedding_dim=CHAR_EMBEDDING_DIMENSION\n        )\n        char_encoder = CharLSTMEncoder(\n            char_emb_dim=CHAR_EMBEDDING_DIMENSION,\n            char_embedder=char_embedder,\n            bidirectional=True,\n            hidden_dim=CHAR_ENCODER_HIDDEN_DIM,\n            combine_strategy=\"concat\",\n            device=torch.device(DEVICE),\n        )\n        embedder = ConcatEmbedders([embedder, char_encoder])\n        EMBEDDING_DIMENSION += 2 * CHAR_ENCODER_HIDDEN_DIM\n\n    lstm2seqencoder = Lstm2SeqEncoder(\n        emb_dim=EMBEDDING_DIMENSION,\n        embedder=embedder,\n        dropout_value=DROPOUT,\n        hidden_dim=HIDDEN_DIM,\n        bidirectional=BIDIRECTIONAL,\n        combine_strategy=COMBINE_STRATEGY,\n        num_layers=NUM_LAYERS,\n        rnn_bias=True,\n        device=torch.device(DEVICE),\n    )\n    model = ScienceIETagger(\n        rnn2seqencoder=lstm2seqencoder,\n        num_classes=NUM_CLASSES,\n        hid_dim=2 * HIDDEN_DIM\n        if BIDIRECTIONAL and COMBINE_STRATEGY == \"concat\"\n        else HIDDEN_DIM,\n        task_constraints=task_constraints,\n        process_constraints=process_constraints,\n        material_constraints=material_constraints,\n        device=torch.device(DEVICE),\n    )\n\n    optimizer = optim.Adam(\n        params=model.parameters(),\n        lr=LEARNING_RATE,\n        weight_decay=REGULARIZATION_STRENGTH,\n    )\n\n    metric = TokenClassificationAccuracy(\n        idx2labelname_mapping=train_dataset.idx2classnames\n    )\n\n    engine = Engine(\n        model=model,\n        train_dataset=train_dataset,\n        validation_dataset=validation_dataset,\n        test_dataset=test_dataset,\n        optimizer=optimizer,\n        batch_size=BATCH_SIZE,\n        save_dir=MODEL_SAVE_DIR,\n        num_epochs=NUM_EPOCHS,\n        save_every=SAVE_EVERY,\n        log_train_metrics_every=LOG_TRAIN_METRICS_EVERY,\n        track_for_best=\"macro_fscore\",\n        device=torch.device(DEVICE),\n        metric=metric,\n        use_wandb=True,\n        experiment_name=EXP_NAME,\n        experiment_hyperparams=config,\n    )\n\n    engine.run()\n","sub_path":"examples/science_ie/science_ie.py","file_name":"science_ie.py","file_ext":"py","file_size_in_byte":13148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"621993784","text":"import numpy as np\nimport scipy.stats as stats\nimport pymc3 as pm\nimport theano.tensor as T\n\ndata = np.loadtxt(\"data/mixture_data.csv\", delimiter=\",\")\n\nwith pm.Model() as model:\n    p1 = pm.Uniform(\"p\", 0, 1)\n    p2 = 1 - p1\n    p = T.stack([p1, p2])\n    assignment = pm.Categorical(\"assignment\", p,\n                             shape=data.shape[0],\n                             testval=np.random.randint(0, 2, data.shape[0]))\n\n    print(\"assignment type: {}\".format(type(assignment)))\n    print(\"assignment: {}\".format(assignment.tag.test_value[:4]))\n\n    sds = pm.Uniform(\"sds\", 0, 100, shape=2)\n    centers = pm.Normal(\"centers\", mu=np.array([120, 190]),\n                                    sd=np.array([10, 10]),\n                                    shape=2)\n    center_i = pm.Deterministic('center_i', centers[assignment])\n    sd_i = pm.Deterministic('sd_i', sds[assignment])\n\n    print(\"center_i: {}\".format(center_i.tag.test_value[:4]))\n    print(\"sd_i: {}\".format(sd_i.tag.test_value[:4]))\n\n    start = pm.find_MAP()\n    step1 = pm.Metropolis(vars=[p, sds, centers])\n    step2 = pm.ElemwiseCategorical(vars=[assignment])\n    trace = pm.sample(25000, step=[step1, step2]) # start=start\n    # Do some investigation\n\n    trace = pm.sample(50000, step=[step1, step2], trace=trace)\n    assignment_trace = trace[\"assignment\"][25000:]\n\n    center_trace = trace[\"centers\"][25000:]\n    prev_center_trace = trace[\"centers\"][:25000]\n\n    std_trace = trace[\"sds\"][25000:]\n    prev_std_trace = trace[\"sds\"][:25000]\n\n    p_trace = trace[\"p\"][25000:]\n    prev_p_trace = trace[\"p\"][:25000]\n\nnorm_pdf = stats.norm.pdf\nx = 175\nv = p_trace * norm_pdf(x, loc=center_trace[:, 0], scale=std_trace[:, 0]) > \\\n        (1 - p_trace) * norm_pdf(x, loc=center_trace[:, 1], scale=std_trace[:, 1])\n\nprint(\"Probability of belonging to cluster 1: {}\".format(v.mean()))\n","sub_path":"Ch3/mixture.py","file_name":"mixture.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"176431588","text":"import pygame\r\nfrom pygame.locals import *\r\nfrom math import ceil, sqrt, floor, radians, cos\r\nimport os\r\nimport time\r\n\r\nSCALE = 200\r\nSCALE2 = SCALE + ceil(SCALE/9)\r\nDECA = SCALE/10\r\nCENTERX = round(SCALE/2)\r\nCENTERY = round((SCALE/2))#-(DECA/2))\r\nRADIUS = ceil(SCALE/2.1)#-((DECA/2)+10)\r\n\r\ndef ClockUpdate(screen, hours, mins, secs, font):\r\n\twhite = (255,255,255)\r\n\tback = (35,40,48)\r\n\twhite = (100,100,100)\r\n\torange = (255, 127, 39)\r\n\tblack=(0,0,0)\r\n\tred = (255,0,0)\r\n\tgreen = (0,255,64)\r\n\tblue = (66,168,223)\r\n\r\n\tscreen.fill((back))\r\n\tHoursSeq(screen, blue, 60, ceil(RADIUS/15), ceil((RADIUS/15)/5))\r\n\tHoursSeq(screen, red, 12, ceil(RADIUS/9), ceil((RADIUS/9)/4))\r\n\t\r\n\tminute_needle(screen, mins, blue)\r\n\tsecond_needle(screen, secs, (45,126,166))\r\n\thour_needle(screen, hours, red)\r\n\r\n\tpygame.draw.circle(screen, back, (CENTERX,CENTERY), 6)\r\n\tpygame.draw.circle(screen, blue, (CENTERX,CENTERY), 3)\r\n\taffi_time2(screen, font, hours, mins, secs)\r\n\r\ndef affi_time2(screen, font, hours=0, mins=0, secs=0):\r\n\thours = floor(hours)\r\n\tmins = floor(mins)\r\n\tif hours < 10.0:\r\n\t\thours = \"0\" + str(hours)\r\n\tif mins < 10.0:\r\n\t\tmins = \"0\" + str(mins)\r\n\tif secs < 10.0:\r\n\t\tsecs = \"0\" + str(secs)\r\n\thours = str(hours) \r\n\tmins = str(mins)\r\n\tsecs = str(secs)\r\n\r\n\ttime = font.render(hours +\" : \" +mins+\" : \" + secs, 1, (255,255,255))\r\n\tpygame.draw.line(screen, (0,0,0), (0,SCALE), (0, SCALE2), 3 )\r\n\tscreen.blit(time, (5,SCALE))\r\n\r\ndef get_time(timep):\r\n\tsecs = round(timep%60, 3)\r\n\tmins = round(((timep/60)%60), 1)\r\n\thours = round(timep/3600, 1)\r\n\treturn hours, mins, secs\r\n\r\ndef hour_needle(screen, hours, color):\r\n\r\n\tangle = get_angle(hours%12, 12)\r\n\thauteur, cos_pos = get_coor(radians(angle), RADIUS-(DECA/1.2))\r\n\thauteur = round(hauteur, 1)\r\n\r\n\tif angle >= 360:\r\n\t\tangle = 0\r\n\tif angle < 0:\r\n\t\tangle = 360\r\n\r\n\tif angle <= 180:\r\n\t\tpygame.draw.line(screen, color, (CENTERX,CENTERY), (CENTERX+cos_pos, CENTERY-round(hauteur)), ceil(RADIUS/80))\r\n\telse:\r\n\t\tpygame.draw.line(screen, color, (CENTERX,CENTERY), (CENTERX-cos_pos, CENTERY-round(hauteur)), ceil(RADIUS/80))\r\n\r\ndef minute_needle(screen, mins, color):\r\n\r\n\tangle = get_angle(mins)\r\n\thauteur, cos_pos = get_coor(radians(angle), RADIUS-(RADIUS/15)-(DECA/3) )\r\n\thauteur = round(hauteur, 1)\r\n\r\n\tif angle >= 360:\r\n\t\tangle = 0\r\n\tif angle < 0:\r\n\t\tangle = 360\r\n\r\n\tif angle <= 180:\r\n\t\tpygame.draw.line(screen, color, (CENTERX,CENTERY), (CENTERX+cos_pos, CENTERY-round(hauteur)), ceil(RADIUS/50))\r\n\telse:\r\n\t\tpygame.draw.line(screen, color, (CENTERX,CENTERY), (CENTERX-cos_pos, CENTERY-round(hauteur)), ceil(RADIUS/50))\r\n\r\ndef second_needle(screen, secs, color):\r\n\tangle = get_angle(secs)\r\n\thauteur, cos_pos = get_coor(radians(angle))\r\n\thauteur = round(hauteur, 1)\r\n\r\n\tif angle >= 360:\r\n\t\tangle = 0\r\n\tif angle < 0:\r\n\t\tangle = 360\r\n\r\n\tif angle <= 180:\r\n\t\tpygame.draw.line(screen, color, (CENTERX,CENTERY), (CENTERX+cos_pos, CENTERY-round(hauteur)), ceil(RADIUS/90))\r\n\telse:\r\n\t\tpygame.draw.line(screen, color, (CENTERX,CENTERY), (CENTERX-cos_pos, CENTERY-round(hauteur)), ceil(RADIUS/90))\r\n\r\ndef HoursSeq(surface, color, unit, high, width, instant = False):\r\n\tdegre = round((360/unit), 4) #360/12H = 30\r\n\r\n\tangle = 0\r\n\t\r\n\twhile angle != 360:\r\n\t\tsin, cos = get_coor(radians(angle), RADIUS-2)\r\n\t\tsin2, cos2 = get_coor(radians(angle), RADIUS-high)\r\n\t\tif angle <= 180:\r\n\t\t\tpygame.draw.line(surface, color, (CENTERX+cos2,CENTERY-sin2), (CENTERX+cos,CENTERY-sin), width)\r\n\t\telse:\r\n\t\t\tpygame.draw.line(surface, color, (CENTERX-cos2,CENTERY+sin2), (CENTERX-cos,CENTERY+sin), width)\r\n\r\n\t\tangle += degre\r\n\r\ndef get_coor(angle, high = RADIUS):\r\n\thauteur = cos(angle)*high\r\n\twidth = sqrt((high*high) - (hauteur*hauteur))\r\n\treturn hauteur, width\r\n\r\ndef get_angle(timep, div=60):\r\n\tcoef = 360\r\n\ttime_spend = timep\r\n\t#360 degree, for 60 seconde\r\n\tif time_spend != 0:\r\n\t\tcoef = div/time_spend\r\n\tif coef == 360:\r\n\t\treturn 0\r\n\telse:\r\n\t\treturn 360/coef\r\n\r\ndef main():\r\n\tpygame.init()\r\n\tscreen = pygame.display.set_mode((SCALE,SCALE2))\r\n\tpygame.display.set_caption(\"Clock\")\r\n\r\n\thauteur = RADIUS\r\n\tangle = 0\r\n\tcenter= ceil(SCALE/2)\r\n\ttime_s0,pend = time.time()\r\n\t\r\n\twhite = (255,255,255)\r\n\tback = (35,40,48)\r\n\twhite = (100,100,100)\r\n\torange = (255, 127, 39)\r\n\tblack=(0,0,0)\r\n\tred = (255,0,0)\r\n\tgreen = (0,255,64)\r\n\tblue = (66,168,223)\r\n\r\n\tfont = pygame.font.SysFont(None, ceil(DECA*1.92))\r\n\tclock = pygame.time.Clock()\r\n\r\n\ttimeT = 0\r\n\trun = True\r\n\twhile True:\r\n\t\tclock.tick(120)\r\n\r\n\t\tif run:\r\n\t\t\ttimep = time.time() - time_spend\r\n\r\n\t\thours, mins, secs = get_time(timep)\r\n\r\n\t\tif run:\r\n\r\n\t\t\tfor event in pygame.event.get():\r\n\t\t\t\tif event.type == QUIT :\r\n\t\t\t\t\tpygame.quit()\r\n\t\t\t\t\texit()\r\n\t\t\t\tif event.type == MOUSEBUTTONDOWN:\r\n\t\t\t\t\trun = False\r\n\t\t\t\t\ttimeT = timep + time_spend\r\n\r\n\r\n\t\t\tClockUpdate(screen, hours, mins, secs, font)\r\n\t\t\t\r\n\t\t\tpygame.display.flip()\r\n\r\n\t\telse :\r\n\t\t\tfor event in pygame.event.get():\r\n\t\t\t\tClockUpdate(screen, hours, mins, secs, font)\r\n\r\n\t\t\t\tif event.type == MOUSEBUTTONDOWN:\r\n\t\t\t\t\ttime_spend = time.time() - timep\r\n\t\t\t\t\trun = True\r\n\t\t\t\tif event.type == QUIT:\r\n\t\t\t\t\tpygame.quit()\r\n\t\t\t\t\texit()\r\n\t\t\t\r\n\r\nif __name__ == '__main__':\r\n\tmain()","sub_path":"Python/Clock/ProtoClock.py","file_name":"ProtoClock.py","file_ext":"py","file_size_in_byte":5065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"200281685","text":"import  warnings\nwarnings.filterwarnings('ignore')\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold, KFold, ShuffleSplit\nfrom sklearn import metrics\n\nimport torch\nfrom torch import nn\nfrom torch.utils.data import Dataset, DataLoader\nimport torch.utils.data\nimport torchvision\nfrom torch import optim\nfrom torch import cuda\nfrom torch.optim.lr_scheduler import CosineAnnealingLR\n\nfrom MLP_Model import Model_Trainer\nfrom DataLoader import CreateDataset, ImbalancedDatasetSampler\n\nfrom tqdm import tqdm_notebook as tqdm\n\nclass Train_Predict:\n    \n    def __init__(self, train_df, test_df, feature, hidden_layers, lr=6e-4, weight_decay=0):\n        self.train_df = train_df\n        self.test_df = test_df\n        self.feature = feature\n        self.hidden_layers = hidden_layers\n        self.lr=lr\n        self.weight_decay = weight_decay\n        \n        self.torch_random_state = 2020\n        torch.cuda.manual_seed_all(self.torch_random_state)\n    \n    def init_model(self):\n        self.model_trainer = Model_Trainer(output_size=2, feature=self.feature, hidden_layers=self.hidden_layers,\n                                           lr=self.lr, weight_decay=self.weight_decay)\n        \n    def make_off_df(self, _label, epoch_num, k):\n        self.train_df['jobflag_2'] = self.train_df['jobflag'].apply(lambda x: 1 if x==_label else 0)\n        \n        cv_loss = []\n        cv_score = []\n        off_df=[]\n\n        for trn, val in tqdm( k.split(self.train_df, self.train_df.jobflag), total=k.n_splits ):\n            trn_df = self.train_df.iloc[trn,:]\n            val_df = self.train_df.iloc[val,:]\n\n            trn_X, trn_y = trn_df[self.feature].values.tolist(),trn_df[['jobflag_2']].values.tolist() \n            val_X, val_y = val_df[self.feature].values.tolist(),val_df[['jobflag_2']].values.tolist() \n\n            trn_data_set = CreateDataset(trn_X, trn_y)\n            trn_dataloader = DataLoader(trn_data_set, shuffle=False, batch_size=256, sampler=ImbalancedDatasetSampler(trn_data_set))\n            val_data_set = CreateDataset(val_X, val_y)\n            val_dataloader = DataLoader(val_data_set, shuffle=False, batch_size=len(val_data_set))\n\n            \n            for e in range(epoch_num):\n                val_df[f'p_{_label}_{e}'] = 0\n                \n            trn_loss_list=[]\n            val_loss_list=[]\n            trn_score_list=[]\n            val_score_list=[]\n            self.init_model()\n                \n            for e in range(epoch_num):\n                trn_loss_avg, trn_preds, trn_labels = self.model_trainer.train(trn_dataloader)\n                val_loss_avg, p = self.model_trainer.eval(val_dataloader)\n                \n                trn_preds = np.round(trn_preds[:,1])\n                trn_score_list.append(metrics.accuracy_score(trn_labels, trn_preds))\n                \n                trn_loss_list.append(trn_loss_avg)\n                val_loss_list.append(val_loss_avg)\n                    \n                val_df[f'p_{_label}_{e}'] += p[:,1]\n            for e in range(epoch_num):\n                p = val_df[[f'p_{_label}_{e}']].values\n                preds = np.round(p)\n                val_score_list.append( metrics.accuracy_score(val_df['jobflag_2'], preds) )\n                \n            cv_loss.append([trn_loss_list, val_loss_list])\n            cv_score.append([trn_score_list, val_score_list])\n            off_df.append(val_df)\n\n        off_df = pd.concat(off_df, axis=0)\n        off_df.sort_values('text_id', inplace=True)\n        off_df.reset_index(drop=True, inplace=True)\n        return off_df, cv_loss, cv_score\n    \n    \n    def predict_test_df(self, _label, epoch_num):\n        self.train_df['jobflag_2'] = self.train_df['jobflag'].apply(lambda x: 1 if x==_label else 0)\n        \n        X, y = self.train_df[self.feature].values.tolist(), self.train_df[['jobflag_2']].values.tolist()\n        trn_data_set = CreateDataset(X, y)\n        trn_dataloader = DataLoader(trn_data_set, shuffle=False, batch_size=256, sampler=ImbalancedDatasetSampler(trn_data_set))\n\n        val_X, val_y = self.test_df[self.feature].values.tolist(), None\n        val_data_set = CreateDataset(val_X, val_y)\n        val_dataloader = DataLoader(val_data_set, shuffle=False, batch_size=len(val_data_set))\n        \n        all_trn_loss = []\n        test_df = self.test_df.copy()\n        \n        #make columns\n        for e in range(epoch_num):\n            test_df[f'p_{_label}_{e}'] = 0\n        \n        for loop in tqdm(range(3)):\n            torch_random_state=self.torch_random_state+loop\n            torch.cuda.manual_seed_all(torch_random_state)\n            \n            self.init_model()\n            trn_loss_list=[]\n            \n            for e in range(epoch_num):\n                trn_loss_avg, _1, _2 = self.model_trainer.train(trn_dataloader)\n                p = self.model_trainer.predict(val_dataloader)\n                test_df[f'p_{_label}_{e}'] += p[:,1]/3\n                trn_loss_list.append(trn_loss_avg)\n                \n            all_trn_loss.append(trn_loss_list)\n            \n        trn_loss_list = np.mean(all_trn_loss, axis=0)\n\n        return test_df, trn_loss_list","sub_path":"one_target.py","file_name":"one_target.py","file_ext":"py","file_size_in_byte":5186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"479512262","text":"import unittest\n\nfrom reprounzip.unpackers.common import unique_names, make_unique_name\nfrom reprounzip.utils import irange\n\n\nclass TestCommon(unittest.TestCase):\n    def test_unique_names(self):\n        \"\"\"Tests the unique_names generator.\"\"\"\n        names = [next(unique_names) for i in irange(3)]\n        for n in names:\n            self.assertTrue(n and isinstance(n, bytes))\n        self.assertEqual(len(set(names)), len(names))\n\n    def test_make_unique_name(self):\n        \"\"\"Tests the make_unique_name() function.\"\"\"\n        names = [make_unique_name(b'/some/prefix_') for i in irange(3)]\n        for n in names:\n            self.assertTrue(n and isinstance(n, bytes) and\n                            n[:13] == b'/some/prefix_')\n        self.assertEqual(len(set(names)), len(names))\n","sub_path":"tests/test_unpackers_common.py","file_name":"test_unpackers_common.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"562255649","text":"import ddg3\nfrom twilio.rest import Client\n# Your Account SID from twilio.com/console\naccount_sid = \"AC97c3b2c95124846c016aee609b362807\"\n# Your Auth Token from twilio.com/console\nauth_token = \"aeb32d9efcfc58dfe862b512d15a8a9b\"\nclient = Client(account_sid, auth_token)\n\ndef textPrompt(info):\n    choice = input(\"Would you like me to text you this information? Enter 1 or 0\")\n    if(choice == '1'):\n        message = client.messages.create(\n        to=\"6303989409\", \n        from_=\"5203946350\",\n        body= \"Hey, its S.O.S.A with your search info: \" + info)\n\ndef ddgSearch(searchQuery):\n\n    r = ddg3.query(searchQuery)\n    print(r.type)\n\n\n    if(r.type == 'disambiguation'):\n        print(\"Here are the first couple results related to what you said:\")\n        \n        print(r.related[0].text)\n        info = \"\\n\" + r.related[0].text\n        info = info + \"\\n\" + r.related[0].url\n        print(r.related[1].text)\n        info = info + \"\\n\" + r.related[1].text\n        info = info + \"\\n\" + r.related[1].url\n\n        textPrompt(info)\n\n\n    elif(r.type == 'answer'):\n        print(r.abstract.text)\n        info = \"\\n\" + r.abstract.text\n        info = info + \"\\n\" + r.abstract.url\n        textPrompt(info)\n    elif(r.type == 'nothing'):\n        print(\"Here are some results related to your search:\")\n        print(r.related)\n        info = \"\\n\" + r.related\n        textPrompt(info)\n\ndef activationPhrases():\n    act = []\n\n    \n    act.append(\"Search for\")\n    \n    \n\n    return act\n    ","sub_path":"duckduck_search/duckDuckGoSearch.py","file_name":"duckDuckGoSearch.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"631914569","text":"from django.core.management.base import BaseCommand, CommandError\nfrom ppm.models import ShuffleSetting, SubscribedService, EmailNotification, EmployeeProfile\nfrom django.contrib.auth.models import Group\nfrom django.core.mail import EmailMessage\nfrom django.db.models import Q\nimport datetime\nimport time\nimport random\nimport math\n\n\nclass Command(BaseCommand):\n    help = 'Send results of Shuffleapplication to email'\n\n    def handle(self, *args, **options):\n        shuffle_settings = self.get_shuffle_settings()\n\n        self.check_dates(shuffle_settings)\n\n    def check_dates(self, shuffle_settings):\n        # Take the number of seconds and convert it to to days\n        today = self.epoch_days()\n\n        for setting in shuffle_settings:\n            shuffle_date = self.epoch_days(setting.date)\n            group = self.get_group(setting.group_id)\n            service = self.get_subscribed_service(setting.group_id)\n            emails = self.get_shuffleapp_emails(group.id)\n            profile_list = self.profiles_object_to_list(group.id)\n\n            # Validate whether a group setting has emails and profiles\n            if emails and profile_list and service:\n                # Check if the amount of days since signup is a multiple of 28 (4*7)\n                if (today - shuffle_date) and ((today - shuffle_date) % setting.interval == 0):\n                    shuffle_list = self.shuffle_list(profile_list, setting.percentage)\n\n                    # Iterate through each email and send the list of expired emails\n                    for e in emails:\n                        pass\n                        self.send_notifications(e.email, shuffle_list, group)\n\n    def shuffle_list(self, profile_list, percentage):\n        \"\"\"\n        This fuction gets a list and shuffles it based on the inputted percentage\n        :param profile_list:\n        :param percentage:\n        :return:\n        \"\"\"\n        list_size = len(profile_list)\n        for x in range(0, 100):\n            random.shuffle(profile_list)\n        end_index = math.ceil(list_size * percentage / 100)\n        result = profile_list[0:end_index]\n        return result\n\n    def epoch_days(self, date=None):\n        \"\"\"\n        This method returns the Unix epoch seconds to be converted into days. It returns today's day if param is\n        none, or returns a date converted from a string.\n        :param date:\n        :return:\n        \"\"\"\n        if date:\n            # Convert string date to timestruct tuple\n            shuffle_timestruct = time.strptime(str(date), '%Y-%m-%d')\n            # Take the Unix epoch time(seconds) and convert it to days: total seconds / 60 min/ 60 secs / 24 hrs\n            shuffle_date = int(time.mktime(shuffle_timestruct) / 60 / 60 / 24)\n            return shuffle_date\n        else:\n            # Take the Unix epoch time(seconds) and convert it to days: total seconds / 60 min/ 60 secs / 24 hrs\n            todays_date = int(time.time() / 60 / 60 / 24)\n            return todays_date\n\n    def get_group(self, group_id):\n        group = Group.objects.get(pk=group_id)\n        return group\n\n    def get_shuffleapp_emails(self, group_id):\n        emails = EmailNotification.objects.filter(app__in=['All', 'Shuffleapp'], group_id=group_id)\n        return emails\n\n    def get_shuffle_settings(self):\n        settings = ShuffleSetting.objects.all()\n        return settings\n\n    def get_subscribed_service(self, group_id):\n        service = SubscribedService.objects.get(group_id=group_id)\n        return service.shuffleapp\n\n    def profiles_object_to_list(self, group_id):\n        \"\"\"\n        This function gets an object of employee profiles and iterates through it finding the names in\n        puts it into a list for use with the random fucntion\n        :return:\n        \"\"\"\n        profiles = EmployeeProfile.objects.filter(group_id=group_id, is_active=1)\n        profile_list = []\n        for profile in profiles:\n            if profile.middle_name:\n                profile_list.append(profile.first_name + ' ' +\n                                    profile.middle_name + ' ' +\n                                    profile.last_name + ' ' +\n                                    profile.badge_id)\n            else:\n                profile_list.append(profile.first_name + ' ' + profile.last_name + ' ' + profile.badge_id)\n        return profile_list\n\n    def send_notifications(self, email, shuffle_list, group):\n        print(shuffle_list, group.name)\n        msg = EmailMessage('Shuffleapp Results', self.message(shuffle_list, group),\n                           'do-not-reply@csc-safety.com',\n                           to=[email])\n        msg.content_subtype = 'html'\n        msg.send()\n\n    def message(self, shuffle_list, group):\n        date = datetime.date.today().strftime('%b %d, %Y')\n        count = len(shuffle_list)\n        message = \"\"\"\\\n        <html>\n            <head>\n                <meta charset=\"UTF-8\">\n                <title>Compliance Headquarters</title>\n            </head>\n        <body style=\"color:gray; font-size:.65em; font-family: \"Lucida Console\", Monaco, monospace\">\n        <table border=\"1\" style=\"margin:auto; border:1px solid gray; border-collapse:collapse\">\n            <tr>\n                <th style=\"background:#FFFF99; padding:10px\">\n                <h3>\"\"\" + group.name + \"\"\": Shuffleapp list generated on <span style=\"color:green\">\"\"\" + str(date) + \"\"\"</span></h3>\n                </th>\n            </tr>\n\n        \"\"\"\n        for row in shuffle_list:\n            message += '<tr><td style=\"padding:10px\">' + row + '</tr></td>'\n        message += \"\"\"\\\n            <tr>\n                <td style=\"background:#FFFF99; padding:10px\">\n                <h3>A total of <span style=\"color:green\">\"\"\" + str(count) + \"\"\" names have been returned successfully</span></h3>\n                </td>\n            <tr>\n        </table>\n        </body>\n        </html>\n        \"\"\"\n        return message\n","sub_path":"ppm/management/commands/shuffleapp_notifier.py","file_name":"shuffleapp_notifier.py","file_ext":"py","file_size_in_byte":5925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"157928736","text":"import numpy as np\n\ndef Gaussian_Kernel_Perceptron(y_t, x_t, model):\n\n    # KernelPerceptron: Non-linear perceptron algorithm by using the kernel trick\n    # --------------------------------------------------------------------------\n    # Reference:\n    # F. Rosenblatt. The perceptron: A probabilistic model for information\n    # storage and organization in the brain.Psychological Review,65:386?07,1958.\n    # --------------------------------------------------------------------------\n    # INPUT:\n    #      y_t:     class label of t-th instance;\n    #      x_t:     t-th training data instance, e.g., X(t,:);\n    #    model:     classifier\n    #\n    # OUTPUT:\n    #    model:     a struct of the weight vector (w) and the SV indexes\n    #  hat_y_t:     predicted class label\n    #      l_t:     suffered loss\n    \n    # Initialization\n    kernel      = model.kernel        # Kernel method to use\n    max_sv      = model.max_sv        # Predefined budget\n    alpha       = model.alpha         # Weight vector {-1, 1} per SV\n    SV          = model.SV            # active support vectors\n    sv_num      = model.sv_num        # Number of support vectors added\n    sigma       = model.sigma         # Hyperparameter of Gaussian Kernel\n    index       = model.index         # Index for budget maintenance\n    \n    # Prediction\n    last_idx = min(sv_num, max_sv)\n    if sv_num != 0:\n        f_t = np.dot(alpha[0:last_idx] ,kernel(SV, x_t, sigma, last_idx))\n    # Make prediction value = 0 when no SVs\n    else:\n        f_t = 0\n    \n    if (f_t>=0):\n        hat_y_t = 1\n    else:\n        hat_y_t = -1\n        \n    # Loss\n    l_t = hat_y_t != y_t                  # Hinge Loss\n\n    # Update on wrong predictions\n    if(l_t > 0):\n        \n        SV[index]     = x_t                # Add new SV\n        alpha[index]  = y_t                # Update alpha weight for this SV\n        index = (index+1) % max_sv\n        \n        model.index   = index\n        model.sv_num += 1\n        model.SV      = SV\n        model.alhpa   = alpha\n    \n    return (model, hat_y_t, l_t)\n","sub_path":"algorithms/Gaussian_Kernel_Perceptron.py","file_name":"Gaussian_Kernel_Perceptron.py","file_ext":"py","file_size_in_byte":2063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"298615169","text":"# author: TFTAuthors@accenture.com\n# Purpose: This file defines a class that helps determine the performance of a model\n# [a] time to inference\n# [b] time it takes to pre_process a batch of data by the model\n# [c] time to load the model\n# [d] time spent on each layer by the model\n\n\nimport time\nimport os\nfrom tft.image.utils_image import read_img_names_labels_csv, read_images, result_message_performance_metrics\nimport tensorflow as tf\nfrom csv import DictWriter, reader\nimport pandas as pd\nfrom tensorflow.python.framework.errors_impl import InvalidArgumentError, FailedPreconditionError\n\n\nclass PerformanceMetrics:\n    def __init__(self, model, input_placeholder_tensor_name, path_to_model, model_file, load_model_file_flag,\n                 path_to_save_results, project_name, image_samples_folder, image_vs_labels_csv, image_height,\n                 image_width, num_samples_batch=64):\n        \"\"\"\n\n        :param model: An object of a model class created by the user. Expected to have the parameters \"self.sess\",\n        \"self.x\" and \"self.logits\", \"self.image_size_height\", \"self.image_size_width\" and \"self.num_channels\"\n        :param input_placeholder_tensor_name: the placeholder that takes a batch of input images\n        :param path_to_model: a string - local absolute path to the model\n        :param model_file: a string - model file name like .meta, .h5 or .pb\n        :param load_model_file_flag : a boolean flag that indicates whether the load_model function in the user defined\n         Model class contains \"path_to_model\"\n        :param path_to_save_results: a string - absolute path to save results\n        :param project_name: a string - represents a name under which the performance metrics is evaluated\n        :param image_samples_folder : a string - absolute path to a folder that contains sample images\n        :param image_vs_labels_csv : A string - a local absolute path to a csv file that contains mapping between image\n               names and corresponding labels. The column names of the csv file must be “ImageName” and “Label”\n               respectively.\n        :param image_height: height of the images that the model is trained to take\n        :param image_width: width of the images that the model is trained to take\n        :param num_samples_batch : batch size on which performance metrics is evaluated. At most 64.\n        \"\"\"\n        self.model = model\n        self.input_placeholder_tensor_name = input_placeholder_tensor_name\n        self.path_to_model = path_to_model\n        self.model_file = model_file\n        self.load_model_file_flag = load_model_file_flag\n        self.path_to_save_results = path_to_save_results\n        self.project_name = project_name\n        self.image_samples_folder = image_samples_folder\n        self.image_vs_labels_csv = image_vs_labels_csv\n        self.image_height = image_height\n        self.image_width = image_width\n        self.num_samples_batch = num_samples_batch\n        self.time_inference = 0  # time it takes to read a batch of data by the model and give output\n        self.time_pre_process = 0  # time it takes to pre_process a batch of data by the model\n        self.time_load_model = 0  # time it takes to load a model from scratch.\n        self.root_folder_to_save_results = os.path.join(self.path_to_save_results, self.project_name)\n\n    def metric_inference(self, image_batch):\n        \"\"\"\n\n        :param image_batch: batch of nd arrays of shape (n_samples, h, w, c)\n        :return: float. Time in seconds - Inference time for a batch of samples\n        \"\"\"\n        assert (image_batch.shape == (self.num_samples_batch, self.image_height, self.image_width, 3)), \\\n            \"the batch shape must be (n_samples, h, w, c)\"\n        tic = time.time()\n        self.model.predict(image_batch)\n        toc = time.time()\n        self.time_inference = toc-tic\n        print(\"Inference time for a batch of '%s' images is \" % (len(image_batch)) + str(self.time_inference) + \" s\")\n        return self.time_inference\n\n    def metric_pre_process_time(self, image_batch):\n        \"\"\"\n\n        :param image_batch: batch of nd arrays of shape (n_samples, h, w, c)\n        :return: float. Time in seconds - time it takes to pre process a batch of images\n        \"\"\"\n        assert (image_batch.shape == (self.num_samples_batch, self.image_height, self.image_width, 3)), \\\n            \"the batch shape must be (n_samples, h, w, c)\"\n        tic = time.time()\n        self.model.pre_process(image_batch)\n        toc = time.time()\n        self.time_pre_process = toc-tic\n        print(\"Time taken to pre_process a batch of '%s' images is \" % (len(image_batch)) + str(self.time_pre_process) +\n              \" s\")\n        return self.time_pre_process\n\n    def metric_load_model(self):\n        # load_model is to be called with in a new variable scope. If not, we get an error as the model would already\n        # be loaded and have a reference inside \"model\" object.\n        # tf.AUTO_REUSE creates variables if they do not exist, and return them otherwise;\n        # For more info: https://www.tensorflow.org/api_docs/python/tf/variable_scope\n        with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):\n            if self.load_model_file_flag:\n                tic = time.time()\n                self.model.load_model(self.path_to_model, self.model_file)\n                toc = time.time()\n                self.time_load_model = toc-tic\n            else:\n                tic = time.time()\n                self.model.load_model(self.path_to_model)\n                toc = time.time()\n                self.time_load_model = toc-tic\n            print(\"Time taken to load the model is \" + str(self.time_load_model) +\n                  \" s\")\n            # assert sess != self.model.sess, \"EQUAL!\"\n        return self.time_load_model\n\n    def metric_layers(self, image_batch, input_tensor_name):\n        \"\"\"\n\n        :param image_batch: batch of nd arrays of shape (n_samples, h, w, c)\n        :param input_tensor_name: the input placeholder name\n        :return: a boolean indicating success or failure\n        \"\"\"\n        print(\"Generating layers_info_detailed.csv...\")\n        # First get all operations in the graph from the session\n        model_graph = self.model.sess.graph\n        all_operations_in_graph = model_graph.get_operations()  # list of all operations in the graph\n        input_placeholder_tensor = model_graph.get_tensor_by_name(input_tensor_name)  # \"Placeholder:0\"\n        assert len(input_placeholder_tensor.shape) == 4, \"The input tensor shape must be of length 4. The first \" \\\n                                                         \"dimension being 'number of samples in the batch', second \" \\\n                                                         \"being 'height of each image', third being 'width of each \" \\\n                                                         \"image' and the last being 'number of channels'\"\n        X = self.model.pre_process(image_batch)\n        first_row = True\n        for an_op in all_operations_in_graph:\n            # Ignore saver operations, placeholder & Assign operations and non fetchable operations in the graph\n            if \"save\" not in an_op.name.lower() and \"placeholder\" not in an_op.name.lower() and \"Assign_\" not in \\\n                    an_op.name.lower() and model_graph.is_fetchable(an_op):\n                # Type of op is  <class 'tensorflow.python.framework.ops.Operation'>\n                # op = self.model.sess.graph.get_operation_by_name(an_op.name)  # op.name is a string\n\n                # get the output tensors of this operation\n                op_outputs = an_op.outputs  # The list of tensor objects representing the outputs of this op\n            # for an_output in op_outputs:\n                # print(\"The operation name \", an_op.name)\n                # print(\"output Tensors \", op_outputs)\n                try:\n                    tic = time.time()\n                    self.model.sess.run(op_outputs, feed_dict={input_placeholder_tensor: X})\n                    toc = time.time()\n                except InvalidArgumentError:\n                    continue\n                except FailedPreconditionError:\n                    continue\n                # print(\"Time to run in seconds-\", (toc-tic))\n\n                # open a csv file in append mode, write a row, close\n                with open(os.path.join(self.root_folder_to_save_results, \"layers_info_detailed.csv\"), \"a\", newline='') \\\n                        as layers_info_detailed_file:\n                    headers = [\"OperationName\", \"Num Of Output Tensors This Op Produces\", \"Time in Seconds\"]\n\n                    layers_info_writer = DictWriter(layers_info_detailed_file, delimiter=',', fieldnames=headers)\n                    if first_row:\n                        layers_info_writer.writeheader()\n                        first_row = False\n                    layers_info_writer.writerow({\"OperationName\": an_op.name, \"Num Of Output Tensors This Op Produces\":\n                                                len(op_outputs), \"Time in Seconds\": toc-tic})\n        print(\"layers_info_detailed.csv generated successfully\")\n\n        # Code to get tokenised Operation Names from detailed CSV\n\n        if os.path.isfile(os.path.join(self.root_folder_to_save_results, \"layers_info_detailed.csv\")):\n            # read the csv\n            df = pd.read_csv(os.path.join(self.root_folder_to_save_results, \"layers_info_detailed.csv\"))\n            list_of_ops = list(df['OperationName'])\n            tokens = []\n            # Getting initial common sub-strings by comparing two consecutive Operation Names\n            for i in range(len(list_of_ops) - 1):\n                str_1 = list_of_ops[i]\n                str_2 = list_of_ops[i + 1]\n                if str_2 in str_1:\n                    tokens.append(str_2)\n            # print(len(tokens))\n            # dataframe for saving sub-string & average time taken by operations starting with the sub-string\n            df3 = pd.DataFrame(columns=['Sub-string in Operation Name',\n                                        'Average Time taken by operations starting with Sub-string'])\n\n            # Getting all the unique Operation Names other than sub-strings\n            m = list_of_ops[1:]\n            for sub_str in tokens:\n                criterion = df['OperationName'].map(lambda x: x.startswith(sub_str))\n                df2 = df[criterion]\n                # saving average time simultaneously for the initial tokens\n                df3 = df3.append({'Sub-string in Operation Name': sub_str,\n                                  'Average Time taken by operations starting with Sub-string': df2['Time in Seconds'].mean()},\n                                 ignore_index=True)\n                list_of_ops_sub_str = df2['OperationName'].tolist()\n                m = [x for x in m if x not in list_of_ops_sub_str]\n\n            # Applying same logic on additional operations obtained to get average time taken\n            for sub_str in m:\n                criterion = df['OperationName'].map(lambda x: x.startswith(sub_str))\n                df2 = df[criterion]\n                df3 = df3.append({'Sub-string in Operation Name': sub_str,\n                                  'Average Time taken by operations starting with Sub-string': df2['Time in Seconds'].mean()},\n                                 ignore_index=True)\n            # finally saving dataframe to CSV\n            df3.to_csv(os.path.join(self.root_folder_to_save_results, \"layers_output_tokened.csv\"), index=False)\n            print(\"layers_output_tokened.csv generated successfully\")\n            res = True\n        else:\n            res = False\n\n        return res\n\n    def compute(self):\n        \"\"\"\n        A function that computes the model's inference metrics & layer related (tensorflow operation) metrics (basically\n        time in seconds)\n\n        :return: a bool - indicates whether results were generated successfully or not\n        \"\"\"\n        first_row = True\n        assert (self.num_samples_batch <= 64), \"Batch sizes must be small, <= 64\"\n        image_names_list, labels = read_img_names_labels_csv(self.image_vs_labels_csv)\n        if len(image_names_list) < 64:\n            self.num_samples_batch = len(image_names_list)\n        samples_path_list = [os.path.join(self.image_samples_folder, img_name)\n                             for img_name in image_names_list[0:self.num_samples_batch]]\n        batch_ndarray = read_images(samples_path_list, self.image_height, self.image_width)\n        model_load_time = self.metric_load_model()\n        pre_process_time = self.metric_pre_process_time(batch_ndarray)\n        inf_time = self.metric_inference(batch_ndarray)\n        col1 = \"Time taken to load the model(in sec)\"\n        col2 = \"Time taken to pre_process a batch of \" + str(self.num_samples_batch) + \" images(in sec)\"\n        col3 = \"Time taken to predict a batch of \" + str(self.num_samples_batch) + \" images(in sec)\"\n\n        if not os.path.isdir(self.root_folder_to_save_results):\n            os.makedirs(self.root_folder_to_save_results, exist_ok=True)\n        with open(os.path.join(self.root_folder_to_save_results, \"model_inference.csv\"), \"w\", newline='') as \\\n                model_inference_file:\n            headers = [col1, col2, col3]\n            model_inference_writer = DictWriter(model_inference_file, delimiter=',', fieldnames=headers)\n            if first_row:\n                model_inference_writer.writeheader()\n                first_row = False\n            model_inference_writer.writerow({col1: model_load_time, col2: pre_process_time, col3: inf_time})\n        print(\"model_inference.csv file generated successfully..\")\n\n        self.metric_layers(batch_ndarray, self.input_placeholder_tensor_name)\n        # Check if the metrics result files have been generated successfully and give a success/failure message\n        return result_message_performance_metrics(self.root_folder_to_save_results)\n","sub_path":"tft/image/performance_image/performance_metrics.py","file_name":"performance_metrics.py","file_ext":"py","file_size_in_byte":13970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"423590923","text":"# coding: utf-8\n\n\"\"\"\n    Pure Storage FlashBlade REST 1.9 Python SDK\n\n    Pure Storage FlashBlade REST 1.9 Python SDK, developed by [Pure Storage, Inc](http://www.purestorage.com/). Documentations can be found at [purity-fb.readthedocs.io](http://purity-fb.readthedocs.io/).\n\n    OpenAPI spec version: 1.9\n    Contact: info@purestorage.com\n    Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\n\nfrom pprint import pformat\nfrom six import iteritems\nimport re\n\n\nclass FileSystemSnapshot(object):\n    \"\"\"\n    NOTE: This class is auto generated by the swagger code generator program.\n    Do not edit the class manually.\n    \"\"\"\n\n\n    \"\"\"\n    Attributes:\n      swagger_types (dict): The key is attribute name\n                            and the value is attribute type.\n      attribute_map (dict): The key is attribute name\n                            and the value is json key in definition.\n    \"\"\"\n    swagger_types = {\n        'name': 'str',\n        'id': 'str',\n        'destroyed': 'bool',\n        'owner': 'Reference',\n        'owner_destroyed': 'bool',\n        'policy': 'LocationReference',\n        'source': 'str',\n        'source_destroyed': 'bool',\n        'source_id': 'str',\n        'source_is_local': 'bool',\n        'source_location': 'Reference',\n        'source_display_name': 'str',\n        'suffix': 'str',\n        'time_remaining': 'int'\n    }\n\n    attribute_map = {\n        'name': 'name',\n        'id': 'id',\n        'destroyed': 'destroyed',\n        'owner': 'owner',\n        'owner_destroyed': 'owner_destroyed',\n        'policy': 'policy',\n        'source': 'source',\n        'source_destroyed': 'source_destroyed',\n        'source_id': 'source_id',\n        'source_is_local': 'source_is_local',\n        'source_location': 'source_location',\n        'source_display_name': 'source_display_name',\n        'suffix': 'suffix',\n        'time_remaining': 'time_remaining'\n    }\n\n    def __init__(self, name=None, id=None, destroyed=None, owner=None, owner_destroyed=None, policy=None, source=None, source_destroyed=None, source_id=None, source_is_local=None, source_location=None, source_display_name=None, suffix=None, time_remaining=None):\n        \"\"\"\n        FileSystemSnapshot - a model defined in Swagger\n        \"\"\"\n\n        self._name = None\n        self._id = None\n        self._destroyed = None\n        self._owner = None\n        self._owner_destroyed = None\n        self._policy = None\n        self._source = None\n        self._source_destroyed = None\n        self._source_id = None\n        self._source_is_local = None\n        self._source_location = None\n        self._source_display_name = None\n        self._suffix = None\n        self._time_remaining = None\n\n        if name is not None:\n          self.name = name\n        if id is not None:\n          self.id = id\n        if destroyed is not None:\n          self.destroyed = destroyed\n        if owner is not None:\n          self.owner = owner\n        if owner_destroyed is not None:\n          self.owner_destroyed = owner_destroyed\n        if policy is not None:\n          self.policy = policy\n        if source is not None:\n          self.source = source\n        if source_destroyed is not None:\n          self.source_destroyed = source_destroyed\n        if source_id is not None:\n          self.source_id = source_id\n        if source_is_local is not None:\n          self.source_is_local = source_is_local\n        if source_location is not None:\n          self.source_location = source_location\n        if source_display_name is not None:\n          self.source_display_name = source_display_name\n        if suffix is not None:\n          self.suffix = suffix\n        if time_remaining is not None:\n          self.time_remaining = time_remaining\n\n    @property\n    def name(self):\n        \"\"\"\n        Gets the name of this FileSystemSnapshot.\n        name of the object\n\n        :return: The name of this FileSystemSnapshot.\n        :rtype: str\n        \"\"\"\n        return self._name\n\n    @name.setter\n    def name(self, name):\n        \"\"\"\n        Sets the name of this FileSystemSnapshot.\n        name of the object\n\n        :param name: The name of this FileSystemSnapshot.\n        :type: str\n        \"\"\"\n\n        self._name = name\n\n    @property\n    def id(self):\n        \"\"\"\n        Gets the id of this FileSystemSnapshot.\n        A unique ID chosen by the system. Cannot change.\n\n        :return: The id of this FileSystemSnapshot.\n        :rtype: str\n        \"\"\"\n        return self._id\n\n    @id.setter\n    def id(self, id):\n        \"\"\"\n        Sets the id of this FileSystemSnapshot.\n        A unique ID chosen by the system. Cannot change.\n\n        :param id: The id of this FileSystemSnapshot.\n        :type: str\n        \"\"\"\n\n        self._id = id\n\n    @property\n    def destroyed(self):\n        \"\"\"\n        Gets the destroyed of this FileSystemSnapshot.\n        Is the file system snapshot destroyed? False by default.\n\n        :return: The destroyed of this FileSystemSnapshot.\n        :rtype: bool\n        \"\"\"\n        return self._destroyed\n\n    @destroyed.setter\n    def destroyed(self, destroyed):\n        \"\"\"\n        Sets the destroyed of this FileSystemSnapshot.\n        Is the file system snapshot destroyed? False by default.\n\n        :param destroyed: The destroyed of this FileSystemSnapshot.\n        :type: bool\n        \"\"\"\n\n        self._destroyed = destroyed\n\n    @property\n    def owner(self):\n        \"\"\"\n        Gets the owner of this FileSystemSnapshot.\n        A reference to the file system that owns this snapshot. If the owner is destroyed, this will be destroyed.\n\n        :return: The owner of this FileSystemSnapshot.\n        :rtype: Reference\n        \"\"\"\n        return self._owner\n\n    @owner.setter\n    def owner(self, owner):\n        \"\"\"\n        Sets the owner of this FileSystemSnapshot.\n        A reference to the file system that owns this snapshot. If the owner is destroyed, this will be destroyed.\n\n        :param owner: The owner of this FileSystemSnapshot.\n        :type: Reference\n        \"\"\"\n\n        self._owner = owner\n\n    @property\n    def owner_destroyed(self):\n        \"\"\"\n        Gets the owner_destroyed of this FileSystemSnapshot.\n        Is the owning file system destroyed?\n\n        :return: The owner_destroyed of this FileSystemSnapshot.\n        :rtype: bool\n        \"\"\"\n        return self._owner_destroyed\n\n    @owner_destroyed.setter\n    def owner_destroyed(self, owner_destroyed):\n        \"\"\"\n        Sets the owner_destroyed of this FileSystemSnapshot.\n        Is the owning file system destroyed?\n\n        :param owner_destroyed: The owner_destroyed of this FileSystemSnapshot.\n        :type: bool\n        \"\"\"\n\n        self._owner_destroyed = owner_destroyed\n\n    @property\n    def policy(self):\n        \"\"\"\n        Gets the policy of this FileSystemSnapshot.\n        A reference to the associated policy.\n\n        :return: The policy of this FileSystemSnapshot.\n        :rtype: LocationReference\n        \"\"\"\n        return self._policy\n\n    @policy.setter\n    def policy(self, policy):\n        \"\"\"\n        Sets the policy of this FileSystemSnapshot.\n        A reference to the associated policy.\n\n        :param policy: The policy of this FileSystemSnapshot.\n        :type: LocationReference\n        \"\"\"\n\n        self._policy = policy\n\n    @property\n    def source(self):\n        \"\"\"\n        Gets the source of this FileSystemSnapshot.\n        The name of the file system that was the source of the data in this snapshot. Normally this is the same as the owner, but if the snapshot is replicated, the source is the original file system.\n\n        :return: The source of this FileSystemSnapshot.\n        :rtype: str\n        \"\"\"\n        return self._source\n\n    @source.setter\n    def source(self, source):\n        \"\"\"\n        Sets the source of this FileSystemSnapshot.\n        The name of the file system that was the source of the data in this snapshot. Normally this is the same as the owner, but if the snapshot is replicated, the source is the original file system.\n\n        :param source: The source of this FileSystemSnapshot.\n        :type: str\n        \"\"\"\n\n        self._source = source\n\n    @property\n    def source_destroyed(self):\n        \"\"\"\n        Gets the source_destroyed of this FileSystemSnapshot.\n        Deprecated. Use `owner_destroyed`. Is the owning file system destroyed?\n\n        :return: The source_destroyed of this FileSystemSnapshot.\n        :rtype: bool\n        \"\"\"\n        return self._source_destroyed\n\n    @source_destroyed.setter\n    def source_destroyed(self, source_destroyed):\n        \"\"\"\n        Sets the source_destroyed of this FileSystemSnapshot.\n        Deprecated. Use `owner_destroyed`. Is the owning file system destroyed?\n\n        :param source_destroyed: The source_destroyed of this FileSystemSnapshot.\n        :type: bool\n        \"\"\"\n\n        self._source_destroyed = source_destroyed\n\n    @property\n    def source_id(self):\n        \"\"\"\n        Gets the source_id of this FileSystemSnapshot.\n        The unique global ID of the source file system.\n\n        :return: The source_id of this FileSystemSnapshot.\n        :rtype: str\n        \"\"\"\n        return self._source_id\n\n    @source_id.setter\n    def source_id(self, source_id):\n        \"\"\"\n        Sets the source_id of this FileSystemSnapshot.\n        The unique global ID of the source file system.\n\n        :param source_id: The source_id of this FileSystemSnapshot.\n        :type: str\n        \"\"\"\n\n        self._source_id = source_id\n\n    @property\n    def source_is_local(self):\n        \"\"\"\n        Gets the source_is_local of this FileSystemSnapshot.\n        Is the source of this file system snapshot on the local array?\n\n        :return: The source_is_local of this FileSystemSnapshot.\n        :rtype: bool\n        \"\"\"\n        return self._source_is_local\n\n    @source_is_local.setter\n    def source_is_local(self, source_is_local):\n        \"\"\"\n        Sets the source_is_local of this FileSystemSnapshot.\n        Is the source of this file system snapshot on the local array?\n\n        :param source_is_local: The source_is_local of this FileSystemSnapshot.\n        :type: bool\n        \"\"\"\n\n        self._source_is_local = source_is_local\n\n    @property\n    def source_location(self):\n        \"\"\"\n        Gets the source_location of this FileSystemSnapshot.\n        A reference to the source array.\n\n        :return: The source_location of this FileSystemSnapshot.\n        :rtype: Reference\n        \"\"\"\n        return self._source_location\n\n    @source_location.setter\n    def source_location(self, source_location):\n        \"\"\"\n        Sets the source_location of this FileSystemSnapshot.\n        A reference to the source array.\n\n        :param source_location: The source_location of this FileSystemSnapshot.\n        :type: Reference\n        \"\"\"\n\n        self._source_location = source_location\n\n    @property\n    def source_display_name(self):\n        \"\"\"\n        Gets the source_display_name of this FileSystemSnapshot.\n        Full name of the source with remote array information. Response will be same as source for local file system snapshots.\n\n        :return: The source_display_name of this FileSystemSnapshot.\n        :rtype: str\n        \"\"\"\n        return self._source_display_name\n\n    @source_display_name.setter\n    def source_display_name(self, source_display_name):\n        \"\"\"\n        Sets the source_display_name of this FileSystemSnapshot.\n        Full name of the source with remote array information. Response will be same as source for local file system snapshots.\n\n        :param source_display_name: The source_display_name of this FileSystemSnapshot.\n        :type: str\n        \"\"\"\n\n        self._source_display_name = source_display_name\n\n    @property\n    def suffix(self):\n        \"\"\"\n        Gets the suffix of this FileSystemSnapshot.\n        The suffix of the snapshot, e.g., snap1.\n\n        :return: The suffix of this FileSystemSnapshot.\n        :rtype: str\n        \"\"\"\n        return self._suffix\n\n    @suffix.setter\n    def suffix(self, suffix):\n        \"\"\"\n        Sets the suffix of this FileSystemSnapshot.\n        The suffix of the snapshot, e.g., snap1.\n\n        :param suffix: The suffix of this FileSystemSnapshot.\n        :type: str\n        \"\"\"\n\n        self._suffix = suffix\n\n    @property\n    def time_remaining(self):\n        \"\"\"\n        Gets the time_remaining of this FileSystemSnapshot.\n        Time in milliseconds before the file system snapshot is eradicated. Null if not destroyed.\n\n        :return: The time_remaining of this FileSystemSnapshot.\n        :rtype: int\n        \"\"\"\n        return self._time_remaining\n\n    @time_remaining.setter\n    def time_remaining(self, time_remaining):\n        \"\"\"\n        Sets the time_remaining of this FileSystemSnapshot.\n        Time in milliseconds before the file system snapshot is eradicated. Null if not destroyed.\n\n        :param time_remaining: The time_remaining of this FileSystemSnapshot.\n        :type: int\n        \"\"\"\n\n        self._time_remaining = time_remaining\n\n    def to_dict(self):\n        \"\"\"\n        Returns the model properties as a dict\n        \"\"\"\n        result = {}\n\n        for attr, _ in iteritems(self.swagger_types):\n            value = getattr(self, attr)\n            if isinstance(value, list):\n                result[attr] = list(map(\n                    lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n                    value\n                ))\n            elif hasattr(value, \"to_dict\"):\n                result[attr] = value.to_dict()\n            elif isinstance(value, dict):\n                result[attr] = dict(map(\n                    lambda item: (item[0], item[1].to_dict())\n                    if hasattr(item[1], \"to_dict\") else item,\n                    value.items()\n                ))\n            else:\n                result[attr] = value\n\n        return result\n\n    def to_str(self):\n        \"\"\"\n        Returns the string representation of the model\n        \"\"\"\n        return pformat(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"\n        For `print` and `pprint`\n        \"\"\"\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"\n        Returns true if both objects are equal\n        \"\"\"\n        if not isinstance(other, FileSystemSnapshot):\n            return False\n\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"\n        Returns true if both objects are not equal\n        \"\"\"\n        return not self == other\n","sub_path":"purity_fb/purity_fb_1dot9/models/file_system_snapshot.py","file_name":"file_system_snapshot.py","file_ext":"py","file_size_in_byte":14508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"63261707","text":"\"\"\"\nCreated on Sep 15, 2014\n\n@author: sfurman\n\"\"\"\n\n\n# Terminal color palette.\nclass Color(object):\n    def __init__(self):\n        pass\n\n    TEXT_RESET_ALL = '\\033[0m'\n    TEXT_RESET = '\\033[39m'  # '\\033[49m'\n    TEXT_BLACK = '\\033[30m'\n    TEXT_RED = '\\033[31m'\n    TEXT_GREEN = '\\033[32m'\n    TEXT_YELLOW = '\\033[33m'\n    TEXT_BLUE = '\\033[34m'\n    TEXT_MAGENTA = '\\033[35m'\n    TEXT_CYAN = '\\033[36m'\n    TEXT_WHITE = '\\033[37m'\n    TEXT_BRIGHT = '\\033[1m'\n    TEXT_NORMAL = '\\033[22m'\n    BACKGROUND_BLACK = '\\033[40m'\n    BACKGROUND_RED = '\\033[41m'\n    BACKGROUND_GREEN = '\\033[42m'\n    BACKGROUND_YELLOW = '\\033[43m'  # olive\n    BACKGROUND_BLUE = '\\033[44m'\n    BACKGROUND_MAGENTA = '\\033[45m'  # purple\n    BACKGROUND_CYAN = '\\033[46m'  # light blue\n    BACKGROUND_WHITE = '\\033[47m'  # grey\n\n# ANSI sequences that 'colorama' converts into win32 calls.\n\n# TEXT:\n# ESC [ 0 m       # Reset all (colors and brightness)\n# ESC [ 1 m       # Bright\n# ESC [ 2 m       # Dim (looks same as normal brightness)\n# ESC [ 22 m      # Normal brightness\n\n# FOREGROUND:\n# ESC [ 30 m      # Black\n# ESC [ 31 m      # Red\n# ESC [ 32 m      # Green\n# ESC [ 33 m      # Yellow\n# ESC [ 34 m      # Blue\n# ESC [ 35 m      # Magenta\n# ESC [ 36 m      # Cyan\n# ESC [ 37 m      # White\n# ESC [ 39 m      # Reset\n\n# BACKGROUND:\n# ESC [ 40 m      # Black\n# ESC [ 41 m      # Red\n# ESC [ 42 m      # Green\n# ESC [ 43 m      # Yellow\n# ESC [ 44 m      # Blue\n# ESC [ 45 m      # Magenta\n# ESC [ 46 m      # Cyan\n# ESC [ 47 m      # White\n# ESC [ 49 m      # Reset\n\n# Cursor positioning:\n# ESC [ y;x H     # Position cursor at x across, y down.\n\n# Clear the screen:\n# ESC [ mode J    # Clear the screen. Only mode 2 (clear entire screen)\n#                   is supported.\n","sub_path":"PythonPackagesValidatorCentOS/colors.py","file_name":"colors.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"556059617","text":"import csp\n\nclass Problem(csp.CSP):\n\n    def __init__(self, fh):\n        # Opening file and constructing the problem\n        with open(fh) as f:\n            ft = f.read().splitlines()\n        print(ft[1].split())\n        T = ([ft[x].split() for x in range(0, len(ft)) if ft[x][0] == 'T'])[0]\n        R = ([ft[x].split() for x in range(0, len(ft)) if ft[x][0] == 'R'])[0]\n        self.S = ([ft[x].split() for x in range(0, len(ft)) if ft[x][0] == 'S'])[0]\n        self.W = ([ft[x].split() for x in range(0, len(ft)) if ft[x][0] == 'W'])[0]\n        self.A = ([ft[x].split() for x in range(0, len(ft)) if ft[x][0] == 'A'])[0]\n        T.pop(0);\n        R.pop(0);\n        self.S.pop(0);\n        self.W.pop(0);\n        self.A.pop(0)\n\n        # List of time slots available to be used in the cost evaluation\n        self.time_slots = sorted(list(set([int(x.split(',')[1]) for x in T if x]))) # ordened list without repetition\n        self.limit = self.time_slots[0]\n\n        # Solution to be exported to file:\n        self.solution = None\n\n        #Dict containing the relation between weekly classes and student_classes\n        self.students_classes = self.stud_classes(self.W, self.S, self.A)\n\n        variables = self.W\n        common_domain = list(self.carth_product(T, R))\n        domains = {x: common_domain for x in self.W}\n        graph = {x: [y for y in self.W if y != x] for x in self.W}\n        constraints_function = self.constraint_function\n        super().__init__(variables, domains, graph, constraints_function)  # used to refer to method _init_ of csp.CSP\n\n\n    def dump_solution(self, fh):\n        # Place here your code to write solution to opened file object fh\n        f = open(fh, \"w\")\n        if self.solution is not None:\n            for x in self.W:\n                f.write(str(x) + ' ' + ' '.join(self.solution[x]) + '\\n')\n        else:\n            f.write(str(None))\n        f.close()\n\n    def constraint_function(self, A, a, B, b):\n        \"\"\"Evaluate if constraint of neighbours A and B is satisfied.\n        It needs to comprise every constraint in the graph.\"\"\"\n        # This is the input of _init_ method of the parent class csp.CSP\n\n        # First Constraint: if a room is assigned for two classes at the same time\n        if a == b:\n            return 0\n\n        # Second Constraint: if  a student class attends to two different times\n        if a[1].split(',') == b[1].split(','):\n            for x in self.students_classes[A]:\n                for y in self.students_classes[B]:\n                    if x == y:\n                        return 0\n\n        # Third Constraint: two weekly classes of the same type and course cannot occur in the same day\n        if A.split(',')[0:2] == B.split(',')[0:2] and a[1].split(',')[0] == b[1].split(',')[0]:\n            return 0\n\n        # Fourth Constraint: Time limit for cost function\n        if int(a[1].split(',')[1]) > self.limit or int(b[1].split(',')[1]) > self.limit:\n            return 0\n\n        # If the constraints are satisfied\n        return 1\n\n    def stud_classes(self, weekly_classes, students_classes, association):\n        # This method associates a list of students classes to each weekly class\n        dict = {}\n        for x in weekly_classes:\n            stud_list = []\n            for y in association:\n                for w in students_classes:  # In case you have a association but not a student class in the specification\n                    if x.split(',')[0] == y.split(',')[1] and w == y.split(',')[0]:\n                        stud_list = stud_list + [y.split(',')[0]]\n            dict[x] = stud_list\n        return dict\n\n    def carth_product(self, *lists):\n        # Recursive method for carthesian product between two or more lists\n        if not lists:\n            return ((),)  # empty tuple to sum in the last iteration of recursion\n        return (carrier + (elements,) for elements in lists[0] for carrier in self.carth_product(*lists[1:]))\n\ndef solve(input_file, output_file):\n\n    p = Problem(input_file)\n    solution = None\n\n    for i in p.time_slots:\n        p.limit = i\n        solution = csp.backtracking_search(p, select_unassigned_variable=csp.mrv,\n                                           order_domain_values=csp.lcv,\n                                           inference=csp.mac)\n        if solution is not None:\n            pass\n\n    p.solution = solution\n\n    p.dump_solution(output_file)\n\n\nif __name__ == \"__main__\":\n    solve('input_file', 'output_file')\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"39218284","text":"\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, utils\nimport matplotlib.pyplot as plt\nfrom PIL import Image\n\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nclass MyDataset(Dataset):\n\t''' Build the dataset '''\n\tdef __init__(self, X, y, transform=None):\n\n\t\tself.img = X\n\t\tself.label = y\n\t\tself.transform = transform # transform on images\n\n\tdef __getitem__(self, idx):\n\n\t\tlabel, img = self.label[idx], self.img[idx]\n\t\tif self.transform:\n\t\t\timg = self.transform(img)\n\t\treturn label, img\n\n\tdef __len__(self):\n\t\treturn len(self.label)\n\n\n# network model\nclass FlowerNet(nn.Module):\n\tdef __init__(self):\n\t\tsuper(FlowerNet, self).__init__()\n\t\tself.layer1 = nn.Sequential(\n\t\t\tnn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),\n\t\t\tnn.ReLU(),\n\t\t\tnn.BatchNorm2d(32),\n\t\t\tnn.MaxPool2d(kernel_size=2, stride=2),\n\t\t\tnn.Dropout(p=0.3))\n\t\tself.layer2 = nn.Sequential(\n\t\t\tnn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),\n\t\t\tnn.ReLU(),\n\t\t\tnn.BatchNorm2d(64),\n\t\t\tnn.MaxPool2d(kernel_size=2, stride=2),\n\t\t\tnn.Dropout(p=0.3))\n\t\tself.layer3 = nn.Sequential(\n\t\t\tnn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),\n\t\t\tnn.ReLU(),\n\t\t\tnn.BatchNorm2d(128),\n\t\t\tnn.MaxPool2d(kernel_size=2, stride=2),\n\t\t\tnn.Dropout(p=0.6))\n\t\tself.fc1 = nn.Sequential(\n\t\t\tnn.Linear(128*4*4,256),\n\t\t\tnn.ReLU(),\n\t\t\tnn.BatchNorm1d(256),\n\t\t\tnn.Dropout(p=0.6)\n\t\t\t)\n\n\t\tself.fc2 = nn.Linear(256, 5)   # 5 classes of flowers\n\n\tdef forward(self, x):\n\t\tout = self.layer1(x)\n\t\tout = self.layer2(out)\n\t\tout = self.layer3(out)\n\t\tout = out.view(out.size(0), -1)\n\t\tout = self.fc1(out)\n\t\tout = self.fc2(out)\n\t\treturn out\n\n\tdef LoadData(self):\n\t\t\"\"\" Load the data and convert them to [0,1] as float32. \n\t\t\tThen divide them into train / test \"\"\"\n\t\tImages = np.load('./Flowers/flower_imgs.npy')\n\t\tLabels = np.load('./Flowers/flower_labels.npy')\n\t\tn = Labels.size\n\t\tindices = list(range(n))\n\t\tsplit = int(np.floor(0.1 * n))\n\t\t# np.random.seed(1234)   # fix the training and test datasets for bettering tuning the parameters.\n\t\tnp.random.shuffle(indices)\n\t\ttrain_indices, test_indices = indices[split:], indices[:split]\n\t\tself.img_mean = np.mean(np.swapaxes(Images/255.0,0,1).reshape(3,-1),1)\n\t\tself.img_std = np.std(np.swapaxes(Images/255.0,0,1).reshape(3,-1),1)\n\n\t\tnormalize = transforms.Normalize(mean=list(self.img_mean), std=list(self.img_std))\n\n\t\tTrainTrans = transforms.Compose([\n\t\t\ttransforms.ToPILImage(),\n        \ttransforms.RandomCrop(28),\n        \ttransforms.Resize(32),\n        \ttransforms.RandomHorizontalFlip(),\n        \ttransforms.RandomRotation(10),\n        \ttransforms.ToTensor(),\n        \tnormalize,\n\t\t\t])\n\t\tTestTrans = transforms.Compose([\n\t\t\ttransforms.ToPILImage(),\n\t\t\ttransforms.ToTensor(),\n\t\t\tnormalize,\n\t\t\t])\n\t\ttrain_dataset = MyDataset(Images[train_indices],Labels[train_indices],TrainTrans)\n\t\ttest_dataset = MyDataset(Images[test_indices], Labels[test_indices], TestTrans)\n\n\t\tself.Train_dataloader = DataLoader(dataset=train_dataset, batch_size = 128, shuffle=True)\n\t\tself.Test_dataloader = DataLoader(dataset=test_dataset, batch_size = 128, shuffle=False)\n\ndef train():\n\n\tmodel = FlowerNet().to(device)\n\tmodel.LoadData()\n\toptimizer = torch.optim.Adadelta(model.parameters(), lr=1.0, rho=0.9, eps=1e-06, weight_decay=0.001)\n\tscheduler = ReduceLROnPlateau(optimizer,mode='min',patience=10)\n\tcriterion = nn.CrossEntropyLoss()  # see the torch.nn.modules.loss document\n\tTrain_epoch = 200\n\ttrain_acc = []\n\ttest_acc = []\n\tfor epoch in range(Train_epoch):\n\t\tcorrect = 0\n\t\ttotal = 0\n\t\ttrain_loss = 0\n\t\tfor batch, (label, image) in enumerate(model.Train_dataloader):\n\t\t\tlabel, image = label.to(device), image.to(device)\n\t\t\toutput = model(image)\n\t\t\tpredicted = torch.argmax(output, dim=1)\n\t\t\ttotal += label.size(0)\n\t\t\tcorrect += (predicted == label).sum().item()\n\t\t\tloss = criterion(output, label)\n\t\t\ttrain_loss += loss.item()\n\n\t\t\toptimizer.zero_grad()\n\t\t\tloss.backward()\n\t\t\toptimizer.step()\n\n\t\tscheduler.step(train_loss)\n\n\t\tprint('Loss Sum:{:.4f} Epoch[{}/{}]'.format(train_loss, epoch+1, Train_epoch))\n\t\tprint('Acc:{:.4f}% Epoch[{}/{}]'.format(100 * correct / total, epoch+1, Train_epoch))\n\t\ttrain_acc.append(correct / total)\n\n\t\t# see the validation accuracy\n\t\tcorrect = 0\n\t\ttotal = 0\n\t\tfor label, image in model.Test_dataloader:\n\t\t\timage = image.to(device)\n\t\t\tlabel = label.to(device)\n\t\t\toutputs = model(image)\n\t\t\tpredicted = torch.argmax(outputs, dim=1)\n\t\t\ttotal += label.size(0)\n\t\t\tcorrect += (predicted == label).sum().item()\n\t\tprint('Test accuracy of the model on the test images:{}%'.format(100 * correct/total))\n\t\ttest_acc.append(correct / total)\n\t# see the learned weights in each layer:\n\tparams = list(model.parameters())\n\tconv1 = params[0]\n\tconv2 = params[4]\n\tconv3 = params[8]\n\tfc1 = params[12]\n\tfc2 = params[16]\n\tprint(\"First conv layer:\")\n\tprint(conv1[0][0])\n\tprint(\"Second conv layer:\")\n\tprint(conv2[0][0])\n\tprint(\"Third conv layer:\")\n\tprint(conv3[0][0])\n\tprint(\"FC1:\")\n\tprint(fc1[0][:20])\n\tprint(\"FC2:\")\n\tprint(fc2[0][:20])\n\n\t# plot the train / validate acc\n\tepochs = np.linspace(1.0, Train_epoch, Train_epoch)\n\n\tfig, ax = plt.subplots(1)\n\tax.plot(epochs, train_acc)\n\tax.plot(epochs, test_acc)\n\tax.legend(['train accuracy', 'test accuracy'])\n\tax.set(xlabel='epochs', ylabel='accuracy',\n\t\ttitle='train and test accuracy over epochs')\n\n\tyticks = np.linspace(0.3, 0.9, 11)\n\n\tax.set_yticks(yticks)\n\tplt.show()\n\n\treturn model\n\ndef test(model):\n\twith torch.no_grad():\n\t\tcorrect = 0\n\t\ttotal = 0\n\t\tmisclassified = []\n\t\tfor label, image in model.Test_dataloader:\n\t\t\timage = image.to(device)\n\t\t\tlabel = label.to(device)\n\t\t\toutputs = model(image)\n\t\t\tpredicted = torch.argmax(outputs, dim=1)\n\t\t\ttotal += label.size(0)\n\t\t\tcorrect += (predicted == label).sum().item()\n\t\t\tmisclassified.extend(image[predicted != label])\n\t\tprint('Test accuracy of the model on the test images:{}%'.format(100 * correct/total))\n\n\t\t# plot first 10 misclassified images:\n\t\tfor i in range(10):\n\t\t\timg = misclassified[i+1].cpu().numpy()\n\t\t\timg[0,:,:] = img[0,:,:] * model.img_std[0] + model.img_mean[0]\n\t\t\timg[1,:,:] = img[1,:,:] * model.img_std[1] + model.img_mean[1]\n\t\t\timg[2,:,:] = img[2,:,:] * model.img_std[2] + model.img_mean[2]\n\t\t\tplt.figure(i)\n\t\t\tplt.imshow(np.transpose(img, (1,2,0)), interpolation='nearest')\n\t\t\tplt.show()\n\ndef main():\n\tmodel = train()\n\ttest(model)\n\nif __name__ == '__main__':\n\tmain()\n\n\n\n","sub_path":"Flowers/Flowers_Model.py","file_name":"Flowers_Model.py","file_ext":"py","file_size_in_byte":6424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"3221301","text":"class ObjDiff(object):\n    \"\"\"Represents a difference between two `CopyDiff` objects.\n\n    Contains a list of `DiffField` objects that represent the changes to each\n    field in the object.\n\n    Also contains the `children` field which may be be a `ChildListField`.\n    \"\"\"\n    def __init__(self, old, new, changes, children=None):\n        self.old = old\n        self.new = new\n        self.changes = changes\n        self.children = children\n\n    def get(self, name):\n        \"\"\"Gets a scalar field with the given name.\"\"\"\n        for field in self.changes:\n            if field.name == name: return field\n        return None\n\nclass DiffField(object):\n    \"\"\"Represents a generic field in a diff.\"\"\"\n    def __init__(self, name):\n        self.name = name\n\nclass ScalarField(DiffField):\n    \"\"\"Represents a change to a scalar field (i.e., not a list or dict)\"\"\"\n    def __init__(self, name, old, new):\n        super().__init__(name)\n        self.new = new\n        self.old = old\n\nclass ChildListField(DiffField):\n    \"\"\"Represents a change to a list of child objects whose changes are also tracked.\"\"\"\n    def __init__(self, name, added, removed, changed):\n        super().__init__(name)\n        self.added = added\n        self.removed = removed\n        self.changed = changed\n\n\nclass CopyDiff(object):\n    \"\"\"Base class providing copy and diff functions\"\"\"\n    def copy(self, **kwargs):\n        copy = self.blank(**kwargs)\n        copy.copy_from(self)\n        return copy\n\n    def copy_from(self, other):\n        \"\"\"Copies changes from `other` to this object.\"\"\"\n        # Copy changes to this object\n        for f in self.copydiff_fields():\n            setattr(self, f, getattr(other, f))\n        # Remove children deleted in other\n        for schild in self.get_children():\n            if not any(map(lambda o: o.same_as(schild), other.get_children())):\n                self.rm_child(schild)\n        # Copy children and add new children to self\n        for ochild in other.get_children():\n            # Find a matching child in this object\n            schild = next(filter(lambda s: ochild.same_as(s), self.get_children()), None)\n            if schild:\n                schild.copy_from(ochild)\n            else:\n                newchild = self.blank_child()\n                newchild.copy_from(ochild)\n                self.add_child(newchild)\n\n    def diff(self, new, prefix=[]):\n        \"\"\"\n        Returns a dict representing the differences between this mod and `new`.\n\n        Return value is an `ObjDiff` representing the differences between the objects.\n\n        Note: At the moment tracked children are always represented by a field\n        called `children` and there can only be one. This can be fixed later,\n        but it's not really needed.\n        \"\"\"\n        changes = []\n\n        for f in self.copydiff_fields():\n            o = getattr(self, f)\n            n = getattr(new, f)\n            if o != n:\n                changes.append(ScalarField(f, old=o, new=n))\n\n        # List removed children\n        added = []\n        removed = []\n        changed = []\n\n        # List removed children.\n        for schild in self.get_children():\n            if not any(map(lambda n: n.same_as(schild), new.get_children())):\n                removed.append(schild)\n\n        # List added and changed children\n        for nchild in new.get_children():\n            # Find a matching child in the old object\n            ochild = next(filter(lambda s: nchild.same_as(s), self.get_children()), None)\n            if ochild:\n                chdiff = ochild.diff(nchild)\n                if len(chdiff.changes) > 0:\n                    changed.append(chdiff)\n            else:\n                added.append(nchild)\n\n        # If children changed, add a `ChildListField` to the diff.\n        if len(added) > 0 or len(removed) > 0 or len(changed) > 0:\n            children = ChildListField('children', added, removed, changed)\n        else:\n            children = None\n\n        print(changes)\n        return ObjDiff(self, new, changes, children)\n\n    def blank(self, **kwargs):\n        \"\"\"Creates an \"empty\" instance of this object\"\"\"\n        raise NotImplementedError\n    def blank_child(self, **kwargs):\n        \"\"\"Creates an \"empty\" instance of a child of this object.\n        None if this object can't have children\"\"\"\n        return None\n    def copydiff_fields(self):\n        \"\"\"Returns a list of fields to be copied or diffed\"\"\"\n        return []\n    def same_as(self, other):\n        scur = hasattr(self, 'cur_id')\n        ocur = hasattr(other, 'cur_id')\n        if self.id == other.id: return True\n        if scur and ocur: return self.cur_id == other.cur_id\n        elif scur: return self.cur_id == other.id\n        elif ocur: return other.cur_id == self.id\n        else: return False\n    def get_children(self): raise NotImplementedError\n    def add_child(self, ch): raise NotImplementedError\n    def rm_child(self, ch): raise NotImplementedError\n\n","sub_path":"mcarch/util/copydiff.py","file_name":"copydiff.py","file_ext":"py","file_size_in_byte":4948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"469301868","text":"\"\"\"\nThis example demonstrates training a simple CNN with tf.keras using the Determined\nNative API.\n\"\"\"\nimport argparse\nimport json\nimport pathlib\n\nimport determined as det\nfrom determined import experimental\n\nimport model_def\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--config\",\n        dest=\"config\",\n        help=\"Specifies Determined Experiment configuration.\",\n        default=\"{}\",\n    )\n    parser.add_argument(\"--local\", action=\"store_true\", help=\"Specifies local mode\")\n    parser.add_argument(\"--test\", action=\"store_true\", help=\"Specifies test mode\")\n    args = parser.parse_args()\n\n    config = {\n        \"hyperparameters\": {\n            \"global_batch_size\": det.Constant(value=32),\n            \"dense1\": det.Constant(value=128),\n        },\n        \"records_per_epoch\": 50000,\n        \"searcher\": {\n            \"name\": \"single\",\n            \"metric\": \"val_accuracy\",\n            \"max_length\": {\n                \"epochs\": 5,\n            }\n        },\n    }\n    config.update(json.loads(args.config))\n\n    experimental.create(\n        trial_def=model_def.FashionMNISTTrial,\n        config=config,\n        local=args.local,\n        test=args.test,\n        context_dir=str(pathlib.Path.cwd()),\n    )\n","sub_path":"examples/official/native/native_fashion_mnist_tf_keras/trial_impl.py","file_name":"trial_impl.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"484555787","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport numpy as np\nimport scipy.sparse as sp\n# Import the sentiment\nfrom keras.datasets import imdb\n\nimport io.io\n\nprint(\"----\")\n\ndef getSplits(vectors, classes, dev=0.8):\n    if len(vectors) != 50000 or len(classes) != 50000:\n        print(\"This is not the standard size of 20NG, expected 50000\")\n        return False\n\n    x_train = vectors[:25000]\n    x_test = vectors[25000:]\n    y_train = classes[:25000]\n    y_test = classes[25000:]\n\n    print(\"Returning development splits\", dev)\n\n    x_dev = x_train[int(len(x_train) * 0.8):]\n    y_dev = y_train[int(len(y_train) * 0.8):]\n    x_train = x_train[:int(len(x_train) * 0.8)]\n    y_train = y_train[:int(len(y_train) * 0.8)]\n\n    print(len(x_test), len(x_test[0]), \"x_test\")\n    print(len(y_test),  \"y_test\")\n    print(len(x_train), len(x_train[0]), \"x_train\")\n    print(len(y_train),  \"y_train\")\n\n    return x_train, y_train, x_test, y_test, x_dev, y_dev\n\ndef everythingElse():\n    np.random.seed(1337)\n    # Get frequencies, PPMI's, classes. Everything needed for directions\n    skip_top = 0\n    lowest_amt = skip_top\n    highest_amt = 0\n    index_from = 2\n    classification = \"all\"\n    bigrams = True\n\n    if bigrams is False:\n        (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=highest_amt, skip_top=skip_top, index_from=index_from)\n    else:\n        (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=0, skip_top=0, index_from=index_from)\n\n    train_len = len(x_train)\n    test_len = len(y_train)\n\n    vectors = np.concatenate((x_train, x_test), axis=0)\n    classes = np.concatenate((y_train, y_test), axis=0)\n        #vectors = x_train[:int(len(x_train) * 0.8)]\n        #classes = y_train[:int(len(y_train) * 0.8)]\n\n\n    word_to_id = imdb.get_word_index()\n    word_to_id = {k:(v+index_from) for k,v in word_to_id.items()}\n    word_to_id[\"<UNK>\"] = 0\n    word_to_id[\"<START>\"] = 1\n    word_to_id[\"<OOV>\"] = 2\n    id_to_word = {value:key for key,value in word_to_id.items()}\n\n    word_vectors = np.empty(shape=(len(vectors)), dtype=np.object) # Have to recreate original word vectors\n    for s in range(len(vectors)):\n        word_sentence = []\n        for w in range(len(vectors[s])):\n            word_sentence.append(id_to_word[vectors[s][w]])\n        word_vectors[s] = word_sentence\n\n    import gensim.models.phrases\n\n    phrases = gensim.models.Phrases(word_vectors)\n    bigram = gensim.models.phrases.Phraser(phrases)\n    phrase_vectors = [bigram[sentence] for sentence in word_vectors]\n\n    from gensim import corpora\n    dictionary = corpora.Dictionary(phrase_vectors)\n    dictionary.filter_extremes(no_below=highest_amt)\n\n    dfs_list = []\n    words = []\n    for i in range(len(dictionary.keys())):\n        words.append(dictionary[i])\n        dfs_list.append(dictionary.dfs[i])\n    io.io.write1dArray(words, \"../data/sentiment/bow/names/\" + str(lowest_amt) + \"-\" + str(highest_amt) + \"-\" + classification + \".txt\")\n    io.io.write1dArray(dfs_list, \"../data/sentiment/bow/frequency/global/\" + str(lowest_amt) + \"-\" + str(highest_amt) + \"-\" + classification + \".txt\")\n    corpus = [dictionary.doc2bow(text) for text in phrase_vectors]\n\n    all_fn = \"../data/sentiment/bow/frequency/phrases/class-all-\"+str(lowest_amt)+\"-\"+str(highest_amt)+\"-\" + classification\n    all_fn_binary = \"../data/sentiment/bow/binary/phrases/class-all-\"+str(lowest_amt)+\"-\"+str(highest_amt)+\"-\" + classification\n\n    import gensim.matutils\n\n    corpus = gensim.matutils.corpus2csc(corpus)\n\n    sp.save_npz(all_fn, corpus)\n\n    \"\"\"\n    #all_fn = \"../data/sentiment/bow/frequency/phrases/class-all-\"+str(lowest_amt)+\"-\"+str(highest_amt)+\"-\" + classification\n    #corpus = sp.load_npz(all_fn + \".npz\")\n    print(\"saving\")\n    \n    ppmi = mt.convertPPMI( corpus)\n    \n    ppmi_sparse = sp.csr_matrix(ppmi)\n    \n    ppmi_fn = \"../data/sentiment/bow/ppmi/class-all-\"+str(lowest_amt)+\"-\"+str(highest_amt)+\"-\" + classification\n    sp.save_npz(ppmi_fn, ppmi_sparse)\n    \"\"\"\n    \"\"\"\n    for key,val in id_to_word.items():\n        if val == \"that\":\n            print(key)\n    \n    ids = np.asarray(list(id_to_word.keys()), dtype=np.int32)\n    words = np.asarray(list(id_to_word.values()), dtype=\"str\")\n    \n    sorted_ids = np.argsort(ids)\n    complete_word_list = words[sorted_ids]\n    \n    word_list = complete_word_list[skip_top+3:highest_amt]\n    \n    new_word_list = []\n    \n    new_word_list.append(\"<UNK>\")\n    new_word_list.append(\"<START>\")\n    new_word_list.append(\"<OOV>\")\n    \n    \n    for i in range(skip_top):\n        new_word_list.append(\"<OOV>\")\n    \n    \n    for w in word_list:\n        new_word_list.append(w)\n    \"\"\"\n    \"\"\"\n    for x in x_train:\n        run = False\n        for n in range(len(x)):\n            if x[n] > highest_amt-1000:\n                run = True\n        if run:\n            try:\n                for id in x_train[0]:\n                    print(id, end=' ')\n                print(\"\")\n                for id in x_train[0]:\n                    print(complete_word_list[id], end=' ')\n                print(\"\")\n                for id in x_train[0]:\n                    print(new_word_list[id], end=' ')\n                print(\"\")\n            except KeyError:\n                print(\"fail\")\n        break\n    \"\"\"\n    \"\"\"\n    import codecs\n    decoding_table = (\n        '\\x00'     #  0x00 -> NULL\n        '\\x01'     #  0x01 -> START OF HEADING\n        '\\x02'     #  0x02 -> START OF TEXT\n        '\\x03'     #  0x03 -> END OF TEXT\n        '\\x04'     #  0x04 -> END OF TRANSMISSION\n        '\\x05'     #  0x05 -> ENQUIRY\n        '\\x06'     #  0x06 -> ACKNOWLEDGE\n        '\\x07'     #  0x07 -> BELL\n        '\\x08'     #  0x08 -> BACKSPACE\n        '\\t'       #  0x09 -> HORIZONTAL TABULATION\n        '\\n'       #  0x0A -> LINE FEED\n        '\\x0b'     #  0x0B -> VERTICAL TABULATION\n        '\\x0c'     #  0x0C -> FORM FEED\n        '\\r'       #  0x0D -> CARRIAGE RETURN\n        '\\x0e'     #  0x0E -> SHIFT OUT\n        '\\x0f'     #  0x0F -> SHIFT IN\n        '\\x10'     #  0x10 -> DATA LINK ESCAPE\n        '\\x11'     #  0x11 -> DEVICE CONTROL ONE\n        '\\x12'     #  0x12 -> DEVICE CONTROL TWO\n        '\\x13'     #  0x13 -> DEVICE CONTROL THREE\n        '\\x14'     #  0x14 -> DEVICE CONTROL FOUR\n        '\\x15'     #  0x15 -> NEGATIVE ACKNOWLEDGE\n        '\\x16'     #  0x16 -> SYNCHRONOUS IDLE\n        '\\x17'     #  0x17 -> END OF TRANSMISSION BLOCK\n        '\\x18'     #  0x18 -> CANCEL\n        '\\x19'     #  0x19 -> END OF MEDIUM\n        '\\x1a'     #  0x1A -> SUBSTITUTE\n        '\\x1b'     #  0x1B -> ESCAPE\n        '\\x1c'     #  0x1C -> FILE SEPARATOR\n        '\\x1d'     #  0x1D -> GROUP SEPARATOR\n        '\\x1e'     #  0x1E -> RECORD SEPARATOR\n        '\\x1f'     #  0x1F -> UNIT SEPARATOR\n        ' '        #  0x20 -> SPACE\n        '!'        #  0x21 -> EXCLAMATION MARK\n        '\"'        #  0x22 -> QUOTATION MARK\n        '#'        #  0x23 -> NUMBER SIGN\n        '$'        #  0x24 -> DOLLAR SIGN\n        '%'        #  0x25 -> PERCENT SIGN\n        '&'        #  0x26 -> AMPERSAND\n        \"'\"        #  0x27 -> APOSTROPHE\n        '('        #  0x28 -> LEFT PARENTHESIS\n        ')'        #  0x29 -> RIGHT PARENTHESIS\n        '*'        #  0x2A -> ASTERISK\n        '+'        #  0x2B -> PLUS SIGN\n        ','        #  0x2C -> COMMA\n        '-'        #  0x2D -> HYPHEN-MINUS\n        '.'        #  0x2E -> FULL STOP\n        '/'        #  0x2F -> SOLIDUS\n        '0'        #  0x30 -> DIGIT ZERO\n        '1'        #  0x31 -> DIGIT ONE\n        '2'        #  0x32 -> DIGIT TWO\n        '3'        #  0x33 -> DIGIT THREE\n        '4'        #  0x34 -> DIGIT FOUR\n        '5'        #  0x35 -> DIGIT FIVE\n        '6'        #  0x36 -> DIGIT SIX\n        '7'        #  0x37 -> DIGIT SEVEN\n        '8'        #  0x38 -> DIGIT EIGHT\n        '9'        #  0x39 -> DIGIT NINE\n        ':'        #  0x3A -> COLON\n        ';'        #  0x3B -> SEMICOLON\n        '<'        #  0x3C -> LESS-THAN SIGN\n        '='        #  0x3D -> EQUALS SIGN\n        '>'        #  0x3E -> GREATER-THAN SIGN\n        '?'        #  0x3F -> QUESTION MARK\n        '@'        #  0x40 -> COMMERCIAL AT\n        'A'        #  0x41 -> LATIN CAPITAL LETTER A\n        'B'        #  0x42 -> LATIN CAPITAL LETTER B\n        'C'        #  0x43 -> LATIN CAPITAL LETTER C\n        'D'        #  0x44 -> LATIN CAPITAL LETTER D\n        'E'        #  0x45 -> LATIN CAPITAL LETTER E\n        'F'        #  0x46 -> LATIN CAPITAL LETTER F\n        'G'        #  0x47 -> LATIN CAPITAL LETTER G\n        'H'        #  0x48 -> LATIN CAPITAL LETTER H\n        'I'        #  0x49 -> LATIN CAPITAL LETTER I\n        'J'        #  0x4A -> LATIN CAPITAL LETTER J\n        'K'        #  0x4B -> LATIN CAPITAL LETTER K\n        'L'        #  0x4C -> LATIN CAPITAL LETTER L\n        'M'        #  0x4D -> LATIN CAPITAL LETTER M\n        'N'        #  0x4E -> LATIN CAPITAL LETTER N\n        'O'        #  0x4F -> LATIN CAPITAL LETTER O\n        'P'        #  0x50 -> LATIN CAPITAL LETTER P\n        'Q'        #  0x51 -> LATIN CAPITAL LETTER Q\n        'R'        #  0x52 -> LATIN CAPITAL LETTER R\n        'S'        #  0x53 -> LATIN CAPITAL LETTER S\n        'T'        #  0x54 -> LATIN CAPITAL LETTER T\n        'U'        #  0x55 -> LATIN CAPITAL LETTER U\n        'V'        #  0x56 -> LATIN CAPITAL LETTER V\n        'W'        #  0x57 -> LATIN CAPITAL LETTER W\n        'X'        #  0x58 -> LATIN CAPITAL LETTER X\n        'Y'        #  0x59 -> LATIN CAPITAL LETTER Y\n        'Z'        #  0x5A -> LATIN CAPITAL LETTER Z\n        '['        #  0x5B -> LEFT SQUARE BRACKET\n        '\\\\'       #  0x5C -> REVERSE SOLIDUS\n        ']'        #  0x5D -> RIGHT SQUARE BRACKET\n        '^'        #  0x5E -> CIRCUMFLEX ACCENT\n        '_'        #  0x5F -> LOW LINE\n        '`'        #  0x60 -> GRAVE ACCENT\n        'a'        #  0x61 -> LATIN SMALL LETTER A\n        'b'        #  0x62 -> LATIN SMALL LETTER B\n        'c'        #  0x63 -> LATIN SMALL LETTER C\n        'd'        #  0x64 -> LATIN SMALL LETTER D\n        'e'        #  0x65 -> LATIN SMALL LETTER E\n        'f'        #  0x66 -> LATIN SMALL LETTER F\n        'g'        #  0x67 -> LATIN SMALL LETTER G\n        'h'        #  0x68 -> LATIN SMALL LETTER H\n        'i'        #  0x69 -> LATIN SMALL LETTER I\n        'j'        #  0x6A -> LATIN SMALL LETTER J\n        'k'        #  0x6B -> LATIN SMALL LETTER K\n        'l'        #  0x6C -> LATIN SMALL LETTER L\n        'm'        #  0x6D -> LATIN SMALL LETTER M\n        'n'        #  0x6E -> LATIN SMALL LETTER N\n        'o'        #  0x6F -> LATIN SMALL LETTER O\n        'p'        #  0x70 -> LATIN SMALL LETTER P\n        'q'        #  0x71 -> LATIN SMALL LETTER Q\n        'r'        #  0x72 -> LATIN SMALL LETTER R\n        's'        #  0x73 -> LATIN SMALL LETTER S\n        't'        #  0x74 -> LATIN SMALL LETTER T\n        'u'        #  0x75 -> LATIN SMALL LETTER U\n        'v'        #  0x76 -> LATIN SMALL LETTER V\n        'w'        #  0x77 -> LATIN SMALL LETTER W\n        'x'        #  0x78 -> LATIN SMALL LETTER X\n        'y'        #  0x79 -> LATIN SMALL LETTER Y\n        'z'        #  0x7A -> LATIN SMALL LETTER Z\n        '{'        #  0x7B -> LEFT CURLY BRACKET\n        '|'        #  0x7C -> VERTICAL LINE\n        '}'        #  0x7D -> RIGHT CURLY BRACKET\n        '~'        #  0x7E -> TILDE\n        '\\x7f'     #  0x7F -> DELETE\n        '\\u20ac'   #  0x80 -> EURO SIGN\n        '\\ufffe'   #  0x81 -> UNDEFINED\n        '\\u201a'   #  0x82 -> SINGLE LOW-9 QUOTATION MARK\n        '\\u0192'   #  0x83 -> LATIN SMALL LETTER F WITH HOOK\n        '\\u201e'   #  0x84 -> DOUBLE LOW-9 QUOTATION MARK\n        '\\u2026'   #  0x85 -> HORIZONTAL ELLIPSIS\n        '\\u2020'   #  0x86 -> DAGGER\n        '\\u2021'   #  0x87 -> DOUBLE DAGGER\n        '\\u02c6'   #  0x88 -> MODIFIER LETTER CIRCUMFLEX ACCENT\n        '\\u2030'   #  0x89 -> PER MILLE SIGN\n        '\\u0160'   #  0x8A -> LATIN CAPITAL LETTER S WITH CARON\n        '\\u2039'   #  0x8B -> SINGLE LEFT-POINTING ANGLE QUOTATION MARK\n        '\\u0152'   #  0x8C -> LATIN CAPITAL LIGATURE OE\n        '\\ufffe'   #  0x8D -> UNDEFINED\n        '\\u017d'   #  0x8E -> LATIN CAPITAL LETTER Z WITH CARON\n        '\\ufffe'   #  0x8F -> UNDEFINED\n        '\\ufffe'   #  0x90 -> UNDEFINED\n        '\\u2018'   #  0x91 -> LEFT SINGLE QUOTATION MARK\n        '\\u2019'   #  0x92 -> RIGHT SINGLE QUOTATION MARK\n        '\\u201c'   #  0x93 -> LEFT DOUBLE QUOTATION MARK\n        '\\u201d'   #  0x94 -> RIGHT DOUBLE QUOTATION MARK\n        '\\u2022'   #  0x95 -> BULLET\n        '\\u2013'   #  0x96 -> EN DASH\n        '\\u2014'   #  0x97 -> EM DASH\n        '\\u02dc'   #  0x98 -> SMALL TILDE\n        '\\u2122'   #  0x99 -> TRADE MARK SIGN\n        '\\u0161'   #  0x9A -> LATIN SMALL LETTER S WITH CARON\n        '\\u203a'   #  0x9B -> SINGLE RIGHT-POINTING ANGLE QUOTATION MARK\n        '\\u0153'   #  0x9C -> LATIN SMALL LIGATURE OE\n        '\\ufffe'   #  0x9D -> UNDEFINED\n        '\\u017e'   #  0x9E -> LATIN SMALL LETTER Z WITH CARON\n        '\\u0178'   #  0x9F -> LATIN CAPITAL LETTER Y WITH DIAERESIS\n        '\\xa0'     #  0xA0 -> NO-BREAK SPACE\n        '\\xa1'     #  0xA1 -> INVERTED EXCLAMATION MARK\n        '\\xa2'     #  0xA2 -> CENT SIGN\n        '\\xa3'     #  0xA3 -> POUND SIGN\n        '\\xa4'     #  0xA4 -> CURRENCY SIGN\n        '\\xa5'     #  0xA5 -> YEN SIGN\n        '\\xa6'     #  0xA6 -> BROKEN BAR\n        '\\xa7'     #  0xA7 -> SECTION SIGN\n        '\\xa8'     #  0xA8 -> DIAERESIS\n        '\\xa9'     #  0xA9 -> COPYRIGHT SIGN\n        '\\xaa'     #  0xAA -> FEMININE ORDINAL INDICATOR\n        '\\xab'     #  0xAB -> LEFT-POINTING DOUBLE ANGLE QUOTATION MARK\n        '\\xac'     #  0xAC -> NOT SIGN\n        '\\xad'     #  0xAD -> SOFT HYPHEN\n        '\\xae'     #  0xAE -> REGISTEREvectorsD SIGN\n        '\\xaf'     #  0xAF -> MACRON\n        '\\xb0'     #  0xB0 -> DEGREE SIGN\n        '\\xb1'     #  0xB1 -> PLUS-MINUS SIGN\n        '\\xb2'     #  0xB2 -> SUPERSCRIPT TWO\n        '\\xb3'     #  0xB3 -> SUPERSCRIPT THREE\n        '\\xb4'     #  0xB4 -> ACUTE ACCENT\n        '\\xb5'     #  0xB5 -> MICRO SIGN\n        '\\xb6'     #  0xB6 -> PILCROW SIGN\n        '\\xb7'     #  0xB7 -> MIDDLE DOT\n        '\\xb8'     #  0xB8 -> CEDILLA\n        '\\xb9'     #  0xB9 -> SUPERSCRIPT ONE\n        '\\xba'     #  0xBA -> MASCULINE ORDINAL INDICATOR\n        '\\xbb'     #  0xBB -> RIGHT-POINTING DOUBLE ANGLE QUOTATION MARK\n        '\\xbc'     #  0xBC -> VULGAR FRACTION ONE QUARTER\n        '\\xbd'     #  0xBD -> VULGAR FRACTION ONE HALF\n        '\\xbe'     #  0xBE -> VULGAR FRACTION THREE QUARTERS\n        '\\xbf'     #  0xBF -> INVERTED QUESTION MARK\n        '\\xc0'     #  0xC0 -> LATIN CAPITAL LETTER A WITH GRAVE\n        '\\xc1'     #  0xC1 -> LATIN CAPITAL LETTER A WITH ACUTE\n        '\\xc2'     #  0xC2 -> LATIN CAPITAL LETTER A WITH CIRCUMFLEX\n        '\\xc3'     #  0xC3 -> LATIN CAPITAL LETTER A WITH TILDE\n        '\\xc4'     #  0xC4 -> LATIN CAPITAL LETTER A WITH DIAERESIS\n        '\\xc5'     #  0xC5 -> LATIN CAPITAL LETTER A WITH RING ABOVE\n        '\\xc6'     #  0xC6 -> LATIN CAPITAL LETTER AE\n        '\\xc7'     #  0xC7 -> LATIN CAPITAL LETTER C WITH CEDILLA\n        '\\xc8'     #  0xC8 -> LATIN CAPITAL LETTER E WITH GRAVE\n        '\\xc9'     #  0xC9 -> LATIN CAPITAL LETTER E WITH ACUTE\n        '\\xca'     #  0xCA -> LATIN CAPITAL LETTER E WITH CIRCUMFLEX\n        '\\xcb'     #  0xCB -> LATIN CAPITAL LETTER E WITH DIAERESIS\n        '\\xcc'     #  0xCC -> LATIN CAPITAL LETTER I WITH GRAVE\n        '\\xcd'     #  0xCD -> LATIN CAPITAL LETTER I WITH ACUTE\n        '\\xce'     #  0xCE -> LATIN CAPITAL LETTER I WITH CIRCUMFLEX\n        '\\xcf'     #  0xCF -> LATIN CAPITAL LETTER I WITH DIAERESIS\n        '\\xd0'     #  0xD0 -> LATIN CAPITAL LETTER ETH\n        '\\xd1'     #  0xD1 -> LATIN CAPITAL LETTER N WITH TILDE\n        '\\xd2'     #  0xD2 -> LATIN CAPITAL LETTER O WITH GRAVE\n        '\\xd3'     #  0xD3 -> LATIN CAPITAL LETTER O WITH ACUTE\n        '\\xd4'     #  0xD4 -> LATIN CAPITAL LETTER O WITH CIRCUMFLEX\n        '\\xd5'     #  0xD5 -> LATIN CAPITAL LETTER O WITH TILDE\n        '\\xd6'     #  0xD6 -> LATIN CAPITAL LETTER O WITH DIAERESIS\n        '\\xd7'     #  0xD7 -> MULTIPLICATION SIGN\n        '\\xd8'     #  0xD8 -> LATIN CAPITAL LETTER O WITH STROKE\n        '\\xd9'     #  0xD9 -> LATIN CAPITAL LETTER U WITH GRAVE\n        '\\xda'     #  0xDA -> LATIN CAPITAL LETTER U WITH ACUTE\n        '\\xdb'     #  0xDB -> LATIN CAPITAL LETTER U WITH CIRCUMFLEX\n        '\\xdc'     #  0xDC -> LATIN CAPITAL LETTER U WITH DIAERESIS\n        '\\xdd'     #  0xDD -> LATIN CAPITAL LETTER Y WITH ACUTE\n        '\\xde'     #  0xDE -> LATIN CAPITAL LETTER THORN\n        '\\xdf'     #  0xDF -> LATIN SMALL LETTER SHARP S\n        '\\xe0'     #  0xE0 -> LATIN SMALL LETTER A WITH GRAVE\n        '\\xe1'     #  0xE1 -> LATIN SMALL LETTER A WITH ACUTE\n        '\\xe2'     #  0xE2 -> LATIN SMALL LETTER A WITH CIRCUMFLEX\n        '\\xe3'     #  0xE3 -> LATIN SMALL LETTER A WITH TILDE\n        '\\xe4'     #  0xE4 -> LATIN SMALL LETTER A WITH DIAERESIS\n        '\\xe5'     #  0xE5 -> LATIN SMALL LETTER A WITH RING ABOVE\n        '\\xe6'     #  0xE6 -> LATIN SMALL LETTER AE\n        '\\xe7'     #  0xE7 -> LATIN SMALL LETTER C WITH CEDILLA\n        '\\xe8'     #  0xE8 -> LATIN SMALL LETTER E WITH GRAVE\n        '\\xe9'     #  0xE9 -> LATIN SMALL LETTER E WITH ACUTE\n        '\\xea'     #  0xEA -> LATIN SMALL LETTER E WITH CIRCUMFLEX\n        '\\xeb'     #  0xEB -> LATIN SMALL LETTER E WITH DIAERESIS\n        '\\xec'     #  0xEC -> LATIN SMALL LETTER I WITH GRAVE\n        '\\xed'     #  0xED -> LATIN SMALL LETTER I WITH ACUTE\n        '\\xee'     #  0xEE -> LATIN SMALL LETTER I WITH CIRCUMFLEX\n        '\\xef'     #  0xEF -> LATIN SMALL LETTER I WITH DIAERESIS\n        '\\xf0'     #  0xF0 -> LATIN SMALL LETTER ETH\n        '\\xf1'     #  0xF1 -> LATIN SMALL LETTER N WITH TILDE\n        '\\xf2'     #  0xF2 -> LATIN SMALL LETTER O WITH GRAVE\n        '\\xf3'     #  0xF3 -> LATIN SMALL LETTER O WITH ACUTE\n        '\\xf4'     #  0xF4 -> LATIN SMALL LETTER O WITH CIRCUMFLEX\n        '\\xf5'     #  0xF5 -> LATIN SMALL LETTER O WITH TILDE\n        '\\xf6'     #  0xF6 -> LATIN SMALL LETTER O WITH DIAERESIS\n        '\\xf7'     #  0xF7 -> DIVISION SIGN\n        '\\xf8'     #  0xF8 -> LATIN SMALL LETTER O WITH STROKE\n        '\\xf9'     #  0xF9 -> LATIN SMALL LETTER U WITH GRAVE\n        '\\xfa'     #  0xFA -> LATIN SMALL LETTER U WITH ACUTE\n        '\\xfb'     #  0xFB -> LATIN SMALL LETTER U WITH CIRCUMFLEX\n        '\\xfc'     #  0xFC -> LATIN SMALL LETTER U WITH DIAERESIS\n        '\\xfd'     #  0xFD -> LATIN SMALL LETTER Y WITH ACUTE\n        '\\xfe'     #  0xFE -> LATIN SMALL LETTER THORN\n        '\\xff'     #  0xFF -> LATIN SMALL LETTER Y WITH DIAERESIS\n    )\n    \n    \n    import collections\n    \n    encoding_table=codecs.charmap_build(decoding_table)\n    mapped_ids = collections.OrderedDict()\n    del_ind = []\n    amt_of_encs = 0\n    for w in range(len(word_list)):\n        try:\n            for char in word_list[w]:\n                codecs.charmap_encode(char,\"strict\", encoding_table)\n        except UnicodeEncodeError:\n            amt_of_encs += 1\n            del_ind.append(w)\n            print(word_list[w], w)\n        mapped_ids[skip_top+3+w] = w - amt_of_encs\n    \n    word_list = np.delete(word_list, del_ind)\n    print(\"----------------------------------------\")\n    \n    dt.write1dArray(word_list, \"../data/sentiment/bow/names/\" + str(lowest_amt) + \"-\" + str(highest_amt) + \"-\" + classification + \".txt\")\n    \n    all_fn = \"../data/sentiment/bow/frequency/phrases/class-all-\"+str(lowest_amt)+\"-\"+str(highest_amt)+\"-\" + classification\n    all_fn_binary = \"../data/sentiment/bow/binary/phrases/class-all-\"+str(lowest_amt)+\"-\"+str(highest_amt)+\"-\" + classification\n    \n    tf = np.zeros(shape=(len(vectors), len(word_list)), dtype=np.int32)\n    tf_binary = np.zeros(shape=(len(vectors), len(word_list)), dtype=np.int32)\n    \n    for ds in range(len(vectors)): # d for document sequence\n        for wi in range(len(vectors[ds])): # every word id in the sequence\n            if vectors[ds][wi] >= skip_top+ 3:\n                new_id = mapped_ids[vectors[ds][wi]]\n                print(ds, new_id)\n                tf[ds][new_id] += 1\n                tf_binary[ds][new_id] = 1\n    print(\"transposing\")\n    tf = np.asarray(tf, dtype=\"int\").transpose()\n    tf_binary = np.asarray(tf_binary, dtype=\"int\").transpose()\n    \n    tf_sparse = sp.csr_matrix(tf)\n    tf_binary_sparse = sp.csr_matrix(tf_binary)\n    \n    sp.save_npz(all_fn_binary, tf_binary_sparse)\n    sp.save_npz(all_fn, tf_sparse)\n    \n    print(\"saving\")\n    \n    #mt.printIndividualFromAll(\"sentiment\",  \"frequency/phrases\", lowest_amt, highest_amt, classification, all_fn=all_fn, names_array=word_list)\n    #mt.printIndividualFromAll(\"sentiment\",  \"binary/phrases\", lowest_amt, highest_amt, classification, all_fn=all_fn_binary, names_array=word_list)\n    \n    ppmi_fn = \"../data/sentiment/bow/ppmi/class-all-\"+str(lowest_amt)+\"-\"+str(highest_amt)+\"-\" + classification\n    #if dt.fileExists(ppmi_fn) is False:\n    \n    ppmi = mt.convertPPMI( tf_sparse)\n    \n    ppmi_sparse = sp.csr_matrix(ppmi)\n    \n    sp.save_npz(ppmi_fn, ppmi_sparse)\n    dt.write2dArray(ppmi, ppmi_fn)\n    #mt.printIndividualFromAll(\"sentiment\",  \"ppmi\", lowest_amt, highest_amt, classification, all_fn=all_fn, names_array=word_list)\n    dt.write2dArray(tf_binary, all_fn_binary)\n    dt.write2dArray(tf, all_fn)\n    \n    \"\"\"\n\n    print(\"1\")\n    classes = np.asarray(classes, dtype=np.int32)\n    print(2)\n    print(3)\n    print(4)\n    names = [\"sentiment\"]\n    io.io.write1dArray(names, \"../data/sentiment/classify/sentiment/names.txt\")\n    io.io.write1dArray(classes, \"../data/sentiment/classify/sentiment/class-\" + \"sentiment\")\n    io.io.write1dArray(classes, \"../data/sentiment/classify/sentiment/class-all\")\n    io.io.write1dArray(list(range(len(classes))), \"../data/sentiment/classify/sentiment/available_entities.txt\")","sub_path":"src/data/sentiment.py","file_name":"sentiment.py","file_ext":"py","file_size_in_byte":21761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"405942391","text":"# \"exercice 2.26 in shoup's introduction to number theory and algebra\"\n# find all elements of Z19 that have order 18\n# 19 being a prime => all elements of Z19 have order Phi(19) = 18\n# altough some elements will have an order k < 18 such as k | 18\n# we solve this the naive way for clarity\n\ndef list_elements_only_18():\n\n    orderk = []\n    order18only = []\n    for i in range(2,19):\n        # loop trough the divisors of 18\n        primitive_root = True\n        for k in [2,3,6,9] :\n            if i**k % 19 == 1:\n                primitive_root = False\n        if not primitive_root:\n            orderk.append(i)\n        if primitive_root :\n            order18only.append(i)\n    return (orderk,order18only)\n\nif __name__ == '__main__':\n    a,b = list_elements_only_18()\n    print(\"Elements with order k (2,3,6,9)\",a)\n    print(\"Elements with order 18 ONLY\",b)\n\n","sub_path":"algebra/ex2-26.py","file_name":"ex2-26.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"540398855","text":"from unittest import TestCase\nimport common as c\n\n\nclass TestCommon(TestCase):\n\n    def test_getSize(self):\n        size = 10\n\n        result = c.get_size()\n\n        self.assertEqual(result, size)\n\n\n    def test_getNumbers(self):\n        # given\n        size = 5\n        expected = [10, 20, 30, 40, 50]\n\n        # when\n        result = c.get_numbers(size)\n\n        # then\n        self.assertEqual(expected, result)","sub_path":"PYTHON/tests/test_common.py","file_name":"test_common.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"192267684","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef nuimage_trajectory_visualization(translations, key_index,\n                                     # sample_token: str,\n                                     # rotation_yaw: float = 0.0,\n                                     # center_key_pose: bool = True\n                                     ) -> None:\n        \"\"\"\n        Render a plot of the trajectory for the clip surrounding the annotated keyframe.\n        A red cross indicates the starting point, a green dot the ego pose of the annotated keyframe.\n        :param sample_token: Sample token.\n        :param rotation_yaw: Rotation of the ego vehicle in the plot.\n            Set to None to use lat/lon coordinates.\n            Set to 0 to point in the driving direction at the time of the keyframe.\n            Set to any other value to rotate relative to the driving direction (in radians).\n        :param center_key_pose: Whether to center the trajectory on the key pose.\n        :param out_path: Optional path to save the rendered figure to disk.\n            If a path is provided, the plot is not shown to the user.\n        \"\"\"\n        # Get the translations or poses.\n        # translations, key_index = self.get_trajectory(sample_token, rotation_yaw=rotation_yaw,\n        #                                               center_key_pose=center_key_pose)\n\n        # Render translations.\n        plt.figure()\n        plt.plot(translations[:, 0], translations[:, 1])\n        plt.plot(translations[key_index, 0], translations[key_index, 1], 'go', markersize=10)  # Key image.\n        plt.plot(translations[0, 0], translations[0, 1], 'rx', markersize=10)  # Start point.\n        max_dist = translations - translations[key_index, :]\n        max_dist = np.ceil(np.max(np.abs(max_dist)) * 1.05)  # Leave some margin.\n        max_dist = np.maximum(10, max_dist)\n        plt.xlim([translations[key_index, 0] - max_dist, translations[key_index, 0] + max_dist])\n        plt.ylim([translations[key_index, 1] - max_dist, translations[key_index, 1] + max_dist])\n        plt.xlabel('x in meters')\n        plt.ylabel('y in meters')\n        # plt.show()\n        plt.draw()\n        plt.pause(0.001)\n        # input()\n\ndef transform_to_image_space(coords, img_size):\n    WIDTH = 75\n    HEIGHT = 75\n    new_coords = []\n\n    # receive coord as x,y\n    for i in range(len(coords)):\n        coords[i][0] += WIDTH/2\n        coords[i][0] = (coords[i][0] / WIDTH) * img_size[0]\n        coords[i][1] = HEIGHT/2 - coords[i][1]\n        coords[i][1] = (coords[i][1] / HEIGHT) * img_size[1]\n        new_coords.append((int(coords[i][0]), int(coords[i][1])))\n    return new_coords\n\n\ndef visualize_traffic(trajectory):\n    # trajectory assumed x, y where x is horizontal axis and y is vertical axis\n    img = np.ones((800, 800, 3))*255\n    positions = transform_to_image_space(trajectory.copy(), (800, 800))\n\n    for i in range(1, len(positions)):\n        img = cv2.line(img, positions[i], positions[i-1], (255, 0, 0), 3)\n\n    nuimage_trajectory_visualization(trajectory, 0)\n\n    cv2.imshow(\"\", img)\n    cv2.waitKey(0)\n\n\ndef visualize_traffic_neighbours(trajectory, map_size):\n    # trajectory assumed x, y where x is horizontal axis and y is vertical axis\n    img = np.ones((800, 800, 3))*255\n\n    for i in range(map_size):\n        filter = (trajectory[i]!= [-64, -64, -64, -64, -64]).all(axis=1).tolist()\n        positions = transform_to_image_space(trajectory[i][filter], (800, 800))\n        R, G, B = np.random.randint(0, 255), np.random.randint(0, 255), np.random.randint(0, 255)\n        R, G, B = 255, 0, 0\n\n        for i in range(1, len(positions)):\n            img = cv2.line(img, positions[i], positions[i-1], (B, G, R), 3)\n            img = cv2.circle(img, positions[i-1], 3, (0, 0, 200), -1)\n            # nuimage_trajectory_visualization(trajectory, 0)\n\n    cv2.imshow(\"\", img)\n    cv2.waitKey(0)\n\nif __name__ == '__main__':\n    visualize_traffic([[0, 0], [5,10], [20, 20]])\n\n","sub_path":"DataVisualization.py","file_name":"DataVisualization.py","file_ext":"py","file_size_in_byte":3977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"208386630","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.remote.webelement import WebElement\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom abc import ABC, abstractmethod\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.metrics import accuracy_score, roc_auc_score, roc_curve\nfrom random import random, choice\nimport multiprocessing as mp\nimport os\nimport csv\nfrom functools import partial\nfrom copy import copy\nfrom tqdm import tqdm\nimport numpy as np\nimport time\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.chrome.options import Options\nimport pickle\n\nos.makedirs(os.path.dirname('./records/'), exist_ok=True)\n\n\nclass element_has_text(object):\n    \"\"\"\n    An expectation for checking that an element has any text contained within it.\n\n    locator: used to find the element\n    returns the WebElement once it has any text.\n    \"\"\"\n\n    def __init__(self, locator):\n        self.locator = locator\n\n    def __call__(self, driver):\n        element = driver.find_element(*self.locator)\n        if element.text:\n            return element\n        else:\n            return False\n\n\ndef start_driver(driver_type='Firefox'):\n    \"\"\"\n    Starts a webdriver of the type driver_type\n\n    :param bool headless: Minimizes the driver immediately, currently only works for chrome.\n    :param str driver_type: Which browser to use.\n    :return: WebDriver\n    \"\"\"\n    driver = {\n        'Chrome': webdriver.Chrome,\n        'Safari': webdriver.Safari,\n        'Edge': webdriver.Edge,\n        'Firefox': webdriver.Firefox\n        }[driver_type]\n    driver = driver()\n    driver.get('http://snarxiv.org/vs-arxiv/')\n    return driver\n\n\ndef get_options(driver: webdriver.Chrome.__class__):\n    \"\"\"\n    This takes the driver which has been already attached to ArXiv vs. SnarXiV and returns the two paper\n    titles as web-elements.\n    :param driver: WebDriver\n    :return: WebElement, WebElement\n    \"\"\"\n    WebDriverWait(driver, 20).until(element_has_text((By.CLASS_NAME, 'corner-0')))\n    lhs = driver.find_element_by_class_name('corner-0')\n    rhs = driver.find_element_by_class_name('corner-1')\n    return lhs, rhs\n\n\nclass Strategy(ABC):\n    \"\"\"\n    This is the generic strategy class. It implements the play function, and requires an optimize function to work.\n    \"\"\"\n    class TextObject:\n        def __init__(self, text: str):\n            self.text = text\n\n    def __init__(self):\n        super(Strategy, self).__init__()\n\n    @abstractmethod\n    def optimize(self, lhs, rhs):\n        \"\"\"\n        This should take in two web-elements that are of the ArXiV vs SnarXiV question format.\n        It returns whichever of them satisfies the strategy that is dictated by this function.\n\n        param WebElement: lhs This is the left hand element.\n        param WebElement: rhs This is the right hand element.\n        :return: WebElement\n        \"\"\"\n        raise NotImplementedError\n\n    def text_opti(self, lhs, rhs):\n        \"\"\"\n        A wrapper of the optimize method which works on strings instead of WebElements.\n\n        param str: lhs Left Hand Side\n        param str: rhs Right Hand Side\n        :return: int 0 for left, 1 for right.\n        \"\"\"\n        best = self.optimize(self.TextObject(lhs), self.TextObject(rhs))\n        if best == lhs:\n            return 0\n        else:\n            return 1\n\n    def play(self, driver_type: str = 'Chrome', n=1000):\n        driver = start_driver(driver_type=driver_type)\n        record = []\n        count = 0\n        while count < n:\n            try:\n                lhs, rhs = get_options(driver)\n                winner = self.optimize(lhs, rhs)\n                winner.click()\n                response = str(lhs.get_attribute('class'))\n                if response[-5:] == 'right':\n                    record.append(True)\n                elif response[-5:] == 'wrong':\n                    record.append(False)\n                count += 1\n            except TimeoutException:\n                driver.close()\n                driver = start_driver(driver_type=driver_type)\n                print('Driver stopped responding, restarting.')\n\n        driver.close()\n        return record\n\n    def play_mp(self, driver_type: str = 'Chrome', n=250, threads=4):\n        if mp.cpu_count() < threads:\n            print('More threads requested than processors available, are you sure?')\n        player = partial(self.play, driver_type=driver_type, n=n)\n        pool = mp.Pool(threads)\n        records = [pool.apply_async(player) for _ in range(threads)]\n        records = [f.get() for f in records]\n        full_record = []\n        for r in records:\n            full_record += r\n        return full_record\n\n\nclass Random(Strategy):\n    def __init__(self, p=.5):\n        super(Random, self).__init__()\n        self.p = p\n\n    def optimize(self, lhs, rhs):\n        choiceseed = random()\n        if choiceseed > self.p:\n            return rhs\n        elif choiceseed < self.p:\n            return lhs\n        else:\n            return choice([lhs, rhs])\n\n\nclass Shortest(Strategy):\n    def __init__(self):\n        super(Shortest, self).__init__()\n\n    def optimize(self, lhs, rhs):\n        if len(lhs.text) < len(rhs.text):\n            return lhs\n        elif len(lhs.text) == len(rhs.text):\n            return choice([lhs, rhs])\n        else:\n            return rhs\n\n\nclass Longest(Strategy):\n    def __init__(self):\n        super(Longest, self).__init__()\n\n    def optimize(self, lhs, rhs):\n        if len(lhs.text) > len(rhs.text):\n            return lhs\n        elif len(lhs.text) == len(rhs.text):\n            return choice([lhs, rhs])\n        else:\n            return rhs\n\nclass NaiveBayes(Strategy):\n    def __init__(self):\n        super(NaiveBayes, self).__init__()\n        try:\n            with open('models/nbmodel.pkl', 'rb+') as f:\n                model, threshold = pickle.load(f)\n        except FileNotFoundError:\n            raise(FileNotFoundError('Could Not Create Strategy, no pickled model found.'))\n        \n        self.model = model\n        self.threshold = threshold\n\n\n    def optimize(self, lhs, rhs):\n        ops = (lhs, rhs)\n        probs = self.model.predict_proba(o.text for o in ops)[:, 1]\n        choice = np.argmax(probs)\n        return ops[choice]\n\nclass ContrastiveNaiveBayes(Strategy):\n    def __init__(self):\n        super(ContrastiveNaiveBayes, self).__init__()\n        try:\n            with open('models/nbmodel_contrastive.pkl', 'rb+') as f:\n                model, threshold = pickle.load(f)\n        except FileNotFoundError:\n            raise(FileNotFoundError('Could Not Create Strategy, no pickled model found.'))\n        \n        self.model = model\n        self.threshold = threshold\n\n\n    def optimize(self, lhs, rhs):\n        ops = (lhs, rhs)\n        probs = self.model.predict_proba([[o.text for o in ops]])\n        choice = np.argmax(probs)\n        return ops[choice]\n\ndef make_contrastive_data(df):\n    \n    df_nu = copy(df)\n    df_nu.columns = ['x0', 'x1']\n    df_nu['correct'] = np.zeros(len(df))\n\n    def scramble_row(row):\n\n        correct = np.random.choice([0, 1])\n        outrow = [0, 0, correct]\n        outrow[correct] = row[0]\n        outrow[not correct] = row[1]\n\n        return outrow\n    \n    df_nu = df.apply(scramble_row, axis=1, result_type='expand')\n    return df_nu\n\ndef add_to_dataset(filename='avs.csv'):\n    dr = start_driver()\n    dr.minimize_window()\n    if not os.path.exists(filename):\n        with open(filename, 'w') as file:\n            writer = csv.writer(file, delimiter=',')\n            writer.writerow(['Right', 'Wrong'])\n    lhs, rhs = get_options(dr)\n    ltext = copy(lhs.text)\n    rtext = copy(rhs.text)\n    lhs.click()\n    response = str(lhs.get_attribute('class'))\n    with open(filename, 'a') as file:\n        writer = csv.writer(file, delimiter=',')\n        if response[-5:] == 'right':\n            writer.writerow([ltext, rtext])\n            dr.close()\n            return [ltext, rtext]\n        elif response[-5:] == 'wrong':\n            writer.writerow([rtext, ltext])\n            dr.close()\n            return [rtext, ltext]\n\n\ndef make_data(n, fname='avs.csv'):\n    p = mp.Pool()\n    _ = p.map(add_to_dataset, tqdm([fname] * n), chunksize=1)\n\n\ndef main():\n    nbstrat = ContrastiveNaiveBayes()\n    record = nbstrat.play_mp(threads=3, n=333)\n    with open('./records/cnb-3-30-2020.pkl', 'wb+') as f:\n        pickle.dump(record, f)\n\n    nbstrat = NaiveBayes()\n    record = nbstrat.play_mp(threads=3, n=333)\n    with open('./records/nb-3-30-2020.pkl', 'wb+') as f:\n        pickle.dump(record, f)\n    \n\nif __name__ == '__main__':\n    main()\n","sub_path":"ArxivVsSnarxiv/strategies.py","file_name":"strategies.py","file_ext":"py","file_size_in_byte":8869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"125866671","text":"import pandas as pd\nfrom rdflib import URIRef, BNode, Literal, Graph\nfrom rdflib.namespace import RDF, RDFS, FOAF, XSD\nfrom rdflib import Namespace\nimport numpy as np\nimport math\nimport sys\nimport argparse\nimport json\nimport urllib\nimport glob\n\nfiles = glob.glob(\"data/add/*.json\")\n\nprev = -1\n\nmap = {\n    1: 30,\n    2: 22,\n    3: 22\n}\n\nfor file in files:\n\n    # jsonファイルを読み込む\n    f = open(file)\n    # jsonデータを読み込んだファイルオブジェクトからPythonデータを作成\n    data = json.load(f)\n    # ファイルを閉じる\n    f.close()\n\n    members = data[\"selections\"][0][\"members\"]\n\n    vol = int(file.split(\"/\")[-1].split(\".\")[0])\n\n    count = 1\n    for member in members:        \n\n        member_id = member[\"@id\"]\n        page = int(member_id.split(\"canvas/p\")[1].split(\"#\")[0])\n\n        if page != prev:\n            count = 1\n\n        prev = page\n\n        page_new = page - map[vol]\n\n        member[\"label\"] = str(vol).zfill(1) + str(page_new).zfill(2)+\"0\"+str(count).zfill(2)\n\n        count += 1\n      \n    with open(file.replace(\"add\", \"add_new\"), 'w') as f:\n      json.dump(data, f, ensure_ascii=False, indent=4,\n              sort_keys=True, separators=(',', ': '))","sub_path":"src/moeller/400_add_id_to_new_curation.py","file_name":"400_add_id_to_new_curation.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"579075794","text":"import logging\nfrom django.contrib import messages\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import get_object_or_404\nfrom django.urls import reverse, reverse_lazy\nfrom django.views.generic import View, ListView\nfrom django.views.generic.edit import CreateView, UpdateView, DeleteView\nfrom zentral.utils.api_views import APIAuthError, JSONPostAPIView\nfrom .api_client import APIClient, APIClientError\nfrom .events import post_jamf_webhook_event\nfrom .forms import JamfInstanceForm\nfrom .models import JamfInstance\n\n\nlogger = logging.getLogger('zentral.contrib.jamf.views')\n\n\n# setup > jamf instances\n\n\nclass JamfInstancesView(LoginRequiredMixin, ListView):\n    model = JamfInstance\n\n    def get_context_data(self, **kwargs):\n        ctx = super().get_context_data(**kwargs)\n        ctx[\"setup\"] = True\n        jamf_instances_count = len(ctx[\"object_list\"])\n        if jamf_instances_count == 0 or jamf_instances_count > 1:\n            suffix = \"s\"\n        else:\n            suffix = \"\"\n        ctx[\"title\"] = \"{} jamf instance{}\".format(jamf_instances_count, suffix)\n        return ctx\n\n\nclass CreateJamfInstanceView(LoginRequiredMixin, CreateView):\n    model = JamfInstance\n    form_class = JamfInstanceForm\n    success_url = reverse_lazy(\"jamf:jamf_instances\")\n\n    def get_context_data(self, **kwargs):\n        ctx = super().get_context_data(**kwargs)\n        ctx[\"setup\"] = True\n        ctx[\"title\"] = \"Create jamf instance\"\n        return ctx\n\n\nclass SetupJamfInstanceView(LoginRequiredMixin, View):\n    def get(self, request, *args, **kwargs):\n        jamf_instance = get_object_or_404(JamfInstance, pk=kwargs[\"pk\"])\n        api_client = APIClient(**jamf_instance.serialize())\n        jamf_instance_base_url = jamf_instance.base_url()\n        try:\n            setup_msg = api_client.setup()\n        except APIClientError:\n            msg = \"Could not setup webhooks on {}.\".format(jamf_instance_base_url)\n            messages.warning(request, msg)\n            logger.exception(msg)\n        else:\n            msg = \"{}: {}\".format(jamf_instance_base_url, setup_msg)\n            messages.info(request, msg)\n            logger.info(msg)\n        return HttpResponseRedirect(reverse(\"jamf:jamf_instances\"))\n\n\nclass UpdateJamfInstanceView(LoginRequiredMixin, UpdateView):\n    model = JamfInstance\n    form_class = JamfInstanceForm\n    success_url = reverse_lazy(\"jamf:jamf_instances\")\n\n    def get_context_data(self, **kwargs):\n        ctx = super().get_context_data(**kwargs)\n        ctx[\"setup\"] = True\n        ctx[\"title\"] = \"Update jamf instance\"\n        return ctx\n\n\nclass DeleteJamfInstanceView(LoginRequiredMixin, DeleteView):\n    model = JamfInstance\n    success_url = reverse_lazy(\"jamf:jamf_instances\")\n\n    def get_context_data(self, **kwargs):\n        ctx = super().get_context_data(**kwargs)\n        ctx[\"setup\"] = True\n        ctx[\"title\"] = \"Delete jamf instance\"\n        return ctx\n\n    def post(self, request, *args, **kwargs):\n        response = super().post(request, *args, **kwargs)\n        jamf_instance_base_url = self.object.base_url()\n        api_client = APIClient(**self.object.serialize())\n        try:\n            api_client.cleanup()\n        except APIClientError:\n            msg = \"Could not remove webhooks configuration on {}.\".format(jamf_instance_base_url)\n            messages.warning(request, msg)\n            logger.exception(msg)\n        else:\n            msg = \"Removed webhooks configuration on {}.\".format(jamf_instance_base_url)\n            messages.info(request, msg)\n            logger.info(msg)\n        return response\n\n\n# API\n\n\nclass PostEventView(JSONPostAPIView):\n    payload_encoding = \"latin-1\"\n\n    def check_request_secret(self, request, *args, **kwargs):\n        try:\n            self.jamf_instance = JamfInstance.objects.select_related(\"business_unit\").get(secret=kwargs[\"secret\"])\n        except JamfInstance.DoesNotExist:\n            raise APIAuthError\n\n    def do_post(self, data):\n        post_jamf_webhook_event(self.jamf_instance, self.user_agent, self.ip, data)\n        return {}\n","sub_path":"zentral/contrib/jamf/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"346160589","text":"import os\nimport json\nfrom lrf.keyword_generator import keyword_generation_micromort as key_gen\nfrom lrf.utilities import utility, risk_categories as rc\n\n\ndef main():\n    lrf_path = os.getcwd()\n    proj_path = os.path.abspath(os.path.join(lrf_path, os.pardir))\n    intermed_data_path = os.path.join(proj_path, 'intermed_data')\n    risk_cat_file = os.path.join(intermed_data_path, 'risk_category_file.json')\n    glove_key_file = os.path.join(intermed_data_path, 'glove_key_subset.json')\n    num_of_keywords = 50\n    feature_selection = 'svc'\n\n    class_mapping = rc.getClassMap()\n\n    category_keywords = key_gen.getNewsKeywords(feature_selection,num_of_keywords,class_mapping)\n\n    print(category_keywords)\n    unique_keywords = []\n    ## To Calculate Unique Keywords:\n    for categ in category_keywords:\n        unique_keywords = unique_keywords+list(category_keywords[categ]['pos'].keys())\n        unique_keywords = unique_keywords+list(category_keywords[categ]['neg'].keys())\n\n    unique_keywords = set(unique_keywords)\n\n    glove_risk_dict = utility.getGloveVec(unique_keywords)\n\n    # final_data = dict()\n    # final_data['glove_risk_dict'] = glove_risk_dict\n    # final_data['category_keywords'] = category_keywords\n\n    with open(risk_cat_file, 'w') as f:\n        json.dump(category_keywords, f)\n    with open(glove_key_file, 'w') as f:\n        json.dump(glove_risk_dict, f)\n\n\nif __name__=='__main__':\n    main()","sub_path":"lrf/keyword_generator/keywords_wrapper.py","file_name":"keywords_wrapper.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"431156375","text":"#-*- coding: utf-8 -*-\n#Created by Hans on 16-10-4\n\nimport os\nimport uuid\nimport socket\n\ntask_id = uuid.uuid4().hex\nhostname = os.uname().nodename\n\nprint('task_id',task_id)\nprint('hostname',hostname)\n\n\ns = socket.socket()\ns.connect(('127.0.0.1',9091))\n\ns.send(task_id.encode())\ns.send(b'\\n')\n\ns.send(hostname.encode())\ns.send(b'\\n')\n\ns.send(b'1\\n')\n\ns.send(b'\\n')\n\ns.send(b'abcd')\ns.close()\n\n","sub_path":"test/test_client.py","file_name":"test_client.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"186809033","text":"class Node:\n    \"\"\"Klasa reprezentująca węzeł listy jednokierunkowej.\"\"\"\n\n    def __init__(self, data=None, next=None):\n        self.data = data\n        self.next = next\n\n    def __str__(self):\n        return str(self.data)   # bardzo ogólnie\n\n\nclass SingleList:\n    \"\"\"Klasa reprezentująca całą listę jednokierunkową.\"\"\"\n\n    def __init__(self, *arguments):\n        self.length = 0         # nie trzeba obliczać za każdym razem\n        self.head = None\n        self.tail = None\n        for item in arguments:\n            self.insert_head(item)\n\n    def is_empty(self):\n        return self.length == 0\n\n    def count(self):      # tworzymy interfejs do odczytu\n        return self.length\n\n    def insert_head(self, data):    # algorytm klasy O(1)\n        node = Node(data)\n        if self.length == 0:\n            self.head = self.tail = node\n        else:                   # dajemy na koniec listy\n            node.next = self.head\n            self.head = node\n        self.length += 1\n\n    def insert_tail(self, data):    # algorytm klasy O(1)\n        node = Node(data)\n        if self.length == 0:\n            self.head = self.tail = node\n        else:                   # dajemy na koniec listy\n            self.tail.next = node\n            self.tail = node\n        self.length += 1\n\n    def remove_head(self):          # algorytm klasy O(1)\n        if self.length == 0:\n            raise Exception(\"pusta lista\")\n        node = self.head\n        self.head = self.head.next\n        self.length -= 1\n        if self.length == 0:   # zabezpieczenie\n            self.tail = None\n        return node   # zwracamy usuwany node\n\n    def printList(self, node):\n        if(node):\n            print(node)\n        if(node.next):\n            self.printList(node.next)","sub_path":"Zadania_9/SingleList.py","file_name":"SingleList.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"372752454","text":"from django.forms import ModelForm, Textarea\nfrom auctions.models import Listings, Bids\n\n\nclass NewListingForm(ModelForm):\n    class Meta:\n        model = Listings\n        fields = ['title', 'description', 'starting_bid','category', 'image']\n        widgets = {\n            'description': Textarea(attrs={'cols': 40, 'rows': 6})\n        }\n    def __init__(self, *args, **kwargs):\n        super(NewListingForm, self).__init__(*args, **kwargs)\n        self.fields['title'].widget.attrs\\\n            .update({\n                'class': 'form-control'\n            })  \n        self.fields['description'].widget.attrs\\\n            .update({\n                'class': 'form-control'\n            })  \n        self.fields['starting_bid'].widget.attrs\\\n            .update({\n                'class': 'form-control col-lg-6'\n            })\n        self.fields['category'].widget.attrs\\\n            .update({\n                'class': 'form-control col-lg-6'\n            }) \n        self.fields['image'].widget.attrs\\\n            .update({\n                'class': 'form-control'\n            })\n          \nclass BidsForm(ModelForm):\n    class Meta:\n        model = Bids\n        fields = ['offer']\n\n    def __init__(self, *args, **kwargs):\n        super(BidsForm, self).__init__(*args, **kwargs)\n        self.fields['offer'].widget.attrs\\\n            .update({\n                'name': 'Offer',\n                'class': 'form-control'\n            })    ","sub_path":"commerce/auctions/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"453130575","text":"import tensorflow as tf\nimport logging\nimport sys\nimport os\nimport numpy as np\nimport math\nfrom vgg16obj_vis import Model\nlogging.basicConfig(level=logging.INFO)\n\n# Tune all the hyper params here\ntf.app.flags.DEFINE_float(\"start_learning_rate\", 1e-5, \"Learning rate.\")\ntf.app.flags.DEFINE_float(\"dropout\", 0.5, \"Fraction of units randomly dropped\")\ntf.app.flags.DEFINE_float(\"reg\", 5e-4, \"L2 regularization to each layer\")\ntf.app.flags.DEFINE_integer(\"batch_size\", 64, \"Batch size to use during training.\")\ntf.app.flags.DEFINE_integer(\"epochs\", 10, \"Number of epochs to train.\")\n\ntf.app.flags.DEFINE_integer(\"data_size\", 22424, \"The number of training samples\")\ntf.app.flags.DEFINE_boolean(\"is_debug\", False, \"Use smaller dataset for debug\")\ntf.app.flags.DEFINE_boolean(\"use_save\", True, \"Save model into checkpoint\")\n\ntf.app.flags.DEFINE_integer(\"print_every\", 64 * 5, \"How many iterations to do per print.\")\ntf.app.flags.DEFINE_integer(\"log_every\", 64 * 5, \"How many iterations to do per log.\")\ntf.app.flags.DEFINE_boolean(\"eval_every_epoch\", True, \"Whether evaluate after each epoch\")\n\ntf.app.flags.DEFINE_boolean(\"is_testing\", False, \"Whether we are testing\")\n\nFLAGS = tf.app.flags.FLAGS\n\nmean_pixel = [95.07919331,  96.92462179,  80.06607895]\nstd_pixel = [85.10712557,  84.6052525 ,  73.99278229] \n\n# Inits the model and reads checkpoints if possible\ndef initialize_model(session, saver, train_dir):\n\tckpt = tf.train.get_checkpoint_state(train_dir)\n\tv2_path = ckpt.model_checkpoint_path + \".index\" if ckpt else \"\"\n\tif ckpt and (tf.gfile.Exists(ckpt.model_checkpoint_path) or tf.gfile.Exists(v2_path)):\n\t\tlogging.info(\"Reading model parameters from %s\" % ckpt.model_checkpoint_path)\n\t\tsaver.restore(session, ckpt.model_checkpoint_path)\n\telse:\n\t\tlogging.info(\"Created model with fresh parameters.\")\n\t\tsession.run(tf.global_variables_initializer())\n\t\tpretrained_vgg16 = np.load('../data/vgg16_weights.npz')\n\t\tkeys = sorted(pretrained_vgg16.keys())\n\t\tfor i, k in enumerate(keys[:-2]): # exclude the last fc layer\n\t\t# for i, k in enumerate(keys[:-4]): # exclude the last three fc layer\n\t\t\t# print (i, k, np.shape(pretrained_vgg16[k]))\n\t\t\tsession.run(tf.trainable_variables()[i].assign(pretrained_vgg16[k]))\n\t\tlogging.info('Num params: %d' % sum(v.get_shape().num_elements() for v in tf.trainable_variables()))\n\n# Loads the data\ndef load_data(debug=True, data_size=1000):\n\tif not debug:\n\t\tdata_size = FLAGS.data_size\n\tX_train = np.load(os.path.join('..', 'data', 'train_data_' + str(data_size) + '.npy'))\n\ty_train = np.load(os.path.join('..', 'data', 'train_label_' + str(data_size) + '.npy'))\n\ttrain_indicies = np.arange(X_train.shape[0])\n\n\t# normalize images by subtracting mean\n\n\tfor c in range(3):\n\t\tX_train[:, :, :, c] = X_train[:, :, :, c] - mean_pixel[c]\n\n\t# np.random.shuffle(train_indicies)\n\t# driver_list = [0,725,1548,2424,3299,4377,5614,6847,8073,9269,10117,10768,11373,11964,\n\t# 12688,13523,14534,15324,16244,16984,17778,18587,19407,20441,20787,21601,22424]\n\t# val_list = np.random.choice(26, 3, replace = False)\n\t# in_val = []\n\t# for i in val_list:\n\t# \tin_val = np.append(in_val, train_indicies[driver_list[i]:driver_list[i+1]])\n\tin_val = train_indicies[20441:]\n\tin_train = train_indicies[:20441]\n\t# in_val = list(map(int, in_val))\n\t# in_train = list(map(int, in_train))\n\tX_val = X_train[in_val]\n\ty_val = y_train[in_val]\n\tX_train = X_train[in_train]\n\ty_train = y_train[in_train]\n\treturn X_train, y_train, X_val, y_val\n\ndef main(_):\n\t# Creates directory for checkpoint and logs\n\tif not os.path.exists('saves'):\n\t\tos.makedirs('saves')\n\tif not os.path.exists('logs'):\n\t\tos.makedirs('logs')\n\n\tif not FLAGS.is_testing:\n\t\tdataset = load_data(FLAGS.is_debug)\n\t\tX_train, y_train, X_val, y_val = dataset\n\t\tprint('data size:')\n\t\tprint(sys.getsizeof(X_train), sys.getsizeof(y_train), sys.getsizeof(X_val), sys.getsizeof(y_val))\n\t\tprint('data shape:')\n\t\tprint(X_train.shape, y_train.shape, X_val.shape, y_val.shape)\n\n\t# Creates the model\n\tmodel = Model()\n\n\twith tf.Session() as sess:\n\t\t# Inits the model\n\t\tinitialize_model(sess, model.saver, './saves/')\n\n\t\t# if not FLAGS.is_testing:\n\t\t# \tprint('Training')\n\n\t\t# \t# Runs the model\n\t\t# \tmodel.run_model(sess, dataset, epochs=FLAGS.epochs, batch_size=FLAGS.batch_size, use_save=True, plot_losses=False)\n\t\t# if FLAGS.is_testing:\n\t\t# \tmodel.run_model(sess, batch_size=FLAGS.batch_size, testing=True)\n\n\t\t# visualization\n\t\tmodel.run_model(sess, dataset, epochs=FLAGS.epochs, batch_size=FLAGS.batch_size, use_save=False, plot_losses=False, get_vis=True)\n\nif __name__ == \"__main__\":\n\ttf.app.run()\n","sub_path":"code/train_vis.py","file_name":"train_vis.py","file_ext":"py","file_size_in_byte":4539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"507113227","text":"from django.urls import path,include\nfrom rest_framework.routers import DefaultRouter\nfrom car import views\n\napp_name = 'car'\nrouter = DefaultRouter()\nrouter.register('person',views.PersonViewSet)\nrouter.register('car-information',views.CarViewSet)\n\n\nurlpatterns = [\n    # path('get-person',views.person_view,name='get_person'),\n    # path('get-car',views.car_view,name='get_car'),\n    # path('inform',views.information,name='info'),\n    path('',include(router.urls))\n]\n","sub_path":"car/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"349194975","text":"# -*- Mode: python; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */\n#\n# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n#\n\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.contrib.auth.decorators import login_required\n\nfrom processor import MinidumpProcessor\n\nfrom crashsubmit import models\nfrom .models import ProcessedCrash\n\n@login_required\ndef process_all(request):\n    # move that to a Manager\n    processed_crashes = ProcessedCrash.objects.values_list('crash_id')\n    print(processed_crashes)\n    crashes = models.UploadedCrash.objects.exclude(crash_id__in=processed_crashes)\n    done = []\n    for crash in crashes:\n        procescor = MinidumpProcessor()\n        procescor.process(crash.crash_id)\n        done.append(crash.crash_id)\n    return HttpResponse(\"\\n\".join(done))\n\n@login_required\ndef process(request, crash_id):\n    processor = MinidumpProcessor()\n    processor.process(crash_id)\n\n    return HttpResponse('CrashID=' + crash_id)\n\n# vim:set shiftwidth=4 softtabstop=4 expandtab: */\n","sub_path":"django/crashreport/processor/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"523707307","text":"\nfrom objects import *\nfrom comm import Communicator\nfrom libraries.rc_motors import RCmotors\n\nfor _ in range(15):\n    pyb.LED(3).toggle()\n    pyb.delay(50)\npyb.LED(3).on()\n\nrc_motors = RCmotors(\"X8\", 200, 320, 50)\n\nleds = [LEDcommand(index, index + 1) for index in range(3)]\nblue_led = BlueLEDcommand(3)\nservo = ServoCommand(4, 1, start_pos=0)\nmotors = MotorCommand(5, rc_motors)\ntilt_servo = ServoCommand(6, 2, start_pos=0)\npan_servo = ServoCommand(7, 3, start_pos=0)\n\n\nencoder = RCencoder(0, rc_motors)\ngps = GPS(1, 6, 4)\nimu = IMU(2, 2, 11)\n\ncommunicator = Communicator(*leds, blue_led, servo, motors, pan_servo, tilt_servo, uart_bus=1)\n\nwhile True:\n    communicator.read_command()\n    \n    if imu.recved_data():\n        sensor_updated = True\n        communicator.write_packet(imu)\n\n    if gps.recved_data():\n        sensor_updated = True\n        communicator.write_packet(gps)\n        pyb.LED(3).toggle()\n\n#    if encoder.recved_data():\n#        sensor_updated = True\n#        communicator.write_packet(encoder)\n#        pyb.LED(2).toggle()\n\n#    if sensor_updated:\n#        sensor_updated = False\n#        print(\"%0.5f, %0.6f, %0.6f, %0.6i\" % (\n#            imu.data[0], gps.data[0], gps.data[1], encoder.data[0]), end='\\r')\n\n    if communicator.should_reset():\n        gps.reset()\n        imu.reset()\n        encoder.reset()\n\n        servo.reset()\n        motors.reset()\n\n        communicator.write_packet(gps)\n        communicator.write_packet(imu)\n        communicator.write_packet(encoder)\n \n","sub_path":"Micropython/backups/rpi/pi_main.py","file_name":"pi_main.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"438095794","text":"\nfrom molmass import ELEMENTS\nimport pandas as pd\nimport numpy as np\n\nimport re\n\ndf = pd.read_csv('/Users/jan/Google Drive/Uni/Uebungen/7. Semester/Nuclear Physics/yield.csv', sep=';')\ns = df['Product'].to_numpy()\n\nZS = [re.split('(\\d+)',s[i])[0] for i in range(len(s))]\nAS = [re.split('(\\d+)',s[i])[1] for i in range(len(s))]\n\nzs = [ELEMENTS[ZS[i]].number for i in range(len(s))]\nas_ = np.array(AS).astype(int)\n\nns = as_ - zs\n\ndf['Z'] = pd.Series(zs, index=df.index)\ndf['N'] = pd.Series(ns, index=df.index)\ndf.to_csv(\"yields2.csv\", sep=';')\n","sub_path":"convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"136295612","text":"from django.http import HttpResponse, Http404\nfrom django.shortcuts import *\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.decorators import permission_required\n\nfrom business.models import *\n\nfrom datetime import *\n\ndef add_timer(request):\n    username = request.user.username\n    \n    # if it's a post then we have to create a new timer\n    if request.method == \"POST\":\n        # initialize new timer       \n        new_timer = Timer(title = request.POST[\"new_timer_title\"])\n        new_timer.last_start = datetime.now()\n        new_timer.hours = 0\n        \n        new_timer.save()\n        \n    return redirect(to=\"/crm/timers\")\n\n# Stops a timer\ndef stop_timer(request, id):\n    # Try to extract the id\n    try:\n        id = int(id)\n    except ValueError:\n        raise Http404()\n    \n    # update the timer\n    timer = Timer.objects.get(id=id)\n    timer.hours = 1\n    timer.last_start = None\n    timer.save()\n    \n    # return to the timers page\n    return redirect(to=\"/crm/timers\")\n    \n\n\n@login_required\ndef timers(request):\n    username = request.user.username\n    \n    timers = Timer.objects.all()\n    \n    return render_to_response('timers.html', ({'username' : username, \n                                               'timers' : timers,\n                                              }))","sub_path":"views/timers.py","file_name":"timers.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"465946828","text":"from flakon import JsonBlueprint\nfrom flask import abort, jsonify, request\n\nfrom bedrock_a_party.classes.party import CannotPartyAloneError, Party, \\\n    NotInvitedGuestError, ItemAlreadyInsertedByUser, NotExistingFoodError\n\nparties = JsonBlueprint('parties', __name__)\n\n_LOADED_PARTIES = {}  # dict of available parties\n_PARTY_NUMBER = 0  # index of the last created party\n\n\n@parties.route(\"/parties\", methods=[\"POST\", \"GET\"])\ndef all_parties():\n    result = None\n    if request.method == 'POST':\n        try:\n            result = create_party(request)\n        except CannotPartyAloneError:\n            abort(400)\n\n    elif request.method == 'GET':\n        result = get_all_parties()\n\n    return result\n\n\n@parties.route(\"/parties/loaded\", methods=['GET'])\ndef loaded_parties():\n    return jsonify({'loaded_parties': _PARTY_NUMBER})\n\n\n@parties.route(\"/party/<id>\", methods=['GET', 'DELETE'])\ndef single_party(id):\n    global _LOADED_PARTIES\n    result = \"\"\n\n    exists_party(id)\n    if 'GET' == request.method:\n        result = jsonify(_LOADED_PARTIES[id].serialize())\n    elif 'DELETE' == request.method:\n        del _LOADED_PARTIES[id]\n    return result\n\n\n@parties.route(\"/party/<id>/foodlist\", methods=['GET'])\ndef get_foodlist(id):\n    global _LOADED_PARTIES\n    result = \"\"\n\n    exists_party(id)\n    if 'GET' == request.method:\n        result = jsonify({'foodlist': _LOADED_PARTIES[id].get_food_list().serialize()})\n    return result\n\n\n@parties.route(\"/party/<id>/foodlist/<user>/<item>\", methods=['POST', 'DELETE'])\ndef edit_foodlist(id, user, item):\n    global _LOADED_PARTIES\n\n    exists_party(id)\n    party = _LOADED_PARTIES[id]\n    result = \"\"\n\n    if 'POST' == request.method:\n        try:\n            result = jsonify(party.add_to_food_list(item=item, user=user).serialize())\n        except NotInvitedGuestError:\n            abort(401)\n        except ItemAlreadyInsertedByUser:\n            abort(400)\n    if 'DELETE' == request.method:\n        try:\n            party.remove_from_food_list(item=item, user=user)\n            result = jsonify({'msg': 'Food deleted!'})\n        except NotExistingFoodError:\n            abort(400)\n    return result\n\n#\n# These are utility functions. Use them, DON'T CHANGE THEM!!\n#\n\n\ndef create_party(req):\n    global _LOADED_PARTIES, _PARTY_NUMBER\n\n    # get data from request\n    json_data = req.get_json()\n\n    # list of guests\n    try:\n        guests = json_data['guests']\n    except:\n        raise CannotPartyAloneError(\"you cannot party alone!\")\n\n    # add party to the loaded parties lists\n    _LOADED_PARTIES[str(_PARTY_NUMBER)] = Party(_PARTY_NUMBER, guests)\n    _PARTY_NUMBER += 1\n\n    return jsonify({'party_number': _PARTY_NUMBER - 1})\n\n\ndef get_all_parties():\n    global _LOADED_PARTIES\n\n    return jsonify(loaded_parties=[party.serialize() for party in _LOADED_PARTIES.values()])\n\n\ndef exists_party(_id):\n    global _PARTY_NUMBER\n    global _LOADED_PARTIES\n\n    if int(_id) > _PARTY_NUMBER:\n        abort(404)  # error 404: Not Found, i.e. wrong URL, resource does not exist\n    elif not(_id in _LOADED_PARTIES):\n        abort(410)  # error 410: Gone, i.e. it existed but it's not there anymore\n","sub_path":"bedrock_a_party/views/parties.py","file_name":"parties.py","file_ext":"py","file_size_in_byte":3149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"164853203","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2006-2007 Edgewall Software\n# Copyright (C) 2006 Matthew Good\n# All rights reserved.\n#\n# This software is licensed as described in the file COPYING, which\n# you should have received as part of this distribution. The terms\n# are also available at http://genshi.edgewall.org/wiki/License.\n#\n# This software consists of voluntary contributions made by many\n# individuals. For the exact contribution history, see the revision\n# history and logs, available at http://genshi.edgewall.org/log/.\n\nimport doctest\nimport os\nimport unittest\n\nfrom genshi.core import Stream\nfrom genshi.output import DocType\nfrom genshi.template import MarkupTemplate, TextTemplate, NewTextTemplate\nfrom genshi.template.plugin import ConfigurationError, \\\n                                   MarkupTemplateEnginePlugin, \\\n                                   TextTemplateEnginePlugin\n\nPACKAGE = 'genshi.template.tests'\n\n\nclass MarkupTemplateEnginePluginTestCase(unittest.TestCase):\n\n    def test_init_no_options(self):\n        plugin = MarkupTemplateEnginePlugin()\n        self.assertEqual(None, plugin.default_encoding)\n        self.assertEqual('html', plugin.default_format)\n        self.assertEqual(None, plugin.default_doctype)\n\n        self.assertEqual([], plugin.loader.search_path)\n        self.assertEqual(True, plugin.loader.auto_reload)\n        self.assertEqual(25, plugin.loader._cache.capacity)\n\n    def test_init_with_loader_options(self):\n        plugin = MarkupTemplateEnginePlugin(options={\n            'genshi.auto_reload': 'off',\n            'genshi.max_cache_size': '100',\n            'genshi.search_path': '/usr/share/tmpl:/usr/local/share/tmpl',\n        })\n        self.assertEqual(['/usr/share/tmpl', '/usr/local/share/tmpl'],\n                         plugin.loader.search_path)\n        self.assertEqual(False, plugin.loader.auto_reload)\n        self.assertEqual(100, plugin.loader._cache.capacity)\n\n    def test_init_with_invalid_cache_size(self):\n        self.assertRaises(ConfigurationError, MarkupTemplateEnginePlugin,\n                          options={'genshi.max_cache_size': 'thirty'})\n\n    def test_init_with_output_options(self):\n        plugin = MarkupTemplateEnginePlugin(options={\n            'genshi.default_encoding': 'iso-8859-15',\n            'genshi.default_format': 'xhtml',\n            'genshi.default_doctype': 'xhtml-strict',\n        })\n        self.assertEqual('iso-8859-15', plugin.default_encoding)\n        self.assertEqual('xhtml', plugin.default_format)\n        self.assertEqual(DocType.XHTML, plugin.default_doctype)\n\n    def test_init_with_invalid_output_format(self):\n        self.assertRaises(ConfigurationError, MarkupTemplateEnginePlugin,\n                          options={'genshi.default_format': 'foobar'})\n\n    def test_init_with_invalid_doctype(self):\n        self.assertRaises(ConfigurationError, MarkupTemplateEnginePlugin,\n                          options={'genshi.default_doctype': 'foobar'})\n\n    def test_load_template_from_file(self):\n        plugin = MarkupTemplateEnginePlugin()\n        tmpl = plugin.load_template(PACKAGE + '.templates.test')\n        self.assertEqual('test.html', os.path.basename(tmpl.filename))\n        assert isinstance(tmpl, MarkupTemplate)\n\n    def test_load_template_from_string(self):\n        plugin = MarkupTemplateEnginePlugin()\n        tmpl = plugin.load_template(None, template_string=\"\"\"<p>\n          $message\n        </p>\"\"\")\n        self.assertEqual(None, tmpl.filename)\n        assert isinstance(tmpl, MarkupTemplate)\n\n    def test_transform_with_load(self):\n        plugin = MarkupTemplateEnginePlugin()\n        tmpl = plugin.load_template(PACKAGE + '.templates.test')\n        stream = plugin.transform({'message': 'Hello'}, tmpl)\n        assert isinstance(stream, Stream)\n\n    def test_transform_without_load(self):\n        plugin = MarkupTemplateEnginePlugin()\n        stream = plugin.transform({'message': 'Hello'},\n                                  PACKAGE + '.templates.test')\n        assert isinstance(stream, Stream)\n\n    def test_render(self):\n        plugin = MarkupTemplateEnginePlugin()\n        tmpl = plugin.load_template(PACKAGE + '.templates.test')\n        output = plugin.render({'message': 'Hello'}, template=tmpl)\n        self.assertEqual(\"\"\"<!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Strict//EN\" \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd\">\n<html lang=\"en\">\n  <head>\n    <title>Test</title>\n  </head>\n  <body>\n    <h1>Test</h1>\n    <p>Hello</p>\n  </body>\n</html>\"\"\", output)\n\n    def test_render_with_format(self):\n        plugin = MarkupTemplateEnginePlugin()\n        tmpl = plugin.load_template(PACKAGE + '.templates.test')\n        output = plugin.render({'message': 'Hello'}, format='xhtml',\n                               template=tmpl)\n        self.assertEqual(\"\"\"<!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Strict//EN\" \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd\">\n<html xmlns=\"http://www.w3.org/1999/xhtml\" lang=\"en\">\n  <head>\n    <title>Test</title>\n  </head>\n  <body>\n    <h1>Test</h1>\n    <p>Hello</p>\n  </body>\n</html>\"\"\", output)\n\n    def test_render_with_doctype(self):\n        plugin = MarkupTemplateEnginePlugin(options={\n            'genshi.default_doctype': 'html-strict',\n        })\n        tmpl = plugin.load_template(PACKAGE + '.templates.test')\n        output = plugin.render({'message': 'Hello'}, template=tmpl)\n        self.assertEqual(\"\"\"<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01//EN\" \"http://www.w3.org/TR/html4/strict.dtd\">\n<html lang=\"en\">\n  <head>\n    <title>Test</title>\n  </head>\n  <body>\n    <h1>Test</h1>\n    <p>Hello</p>\n  </body>\n</html>\"\"\", output)\n\n    def test_render_fragment_with_doctype(self):\n        plugin = MarkupTemplateEnginePlugin(options={\n            'genshi.default_doctype': 'html-strict',\n        })\n        tmpl = plugin.load_template(PACKAGE + '.templates.test_no_doctype')\n        output = plugin.render({'message': 'Hello'}, template=tmpl,\n                               fragment=True)\n        self.assertEqual(\"\"\"<html lang=\"en\">\n  <head>\n    <title>Test</title>\n  </head>\n  <body>\n    <h1>Test</h1>\n    <p>Hello</p>\n  </body>\n</html>\"\"\", output)\n\n    def test_helper_functions(self):\n        plugin = MarkupTemplateEnginePlugin()\n        tmpl = plugin.load_template(PACKAGE + '.templates.functions')\n        output = plugin.render({'snippet': '<b>Foo</b>'}, template=tmpl)\n        self.assertEqual(\"\"\"<div>\nFalse\nbar\n<b>Foo</b>\n<b>Foo</b>\n</div>\"\"\", output)\n\n\nclass TextTemplateEnginePluginTestCase(unittest.TestCase):\n\n    def test_init_no_options(self):\n        plugin = TextTemplateEnginePlugin()\n        self.assertEqual(None, plugin.default_encoding)\n        self.assertEqual('text', plugin.default_format)\n\n        self.assertEqual([], plugin.loader.search_path)\n        self.assertEqual(True, plugin.loader.auto_reload)\n        self.assertEqual(25, plugin.loader._cache.capacity)\n\n    def test_init_with_loader_options(self):\n        plugin = TextTemplateEnginePlugin(options={\n            'genshi.auto_reload': 'off',\n            'genshi.max_cache_size': '100',\n            'genshi.search_path': '/usr/share/tmpl:/usr/local/share/tmpl',\n        })\n        self.assertEqual(['/usr/share/tmpl', '/usr/local/share/tmpl'],\n                         plugin.loader.search_path)\n        self.assertEqual(False, plugin.loader.auto_reload)\n        self.assertEqual(100, plugin.loader._cache.capacity)\n\n    def test_init_with_output_options(self):\n        plugin = TextTemplateEnginePlugin(options={\n            'genshi.default_encoding': 'iso-8859-15',\n        })\n        self.assertEqual('iso-8859-15', plugin.default_encoding)\n\n    def test_init_with_new_syntax(self):\n        plugin = TextTemplateEnginePlugin(options={\n            'genshi.new_text_syntax': 'yes',\n        })\n        self.assertEqual(NewTextTemplate, plugin.template_class)\n        tmpl = plugin.load_template(PACKAGE + '.templates.new_syntax')\n        output = plugin.render({'foo': True}, template=tmpl)\n        self.assertEqual('bar', output)\n\n    def test_load_template_from_file(self):\n        plugin = TextTemplateEnginePlugin()\n        tmpl = plugin.load_template(PACKAGE + '.templates.test')\n        assert isinstance(tmpl, TextTemplate)\n        self.assertEqual('test.txt', os.path.basename(tmpl.filename))\n\n    def test_load_template_from_string(self):\n        plugin = TextTemplateEnginePlugin()\n        tmpl = plugin.load_template(None, template_string=\"$message\")\n        self.assertEqual(None, tmpl.filename)\n        assert isinstance(tmpl, TextTemplate)\n\n    def test_transform_without_load(self):\n        plugin = TextTemplateEnginePlugin()\n        stream = plugin.transform({'message': 'Hello'},\n                                  PACKAGE + '.templates.test')\n        assert isinstance(stream, Stream)\n\n    def test_transform_with_load(self):\n        plugin = TextTemplateEnginePlugin()\n        tmpl = plugin.load_template(PACKAGE + '.templates.test')\n        stream = plugin.transform({'message': 'Hello'}, tmpl)\n        assert isinstance(stream, Stream)\n\n    def test_render(self):\n        plugin = TextTemplateEnginePlugin()\n        tmpl = plugin.load_template(PACKAGE + '.templates.test')\n        output = plugin.render({'message': 'Hello'}, template=tmpl)\n        self.assertEqual(\"\"\"Test\n====\n\nHello\n\"\"\", output)\n\n    def test_helper_functions(self):\n        plugin = TextTemplateEnginePlugin()\n        tmpl = plugin.load_template(PACKAGE + '.templates.functions')\n        output = plugin.render({}, template=tmpl)\n        self.assertEqual(\"\"\"False\nbar\n\"\"\", output)\n\n\ndef suite():\n    suite = unittest.TestSuite()\n    suite.addTest(unittest.makeSuite(MarkupTemplateEnginePluginTestCase, 'test'))\n    suite.addTest(unittest.makeSuite(TextTemplateEnginePluginTestCase, 'test'))\n    return suite\n\nif __name__ == '__main__':\n    unittest.main(defaultTest='suite')\n","sub_path":"OmniMarkupLib/Renderers/libs/python3/genshi/template/tests/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":9861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"110915502","text":"#!C:/Users/user/AppData/Local/Programs/Python/Python37-32/python\nimport socket\n\ns=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n\nserver = 'localhost'\n\nport = 80\n\nrequest = \"GET / HTTP/1.1\\r\\nHost:\" + server + \":\" + str(port) + \"\\r\\n\\r\\n\"\n\ns.connect((server, port))\n\ns.send(request.encode('UTF-8'))\n\nprint(\"Content-Type: text/html\\n\\n\")\n\nresult = \".\"\n\nwhile len(result)>0:\n\tresult = s.recv(4096)\n\tchunk = result.decode('UTF-8')\n\tprint(chunk)","sub_path":"Server/PythonWeb.py","file_name":"PythonWeb.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"524982971","text":"def is_prime(z):\n    a = []\n    for i in range(1, z+1):\n        if z % i == 0:\n            a.append(i)\n    if len(a) == 2:\n        return True\n    elif z == 1:\n        return True\n    else:\n        return False\n\n\ni = 0\nj = 0\nprimes = []\nwhile True:\n    i += 1\n    if is_prime(i) == True:\n        j += 1\n    if j == 10001:\n        break\n\n\nprint(i)\n# 104743\n","sub_path":"7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"192302322","text":"import json\nimport nnvm.symbol as sym\nimport nnvm.graph as graph\n\ndef grad(ys, xs, ys_grads):\n    g = graph.create(ys)\n    g._set_symbol_list_attr('grad_ys', ys)\n    g._set_symbol_list_attr('grad_xs', xs)\n    g._set_symbol_list_attr('grad_ys_out_grad', ys_grads)\n    return g.apply('Gradient')\n\ndef test_graph_gradient():\n    x0 = sym.Variable('x0')\n    x1 = sym.Variable('x1')\n    yg = sym.Variable('yg')\n    y = sym.exp(sym.mul(x0, x1))\n    grad_graph = grad(y, [x0], yg)\n    print(\"Original graph\")\n    print(y.debug_str())\n    print(\"Gradient  graph\")\n    print(grad_graph.symbol.debug_str())\n\nif __name__ == \"__main__\":\n    test_graph_gradient()\n","sub_path":"nnvm/tests/python/test_gradient.py","file_name":"test_gradient.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"199011317","text":"# coding:utf-8\r\n'''\r\ntests for the module app1\r\n'''\r\nimport unittest\r\nimport os\r\nimport sys\r\nfrom contextlib import contextmanager\r\nfrom io import StringIO\r\nimport app1 as app\r\n\r\nclass Test(unittest.TestCase):\r\n\r\n    @contextmanager\r\n    def captured_output(self):\r\n        new_out, new_err = StringIO(), StringIO()\r\n        old_out, old_err = sys.stdout, sys.stderr\r\n        try:\r\n            sys.stdout, sys.stderr = new_out, new_err\r\n            yield sys.stdout, sys.stderr\r\n        finally:\r\n            sys.stdout, sys.stderr = old_out, old_err\r\n\r\n    def setUp(self):\r\n        self.app = app.App()\r\n        app_path = os.path.dirname(app.__file__)\r\n        os.chdir(app_path)\r\n\r\n    def tearDown(self):\r\n        self.app = None\r\n\r\n\r\n    def test_language_default(self):\r\n\r\n        self.app.set_language()\r\n        with self.captured_output() as (out, err):\r\n            self.app.set_translation()\r\n            self.app.test_print()\r\n        out_msg = out.getvalue().strip()\r\n        err_msg = err.getvalue().strip()\r\n#         print(out_msg)\r\n#         print(err_msg)\r\n        self.assertEqual(err_msg, \"[Errno 2] No translation file found for domain: 'app'\", \"unknown default language error\")\r\n        self.assertEqual(out_msg, \"This is a test message. Translated from default language 'en' to 'en'\")\r\n\r\n    def test_language_known_de(self):\r\n\r\n        language = \"de\" \r\n        self.app.set_language(language)\r\n        with self.captured_output() as (out, err):\r\n            self.app.set_translation(language)\r\n            self.app.test_print()\r\n        out_msg = out.getvalue().strip()\r\n        err_msg = err.getvalue().strip()\r\n#         print(out_msg)\r\n#         print(err_msg)\r\n        self.assertEqual(err_msg, \"\", \"error\")\r\n        self.assertEqual(out_msg, \"Dieses ist eine Test-Meldung. Übersetzt aus der Vorgabe-Sprache 'en' nach 'de'\")\r\n\r\n    def test_language_known_fr(self):\r\n \r\n        language = \"fr\" \r\n        self.app.set_language(language)\r\n        with self.captured_output() as (out, err):\r\n            self.app.set_translation(language)\r\n            self.app.test_print()\r\n        out_msg = out.getvalue().strip()\r\n        err_msg = err.getvalue().strip()\r\n#         print(out_msg)\r\n#         print(err_msg)\r\n        self.assertEqual(err_msg, \"\", \"error\")\r\n        self.assertEqual(out_msg, \"C'est une message de test. Traduit de la langue par défault 'en' en fr'\")\r\n\r\n    def test_language_unknown_es(self):\r\n\r\n        language = \"es\" \r\n        self.app.set_language(language)\r\n        with self.captured_output() as (out, err):\r\n            self.app.set_translation(language)\r\n            self.app.test_print()\r\n        out_msg = out.getvalue().strip()\r\n        err_msg = err.getvalue().strip()\r\n#         print(out_msg)\r\n#         print(err_msg)\r\n        self.assertEqual(err_msg, \"[Errno 2] No translation file found for domain: 'app'\", \"unknown language 'es' error\")\r\n        self.assertEqual(out_msg, \"This is a test message. Translated from default language 'en' to 'es'\")\r\n","sub_path":"tests/test_app1_01.py","file_name":"test_app1_01.py","file_ext":"py","file_size_in_byte":3012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"572303503","text":"from __future__ import absolute_import, division, print_function\n\nimport threading\nimport numpy as np\nimport h5py\nimport tensorflow as tf\n\n\nclass HDF5DataProvider(object):\n    def __init__(self,\n                 hdf5source,\n                 sourcelist,\n                 batch_size,\n                 subslice=None,\n                 mini_batch_size=None,\n                 preprocess=None,\n                 postprocess=None,\n                 pad=False):\n\n        self.hdf5source = hdf5source\n        self.file = h5py.File(self.hdf5source, 'r')\n        self.sourcelist = sourcelist\n        self.subslice = subslice\n        self.subsliceinds = None\n        self.preprocess = {} if preprocess is None else preprocess\n        self.postprocess = {} if postprocess is None else postprocess\n\n        self.data = {}\n        self.sizes = {}\n        for source in self.sourcelist:\n            self.data[source] = self.file[source]\n            if source in self.preprocess:\n                print('Preprocessing %s...' % source)\n                self.data[source] = self.preprocess[source](self.data[source])\n                print('...done')\n\n        for source in sourcelist:\n            if self.subslice is None:\n                self.sizes[source] = self.data[source].shape\n            else:\n                if self.subsliceinds is None:\n                    if isinstance(self.subslice, str):\n                        self.subsliceinds = self.file[self.subslice][:]\n                    elif hasattr(self.subslice, '__call__'):\n                        self.subsliceinds = self.subslice(self.file, self.sourcelist)\n                    elif len(self.subslice) == self.data[source].shape[0]:\n                        self.subsliceinds = self.subslice[:]\n                    else:\n                        self.subsliceinds = np.zeros(self.data[source].shape[0]).astype(np.bool)\n                        self.subsliceinds[self.subslice] = True\n                    self.subsliceinds = self.subsliceinds.nonzero()[0].astype(int)\n                sz = self.data[source].shape\n                self.sizes[source] = (self.subsliceinds.shape[0],) + sz[1:]\n            if not hasattr(self, 'data_length'):\n                self.data_length = self.sizes[source][0]\n            assert self.sizes[source][0] == self.data_length, (self.sizes[source], self.data_length)\n\n        self.batch_size = batch_size\n        if mini_batch_size is None:\n            mini_batch_size = self.batch_size\n        self.mini_batch_size = mini_batch_size\n        self.total_batches = self.data_length // self.batch_size + 1\n        self.curr_batch_num = 0\n        self.curr_epoch = 1\n        self.pad = pad\n\n    def set_epoch_batch(self, epoch, batch_num):\n        self.curr_epoch = epoch\n        self.curr_batch_num = batch_num\n\n    def get_next_batch(self):\n        data = self.get_batch(self.curr_batch_num)\n        self.increment_batch_num()\n        return data\n\n    def __iter__(self):\n        return self\n\n    def next(self):\n        return self.get_next_batch()\n\n    def increment_batch_num(self):\n        m = self.total_batches\n        if (self.curr_batch_num >= m - 1):\n            self.curr_epoch += 1\n        self.curr_batch_num = (self.curr_batch_num + 1) % m\n\n    def get_batch(self, cbn):\n        data = {}\n        startv = cbn * self.batch_size\n        endv = (cbn + 1) * self.batch_size\n        if self.pad and endv > self.data_length:\n            startv = self.data_length - self.batch_size\n            endv = startv + self.batch_size\n        sourcelist = self.sourcelist\n        for source in sourcelist:\n            data[source] = self.get_data(self.data[source], slice(startv, endv))\n            if source in self.postprocess:\n                data[source] = self.postprocess[source](data[source], self.file)\n        return data\n\n    def get_data(self, dsource, sliceval):\n        if self.subslice is None:\n            return dsource[sliceval]\n        else:\n            subslice_inds = self.subsliceinds[sliceval]\n            mbs = self.mini_batch_size\n            bn0 = subslice_inds.min() // mbs\n            bn1 = subslice_inds.max() // mbs\n            stims = []\n            for _bn in range(bn0, bn1 + 1):\n                _s = np.asarray(dsource[_bn * mbs: (_bn + 1) * mbs])\n                new_inds = isin(np.arange(_bn * mbs, (_bn + 1) * mbs), subslice_inds)\n                new_array = _s[new_inds]\n                stims.append(new_array)\n            stims = np.concatenate(stims)\n            return stims\n\n\ndef get_unique_labels(larray):\n    larray = larray[:]\n    labels_unique = np.unique(larray)\n    s = larray.argsort()\n    cat_s = larray[s]\n    ss = np.array([0] + ((cat_s[1:] != cat_s[:-1]).nonzero()[0] + 1).tolist() + [len(cat_s)])\n    ssd = ss[1:] - ss[:-1]\n    labels = np.repeat(np.arange(len(labels_unique)), ssd)\n    larray = labels[perminverse(s)]\n    return larray.astype(np.int64)\n\n\ndef perminverse(s):\n    \"\"\"\n    Fast inverse of a (numpy) permutation.\n\n    From yamutils\n    \"\"\"\n    X = np.array(range(len(s)))\n    X[s] = range(len(s))\n    return X\n\n\ndef isin(X,Y):\n    \"\"\"\n    Indices of elements in a numpy array that appear in another.\n    Fast routine for determining indices of elements in numpy array `X` that\n    appear in numpy array `Y`, returning a boolean array `Z` such that::\n            Z[i] = X[i] in Y\n    **Parameters**\n            **X** :  numpy array\n                    Numpy array to comapare to numpy array `Y`.  For each\n                    element of `X`, ask if it is in `Y`.\n            **Y** :  numpy array\n                    Numpy array to which numpy array `X` is compared.  For each\n                    element of `X`, ask if it is in `Y`.\n    **Returns**\n            **b** :  numpy array (bool)\n                    Boolean numpy array, `len(b) = len(X)`.\n    **See Also:**\n            :func:`tabular.fast.recarrayisin`,\n            :func:`tabular.fast.arraydifference`\n    \"\"\"\n    if len(Y) > 0:\n        T = Y.copy()\n        T.sort()\n        D = T.searchsorted(X)\n        T = np.append(T,np.array([0]))\n        W = (T[D] == X)\n        if isinstance(W,bool):\n            return np.zeros((len(X),),bool)\n        else:\n            return (T[D] == X)\n    else:\n        return np.zeros((len(X),),bool)\n\n\nclass CustomQueue(object):\n\n    def __init__(self, node, data_iter, queue_batch_size=128, n_threads=4):\n        \"\"\"\n        A generic queue for reading data\n\n        Based on https://indico.io/blog/tensorflow-data-input-part2-extensions/\n        \"\"\"\n        self.node = node\n        self.data_iter = data_iter\n        self.queue_batch_size = queue_batch_size\n        self.n_threads = n_threads\n        self._continue = True\n\n        dtypes = [d.dtype for d in node.values()]\n        shapes = [d.get_shape() for d in node.values()]\n        self.queue = tf.RandomShuffleQueue(capacity=n_threads * queue_batch_size * 2,\n                                        min_after_dequeue=n_threads * queue_batch_size,\n                                        dtypes=dtypes,\n                                        shapes=shapes)\n        self.enqueue_op = self.queue.enqueue(node.values())\n        data_batch = self.queue.dequeue_many(queue_batch_size)\n        self.batch = {k:v for k,v in zip(node.keys(), data_batch)}\n\n    def thread_main(self, sess):\n        \"\"\"\n        Function run on alternate thread. Basically, keep adding data to the queue.\n        \"\"\"\n        for batch in self.data_iter:\n            if self._continue:\n                sess.run(self.enqueue_op, feed_dict=batch)\n\n    def start_threads(self, sess):\n        \"\"\" Start background threads to feed queue \"\"\"\n        threads = []\n        for n in range(self.n_threads):\n            t = threading.Thread(target=self.thread_main, args=(sess,))\n            t.daemon = True  # thread will close when parent quits\n            t.start()\n            threads.append(t)\n        return threads\n\n\nclass ImageNet(HDF5DataProvider, CustomQueue):\n\n    def __init__(self, data_path, subslice, crop_size=None,\n                 batch_size=256, n_threads=4, *args, **kwargs):\n        \"\"\"\n        A specific reader for IamgeNet stored as a HDF5 file\n\n        Args:\n            - data_path: path to imagenet data\n            - subslice: np array for training or eval slice\n            - crop_size: for center crop (crop_size x crop_size)\n            - *args: extra arguments for HDF5DataProvider\n        Kwargs:\n            - **kwargs: extra keyword arguments for HDF5DataProvider\n        \"\"\"\n        HDF5DataProvider.__init__(self,\n            data_path,\n            ['data', 'labels'],\n            batch_size=1,  # filll up the queue one image at a time\n            subslice=subslice,\n            preprocess={'labels': get_unique_labels},\n            postprocess={'data': self.postproc},\n            pad=True,\n            *args, **kwargs)\n        if crop_size is None:\n            self.crop_size = 256\n        else:\n            self.crop_size = crop_size\n        node = {'data': tf.placeholder(tf.float32,\n                                            shape=(self.crop_size, self.crop_size, 3),\n                                            name='data'),\n                     'labels': tf.placeholder(tf.int64,\n                                              shape=[],\n                                              name='labels')}\n        CustomQueue.__init__(self, node, self, queue_batch_size=batch_size,\n                             n_threads=n_threads)\n\n    def postproc(self, ims, f):\n        norm = ims / 255. - .5\n        resh = norm.reshape((3, 256, 256))\n        sw = resh.swapaxes(0, 1).swapaxes(1, 2)\n        off = np.random.randint(0, 256 - self.crop_size, size=2)\n        images_batch = sw[off[0]: off[0] + self.crop_size,\n                          off[1]: off[1] + self.crop_size]\n        return images_batch.astype(np.float32)\n\n    def next(self):\n        batch = super(ImageNet, self).next()\n        feed_dict = {self.node['data']: batch['data'],\n                     self.node['labels']: batch['labels'][0]}\n        return feed_dict","sub_path":"tfutils/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":10001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"381476338","text":"#########################################################################\n##\n## Structure of network.\n##\n#########################################################################\nimport torch\nimport torch.nn as nn\nfrom util_hourglass import *\nfrom torch.quantization import QuantStub, DeQuantStub\n\n####################################################################\n##\n## lane_detection_network\n##\n####################################################################\nclass lane_detection_network(nn.Module):\n    def __init__(self):\n        super(lane_detection_network, self).__init__()\n        self.resizing = resize_layer(3, 32)\n        # feature extraction\n        self.layer1 = hourglass_block(32, 32)\n        self.layer2 = hourglass_block(32, 32)\n        self.quant = QuantStub()\n        self.dequant = DeQuantStub()\n    def fuse_model(self):\n        modules_list = [m for m in self.modules()]\n        for i, m in enumerate(modules_list):\n            if type(m) == Conv2D_BatchNorm_Relu:\n                if type(m.cbr_unit) == nn.Sequential and len(m.cbr_unit) == 3:\n                    for idx in range(len(m.cbr_unit)):\n                        if type(m.cbr_unit[idx]) == nn.Conv2d:\n                            torch.quantization.fuse_modules(m.cbr_unit, [str(idx), str(idx + 1), str(idx + 2)],\n                                                            inplace=True)\n            if type(m) == UNet_2Plus:\n                m.fuse_model()\n            if type(m) == nn.Sequential and len(m) == 2:\n                torch.quantization.fuse_modules(m, [\"0\", \"1\"], inplace=True)\n\n    def forward(self, inputs):\n        # feature extraction\n        inputs = self.quant(inputs)\n        out = self.resizing(inputs)\n        result1, out, feature1 = self.layer1(out)\n        result2, out, feature2 = self.layer2(out)\n        for i,res in enumerate(result1):\n            result1[i] = self.dequant(res)\n        for i,res in enumerate(result2):\n            result2[i] = self.dequant(res)\n        feature1 = self.dequant(feature1)\n        feature2 = self.dequant(feature2)\n        return [result1, result2], [feature1, feature2]","sub_path":"pinet_quantization_train/hourglass_network.py","file_name":"hourglass_network.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"247493352","text":"from metalearn import Metafeatures\nfrom DataExplorationMethods import RemoveDatasetIssuesMethods as RDIM\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nimport matplotlib.pyplot as plt\n\n\ndef find_dataset_issues(X, y, features, missing_values='Unknown', preprocessing=False, focus=False, plots=False):\n    \"\"\"\n\n    :param X:\n    :param y:\n    :param features:\n    :param missing_values:\n    :param focus:\n    :param plots:\n    :return:\n    \"\"\"\n\n    dfX, dfy = prepare_dataset(X, y, features, missing_values, output_class=True)\n\n    exploration_results = explore_dataset(dfX, dfy, features, plots=plots, focus=focus)\n\n    if preprocessing:\n        X_new, y_new, f_new = RDIM.preprocess_dataset(dfX, dfy, exploration_results)\n\n        return X_new, y_new, f_new, exploration_results\n\n\ndef prepare_dataset(X, y, features, missing_values='Unknown', output_class=True):\n    \"\"\"\n\n    :param X:\n    :param y:\n    :param features:\n    :param missing_values:\n    :param output_class:\n    :return:\n    \"\"\"\n\n    # Create dataframe\n    dfX = pd.DataFrame(X, columns=features)\n\n    dfX = prepare_missing_values(dfX, missing_values)\n\n    dfX = prepare_data_types(dfX)\n\n    if output_class:\n        dfy = pd.Series(y, dtype='category')\n    else:\n        dfy = pd.Series(y, dtype=float)\n\n    dfy.name = \"Output\"\n\n    return dfX, dfy\n\n\ndef prepare_missing_values(dfX, missing_values='Unknown'):\n    \"\"\"\n\n    :param dfX:\n    :param missing_values:\n    :return:\n    \"\"\"\n\n    # Change possible missing values to NaN values\n    if type(missing_values) == list:\n        for missing_value in missing_values:\n            dfX = dfX.replace(missing_value, np.NaN)\n\n    elif missing_values == 'Unknown':\n        for missing_value in ['', 'None']:\n            dfX = dfX.replace(missing_value, np.NaN)\n\n    else:\n        dfX = dfX.replace(missing_values, np.NaN)\n\n    return dfX\n\n\ndef prepare_data_types(dfX):\n    \"\"\"\n\n    :param dfX:\n    :return:\n    \"\"\"\n\n    for header in list(dfX):\n        X_f = dfX[header].values\n        try:\n            X_f.astype(float)\n            dfX[header] = pd.to_numeric(dfX[header], errors='coerce')\n        except:\n            dfX[header] = dfX[header].astype('category')\n\n    return dfX\n\n\ndef explore_dataset(dfX, dfy, features, focus=False, plots=False):\n    \"\"\"\n\n    :param dfX:\n    :param dfy:\n    :param features:\n    :param focus:\n    :param plots:\n    :return:\n    \"\"\"\n\n    mf = Metafeatures()\n    metafeatures = mf.compute(dfX, dfy)\n\n\n    exploration_results = {}\n    exploration_results['cat'] = False\n    exploration_results['num'] = False\n\n    # Feature types\n    if metafeatures['NumberOfCategoricalFeatures']['value'] > 0:\n        exploration_results['cat'] = True\n        print(\"%i categorical features are present, hot encoding is recommended for machine learning analysis\"\n              % metafeatures['NumberOfCategoricalFeatures']['value'])\n        if metafeatures['NumberOfNumericFeatures']['value'] > 0:\n            exploration_results['num'] = True\n            print(\"Also %i numerical features are present. Either hot encoding or binning must occur for \"\n                  \"further analysis\" % metafeatures['NumberOfNumericFeatures']['value'])\n    elif metafeatures['NumberOfNumericFeatures']['value'] > 0:\n        exploration_results['num'] = True\n\n    exploration_results['fs'] = False\n\n    # Feature dimensionality\n    if metafeatures['Dimensionality']['value'] > 1:\n        exploration_results['fs'] = True\n        print(\"Dimensionality is higher than 1: %.2f, feature selection is recommended\"\n              % metafeatures['Dimensionality']['value'])\n\n    min_mi = min(metafeatures['MinCategoricalMutualInformation']['value'],\n                 metafeatures['MinNumericMutualInformation']['value'])\n\n    if min_mi < 0.01:\n        exploration_results['fs'] = True\n        print(\"The mutual information of at least one feature is lower than 0.05: %.2f. \"\n              \"Feature selection is recommended\" % min_mi)\n\n    exploration_results['mv'] = False\n    exploration_results['cca'] = True\n    exploration_results['aca'] = True\n\n    if focus and exploration_results['cat']:\n        feat = metafeatures['CategoricalMutualInformationOutlierFeatures']['value']\n        out = [round(elem, 2) for elem in metafeatures['CategoricalMutualInformationOutliers']['value']]\n        print(\"The categorical features %s have the lowest mutual information respectively %s\" %\n              (feat[0:3], out[0:3]))\n        print(\"The categorical features %s have the highest mutual information respectively %s\" %\n              (feat[3:6], out[3:6]))\n        feat = metafeatures['NumericMutualInformationOutlierFeatures']['value']\n        out = [round(elem, 2) for elem in metafeatures['NumericMutualInformationOutliers']['value']]\n        print(\"The numeric features %s have the lowest mutual information respectively %s\" %\n              (feat[0:3], out[0:3]))\n        print(\"The numeric features %s have the highest mutual information respectively %s\" %\n              (feat[3:6], out[3:6]))\n\n    # Missing values\n    if metafeatures['NumberOfMissingValues']['value'] > 0:\n        exploration_results['mv'] = True\n        print(\"%i missing values are present, missing value handling should occur\"\n              % metafeatures['NumberOfMissingValues']['value'])\n        if metafeatures['RatioOfInstancesWithMissingValues']['value'] > 0.10:\n            print(\"The ratio of samples with missing values is not small %.2f, \"\n                  \"only complete case analysis is not advised\"\n                  % metafeatures['RatioOfInstancesWithMissingValues']['value'])\n            exploration_results['cca'] = False\n        if metafeatures['RatioOfFeaturesWithMissingValues']['value'] > 0.10:\n            print(\"The ratio of features with missing values is not small %.2f, \"\n                  \"only available case analysis is not advised\"\n                  % metafeatures['RatioOfInstancesWithMissingValues']['value'])\n            exploration_results['aca'] = False\n\n        if focus:\n            feat = metafeatures['FeaturesWithMostMissingValues']['value']\n            ratios = [round(elem, 2) for elem in metafeatures['RatioOfFeaturesWithMostMissingValues']['value']]\n            print(\"The features %s have the most i values, with ratios of respectively %s\" % (feat, ratios))\n\n\n    exploration_results['imbalance'] = False\n\n    # Output imbalance\n    if metafeatures['MinClassProbability']['value'] + 0.10 / metafeatures['NumberOfClasses']['value'] \\\n            < metafeatures['MaxClassProbability']['value']:\n        exploration_results['imbalance'] = True\n        print(\n            \"Minority class probability is %.2f smaller than the majority class probability. Imbalance seems present,\"\n            \" and should be taken into account\" %\n            (metafeatures['MaxClassProbability']['value'] - metafeatures['MinClassProbability']['value']))\n\n        if focus:\n            num_class = min(3, int(metafeatures['NumberOfClasses']['value'] / 2))\n            out = [round(elem, 2) for elem in metafeatures['OutlierClassProbabilities']['value']]\n            print(\"The minority classes %s have a probability of %s\" %\n                  (metafeatures['OutlierClasses']['value'][0: num_class], out[0: num_class]))\n            print(\"The majority classes %s have a probability of %s\" %\n                  (metafeatures['OutlierClasses']['value'][-num_class:], out[-num_class:]))\n\n    exploration_results['irrelevance'] = 0\n\n    # Irrelevant features\n    if metafeatures['MinCardinalityOfCategoricalFeatures']['value'] == 1 or \\\n                    metafeatures['MinCardinalityOfNumericFeatures']['value'] == 1:\n        exploration_results['irrelevance'] = metafeatures['MinimumCardinalityFeaturesCount']['value']\n        print(\"%i features without any information are present\" % metafeatures['MinimumCardinalityFeaturesCount'][\n            'value'])\n        exploration_results['irrelevant_features'] = metafeatures['MinimumCardinalityFeatures']['value']\n\n\n        if focus:\n            print('The features without any information present are %s' %\n                  features[metafeatures['MinimumCardinalityFeatures']['value']].tolist())\n\n    # Normalisation\n        exploration_results['norm_means'] = False\n        exploration_results['norm_stdev'] = False\n        exploration_results['stand'] = False\n\n    mean_means = metafeatures['MeanMeansOfNumericFeatures']['value']\n    stdev_means = metafeatures['StdevMeansOfNumericFeatures']['value']\n    mean_stdevs = metafeatures['MeanStdDevOfNumericFeatures']['value']\n    stdev_stdevs = metafeatures['StdevStdDevOfNumericFeatures']['value']\n    if mean_means != 0 and stdev_means / mean_means > 0.01:\n        print(\"There is a high variance in the means of the features. Normalisation or standardisation of the \"\n              \"mean is recommended.\")\n        exploration_results['norm_means'] = True\n\n    if mean_stdevs != 0 and stdev_stdevs / mean_stdevs > 0.01:\n        print(\"There is a high variance in the standard deviation in the features. Normalisation or standardisation \"\n              \"of the standard deviation is recommended\")\n        exploration_results['norm_stdev'] = True\n\n    if abs(metafeatures['MeanSkewnessOfNumericFeatures']['value']) > 1:\n        print(\"The average skewness of the numeric features is highly different from the normal distribution. \"\n              \"Keep in mind a significant number of the features is not normally distributed\")\n        exploration_results['stand'] = True\n\n    if abs(metafeatures['MeanKurtosisOfNumericFeatures']['value'] - 3) > 1:\n        print(\"The average kurtosis of the numeric features is highly different from the normal distribution. \"\n              \"Keep in mind a significant number of the features is not normally distributed\")\n        exploration_results['stand'] = True\n\n    # Multicollinearity\n    mc_threshold = 1 / ((metafeatures['NumberOfFeatures']['value']) ** (1 / 2))\n\n    exploration_results['mc'] = False\n\n    if metafeatures['PredPCA1']['value'] > mc_threshold:\n        print('The first principal component already explains %.2f variance in the dataset, showing a high amount of '\n              'multicollinearity being present' % metafeatures['PredPCA1']['value'])\n        exploration_results['mc'] = True\n    elif metafeatures['PredPCA1']['value'] + metafeatures['PredPCA2']['value'] > mc_threshold:\n        print(\n            'The first two principal component already explain %.2f variance in the dataset, showing a high amount of '\n            'multicollinearity being present' %\n            metafeatures['PredPCA1']['value'] + metafeatures['PredPCA2']['value'])\n        exploration_results['mc'] = True\n    elif metafeatures['PredPCA1']['value'] + metafeatures['PredPCA2']['value'] + metafeatures['PredPCA3'][\n        'value'] > mc_threshold:\n        print(\n            'The first three principal component already contain %.2f variance in the dataset, showing a high amount of '\n            'multicollinearity being present' %\n            metafeatures['PredPCA1']['value'] + metafeatures['PredPCA2']['value'] + metafeatures['PredPCA3']['value'])\n        exploration_results['mc'] = True\n\n    if metafeatures['MeanCorrelation']['value'] != 'Too many features for correlation testing':\n        if metafeatures['MaxCorrelation']['value'] > 0.5:\n            print('At least two features have a high correlation value of %.2f' %\n                  metafeatures['MaxCorrelation']['value'])\n            exploration_results['mc'] = True\n        if metafeatures['MinCorrelation']['value'] < -0.5:\n            print('At least two features have a high negative correlation value of %.2f' %\n                  metafeatures['MinCorrelation']['value'])\n            exploration_results['mc'] = True\n\n        if focus:\n            feat = metafeatures['CorrelationOutlierFeatures']['value']\n            out = [round(elem, 2) for elem in metafeatures['CorrelationOutliers']['value']]\n            print(\"The combination of the features %s have a negative correlation of %s\" % (feat[0:3], out[0:3]))\n            print(\"The combination of the features %s have a positive correlation of %s\" % (feat[3:6], out[3:6]))\n\n    if plots:\n        plt.show()\n\n    return exploration_results\n\n","sub_path":"Testing/DataExplorationMethods/FindDatasetIssuesMethods.py","file_name":"FindDatasetIssuesMethods.py","file_ext":"py","file_size_in_byte":12215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"119094275","text":"from typing import TYPE_CHECKING\n\nfrom funcy import rpartial, lsplit\n\nfrom dvc.dependency import ParamsDependency\nfrom dvc.utils.collections import apply_diff\nfrom dvc.utils.stage import parse_stage_for_update\nfrom typing import List\n\nif TYPE_CHECKING:\n    from dvc.stage import PipelineStage, Stage\n\nPARAM_PATH = ParamsDependency.PARAM_PATH\nPARAM_PARAMS = ParamsDependency.PARAM_PARAMS\nDEFAULT_PARAMS_FILE = ParamsDependency.DEFAULT_PARAMS_FILE\n\n\ndef _get_outs(stage: \"PipelineStage\"):\n    outs_bucket = {}\n    for o in stage.outs:\n        bucket_key = [\"metrics\"] if o.metric else [\"outs\"]\n\n        if not o.metric and o.persist:\n            bucket_key += [\"persist\"]\n        if not o.use_cache:\n            bucket_key += [\"no_cache\"]\n        key = \"_\".join(bucket_key)\n        outs_bucket[key] = outs_bucket.get(key, []) + [o.def_path]\n    return outs_bucket\n\n\ndef get_params_deps(stage: \"PipelineStage\"):\n    return lsplit(rpartial(isinstance, ParamsDependency), stage.deps)\n\n\ndef _serialize_params(params: List[ParamsDependency]):\n    \"\"\"Return two types of values from stage:\n\n    `keys` - which is list of params without values, used in a pipeline file\n\n    which is in the shape of:\n        ['lr', 'train', {'params2.yaml': ['lr']}]\n    `key_vals` - which is list of params with values, used in a lockfile\n    which is in the shape of:\n        {'params.yaml': {'lr': '1', 'train': 2}, {'params2.yaml': {'lr': '1'}}\n    \"\"\"\n    keys = []\n    key_vals = {}\n\n    for param_dep in params:\n        dump = param_dep.dumpd()\n        path, params = dump[PARAM_PATH], dump[PARAM_PARAMS]\n        k = list(params.keys())\n        if not k:\n            continue\n        # if it's not a default file, change the shape\n        # to: {path: k}\n        keys.extend(k if path == DEFAULT_PARAMS_FILE else [{path: k}])\n        key_vals.update({path: params})\n\n    return keys, key_vals\n\n\ndef to_pipeline_file(stage: \"PipelineStage\"):\n    params, deps = get_params_deps(stage)\n    serialized_params, _ = _serialize_params(params)\n\n    return {\n        stage.name: {\n            key: value\n            for key, value in {\n                stage.PARAM_CMD: stage.cmd,\n                stage.PARAM_WDIR: stage.resolve_wdir(),\n                stage.PARAM_DEPS: [d.def_path for d in deps],\n                stage.PARAM_PARAMS: serialized_params,\n                **_get_outs(stage),\n                stage.PARAM_LOCKED: stage.locked,\n                stage.PARAM_ALWAYS_CHANGED: stage.always_changed,\n            }.items()\n            if value\n        }\n    }\n\n\ndef to_lockfile(stage: \"PipelineStage\") -> dict:\n    assert stage.cmd\n    assert stage.name\n\n    res = {\"cmd\": stage.cmd}\n    params, deps = get_params_deps(stage)\n    deps = [\n        {\"path\": dep.def_path, dep.checksum_type: dep.get_checksum()}\n        for dep in deps\n    ]\n    outs = [\n        {\"path\": out.def_path, out.checksum_type: out.get_checksum()}\n        for out in stage.outs\n    ]\n    if deps:\n        res[\"deps\"] = deps\n    if params:\n        _, res[\"params\"] = _serialize_params(params)\n    if outs:\n        res[\"outs\"] = outs\n\n    return {stage.name: res}\n\n\ndef to_single_stage_file(stage: \"Stage\"):\n    state = stage.dumpd()\n\n    # When we load a stage we parse yaml with a fast parser, which strips\n    # off all the comments and formatting. To retain those on update we do\n    # a trick here:\n    # - reparse the same yaml text with a slow but smart ruamel yaml parser\n    # - apply changes to a returned structure\n    # - serialize it\n    if stage._stage_text is not None:\n        saved_state = parse_stage_for_update(stage._stage_text, stage.path)\n        # Stage doesn't work with meta in any way, so .dumpd() doesn't\n        # have it. We simply copy it over.\n        if \"meta\" in saved_state:\n            state[\"meta\"] = saved_state[\"meta\"]\n        apply_diff(state, saved_state)\n        state = saved_state\n    return state\n","sub_path":"dvc/serialize.py","file_name":"serialize.py","file_ext":"py","file_size_in_byte":3889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"229023108","text":"from subprocess import check_output, CalledProcessError\nfrom typing import Tuple, Optional\n\n\ndef git_info(project_home: str = None) -> Tuple[Optional[str], Optional[str]]:\n    \"\"\" Read the git branch and commit of the project's home folder.\n    This is useful to log your branch and commit has for referencing results\n\n    Args:\n        project_home: The project home folder\n        (usually the location main.py is called from)\n    Returns:\n        git_branch: Associated git tag/branch name. None if not possible to get.\n        git_commit: The git hash, if exists. None if not possible to get.\n\n    \"\"\"\n\n    git_branch = None\n    git_commit = None\n\n    cmd = [\"git\", \"rev-parse\", \"HEAD\"]\n    try:\n        _hash = check_output(cmd, cwd=project_home)\n        git_commit = str(_hash, \"utf-8\").strip()\n    except CalledProcessError:\n        print(\"Not able to get git commit hash: {cmd} @ {project_home}\").format(\n            cmd=cmd, project_home=project_home\n        )\n\n    cmd = \"git symbolic-ref -q --short HEAD || git describe --tags --exact-match\"\n    try:\n        _name = check_output(cmd, cwd=project_home, shell=True)\n        git_branch = str(_name, \"utf-8\").strip()\n    except CalledProcessError:\n        print(\"Not able to get git branch name: '{cmd}' @ {project_home}\").format(\n            cmd=cmd, project_home=project_home\n        )\n\n    return git_branch, git_commit\n","sub_path":"src/utils/git_utils.py","file_name":"git_utils.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"529510843","text":"from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\n\nhtml = urlopen('https://www.ivips.co.kr:7002/benefit/beCard.asp')\n# 빕스 제휴 카드 홈페이지\nbsobj = BeautifulSoup(html, \"html.parser\")\n\n\nname=bsobj.findAll('th',{'scope':\"row\"})\nget=bsobj.findAll('td',{'class':'ac-txt rline'})\ndetail=bsobj.findAll('ul',{'class':\"list01\"})\n빕스=[]\nfor i in range(len(name)):\n    빕스.append([0,0,0,0,0])\nfor x in range(len(name)):\n    빕스[x][0]=name[x].text.strip('\\n \\t \\r')\n    #print(get[x].text,'\\n')\n    빕스[x][4]=get[x].text+detail[x].text\n    for i in range(5,100,5):\n        if str(i) in get[x].text:\n            빕스[x][2]='x*'+str((100-i)/100)\n\n# 홈페이지에서 각각의 할인 정보를 추출하여 추출합니다.\n#%% 데이터 저장 양식\n'''\n빕스[0][2]='x*1'\n빕스[1][2]='x*1'\n'''\na=['우리체크', 0 ,'x*0.8',0,'20프로 할인']\nb=['우리신용', 10000 ,'x-3000.0',0,'결제 건당 3000원 할인']\nc=['CU·배달의민족 삼성카드 taptap', 1500, 'x-(x//1500)*200',0,'1500원 당 200원 할인']\n소지카드=['우리체크','CU·배달의민족 삼성카드 taptap']\n편의점=[a,b,c]\n스팅=[a,b,c]\n주유소=[a,b,c]\n가맹점명=[\"편의점\", \"주유소\", \"스팅\", \"빕스\"]\n가맹점=[편의점, 주유소, 스팅, 빕스]\n#print(가맹점)\n\nfrom def_cardlist_with_sort import *\nimin=\"빕스\"\nls=card_sort(where(imin, 가맹점명, 가맹점),  800000)\nfor i in range(len(ls)):\n    print(ls[i][0],'\\t',ls[i][3])\nprint(ls[0][4])\n","sub_path":"flask_test/my_crawler.py","file_name":"my_crawler.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"309734785","text":"import logging\nimport random\nfrom os import environ\nfrom urllib import parse\n\nimport discord\nfrom discord.ext import commands\n\nTMDB_API_KEY = environ.get(\"TMDB_API_KEY\")\n\nlog = logging.getLogger(__name__)\n\n\nasync def send_random_movie_embed(bot: commands.Bot, ctx: commands.Context, pages: int = 1, icon: str = '',\n                                  genre_code: str = None, keyword_code: str = None) -> None:\n    \"\"\"Randomly selects a movie and sends an embed with its details.\"\"\"\n    # Selecting a random int to parse it to the page parameter\n    random_page = random.randint(0, pages)\n    # TMDB api params\n    params = {\n        \"api_key\": TMDB_API_KEY,\n        \"language\": \"en-US\",\n        \"sort_by\": \"popularity.desc\",\n        \"include_adult\": \"false\",\n        \"include_video\": \"false\",\n        \"page\": random_page\n    }\n    if genre_code:\n        params[\"with_genres\"] = genre_code\n    if keyword_code:\n        params[\"with_keywords\"] = keyword_code\n    # The api request url\n    request_url = \"https://api.themoviedb.org/3/discover/movie?\" + parse.urlencode(params)\n    async with bot.http_session.get(request_url) as resp:\n        # Trying to load the json file returned from the api\n        try:\n            data = await resp.json()\n            # Selecting random result from results object in the json file\n            selected_movie = random.choice(data[\"results\"])\n\n            embed = discord.Embed(\n                title=f\"{icon} {selected_movie['title']} {icon}\",\n                description=selected_movie[\"overview\"],\n            )\n            embed.set_image(url=f\"http://image.tmdb.org/t/p/w200/{selected_movie['poster_path']}\")\n            embed.add_field(name=\"Release date :clock1:\", value=selected_movie[\"release_date\"])\n            embed.add_field(name=\"Rating :star2:\", value=selected_movie[\"vote_average\"])\n            await ctx.send(embed=embed)\n        except KeyError:\n            warning_message = \"A KeyError was raised while fetching information on the movie. The API service\" \\\n                              \" could be unavailable or the API key could be set incorrectly.\"\n            embed = discord.Embed(title=warning_message)\n            log.warning(warning_message)\n            await ctx.send(embed=embed)\n","sub_path":"bot/utils/movies.py","file_name":"movies.py","file_ext":"py","file_size_in_byte":2243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"627384686","text":"def primeSub(x,y):\r\n    n = [2,3,5,7,11,13,17,19,23,29,31,37,41,43,47]\r\n    p = (x-y)\r\n    init = 'NO'\r\n    for i in range(len(n)):\r\n        if p % n[i] == 0:\r\n            init = 'YES'\r\n            return('YES')\r\n            break\r\n        elif p % n[i] != 0:\r\n            init = 'NO'\r\n    if init == 'NO' and init != 'YES':\r\n        return('NO')\r\n\r\n\r\n\r\n\r\nq = int(input())\r\nfor i in range(q):\r\n    arr = list(map(int,input().split()))\r\n    print(primeSub(arr[0],arr[1]))\r\n    \r\n                \r\n        ","sub_path":"codeforces/prime_subtraction.py","file_name":"prime_subtraction.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"99942607","text":"# Remove the minimum number of invalid parentheses in order to make the \n# input string valid. Return all possible results.\n\n# Note: The input string may contain letters other than the parentheses ( and ).\n\n# Examples:\n# \"()())()\" -> [\"()()()\", \"(())()\"]\n# \"(a)())()\" -> [\"(a)()()\", \"(a())()\"]\n# \")(\" -> [\"\"]\n\ndef uniplz(array):\n    unique = {}\n    for i in range(len(array)):\n        if array[i] not in unique:\n            unique[array[i]] = 1\n        elif array[i] in unique:\n            unique[array[i]] += 1\n    for key in unique:\n        if unique[key] == 1:\n            return key\n    return False\nprint(uniplz([1,2,3,2,3,1,7,3,4,5,5,4]))\nprint(uniplz([7,8,2,10,4,2,8,7,9]))\n","sub_path":"Python problems/paratheisis.py","file_name":"paratheisis.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"540022877","text":"import matplotlib.pyplot as plt\nfrom IPython.display import display\nfrom sympy import *\nimport numpy as np\nfrom math import *\nfrom scipy.integrate import quad\nfrom scipy.integrate import simps\nfrom numpy import trapz\nfrom mpl_toolkits.mplot3d import Axes3D\n\n# we'll start with a quick explanation of what happens and hints to two functions\nprint('----------------------------------------------------')\nprint('')\nprint('TORUS VOLUME CALCULATOR')\nprint('')\nprint('this python sheet is designed to compare different methods to calculate')\nprint('the volume of a rotated circle body, i.e. torus')\nprint('')\nprint('we will use following methods:')\nprint('')\nprint('- exact calculation')\nprint('- iterated trapeze method')\nprint('- trapeze library method')\nprint('- iterated simpson method')\nprint('- simpson library method')\nprint('- integrated method')\nprint('')\nprint('following functions are available:')\nprint('')\nprint('newTorus(hole_rad, diam_tube) \\t new calculation for specific torus volume')\nprint('printIterations() \\t\\t overview of trapeze/simpson iterations')\nprint('')\nprint('----------------------------------------------------')\n\n# start\ninit_printing(use_latex='mathjax', no_global=True)\nx, r, R = symbols('x r R')\nf, g, h = symbols('f g h', cls=Function)\n\n# ---------------------- change circle attributes here ----------------------\n\n# definition of rotated circle\nrotated_circle_start_height = 1  # circle is placed this high above x axis\nrotated_circle_diameter = 1  # circle diameter\n\n# ---------------------- change circle attributes above ----------------------\n\n# global variables\nrad_max = 0  # translation to max radius of rotation body\nrad_hole = 0  # translation to min radius of rotation body (i.e. 'hole')\nrad_tube = 0  # radius of torus tube (i.e. 'r')\nrad_torus = 0  # radius of torus (i.e. 'R')\nresult_t = 0  # trapeze value calculated with iterations\nresult_s = 0  # simpson value calculated with iterations\nlib_t = 0  # trapeze value calculated with numpy library\nlib_s = 0  # simpson value calculated with scipy library\nx_ = 0  # x values for 3d plot\ny_ = 0  # y values for 3d plot\nz_ = 0  # z values for 3d plot\nintegrated = 0  # variable will store integrated result\nV = 0  # variable for exact volume\n\nlim_a = 0  # x value for starting integral calculations\nn_iter = 10  # iteration base value will be 2**n_iter\ntheta = np.linspace(0, 2 * pi, 100)  # theta value for 3d plot\nphi = np.linspace(0, 2 * pi, 100)  # phi value for 3d plot\ntheta, phi = np.meshgrid(theta, phi)  # theta and phi as mesh grid\n\n# formula for circle scope\nexpr_a = 'sqrt(r**2-x**2)'\nf = lambdify([x, r], sympify(expr_a), \"numpy\")\n\n# formulas for circle volume\ng = lambda x, R, r: x * np.sqrt(r ** 2 - (x - R) ** 2)\nh = lambda R, r: 2 * pi ** 2 * R * r ** 2\n\n\n# helper functions\n# source: http://www.imn.htwk-leipzig.de/~lueders/informatik/lehrinhalte/torus.pdf\ndef volume(a, b):\n    \"\"\"function will return the volume of a torus.\n    parameters:\n        a: 1/2 area of cut torus 'cap' (equals half circle area of rotated circle)\n        b: 1/2 area of cut torus itself\"\"\"\n    return ((2 * b - (2 * b - 4 * a)) * rad_torus) * pi * 2\n\n\n# source: https://scipython.com/book/chapter-7-matplotlib/examples/a-torus/\ndef plot():\n    \"\"\"function will plot 3d graph of torus\"\"\"\n    fig = plt.figure()\n    ax = fig.add_subplot(111, projection='3d')\n    ax.set_zlim(-rad_torus, rad_torus)\n    ax.plot_surface(x_, y_, z_, rstride=5, cstride=5, color='#FF9321', edgecolors='w')\n    ax.view_init(45, 30)\n    plt.title('volume will be calculated for displayed torus', y=1.08)\n    plt.show()\n\n\n# source: https://stackoverflow.com/questions/46431826/using-trapezoidal-numerical-integration-in-python\ndef trapeze(add, n):\n    \"\"\"trapeze approximation formula, iteration will be done n times\"\"\"\n    a = lim_a\n    b = rad_tube\n    r = b\n    h = (b - a) / n\n    sum = ((f(a, r) + add) + (f(b, r)) + add) / 2.\n    for i in range(1, n):\n        sum = sum + (f((a + (h * i)), r) + add)\n    return sum * h\n\n\n# source: https://stackoverflow.com/questions/16001157/simpsons-rule-in-python\ndef simpson(add, n):\n    \"\"\"simpson approximation formula, iteration will be done n times\"\"\"\n    a = lim_a\n    b = rad_tube\n    r = b\n    h = (b - a) / n\n    k = 0.0\n    x = a + h\n    for i in range(1, int(n / 2 + 1)):\n        k += 4 * (f(x, r) + add)\n        x += 2 * h\n    x = a + 2 * h\n    for i in range(1, int(n / 2)):\n        k += 2 * (f(x, r) + add)\n        x += 2 * h\n    return (h / 3) * ((f(a, r) + add) + (f(b, r) + add) + k)\n\n\ndef prepareIterations(title, function, iterations):\n    \"\"\"function to display multiple iterations of either the trapeze or simpson formula\"\"\"\n    print(title + ' calculation:')\n    print('iterations', '\\tresult', '\\t\\terror (delta exact volume)')\n    for i in range(1, iterations + 1):\n        n = 2 ** i\n        t1 = function(0, n)\n        t2 = function(rad_torus, n)\n        t = volume(t1, t2)\n        print('%d \\t\\t%f \\t%s' % (n, t, error(t)))\n    print('')\n\n\ndef printIterations():\n    \"\"\"function prints multiple iterations of trapeze and simpson formula\"\"\"\n    prepareIterations('trapeze', trapeze, n_iter)\n    prepareIterations('simpson', simpson, n_iter)\n\n\ndef calcResult(function, iterations):\n    \"\"\"function will return the result for either the trapeze or simpson formula\n    for specific iterations\"\"\"\n    t1 = function(0, iterations)\n    t2 = function(rad_torus, iterations)\n    t = volume(t1, t2)\n    return t\n\n\ndef newTorus(hole_rad, diam_tube):\n    \"\"\"function allows to run the calculations for another rotated circle body (=torus).\n    if hole_rad or diam_tube are null or smaller, it will be set to 0.000001 to prevent errors.\n    parameters are:\n        - hole_rad (radius of the torus hole)\n        - diam_tube (diameter of the circle to be rotated)\"\"\"\n    global rotated_circle_start_height\n    global rotated_circle_diameter\n    if hole_rad <= 0:\n        hole_rad = 0.000001\n    if diam_tube <= 0:\n        diam_tube = 0.000001\n    rotated_circle_start_height = hole_rad\n    rotated_circle_diameter = diam_tube\n    perform()\n\n\ndef calc():\n    \"\"\"function will calculate all needed values for a specific torus\"\"\"\n    # importing global variables\n    global rad_max\n    global rad_hole\n    global rad_tube\n    global rad_torus\n    global x_\n    global y_\n    global z_\n    global V\n    global result_t\n    global result_s\n    global lib_t\n    global lib_s\n    global integrated\n\n    # sets upper and lower radius\n    rad_max = rotated_circle_start_height + rotated_circle_diameter\n    rad_hole = rotated_circle_start_height\n\n    # sets R and r value of torus\n    rad_tube = (rad_max - rad_hole) / 2\n    rad_torus = rad_max - rad_tube\n\n    # sets axe values for 3d plot\n    x_ = (rad_torus + rad_tube * np.cos(theta)) * np.cos(phi)\n    y_ = (rad_torus + rad_tube * np.cos(theta)) * np.sin(phi)\n    z_ = rad_tube * np.sin(theta)\n\n    # gets exact volume\n    V = h(rad_torus, rad_tube)\n\n    # calculates volume by integral\n    integrated, err = quad(g, rad_hole, rad_max, args=(rad_torus, rad_tube))\n    integrated *= 4 * pi\n\n    # calculates volume for trapeze and simpson method\n    result_t = calcResult(trapeze, 2 ** n_iter)\n    result_s = calcResult(simpson, 2 ** n_iter)\n\n    # calculates volume for trapeze and simpson method from libraries\n    # setup data points for calculations\n    x_num = np.linspace(0, rad_tube, 100)\n    y_num_a = f(x_num, rad_tube)\n    y_num_b = f(x_num, rad_tube) + rad_torus\n\n    # calculate volume of torus\n    lib_t = volume(trapz(y_num_a, x=x_num), trapz(y_num_b, x=x_num))\n    lib_s = volume(simps(y_num_a, x=x_num), simps(y_num_b, x=x_num))\n\n\ndef error(x):\n    \"\"\"function will return the delta error.\n    the value to be checked will be compared to the known exact value\"\"\"\n    return str(100 / V * (V - x)) + ' %'\n\n\ndef printOverview():\n    \"\"\"function will display an overview of all calculations\"\"\"\n    print('----------------------------------------------------')\n    print('')\n    print('OVERVIEW:')\n    print('')\n    print('exact volume: \\t\\t' + str(V))\n    print('')\n    print('trapeze value: \\t\\t' + str(result_t) + '\\terror: ' + error(result_t))\n    print('numpy.trapz: \\t\\t' + str(lib_t) + '\\terror: ' + error(lib_t))\n    print('')\n    print('simpson value: \\t\\t' + str(result_s) + '\\terror: ' + error(result_s))\n    print('scipy.simps: \\t\\t' + str(lib_s) + '\\terror: ' + error(lib_s))\n    print('')\n    print('integrated value: \\t' + str(integrated) + '\\terror: ' + error(integrated))\n    print('')\n    print('----------------------------------------------------')\n\n\ndef perform():\n    \"\"\"calculates new values, plots torus and prints overview\"\"\"\n    calc()\n    plot()\n    printOverview()\n\n\n# executed as this py sheet is started\nperform()","sub_path":"integrationV/numIntFinal.py","file_name":"numIntFinal.py","file_ext":"py","file_size_in_byte":8718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"600016233","text":"from django.db import models\nfrom cms.models.pluginmodel import CMSPlugin\nfrom django.template.defaultfilters import slugify\nfrom django.utils.translation import ugettext_lazy as _\n\n\nclass Organization(models.Model):\n    \"\"\"\n    Defines an organization.\n    These can have parent/child relationships.\n    \"\"\"\n    name = models.CharField(\n        _('name'),\n        max_length=100\n    )\n    slug = models.SlugField(\n        _('slug'),\n        max_length=100,\n        unique=True,\n        editable=False\n    )\n    description = models.TextField(\n        _('description'),\n        null=True,\n        blank=True\n    )\n    parent = models.ForeignKey(\n        'self',\n        related_name='children',\n        blank=True,\n        null=True\n    )\n    date_added = models.DateTimeField(\n        _('date added'),\n        auto_now_add=True\n    )\n    date_modified = models.DateTimeField(\n        _('date modified'),\n        auto_now=True\n    )\n\n    \"\"\"\n    Overide the save method to auto-slug.\n    \"\"\"\n    def save(self, *args, **kwargs):\n        if not self.slug:\n            # Set slug only if new to keep from breaking links.\n            self.slug = slugify(self.name)\n\n        super(Organization, self).save(*args, **kwargs)\n\n    def __unicode__(self):\n        return self.name\n\n    class Meta:\n        verbose_name = 'organization'\n        verbose_name_plural = 'organizations'\n        app_label = 'resources'\n\n\nclass PluginOrganization(CMSPlugin):\n    \"\"\"\n    Defines the organization scope of each plugin.\n    \"\"\"\n    organization = models.ForeignKey('Organization')\n\n    class Meta:\n        app_label = 'resources'\n","sub_path":"resources/models/organizations.py","file_name":"organizations.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"62210663","text":"# -*- coding: utf-8 -*-\n# @Time    : 2021/7/16 15:01\n# @Author  : yuting\n# @FileName: timeData_WRFmode.py\nimport pandas as pd\nimport os\nfrom loguru import logger\n\n# WRF文件路径\nSIMULATED_FILE = \"./模拟数据\"\n# 提取到的做时间序列预测的模拟数据\nOUTPUT_FILE = \"./时序数据\"\n\n# 选取的地理位置\nNX = 1\nNY = 1\nNL = 1\n\n\n# 提取某一点三个月的时间数据\ndef getTimeData(PATH):\n    data = pd.read_csv(PATH)\n    data_choose = data.loc[(data['nx'] == NX) & (data['ny'] == NY) & (data['nl'] == NL)]\n    return data_choose\n\n\nif __name__ == \"__main__\":\n    g = os.walk(SIMULATED_FILE)\n    for path, dir_list, file_list in g:\n        for file_name in file_list:\n            data = getTimeData(os.path.join(path, file_name))\n\n            # 处理异常数据，2月4日的数据只有十条\n            if file_name == \"wrfout_d02_2017-02-04_00_00_00.csv\":\n                # 异常数据，打算不输出，若想输出，则执行以下代码\n                '''\n                data.to_csv(OUTPUT_FILE + \"\\\\nx=\" + str(NX) + \"ny=\" + str(NY) + \"nl=\" + str(NL) + \"_wrfMode.csv\", mode='a',\n                            encoding=\"utf-8\",\n                            header=None, index=None)\n                '''\n                continue\n            elif file_name == \"wrfout_d02_2017-01-01_00_00_00.csv\":\n                # data.drop([len(data) - 1], inplace=True)\n                data.to_csv(OUTPUT_FILE + \"\\\\nx=\" + str(NX) + \"ny=\" + str(NY) + \"nl=\" + str(NL) + \"_wrfMode.csv\",\n                            encoding=\"utf-8\",\n                            header=True, index=None)\n            else:\n                # data.drop([len(data) - 1], inplace=True)\n                data.to_csv(OUTPUT_FILE + \"\\\\nx=\" + str(NX) + \"ny=\" + str(NY) + \"nl=\" + str(NL) + \"_wrfMode.csv\",\n                            mode='a',\n                            encoding=\"utf-8\",\n                            header=None, index=None)\n\n            logger.info(\"已加入\" + file_name[11:21] + \"的数据\")\n","sub_path":"timeData_WRFmode.py","file_name":"timeData_WRFmode.py","file_ext":"py","file_size_in_byte":1997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"523182340","text":"import time\nimport atexit\nfrom os import environ\nfrom events import Events\nfrom .models.base import Model\nfrom .processor import BatchProcessor\n\nENVIRONMENT_AUTHORIZATION_TOKEN = environ.get('PANAMAH_AUTHORIZATION_TOKEN')\nENVIRONMENT_SECRET = environ.get('PANAMAH_SECRET')\nENVIRONMENT_ASSINANTE_ID = environ.get('PANAMAH_ASSINANTE_ID')\n\n\nclass BoolHook:\n    def __init__(self, initial_result=True):\n        self.result = initial_result\n\n    def set_false(self):\n        self.result = False\n\n    def set_true(self):\n        self.result = True\n\n\nclass PanamahStream():\n    class _PanamahStream():\n        def __init__(self, authorization_token, secret, assinante_id):\n            self.authorization_token = authorization_token\n            self.secret = secret\n            self.assinante_id = assinante_id\n            self.processor = BatchProcessor(\n                authorization_token, secret, assinante_id)\n            self.processor.start()\n            self.events = Events()\n\n            def flush():\n                try:\n                    self.processor.flush()\n                except Exception as e:\n                    print('Flushing falhou com: ', str(e))\n            # atexit.register(flush)\n\n        def is_acceptable_model(self, model):\n            return hasattr(model, 'name') and model.name != 'ASSINANTE'\n\n        def emit(self, event, *args):\n            event_slot = getattr(self.events, event)\n            event_slot(*args)\n\n        def on(self, event, fn):\n            event_slot = getattr(self.events, event)\n            event_slot += fn\n\n        def off(self, event, fn):\n            event_slot = getattr(self.events, event)\n            event_slot -= fn\n\n        def save(self, model, assinante_id=None):\n            models = model if isinstance(model, list) else [model]\n            for item in models:\n                if isinstance(item, Model) and self.is_acceptable_model(item):\n                    allow_save = BoolHook()\n                    self.emit('before_save', item, allow_save.set_false)\n                    if allow_save.result:\n                        self.processor.save(item, assinante_id)\n                else:\n                    raise ValueError(\n                        'model deve ser um modelo valido do Panamah')\n\n        def delete(self, model, assinante_id=None):\n            models = model if isinstance(model, list) else [model]\n            for item in models:\n                if isinstance(item, Model) and self.is_acceptable_model(item):\n                    allow_delete = BoolHook()\n                    self.emit('before_delete', item, allow_delete.set_false)\n                    if allow_delete.result:\n                        self.processor.delete(item, assinante_id)\n                else:\n                    raise ValueError(\n                        'model deve ser um modelo valido do Panamah')\n\n        def flush(self):\n            self.processor.flush()\n\n    instance = None\n\n    def __init__(self, authorization_token=ENVIRONMENT_AUTHORIZATION_TOKEN, secret=ENVIRONMENT_SECRET, assinante_id=ENVIRONMENT_ASSINANTE_ID or '*'):\n        if self.instance is None:\n            self.instance = PanamahStream._PanamahStream(\n                authorization_token, secret, assinante_id)\n        PanamahStream.authorization_token = authorization_token\n        PanamahStream.secret = secret\n        PanamahStream.assinante_id = assinante_id\n\n    def __getattr__(self, name):\n        return getattr(self.instance, name)\n","sub_path":"panamah_sdk/stream.py","file_name":"stream.py","file_ext":"py","file_size_in_byte":3466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"95458465","text":"from PIL import Image\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.decorators import user_passes_test\nfrom django.contrib.auth.models import User\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.core.urlresolvers import reverse\nfrom django.db.models import Count\nfrom django.db.models.functions import Lower\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import redirect\nfrom django.shortcuts import render\n\nfrom .forms import UserProfileForm, ClanForm, UserForm, CommentForm, ClanWarForm, BattleForm\nfrom .models import Clan, UserProfile, ClanWar, Battle, Comment, News\n\n\ndef index(request):\n    return render(request, 'dk/index.html')\n\n\ndef is_admin_check(user):\n    return user.groups.filter(name__in=['Admin']).exists()\n\n@user_passes_test(is_admin_check, login_url=\"/dk/news/\", redirect_field_name=None)\ndef comment_delete(request,id):\n    try:\n        comment = Comment.objects.get(id=id)\n    except Comment.DoesNotExist:\n        return redirect('index')\n\n    comment.delete()\n\n    if request.GET.get('next'):\n        kam = request.GET['next']\n        messages.add_message(request, messages.SUCCESS, 'Comment smazan')\n        return redirect(kam)\n    else:\n        return redirect('/dk/')\n\ndef news_detail(request, id):\n    news = News.objects.get(id=id)\n    admin = is_admin_check(request.user)\n\n    if request.method == 'POST':\n        form = CommentForm(data=request.POST)\n        if form.is_valid():\n            comment = form.save(commit=False)\n            comment.section = news.sekce_novinky\n            comment.save()\n\n            messages.add_message(request, messages.SUCCESS, 'Comment pridan')\n            return redirect('/dk/news/' + id)\n        else:\n            print(form.errors, )\n    else:\n        form = CommentForm(initial={'name': request.user.username})\n\n    comments = news.sekce_novinky.comment_sekce.all()\n    content = {'news': news, 'comments': comments, 'form': form, 'admin':admin}\n    return render(request, 'dk/news_detail.html', content)\n\n\ndef news(request):\n    news = News.objects.order_by('-datum')\n    news = news.annotate(num_comments=Count('sekce_novinky__comment_sekce'))\n    news = news.select_related()\n\n    paginator = Paginator(news, 10)\n    page = request.GET.get('page')\n    try:\n        news = paginator.page(page)\n    except PageNotAnInteger:\n        news = paginator.page(1)\n    except EmptyPage:\n        news = paginator.page(paginator.num_pages)\n    return render(request, 'dk/news.html', {'news': news})\n\n\ndef cw(request):\n    cws = ClanWar.objects.annotate(num_comments=Count('sekce_cw__comment_sekce', distinct=True))\n    cws = cws.select_related()\n\n    paginator = Paginator(cws, 20)\n    page = request.GET.get('page')\n    try:\n        cws = paginator.page(page)\n    except PageNotAnInteger:\n        cws = paginator.page(1)\n    except EmptyPage:\n        cws = paginator.page(paginator.num_pages)\n\n    if request.method == 'POST':\n        form = ClanWarForm(data=request.POST)\n        if form.is_valid():\n            form.save()\n            return redirect('/dk/cw/')\n    else:\n        form = ClanWarForm()\n    return render(request, 'dk/cw.html', {'cws': cws, 'form': form})\n\n\ndef cw_detail(request, id):\n    cw = ClanWar.objects.select_related('enemy').get(id=id)\n    admin = is_admin_check(request.user)\n\n    if request.method == 'POST':\n        if 'comment_sub' in request.POST:\n            form = CommentForm(data=request.POST)\n            if form.is_valid():\n                comment = form.save(commit=False)\n                comment.section = cw.sekce_cw\n\n                comment.save()\n\n                messages.add_message(request, messages.SUCCESS, 'Comment pridan')\n                return redirect('/dk/cw/' + id)\n            else:\n                print(form.errors, )\n\n        if 'battle_sub' in request.POST:\n            form = BattleForm(data=request.POST)\n            if form.is_valid():\n                battle = form.save(commit=False)\n                battle.cw = cw\n                battle.save()\n                return redirect('/dk/cw/' + id)\n            else:\n                print(form.errors, )\n    else:\n        form = CommentForm(initial={'name': request.user.username})\n        form2 = BattleForm()\n\n    comments = Comment.objects.filter(section__cw=cw)\n    battles = Battle.objects.filter(cw=id).select_related()\n    content = {'cw': cw, 'battles': battles, 'comments': comments, 'form': form,'form2': form2, 'admin':admin}\n    return render(request, 'dk/cw_detail.html',content)\n\n\n@user_passes_test(is_admin_check, login_url=\"/dk/members/\", redirect_field_name=None)\ndef change_status(request, id):\n    user = User.objects.get(id=id)\n    if not is_admin_check(user):\n        if user.is_active:\n            user.is_active = False\n            messages.add_message(request, messages.SUCCESS, 'Uzivatel ' + user.username + ' deaktivovan')\n        else:\n            user.is_active = True\n            messages.add_message(request, messages.SUCCESS, 'Uzivatel ' + user.username + ' aktivovan')\n        user.save()\n\n    else:\n        messages.add_message(request, messages.ERROR, 'Nemuzes deaktivovat admina')\n    return redirect('/dk/members/')\n\n\ndef members(request):\n    users = UserProfile.objects.order_by(Lower('user__username'))\n    users = users.annotate(num_battles=Count('user__news'))\n    users = users.select_related()\n\n    admin = is_admin_check(request.user)\n    if request.method == 'POST' and admin:\n        user_form = UserForm(data=request.POST)\n        profile_form = UserProfileForm(data=request.POST)\n\n        if user_form.is_valid() and profile_form.is_valid():\n            user = user_form.save()\n            user.set_password(user.password)\n            user.save()\n\n            profile = profile_form.save(commit=False)\n            profile.user = user\n\n            profile.save()\n            messages.add_message(request, messages.SUCCESS, 'Uzivatel registrovan.')\n            return redirect('/dk/members/')\n        else:\n            messages.add_message(request, messages.ERROR, 'Chyba v registraci. Zkontroluj formular.')\n    else:\n        user_form = UserForm()\n        profile_form = UserProfileForm()\n\n    content = {'user_form': user_form, 'profile_form': profile_form, 'users': users,'admin':admin}\n    return render(request, 'dk/members.html', content)\n\n\ndef clans(request):\n    try:\n        clans_all = Clan.objects.select_related()\n    except User.DoesNotExist:\n        return redirect('index')\n\n    paginator = Paginator(clans_all, 25)\n    page = request.GET.get('page')\n    try:\n        clans = paginator.page(page)\n    except PageNotAnInteger:\n        clans = paginator.page(1)\n    except EmptyPage:\n        clans = paginator.page(paginator.num_pages)\n\n    if request.method == 'POST':\n        form = ClanForm(request.POST)\n        if form.is_valid():\n            form.save()\n\n            return redirect('/dk/clans/')\n        else:\n            print(form.errors)\n    else:\n        form = ClanForm()\n\n    return render(request, 'dk/clans.html', {'form': form, 'clans': clans})\n\n\ndef user_login(request):\n    if request.method == 'POST':\n        username = request.POST.get('username')\n        password = request.POST.get('password')\n\n        user = authenticate(username=username, password=password)\n\n        if user:\n            if user.is_active:\n                login(request, user)\n                messages.add_message(request, messages.SUCCESS, 'Prihlaseni OK.')\n                return HttpResponseRedirect(reverse('index'))\n            else:\n                return HttpResponse('Your DK account is disabled')\n\n        else:\n            # print(\"Invalid login details: {0}, {1}\".format(username, password))\n            messages.add_message(request, messages.ERROR, 'Invalid login details supplied.')\n            return HttpResponseRedirect(reverse('index'))\n            # return HttpResponse(\"Invalid login details supplied\")\n\n    else:\n        return render(request, 'dk/login.html', {})\n\n\n@login_required\ndef user_logout(request):\n    # Since we know the user is logged in, we can now just log them out.\n    logout(request)\n    # Take the user back to the homepage.\n    messages.add_message(request, messages.SUCCESS, 'Odhlaseni OK.')\n    return HttpResponseRedirect(reverse('index'))\n\n\n@login_required\ndef user_profile(request):\n    try:\n        user = User.objects.get(username=request.user)\n    except User.DoesNotExist:\n        return redirect('index')\n\n    userprofile = UserProfile.objects.get_or_create(user=user)[0]\n    form = UserProfileForm({'info': userprofile.info, 'birthday': userprofile.birthday, 'picture': userprofile.picture})\n\n    if request.method == 'POST':\n        form = UserProfileForm(data=request.POST, instance=userprofile)\n        if form.is_valid():\n            profile = form.save(commit=False)\n\n            if 'picture' in request.FILES:\n                profile.picture = request.FILES['picture']\n\n            profile.save()\n\n            if 'picture' in request.FILES:\n                basewidth = 128\n\n                cesta = profile.picture.path\n                obrazek = Image.open(cesta)\n\n                wpercent = (basewidth / float(obrazek.size[0]))\n                hsize = int((float(obrazek.size[1]) * float(wpercent)))\n                obrazek = obrazek.resize((basewidth, hsize), Image.ANTIALIAS)\n                obrazek.save(cesta)\n\n                print(profile.picture.path)\n            return redirect('user_profile')\n        else:\n            print(form.errors)\n\n    return render(request, 'dk/profile.html', {'form': form, 'userprofile': userprofile, })\n","sub_path":"dk/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"613945689","text":"#--*-- coding: utf-8 --*--\n\nimport pathlib\nfrom app.handlers.api.mail import Mail\n\n\nPROJECT_ROOT = pathlib.Path(__file__).parent\n\n\ndef init_routers(app):\n    mail = Mail()\n\n    prefix = '/api'\n\n    app.router.add_post(prefix + '/alarm/mail', mail.send_mail)\n\n\n","sub_path":"monitor_alarm/app/init/routers.py","file_name":"routers.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"117065761","text":"import boto3\nimport os\nimport re\nfrom tabulate import tabulate\n\nold_profile_name=\"uat\"\nnew_profile_name=\"newuat\"\nsourcesession = boto3.Session(profile_name=old_profile_name)\ndynamoclient = sourcesession.client('dynamodb', region_name='us-east-1')\n\ntargetsession = boto3.Session(profile_name=new_profile_name)\ndynamotargetclient = targetsession.client('dynamodb', region_name='us-east-1')\n\n\n\n#getting the \ntablelist = dynamoclient.list_tables()['TableNames']\n\nheaders = [\"Table_Name\", \"Old_uat_Count\", \"New_uat_Count\", \"validation\"]\n\n#tablelist=[\"test-shever\"]\ntables=[]\nloop=0\nprint (\"validation is currently in progress...\")\nfor table in tablelist:\n  #oldcount=int(dynamoclient.describe_table(TableName=table)['Table']['ItemCount'])\n  old_metadata=dynamoclient.scan(TableName=table)\n  oldcount=int(old_metadata['ScannedCount'])\n\n  while old_metadata.get(\"LastEvaluatedKey\"):\n    oldcount=int(old_metadata['ScannedCount']) + oldcount\n    old_metadata=dynamoclient.scan(TableName=table, ExclusiveStartKey=old_metadata.get(\"LastEvaluatedKey\"))\n\n  try:\n    #newcount=int(dynamotargetclient.describe_table(TableName=table)['Table']['ItemCount'])\n    new_metadata=dynamotargetclient.scan(TableName=table.replace(\"uat-02\", \"uat-03\"))\n    newcount=int(new_metadata['ScannedCount'])\n    while new_metadata.get(\"LastEvaluatedKey\"):\n      newcount=int(new_metadata['ScannedCount']) + newcount\n      new_metadata=dynamotargetclient.scan(TableName=table, ExclusiveStartKey=new_metadata.get(\"LastEvaluatedKey\"))\n\n  except dynamotargetclient.exceptions.ResourceNotFoundException:\n    newcount=\"Null\"\n\n  #print(\"{} {} {}\".format(table, oldcount, newcount))\n  if oldcount == newcount :\n  \tvalide=\"Sucess\"\n  elif newcount == \"Null\":\n  \tvalide=\"NA\"\n  else:\n     valide=\"Failed\"\n\n  print (\"validation completed for table {} oldcount {} newcount {} validation {}\".format(table, oldcount, newcount, valide))\n  tables.append([table, oldcount, newcount, valide])\n  del newcount\n  del oldcount\n  \n\n  # if loop == 0 :\n  # \tprint(tabulate(tables, headers, tablefmt=\"simple\"))\n  #   loop=1\n  # else:\n  #   print(tabulate(tables, tablefmt=\"simple\"))\n  # tables.clear()\nprint(tabulate(tables, headers, tablefmt=\"simple\"))\n","sub_path":"AWS/dynamodbvalidatw.py","file_name":"dynamodbvalidatw.py","file_ext":"py","file_size_in_byte":2193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"374676","text":"import boot\nimport py2pyx\nimport os\nimport shutil\nimport logging\npy2pyx.set_encode(boot.encode)\npy2pyx.set_decode(boot.decode)\n\n#project entry\nentry = \"app.py\"\npy_boot = \"boot.py\"\n\noutput_dir = \"guard\"\n\ndef clear_workspace():\n    if os.path.exists(output_dir):\n        for dirpath,dirs,filenames in os.walk(output_dir):\n            for name in filenames:\n                os.remove(os.path.join(dirpath,name))\n    else:\n        os.mkdir(output_dir)\n    pass\n\ndef build():\n\n    clear_workspace()\n\n    # project source excluce entry\n    project_src = []\n    for dirpath, dirnames, filenames in os.walk(\"./\"):\n        for fname in filenames:\n            if not fname.endswith(\".py\") or fname == entry or \\\n                    fname == py_boot or fname == \"guard.py\" or fname == \"setup.py\":\n                continue\n            project_src.append(os.path.join(dirpath, fname))\n\n    logging.basicConfig(level=logging.DEBUG, format='%(levelname)s: %(message)s')\n\n    logging.info(\"start encode....\")\n\n    for src in project_src:\n        if not src.endswith(\".py\"):\n            continue\n        logging.info(src)\n        pyc_path = src + \"c\"\n\n        if os.path.exists(pyc_path):\n            os.remove(pyc_path)\n\n        py2pyx.py2pyx(src)\n\n        pyc_guard = os.path.join(output_dir, os.path.basename(pyc_path))\n        if os.path.exists(pyc_path):\n            shutil.move(pyc_path, pyc_guard)\n\n        if not os.path.exists(pyc_guard):\n            logging.error(src + \" encode fail\")\n            exit(-1)\n\n    logging.info(py_boot)\n    py2pyx.boot_encode(py_boot)\n    pyc_boot_path = py_boot + 'c'\n    if not os.path.exists(pyc_boot_path):\n        logging.error(py_boot + ' encode fail')\n        exit(-1)\n    shutil.move(pyc_boot_path, os.path.join(output_dir, os.path.basename(pyc_boot_path)))\n\n    logging.info(entry)\n    shutil.copy(entry, os.path.join(output_dir, os.path.basename(entry)))\n\n    # logging.info(\"py_guard.so\")\n    # shutil.copy(\"py_guard.so\",os.path.join(output_dir,os.path.basename(\"py_guard.so\")))\n\n    logging.info(\"[success! %d file encode to %s]\" % (len(project_src), output_dir))\n\n    pass\n\n\nif __name__ == \"__main__\":\n    build()\n\n","sub_path":"example/guard.py","file_name":"guard.py","file_ext":"py","file_size_in_byte":2152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"469894254","text":"import taskloaf.worker\nimport taskloaf.promise\nimport taskloaf.allocator\nimport taskloaf.refcounting\nimport taskloaf.object_ref\nimport contextlib\n\ndef add_plugins(worker):\n    taskloaf.allocator.setup_plugin(worker)\n    taskloaf.refcounting.setup_plugin(worker)\n    taskloaf.object_ref.setup_plugin(worker)\n    taskloaf.promise.setup_plugin(worker)\n    return worker\n\n@contextlib.contextmanager\ndef null_comm_worker():\n    cfg = dict()\n    try:\n        with taskloaf.worker.Worker(taskloaf.worker.NullComm(), cfg) as worker:\n            add_plugins(worker)\n            yield worker\n    finally:\n        pass\n\ndef run(coro, cfg = None):\n    if cfg is None:\n        cfg = dict()\n\n    async def wrapper(worker):\n        try:\n            worker.result = await coro(worker)\n        finally:\n            taskloaf.worker.shutdown(worker)\n\n    with null_comm_worker() as worker:\n        worker.start(wrapper)\n        return worker.result\n","sub_path":"taskloaf/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"444820443","text":"\"\"\"\nOpenCV Note\n\npipenv install opencv-python\n\nこのノートは ↑ install すれば実行できる。\n\"\"\"\n\n\nimport io\nimport cv2\nimport numpy\n\n\n# ローカルファイルから mat ファイルを作る。\n# WARNING: 指定したファイルがなくてもエラーが発生しません。 None が出ます。\nmat = cv2.imread('./file.png')\nassert mat is not None, 'ファイルないよ。'\n\n\n# mat ファイルをリサイズする。今回はどんな画像も 200x200 にします。\n# WARNING: これを復活させると、下のブランク画像を作ってるところも (200, 200) にしないとダメよ。\n# mat = cv2.resize(mat, (200, 200))\n\n\n# グレースケールで作る。\ngray_scale_mat = cv2.imread('./file.png', cv2.IMREAD_GRAYSCALE)\n\n\n# バイナリから mat ファイルを作る。\ndef imread_from_bytes(b: bytes) -> numpy.ndarray:\n    numpy_array = numpy.frombuffer(b, numpy.uint8)\n    numpy_ndarray = cv2.imdecode(numpy_array, cv2.IMREAD_COLOR)\n    return numpy_ndarray\n\n\n# NOTE: mat ってのは numpy の1,2次元配列です。\n# NOTE: type(mat) -> <class 'numpy.ndarray'>\n# NOTE: 1次元のことを channel といい、2次元のことを dimension といいます。\n# print(mat)\n# [[[ 37  40  49]\n#   [ 34  37  44]\n#   [ 47  51  66]\n#   ...\n#   [ 48  61  74]\n#   [ 47  59  74]\n#   [ 47  61  73]]\n#   ...\n#  [[ 80 115 139]\n#   [ 88 127 152]\n#   [130 166 190]\n#   ...\n#   [ 97 133 166]\n#   [ 73 108 143]\n#   [ 64  94 127]]]\n\n# mat を画像で閲覧できます。ニュッと GUI で出てくる。\ncv2.imshow('Push any key to close the window', mat)\ncv2.waitKey(0)\n\n# mat の情報は普通に numpy.ndarray の持っているメソッドで確認できる。\n# channel はカラー画像のとき存在します。\n# NOTE: グレースケールと見分けるのに使われるようだ。\nwidth, height, channel = mat.shape\n# print(width, height, channel)\n# Example:100     100        3\n\n\n# 画像(mat)同士を連結。 mat 自体を2次元配列にしたものを渡すと、1つの mat になって返ってきます。\ndef concatenate_tile(mat_list_2d: numpy.ndarray) -> numpy.ndarray:\n\n    # mat_file の1次元配列を受け取り、タイル状に連結します。\n    return cv2.vconcat([cv2.hconcat(list_1d) for list_1d in mat_list_2d])\n\n\n# ブランク画像です。\n# NOTE: 黒画像なら numpy.zeros((100, 100, 3), numpy.uint8)\nblank_mat = numpy.ones((100, 100, 3), numpy.uint8) * 255\n\n# 今回は試しに2x2にしてみる。(file.png 3枚とブランク1枚で。)\nmat_list_1d = [mat, mat, mat]\n# 数が合わないと(4枚じゃないと)ダメ。\ngraft_list = [blank_mat] * (4 - len(mat_list_1d))\nmat_list_1d.extend(graft_list)\n# 4x4にします。 numpy の reshape を使ってもいい気がするが大袈裟かなと。\nmat_list_2d = [\n    mat_list_1d[0:2],\n    mat_list_1d[2:4],\n]\n# 連結。\nconcatenated_mat = concatenate_tile(mat_list_2d)\n\n# 確認します。\ncv2.imshow('Push any key to close the window', concatenated_mat)\ncv2.waitKey(0)\n\n# ローカルへの保存のやりかた2通りです。\n# 1. cv2.imwrite を使う。\ncv2.imwrite('./use_imwrite.png', concatenated_mat)\n# 2. cv2.imencode と tobytes でバイナリに変換してから保存する。\nencode_succeeded, buffer = cv2.imencode('.png', concatenated_mat)\nbytes_image = buffer.tobytes()\nwith open('./use_imencode.png', 'wb') as f:\n    f.write(bytes_image)\n\n# もし「ストリーム」が必要だと言われたら io を使わないといけない。\n# _io.BytesIO というオブジェクト。\nstream = io.BytesIO(buffer)\n# これは f.write には回せない。\n# バイナリにするには stream.read()\n","sub_path":"01_Python/(2020-07-15)OpenCvNote.py","file_name":"(2020-07-15)OpenCvNote.py","file_ext":"py","file_size_in_byte":3660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"367578883","text":"from django.urls import path\nfrom.import views\n\n\napp_name='store'\nurlpatterns=[\n    path(\"accueil/\",views.accueil,name=\"accueil\"),\n    path('detail/<product_id>',views.detail,name=\"detail\"),\n    path('display/',views.display,name=\"display\"),\n    path('categorie/<categorie_id>',views.categorie,name=\"categorie\"),\n    path(\"commande/<product_id>\",views.commande,name='commande'),\n    path(\"search/\",views.search,name='search'),\n    path(\"about/\",views.about,name='about'),\n    path(\"confidentialite/\",views.confidentialite,name='confidentialite'),\n    path(\"merci/\",views.merci,name='merci'),\n    ]\n","sub_path":"Lib/site-packages/django/bin/commerce/store/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"632095099","text":"\n\n### one code one day\n### leetcode 135 分发糖果\n### 2020/06/22\n\ndef candy(self, ratings: List[int]) -> int:\n    N = len(ratings)\n    ratings.insert(0, float('inf'))\n    ratings.append(float('inf'))\n    left = [1] * (N+2)\n    right = [1] * (N+2)\n    for i in range(2, N+1):\n        if(ratings[i] > ratings[i-1]):\n            left[i] = left[i-1] + 1\n    for i in range(N-1, 0, -1):\n        if(ratings[i] > ratings[i+1]):\n            right[i] = right[i+1] + 1\n    res = 0\n    for i in range(1, N+1):\n        res += max(left[i], right[i])\n    return res\n","sub_path":"数组/candy.py","file_name":"candy.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"230256898","text":"#!/usr/bin/env python\n\n\"\"\"Directory is a frontend to a system-wide directory datastore, where system config and definitions live.\"\"\"\n\n__author__ = 'Thomas R. Lennan, Michael Meisinger'\n__license__ = 'Apache 2.0'\n\nimport inspect\n\nfrom pyon.core import bootstrap\nfrom pyon.core.bootstrap import IonObject, CFG\nfrom pyon.core.exception import Conflict, NotFound, BadRequest\nfrom pyon.core.object import IonObjectBase\nfrom pyon.datastore.datastore import DataStore\nfrom pyon.util.log import log\n\nfrom interface.objects import DirEntry\n\n\nclass Directory(object):\n    \"\"\"\n    Class that uses a data store to provide a directory lookup mechanism.\n    \"\"\"\n\n    def __init__(self, datastore_manager=None, orgname=None):\n        # Get an instance of datastore configured as directory.\n        # May be persistent or mock, forced clean, with indexes\n        datastore_manager = datastore_manager or bootstrap.container_instance.datastore_manager\n        self.dir_store = datastore_manager.get_datastore(\"directory\", DataStore.DS_PROFILE.DIRECTORY)\n\n        self.orgname = orgname or CFG.system.root_org\n        self.is_root = (self.orgname == CFG.system.root_org)\n\n        self._init()\n\n    def close(self):\n        \"\"\"\n        Pass-through method to close the underlying datastore.\n        \"\"\"\n        self.dir_store.close()\n\n    def _get_dn(self, parent, key=None, org=None):\n        org = org or self.orgname\n        if parent == '/':\n            return \"%s/%s\" % (org, key) if key is not None else org\n        elif parent.startswith(\"/\"):\n            return \"%s%s/%s\" % (org, parent,key) if key is not None else \"%s%s\" % (org, parent)\n        else:\n            raise BadRequest(\"Illegal directory parent: %s\" % parent)\n\n    def _init(self):\n        self._assert_existence(\"/\", \"Agents\",\n                description=\"Running agents are registered here\")\n\n        if not self._assert_existence(\"/\", \"Config\",\n                description=\"System configuration is registered here\"):\n            if self.is_root:\n                self._register_config()\n\n        self._assert_existence(\"/\", \"Containers\",\n                description=\"Running containers are registered here\")\n\n        if not self._assert_existence(\"/\", \"ObjectTypes\",\n                description=\"ObjectTypes are registered here\"):\n            if self.is_root:\n                self._register_object_types()\n\n        self._assert_existence(\"/\", \"Org\",\n                description=\"Org specifics are registered here\",\n                is_root=self.is_root)\n\n        self._assert_existence(\"/Org\", \"Resources\",\n                description=\"Shared Org resources are registered here\")\n\n        self._assert_existence(\"/\", \"ResourceTypes\",\n                description=\"Resource types are registered here\")\n\n        if not self._assert_existence(\"/\", \"ServiceInterfaces\",\n                description=\"Service interface definitions are registered here\"):\n            if self.is_root:\n                self._register_service_definitions()\n\n        self._assert_existence(\"/\", \"Services\",\n                description=\"Service instances are registered here\")\n\n    def _assert_existence(self, parent, key, **kwargs):\n        \"\"\"\n        Make sure an entry is in the directory.\n        @retval True if entry existed\n        \"\"\"\n        dn = self._get_dn(parent, key)\n        direntry = self._safe_read(dn)\n        existed = bool(direntry)\n        if not direntry:\n            direntry = DirEntry(parent=parent, key=key, attributes=kwargs)\n            # TODO: This may fail because of concurrent create\n            self.dir_store.create(direntry, dn)\n        return existed\n\n    def _safe_read(self, key):\n        try:\n            res = self.dir_store.read(key)\n            return res\n        except NotFound:\n            return None\n        except BadRequest:\n            return None\n\n    def _create(self):\n        \"\"\"\n        Method which will create the underlying data store and\n        persist an empty Directory object.\n        \"\"\"\n        log.debug(\"Creating data store and Directory\")\n        self.dir_store.create_datastore()\n\n        # Persist empty Directory object under known name\n        self.dir_name = bootstrap.get_sys_name()\n        directory_obj = IonObject('Directory', name=self.dir_name)\n        dir_id,rev = self.dir_store.create(directory_obj, 'DIR')\n\n        # Persist ROOT Directory object\n        root_obj = DirEntry(parent='/', key=\"ROOT\", attributes=dict(sys_name=bootstrap.get_sys_name()))\n        root_id,rev = self.dir_store.create(root_obj, self._get_dn(root_obj.parent, root_obj.key))\n\n    def register(self, parent, key, **kwargs):\n        \"\"\"\n        Add/replace an entry to directory below a parent node.\n        Note: Does not merge the attribute values of the entry if existing\n        \"\"\"\n        assert parent and key, \"Malformed Directory register\"\n        dn = self._get_dn(parent, key)\n        log.debug(\"Directory.add(%s): %s\" % (dn, kwargs))\n\n        entry_old = None\n        direntry = self._safe_read(dn)\n        if direntry:\n            entry_old = direntry.attributes\n            direntry.attributes = kwargs\n            # TODO: This may fail because of concurrent update\n            self.dir_store.update(direntry)\n        else:\n            direntry = DirEntry(parent=parent, key=key, attributes=kwargs)\n            # TODO: This may fail because of concurrent create\n            self.dir_store.create(direntry, dn)\n\n        return entry_old\n\n    def register_safe(self, parent, key, **kwargs):\n        try:\n            return self.register(parent, key, **kwargs)\n        except Exception as ex:\n            log.exception(\"Error registering key=%s/%s, args=%s\" % (parent, key, kwargs))\n\n    def register_mult(self, entries):\n        if type(entries) not in (list, tuple):\n            raise BadRequest(\"Bad type\")\n        de_list = []\n        deid_list = []\n        for parent, key, attrs in entries:\n            de = DirEntry(parent=parent, key=key, attributes=attrs)\n            de_list.append(de)\n            dn = self._get_dn(parent, key)\n            deid_list.append(dn)\n        self.dir_store.create_mult(de_list, deid_list)\n\n    def lookup(self, qualified_key='/'):\n        \"\"\"\n        Read entry residing in directory at parent node level.\n        \"\"\"\n        log.debug(\"Reading content at path %s\" % qualified_key)\n        dn = self._get_dn(qualified_key)\n        direntry = self._safe_read(dn)\n        return direntry.attributes if direntry else None\n\n    def unregister(self, parent, key):\n        \"\"\"\n        Remove entry residing in directory at parent node level.\n        \"\"\"\n        dn = self._get_dn(parent, key)\n        log.debug(\"Removing content at path %s\" % dn)\n\n        entry_old = None\n        direntry = self._safe_read(dn)\n        if direntry:\n            entry_old = direntry.attributes\n            self.dir_store.delete(direntry)\n\n        return entry_old\n\n    def unregister_safe(self, parent, key):\n        try:\n            return self.unregister(parent, key)\n        except Exception as ex:\n            log.exception(\"Error unregistering key=%s/%s\" % (parent, key))\n\n    def find_entries(self, qname='/'):\n        if not str(qname).startswith('/'):\n            raise BadRequest(\"Illegal directory node: qname=%s\" % qname)\n        delist = self.dir_store.find_dir_entries(qname)\n        return delist\n\n    # ------------------------------------------\n    # Specific directory entry methods\n\n\n    # ------------------------------------------\n    # Internal methods\n\n    def _register_config(self):\n        self.register(\"/Config\", \"CFG\", **CFG.copy())\n\n    def _load_config(self):\n        de = self.lookup(\"/Config/CFG\")\n        if not de:\n            raise Conflict(\"Expected /Config/CFG in directory. Correct Org??\")\n\n    def _register_service_definitions(self):\n        from pyon.core.bootstrap import service_registry\n        svc_list = []\n        for svcname, svc in service_registry.services.iteritems():\n            svc_list.append((\"/ServiceInterfaces\", svcname, {}))\n            #log.debug(\"Register service: %s\" % svcname)\n        self.register_mult(svc_list)\n        log.info(\"Registered %d services in directory\" % len(svc_list))\n\n    def _register_object_types(self):\n        from interface import objects\n        delist = []\n        for cname, cobj in inspect.getmembers(objects, inspect.isclass):\n            if issubclass(cobj, IonObjectBase) and cobj != IonObjectBase:\n                parentlist = [parent.__name__ for parent in cobj.__mro__ if parent.__name__ not in ['IonObjectBase','object']]\n                delist.append((\"/ObjectTypes\", cname, dict(schema=cobj._schema, extends=parentlist)))\n                #log.debug(\"Register object: %s\" % cname)\n        self.register_mult(delist)\n        log.info(\"Registered %d objects in directory\" % len(delist))\n\n    def load_definitions(self):\n        pass\n","sub_path":"pyon/ion/directory.py","file_name":"directory.py","file_ext":"py","file_size_in_byte":8838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"182662302","text":"from django.urls import path\n\nfrom ..views import shop_view\n\nCOMMERCE_URL = [\n    # Admin routes for the seller.\n    path(\"create_shop/\", shop_view.create_shop, name=\"create_shop\"),\n    path(\"create_tags/\", shop_view.create_tags, name=\"create_tags\"),\n    path(\"create_product/\", shop_view.create_product, name=\"create_product\"),\n    path(\"delete_products/\", shop_view.delete_products, name=\"delete_products\"),\n    path(\n        \"delete_products/<str:p_k>/\",\n        shop_view.delete_products_mobile,\n        name=\"delete_products_mobile\",\n    ),\n    path(\"shop_products/\", shop_view.shop_products, name=\"shop_products\"),\n    path(\"catergory_list/\", shop_view.get_catergories, name=\"catergory_list\"),\n    path(\"get_info/\", shop_view.get_info, name=\"get_info\"),\n    path(\"save_info/\", shop_view.save_info, name=\"save_info\"),\n    path(\"edit_products/\", shop_view.edit_products, name=\"edit_produts\"),\n    path(\"delete_tags/\", shop_view.delete_tags, name=\"delete_tags\"),\n]\n","sub_path":"api/urls_route/commerce.py","file_name":"commerce.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"540050750","text":"# python 实现相关度和 R平方(决定系数)\nimport numpy as np\nfrom astropy.units import Ybarn\nimport math\n\n# 计算皮尔逊相关系数\ndef computeCorrelation(X, Y):\n    xBar = np.mean(X)\n    yBar = np.mean(Y)\n    SSR = 0\n    varX = 0\n    varY = 0\n    for i in range(0, len(X)):\n        diffXvsXbar = X[i] - xBar\n        diffYvsYbar = Y[i] - yBar\n        SSR += (diffXvsXbar * diffYvsYbar)\n        varX += diffXvsXbar**2\n        varY += diffYvsYbar**2\n\n    SST = math.sqrt(varX * varY)\n    return SSR / SST\n\n\n\n# degree --次. x^2就是二次 这里是一次 没有x平方的值\ndef polyfix(x,y,degree):\n    results= {}\n    coeffs = np.polyfit(x, y, degree)\n\n    # results['polynomial'] 包含斜率和截距，一个方程的必要参数\n    results['polynomial'] = coeffs.tolist() #字典to list\n\n    # r squared\n    p = np.poly1d(coeffs) #一维线性回归 这个p就是估计的理想方程\n    yhat = p(x)\n    ybar = np.sum(y)/len(y) # np.mean(Y)\n    ssreg = np.sum((yhat-ybar)**2)\n    sstotal = np.sum((y-ybar)**2)\n    results['determination'] = ssreg / sstotal\n    return results\n\n\n\n\ntextX = [1,3,8,7,9]\ntextY = [10,12,24,21,34]\n\nprint (\"r: {}\".format(computeCorrelation(textX, textY)))\nprint (\"r^2: {}\".format(computeCorrelation(textX, textY)**2))\n\nprint (\"r^2: {}\".format(polyfix(textX, textY, 1)['determination']))","sub_path":"multiple-regression/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":1332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"442638636","text":"import cv2\nimport sys\nimport os.path\n\ncascade_file = \"./lbpcascade_animeface.xml\"\ncascade = cv2.CascadeClassifier(cascade_file)\n\ndef detect(image):\n    global cascade\n    if not os.path.isfile(cascade_file):\n        raise RuntimeError(\"%s: not found\" % cascade_file)\n\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    gray = cv2.equalizeHist(gray)\n\n    faces = cascade.detectMultiScale(gray,\n                                     # detector options\n                                     scaleFactor = 1.1,\n                                     minNeighbors = 5,\n                                     minSize = (24, 24))\n    for (x, y, w, h) in faces:\n        yield(x, y, w, h)\n\ninput = cv2.VideoCapture(sys.argv[1])\nprint(input.get(cv2.CAP_PROP_FOURCC))\n\nwidth = input.get(cv2.CAP_PROP_FRAME_WIDTH)\nheight = input.get(cv2.CAP_PROP_FRAME_HEIGHT)\nfps = input.get(cv2.CAP_PROP_FPS)\n\nprint(fps)\n\nfourcc = cv2.VideoWriter_fourcc(*'mp4v')\noutput = cv2.VideoWriter('output.avi',\n    fourcc,\n    60,\n    (int(width), int(height)))\n\ni = 0\nwhile True:\n    ret, frame = input.read()\n\n    i += 1\n    if ret == True:\n        for (x,y, w,h) in detect(frame):\n            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)\n\n        output.write(frame)\n        # cv2.imwrite('tmp/mov%d.png' % i, frame)\n        print('mov%d' % i)\n    else:\n        break\n","sub_path":"face_detect.py","file_name":"face_detect.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"297002271","text":"import pytest\nfrom immudb.client import ImmudbClient\nimport string,random\nimport grpc._channel\n\n\n\ndef get_random_string(length):\n    return ''.join(random.choice(string.printable) for i in range(length))\n\nclass TestGetSet:\n    def test_get_set_massive(self):\n        try:\n            a = ImmudbClient(\"localhost:3322\")\n            a.login(\"immudb\",\"immudb\")\n        except grpc._channel._InactiveRpcError as e:\n            pytest.skip(\"Cannot reach immudb server\")\n        xset={}\n        for i in range(0,10000):\n            xset[\"massif:{:04X}\".format(i).encode('utf8')]=get_random_string(32).encode('utf8')\n        assert type(a.setAll(xset))!=int\n        # test getAll\n        resp=a.getAll(xset.keys())\n        for i in resp.keys():\n            assert i in xset\n            assert xset[i]==resp[i]\n        for i in xset.keys():\n            assert i in resp\n            assert xset[i]==resp[i]\n        # test getAllItems\n        resp=a.getAllItems(xset.keys())\n        for i in resp.keys():\n            assert i in xset\n            assert xset[i]==resp[i]\n\n\n\n","sub_path":"tests/immu/test_massive_operation.py","file_name":"test_massive_operation.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"55586220","text":"\"\"\"Process data from the mini and portables \"\"\"\nfrom __future__ import print_function\nimport datetime\n\nimport pytz\nimport psycopg2.extras\nfrom pyiem.observation import Observation\nfrom pyiem.datatypes import temperature, humidity\nfrom pyiem import meteorology\nfrom pyiem.util import get_dbconn\n\n\nLOOKUP = {'miniExportM1.csv': 'RAII4',\n          'miniExportM2.csv': 'RMYI4',\n          'portableExportP1.csv': 'RSWI4',\n          'portableExportP2.csv': 'RAGI4',\n          'portableExportP3.csv': 'RLMI4',\n          # 'portableExportPT.csv': 'ROCI4',\n          'portableExportPT.csv': 'RRCI4',\n          'miniExportIFB.csv': 'RIFI4',\n          }\n\nTHRESHOLD = datetime.datetime.utcnow() - datetime.timedelta(minutes=180)\nTHRESHOLD = THRESHOLD.replace(tzinfo=pytz.UTC)\n\n\ndef processfile(icursor, filename):\n    \"\"\"Process this file\"\"\"\n    lines = open(\"/mesonet/data/incoming/rwis/%s\" % (filename,),\n                 'r').readlines()\n    if len(lines) < 2:\n        return\n    heading = lines[0].split(\",\")\n    cols = lines[1].split(\",\")\n    data = {}\n    if len(cols) < len(heading):\n        return\n    for i, h in enumerate(heading):\n        if cols[i].strip() != \"/\":\n            data[h.strip()] = cols[i].strip()\n\n    nwsli = LOOKUP[filename]\n    if filename in ['portableExportP1.csv', 'miniExportIFB.csv']:\n        ts = datetime.datetime.strptime(data['date_time'][:16],\n                                        '%Y-%m-%d %H:%M')\n    else:\n        ts = datetime.datetime.strptime(data['date_time'][:-6],\n                                        '%Y-%m-%d %H:%M')\n    ts = ts.replace(tzinfo=pytz.UTC)\n    iem = Observation(nwsli, 'IA_RWIS', ts)\n    if ts.year < 2010:\n        print((\"rwis/mini_portable.py file: %s bad timestamp: %s\"\n               \"\") % (filename, data['date_time']))\n        return\n    iem.load(icursor)\n\n    # IEM Tracker stuff is missing\n\n    if data.get('wind_speed', '') != '':\n        iem.data['sknt'] = float(data['wind_speed']) * 1.17\n    if data.get('sub', '') != '':\n        iem.data['rwis_subf'] = float(data['sub'])\n    if data.get('air_temp', '') != '':\n        iem.data['tmpf'] = float(data['air_temp'])\n    if data.get('pave_temp', '') != '':\n        iem.data['tsf0'] = float(data['pave_temp'])\n    if data.get('pave_temp2', '') != '':\n        iem.data['tsf1'] = float(data['pave_temp2'])\n    if data.get('press', '') != '':\n        iem.data['alti'] = float(data['press'])\n    if data.get('RH', '') != '':\n        if float(data['RH']) > 1:\n            t = temperature(iem.data['tmpf'], 'F')\n            rh = humidity(float(data['RH']), '%')\n            iem.data['dwpf'] = meteorology.dewpoint(t, rh).value('F')\n    if data.get('wind_dir', '') != '':\n        iem.data['drct'] = float(data['wind_dir'])\n    iem.save(icursor)\n\n\ndef main():\n    \"\"\"Go main Go\"\"\"\n    pgconn = get_dbconn('iem')\n    icursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n    for k in LOOKUP:\n        processfile(icursor, k)\n\n    pgconn.commit()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"scripts/ingestors/rwis/mini_portable.py","file_name":"mini_portable.py","file_ext":"py","file_size_in_byte":3004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"292430750","text":"import socket\n#实例化一个socket对象\ntcp_client = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n#连接的服务端地址\nport_ip = ('127.0.0.1',8000)\n#连接到服务端\ntcp_client.connect(port_ip)\n\n#通讯循环\nwhile True:\n    msg = input('>>')\n    #对信息进行处理\n    if not msg:continue\n    if msg == 'quit':break\n    #接通电话后发信息\n    tcp_client.send(msg.encode('utf-8'))\n    print('成功发送信息：',msg)\n    #接收反馈信息\n    feedback = tcp_client.recv(1000)\n    print('成功接收到反馈信息',feedback.decode('gbk'))\n#关闭连接\ntcp_client.close()\n","sub_path":"网络编程/基于tcp协议实现远程连接client端.py","file_name":"基于tcp协议实现远程连接client端.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"511634442","text":"\"\"\"\nFile: Lagrange_poly2b.py\nExercise 5.25\nCopyright (c) 2016 Andrew Malfavon\nLicense: MIT\nInvestigate the behavior of Lagrange's interpolating polynomials.\n\"\"\"\n\nimport Lagrange_poly2 as p1\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#Graph for n = 2, 4, 6, and 10\ndef graph1():\n    n = [2, 4, 6, 10]\n    p1.graph2(np.abs, n, -2, 2)\n    plt.title('f(x) = |x| and Interpolation Points')\n    plt.xlim(-2.0, 2.0)\n    plt.ylim(0.0, 2.1)\n\n#graph for n = 13, and 20\ndef graph2():\n    n = [13, 20]\n    p1.graph2(np.abs, n, -2, 2)\n    plt.title('f(x) = |x| and Interpolation Points')\n    plt.xlim(-2.0, 2.0)\n    plt.ylim(-4.5, 5.5)","sub_path":"Lagrange_poly2b.py","file_name":"Lagrange_poly2b.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"501436481","text":"from dbfread import DBF\nfrom .repository import FacultyRepository, StudentRepository\nfrom .entity import Student\n\nclass DbfSyncer(object):\n    def __init__(self, students_file, faculty_file, db_helper):\n        self.students_file = students_file\n        self.faculty_file = faculty_file\n        self.helper = db_helper\n        self.faculty_repository = FacultyRepository(self.helper)\n        self.students_repository = StudentRepository(self.helper)\n\n    def sync(self, progress_listener):\n        self.sync_faculty(progress_listener)\n        self.sync_students(progress_listener)\n        progress_listener(None, None, None, True)\n\n    def sync_students(self, progress_listener):\n        self.students_repository.delete_all()\n        dbf = DBF(self.students_file, encoding='cp866')\n        size = len(dbf)\n        for index, record in enumerate(dbf):\n            progress_listener(self.students_file, size, index, False)\n            self.read_students_record(record)\n\n    def sync_faculty(self, progress_listener):\n        self.faculty_repository.delete_all()\n        dbf = DBF(self.faculty_file, encoding='cp866')\n        size = len(dbf)\n        for index, record in enumerate(dbf):\n            progress_listener(self.faculty_file, size, index, False)\n            self.read_faculty_record(record)\n\n    def read_faculty_record(self, record):\n        self.faculty_repository.create(record['FAK'], record['NAME_FAK'])\n\n    def read_students_record(self, record):\n        student = Student()\n        student.tab_num = record['TABNOM']\n        self.set_student_name(student, record['FIO'])\n        student.inn = record['ID_KOD']\n        student.faculty = self.faculty_repository.find_by_id(record['FAK'])\n        self.students_repository.save(student)\n\n    @staticmethod\n    def set_student_name(student, full_name):\n        splited = full_name.split(' ')\n        student.last_name = splited[0].strip()\n        student.first_name = splited[1].strip()\n        if len(splited) > 2:\n            student.middle_name = splited[2].strip()\n","sub_path":"db/dbf_syncer.py","file_name":"dbf_syncer.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"286332374","text":"#!/usr/bin/env python3\nfrom argparse import ArgumentParser\nimport csv\nfrom math import sqrt\nfrom pathlib import Path\nimport pickle\nfrom typing import Any, Iterable\n\nfrom data_path_utils import create_data_path\nimport networkx as nx\n\nHIPPIE_GENE_INCIDES = ['ID Interactor A', 'ID Interactor B']\nHIPPIE_WEIGHT_INDEX = 'Confidence Value'\ndef get_hippie_proteins_from_line(line):\n    data = []\n    for index in HIPPIE_GENE_INCIDES:\n        value = line[index]\n        pieces = value.split(':')\n        try:\n            data.append(pieces[1])\n        except IndexError:\n            print('Bad gene data: {}'.format(value))\n            return\n    if any(d is None for d in data):\n        return\n    weight = float(line[HIPPIE_WEIGHT_INDEX])\n    if weight == 0:\n        return\n    data.append({'weight': weight})\n    return tuple(data)\n\ndef get_hippie_proteins(filename: Path):\n    with filename.open() as f:\n        r = csv.DictReader(f, delimiter='\\t')\n        for i, line in enumerate(r, 1):\n            proteins = get_hippie_proteins_from_line(line)\n            if proteins:\n                yield proteins\n            else:\n                print('Line {} bad'.format(i))\n\ndef build_hippie_network(filename: Path) -> nx.Graph:\n    g = nx.Graph()\n    g.add_edges_from(get_hippie_proteins(filename))\n    return g\n\ndef join_string_keys(args: Iterable[Any]) -> str:\n    return '_'.join(str(arg) for arg in args)\n\ndef insert_dummy_edge_nodes(input_network: nx.Graph, edge_name_func=tuple) -> nx.Graph:\n    \"\"\"\n    :param input_network: undirected, maybe weighted\n    :param edge_name_func: Function used to create names for new nodes. Takes one\n    iterable argument of the (sorted) node names, and returns a single object used\n    for new node labels. By default, this is `tuple`, to preserve as much\n    information as possible about the input network, but this can be overridden to\n    create string labels or anything else desired. The function `join_string_keys`\n    is designed to be used as an alternative to `tuple`.\n    :return: A new network, with dummy nodes inserted in the middle of each edge.\n\n    For an original edge (A, B) with weight w(A, B), the new edge weights w((A,), (A, B))\n    and w((A, B), (B,)), are equal to √w(A, B), preserving the property that for a\n    path P, the reliability r(P) = ∏_{n1, n2 ��� P} w(n1, n2) is unchanged after this\n    transformation.\n    \"\"\"\n    network = nx.Graph()\n\n    for node in input_network.nodes:\n        network.add_node(edge_name_func([node]))\n\n    for n1, n2, weight in input_network.edges.data('weight'):\n        new_n1 = edge_name_func([n1])\n        new_n2 = edge_name_func([n2])\n        new_node = edge_name_func(sorted([n1, n2]))\n        if weight is None:\n            new_weight = 1\n        else:\n            new_weight = sqrt(weight)\n\n        network.add_edge(new_n1, new_node, weight=new_weight)\n        network.add_edge(new_node, new_n2, weight=new_weight)\n\n    return network\n\nif __name__ == '__main__':\n    p = ArgumentParser()\n    p.add_argument('hippie_input_path', type=Path)\n    args = p.parse_args()\n\n    data_path = create_data_path('build_hippie_network')\n    print('Reading HIPPIE network from', args.hippie_input_path)\n    network = build_hippie_network(args.hippie_input_path)\n    print('Network contains {} nodes, {} edges'.format(len(network.nodes()), len(network.edges())))\n    network_output_path = data_path / 'network.pickle'\n    print('Saving network to', network_output_path)\n    with network_output_path.open('wb') as f:\n        pickle.dump(network, f, protocol=pickle.HIGHEST_PROTOCOL)\n","sub_path":"network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":3575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"542900037","text":"## Quick Sort vs Merge Sort\n# Both of them use the divide and conquer strategy.\n# The key of quick sort is \"Pivot\", and \"partition\".\n# The key of merge sort is the \"midpoint\" and \"merge\".\n\n# The quick sort partitions the sequence by dividing it into two segments based on a selected pivot key.\n# In addition, the quick sort can be implemented to work with virutal subsequences without the need \n# for temporary storage.\n\n# The quick sort is a simple recursive algorithm that can be used to sort keys stored in either an array or list.\n\n# The FIRST key is selected as the pivot.\n\n# A simple implementation using the slice operation can be devised for the quick sort algorithm as was done \n# with the merge sort but it would require the use of temporary storage.\n\n# An efficient solution can be deisigned to work with virtual subsequences or segments that does not \n# require temporary storage.\n# However, it is not as easily implemented since the partitioning must be done using the same sequence\n# structure.\n\n## Efficiency Analysis\n# The quick sort algorithm has an average or expected time of O(nlogn)\n# BUT runs in O(N^2) in the worst case.\n# Even though quick sort is quadratic in the worst case, it does approach the average\n# case in many instances and has the advantage of not requiring additonal temporary\n# storage as in the case with the merge sort.\n# The quick sort is the commonly used algorithm to implement sorting in language libraries.\n# Earlier version of Python used quick sort to implement the sort() method of the list structure.\n# In a current version, a hybrid algorithm that combines the insertion and merge sort\n# algorithm is used instead.\n\n\n## Implementation - in place sort\ndef quickSort(seq):\n    N  = len(seq)\n    recQuickSort(seq, 0, N-1)\n\ndef recQuickSort(seq, start, end):\n    # base case\n    # start > end to handle the seq empty case.\n    if start >= end:\n        return    \n    pivot = seq[start]\n    # divide\n    # find the position index\n    pos = partitionSeq(seq, start, end)\n    # after partition, the pivot index will be changed.\n    recQuickSort(seq, start, pos-1)\n    # in other words, the pivot is settle down, no need to put it into the rec sort seq indexes.\n    recQuickSort(seq, pos+1, end)\n    \n\n# O(N)\n# compare with pivot, like the insertion sort?\n# \ndef partitionSeq(seq, start, end):\n    pivot = seq[start]\n    left, right = start + 1, end\n    while left <= right:\n        while left <= right and seq[left] < pivot:\n            left += 1\n        while left <= right and seq[right] >= pivot:\n            right -= 1\n        # now the left > pivot, and the right < pivot, exchange them\n        if left < right:\n            seq[left], seq[right] = seq[right], seq[left]\n            \n        # put the pivot the proper place\n        # now, left should be the first one that greater than \n    if start != right:\n        seq[start], seq[right] = seq[right], seq[start]\n    # Now, right is the pivot index\n    return right\n\n            \n    \nif __name__ == \"__main__\":\n    a = [9, 7, 6, 2, 3, 7, 7, 7]\n    b = [2,2,2,2]\n    print(\"Before quick sort:\", a)\n    quickSort(a)\n    quickSort(b)\n    print(\"After quick sort:\", a)\n    print(\"After quick sort:\", b)\n\n## How fast can we sort?\n# The comparison sort algorithms achieve their goal by comparing the individual sort keys\n# to other keys in the list.\n# We have reviewed five sorting algorithms. The first three, bubble, selection, and insertion, have a \n# worse case time of O(N^2) while the merge sort has a worst case time of O(NlogN).\n# The quick sort, the more commonly used algorithm in language libraries, is O(N^2) in the worst case \n# but it has an expected or average time of O(NlogN).\n# For a comparison sort, the answer is no.\n# It can be shown, with the use of a decision tree and examining the permutations of all possible\n# comparisons among the sort keys, that the worst case time for a comparison sort can be no better than O(logN).\n# A distribution sort algorithm that works in linear time.\n# Distrbution sort algorithms use techniques other than comparsions among the keys themselves to sort \n# the sequence of keys.\n# While there distribution algorithms are fast, they are not general purpose sorting algorithms.\n# In other words, they cannot be applied to just any sequence of keys.\n# Typically, these algorithms are used when the keys have certain characteristics and for specific types of \n# applications.\n \n\n\n","sub_path":"bookmark/data_structures_and_algorithms_using_python/12-advanced_sorting/quick_sort.py","file_name":"quick_sort.py","file_ext":"py","file_size_in_byte":4427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"153415450","text":"from __future__ import absolute_import, print_function\n\nfrom flask import Blueprint, render_template, request\nfrom .models import ApiRegistrations\n\nimport sys\nfrom invenio_db import db\n\nblueprint = Blueprint(\n    'scoap3_api',\n    __name__,\n    url_prefix='/partners',\n    template_folder='templates',\n    static_folder='static',\n)\n\n\nclass EmailRegisterdException(Exception):\n    pass\n\n\nclass NameUsedException(Exception):\n    pass\n\n\n@blueprint.route('/')\ndef index():\n    return render_template(\n        'scoap3_api/index.html',\n        title='SCOAP3 Repository - API'\n    )\n\n\n@blueprint.route('/register', methods=['GET', 'POST'])\ndef register():\n    message = ''\n    if request.method == 'POST':\n        try:\n            new_reg = ApiRegistrations(partner=bool(int(request.form['partner'])),\n                                       name=request.form['name'],\n                                       email=request.form['email'],\n                                       organization=request.form['organization'],\n                                       role=request.form['role'],\n                                       country=request.form['country'],\n                                       description=request.form['description']\n                                       )\n            db.session.add(new_reg)\n            db.session.commit()\n            message = ('success', \"Registration succesful. We will confirm your registration shortly.\")\n        except EmailRegisterdException():\n            message = (\n                'error',\n                \"User with this <b>email</b>: {} is already registered.\".format(request.args.get('inputEmail')))\n        except NameUsedException():\n            message = (\n                'error', \"User with this <b>name</b>: {} is already registered\".format(request.args.get('inputName')))\n        except Exception():\n            message = ('error', sys.exc_info()[0])\n\n    return render_template(\n        'scoap3_api/register.html',\n        title='SCOAP3 Repository - Tools registration',\n        message=message,\n    )\n","sub_path":"scoap3/modules/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"440436133","text":"#! /usr/bin/env python\nimport subprocess\nimport argparse\nfrom datetime import datetime\nimport os,sys,glob,shutil\nfrom rpcm import geo\nimport numpy as np\nimport geopandas as gpd\nfrom distutils.spawn import find_executable\nfrom skysat_stereo import misc_geospatial as misc\nfrom skysat_stereo import asp_utils as asp\nfrom skysat_stereo import bundle_adjustment_lib as ba\nfrom skysat_stereo import skysat_stereo_workflow as workflow\n\n\"\"\"\nScript for running the full pipeline based on workflow described in ISPRS 2020 submission\nNeed to specify input image folder, input refrence DEM folder\n\"\"\"\n#TODO:\n# Add an option of cleaning up the lots of intermediate files produced\n\ndef getparser():\n    parser = argparse.ArgumentParser(description='Wrapper script to run full triplet stereo workflow')\n    parser.add_argument('-in_img',default=None,type=str,help='path to Folder containing L1B imagery')\n    parser.add_argument('-aoi_bbox',default=None,type=str,help='path to bounding box shapefile if limiting processing to a smaller aoi')\n    parser.add_argument('-orthodem',default=None,type=str,help='path to Reference DEM to use in orthorectification and camera resection, if not provided, will use coregdem')\n    parser.add_argument('-coregdem',default=None,type=str,help='path to reference DEM to use in coregisteration')\n    parser.add_argument('-mask_dem',default=1,type=int,choices=[1,0],help='mask reference DEM for static surfaces before coreg (default: %(default)s)')\n    mask_opt = ['glaciers','glaciers+nlcd']\n    parser.add_argument('-mask_dem_opt',default='glaciers',choices=mask_opt,help='surfaces to mask if -mask_dem=1, default is glaciers which uses RGI polygons.\\\n                        If processing in CONUS, the option of glaciers+nlcd also additionaly masks out forest surfaces')\n    parser.add_argument('-ortho_workflow',default=1,type=int,choices=[1,0],help='option to orthorectify before stereo or not')\n    parser.add_argument('-block_matching',default=0,type=int,choices=[1,0],help='whether to use block matching in final stereo matching, default is 0 (not)')\n    parser.add_argument('-job_name',default=None,type=str,help='identifier for output folder and final composite products')\n    parser.add_argument('-outfolder',default=None,type=str,help='path to output folder to save results in')\n    bin_choice = [1,0]\n    parser.add_argument('-full_workflow',choices=bin_choice,type=int,default=1,help='Specify 1 to run full workflow (default: %(default)s)')\n    parser.add_argument('-partial_workflow_steps',nargs='*',help='specify steps of workflow to run')\n    return parser\n\ndef main():\n    parser = getparser()\n    args = parser.parse_args()\n    img_folder = args.in_img\n    coreg_dem = args.coregdem\n    if args.orthodem is not None:\n        ortho_dem = args.orthodem\n    else:\n        ortho_dem = coreg_dem\n    # Check for input files\n    img_list = glob.glob(os.path.join(img_folder,'*.tif'))+glob.glob(os.path.join(img_folder,'*.tiff'))\n    if len(img_list)<2:\n        print(f\"Only {len(img_list)} images detected, exiting\")\n        sys.exit()\n    if not os.path.exists(coreg_dem):\n        print(f\"Coreg dem {coreg_dem} could not be located, exiting\")\n        sys.exit()\n    if not os.path.exists(ortho_dem):\n        print(f\"Ortho dem {ortho_dem} could not be located, exiting\")\n        sys.exit()\n\n    # structure for output folder\n    out_fol = os.path.join(args.outfolder,'proc_out')\n    job_name = args.job_name\n    \n    #Universal Args\n    if args.ortho_workflow == 1:\n        map = True\n    else:\n        map = False\n    if map:\n        init_stereo_session = 'rpcmaprpc'\n        init_ortho_session = 'rpc'\n        final_stereo_session = 'pinholemappinhole'\n        final_ortho_session = 'pinhole'\n    else:\n        init_stereo_session = 'rpc'\n        init_ortho_session = 'rpc'\n        final_stereo_session, final_ortho_session = ['nadirpinhole','pinhole']\n\n    # For consistency, lets hardcode expected file names,folder names here :)\n    # step1 outputs\n    overlap_full_txt = os.path.join(out_fol,'overlap.txt')\n    overlap_full_pkl = os.path.splitext(overlap_full_txt)[0]+'_with_overlap_perc.pkl'\n    overlap_stereo_pkl = os.path.splitext(overlap_full_pkl)[0]+'_stereo_only.pkl'\n    overlap_stereo_txt = os.path.splitext(overlap_full_pkl)[0]+'_stereo_only.txt'\n    bound_fn = os.path.splitext(overlap_full_txt)[0]+'_bound.gpkg'\n    bound_buffer_fn = os.path.splitext(bound_fn)[0]+'_1km_buffer.gpkg'\n\n    # step2 outputs\n    cam_gcp_directory = os.path.join(out_fol,'camgen_cam_gcp')\n    \n    # step3 outputs\n    init_ortho_dir = os.path.join(out_fol,'init_rpc_ortho')\n    init_stereo_dir = os.path.join(out_fol,'init_rpc_stereo')\n    \n    # step4 bundle_adjust dense matches\n    init_ba = os.path.join(out_fol,'ba_pinhole')\n    ba_prefix = os.path.join(init_ba,'run')\n    \n    # step5 stereo_args\n    intermediate_ortho_dir = os.path.join(out_fol,'intermediate_pinhole_ortho')\n    final_stereo_dir = os.path.join(out_fol,'final_pinhole_stereo')\n\n    # step 6, dem gridding and mosaicing\n    mos_dem_dir = os.path.join(final_stereo_dir,'composite_dems')\n\n    # step 7. dem_alignment\n    alignment_dir = os.path.join(out_fol,'georegistered_dem_mos')\n \n    # step 8, camera alignment\n    aligned_cam_dir = os.path.join(out_fol,'georegistered_cameras')\n \n    # step 9, final orthorectification\n    final_ortho_dir = os.path.join(out_fol,'georegistered_orthomosaics')\n    \n    # step 10, plot figure\n    final_figure = os.path.join(out_fol,f\"{job_name}_result.jpg\")\n    \n    # step 11, experimental rpc production\n    \n    if args.full_workflow == 1:\n         steps2run = np.arange(0,11) # run the entire 9 steps\n    else:\n        steps2run = np.array(args.partial_workflow_steps).astype(int)\n\n    #workflow_steps\n    # create output directory\n    if not os.path.exists(out_fol):\n        os.makedirs(out_fol)\n    \n    #copy reference DEM(s) to refdem directory\n    # if parallel runs on different nodes use the same DEM, then will have issues\n    refdem_dir = os.path.join(out_fol,'refdem')\n    if not os.path.exists(refdem_dir):\n        os.makedirs(refdem_dir)\n    shutil.copy2(coreg_dem,os.path.join(refdem_dir,os.path.basename(coreg_dem)))\n    if coreg_dem != ortho_dem:\n        diff_dem = True\n        shutil.copy2(ortho_dem,os.path.join(refdem_dir,os.path.basename(ortho_dem)))\n    else:\n        diff_dem = False\n    # replace old variable names\n    coreg_dem = os.path.join(refdem_dir,os.path.basename(coreg_dem))\n    ortho_dem = os.path.join(refdem_dir,os.path.basename(ortho_dem))\n\n\n    if 1 in steps2run:\n        print(\"Computing overlapping pairs\")\n        # Step 1 Compute overlapping pairs\n        # Inputs: Image directory, minimum overlap percentage \n        overlap_perc = 0.01 # 1 percent essentially\n        \n        workflow.prepare_stereopair_list_rtree(img_folder,overlap_perc,overlap_full_txt,\n                                         aoi_bbox=args.aoi_bbox)\n        \n\n    print(\"Computing Target UTM zones for orthorectification\")\n    gdf = gpd.read_file(bound_fn)\n    clon,clat = [gdf.centroid.x.values,gdf.centroid.y.values]\n    epsg_code = f'EPSG:{geo.compute_epsg(clon,clat)}'\n    print(f\"Detected UTM zone is {epsg_code}\")\n    if not os.path.exists(bound_buffer_fn):\n        print(\"Creating buffered shapefile\")\n        gdf_proj = gdf.to_crs(epsg_code)\n        gdf_proj['geometry'] = gdf_proj.buffer(1000)\n        gdf_proj.to_file(bound_buffer_fn,driver='GPKG')\n\n    print(\"Cropping reference DEMs to extent of SkySat footprint + 1 km buffer\")\n    misc.clip_raster_by_shp_disk(coreg_dem,bound_buffer_fn)\n    misc.ndvtrim_function(os.path.splitext(coreg_dem)[0]+'_shpclip.tif')\n    coreg_dem = os.path.splitext(coreg_dem)[0]+'_shpclip_trim.tif'\n\n    if diff_dem:\n        misc.clip_raster_by_shp_disk(ortho_dem,bound_buffer_fn)\n        misc.ndvtrim_function(os.path.splitext(ortho_dem)[0]+'_shpclip.tif')\n        ortho_dem = os.path.splitext(ortho_dem)[0]+'_shpclip_trim.tif'    \n    else:\n        ortho_dem = coreg_dem\n\n\n    if 2 in steps2run:\n        print(\"Generating Frame Cameras\")\n        cam_gen_log = workflow.skysat_preprocess(img_folder,mode='triplet',\n        product_level='l1b',overlap_pkl=overlap_stereo_pkl,dem=ortho_dem,\n        outdir=cam_gcp_directory)\n\n        now = datetime.now()\n        log_fn = os.path.join(cam_gcp_directory,'camgen_{}.log'.format(now))\n        print(\"saving subprocess camgen log at {}\".format(log_fn))\n        with open(log_fn,'w') as f:\n            for log in cam_gen_log:\n                f.write(log)\n\n    if 3 in steps2run:\n        # specify whether to run using maprojected sessions or not\n   \n        if map:\n            # orthorectify all the images first\n            print(\"Orthorectifying images using RPC camera\")\n            workflow.execute_skysat_orhtorectification(images=img_list,data='triplet',session=init_ortho_session,\n                                                       outdir=init_ortho_dir,tsrs=epsg_code,dem=ortho_dem,mode='science',\n                                                       overlap_list=overlap_stereo_txt,copy_rpc=1,orthomosaic=0)\n            init_stereo_input_img_folder = init_ortho_dir\n        else:\n            init_stereo_input_img_folder = img_folder\n        print(\"Running stereo using RPC cameras\")\n        # Note crop_map = 0 option, this does not do warping to common extent and resolution for orthoimages before stereo, because we want to \n        # presrve this crucail information for correctly unwarped dense match points\n        workflow.execute_skysat_stereo(init_stereo_input_img_folder,init_stereo_dir,\n                                       mode='triplet',session=init_stereo_session,\n                                       dem=ortho_dem,texture='normal',writeout_only=False,\n                                       block=1,crop_map=0,threads=2,overlap_pkl=overlap_stereo_pkl,\n                                       cross_track=False)\n        \n\n        # copy dense match file to ba directory\n        workflow.dense_match_wrapper(stereo_master_dir=os.path.abspath(init_stereo_dir),\n                                     ba_dir=os.path.abspath(init_ba),modify_overlap=0) \n\n\n    if 4 in steps2run:\n        # this is bundle adjustment step\n        # we use dense files copied from previous step\n        \n        ba_prefix = os.path.join(init_ba,'run')\n        print(\"running bundle adjustment\")\n        ba.bundle_adjust_stable(img=img_folder,ba_prefix=ba_prefix,cam=os.path.abspath(cam_gcp_directory),\n                                session='nadirpinhole',overlap_list=overlap_stereo_txt,\n                                num_iter=700,num_pass=2,mode='full_triplet')\n        \n\n\n    if 5 in steps2run:\n        # this is where final stereo will take place\n        # first we orthorectify again, if map = True\n        if map:\n            workflow.execute_skysat_orhtorectification(images=img_list,data='triplet',session=final_ortho_session,\n                                                       outdir=intermediate_ortho_dir,tsrs=epsg_code,dem=ortho_dem,\n                                                       ba_prefix=ba_prefix+'-run',mode='science',overlap_list=overlap_stereo_txt,\n                                                       copy_rpc=1,orthomosaic=0)\n            print(\"Running intermediate orthorectification with bundle adjusted pinhole cameras\")\n            \n            final_stereo_input_img_folder = intermediate_ortho_dir\n        else:\n            final_stereo_input_img_folder = img_folder\n        # now run stereo\n        print(\"Running final stereo reconstruction\")\n        workflow.execute_skysat_stereo(final_stereo_input_img_folder,\n                                       final_stereo_dir,ba_prefix=ba_prefix+'-run',\n                                       mode='triplet',session=final_stereo_session,\n                                       dem=ortho_dem,texture='normal',writeout_only=False,\n                                       block=args.block_matching,crop_map=1,threads=2,overlap_pkl=overlap_stereo_pkl,\n                                       cross_track=False)\n        \n\n   \n    if 6 in steps2run:\n\n        pc_list = sorted(glob.glob(os.path.join(final_stereo_dir,'20*/2*/run-PC.tif'))) \n        print(f\"Identified {len(pc_list)} clouds\")\n        \n        # this is dem gridding followed by mosaicing\n        workflow.gridding_wrapper(pc_list,tr=2)\n        \n        print(\"Mosaicing DEMs\")\n        \n        workflow.dem_mosaic_wrapper(dir=os.path.abspath(final_stereo_dir),mode='triplet',\n                                    out_folder=os.path.abspath(mos_dem_dir))\n\n    if 7 in steps2run:\n        # this is DEM alignment step\n        # add option to mask coreg_dem for static surfaces\n        # might want to remove glaciers, forest et al. before coregisteration\n        # this can potentially be done in asp_utils step\n        # actually use dem_mask.py with options of nlcd, nlcd_filter (not_forest) and of course RGI glacier polygons\n        if args.mask_dem == 1: \n            # this might change for non-US sites, best to use bareground files\n            if args.mask_dem_opt == 'glaciers':\n                mask_list = ['glaciers']\n            elif args.msak_dem_opt == 'glaciers+nlcd':\n                mask_list = ['nlcd','glaciers'] \n            print(\"Masking reference DEM to static surfaces\") \n            misc.dem_mask_disk(mask_list,os.path.abspath(coreg_dem))\n            coreg_dem = os.path.splitext(coreg_dem)[0]+'_ref.tif'\n        \n        #now perform alignment\n        median_mos_dem = glob.glob(os.path.join(mos_dem_dir,'multiview_*_median_mos.tif'))[0]\n        print(\"Aligning DEMs\")\n        workflow.alignment_wrapper_single(coreg_dem,source_dem=median_mos_dem,max_displacement=40,\n                                          outprefix=os.path.join(alignment_dir,'run'))\n        \n    if 8 in steps2run:\n        # this steps aligns the frame camera models\n        camera_list = sorted(glob.glob(os.path.join(init_ba,'run-run-*.tsai')))\n        print(f\"Detected {len(camera_list)} cameras to be registered to DEM\")\n        alignment_vector = glob.glob(os.path.join(alignment_dir,'alignment_vector.txt'))[0]\n        if not os.path.exists(aligned_cam_dir):\n            os.makedirs(aligned_cam_dir)\n        print(\"Aligning cameras\")\n        workflow.align_cameras_wrapper(input_camera_list=camera_list,transform_txt=alignment_vector,\n                                       outfolder=aligned_cam_dir)\n\n    if 9 in steps2run:\n        # this produces final georegistered orthomosaics\n        georegistered_median_dem = glob.glob(os.path.join(alignment_dir,'run-trans_*DEM.tif'))[0]\n        print(\"Running final orthomsaic creation\")\n        workflow.execute_skysat_orhtorectification(images=img_list,data='triplet',session=final_ortho_session,\n                                                       outdir=final_ortho_dir,tsrs=epsg_code,dem=georegistered_median_dem,\n                                                       ba_prefix=os.path.join(aligned_cam_dir,'run-run'),mode='science',\n                                                       overlap_list=overlap_stereo_txt,copy_rpc=0,orthomosaic=1)\n        \n    if 10 in steps2run:\n        # this produces a final plot of orthoimage,DEM, NMAD and countmaps\n        ortho = glob.glob(os.path.join(final_ortho_dir,'*finest_orthomosaic.tif'))[0]\n        count = glob.glob(os.path.join(mos_dem_dir,'*count*.tif'))[0]\n        nmad = glob.glob(os.path.join(mos_dem_dir,'*nmad*.tif'))[0]\n        georegistered_median_dem = glob.glob(os.path.join(alignment_dir,'run-trans_*DEM.tif'))[0]\n        print(\"plotting final figure\")\n        misc.plot_composite_fig(ortho,georegistered_median_dem,count,nmad,outfn=final_figure)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"scripts/skysat_triplet_pipeline.py","file_name":"skysat_triplet_pipeline.py","file_ext":"py","file_size_in_byte":15701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"19789658","text":"import numpy as np\r\nimport pandas as pd\r\n\r\ndef sigmoid(z):\r\n    return 1/(1+np.exp(-z))\r\n\r\ndef forward(X,w1,w2,w3):\r\n    global W1, W2,W3, z2,a2,a2b,z3,a3,a3b,z4\r\n    W1 = w1                                             # initialization of weights(may be redundant)\r\n    W2 = w2\r\n    W3 = w3\r\n    Xt = np.vstack((1,np.transpose(X)))                 # X is a [n*1] row matrix, converting it to column matrix and adding bias-> we get Xt\r\n    z2 = np.matmul(W1, Xt)                              # multiplying Xt with W1 to get input to hidden layer i.e. z2\r\n    a2 = sigmoid(z2)                                    # activation on hidden layer to get output of hidden layer i.e. a2\r\n    a2b = np.vstack((1,a2))                             # adding bias to the activation of hidden unit, we get a2b (activation of layer 2 with bias)\r\n    z3 = np.matmul(W2,a2b)                              # multiplying a2b with W2 to get input to the output layer ie z3\r\n    a3 = sigmoid(z3)\r\n    a3b = np.vstack((1,a3))\r\n    z4= np.matmul(W3, a3b)\r\n    yHat = sigmoid(z4)                                  # applying activation to z3 to get the observed output yHat\r\n    return(yHat)                                        # return observerd output in the order of n * 1\r\n\r\n\r\ndef compute(ip):\r\n    databaseDisease = pd.read_csv('/home/phoenix/minor/checker/SmallDiseaseSet38.csv')\r\n    databaseSymptom = pd.read_csv('/home/phoenix/minor/checker/symptomlabel.csv')\r\n\r\n    a = np.array(databaseDisease)\r\n    b = np.array(databaseSymptom)\r\n\r\n    diseaseLabel = a.T[0:1]\r\n    symptomLabel = np.delete(b[0:1], 0, axis=1)\r\n\r\n\r\n    w1 = np.loadtxt('/home/phoenix/minor/checker/weights_W1')\r\n    w2 = np.loadtxt('/home/phoenix/minor/checker/weights_W2')\r\n    w3 = np.loadtxt('/home/phoenix/minor/checker/weights_W3')\r\n    W1 = np.matrix(w1)\r\n    W2 = np.matrix(w2)\r\n    W3 = np.matrix(w3)\r\n\r\n    #print(symptomLabel)\r\n    #print(diseaseLabel)\r\n\r\n\r\n    symptomInput = []\r\n    inputNet = []\r\n    symptomInput = ip.splitlines()\r\n    \r\n\r\n    for i in range(len(symptomInput)):\r\n        symptomInput[i] = symptomInput[i].upper()\r\n\r\n\r\n    for item in symptomLabel[0]:\r\n        if item.upper() in symptomInput:\r\n            inputNet.append(1)\r\n        else:\r\n            inputNet.append(0)\r\n    #print(inputNet)\r\n\r\n    X = np.matrix(inputNet)\r\n\r\n\r\n\r\n\r\n    outputNet = forward(X,W1,W2,W3).T\r\n    outputNet[np.logical_or(outputNet<0.01,outputNet>1)]=0.0\r\n    #outputNet = np.round(outputNet)\r\n\r\n    \r\n\r\n    s=[]\r\n\r\n    s = np.squeeze(np.asarray(outputNet))\r\n\r\n\r\n\r\n    possibleDiseases = []\r\n    counter = 0\r\n    for j in s:\r\n        if j != 0.0:\r\n            possibleDiseases.append(diseaseLabel[0][counter])\r\n        counter = counter + 1\r\n\r\n    return(possibleDiseases)","sub_path":"checker/nn3.py","file_name":"nn3.py","file_ext":"py","file_size_in_byte":2733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"140834545","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Feb  5 14:53:54 2021\n\n@author: rakshit\n\nThis scrip extracts two types of video sequences from the Gaze-in-Wild project\nA) Calibration eye and scene videos including the VOR sequence\nB) Eye videos with manual annotations\n\"\"\"\nimport os\nimport sys\nimport pickle\nimport msgpack\nimport argparse\nimport itertools\nimport matlab.engine\nimport scipy.io as scio\n\nimport numpy as np\n\nsys.path.append('/home/rakshit/Documents/MATLAB/gaze-in-wild/SupportFunctions/PythonSupport')\n\nfrom pprint import pprint\nfrom HelperFunctions import convert_ProcessData_to_pycompat\n\neng = matlab.engine.start_matlab()\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--mode', help='extract calibration sequence or labels',\n                    type=str, default='calib')\nparser.add_argument('--path2ds', help='Path to datasets',\n                    type=str, default='/media/rakshit/tank/Dataset')\nparser.add_argument('--path_save', help='directory to save outputs',\n                    type=str, default='/media/rakshit/tank/GIW_op')\nparser.add_argument('--path2extracted', default='/media/rakshit/tank/GIW_rearranged')\nargs = parser.parse_args()\n\nprint('Argument list: ')\npprint(vars(args))\n\ndef _load_object_legacy(file_path):\n    file_path = os.path.expanduser(file_path)\n    with open(file_path, 'rb') as fh:\n        data = pickle.load(fh, encoding='bytes')\n    return data\n\ndef load_object(file_path, allow_legacy=True):\n    import gc\n    file_path = os.path.expanduser(file_path)\n    with open(file_path, 'rb') as fh:\n        try:\n            gc.disable()  # speeds deserialization up.\n            data = msgpack.unpack(fh, raw=False, strict_map_key=False)\n        except Exception as e:\n            if not allow_legacy:\n                raise e\n            else:\n                print('{} has a deprecated format: Will be updated on save'.format(file_path))\n                data = _load_object_legacy(file_path)\n        finally:\n            gc.enable()\n    return data\n\ndef match_stamps_and_get_data(timestamps, pupil_data, cond):\n    data = {}\n    temp = [[ele['timestamp'], loc_entry] for loc_entry, ele in enumerate(pupil_data['pupil_positions']) if ele['id']==cond]\n    global_stamps, loc_entry = zip(*temp)\n    global_stamps = np.array(global_stamps)\n\n    # For a given stamp for each individual eye video, find consecutive stamp\n    # in pupil_data stamps\n    for frame, stamp in np.ndenumerate(timestamps):\n        # frame returned as a tuple so just pick the actual entry value\n        loc = np.argmin(np.abs(stamp - global_stamps))\n        data[frame[0]] = {'pupil_positions':[], 'gaze_positions':[]}\n        data[frame[0]]['pupil_positions'] = pupil_data['pupil_positions'][loc_entry[loc]]\n\n    temp = [[ele['timestamp'], loc_entry] for loc_entry, ele in enumerate(pupil_data['gaze_positions'])]\n    global_stamps, loc_entry = zip(*temp)\n    global_stamps = np.array(global_stamps)\n\n    for frame, stamp in np.ndenumerate(timestamps):\n        # frame returned as a tuple so just pick the actual entry value\n        loc = np.argmin(np.abs(stamp - global_stamps))\n        data[frame[0]]['gaze_positions'] = pupil_data['gaze_positions'][loc_entry[loc]]\n\n    return data\n\ndef extract_calib_pupil_data(path_data, PrIdx, TrIdx):\n    # Load calibration points\n    path_calib_data = os.path.join(path_data, str(PrIdx), str(TrIdx), 'Gaze', 'offline_data')\n    calib_data = load_object(path_calib_data + '/offline_calibration_gaze')\n\n    N = len(calib_data['manual_ref_positions'])\n\n    frames = []\n    calib_dict = {'calib': {}}\n    for i in range(N):\n        frames.append(calib_data['manual_ref_positions'][i]['index_range'])\n        calib_dict['calib'][i] = calib_data['manual_ref_positions'][i]\n\n    frame_range = list(itertools.chain(*frames))\n    calib_dict['calib_start'] = min(frame_range)\n    calib_dict['calib_stop'] = max(frame_range)\n\n    # Load timestamps\n    timestamps_eye0 = np.load(os.path.join(path_data, str(PrIdx), str(TrIdx), 'Gaze', 'eye0_timestamps.npy'))\n    timestamps_eye1 = np.load(os.path.join(path_data, str(PrIdx), str(TrIdx), 'Gaze', 'eye1_timestamps.npy'))\n\n    # Load pupil data\n    pupil_data = load_object(os.path.join(path_data, str(PrIdx), str(TrIdx), 'Gaze', 'pupil_data'))\n\n    # match timestamps to stamps from pupil_data and save out pupil data\n    calib_dict['pupil_0'] = match_stamps_and_get_data(np.array(timestamps_eye0).squeeze(),\n                                                      pupil_data, cond=0)\n    calib_dict['pupil_1'] = match_stamps_and_get_data(np.array(timestamps_eye1).squeeze(),\n                                                      pupil_data, cond=1)\n\n    return calib_dict\n\n\ndef loadmat(filename):\n    '''\n    this function should be called instead of direct spio.loadmat\n    as it cures the problem of not properly recovering python dictionaries\n    from mat files. It calls the function check keys to cure all entries\n    which are still mat-objects\n    '''\n    data = scio.loadmat(filename, struct_as_record=False, squeeze_me=True)\n    return _check_keys(data)\n\ndef _check_keys(dict):\n    '''\n    checks if entries in dictionary are mat-objects. If yes\n    todict is called to change them to nested dictionaries\n    '''\n    for key in dict:\n        if isinstance(dict[key], scio.matlab.mio5_params.mat_struct):\n            dict[key] = _todict(dict[key])\n    return dict\n\ndef _todict(matobj):\n    '''\n    A recursive function which constructs from matobjects nested dictionaries\n    '''\n    dict = {}\n    for strg in matobj._fieldnames:\n        elem = matobj.__dict__[strg]\n        if isinstance(elem, scio.matlab.mio5_params.mat_struct):\n            dict[strg] = _todict(elem)\n        else:\n            dict[strg] = elem\n    return dict\n\nif __name__=='__main__':\n    list_name = np.arange(1, 24)\n    loc_prune = np.in1d(list_name, [4, 5, 7, 21])\n    list_name = list_name[~loc_prune]\n\n    path_data = os.path.join(args.path2ds, 'Gaze-in-Wild')\n\n    list_task = ['Indoor_Walk', 'Ball_Catch', 'Visual_Search', 'Tea_Making']\n\n    # all_calib_pupil_data = {}\n    for task in list_task:\n\n        path_Labels = os.path.join(path_data, 'extracted_data', task, 'Labels')\n        path_ProcessData = os.path.join(path_data, 'extracted_data', task, 'ProcessData_cleaned')\n        path_OperationalData = os.path.join(path_data, 'extracted_data', task, 'TempData')\n\n        list_ProcessData = os.listdir(path_ProcessData)\n\n        for fid_pd in list_ProcessData:\n            ProcessData = loadmat(os.path.join(path_ProcessData, fid_pd))['ProcessData']\n            # ProcessData = convert_ProcessData_to_pycompat(ProcessData)\n            # ProcessData = eng.load(os.path.join(path_ProcessData, fid_pd))\n\n            PrIdx = ProcessData['PrIdx']\n            TrIdx = ProcessData['TrIdx']\n\n            OperationalData = eng.load(os.path.join(path_OperationalData,\n                                                    'Params_PrIdx_{}_TrIdx_{}.mat'.format(PrIdx, TrIdx)))\n            RotMat_etg = OperationalData['RotMat_etg']\n            R0 = ProcessData['R0']\n            R1 = ProcessData['R1']\n\n            if args.mode == 'calib':\n                calib_pupil_data = extract_calib_pupil_data(path_data, PrIdx, TrIdx)\n                # all_calib_pupil_data[(PrIdx.item(), TrIdx.item())] = calib_pupil_data\n                calib_pupil_data['eye0_GIW_to_PL'] = np.linalg.inv(R0).dot(np.linalg.inv(RotMat_etg))\n                calib_pupil_data['eye1_GIW_to_PL'] = np.linalg.inv(R1).dot(np.linalg.inv(RotMat_etg))\n                calib_pupil_data['eyec_GIW_to_PL'] = np.linalg.inv(RotMat_etg)\n\n            elif args.mode == 'classification':\n                sys.exit('Not imlemented')\n\n            with open(args.path_save+'/calib_pupil_data_PrIdx_{}_TrIdx_{}.pkl'.format(PrIdx, TrIdx), 'wb') as fid:\n                pickle.dump(calib_pupil_data, fid)\n","sub_path":"ritnet/Ellseg_v2/dataset_generation/ExtractedGIW.py","file_name":"ExtractedGIW.py","file_ext":"py","file_size_in_byte":7853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"378332871","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.utils.timezone\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Subscription',\n            fields=[\n                ('id', models.AutoField(serialize=False, auto_created=True, primary_key=True, verbose_name='ID')),\n                ('Name', models.CharField(max_length=50)),\n                ('Type', models.CharField(max_length=3, choices=[('Rss', 'Rss subscription'), ('Url', 'Site url subscription')])),\n                ('Address', models.URLField()),\n                ('LastUpdateDate', models.DateTimeField(blank=True)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='SubscriptionItem',\n            fields=[\n                ('id', models.AutoField(serialize=False, auto_created=True, primary_key=True, verbose_name='ID')),\n                ('Url', models.URLField()),\n                ('ImageUrl', models.URLField(max_length=120)),\n                ('Description', models.CharField(max_length=145)),\n                ('AddedDate', models.DateTimeField()),\n                ('Subscription', models.ForeignKey(to='BeardlyImageApp.Subscription')),\n            ],\n        ),\n        migrations.CreateModel(\n            name='UsersSubscriptions',\n            fields=[\n                ('id', models.AutoField(serialize=False, auto_created=True, primary_key=True, verbose_name='ID')),\n                ('AddedDate', models.DateTimeField(default=django.utils.timezone.now)),\n                ('Subscription', models.ForeignKey(to='BeardlyImageApp.Subscription', related_name='membership')),\n                ('User', models.ForeignKey(to=settings.AUTH_USER_MODEL, related_name='membership')),\n            ],\n        ),\n        migrations.CreateModel(\n            name='UserVerification',\n            fields=[\n                ('id', models.AutoField(serialize=False, auto_created=True, primary_key=True, verbose_name='ID')),\n                ('ActivationKey', models.CharField(blank=True, max_length=40)),\n                ('KeyExpires', models.DateTimeField(default=django.utils.timezone.now)),\n                ('User', models.OneToOneField(to=settings.AUTH_USER_MODEL)),\n            ],\n            options={\n                'verbose_name_plural': 'User profiles',\n            },\n        ),\n        migrations.AddField(\n            model_name='subscription',\n            name='Users',\n            field=models.ManyToManyField(through='BeardlyImageApp.UsersSubscriptions', to=settings.AUTH_USER_MODEL),\n        ),\n    ]\n","sub_path":"BeardlyImageApp/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":2742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"443032562","text":"from HyperMap.models import *\nfrom HyperMap.forms import *\nfrom HyperMap.serializers import *\n\nfrom datetime import datetime\n\nfrom rest_framework import status\nfrom rest_framework.renderers import JSONRenderer\nfrom rest_framework.parsers import JSONParser, MultiPartParser\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework.authentication import TokenAuthentication\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.views import APIView\nfrom rest_framework.views import exception_handler\nfrom rest_framework.decorators import authentication_classes, permission_classes\nfrom rest_framework.authtoken.views import obtain_auth_token\nimport json\nfrom rest_framework.request import Request\nfrom django.utils.functional import SimpleLazyObject\nfrom django.contrib.auth.middleware import get_user\n\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.http import HttpResponse, JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.core.urlresolvers import reverse\nfrom django.core.mail import send_mail\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.tokens import default_token_generator\nfrom django.core import serializers\nfrom django.db.models import Count, Case, When, Value, IntegerField\nimport random\nfrom django.db.models import Q\nimport time\nimport pytz\nimport operator\n\n# Create your views here.\ndef home(request):\n    return redirect('/main/')#return render(request, 'index.html', {})\n\n#@api_view([\"GET\"])\n#@authentication_classes((TokenAuthentication,))\n#@permission_classes((IsAuthenticated,))\ndef react_render(request):\n    return render(request, 'index.html', {})\n\n#@api_view([\"GET\", \"POST\"])\n@csrf_exempt\ndef register(request):\n    #if request.user.is_authenticated():\n    #    return redirect('/main/')\n    if request.method == 'GET':\n        return render(request, 'index.html', {})\n    elif request.method == 'POST':\n        data = JSONParser().parse(request)\n        serializer = UserSerializer(data=data)#data)\n        # print data\n        #print(serializer)\n        if serializer.is_valid():\n            serializer.save()\n            new_user = User.objects.get(username=serializer.data['username'])\n            token = default_token_generator.make_token(new_user)\n            email_body = \"\"\"\n            Welcome to HyperMap. Please click the link below to verify your email address and complete the registration of your account:\n            http://%s%s\n            \"\"\" % (request.get_host(), reverse('confirm_registration', kwargs={'id':new_user.id, 'token':token}))\n            send_mail(subject=\"[HyperMap]Verify your email address\", message=email_body, from_email=\"admin@hypermap.com\", recipient_list=[new_user.email])\n            return JsonResponse({'success': 'You register account successfully!'}, status=201)\n        return JsonResponse(serializer.errors, status=400)\n\ndef confirm_registration(request, id, token):\n    try:\n        user = User.objects.get(id=id)\n    except ObjectDoesNotExist:\n        return redirect(\"/error/\")# JsonResponse({'errors': 'User does not exist!'}, status=400)\n    if user != None and default_token_generator.check_token(user, token):\n        user.is_active = True\n        user.save()\n        return redirect(\"/main/\")\n    else:\n        return redirect(\"/error/\")# JsonResponse({'errors': 'Wrong confirmation link!'}, status=400)\n\n@api_view([\"GET\",\"POST\"])\n@csrf_exempt\ndef user_login(request):\n    if request.method == \"GET\":\n        return render(request, 'index.html', {})\n    data = JSONParser().parse(request)\n    #if not \"username\" in data or not \"password\" in data:\n    #    return JsonResponse({'errors': 'Please enter username and password!'}, status=400)\n    if not data[\"username\"] or not data[\"password\"]:\n        return JsonResponse({'error': 'Please enter username and password!'}, status=400)\n    try:\n        user = User.objects.get(username=data[\"username\"])\n    except ObjectDoesNotExist:\n        return JsonResponse({'error': 'User does not exist!'}, status=400)\n    if not user.check_password(data[\"password\"]):\n        return JsonResponse({'error': 'Wrong password!'}, status=403)\n    if not user.is_active:\n        return JsonResponse({'error': 'Need confirmation!'}, status=403)\n    token, created = Token.objects.get_or_create(user=user)\n    return JsonResponse({'token': token.key}, status=200)\n\n@api_view([\"GET\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_all_user(request):\n    #print(request.user)\n    users = User.objects.exclude(is_active=False).exclude(username=request.user.username).values('username', 'id')\n    user_list = list(users)\n    return JsonResponse({'users': user_list}, status=200)\n\n@api_view([\"GET\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_user_info(request):\n    serializer = UserSerializer(request.user)\n    return Response(serializer.data)\n\n@api_view([\"GET\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_user_profile(request):\n    profile = Profile.objects.get(user=request.user)\n    serializer = ProfileSerializer(profile)\n    return Response(serializer.data)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef edit_profile(request):\n    #data = JSONParser().parse(request)\n    profile = Profile.objects.get(user=request.user)\n    serializer = ProfileSerializer(profile, data=request.data, partial=True)\n    # print(serializer)\n    if serializer.is_valid():\n        serializer.save()\n        return JsonResponse({'success': 'Profile is edited successfully!'}, status=202)\n    # print(serializer.errors)\n    return JsonResponse(serializer.errors, status=400)\n\n@api_view([\"GET\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_recommendation_events(request):\n    profile = Profile.objects.get(user=request.user)\n    # print(len(profile.footprint.all()))\n    if len(profile.footprint.all()) == 0:\n        events = Event.objects.exclude(creator=request.user.id).exclude(session_end__lt=datetime.now()).annotate(num_registered=Count('registered')).order_by('-num_registered').values('id','name','category')[:5]\n        event_list = list(events)\n    else:\n        rec = {}\n        for registered_event in profile.footprint.all():\n            registered_users = registered_event.registered.exclude(username=request.user.username)\n            for user in registered_users:\n                registered_profile = Profile.objects.get(user=user)\n                events = registered_profile.footprint.exclude(pk__in=profile.footprint.all().values('id')).exclude(session_end__lt=datetime.now())\n                for event in events:\n                    if event.pk in rec:\n                        rec[event.pk] += 1\n                    else:\n                        rec[event.pk] = 1\n        sorted_rec = sorted(rec.items(), key=operator.itemgetter(1), reverse=True)[:5]\n        rec_list = []\n        for rec in sorted_rec:\n            rec_list.append(rec[0])\n        events = Event.objects.filter(pk__in=rec_list).values('id','name','category')\n        event_list = list(events)\n        if len(event_list) < 5:\n            more_events = Event.objects.exclude(pk__in=profile.footprint.all().values('id')).exclude(pk__in=events.values('id')).exclude(creator=request.user.id).exclude(session_end__lt=datetime.now()).annotate(num_registered=Count('registered')).order_by('-num_registered').values('id','name','category')[:5-len(event_list)]\n            for event in list(more_events):\n                event_list.append(event)\n    for event in event_list:\n        event['image'] = Event.objects.get(id=event['id']).image.url\n    return JsonResponse({'events': event_list}, status=200)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef create_event(request):\n    serializer = EventSerializer(data=request.data, context=request)\n    # print(serializer)\n    if serializer.is_valid():\n        serializer.save()\n        # debug = serializer.data\n        return JsonResponse({'success': 'Event is created successfully!'}, status=201)\n    # print(serializer.errors)\n    return JsonResponse(serializer.errors, status=400)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_event(request):\n    data = JSONParser().parse(request)\n    try:\n        event = Event.objects.get(pk=data['id'])\n    except ObjectDoesNotExist:\n        return JsonResponse({'error': 'Event does not exist or has already been deleted!'}, status=400)\n    event = Event.objects.filter(pk=data['id']).values('id', 'name', 'content', 'category', 'session_begin', 'session_end', 'position',\n                           'lat', 'lng', 'creator')\n    event = list(event)[0]\n    profile = Profile.objects.get(user=request.user)\n    if request.user.id == event['creator']:\n        unconfirmed = list(Event.objects.get(id=event[\"id\"]).unconfirmed.all().values('id','username','first_name','last_name'))\n        event[\"unconfirmed\"] = unconfirmed\n        registered = list(Event.objects.get(id=event[\"id\"]).registered.all().values('id','username','first_name','last_name'))\n        event[\"registered\"] = registered\n    else:\n        if profile.footprint.filter(pk=event['id']):\n            event[\"isRegistered\"] = True\n        else:\n            event[\"isRegistered\"] = False\n        if event['session_end'] < datetime.now():\n            event[\"isValid\"] = False\n        else:\n            event[\"isValid\"] = True\n    event['creator_firstname'] = User.objects.get(id=event['creator']).first_name\n    event['creator_lastname'] = User.objects.get(id=event['creator']).last_name\n    event['creator'] = User.objects.get(id=event['creator']).username\n    event['present_user'] = request.user.username\n    return JsonResponse({'eventInfo': event}, status=200)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef register_event(request):\n    data = JSONParser().parse(request)\n    try:\n        event = Event.objects.get(pk=data['id'])\n    except ObjectDoesNotExist:\n        return JsonResponse({'error': 'Event does not exist or has already been deleted!'}, status=400)\n    data['registered'] = [request.user.id]\n    serializer = EventSerializer(event, data=data, partial=True)\n    # print(serializer)\n    if serializer.is_valid():\n        serializer.save()\n        # print(len(event.registered.all()))\n        if event.registered.filter(id=request.user.id).exists():\n            return JsonResponse({'success': 'You register this event successfully!'}, status=202)\n        else:\n            return JsonResponse({'success': 'You unregister this event successfully!'}, status=202)\n    # print(serializer.errors)\n    return JsonResponse(serializer.errors, status=400)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_filtered_events(request):\n    data = JSONParser().parse(request)\n    selectedlist = data[\"selectedlist\"].split(',')\n    # transfer timestamp to datetime object\n    selecteddate = datetime.fromtimestamp(data[\"selecteddate\"]).date()\n    # print(selecteddate)\n    events = Event.objects.filter(category__in=selectedlist, session_begin__date__lte=selecteddate, session_end__date__gte=selecteddate).values('id','name','category','lat','lng')\n    event_list = list(events)\n    for event in event_list:\n        event['image'] = Event.objects.get(id=event['id']).image.url\n        event['lat'] += (random.uniform(0, 1) - 0.5) / 2500\n    return JsonResponse({'events': event_list, 'user': request.user.id}, status=200)\n\n@api_view([\"GET\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_all_events(request):\n    events = Event.objects.annotate(num_registered=Count('registered')).annotate(\n                        is_valid=Case(\n                            When(session_end__gt=datetime.now(),\n                                then=Value(1)),\n                            default=Value(0),\n                            output_field=IntegerField())).order_by('-is_valid','session_begin','-num_registered').values('id','name','category')\n    event_list = list(events)\n    for event in event_list:\n        event['image'] = Event.objects.get(id=event['id']).image.url\n    #print(event_lists)\n    return JsonResponse({'events': event_list}, status=200)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_registered_users(request):\n    data = JSONParser().parse(request)\n    try:\n        event = Event.objects.get(pk=data['id'])\n    except ObjectDoesNotExist:\n        return JsonResponse({'error': 'Event does not exist or has already been deleted!'}, status=400)\n    users = event.registered.all().values('first_name','last_name')\n    user_list = list(users)\n    return JsonResponse({'users': user_list}, status=200)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_group_users(request):\n    data = JSONParser().parse(request)\n    try:\n        group = Group.objects.get(name=data['id'])\n    except ObjectDoesNotExist:\n        return JsonResponse({'error': 'Group does not exist or has already been deleted!'}, status=400)\n    users = group.member.all().values('first_name','last_name')\n    user_list = list(users)\n    return JsonResponse({'users': user_list}, status=200)\n\n@api_view([\"GET\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_user_events(request):\n    profile = Profile.objects.get(user_id=request.user.id)\n    events = profile.posted_event.annotate(num_registered=Count('registered')).annotate(\n                        is_valid=Case(\n                            When(session_end__gt=datetime.now(),\n                                then=Value(1)),\n                            default=Value(0),\n                            output_field=IntegerField())).order_by('-is_valid','session_begin','-num_registered').values('id', 'name', 'category')\n    event_list = list(events)\n    for event in event_list:\n        event[\"registered\"] = len(Event.objects.get(id=event['id']).registered.all())\n        event[\"unconfirmed\"] = len(Event.objects.get(id=event['id']).unconfirmed.all().values('id'))\n        event['image'] = Event.objects.get(id=event['id']).image.url\n    # print(event_lists)\n    return JsonResponse({'events': event_list}, status=200)\n\n@api_view([\"GET\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_footprint(request):\n    profile = Profile.objects.get(user=request.user)\n    events = profile.footprint.annotate(num_registered=Count('registered')).annotate(\n                        is_valid=Case(\n                            When(session_end__gt=datetime.now(),\n                                then=Value(1)),\n                            default=Value(0),\n                            output_field=IntegerField())).order_by('-is_valid','session_begin','-num_registered').values('id', 'name', 'category')\n    event_list = list(events)\n    for event in event_list:\n        event['image'] = Event.objects.get(id=event['id']).image.url\n    return JsonResponse({'events': event_list}, status=200)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef create_group(request):\n    data = JSONParser().parse(request)\n    if Group.objects.filter(name=data['name']).exists():\n        return JsonResponse({'error': 'This group name has already been registered.'}, status=400)\n    data['creator'] = request.user.id\n    #print(data)\n    data['member'] = [request.user.id]\n    for member in data['members']:\n        data['member'].append(member['id'])\n    serializer = GroupSerializer(data=data)\n    #print(serializer)\n    if serializer.is_valid():\n        serializer.save()\n        return JsonResponse({'success': 'Group is created successfully!'}, status=201)\n    # print(serializer.errors)\n    return JsonResponse(serializer.errors, status=400)\n\n@api_view([\"GET\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef get_groups(request):\n    groups = Group.objects.filter(member__id__contains=request.user.id).values('name','creator')\n    group_list = list(groups)\n    for group in group_list:\n        group[\"number\"] = len(Group.objects.get(name=group[\"name\"]).member.all())\n        group[\"creator\"] = User.objects.get(id=group[\"creator\"]).username\n        if request.user.username == group[\"creator\"]:\n            group[\"isCreator\"] = True\n        else:\n            group[\"isCreator\"] = False\n    return JsonResponse({'groups': group_list}, status=200)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef confirm_event(request):\n    data = JSONParser().parse(request)\n    try:\n        event = Event.objects.get(pk=data['id'])\n    except ObjectDoesNotExist:\n        return JsonResponse({'error': 'Event does not exist or has already been deleted!'}, status=400)\n    # data['unconfirmed'] = [request.user.id]\n    data['unconfirmed'] = [data['user']]\n    serializer = EventSerializer(event, data=data, partial=True)\n    if serializer.is_valid():\n        serializer.save()\n        # debug = serializer.data\n        return JsonResponse({'success': 'This user is confirmed successfully!'}, status=202)\n    # print(serializer.errors)\n    return JsonResponse(serializer.errors, status=400)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef withdraw_group(request):\n    data = JSONParser().parse(request)\n    try:\n        group = Group.objects.get(name=data['group'])\n    except ObjectDoesNotExist:\n        return JsonResponse({'error': 'Group does not exist or has already been deleted!'}, status=400)\n    if group.creator.id == request.user.id:\n        group.delete()\n        return JsonResponse({'success': 'Group is deleted successfully!'}, status=202)\n    else:\n        group.member.remove(request.user)\n        group.save()\n        return JsonResponse({'success': 'Group is withdrawn successfully!'}, status=202)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef delete_event(request):\n    data = JSONParser().parse(request)\n    try:\n        event = Event.objects.get(pk=data['id'])\n    except ObjectDoesNotExist:\n        return JsonResponse({'error': 'Event does not exist or has already been deleted!'}, status=400)\n    if event.creator != request.user:\n        return JsonResponse({'error': 'Your are not the creator of this event!'}, status=403)\n    event.delete()\n    return JsonResponse({'success': 'Event is deleted successfully!'}, status=202)\n\n@api_view([\"POST\"])\n@authentication_classes((TokenAuthentication,))\n@permission_classes((IsAuthenticated,))\ndef edit_event(request):\n    try:\n        event = Event.objects.get(id=request.data['id'])\n    except ObjectDoesNotExist:\n        return JsonResponse({'error': 'Event does not exist or has already been deleted!'}, status=400)\n    if event.creator != request.user:\n        return JsonResponse({'error': 'Your are not the creator of this event!'}, status=403)\n    serializer = EventSerializer(event, data=request.data, partial=True)\n    # print(serializer)\n    if serializer.is_valid():\n        serializer.save()\n        return JsonResponse({'success': 'Event is edited successfully!'}, status=202)\n    # print(serializer.errors)\n    return JsonResponse(serializer.errors, status=400)\n\ndef error404(request):\n    return redirect('/error/')\n\ndef custom_exception_handler(exc, context):\n    # Call REST framework's default exception handler first,\n    # to get the standard error response.\n    response = exception_handler(exc, context)\n    # Now add the HTTP status code to the response.\n    #print (response.status_code)\n    if response is not None:\n        response.data['status_code'] = response.status_code\n        if response.status_code == 401:\n            return redirect('/login/')\n    #if response.status_code == 400:\n    #    return redirect('/login/')\n    return response\n","sub_path":"src/webapps/HyperMap/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":20475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"49560784","text":"class Solution:\n    \"\"\"\n    @param A: An integer array\n    @param queries: The query list\n    @return: The number of element in the array that are smaller that the given integer\n    \"\"\"\n    def countOfSmallerNumber(self, A, queries):\n        A = sorted(A)\n        n = len(A)\n        results = []\n\n        for query in queries:\n            results.append(self.count(A, n, query))\n\n        return results\n\n    def count(self, A, n, query):\n        if not A:\n            return 0\n\n        left, right = 0, n - 1\n        while left + 1 < right:\n            mid = (left + right) // 2\n            if A[mid] < query:\n                left = mid\n            else:\n                right = mid\n\n        if A[right] < query:\n            return right + 1\n        if A[left] < query:\n            return left + 1\n        return 0\n","sub_path":"Databricks/248. Count of Smaller Number.py","file_name":"248. Count of Smaller Number.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"77511561","text":"\"\"\"\nThis file produces the data for Task 2. Running it prompts the user to input initial values for the system,\nafter which the necessary data is produced and output to a file in the parent directory.\n\nThis file must be in the same folder as sources.py to be run successfully.\n\"\"\"\nfrom sources import *\n\n# ==================================================\n# #### Defining constants #####\n# ==================================================\nG = 1\nomega = 1  # angular velocity\nm_1 = set_value(\"Please enter the mass of the first primary in kg: \")  # = 1.9885E30 (Sun)\nm_2 = set_value(\"Please enter the mass of the second primary in kg: \")  # = 1.89819E27 (Jupiter)\nm1 = m_1 / (m_1 + m_2)\nm2 = m_2 / (m_1 + m_2)\nEND_TIME = 1000\n\n\n# =========================================================\n# #### Defining derivatives of the object's trajectory#####\n# =========================================================\ndef derivatives_threebody(variables, *_, g=G):\n    \"\"\"Calculates the derivatives with respect to time of the variables in the restricted three body system.\n\n    We make use of the EOM for a corotating frame of reference in which the two primary masses are stationary.\n    Note that these EOM have no explicit time depencency; the second positional argument of this function is\n    in place for compatibility with runge_kutta(), but we don't need any actual time input.\n\n    Args:\n        variables: NumPy array containing the relative variables x, y, vx, and vy\n        g (optional): Gravitational constant G in Newton's law of gravitation.\n\n    Returns:\n        A NumPy array containing the derivatives dx/dt, dy/dt, dvx/dt, and dvy/dt.\n    \"\"\"\n    x, y, vx, vy = variables\n\n    # for use in dvx and dvy\n    x1, x2, y1, y2 = positions(1, 0, m1, m2)\n    r1, r2 = abs(x1), abs(x2)\n    R1 = np.sqrt((x - x1)**2 + (y - y1)**2)\n    R2 = np.sqrt((x - x2)**2 + (y - y2)**2)\n    const1 = -g * m1 / R1**3   \n    const2 = -g * m2 / R2**3\n\n    # We calculate the actual derivatives\n    dx = vx\n    dy = vy\n    dvx = const1 * (x - r1) + const2 * (x + r2) + omega**2 * x + 2*omega*vy\n    dvy = y * (const1 + const2) + omega**2 * y - 2*omega*vx\n\n    return np.array([dx, dy, dvx, dvy])\n\n\n# ==================================================\n# #### Producing data file and running simulation#####\n# ==================================================\ndef main():\n    x = set_value(\"Please enter the initial x position of the asteroid: \")\n    y = set_value(\"Please enter the initial y position of the asteroid: \")\n    vx = set_value(\"Please enter the initial x velocity of the asteroid: \")\n    vy = set_value(\"Please enter the initial y velocity of the asteroid: \")\n    dt = set_value(\"Please enter the time step dt for integration in years: \")\n    variables = np.array([x, y, vx, vy])\n    produce_data(2, variables, derivatives_threebody, END_TIME, dt, m1, m2)\n\n\nmain()\n","sub_path":"Project 2/Task_2_data.py","file_name":"Task_2_data.py","file_ext":"py","file_size_in_byte":2865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"8919578","text":"from ccdc import io\nfrom hotspots.hs_io import HotspotReader\n\n\nhs = HotspotReader(\"out.zip\").read()\nc = hs._docking_constraint_atoms(max_constraints=1)\nd = c.to_molecule()\n\nwith io.MoleculeWriter(\"constraints.mol2\") as w:\n    w.write(d)","sub_path":"examples/2_docking/virtual_screening/hotspot/scripts/cosnt.py","file_name":"cosnt.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"361821423","text":"# -*- coding: utf-8 -*-\n\"\"\"\n    zeit.api.queries\n    ~~~~~~~~~~~~~~~~\n\n    This module contains classes for the different kind of queries a client\n    may submit to the API. These define allowed parameters and values, methods\n    to fetch a raw result set from a Solr server and transform or enrich the\n    received results.\n\n    Copyright: (c) 2013 by ZEIT ONLINE.\n    License: BSD, see LICENSE.md for more details.\n\"\"\"\n\nimport json\nimport exceptions\nimport os\nimport time\nimport urllib\nimport urllib2\nimport warnings\n\nfrom flask import g, current_app as current_app, request\n\nfrom zeit.api import parameters, util\nfrom zeit.api.exceptions import BadRequest, EndpointNotFound, \\\n    ResourceNotFound, ServiceUnavailable\n\n\nclass Query(object):\n    \"\"\"The base class for all API queries.\"\"\"\n\n    def __getitem__(self, key):\n        if key not in self.__dict__.keys():\n            raise exceptions.KeyError()\n        return self.__dict__[key]\n\n    def __init__(self, **kwargs):\n        for kw in kwargs:\n            if kw in self.__dict__.keys():\n                getattr(self, kw).value = kwargs[kw]\n            elif kw == 'callback' or kw == 'api_key':\n                pass\n            else:\n                warnings.warn('Unsupported parameter key %s' % kw)\n\n    def __iter__(self):\n        for param in self.__dict__.values():\n            if isinstance(param, parameters.Param):\n                yield (param.origin or param.key, param.value)\n\n    def __str__(self):\n        return str(dict(self.__iter__()))\n\n    def items(self):\n        return list(self.__iter__())\n\n    def params(self):\n        for param in self.__dict__.values():\n            if isinstance(param, parameters.Param) and param.key:\n                yield (param.key, param.value)\n\n\nclass SearchQuery(Query):\n    \"\"\"The base class for all SQL-based searches.\"\"\"\n\n    _default_field = 'value'\n\n    def __init__(self, endpoint, **kwargs):\n        self.q = parameters.SqlQParam()\n        self.limit = parameters.LimitParam()\n        self.offset = parameters.OffsetParam()\n        self._endpoint = endpoint\n        super(SearchQuery, self).__init__(**kwargs)\n\n    def _fetch_raw(self):\n        sql = 'SELECT * FROM %s WHERE %s LIKE ? LIMIT ?, ?;'\n        query = sql % (self._endpoint, self._default_field)\n        options = (self.q.value, self.offset.value, self.limit.value)\n        result = g.db.execute(query, options).fetchall()\n        return result\n\n    def _fetch_count(self):\n        sql = 'SELECT COUNT(*) FROM %s WHERE %s LIKE ?;'\n        query = sql % (self._endpoint, self._default_field)\n        result = g.db.execute(query, (self.q.value,)).fetchone()\n        return result[0]\n\n    def _matches(self):\n        for row in self._fetch_raw():\n            match = dict()\n            for key in self.fields:\n                position = self.fields.default.split(',').index(key)\n                match[key] = row[position]\n            yield match\n\n    def fetch(self):\n        return dict(\n            matches=list(self._matches()),\n            found=self._fetch_count(),\n            limit=int(self.limit.value),\n            offset=int(self.offset.value)\n        )\n\n\nclass AuthorSearchQuery(SearchQuery):\n    \"\"\"Search query for content authors.\"\"\"\n\n    def __init__(self, **kwargs):\n        self.fields = parameters.FieldsParam(\n            default='href,id,type,uri,value'\n        )\n        super(AuthorSearchQuery, self).__init__('author', **kwargs)\n\n\nclass DepartmentSearchQuery(SearchQuery):\n    \"\"\"Search query for newspaper departments.\"\"\"\n\n    def __init__(self, **kwargs):\n        self.fields = parameters.FieldsParam(\n            default='href,id,parent,uri,value'\n        )\n        super(DepartmentSearchQuery, self).__init__('department', **kwargs)\n\n\nclass KeywordSearchQuery(SearchQuery):\n    \"\"\"Search query for available keywords.\"\"\"\n\n    def __init__(self, **kwargs):\n        self.fields = parameters.FieldsParam(\n            default='href,id,lexical,score,type,uri,value'\n        )\n        super(KeywordSearchQuery, self).__init__('keyword', **kwargs)\n\n\nclass ProductSearchQuery(SearchQuery):\n    \"\"\"Search query for publication products.\"\"\"\n\n    def __init__(self, **kwargs):\n        self.fields = parameters.FieldsParam(\n            default='href,id,uri,value'\n        )\n        super(ProductSearchQuery, self).__init__('product', **kwargs)\n\n\nclass SeriesSearchQuery(SearchQuery):\n    \"\"\"Search query for article series.\"\"\"\n\n    def __init__(self, **kwargs):\n        self.fields = parameters.FieldsParam(\n            default='href,id,name,uri,value'\n        )\n        super(SeriesSearchQuery, self).__init__('series', **kwargs)\n\n\nclass ContentSearchQuery(Query):\n    \"\"\"Unfiltered content search query.\"\"\"\n\n    _endpoint = 'content'\n    _action = 'search'\n\n    def __init__(self, **kwargs):\n        self.q = parameters.StrParam(\n            key='q',\n            origin='q',\n            default='*:*'\n        )\n        self.sort = parameters.StrParam(\n            key='sort',\n            origin='sort',\n            default='release_date desc'\n        )\n        self.fields = parameters.FieldsParam(\n            default=('subtitle,uuid,title,href,release_date,'\n                'uri,snippet,supertitle,teaser_title,teaser_text'),\n            enforce='uuid'\n        )\n        self.facet_date = parameters.FacetDateParam()\n        self.facet_field = parameters.FacetFieldParam()\n        self.limit = parameters.LimitParam()\n        self.offset = parameters.OffsetParam()\n        super(ContentSearchQuery, self).__init__(**kwargs)\n        if self.facet_field.value != '' or self.facet_date.value != '':\n            self.facet = parameters.StrParam(\n                key='facet',\n                origin='facet',\n                default='true'\n            )\n        if self.facet_date.value != '':\n            self.facet_date_field = parameters.StrParam(\n                origin='facet.date',\n                default='release_date'\n            )\n\n    def _fetch_raw(self):\n        query = util.url_encode(dict(self))\n        url = '%s/%s/%s?%s' % (current_app.config['SOLR_URL'],\n            self.__class__._endpoint, self._action, query)\n        try:\n            data = urllib2.urlopen(url)\n            return json.load(data)\n        except urllib2.URLError:\n            raise ServiceUnavailable()\n\n    def _make_matches(self, raw):\n        for match in raw['response']['docs']:\n            if 'uri' in self.fields:\n                match['uri'] = '%s/%s/%s' % (current_app.config['API_URL'],\n                    self.__class__._endpoint, match['uuid'])\n            if 'snippet' in self.fields and self.q.value != self.q.default:\n                snippet = raw['highlighting'][match['uuid']]\n                if len(snippet.values()) > 0:\n                    match['snippet'] = snippet.values()[0][0]\n            if self.fields._value:\n                if 'uuid' not in self.fields._value:\n                    del match['uuid']\n\n    def fetch(self):\n        raw = self._fetch_raw()\n        self._make_matches(raw)\n        response = dict(\n            matches=raw['response']['docs'],\n            found=raw['response']['numFound'],\n            limit=int(self.limit.value),\n            offset=int(self.offset.value)\n        )\n        if  hasattr(self, 'facet'):\n            response['facets'] = dict()\n            response['facets'].update(\n                raw['facet_counts']['facet_dates'])\n            response['facets'].update(\n                raw['facet_counts']['facet_fields'])\n        return response\n\n\nclass FilteredContentSearchQuery(ContentSearchQuery):\n    \"\"\"Pre-filtered search query.\"\"\"\n\n    _default_field = 'id'\n\n    def __init__(self, endpoint='', filter_id='', **kwargs):\n\n        self.fq = parameters.StrParam(\n            default='%s:%s' % (endpoint, filter_id),\n            origin='fq'\n        )\n        super(FilteredContentSearchQuery, self).__init__(**kwargs)\n        self._endpoint = endpoint\n        self._id = filter_id\n\n    def fetch(self):\n        if self._endpoint not in ('author', 'content', 'department', 'keyword',\n                'product', 'series'):\n            raise EndpointNotFound()\n        sql = 'SELECT * FROM %s WHERE %s LIKE :q;'\n        query = sql % (self._endpoint, self._default_field)\n        result = g.db.execute(query, {'q': self._id}).fetchall()\n        if len(result) == 0:\n            raise ResourceNotFound()\n        if self._endpoint == 'author':\n            self.fq.value = self.fq.value.replace('-', '*')\n        meta = dict(zip(QueryFactory(self._endpoint).fields, result[0]))\n        if self._endpoint == 'department':\n            if meta['parent'] != '':\n                self.fq.value = 'sub_department:%s' % (self._id)\n        meta.update(super(FilteredContentSearchQuery, self).fetch())\n        return meta\n\n\nclass DisplayClientQuery(Query):\n    \"\"\"Display client information and API usage stats.\"\"\"\n\n    def __init__(self, **kwargs):\n        super(DisplayClientQuery, self).__init__(**kwargs)\n\n    def fetch(self):\n        row = g.db.execute('SELECT * FROM client WHERE api_key=?',\n                (g.api_key,)).fetchone()\n        return dict(\n            api_key=row[0],\n            tier=row[1],\n            name=row[2],\n            email=row[3],\n            requests=row[4],\n            reset=row[5],\n            quota=current_app.config['ACCESS_TIERS'][row[1]]\n        )\n\n\nclass RegisterClientQuery(Query):\n    \"\"\"Register a new API client with a POST request.\"\"\"\n\n    def __init__(self, **kwargs):\n        self.name = parameters.StrParam()\n        self.email = parameters.StrParam()\n        self.challenge = parameters.StrParam()\n        self.response = parameters.StrParam()\n        super(RegisterClientQuery, self).__init__(**kwargs)\n\n    def _verify_captcha(self):\n        if current_app.config['TESTING']:\n            return True\n        data = urllib.urlencode(\n            dict(\n                privatekey=current_app.config['RECAPTCHA_PRIVATE_KEY'],\n                remoteip=request.remote_addr,\n                challenge=self.challenge.value,\n                response=self.response.value\n            )\n        )\n        url = 'http://www.google.com/recaptcha/api/verify'\n        return 'true' in urllib.urlopen(url, data).readline()\n\n    def push(self):\n        empty = self.name.value == '' or self.email.value == ''\n        if empty or not self._verify_captcha():\n            raise BadRequest()\n        g.api_key = str(os.urandom(26).encode('hex'))\n        tier = 'free'\n        name = self.name.value\n        email = self.email.value\n        requests = 0\n        reset = int(time.time())\n        query = 'INSERT INTO client VALUES (?, ?, ?, ?, ?, ?)'\n        g.db.execute(query, (g.api_key, tier, name, email, requests, reset))\n        return DisplayClientQuery().fetch()\n\n\nclass QueryFactory(object):\n    \"\"\"A factory class for general endpoint queries.\"\"\"\n\n    def __init__(self, endpoint='', **kwargs):\n        if endpoint == 'author':\n            self.__class__ = AuthorSearchQuery\n        elif endpoint == 'client':\n            self.__class__ = DisplayClientQuery\n        elif endpoint == 'content':\n            self.__class__ = ContentSearchQuery\n        elif endpoint == 'department':\n            self.__class__ = DepartmentSearchQuery\n        elif endpoint == 'keyword':\n            self.__class__ = KeywordSearchQuery\n        elif endpoint == 'product':\n            self.__class__ = ProductSearchQuery\n        elif endpoint == 'series':\n            self.__class__ = SeriesSearchQuery\n        else:\n            raise En()\n        self.__init__(**kwargs)\n\n\nclass ContentIdQuery(Query):\n    \"\"\"Display content item with the given id.\"\"\"\n\n    _endpoint = 'content'\n    _action = 'id'\n\n    def __init__(self, content_id='', **kwargs):\n        self.fields = parameters.CsvParam(\n            default=('categories,creators,href,keywords,relations,release_'\n                'date,supertitle,teaser_text,teaser_title,title,uri,uuid'),\n            key='fields'\n        )\n        self.fq = parameters.StrParam(\n            default='uuid:%s' % content_id,\n            origin='fq'\n        )\n        self.q = parameters.StrParam(\n            origin='q',\n            default='*:*'\n        )\n        super(ContentIdQuery, self).__init__(**kwargs)\n\n    def _fetch_raw(self):\n        query = util.url_encode(dict(self))\n        url = '%s/%s/%s?%s' % (current_app.config['SOLR_URL'],\n            self.__class__._endpoint, self._action, query)\n        try:\n            data = urllib2.urlopen(url)\n            return json.load(data)\n        except urllib2.URLError:\n            raise ServiceUnavailable()\n\n    def _fetch_keywords(self, keywords):\n        for keyword in keywords:\n            query = 'SELECT type, value FROM keyword WHERE id = ?;'\n            row = g.db.execute(query, (keyword,)).fetchone()\n            if row is None:\n                continue\n            yield dict(\n                rel=row[0],\n                name=row[1],\n                uri='%s/keyword/%s' % (current_app.config['API_URL'],\n                    keyword)\n            )\n\n    def _fetch_relations(self, relations):\n        for relation in relations:\n            url = '%s/content/id?q=%s' % (current_app.config['SOLR_URL'],\n                relation)\n            solr_data = urllib2.urlopen(url)\n            try:\n                parsed = json.load(solr_data)\n                title = parsed['response']['docs'][0]['title']\n            except:\n                continue\n            yield dict(\n                rel='related',\n                name=title,\n                uri='%s/content/%s' % (current_app.config['API_URL'],\n                    relation)\n            )\n\n    def _fetch_authors(self, authors):\n        for author in authors:\n            yield dict(\n                rel='author',\n                name=author,\n                uri='%s/author/%s' % (current_app.config['API_URL'],\n                    author.replace(' ', '-'))\n            )\n\n    def _fetch_category(self, endpoint, cat_id, type):\n        query = 'SELECT value FROM %s WHERE id = \"%s\";' % (endpoint, cat_id)\n        row = g.db.execute(query).fetchone()\n        if row:\n            yield dict(\n                rel=type,\n                name=row[0],\n                uri='%s/%s/%s' % (current_app.config['API_URL'],\n                    endpoint, cat_id)\n            )\n\n    def fetch(self):\n        raw = self._fetch_raw()\n\n        if len(raw['response']['docs']) == 0:\n            raise ResourceNotFound()\n        else:\n            doc = raw['response']['docs'][0]\n\n        categories = [('department', 'department'), ('product', 'product'),\n            ('sub_department', 'department'), ('series', 'series')]\n        whitelist = ['categories', 'creators', 'keywords', 'relations']\n        blacklist = ['body']\n\n        for key in whitelist:\n            doc[key] = []\n\n        for key in blacklist:\n            if key in doc:\n                del doc[key]\n\n        if 'uri' in self.fields:\n            doc['uri'] = '%s/%s/%s' % (current_app.config['API_URL'],\n                self._endpoint, doc['uuid'])\n\n        if 'keyword' in doc and 'keywords' in self.fields:\n            kw = self._fetch_keywords(doc['keyword'])\n            doc['keywords'].extend(kw)\n            del doc['keyword']\n\n        if 'related' in doc and 'relations' in self.fields:\n            rl = self._fetch_relations(doc['related'])\n            doc['relations'].extend(rl)\n            del doc['related']\n\n        if 'author' in doc and 'creators' in self.fields:\n            au = self._fetch_authors(doc['author'])\n            doc['creators'].extend(au)\n            del doc['author']\n\n        for category, endpoint in categories:\n            if category in doc and 'categories' in self.fields:\n                cat_id = doc[category]\n                cat = self._fetch_category(endpoint, cat_id, category)\n                doc['categories'].extend(cat)\n                del doc[category]\n\n        for key in doc.keys():\n            if key not in self.fields:\n                del doc[key]\n\n        return doc\n\n\nclass DefinitionQuery(Query):\n    \"\"\"Return API meta information for all available queries.\"\"\"\n\n    def __init__(self, **kwargs):\n        super(DefinitionQuery, self).__init__(**kwargs)\n\n    def _make_def(self, endpoint, query_class):\n        url = '%s/%s' % (current_app.config['API_URL'], endpoint)\n        params = dict(p for p in query_class().params())\n        doc = query_class().__doc__\n        return endpoint, dict(url=url, params=params, doc=doc)\n\n    def fetch(self):\n        endpoints = {'author': AuthorSearchQuery,\n            'author/{id}': FilteredContentSearchQuery,\n            'client': DisplayClientQuery,\n            'content': ContentSearchQuery,\n            'content/{id}': ContentIdQuery,\n            'department': DepartmentSearchQuery,\n            'department/{id}': FilteredContentSearchQuery,\n            'keyword': KeywordSearchQuery,\n            'keyword/{id}': FilteredContentSearchQuery,\n            'product': ProductSearchQuery,\n            'product/{id}': FilteredContentSearchQuery,\n            'series': SeriesSearchQuery,\n            'series/{id}': FilteredContentSearchQuery}\n        return dict(self._make_def(ep, endpoints[ep]) for ep in endpoints)\n","sub_path":"src/zeit/api/queries.py","file_name":"queries.py","file_ext":"py","file_size_in_byte":17166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"555176703","text":"import json\n\nimport pytest\n\nimport contacts.handler as handler\nfrom fixtures import provision_database  # noqa: F401\nfrom utils import make_mock_event\n\n\n@pytest.mark.usefixtures('provision_database')\nclass TestGetContacts(object):\n    def test_get_contacts(self):\n\n        userId = 'user2'\n        mock_event = make_mock_event(userId, None)\n        response = handler.get_contacts(mock_event, None)\n\n        contacts = response['contacts']\n        assert contacts is not None\n        assert len(contacts) == 2\n\n        contact = contacts[0]\n        assert contact['id'] == '1002'\n        assert contact['nickname'] == 'contact-a-2'\n        assert contact['thumbnail'] == 'thumbnail-2'\n\n        contact = contacts[1]\n        assert contact['id'] == '1001'\n        assert contact['nickname'] == 'contact-z-1'\n        assert contact['thumbnail'] == 'thumbnail-1'\n\n    def test_get_existing_contact(self):\n        userId = 'user2'\n        mock_event = make_mock_event(userId, {'id': '1001'})\n        contact = handler.get_contact(mock_event, None)\n\n        assert contact is not None\n        assert contact['id'] == '1001'\n        assert contact['nickname'] == 'contact-z-1'\n        assert contact['thumbnail'] == 'thumbnail-1'\n\n    def test_get_unknown_contact(self):\n        userId = 'user2'\n        mock_event = make_mock_event(userId, {'id': '9999'})\n        contact = handler.get_contact(mock_event, None)\n\n        assert contact is None\n","sub_path":"tests/test_get_contacts.py","file_name":"test_get_contacts.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"437222190","text":"#Question 1\r\n\r\ndef print_square():\r\n    print(\"*\"*5)\r\n    i= 0\r\n    while i < 3:\r\n        mid= \"*\"+ \"   \"+\"*\"\r\n        print(mid)\r\n        i+=1\r\n    print(\"*\"*5) \r\n    \r\n#===========part 2==========\r\ndef print_rectangle(width,height):\r\n    print(\"*\"*width)\r\n    i=0\r\n    while i <(height-2):\r\n        mid= \"*\"+ \" \"*(width-2)+\"*\"\r\n        print(mid)\r\n        i+=1\r\n    print(\"*\"*width)  \r\n    \r\n#print_rectangle(5,7)       \r\n        \r\n        \r\n#==========part 3===========\r\ndef get_rectangle(width,height):\r\n    angle= \"*\"*width +\"\\n\"\r\n    i= 0\r\n    while i < height-2:\r\n        angle+= (\"*\" + \" \"*(width-2) + \"*\\n\")\r\n        i+=1\r\n    angle+= (\"*\"*width)\r\n    return angle\r\n    \r\n        \r\n       \r\n","sub_path":"examples/data/Assignment_4/mthsph007/boxes.py","file_name":"boxes.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"1689789","text":"from db.engine_logic import BaseEngine, Singleton\nimport sqlite3\nimport os\n\n\nclass SQLITE(BaseEngine, metaclass=Singleton):\n    BASE_DIR = os.path.dirname(os.path.abspath(__file__))\n\n    def __init__(self):\n        self.DATABASE = os.path.join(self.BASE_DIR, 'sqlite.db')\n        print(self.DATABASE)\n\n        # if there's no sqlite engine:\n        if not os.path.exists(self.DATABASE):\n            self.conn = None\n            self.cursor = None\n            self.restart()\n        else:\n            self.conn = sqlite3.connect(self.DATABASE)\n            self.cursor = self.conn.cursor()\n\n    def execute(self, request, data=None):\n        \"Raw execute if it's needed\"\n        print(request)\n        data = tuple(data) if data else ()\n        print(data)\n        self.cursor.execute(request, data)\n        result = self.cursor.fetchall()\n        self.conn.commit()\n        if result != None:\n            return result\n\n    def create_table(self):\n        sql =\"\"\"CREATE TABLE weather\n                (id_town INTEGER PRIMARY KEY, town VARCHAR(255),\n                date DATE, temperture INTEGER, id_weather INTEGER)\n             \"\"\"\n        self.execute(sql)\n\n    def include(self, **field_with_data):\n        \"find records by id, town and so on\"\n        # list_data = self._dict_to_list(field_with_data)\n        sql = \"\"\"SELECT * from weather WHERE \"\"\"\n        list_data = list()\n        for column, data in field_with_data.items():\n            sql += \"{}=?, \".format(column)\n            list_data.append(data)\n        return self.execute(sql[:-2], list_data)\n\n    def _dict_to_list(self, dictionary):\n        result = []\n        for key, value in dictionary.items():\n            result.append(key)\n            result.append(value)\n        return result\n\n    def insert(self, *list_data):\n        sql = \"\"\"\n        INSERT INTO weather\n        VALUES (?{})\n            \"\"\".format(', ?'*(len(list_data)-1))\n        self.execute(sql, list_data)\n\n    def remove(self, id_town):\n        \"remove by id_town\"\n        sql = \"\"\"DELETE FROM weather WHERE id_town=?\"\"\"\n        self.execute(sql, id_town)\n\n    def restart(self):\n        open(self.DATABASE, 'w')\n        os.remove(self.DATABASE)\n        # update conn and cursor\n        self.conn = sqlite3.connect(self.DATABASE)\n        self.cursor = self.conn.cursor()\n        self.create_table()\n","sub_path":"db/sqlite_engine.py","file_name":"sqlite_engine.py","file_ext":"py","file_size_in_byte":2337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"95284911","text":"# coding=utf-8\n\nUS_STATES = {\n    'ak': 'alaska',\n    'al': 'alabama',\n    'ar': 'arkansas',\n    'as': 'american samoa',\n    'az': 'arizona',\n    'ca': 'california',\n    'co': 'colorado',\n    'ct': 'connecticut',\n    'dc': 'district of columbia',\n    'de': 'delaware',\n    'fl': 'florida',\n    'ga': 'georgia',\n    'gu': 'guam',\n    'hi': 'hawaii',\n    'ia': 'iowa',\n    'id': 'idaho',\n    'il': 'illinois',\n    'in': 'indiana',\n    'ks': 'kansas',\n    'ky': 'kentucky',\n    'la': 'louisiana',\n    'ma': 'massachusetts',\n    'md': 'maryland',\n    'me': 'maine',\n    'mi': 'michigan',\n    'mn': 'minnesota',\n    'mo': 'missouri',\n    'mp': 'northern mariana islands',\n    'ms': 'mississippi',\n    'mt': 'montana',\n    'na': 'national',\n    'nc': 'north carolina',\n    'nd': 'north dakota',\n    'ne': 'nebraska',\n    'nh': 'new hampshire',\n    'nj': 'new jersey',\n    'nm': 'new mexico',\n    'nv': 'nevada',\n    'ny': 'new york',\n    'oh': 'ohio',\n    'ok': 'oklahoma',\n    'or': 'oregon',\n    'pa': 'pennsylvania',\n    'pr': 'puerto Rico',\n    'ri': 'rhode Island',\n    'sc': 'south Carolina',\n    'sd': 'south Dakota',\n    'tn': 'tennessee',\n    'tx': 'texas',\n    'ut': 'utah',\n    'va': 'virginia',\n    'vi': 'virgin Islands',\n    'vt': 'vermont',\n    'wa': 'washington',\n    'wi': 'wisconsin',\n    'wv': 'west Virginia',\n    'wy': 'wyoming'\n    }\n\n\nUS_STATES_LIST = [\"Alabama\",\"Alaska\",\"Arizona\",\"Arkansas\",\"California\",\"Colorado\",\n  \"Connecticut\",\"Delaware\",\"Florida\",\"Georgia\",\"Hawaii\",\"Idaho\",\"Illinois\",\n  \"Indiana\",\"Iowa\",\"Kansas\",\"Kentucky\",\"Louisiana\",\"Maine\",\"Maryland\",\n  \"Massachusetts\",\"Michigan\",\"Minnesota\",\"Mississippi\",\"Missouri\",\"Montana\",\n  \"Nebraska\",\"Nevada\",\"New Hampshire\",\"New Jersey\",\"New Mexico\",\"New York\",\n  \"North Carolina\",\"North Dakota\",\"Ohio\",\"Oklahoma\",\"Oregon\",\"Pennsylvania\",\n  \"Rhode Island\",\"South Carolina\",\"South Dakota\",\"Tennessee\",\"Texas\",\"Utah\",\n  \"Vermont\",\"Virginia\",\"Washington\",\"West Virginia\",\"Wisconsin\",\"Wyoming\"]\n\nMONTH_DICT = {\n        \"Jan\":\"01\",\n        \"Feb\":\"02\",\n        \"Mar\":\"03\",\n        \"Apr\":\"04\",\n        \"May\":\"05\",\n        \"Jun\":\"06\",\n        \"Jul\":\"07\",\n        \"Aug\":\"08\",\n        \"Sep\":\"09\",\n        \"Oct\":\"10\",\n        \"Nov\":\"11\",\n        \"Dec\":\"12\"\n        }\n\nACCEPTED_STATUSES = ['ace club', 'eagle club', 'birdie club', 'active', 'current']\n\n\nHISTORY_FIELDS = [\n        \"latest_update\",\n        \"membership\",\n        \"membership_status\",\n        \"membership_status_expiration_date\",\n        \"first_name\", \n        \"middle_name\",\n        \"last_name\",\n        \"city\",\n        \"state\",\n        \"country\",\n        \"classification\",\n        \"career_earnings\",\n        \"total_wins\",\n        \"lowest_rating\",\n        \"highest_rating\",\n        \"current_rating\",\n        \"rating_difference\",\n        \"individual_tournament_years\",\n        \"certified_status\",\n        \"certified_status_expiration_date\",\n    ]\n\n\nHISTORY_FIELDS_TOURNAMENT = [\n        \"latest_update\",\n        \"tournament_name\",\n        \"city\",\n        \"state\",\n        \"country\",\n        \"tournament_start\",\n        \"tournament_end\",\n        \"tournament_length_days\",\n        \"tournament_director\",\n        \"tournament_director_id\",\n        \"assistant_director\",\n        \"assistant_director_id\",\n        \"tournament_tier\",\n        \"tournament_website\",\n        \"tournament_phone\",\n        \"tournament_email\",\n        \"total_players\",\n        \"tournament_classification\",\n        \"event_results_status\",\n        \"pdga_latest_update\",\n        \"pro_prize_money\",\n        \"tournament_type\",\n        \"players\",\n    ]\n\n\nFEMALE_DIVISIONS = [\n        'FPO', \n        'FP40', \n        'FP50', \n        'FP55', \n        'FP60', \n        'FP65', \n        'FP70', \n        'FA1', \n        'FA2', \n        'FA3', \n        'FA4', \n        'FA40', \n        'FA50', \n        'FA55', \n        'FA60', \n        'FA65', \n        'FA70', \n        'FJ18', \n        'FJ15', \n        'FJ12', \n        'FJ10', \n        'FJ08', \n        'FJ06'\n]\n\nJUNIOR_DIVISIONS = [\n        'MJ18', \n        'MJ15', \n        'MJ12', \n        'MJ10', \n        'MJ08', \n        'MJ06'\n]","sub_path":"project/helpers/helper_data.py","file_name":"helper_data.py","file_ext":"py","file_size_in_byte":4072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"335865853","text":"from ekphrasis.classes.textpp import TextPreProcessor\nfrom ekphrasis.classes.tokenizer import SocialTokenizer\nfrom ekphrasis.dicts.emoticons import emoticons\nfrom sklearn import svm\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_selection import SelectKBest, mutual_info_classif\nfrom sklearn.metrics import f1_score, recall_score, precision_score\nfrom sklearn.preprocessing import Normalizer, StandardScaler\n\nfrom dataset.data_loader import SemEvalDataLoader\nfrom sk_transformers.CustomPreProcessor import CustomPreProcessor\nfrom sk_transformers.DBOWFeatureExtractor import DBOWFeatureExtractor\nfrom utilities.data_loader import get_embeddings\nfrom utilities.ignore_warnings import set_ignores\n\nset_ignores()\nimport numpy\n\nnumpy.random.seed(1337)  # for reproducibility\nfrom sklearn.pipeline import Pipeline\n\n\ndef eval_clf(model, X, y, X_test, y_test):\n    model.fit(X, y)\n    y_p = model.predict(X_test)\n\n    f1 = round(f1_score(y_test, y_p, labels=['positive', 'negative'],\n                        average='macro'), 3)\n    recall = round(recall_score(y_test, y_p, average='macro'), 3)\n    precision = round(precision_score(y_test, y_p, average='macro'), 3)\n    print(\"f1\", f1)\n    print(\"recall\", recall)\n    print(\"precision\", precision)\n    print(\"{} & {} & {}\".format(recall, f1, precision))\n\n\ndef tok(text):\n    return text\n\n\ndefault_pp = TextPreProcessor(\n    backoff=['url', 'email', 'percent', 'money', 'phone', 'user', 'time', 'url',\n             'date', 'number'],\n    include_tags={\"hashtag\", \"allcaps\", \"elongated\", \"repeated\", 'emphasis',\n                  'censored'},\n    fix_html=True,\n    segmenter=\"twitter\",\n    corrector=\"twitter\",\n    unpack_hashtags=True,\n    unpack_contractions=True,\n    spell_correct_elong=False,\n    tokenizer=SocialTokenizer(lowercase=True).tokenize,\n    dicts=[emoticons])\n\nWV_CORPUS = \"datastories.twitter\"\nWV_DIM = 300\nembeddings, word_indices = get_embeddings(corpus=WV_CORPUS, dim=WV_DIM)\n\n# bot = Pipeline([\n#     ('preprocess', CustomPreProcessor(default_pp, to_list=True)),\n#     ('fw', TfidfVectorizer(tokenizer=tok,\n#                            min_df=5,\n#                            # max_df=0.9,\n#                            sublinear_tf=True, binary=True,\n#                            lowercase=False)),\n#     ('classifier', svm.LinearSVC(C=0.6, class_weight='balanced',\n#                                  loss='hinge',\n#                                  random_state=0)),\n#     # ('classifier', LogisticRegression(C=0.6, class_weight='balanced',\n#     #                                   random_state=0, n_jobs=-1)),\n# ])\n\ndbow = Pipeline([\n    ('preprocess', CustomPreProcessor(default_pp, to_list=True)),\n    ('ext', DBOWFeatureExtractor(aggregation=\"mean\",\n                                 negation=True,\n                                 # context_diff=\"abs\",\n                                 word_vectors=embeddings,\n                                 word_indices=word_indices,\n                                 neg_comma=False, neg_modals=True,\n                                 # idf_weight=True, fs_weight=False,\n                                 stopwords=True)),\n    # ('normalizer', StandardScaler()),\n    ('normalizer2', Normalizer(norm='l2', copy=False)),\n    ('classifier', svm.LinearSVC(C=0.6, class_weight='balanced',\n                                 loss='hinge',\n                                 random_state=0)),\n])\n\ntrain_set = SemEvalDataLoader(verbose=False).get_data(task=\"A\",\n                                                      years=None,\n                                                      datasets=None,\n                                                      only_semeval=True)\nX = [obs[1] for obs in train_set]\ny = [obs[0] for obs in train_set]\n\ntest_data = SemEvalDataLoader(verbose=False).get_gold(task=\"A\")\nX_test = [obs[1] for obs in test_data]\ny_test = [obs[0] for obs in test_data]\n\n# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,\n#                                                     stratify=y,\n#                                                     random_state=42)\n\n# print_dataset_statistics(y)\nprint(\"-----------------------------\")\nprint(\"LinearSVC\")\neval_clf(dbow, X, y, X_test, y_test)\n# eval_clf(bot, X, y, X_test, y_test)\nprint(\"-----------------------------\")\n","sub_path":"models/hand/nbow.py","file_name":"nbow.py","file_ext":"py","file_size_in_byte":4323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"200241287","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\n# deprecated in version 1.8\nfrom django.conf.urls.i18n import i18n_patterns\nfrom django.conf.urls.static import static\n\nfrom django.conf import settings\nfrom hosting.views import RailsHostingView, DjangoHostingView, NodeJSHostingView\nfrom membership import urls as membership_urls\nfrom ungleich_page.views import LandingView\nfrom django.views.generic import RedirectView\nfrom django.core.urlresolvers import reverse_lazy\nimport debug_toolbar\n\nurlpatterns = [   url(r'^index.html$', LandingView.as_view()),\n                  url(r'^hosting/', include('hosting.urls', namespace=\"hosting\")),\n                  url(r'^open_api/', include('opennebula_api.urls',\n                      namespace='opennebula_api')),\n                  url(r'^railshosting/', RailsHostingView.as_view(), name=\"rails.hosting\"),\n                  url(r'^nodehosting/', NodeJSHostingView.as_view(), name=\"node.hosting\"),\n                  url(r'^djangohosting/', DjangoHostingView.as_view(), name=\"django.hosting\"),\n                  url(r'^nosystemd/', include('nosystemd.urls', namespace=\"nosystemd\")),\n                  url(r'^taggit_autosuggest/', include('taggit_autosuggest.urls')),\n                  url(r'^jsi18n/(?P<packages>\\S+?)/$',\n                      'django.views.i18n.javascript_catalog'),\n              ] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n\n# note the django CMS URLs included via i18n_patterns\nurlpatterns += i18n_patterns('',\n                             url(r'^/?$', LandingView.as_view()),\n                             url(r'^admin/', include(admin.site.urls)),\n                             url(r'^datacenterlight', include('datacenterlight.urls', namespace=\"datacenterlight\")),\n                             url(r'^hosting/', RedirectView.as_view(url=reverse_lazy('hosting:login')), name='redirect_hosting_login'),\n                             url(r'^alplora', include('alplora.urls', namespace=\"alplora\")),\n                             url(r'^membership/', include(membership_urls)),\n                             url(r'^digitalglarus/', include('digitalglarus.urls',\n                                                             namespace=\"digitalglarus\")),\n                             #url(r'^blog/', include('ungleich.urls', namespace='ungleich')),\n                             url(r'^',\n                                 include('ungleich_page.urls', namespace='ungleich_page'),\n                                 name='ungleich_page'),\n                             url(r'^blog/', include('ungleich.urls', namespace='ungleich')),\n                             url(r'^', include('cms.urls'))\n                             )\n\nif settings.DEBUG:\n    urlpatterns += patterns('',\n                            url(r'^media/(?P<path>.*)$',\n                                'django.views.static.serve', {\n                                    'document_root': settings.MEDIA_ROOT,\n                                }),\n                            )\n    urlpatterns += patterns('',url(r'^__debug__/', include(debug_toolbar.urls)))\n","sub_path":"dynamicweb/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"71487087","text":"# Copyright (c) 2017 UPV-GryCAP & UFCG-LSD.\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\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport datetime\nimport json\nimport redis\nimport requests\nimport six\nimport threading\nimport time\nimport uuid\n\nfrom broker.service import api\nfrom broker.plugins import base\nfrom broker.persistence.etcd_db import plugin as etcd\nfrom broker.persistence.sqlite import plugin as sqlite\nfrom broker.utils import ids\nfrom broker.utils import logger\nfrom broker.utils.plugins import k8s\nfrom broker.utils.framework import monitor, controller, visualizer\nfrom broker import exceptions as ex\n\n\nKUBEJOBS_LOG = logger.Log(\"KubeJobsPlugin\", \"logs/kubejobs.log\")\napplication_time_log = \\\n    logger.Log(\"Application_time\", \"logs/application_time.log\")\n\n\nclass KubeJobsExecutor(base.GenericApplicationExecutor):\n\n    def __init__(self, app_id, starting_time=None,\n                 redis=None, status='created',\n                 job_completed=False,\n                 terminated=False,\n                 visualizer_url=\"URL not generated!\",\n                 enable_visualizer=False,\n                 data=None, enable_detailed_report=False,\n                 job_resources_lifetime=0, report={},\n                 del_resources_authorization=False, finish_time=None,\n                 redis_ip=None, redis_port=None):\n\n        self.job_resources_lifetime = job_resources_lifetime\n        self.id = ids.ID_Generator().get_ID()\n        self.app_id = app_id\n        self.starting_time = starting_time\n        self.rds = redis\n        self.redis_ip = redis_ip\n        self.redis_port = redis_port\n        self.status = status\n        self.job_completed = job_completed\n        self.terminated = terminated\n        self.visualizer_url = visualizer_url\n        self.k8s = k8s\n        self.db_connector = self.get_db_connector()\n        self.enable_visualizer = enable_visualizer\n        self.enable_detailed_report = enable_detailed_report\n        self.report = report\n        self.data = data\n        self.finish_time = finish_time\n        self.del_resources_authorization = del_resources_authorization\n\n    def __repr__(self):\n\n        representation = {\n            \"app_id\": self.app_id,\n            \"starting_time\": str(self.get_application_start_time()),\n            \"status\": self.status,\n            \"visualizer_url\": self.visualizer_url,\n            \"redis_ip\": self.redis_ip,\n            \"redis_port\": self.redis_port\n        }\n\n        representation.update(self.report)\n        return json.dumps(representation)\n\n    def get_report(self):\n        report = {}\n        status_code = -1\n        while status_code != 200 and status_code != 400:\n            status_code, report = monitor.get_job_report(\n                                            api.monitor_url,\n                                            self.app_id,\n                                            self.data['monitor_plugin'],\n                                            self.data['monitor_info'])\n            time.sleep(1)\n\n        if status_code == 400:\n            report = {'message': 'Monitoring does not exists '\n                      'yet or has been deleted!'}\n        self.report = report\n\n    def get_detailed_report(self):\n        if not self.enable_detailed_report:\n            report = {'message': 'The detailed report is '\n                      'disabled to this job!'}\n        else:\n            report = monitor.get_detailed_report(api.monitor_url,\n                                                 self.app_id,\n                                                 self.data['monitor_plugin'],\n                                                 self.data['monitor_info'])\n            if \"error_code\" in report:\n                report = {'message': 'Monitoring does not exists '\n                          'yet or has been deleted!'}\n        return report\n\n    def __reduce__(self):\n        return (rebuild, (self.app_id,\n                          self.starting_time,\n                          self.status,\n                          self.visualizer_url,\n                          self.data,\n                          self.report,\n                          self.del_resources_authorization,\n                          self.finish_time,\n                          self.job_resources_lifetime,\n                          self.terminated,\n                          self.job_completed,\n                          self.enable_visualizer,\n                          self.redis_ip,\n                          self.redis_port))\n\n    def get_db_connector(self):\n        if (api.plugin_name == \"etcd\"):\n            return etcd.Etcd3JobPersistence(api.persistence_ip,\n                                            api.persistence_port)\n\n        elif (api.plugin_name == \"sqlite\"):\n            return sqlite.SqliteJobPersistence()\n\n    def enable_detailed_report_if_visualizer_is_enabled(self):\n        if self.data['enable_visualizer']:\n            self.enable_detailed_report = True\n\n    def start_application(self, data):\n        try:\n            self.data = data\n            self.persist_state()\n            self.validate(data)\n            self.enable_detailed_report_if_visualizer_is_enabled()\n            self.activate_related_cluster(data)\n            self.update_env_vars(data)\n            self.setup_redis()\n            database_data, datasource_type = \\\n                self.setup_metric_persistence(data)\n            self.update_visualizer_info(data, database_data, self.redis_ip)\n            self.start_visualization(data)\n            self.persist_state()\n            queue_size = self.push_jobs_to_redis(data)\n            self.trigger_job(data)\n            self.persist_state()\n            self.update_monitor_info(database_data, datasource_type,\n                                     queue_size)\n            self.start_monitoring(data)\n            self.add_redis_info_to_data()\n            self.start_controlling(data)\n            self.wait_job_finish(check_interval=1)\n\n        except Exception as ex:\n            self.terminated = True\n            self.update_application_state(\"error\")\n            KUBEJOBS_LOG.log(\"ERROR: %s\" % ex)\n            raise\n\n        KUBEJOBS_LOG.log(\"Application finished.\")\n\n    def add_redis_info_to_data(self):\n        self.data.update({'redis_ip': self.redis_ip,\n                          'redis_port': self.redis_port})\n\n    def get_workload(self, data):\n        # Download files that contains the items\n        jobs = requests.get(data['redis_workload']).text.\\\n            split('\\n')[:-1]\n\n        return jobs\n\n    def activate_related_cluster(self, data):\n        # If the cluster name is informed in data, active the cluster\n        if('cluster_name' in data.keys()):\n            api.v10.activate_cluster(data['cluster_name'], data)\n\n    def update_env_vars(self, data):\n        # inject REDIS_HOST in the environment\n        data['env_vars']['REDIS_HOST'] = 'redis-%s' % self.app_id\n\n        # inject SCONE_CONFIG_ID in the environment\n        config_id = data.get('config_id')\n        if config_id is not None:\n            KUBEJOBS_LOG.log(\"You should pass the config_id variable \"\n                             \"through the env_var parameter. Passing this way\"\n                             \"is deprecated.\")\n            data['env_vars']['SCONE_CONFIG_ID'] = config_id\n\n    def setup_redis(self):\n        # Provision a redis database for the job. Die in case of error.\n        self.redis_ip, self.redis_port = \\\n            self.k8s.provision_redis_or_die(self.app_id)\n\n        # create a new Redis client and fill the work queue\n        if(self.rds is None):\n            self.rds = redis.StrictRedis(host=self.redis_ip,\n                                         port=self.redis_port)\n\n    def setup_metric_persistence(self, data):\n\n        if 'enable_detailed_report' in data:\n            self.enable_detailed_report = data['enable_detailed_report']\n        datasource_type = None\n        database_data = {}\n        if self.enable_detailed_report:\n            KUBEJOBS_LOG.log(\"Creating metrics persistence platform...\")\n            datasource_type = data['visualizer_info']['datasource_type']\n            database_data = self.setup_datasource(datasource_type)\n\n        return database_data, datasource_type\n\n    def setup_datasource(self, datasource_type):\n        if datasource_type == \"influxdb\":\n            database_data = self.k8s.create_influxdb(self.app_id)\n\n        return database_data\n\n    def update_visualizer_info(self, data, database_data, redis_ip):\n\n        database_data.update({\"url\": redis_ip})\n        data['visualizer_info'].\\\n            update({'database_data': database_data,\n                    'enable_visualizer': data['enable_visualizer'],\n                    'plugin': data['monitor_plugin'],\n                    'visualizer_plugin': data['visualizer_plugin'],\n                    'username': data['username'],\n                    'password': data['password']})\n\n    def update_monitor_info(self, database_data,\n                            datasource_type, queue_size):\n\n        schedule_strategy, heuristic_options = \\\n            self._get_control_parameters()\n\n        self.data['monitor_info'].\\\n            update({'database_data': database_data,\n                    'datasource_type': datasource_type,\n                    'number_of_jobs': queue_size,\n                    'submission_time': self.starting_time.\n                    strftime('%Y-%m-%dT%H:%M:%S.%fGMT'),\n                    'redis_ip': self.redis_ip,\n                    'redis_port': self.redis_port,\n                    'enable_visualizer': self.enable_visualizer,\n                    'enable_detailed_report': self.enable_detailed_report,\n                    'scaling_strategy': schedule_strategy,\n                    'heuristic_options': heuristic_options,\n                    'max_replicas': self.data[\"control_parameters\"][\"max_rep\"],\n                    'min_replicas': self.data[\"control_parameters\"][\"min_rep\"]\n                    })\n        # 'cpu_agent_port': agent_port})\n\n    def _get_control_parameters(self):\n\n        control_parameters = self.data['control_parameters']\n        schedule_strategy = 'default'\n        heuristic_options = None\n        if 'schedule_strategy' in control_parameters:\n            schedule_strategy = control_parameters['schedule_strategy']\n\n        if 'heuristic_options' in control_parameters:\n            heuristic_options = control_parameters['heuristic_options']\n\n        return schedule_strategy, heuristic_options\n\n    def start_visualization(self, data):\n        self.enable_visualizer = data['enable_visualizer']\n        if self.enable_visualizer:\n            visualizer.start_visualization(\n                api.visualizer_url, self.app_id, data['visualizer_info'])\n\n            self.visualizer_url = visualizer.get_visualizer_url(\n                        api.visualizer_url, self.app_id)\n\n            KUBEJOBS_LOG.log(\n                \"Dashboard of the job created on: %s\" %\n                (self.visualizer_url))\n\n    def push_jobs_to_redis(self, data):\n\n        jobs = self.get_workload(data)\n        KUBEJOBS_LOG.log(\"Creating Redis queue\")\n        for job in jobs:\n            self.rds.rpush(\"job\", job)\n\n        return len(jobs)\n\n    def trigger_job(self, data):\n        KUBEJOBS_LOG.log(\"Creating Job\")\n        termination_grace = data.get('termination_grace_period_seconds',\n                                     30)\n        kwargs = {\n            'app_id': self.app_id,\n            'cmd': data['cmd'],\n            'img': data['img'],\n            'init_size': data['init_size'],\n            'env_vars': data['env_vars'],\n            'config_id': data.get('config_id'),\n            'job_termination_grace_period_seconds': termination_grace\n        }\n\n        if data.get('selectors'):\n            kwargs.update({\n                'selectors': data['selectors']\n            })\n\n        if data.get(\"k8s_resources_control\"):\n            kwargs.update({\n                \"limits\": data[\"k8s_resources_control\"]['limits'],\n                \"requests\": data[\"k8s_resources_control\"]['requests']\n                           })\n\n        if data.get(\"secrets\"):\n            kwargs.update({\"secrets\": data[\"secrets\"]})\n\n        self.k8s.create_job(**kwargs)\n\n        KUBEJOBS_LOG.log(\"Job running...\")\n        self.update_application_state(\"ongoing\")\n        self.starting_time = datetime.datetime.now()\n\n    def start_monitoring(self, data, collect_period=1):\n        monitor.start_monitor(api.monitor_url, self.app_id,\n                              data['monitor_plugin'],\n                              data['monitor_info'], collect_period)\n\n    def start_controlling(self, data):\n        controller.start_controller_k8s(api.controller_url,\n                                        self.app_id, data)\n\n    def wait_job_finish(self, check_interval=1):\n        if not self.job_completed and not self.terminated:\n            while not self.job_completed and not self.terminated:\n                self.synchronize()\n                time.sleep(check_interval)\n            KUBEJOBS_LOG.log(\"Job finished - Status: \"\n                             + self.get_application_state())\n            self.get_report()\n            self.finish_time = datetime.datetime.now()\n            self.set_job_resources_lifetime()\n            self.del_resources_authorization = True\n            self.persist_state()\n            self.schedule_resources_deletion()\n\n    def set_job_resources_lifetime(self):\n        if \"job_resources_lifetime\" in self.data:\n            try:\n                self.job_resources_lifetime = \\\n                    int(self.data[\"job_resources_lifetime\"])\n\n            except Exception:\n                KUBEJOBS_LOG.log(\"The variable 'job_resources_lifetime' \"\n                                 \"must be int! The default value 0 has \"\n                                 \"been setted for this job!\")\n\n    def schedule_resources_deletion(self):\n        if self.job_resources_lifetime > 0:\n            api.v10.job_cleaner_svc.\\\n                insert_element(self.app_id, self.job_resources_lifetime)\n        else:\n            self.delete_job_resources()\n\n    def delete_job_resources(self):\n        try:\n            if self.enable_visualizer:\n                visualizer.stop_visualization(api.visualizer_url,\n                                              self.app_id,\n                                              self.data['visualizer_info'])\n\n            monitor.stop_monitor(api.monitor_url, self.app_id)\n            controller.stop_controller(api.controller_url,\n                                       self.app_id)\n\n            self.visualizer_url = \"Url is dead!\"\n            KUBEJOBS_LOG.log(\"Stoped services\")\n\n            # delete redis resources\n            if not self.get_application_state() == 'terminated':\n                self.k8s.terminate_job(self.app_id)\n        except Exception:\n            KUBEJOBS_LOG.log(\"Job \" + self.app_id +\n                             \" resources already deleted!\")\n        self.del_resources_authorization = False\n        self.persist_state()\n\n    def get_application_state(self):\n        return self.status\n\n    def get_visualizer_url(self):\n        return self.visualizer_url\n\n    def get_application_start_time(self):\n        if(self.starting_time is not None):\n            return self.starting_time.strftime('%Y-%m-%dT%H:%M:%S.%fGMT')\n        else:\n            return \"Job is not running yet!\"\n\n    def update_application_state(self, state):\n        self.status = state\n        self.persist_state()\n\n    def terminate_job(self):\n        self.k8s.terminate_job(self.app_id)\n        self.update_application_state(\"terminated\")\n        self.finish_time = datetime.datetime.now()\n        self.del_resources_authorization = True\n\n    def stop_application(self):\n        self.rds.delete(\"job\")\n        self.rds.rpush(\"stop\", \"stop\")\n        self.finish_time = datetime.datetime.now()\n        self.del_resources_authorization = True\n        self.terminated = True\n        self.update_application_state(\"stopped\")\n\n    def errors(self):\n        try:\n            self.rds.ping()\n        except redis.exceptions.ConnectionError:\n            return ()\n        return self.rds.lrange(\"job:errors\", 0, -1)\n\n    def persist_state(self):\n        self.db_connector.\\\n            put(self.app_id, self)\n\n    def synchronize(self, tries=10):\n        \"\"\" Infer the job state from job status in Kubernetes.\n        If a job is active in Kubernetes, its state is 'ongoing'.\n        If a job is not active in Kubernetes, it can be\n        'completed' or 'failed'.\n        If an exception has been thrown, the job does not exist,\n        so its state is 'not found'.\n\n        Returns:\n        None -\n        \"\"\"\n        while tries > 0:\n            try:\n                current_status = self.k8s.get_job_status(self.app_id)\n                if current_status.active is not None:\n                    if self.get_application_state() != 'ongoing':\n                        self.update_application_state(\"ongoing\")\n                else:\n                    condition = current_status.conditions.pop().type\n                    if condition == 'Complete':\n                        if self.get_application_state() != 'stopped':\n                            self.job_completed = True\n                            self.update_application_state(\"completed\")\n                        else:\n                            self.terminated = True\n                    else:\n                        self.terminated = True\n                        self.update_application_state(\"failed\")\n                break\n            except Exception:\n                tries -= 1\n                if tries <= 0:\n                    self.terminated = True\n                    final_states = ['completed', 'failed',\n                                    'error', 'created', 'stopped']\n\n                    if self.status not in final_states:\n\n                        self.update_application_state('not found')\n                    self.persist_state()\n\n    def validate(self, data):\n        data_model = {\n            \"cmd\": list,\n            \"control_parameters\": dict,\n            \"control_plugin\": six.string_types,\n            \"env_vars\": dict,\n            \"img\": six.string_types,\n            \"init_size\": int,\n            \"monitor_info\": dict,\n            \"monitor_plugin\": six.string_types,\n            \"redis_workload\": six.string_types,\n            \"enable_visualizer\": bool\n            # The parameters below are only needed if enable_visualizer is True\n            # \"visualizer_plugin\": six.string_types\n            # \"visualizer_info\":dict\n        }\n\n        for key in data_model:\n            if (key not in data):\n                raise ex.BadRequestException(\n                    \"Variable \\\"{}\\\" is missing\".format(key))\n            if (not isinstance(data[key], data_model[key])):\n                raise ex.BadRequestException(\n                    \"\\\"{}\\\" has unexpected variable type: {}. Was expecting {}\"\n                    .format(key, type(data[key]), data_model[key]))\n\n        if (data[\"enable_visualizer\"]):\n            if (\"visualizer_plugin\" not in data):\n                raise ex.BadRequestException(\n                    \"Variable \\\"visualizer_plugin\\\" is missing\")\n\n            if (not isinstance(data[\"visualizer_plugin\"], six.string_types)):\n                raise ex.BadRequestException(\n                    \"\\\"visualizer_plugin\\\" has unexpected variable type: {}.\\\n                     Was expecting {}\"\n                    .format(type(data[\"visualizer_plugin\"]),\n                            data_model[\"visualizer_plugin\"]))\n\n            if (\"visualizer_info\" not in data):\n                raise ex.BadRequestException(\n                    \"Variable \\\"visualizer_info\\\" is missing\")\n\n            if (not isinstance(data[\"visualizer_info\"], dict)):\n                raise ex.BadRequestException(\n                    \"\\\"visualizer_info\\\" has unexpected variable type: {}.\\\n                    Was expecting {}\"\n                    .format(type(data[\"visualizer_info\"]),\n                            data_model[\"visualizer_info\"]))\n\n        if (not data[\"init_size\"] > 0):\n            raise ex.BadRequestException(\n                \"Variable \\\"init_size\\\" should be greater than 0\")\n\n\nclass KubeJobsProvider(base.PluginInterface):\n\n    def __init__(self):\n        self.id_generator = ids.ID_Generator()\n\n    def get_title(self):\n        return 'Kubernetes Batch Jobs Plugin'\n\n    def get_description(self):\n        return ('Plugin that allows utilization of '\n                'Batch Jobs over a k8s cluster')\n\n    def to_dict(self):\n        return {\n            'name': self.name,\n            'title': self.get_title(),\n            'description': self.get_description(),\n        }\n\n    def execute(self, data):\n        app_id = 'kj-' + str(uuid.uuid4())[0:7]\n        executor = KubeJobsExecutor(app_id)\n\n        handling_thread = threading.Thread(target=executor.start_application,\n                                           args=(data,))\n        handling_thread.start()\n        return app_id, executor\n\n\ndef rebuild(app_id, starting_time,\n            status, visualizer_url,\n            data, report,\n            del_resources_auth, finish_time,\n            job_resources_lifetime,\n            terminated, job_completed,\n            enable_visualizer, redis_ip, redis_port):\n\n    obj = KubeJobsExecutor(app_id=app_id,\n                           starting_time=starting_time,\n                           status=status,\n                           visualizer_url=visualizer_url,\n                           data=data,\n                           report=report,\n                           del_resources_authorization=del_resources_auth,\n                           finish_time=finish_time,\n                           job_resources_lifetime=job_resources_lifetime,\n                           terminated=terminated,\n                           job_completed=job_completed,\n                           enable_visualizer=enable_visualizer,\n                           redis_ip=redis_ip,\n                           redis_port=redis_port)\n    return obj\n\n\nPLUGIN = KubeJobsProvider\n","sub_path":"kubejobs/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":22587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"514145967","text":"import sys\nimport os, os.path as osp\nimport datetime as dt\n\ndef pardir(path, level = 1):\n    for _ in range(level):\n        path = osp.dirname(path)\n    return path\n\nBASEDIR = osp.abspath(pardir(__file__, 2))\nNOW     = dt.datetime.now()\n\nsys.path.insert(0, BASEDIR)\n\nimport shank\n\nproject   = shank.__name__\nauthor    = shank.__author__\ncopyright = \"%s %s\" % (NOW.year, shank.__author__)\n\nversion   = shank.__version__\nrelease   = shank.__version__\n\nsource_suffix  = \".md\"\nsource_parsers = { \".md\": \"recommonmark.parser.CommonMarkParser\" }\n\nmaster_doc     = \"index\"","sub_path":"docs/source/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"446835285","text":" # -*- coding: utf-8 -*-\n\n\nLUNA16ANNOTATIONPATH=\"/home/liubo/data/LUNA16/\"  # 标签\nDOWNLOADLUNA16PATH=\"/home/liubo/data/LUNA16/\"   # 图片\nLUNA16SEGMENTATIONPATH = \"/home/liubo/data/LUNA16/\" #分割\nLUNA16PROCESSPATH=\"/home/liubo/data/LUNA16_PROCESS/\" # 处理结果\n\n\nconfig = {'train_data_path':[\n                             DOWNLOADLUNA16PATH+'/subset0/',    # 训练路径  \n                             DOWNLOADLUNA16PATH+'/subset1/',\n                             DOWNLOADLUNA16PATH+'/subset2/', \n                             DOWNLOADLUNA16PATH+'/subset3/',\n                             DOWNLOADLUNA16PATH+'/subset4/',\n                             DOWNLOADLUNA16PATH+'/subset5/',\n                             DOWNLOADLUNA16PATH+'/subset6/',\n                             DOWNLOADLUNA16PATH+'/subset7/',\n                             DOWNLOADLUNA16PATH+'/subset8/'],\n          'val_data_path':[DOWNLOADLUNA16PATH+'/subset9/'],     # 验证数据路径\n          'test_data_path':[DOWNLOADLUNA16PATH+'/subset9/'],    # 测试数据路径\n          \n          'train_preprocess_result_path':LUNA16PROCESSPATH, # contains numpy for the data and label, which is generated by prepare.py   # numpy 格式的训练数据和标签\n          'val_preprocess_result_path':LUNA16PROCESSPATH,  # make sure copy all the numpy into one folder after prepare.py     # numpy 格式的验证数据和标签\n          'test_preprocess_result_path':LUNA16PROCESSPATH,     # numpy 格式的测试数据和标签\n          \n          'train_annos_path':LUNA16ANNOTATIONPATH+'/CSVFILES/annotations.csv',\n          'val_annos_path':LUNA16ANNOTATIONPATH+'/CSVFILES/annotations.csv',\n          'test_annos_path':LUNA16ANNOTATIONPATH+'/CSVFILES/annotations.csv',\n\n          'black_list':[],   # 处理的黑名单\n          \n          'preprocessing_backend':'python',  \n\n          'luna_segment':LUNA16SEGMENTATIONPATH+'/seg-lungs-LUNA16/', # download from https://luna16.grand-challenge.org/data/\n          'preprocess_result_path':LUNA16PROCESSPATH,\n          'luna_data':DOWNLOADLUNA16PATH,\n          'luna_label':LUNA16ANNOTATIONPATH+'/CSVFILES/annotations.csv'\n         } \n","sub_path":"config_training.py","file_name":"config_training.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"332999604","text":"from typing import List, Dict, Set, Callable\nimport enum\n\n\nclass Parity(enum.Enum):\n    ODD = 0\n    EVEN = 1\n\n\ndef gen_list(start: int, stop: int, parity: Parity) -> List[int]:\n    \"\"\"\n    :param start: Where to start the list (inclusive).\n    :param stop: Where to stop the list (exclusive).\n    :param parity: Based on which parity is passed, the method will be applied to each item in the list\n    :return: Returns a list in specified range parity\n    \"\"\"\n    if parity == Parity.ODD:\n        list1 = list(range(start, stop, 1))\n        new_list = list(filter((lambda x: x % 2 == 1), list1))\n        return new_list\n    elif parity == Parity.EVEN:\n        list1 = list(range(start, stop, 1))\n        new_list = list(filter((lambda x: x % 2 == 0), list1))\n        return new_list\n    \n\n\ndef gen_dict(start: int, stop: int, strategy: Callable) -> Dict:\n    \"\"\"\n    :param start: Key value start (inclusive).\n    :param stop: Key value end (exclusive).\n    :param strategy: takes in a lambda function to carryout on each item in the list\n    :return: Returns a dict with keys in range, and a value with a strategy applied for each key value\n    \"\"\"\n    dicts = {}\n    keys = range(start, stop)\n    for i in keys:\n        dicts[i] = strategy(i)\n    return dicts\n\n\ndef gen_set(val_in: str) -> set:\n    \"\"\"\n    :param val_in: Takes in a string\n    :return: Returns a set of only lower case distanct characters from the string, and returns them capitalized in the set.\n    \"\"\"\n    \n    lst =list(val_in)\n    lower_case = []\n    upper_case = []\n    for i in lst:\n        if (i.islower()):\n            lower_case.append(i)\n    for i in lower_case:\n        upper_case.append(i.upper())\n    gen_set = set(upper_case)\n    return gen_set\n","sub_path":"viva_comprehensions.py","file_name":"viva_comprehensions.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"141503316","text":"# flake8: noqa\nimport os\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader\n\nfrom catalyst import dl\nfrom catalyst.contrib.data.transforms import ToTensor\nfrom catalyst.contrib.datasets import MNIST\nfrom catalyst.utils import metrics\n\n\nclass ClassifyAE(torch.nn.Module):\n    def __init__(self, in_features, hid_features, out_features):\n        super().__init__()\n        self.encoder = nn.Sequential(\n            nn.Linear(in_features, hid_features), nn.Tanh()\n        )\n        self.decoder = nn.Sequential(\n            nn.Linear(hid_features, in_features), nn.Sigmoid()\n        )\n        self.clf = nn.Linear(hid_features, out_features)\n\n    def forward(self, x):\n        z = self.encoder(x)\n        y_hat = self.clf(z)\n        x_ = self.decoder(z)\n        return y_hat, x_\n\n\nclass CustomRunner(dl.Runner):\n    def _handle_batch(self, batch):\n        x, y = batch\n        x = x.view(x.size(0), -1)\n        y_hat, x_ = self.model(x)\n        loss_clf = F.cross_entropy(y_hat, y)\n        loss_ae = F.mse_loss(x_, x)\n        loss = loss_clf + loss_ae\n        accuracy01, accuracy03, accuracy05 = metrics.accuracy(\n            y_hat, y, topk=(1, 3, 5)\n        )\n\n        self.batch_metrics = {\n            \"loss_clf\": loss_clf,\n            \"loss_ae\": loss_ae,\n            \"loss\": loss,\n            \"accuracy01\": accuracy01,\n            \"accuracy03\": accuracy03,\n            \"accuracy05\": accuracy05,\n        }\n\n        if self.is_train_loader:\n            loss.backward()\n            self.optimizer.step()\n            self.optimizer.zero_grad()\n\n\ndef main():\n    model = ClassifyAE(28 * 28, 128, 10)\n    optimizer = torch.optim.Adam(model.parameters(), lr=0.02)\n\n    loaders = {\n        \"train\": DataLoader(\n            MNIST(\n                os.getcwd(), train=False, download=True, transform=ToTensor(),\n            ),\n            batch_size=32,\n        ),\n        \"valid\": DataLoader(\n            MNIST(\n                os.getcwd(), train=False, download=True, transform=ToTensor(),\n            ),\n            batch_size=32,\n        ),\n    }\n\n    runner = CustomRunner()\n    runner.train(\n        model=model,\n        optimizer=optimizer,\n        loaders=loaders,\n        verbose=True,\n        check=True,\n    )\n\n\nif __name__ == \"__main__\":\n    if os.getenv(\"USE_APEX\", \"0\") == \"0\" and os.getenv(\"USE_DDP\", \"0\") == \"0\":\n        main()\n","sub_path":"tests/_tests_scripts/dl_z_mvp_mnist_ae.py","file_name":"dl_z_mvp_mnist_ae.py","file_ext":"py","file_size_in_byte":2396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"49182035","text":"# python3\nimport os\nimport threading\nimport time\n\nimport itchat\nfrom itchat.content import *\nfrom sanic.log import logger\n\nfrom wechat.robot import robot\nfrom .wx_status import WxStatus, Status\n\n\nclass WxThead(threading.Thread):\n\n    def __init__(self, wx: WxStatus):\n        threading.Thread.__init__(self)\n        self.wx = wx\n        self.qr_path = \"./page/qrcode.png\"\n        self.waitForConfirm = False\n\n    def run(self):\n        self.login()\n        itchat.run()\n\n    def login(self):\n        def exit_callback():\n            if os.path.exists(self.qr_path):\n                os.remove(self.qr_path)\n            if os.path.exists(\"./itchat.pkl\"):\n                os.remove(\"./itchat.pkl\")\n            self.wx.update(Status.logout)\n            logger.info(\"==========exit=============\")\n            self.login()\n            raise Exception(\"logout\")\n\n        def qr_callback(uuid, status, qrcode):\n            with open(self.qr_path, \"wb\") as f:\n                f.write(qrcode)\n                self.wx.update(Status.qr)\n                logger.info(\"==========login============\")\n\n        def open_qr():\n            for get_count in range(10):\n                logger.info('Getting uuid')\n                uuid = itchat.get_QRuuid()\n                while uuid is None:\n                    uuid = itchat.get_QRuuid()\n                    time.sleep(1)\n                logger.info('Getting QR Code')\n                if itchat.get_QR(uuid, qrCallback=qr_callback):\n                    break\n                elif get_count >= 9:\n                    self.wx.update(Status.error)\n                    logger.info('Failed to get QR Code, please restart the program')\n            logger.info('Please scan the QR Code')\n            return uuid\n\n        uuid = open_qr()\n        while 1:\n            status = itchat.check_login(uuid)\n            print(\"========\" + status + \"========\")\n            if status == '200':\n                self.wx.update(Status.success)\n                break\n            elif status == '201':\n                if self.waitForConfirm:\n                    logger.info('Please press confirm')\n                    self.waitForConfirm = True\n            elif status == '408':\n                logger.info('Reloading QR Code')\n                uuid = open_qr()\n                self.waitForConfirm = False\n        self.wx.userInfo = itchat.web_init()\n        itchat.show_mobile_login()\n        itchat.get_friends(True)\n        itchat.start_receiving(exit_callback)\n\n        @itchat.msg_register(TEXT, isFriendChat=True)\n        def text_reply(msg):\n            rep = robot(msg.Content) + \"[智能管家]\"\n            itchat.send_msg(rep, msg.FromUserName)\n\n        @itchat.msg_register(FRIENDS)\n        def friend_reply(msg):\n            itchat.add_friend(**msg['Text'])\n            self.wx.new_friends.append(msg)\n","sub_path":"wechat/wx_thread.py","file_name":"wx_thread.py","file_ext":"py","file_size_in_byte":2823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"33674402","text":"n = int(input())\nh = list(map(int,input().split()))\nans,cnt = [0]*n,0\nh.append(1)\nans.append(1)\nwhile(h!=ans):\n\tfor i in range(n):\n\t\tif h[i] > 0:\n\t\t\th[i] -= 1\n\t\t\tif h[i+1] == 0:\n\t\t\t\tbreak\n\tcnt += 1\nprint(cnt)\n","sub_path":"abc116/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"243951008","text":"import numpy as np\nfrom keras.preprocessing.image import load_img, img_to_array\nfrom keras.models import load_model\nfrom tkinter import * \nfrom tkinter import filedialog\nfrom PIL import ImageTk, Image \n\nlongitud, altura = 150, 150\nmodelo = './conocimiento/modelo.h5'\npesos_modelo = './conocimiento/pesos.h5'\ncnn = load_model(modelo)\ncnn.load_weights(pesos_modelo)\n\nroot = Tk() \nroot.geometry(\"550x300+300+150\") \nroot.title = \"Identificador de piezas de Ajedrez\"\nroot.resizable(width=True, height=True) \n\ndef predict(file):\n  x = load_img(file, target_size=(longitud, altura))\n  x = img_to_array(x)\n  text.set(\"Espere...\")\n  x = np.expand_dims(x, axis=0)\n  array = cnn.predict(x)\n  print(array)\n  result = array[0]\n  answer = np.argmax(result)\n  \n  if answer == 0:\n    print(\"pred: Alfil\")\n    text.set(\"Alfil\")\n  elif answer == 1:\n    print(\"pred: Caballo\")\n    text.set(\"Caballo\")\n  elif answer == 2:\n    print(\"pred: Dama\")\n    text.set(\"Dama\")\n  elif answer == 3:\n    print(\"pred: Peón\")\n    text.set(\"Peón\")\n  elif answer == 4:\n    print(\"pred: Rey\")\n    text.set(\"Rey\")\n  elif answer == 5:\n    print(\"pred: Torre\")\n    text.set(\"Torre\")\n\n  \n  return answer\n\ndef openfn(): \n    filename = filedialog.askopenfilename(title='Selecciona una imágen') \n    return filename \ndef open_img():\n    children = root.winfo_children()\n    for child in children: \n      child.destroy() \n    \n    x = openfn() \n\n    img = Image.open(x) \n    img = img.resize((250, 250), Image.ANTIALIAS) \n    img = ImageTk.PhotoImage(img) \n    panel = Label(root, image=img) \n    panel.image = img \n    panel.pack() \n    predict(x)\n    text.set(\"\")\n    label = Label(root,textvariable=text).pack()\n    btn = Button(root, text='open image', command=open_img).pack()\n\ntext = StringVar()\ntext.set(\"Comienza\")\nlabel = Label(root,textvariable=text).pack()\nbtn = Button(root, text='open image', command=open_img).pack() \n\nroot.mainloop() \n\n\n\n\n\n\n\n\n\n","sub_path":"identificador.py","file_name":"identificador.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"37643222","text":"from twisted.internet import defer\nfrom twisted.python import failure\n\nfrom feat.test import common as test_common\nfrom feat.test.integration import common\n\nfrom feat.agents.base import agent, descriptor, replay, message\nfrom feat.agents.base import requester, replier\nfrom feat.common import fiber\nfrom feat.common.text_helper import format_block\n\nfrom feat.interface.recipient import *\n\n\n@descriptor.register(\"protoser_test_agent\")\nclass Descriptor(descriptor.Descriptor):\n    pass\n\n\n@agent.register(\"protoser_test_agent\")\nclass Agent(agent.BaseAgent):\n\n    @replay.mutable\n    def initiate(self, state):\n        state.medium.register_interest(NormalReplier)\n        state.medium.register_interest(SerializedReplier)\n        state.medium.register_interest(PooledReplier)\n        self.reset()\n\n    @replay.immutable\n    def get_count(self, state):\n        return state.count\n\n    @replay.immutable\n    def get_curr(self, state):\n        return state.curr\n\n    @replay.immutable\n    def get_max(self, state):\n        return state.max\n\n    @replay.mutable\n    def reset(self, state):\n        state.count = 0\n        state.curr = 0\n        state.max = 0\n\n    @replay.mutable\n    def protocol_started(self, state):\n        state.curr += 1\n        state.count += 1\n        state.max = max(state.curr, state.max)\n\n    @replay.mutable\n    def protocol_terminated(self, state):\n        state.curr -= 1\n        assert state.curr >= 0\n\n\nclass NormalRequester(requester.BaseRequester):\n\n    protocol_id = 'test_normal'\n    timeout = 10\n\n    @replay.entry_point\n    def initiate(self, state):\n        msg = message.RequestMessage()\n        state.medium.request(msg)\n\n\nclass NormalReplier(replier.BaseReplier):\n\n    protocol_id = 'test_normal'\n\n    @replay.entry_point\n    def requested(self, state, request):\n        state.agent.protocol_started()\n        f = fiber.Fiber()\n        f.add_callback(test_common.delay, 1)\n        f.add_callback(self.done)\n        return f.succeed()\n\n    @replay.mutable\n    def done(self, state, _):\n        response = message.ResponseMessage()\n        state.medium.reply(response)\n        state.agent.protocol_terminated()\n\n\nclass SerializedRequester(NormalRequester):\n\n    protocol_id = 'test_serialized'\n\n\nclass SerializedReplier(NormalReplier):\n\n    protocol_id = 'test_serialized'\n    concurrency = 1\n\n\nclass PooledRequester(NormalRequester):\n\n    protocol_id = 'test_pooled'\n\n\nclass PooledReplier(NormalReplier):\n\n    protocol_id = 'test_pooled'\n    concurrency = 3\n\n\n@common.attr(timescale=0.2)\nclass ProtoSerializationTest(common.SimulationTest):\n\n    timeout = 15\n\n    def prolog(self):\n        setup = format_block(\"\"\"\n        agency = spawn_agency()\n        agency.disable_protocol('setup-monitoring', 'Task')\n        desc1 = descriptor_factory('protoser_test_agent')\n        desc2 = descriptor_factory('protoser_test_agent')\n        medium1 = agency.start_agent(desc1)\n        medium2 = agency.start_agent(desc2)\n        agent1 = medium1.get_agent()\n        agent2 = medium2.get_agent()\n        \"\"\")\n        return self.process(setup)\n\n    @defer.inlineCallbacks\n    def checkMultipleRequest(self, factory, count, max):\n        agent1 = self.get_local('agent1')\n        agent2 = self.get_local('agent2')\n        recip2 = IRecipient(agent2)\n\n        self.assertEqual(agent2.get_curr(), 0)\n        self.assertEqual(agent2.get_max(), 0)\n        self.assertEqual(agent2.get_count(), 0)\n\n        dl = []\n        for _ in range(count):\n            p = agent1.initiate_protocol(factory, recip2)\n            dl.append(p.notify_finish())\n\n        yield defer.DeferredList(dl)\n        yield self.wait_for_idle(10)\n\n        self.assertEqual(agent2.get_curr(), 0)\n        self.assertEqual(agent2.get_max(), max)\n        self.assertEqual(agent2.get_count(), count)\n\n    def testValidateProlog(self):\n        agents = [x for x in self.driver.iter_agents()]\n        self.assertEqual(2, len(agents))\n\n    def testNormalRequester(self):\n        return self.checkMultipleRequest(NormalRequester, 8, 8)\n\n    def testSerializedRequester(self):\n        return self.checkMultipleRequest(SerializedRequester, 8, 1)\n\n    def testPooledRequester(self):\n        return self.checkMultipleRequest(PooledRequester, 8, 3)\n","sub_path":"src/feat/test/integration/test_simulation_protocol_serialization.py","file_name":"test_simulation_protocol_serialization.py","file_ext":"py","file_size_in_byte":4224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"623764695","text":"import sys\n\nimport socket\nfrom cStringIO import StringIO\nfrom lambdaApiLib import *\nimport logging\n\n\ndef create_socket(proxyHost):\n    try:\n        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        #logging.info(\"Socket successfully created\")\n    except socket.error as err:\n        logging.error(\"socket creation failed with error %s\" % err)\n\n    s.connect((proxyHost, int('80')))\n\n    #logging.info(\"The socket has successfully connected to the Mediator: \" + str(proxyHost) + ' on port: ' + str('80'))\n    return s\n\n\ndef send_socket_msg(s, message):\n    try:\n        # Add null byte to the end of all messages\n        message += '\\00'\n        #logging.debug('send' + message)\n        s.sendall(message)\n    except Exception as e:\n        # Send failed\n        logging.error('Send failed' + str(e))\n        sys.exit()\n    #logging.debug('Message: ' + '\\\"' + message + '\\\"' + ' send successful')\n\ndef recv_1024byte(sock):\n    buf1 = StringIO()\n    sock.settimeout(2)\n    while True:\n        try:  # Try receiving\n            received = sock.recv(1024)\n        except socket.timeout as e:  # If timeout occurs, there is no more data to be received through socket\n            err = e.args[0]\n            if err == 'timed out':  # Handle the timeout and process data as timeout means no more to be received\n                payload = buf1.getvalue()\n                payloadlist = payload.split('\\00')\n                loadUrl = [s for s in payloadlist if \"componentUri\" in s]  # Find the componentUri\n                try:\n                    loadUrl = str(loadUrl[0]).strip('\\00')  # strip the null byte then return the string\n                except Exception as e:\n                    logging.error(e)\n\n                logging.info(loadUrl)\n                break\n            else:  # if we have an error terminate the program with error 1\n                logging.error(e)\n                sys.exit(1)\n        else:\n            if len(received) == 0:\n                logging.info('Received length = 0, server finished sending')\n                sys.exit(0)\n            else:\n                #print received\n                try:\n                    buf1.write(received)\n                except Exception as e:\n                    logging.info(e)\n    return loadUrl  #return load string url for zip or png/jpg\n\n\n\n\n\n","sub_path":"socketLib.py","file_name":"socketLib.py","file_ext":"py","file_size_in_byte":2317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"324949804","text":"import re\n\nfrom collections import OrderedDict\nfrom functools import lru_cache\nfrom hashlib import md5\nfrom os import makedirs, path\n\nimport click\nimport yaml\n\nfrom kubetools.exceptions import KubeDevError\nfrom kubetools.settings import get_settings\n\nNON_COMPOSE_KEYS = (\n    # Kubetools specific\n    'minKubetoolsVersion',\n\n    # Kubetools dev specific\n    'devScripts',\n    'containerContext',\n\n    # Kubetools -> Kubernetes specific\n    'servicePorts',\n    'probes',\n    'resources',\n\n    # Kubernetes specific\n    'livenessProbe',\n    'readinessProbe',\n\n    # Internal flags for `ktd status`\n    'is_deployment',\n    'is_dependency',\n)\n\nCONTAINER_KEYS = ('dependencies', 'deployments')\nCONTAINER_KEY_TO_FLAG = {\n    'dependencies': 'is_dependency',\n    'deployments': 'is_deployment',\n}\n\n\ndef dockerise_label(value):\n    # Unfortunate egg from Docker engine pre label support, see:\n    # https://github.com/docker/compose/issues/2119\n    return re.sub(r'[^a-z0-9]', '', value.lower())\n\n\ndef get_project_name(kubetools_config):\n    name = kubetools_config['name']\n    env = kubetools_config['env']\n\n    # Compose name is APP-ENV\n    return '-'.join((name, env))\n\n\ndef get_compose_name(kubetools_config):\n    name_env = get_project_name(kubetools_config)\n    return dockerise_label(name_env)\n\n\ndef get_compose_dirname(kubetools_config):\n    settings = get_settings()\n    return path.join(\n        path.dirname(kubetools_config['_filename']),\n        settings.DEV_CONFIG_DIRNAME,\n    )\n\n\ndef get_compose_filename(kubetools_config):\n    env = kubetools_config['env']\n    compose_filename = '{0}-compose.yml'.format(env)\n    return path.join(get_compose_dirname(kubetools_config), compose_filename)\n\n\ndef get_all_containers(kubetools_config, container_keys=CONTAINER_KEYS):\n    containers = []\n\n    for key_name in container_keys:\n        deployments = kubetools_config.get(key_name, {})\n\n        for deployment_name, deployment_config in deployments.items():\n            if 'containers' not in deployment_config:\n                raise KubeDevError('Deployment {0} is missing containers'.format(\n                    deployment_name,\n                ))\n\n            deployment_containers = deployment_config['containers']\n\n            for container_name, config in deployment_containers.items():\n                config[CONTAINER_KEY_TO_FLAG[key_name]] = True\n                containers.append((container_name, config))\n\n    return containers\n\n\ndef get_all_containers_by_name(kubetools_config, container_keys=CONTAINER_KEYS):\n    return OrderedDict(get_all_containers(\n        kubetools_config,\n        container_keys=container_keys,\n    ))\n\n\ndef _create_compose_service(kubetools_config, name, config, envvars=None):\n    for invalid_build_key in (\n        'preBuildCommands',\n        'registry',\n    ):\n        if invalid_build_key in config.get('build', {}):\n            config['build'].pop(invalid_build_key)\n\n    # Because this is one of our containers (buildContexts are relevant to\n    # the project) - setup a TTY and STDIN so we can attach and be interactive\n    # when attached (eg ipdb).\n    service = {\n        'tty': True,\n        'stdin_open': True,\n        'labels': {\n            'kubetools.project.name': kubetools_config['name'],\n            'kubetools.project.env': kubetools_config['env'],\n        },\n    }\n\n    service.update(config)\n\n    # Translate k8s command/args to docker-compose entrypoint/command\n    if 'command' in service:\n        service['entrypoint'] = service.pop('command')\n    if 'args' in config:\n        service['command'] = service.pop('args')\n\n    if 'build' in service and 'context' not in service['build']:\n        service['build']['context'] = '.'\n\n    if 'ports' in config:\n        # Generate a consistent base port for this project/container combo\n        hash_string = '{0}-{1}'.format(get_project_name(kubetools_config), name)\n\n        # MD5 the string, integer that and then modulus 10k to shorten it down\n        port_base = int(md5(hash_string.encode('utf-8')).hexdigest(), 16) % 10000\n        # And bump by 10k so we don't stray into the privileged port range (<1025)\n        port_base += 10000\n\n        # Reassign ports with explicit host port numbers\n        ports = []\n\n        for port in config['ports']:\n            if isinstance(port, dict):\n                port = port['port']\n\n            ports.append(\n                '{0}:{1}'.format(int(port_base) + int(port), port),\n            )\n\n        service['ports'] = ports\n\n    # Make our service - drop anything kubernetes or kubetools specific\n    service = {\n        key: value\n        for key, value in service.items()\n        if key not in NON_COMPOSE_KEYS\n    }\n\n    # Provide project-specific aliases for all containers. This means we can up\n    # the same containers (eg mariadb x2) under the same dev network, but have\n    # each app speak to it's own mariadb instance (eg app-mariadb).\n    compose_name = kubetools_config['name']\n\n    service['networks'] = {\n        'default': {\n            'aliases': [\n                '{0}-{1}'.format(compose_name, name),\n            ],\n        },\n    }\n\n    # Add any *missing* extra envvars\n    if envvars:\n        environment = service.setdefault('environment', [])\n        for envar in envvars:\n            if envar not in environment:\n                environment.append(envar)\n\n    return service\n\n\n@lru_cache(maxsize=1)\ndef get_dev_network_environment_variables():\n    # This \"fixes\" a horrible circular dependency between config/docker_util\n    from .docker_util import get_all_docker_dev_network_containers\n    containers = get_all_docker_dev_network_containers()\n\n    envvars = set()\n\n    for container in containers:\n        networks = container.attrs['NetworkSettings']['Networks']\n        if 'dev' not in networks:\n            continue\n\n        aliases = networks['dev']['Aliases']\n        for alias in aliases:\n            if '-' in alias:\n                break\n        else:  # no alias with \"-\"\n            continue\n\n        envar = alias.upper().replace('-', '_')\n        envvars.add('DEV_{0}={1}'.format(envar, alias))\n    return list(envvars)\n\n\ndef create_compose_config(kubetools_config):\n    # If we're not in a custom env, everything sits on the \"dev\" network. Envs\n    # remain encapsulated inside their own network.\n    DEV_DEFAULT_ENV = get_settings().DEV_DEFAULT_ENV\n    ktd_env = kubetools_config.get('env', DEV_DEFAULT_ENV)\n    dev_network = ktd_env == DEV_DEFAULT_ENV\n\n    all_containers = get_all_containers(kubetools_config)\n\n    envvars = [\n        'KTD_ENV={0}'.format(ktd_env),\n    ]\n\n    dev_network_envvars = None\n    if dev_network:\n        dev_network_envvars = get_dev_network_environment_variables()\n        if dev_network_envvars:\n            envvars.extend(dev_network_envvars)\n\n    click.echo('--> Injecting environment variables:')\n    for envar in envvars:\n        click.echo('    {0}'.format(envar))\n\n    services = {\n        name: _create_compose_service(\n            kubetools_config, name, config,\n            envvars=envvars,\n        )\n        for name, config in all_containers\n    }\n\n    compose_config = {\n        'version': '3',\n        'services': services,\n    }\n\n    if dev_network:\n        compose_config['networks'] = {\n            'default': {\n                'external': {\n                    'name': 'dev',\n                },\n            },\n        }\n\n    yaml_data = yaml.safe_dump(compose_config)\n\n    compose_dirname = get_compose_dirname(kubetools_config)\n    if not path.exists(compose_dirname):\n        makedirs(compose_dirname)\n\n    with click.open_file(get_compose_filename(kubetools_config), 'w') as f:\n        f.write(yaml_data)\n","sub_path":"kubetools/dev/backends/docker_compose/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":7650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"380078128","text":"def _are_the_alleles_equal(allele_one, allele_two):\n    if allele_one == allele_two:\n        return True\n    else:\n        return False\n\ndef _is_integer(value): \n    try: \n        int(value)\n        return True\n    except ValueError:\n        return False   \n\ndef _does_allele_consist_of_legal_characters(allele):\n \n    characters_to_accept_in_basic_allele = {'A', 'T', 'C', 'G', 'I', 'D', '0'} \n    characters_to_accept_in_multicharacter_allele = {'A', 'T', 'C', 'G'}\n   \n    if len(allele) == 1: \n        if allele not in characters_to_accept_in_basic_allele:\n            return False\n    else: \n        for character in allele:\n            if character not in characters_to_accept_in_multicharacter_allele:\n                return False\n    return True\n\ndef _is_chromosome_legal(chromosome):\n    \n    chromosomes_to_accept = [str(i) for i in range(1,27)]\n    if chromosome in chromosomes_to_accept:\n        return True\n    else:\n        return False\n\ndef is_snp_valid(chromosome, position_corrected, allele_one, allele_two):\n\n    if not _is_chromosome_legal(chromosome):\n        return False\n             \n    if not _is_integer(position_corrected):\n        return False\n         \n    if not _is_integer(chromosome):\n        return False\n    \n    if not _does_allele_consist_of_legal_characters(allele_one) or not _does_allele_consist_of_legal_characters(allele_two):\n\n        return False\n   \n    if _are_the_alleles_equal(allele_one, allele_two):\n        return False\n         \n    return True\n    \n","sub_path":"snp_info/snp_valid.py","file_name":"snp_valid.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"175633866","text":"#Given a matrix of m x n elements (m rows, n columns),\n# return all elements of the matrix in spiral order.\n\n#Given a matrix of m x n elements (m rows, n columns),\n#return all elements of the matrix in spiral order.\n#\n#For example,\n#Given the following matrix:\n#\n# [\n#  [ 1, 2, 3 ],\n#  [ 4, 5, 6 ],\n#  [ 7, 8, 9 ]\n# ]\n# You should return [1,2,3,6,9,8,7,4,5]\n\nclass Solution(object):\n\tdef spiralOrder(self,matrix):\n\t\t\"\"\"\n\t\t:type matrix: List[List[int]]\n\t\t:rtype: List[int]\n\t\t\"\"\"\n\t\tif not matrix:\n\t\t\treturn matrix\n\t\tcol=len(matrix[0])\n\t\trow=len(matrix)\n\t\tcol_begin=0\n\t\tcol_end=col-1\n\t\trow_begin=0\n\t\trow_end=row-1\n\t\tres=[]\n\t\twhile col_begin<=col_end and row_begin<=row_end:\n\t\t\t#-->right\n\t\t\tfor i in range(col_begin,col_end+1,1):\n\t\t\t\tres.append(matrix[row_begin][i])\n\t\t\trow_begin+=1\n\t\t\t#-->down\n\t\t\tfor j in range(row_begin,row_end+1,1):\n\t\t\t\tres.append(matrix[j][col_end])\n\t\t\tcol_end-=1\n\t\t\t#-->left\n\t\t\t#存在可以向左移动的列\n\t\t\tif row_begin<=row_end:\n\t\t\t\tfor i in range(col_end,col_begin-1,-1):\n\t\t\t\t\tres.append(matrix[row_end][i])\n\t\t\t\trow_end-=1\n\t\t\t#-->up\n\t\t\t#存在可以向上移动的行\n\t\t\tif col_begin<=col_end:\n\t\t\t\tfor j in range(row_end,row_begin-1,-1):\n\t\t\t\t\tres.append(matrix[j][col_begin])\n\t\t\t\tcol_begin+=1\n\t\t\n\t\treturn res\n\t\t\nso=Solution()\nmatrix=[[1,2,3],[4,5,6],[7,8,9]]\nres=so.spiralOrder(matrix)\nprint(res)\n\n\n","sub_path":"LeetCode/partI/Spiral_Matrix/spiral_matrix.py","file_name":"spiral_matrix.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"140066923","text":"\"\"\"\n练习01：\n编写一个函数，参数是一个单词，查询这个单词的\n解释\n提示： 考虑到查找不到的情况\n      split()\n\"\"\"\n\ndef find_word(word):\n    \"\"\"\n    :param word: 要查找的单词\n    :return: 该单词的解释 None\n    \"\"\"\n    file = open(\"dict.txt\") # 默认读\n    # 每次提取一行\n    for line in file:\n        tmp = line.split(' ',1)\n        # 如果遍历到的单词已经大于word就不用再找了\n        if tmp[0] > word:\n            file.close()\n            return\n        elif word == tmp[0]:\n            file.close()\n            return tmp[1].strip() # 去除字符串两侧空格\n\nprint(find_word(\"zzz\"))","sub_path":"note/day03_all/day03/exercise01.py","file_name":"exercise01.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"490353634","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nusage:\n  python termex_janome_store_lr2.py 01.txt\n\n　・引数に入力とする日本語テキストファイル(utf8)を指定\n　・事前学習結果をもとに、引数で与えたテキストファイルの\n　　用語重要度を出力\n　・処理結果ファイル: 　janome_extracted-store_lr.txt\n\n 　本パッケージ中のテストデータを使う例は以下\n 　python termex_janome_store_lr2.py ../test_data/jpn_sample.txt\n\n\"\"\"\n\nimport sys\nimport collections\nimport dbm\n\nfrom janome.tokenizer import Tokenizer\nimport termextract.janome\nimport termextract.core\n\ndef output(data):\n    \"\"\"\n    処理結果を\"janome_extracted-store_lr.txt\"に出力\n    \"\"\"\n    outfile = open(\"janome_extracted-store_lr.txt\", \"w\", encoding=\"utf-8\")\n    data_collection = collections.Counter(data)\n    for cmp_noun, value in data_collection.most_common():\n        outfile.write(termextract.core.modify_agglutinative_lang(cmp_noun))\n        outfile.write(\"\\t\")\n        outfile.write(str(value))\n        outfile.write(\"\\n\")\n    outfile.close()\n\nif __name__ == \"__main__\":\n    ARGVS = sys.argv\n    t = Tokenizer()\n    # 専門用語抽出対象の文書を読み込み\n    infile = open(ARGVS[1], \"r\", encoding=\"utf-8\")\n    text = infile.read()\n    infile.close()\n    tokenize_text = t.tokenize(text)\n    frequency = termextract.janome.cmp_noun_dict(tokenize_text)\n\n    # 蓄積したLR情報からLRを呼び出し\n    DB = dbm.open(\"store-lr\", \"r\")\n    lr = termextract.core.score_lr(\n        frequency,\n        ignore_words=termextract.janome.IGNORE_WORDS,\n        lr_mode=1, average_rate=1, dbm=DB)\n    DB.close()\n\n    # 重要度をfrequencyとLRの組み合わせとし、ファイル出力\n    term_imp = termextract.core.term_importance(frequency, lr)\n    output(term_imp)\n","sub_path":"pytermex/termex_janome_store_lr2.py","file_name":"termex_janome_store_lr2.py","file_ext":"py","file_size_in_byte":1832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"235465149","text":"# There can be three types of methods in classes in Python\n\n# 1 - class method  -- we can use class methods as alternative constructor\n# 2 - static method -- are used for methods which don't have direct connection to the class\n# 3 - regular method -- they are just normal methods\n\n\nclass Student:\n\n    # initializing the class\n    def __init__(self, name, roll, branch):\n        self.name = name\n        self.roll = roll\n        self.branch = branch\n\n    # Function to return the details of the Student\n    def get_details(self):\n        print(\"The Details of the Student is :\")\n        print(f'Name : {self.name}')\n        print(f'RollNo : {self.roll}')\n        print(f'Branch : {self.branch}')\n\n    # This method is used as an alternative constructor to accept raw string as its input and return a {Student} class object\n    @classmethod\n    def extract_info(cls, raw_string):\n        name, roll, branch = raw_string.split('+')\n        return cls(name, roll, branch)\n\n    # This method doesn't contains any diret relation or use of any class variables\n    @staticmethod\n    def is_of_college(college):\n        if college == 'PSIT':\n            print(True)\n        else:\n            print(False)\n\n\n# This function provides documentation on any object that we need help for\n# print(help(map))\n\n\n# Creating an instance of the Student Class and passing the parameters and using methods of the Student class.\n\n# s1 = Student('shiv kumar katheriya', 1716410223, 'CSE')\n# s1.get_details()\n# s2 = Student('harsh Chaurasia', 1716410223, 'CSE')\n# s2.get_details()\n# s3 = Student('Prakhar kumar', 1716410223, 'CSE')\n# s3.get_details()\n# s1.is_of_college(\"PSIT\")\n\n\n# Creating an instance of the Student class via {extract_info}-classmethod.\n\ns2 = Student.extract_info(\"Harsh Chaurasia+1716410101+CSE\")\ns2.get_details()\n# s2.is_of_college(\"KIT\")\n\n\n# This function can be used to know if an object is an instance of a class or not\n# print(isinstance(s1, Student))","sub_path":"Coding/Python_Prgs/methods.py","file_name":"methods.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"220312315","text":"# -*- coding=utf-8 -*-\n\nfrom sklearn.svm import SVC,NuSVC\nimport numpy as np\nimport pickle\n\nX = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])\ny = np.array([1, 1, 2, 2])\n\ndef SVC(dataset,labels):\n    \"\"\"\n    SVC参数解释\n    （1）C: 目标函数的惩罚系数C，用来平衡分类间隔margin和错分样本的，default C = 1.0；\n    （2）kernel：参数选择有RBF, Linear, Poly, Sigmoid, 默认的是\"RBF\";\n    （3）degree：if you choose 'Poly' in param 2, this is effective, degree决定了多项式的最高次幂；\n    （4）gamma：核函数的系数('Poly', 'RBF' and 'Sigmoid'), 默认是gamma = 1 / n_features;\n    （5）coef0：核函数中的独立项，'RBF' and 'Poly'有效；\n    （6）probablity: 可能性估计是否使用(true or false)；\n    （7）shrinking：是否进行启发式；\n    （8）tol（default = 1e - 3）: svm结束标准的精度;\n    （9）cache_size: 制定训练所需要的内存（以MB为单位）；\n    （10）class_weight: 每个类所占据的权重，不同的类设置不同的惩罚参数C, 缺省的话自适应；\n    （11）verbose: 跟多线程有关，不大明白啥意思具体；\n    （12）max_iter: 最大迭代次数，default = 1， if max_iter = -1, no limited;\n    （13）decision_function_shape ： ‘ovo’ 一对一, ‘ovr’ 多对多  or None 无, default=None\n    （14）random_state ：用于概率估计的数据重排时的伪随机数生成器的种子。\n     ps：7,8,9一般不考虑。\n    \"\"\"\n    clf = SVC()\n    clf.fit(X, y)\n    with open('train.data','wb+') as f:\n        pickle.dump((clf,clf.fit(X, y)),f)\n    with open('train.data', 'rb+') as f:\n        clf1, svc = pickle.load(f)\n\n    print(clf1.predict([[-0.8, -1]]))\n\ndef NuSVC():\n    '''''\n    NuSVC参数\n    nu：训练误差的一个上界和支持向量的分数的下界。应在间隔（0，1 ]。\n    其余同SVC\n    '''\n    X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])\n    y = np.array([1, 1, 2, 2])\n\n    clf = NuSVC()\n    clf.fit(X, y)\n    print(clf.fit(X, y))\n    print(clf.predict([[-0.8, -1]]))\n\ndef LinearSVC():\n    '''\n    LinearSVC 参数解释\n    C：目标函数的惩罚系数C，用来平衡分类间隔margin和错分样本的，default C = 1.0；\n    loss ：指定损失函数\n    penalty ：\n    dual ：选择算法来解决对偶或原始优化问题。当n_samples > n_features 时dual=false。\n    tol ：（default = 1e - 3）: svm结束标准的精度;\n    multi_class：如果y输出类别包含多类，用来确定多类策略， ovr表示一对多，“crammer_singer”优化所有类别的一个共同的目标\n    如果选择“crammer_singer”，损失、惩罚和优化将会被被忽略。\n    fit_intercept ：\n    intercept_scaling ：\n    class_weight ：对于每一个类别i设置惩罚系数C = class_weight[i]*C,如果不给出，权重自动调整为 n_samples / (n_classes * np.bincount(y))\n    verbose：跟多线程有关，不大明白啥意思具体\n    '''\n\n    from sklearn.svm import SVC\n\n    X=[[0],[1],[2],[3]]\n    Y = [0,1,2,3]\n\n    clf = SVC(decision_function_shape='ovo') #ovo为一对一\n    clf.fit(X,Y)\n    print(clf.fit(X,Y))\n\n    dec = clf.decision_function([[1]])    #返回的是样本距离超平面的距离\n    print(dec)\n\n    clf.decision_function_shape = \"ovr\"\n    dec =clf.decision_function([1]) #返回的是样本距离超平面的距离\n    print(dec)\n\n    #预测\n    print(clf.predict([1]))\n\nif __name__ == '__main__':\n    SVC(None,None)","sub_path":"algorithm/scikit-learn/SVM/svm.py","file_name":"svm.py","file_ext":"py","file_size_in_byte":3494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"409489570","text":"# Discrete Hidden Markov Model (HMM) with scaling\nfrom __future__ import print_function, division\nfrom builtins import range\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef random_normalized(d1, d2):\n\tx = np.random.random((d1, d2))\n\treturn x / x.sum(axis=1, keepdims=True)\n\n\nclass HMM:\n\tdef __init__(self, hidden_states):\n\n\t\t\"\"\"\n\t\t:param hidden_states: number of hidden states\n\t\t\"\"\"\n\t\tself.hidden_states = hidden_states\n\n\tdef fit(self, X, max_iter=30):\n\t\tnp.random.seed(123)\n\n\t\t\"\"\"\n\t\tTrain the HMM model using the Baum-Welch algorithm a specific instance of the expectation-maximization algorithm\n\t\tdetermine V, the vocabulary size.\n\n\t\tThis algorithm function assumes that the observation are already in integers ranging from 0 - V-1\n\t\t:param data: a jagged array of observed sequences\n\t\t:param max_iter: maximum number of iterations\n\t\t\"\"\"\n\t\tV = max(max(x) for x in X) + 1\n\t\tN = len(X)\n\n\t\tself.initial_state_distribution = np.ones(self.hidden_states) / self.hidden_states\n\t\tself.state_transition_matrix = random_normalized(self.hidden_states, self.hidden_states)\n\t\tself.output_distribution = random_normalized(self.hidden_states, V)\n\n\t\t# print(\"initial A:\", self.state_transition_matrix)\n\t\t# print(\"initial B:\", self.output_distribution)\n\n\t\tcosts = []\n\t\tfor it in range(max_iter):\n\t\t\tif it % 10 == 0:\n\t\t\t\tprint(\"it:\", it)\n\t\t\t# alpha1 = np.zeros((N, self.M))\n\t\t\talphas = []\n\t\t\tbetas = []\n\t\t\tscales = []\n\t\t\tlogP = np.zeros(N)\n\t\t\tfor n in range(N):\n\t\t\t\tx = X[n]\n\t\t\t\tT = len(x)\n\t\t\t\tscale = np.zeros(T)\n\t\t\t\t# alpha1[n] = self.pi*self.B[:,x[0]]\n\t\t\t\talpha = np.zeros((T, self.hidden_states))\n\t\t\t\talpha[0] = self.initial_state_distribution * self.output_distribution[:, x[0]]\n\t\t\t\tscale[0] = alpha[0].sum()\n\t\t\t\talpha[0] /= scale[0]\n\t\t\t\tfor t in range(1, T):\n\t\t\t\t\talpha_t_prime = alpha[t - 1].dot(self.state_transition_matrix) * self.output_distribution[:, x[t]]\n\t\t\t\t\tscale[t] = alpha_t_prime.sum()\n\t\t\t\t\talpha[t] = alpha_t_prime / scale[t]\n\t\t\t\tlogP[n] = np.log(scale).sum()\n\t\t\t\talphas.append(alpha)\n\t\t\t\tscales.append(scale)\n\n\t\t\t\tbeta = np.zeros((T, self.hidden_states))\n\t\t\t\tbeta[-1] = 1\n\t\t\t\tfor t in range(T - 2, -1, -1):\n\t\t\t\t\tbeta[t] = self.state_transition_matrix.dot(self.output_distribution[:, x[t + 1]] * beta[t + 1]) / scale[t + 1]\n\t\t\t\tbetas.append(beta)\n\n\t\t\tcost = np.sum(logP)\n\t\t\tcosts.append(cost)\n\n\t\t\t# now re-estimate the initial state distribution, transition matrix, and output distibution\n\t\t\tself.initial_state_distribution = np.sum((alphas[n][0] * betas[n][0]) for n in range(N)) / N\n\n\t\t\tden1 = np.zeros((self.hidden_states, 1))\n\t\t\tden2 = np.zeros((self.hidden_states, 1))\n\t\t\ta_num = np.zeros((self.hidden_states, self.hidden_states))\n\t\t\tb_num = np.zeros((self.hidden_states, V))\n\t\t\tfor n in range(N):\n\t\t\t\tx = X[n]\n\t\t\t\tT = len(x)\n\t\t\t\tden1 += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T\n\t\t\t\tden2 += (alphas[n] * betas[n]).sum(axis=0, keepdims=True).T\n\n\t\t\t\t# numerator for A\n\t\t\t\t# a_num_n = np.zeros((self.M, self.M))\n\t\t\t\tfor i in range(self.hidden_states):\n\t\t\t\t\tfor j in range(self.hidden_states):\n\t\t\t\t\t\tfor t in range(T - 1):\n\t\t\t\t\t\t\ta_num[i, j] += alphas[n][t, i] * betas[n][t + 1, j] * self.state_transition_matrix[i, j] * self.output_distribution[j, x[t + 1]] / \\\n\t\t\t\t\t\t\t               scales[n][t + 1]\n\n\t\t\t\tfor i in range(self.hidden_states):\n\t\t\t\t\tfor t in range(T):\n\t\t\t\t\t\tb_num[i, x[t]] += alphas[n][t, i] * betas[n][t, i]\n\t\t\tself.state_transition_matrix = a_num / den1\n\t\t\tself.output_distribution = b_num / den2\n# \t\tprint(\"A:\", self.state_transition_matrix)\n# \t\tprint(\"B:\", self.output_distribution)\n# \t\tprint(\"pi:\", self.initial_state_distribution)\n\n# \t\tplt.plot(costs)\n# \t\tplt.show()\n\n\tdef log_likelihood(self, x):\n\t\t\"\"\"\"\n\t\treturns log P(x | model) using the forward part of the forward-backward algorithm\n\t\t\"\"\"\n\t\tT = len(x)\n\t\tscale = np.zeros(T)\n\t\talpha = np.zeros((T, self.hidden_states))\n\t\talpha[0] = self.initial_state_distribution * self.output_distribution[:, x[0]]\n\t\tscale[0] = alpha[0].sum()\n\t\talpha[0] /= scale[0]\n\t\tfor t in range(1, T):\n\t\t\talpha_t_prime = alpha[t - 1].dot(self.state_transition_matrix) * self.output_distribution[:, x[t]]\n\t\t\tscale[t] = alpha_t_prime.sum()\n\t\t\talpha[t] = alpha_t_prime / scale[t]\n\t\treturn np.log(scale).sum()\n\n\tdef log_likelihood_multi(self, X):\n\t\treturn np.array([self.log_likelihood(x) for x in X])\n\n\tdef get_state_sequence(self, x):\n\t\t\"\"\"\"\n\t\tReturns the most likely state sequence given observed sequence x using the Viterbi algorithm\n\t\t\"\"\"\n\t\tT = len(x)\n\t\tdelta = np.zeros((T, self.hidden_states))\n\t\tpsi = np.zeros((T, self.hidden_states))\n\t\tdelta[0] = np.log(self.initial_state_distribution) + np.log(self.output_distribution[:, x[0]])\n\t\tfor t in range(1, T):\n\t\t\tfor j in range(self.hidden_states):\n\t\t\t\tdelta[t, j] = np.max(delta[t - 1] + np.log(self.state_transition_matrix[:, j])) + np.log(self.output_distribution[j, x[t]])\n\t\t\t\tpsi[t, j] = np.argmax(delta[t - 1] + np.log(self.state_transition_matrix[:, j]))\n\n\t\t# backtrack\n\t\tstates = np.zeros(T, dtype=np.int32)\n\t\tstates[T - 1] = np.argmax(delta[T - 1])\n\t\tfor t in range(T - 2, -1, -1):\n\t\t\tstates[t] = psi[t + 1, states[t + 1]]\n\t\treturn states\n\n# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python\n# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python\n# http://lazyprogrammer.me\n\n","sub_path":"Models/Q1_Mouse Activity/Hidden Markov/.ipynb_checkpoints/hmm_scaled-checkpoint.py","file_name":"hmm_scaled-checkpoint.py","file_ext":"py","file_size_in_byte":5291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"272145442","text":"##############################################################################\n# Author: David Klee\n# Date: May 4, 2021\n#\n# The purpose of this script is to record a list of arm joint positions by\n# passively moving the robot.  Then the list of waypoints is played back\n# by issuing motion commands to the arm.\n#\n##############################################################################\n\nimport time\nfrom neu_ro_arm.robot.robot_arm import RobotArm\n\ndef collect_waypoints(robot):\n    '''Returns list of arm joint positions that user records\n    '''\n    # initialize an empty list to store arm joint positions\n    waypoints = []\n\n    # use inp function to determine when to record the arm joint position\n    while True:\n        user_input = input(\"Store current arm joint position as waypoint? [y/N] : \")\n        if user_input == 'y':\n            # record joint position and add it to the list of waypoints\n            arm_jpos = robot.get_arm_jpos()\n            waypoints.append(arm_jpos)\n            print('Waypoint recorded.')\n\n        elif user_input == 'N':\n            # end the while loop\n            print('----- Waypoint collection has ended -----')\n            break\n\n        else:\n            # unknown input so ignore\n            continue\n\n    return waypoints\n\ndef follow_waypoints(robot, waypoints):\n    '''Issues movement commands to follow list of arm joint positions\n    '''\n    # allow user to decide when to begin\n    # program will sit here until input is received\n    input('Press enter when ready to perform movement... ')\n\n    # execute commands sequentially\n    for arm_jpos in waypoints:\n        # issue movement command to robot\n        robot.move_arm_jpos(arm_jpos)\n\n        # you may want a delay between movements\n        delay = 1 # seconds\n        time.sleep(delay)\n\n# initialize instance of RobotArm\n# connects to robot automatically\nrobot = RobotArm()\n\n# enter passive mode, so arm can be moved by hand\nrobot.passive_mode()\n\nwaypoints = collect_waypoints(robot)\n\n# enter active mode to prepare for movement commands\n# at this point, arm should not be moved by hand\nrobot.active_mode()\n\nfollow_waypoints(robot, waypoints)\n\n","sub_path":"class_material/week2/move_with_waypoints.py","file_name":"move_with_waypoints.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"228509085","text":"#/usr/bin/python\n\nimport os\nimport subprocess\nimport datetime\nimport re\nimport sys\n\nfrom collections import OrderedDict\nfrom pprint import pprint\n\ntrue = True\nfalse = False\n\n# sys.argv[1] represents the printer\n# sys.argv[2] represents the number of lines we will print out to\n\nprinter = sys.argv[1]\nnumber_of_lines = int(sys.argv[2])\n\nlog_output = \"/var/www/res/\"\nlog_file = \"/var/log/cups/page_log\"\nfile = open(log_output + printer +  \"-display.log\", 'w')\n\nif(os.path.exists(log_file) == false):\n\tprint(\"Bad file to cups page_log : \" + log_file + \"\\n\\tExiting Program...\")\n\tsys.exit(-1)\n\noutput_list = list()\n\ngrep_cmd_txt = \"grep \" + sys.argv[1] + \" \" + log_file\nprint(\"Running command\\t-\\t\" + grep_cmd_txt)\ngrep_cmd = os.popen(grep_cmd_txt)\ngrep_output = grep_cmd.read()\ngrep_output_list = grep_output.splitlines()\ngrep_cmd.close()\n\nhash_dictionary = OrderedDict()\nhash_list = list()\n\nfor line in grep_output_list:\n\tline_split = line.split()\n\tjob_id = int(line_split[2])\n\thash_dictionary[job_id] = line\n\n#print(\"---Hash Dictionary---\")\n#pprint(hash_dictionary) \t\t\t#<-- debug line that would output the dictionary in a readable format\nhash_list = hash_dictionary.values()\n#print(\"---Hash list---\")\n#pprint(hash_list)\t\t\t\t\nhash_list.reverse()\n#print(\"---reversed hash list---\")\n#pprint(hash_list)\n\ncounter = 0;\nfor line in hash_list:\n\tif counter == number_of_lines:\n\t\tbreak\n\tlist = line.split()\n\tprinter_name = list[0]\n\tuser = list[1]\n\tjob_id = list[2]\n\tdate = list[3][1:-3]\n\tdate = date.replace(':', ' - ', 1)\n\tpage_num = list[5]\n\tcopies = list[6]\n\tcomp_ip = list[8]\n\tjob_name = \"\"\n\tfor i in range(10, len(list)):\n\t\tjob_name = job_name + list[i]\n\n\ttemp_list = [user, job_id, date]\n\t\n\toutput = \"<tr>\"\n\tfor temp in temp_list:\n\t\toutput += (\"<td>\" + temp + \"</td>\")\n\toutput += \"</tr>\"\t\n\n\toutput_list.append(output)\n\tcounter = counter + 1\n\t\nfor temp in output_list:\n\tfile.write(temp + \"\\n\")\n\nprint(\"wrote to file: \" + file.name)\n\nfile.close()\n","sub_path":"display_station_output.py","file_name":"display_station_output.py","file_ext":"py","file_size_in_byte":1940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"164455074","text":"\"\"\"\n    Stack structure\n    - LIFO logic\n\"\"\"\n\nclass Stack():\n    # Stack stored using an array, of predefined size\n    def __init__(self, size = 15):\n        self.__size = size\n        self.__pointer = -1\n        self.__stack = [None for x in range(size)]\n\n    # Getter for stack size\n    @property\n    def size(self):\n        return self.__size\n\n    # Pushes new data to stack\n    def push(self, toPush):\n        if self.__pointer < self.__size-1:\n            self.__pointer += 1\n            self.__stack[self.__pointer] = toPush\n        else:\n            return None\n\n    # Removes top object from stack\n    def pop(self):\n        if self.__pointer >= 0:\n            popped = self.__stack[self.__pointer]\n            self.__stack[self.__pointer] = None\n            self.__pointer -= 1\n            return popped\n        else:\n            return None\n\n    # Reveals object at top of stack\n    def peek(self):\n        if not self.isEmpty():\n            return self.__stack[self.__pointer]\n        else:\n            return None\n\n    def isEmpty(self):\n        return (self.__pointer == -1)\n\nif __name__ == \"__main__\":\n    size = 10\n    a = Stack(size)\n\n    # Testing\n    a.pop()\n\n    #for x in range(10):\n    #    a.push(x)\n        #print(\"peek: \", end=\"\")\n        #a.peek()\n        #print(\"pop: \" + str(a.pop()))\n\n    for x in range(size):\n        a.push(x)\n\n    a.peek()\n    a.push(1)\n\n    try:\n        print(a.stack)\n    except AttributeError as e:\n        print(\"your stuff works\")\n","sub_path":"Code/Structs/Stack.py","file_name":"Stack.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}
+{"seq_id":"42560788","text":"from room import Room\nfrom player import Player\nfrom world import World\n\nimport random\nfrom ast import literal_eval\n\n\ndef traverse(room, visited=set()):\n    traversed_path = []\n\n    # add starting room to visited set\n    visited.add(room.id)\n\n    # for each direction provided in room exits method\n    for direction in room.get_exits():\n        # move to that room and store in next_room\n        next_room = room.get_room_in_direction(direction)\n\n        # need reverse directions because they are opposite based on what appears on map visual\n        reversed_directions = {'n': 's', 's': 'n', 'e': 'w', 'w': 'e'}\n\n        # if that room has not already been visited...\n        if next_room.id not in visited:\n            # recurse on that room\n            next_room_path = traverse(next_room, visited)\n\n            # after recursing, if valid direction path, store path\n            if next_room_path:\n                path = [direction] + next_room_path + \\\n                    [reversed_directions[direction]]\n            # not valid path so reverse direction and store that path\n            else:\n                path = [direction, reversed_directions[direction]]\n            # add path taken to traversed array\n            traversed_path = traversed_path + path\n\n    # return complete path to traversing the maze\n    return traversed_path\n\n\n# Load world\nworld = World()\n\n\n# You may uncomment the smaller graphs for development and testing purposes.\n# map_file = \"maps/test_line.txt\"\n# map_file = \"maps/test_cross.txt\"\n# map_file = \"maps/test_loop.txt\"\n# map_file = \"maps/test_loop_fork.txt\"\nmap_file = \"maps/main_maze.txt\"\n\n# Loads the map into a dictionary\nroom_graph = literal_eval(open(map_file, \"r\").read())\nworld.load_graph(room_graph)\n\n# Print an ASCII map\nworld.print_rooms()\n\nplayer = Player(world.starting_room)\n\n# Fill this out with directions to walk\ntraversal_path = traverse(player.current_room)\n\n\n# TRAVERSAL TEST\nvisited_rooms = set()\nplayer.current_room = world.starting_room\nvisited_rooms.add(player.current_room)\n\nfor move in traversal_path:\n    player.travel(move)\n    visited_rooms.add(player.current_room)\n\nif len(visited_rooms) == len(room_graph):\n    print(\n        f\"TESTS PASSED: {len(traversal_path)} moves, {len(visited_rooms)} rooms visited\")\nelse:\n    print(\"TESTS FAILED: INCOMPLETE TRAVERSAL\")\n    print(f\"{len(room_graph) - len(visited_rooms)} unvisited rooms\")\n\n\n#######\n# UNCOMMENT TO WALK AROUND\n#######\n# player.current_room.print_room_description(player)\n# while True:\n#     cmds = input(\"-> \").lower().split(\" \")\n#     if cmds[0] in [\"n\", \"s\", \"e\", \"w\"]:\n#         player.travel(cmds[0], True)\n#     elif cmds[0] == \"q\":\n#         break\n#     else:\n#         print(\"I did not understand that command.\")\n","sub_path":"projects/adventure/adv.py","file_name":"adv.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"61"}